diff --git a/.DS_Store b/.DS_Store index 5378934..b8e89c4 100644 Binary files a/.DS_Store and b/.DS_Store differ diff --git a/bfm17/BFM_standalone_pelagic.nc b/bfm17/BFM_standalone_pelagic.nc deleted file mode 100644 index 36021ea..0000000 Binary files a/bfm17/BFM_standalone_pelagic.nc and /dev/null differ diff --git a/bfm17/bfm_domc.jpg b/bfm17/bfm_domc.jpg deleted file mode 100644 index cfbfc67..0000000 Binary files a/bfm17/bfm_domc.jpg and /dev/null differ diff --git a/bfm17/bfm_domn.jpg b/bfm17/bfm_domn.jpg deleted file mode 100644 index 010cde4..0000000 Binary files a/bfm17/bfm_domn.jpg and /dev/null differ diff --git a/bfm17/bfm_domp.jpg b/bfm17/bfm_domp.jpg deleted file mode 100644 index 8e661f3..0000000 Binary files a/bfm17/bfm_domp.jpg and /dev/null differ diff --git a/bfm17/bfm_exu.jpg b/bfm17/bfm_exu.jpg deleted file mode 100644 index aae4009..0000000 Binary files a/bfm17/bfm_exu.jpg and /dev/null differ diff --git a/bfm17/bfm_fI.jpg b/bfm17/bfm_fI.jpg deleted file mode 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7838e52..0000000 Binary files a/bfm17/bfm_pp.jpg and /dev/null differ diff --git a/bfm17/bfm_resz.jpg b/bfm17/bfm_resz.jpg deleted file mode 100644 index 540959d..0000000 Binary files a/bfm17/bfm_resz.jpg and /dev/null differ diff --git a/bfm17/bfm_standalone.py b/bfm17/bfm_standalone.py deleted file mode 100644 index 745c30d..0000000 --- a/bfm17/bfm_standalone.py +++ /dev/null @@ -1,248 +0,0 @@ -import netCDF4 as nc -import os -import numpy as np -from matplotlib import pyplot as plt - -path = os.getcwd() + '/bfm17/BFM_standalone_pelagic-0515.nc' -variables = nc.Dataset(path).variables - -o2 = np.asarray(variables['O2o'][:]) -po4 = np.asarray(variables['N1p'][:]) -no3 = np.asarray(variables['N3n'][:]) -nh4 = np.asarray(variables['N4n'][:]) - -pc = np.asarray(variables['P2c'][:]) -pn = np.asarray(variables['P2n'][:]) -pp = np.asarray(variables['P2p'][:]) -pl = np.asarray(variables['P2l'][:]) -exu = np.asarray(variables['exPPYc'][:]) -gpp = np.asarray(variables['ruPPYc'][:]) -uptn = np.asarray(variables['ruPPYn'][:]) -uptp = np.asarray(variables['ruPPYp'][:]) - -domc = np.asarray(variables['R1c'][:]) -domn = np.asarray(variables['R1n'][:]) -domp = np.asarray(variables['R1p'][:]) - -pomc = np.asarray(variables['R6c'][:]) -pomn = np.asarray(variables['R6n'][:]) -pomp = np.asarray(variables['R6p'][:]) - -resz = np.asarray(variables['resZOOc'][:]) -nspz = np.asarray(variables['ruZOOc'][:]) -zc = np.asarray(variables['Z5c'][:]) - -remzn = np.asarray(variables['remZOOn'][:]) -remzp = np.asarray(variables['remZOOp']) - -fI = np.asarray(variables['eiPPY_iiP2_'][:]) -ETW = np.asarray(variables['ETW'][:]) -EIR = np.asarray(variables['EIR'][:]) - -x = np.linspace(0,729,730) -# x = np.linspace(15,744,730) - -# fig, ax = plt.subplots() -# ax.plot(x,ETW[0:730]) -# plt.xlim([0,729]) -# # ax.plot(x,ETW[15:745]) -# # plt.xlim([15,744]) -# plt.title("Temperature") -# plt.xlabel("Time [d]") -# plt.savefig("bfm_temp.jpg") - -# fig, ax = plt.subplots() -# ax.plot(x,EIR[0:730]) -# plt.xlim([0,729]) -# # ax.plot(x,EIR[15:745]) -# # plt.xlim([15,744]) -# plt.title("Irradiance") -# plt.xlabel("Time [d]") -# plt.savefig("bfm_irr.jpg") - -# fig, ax = plt.subplots() -# ax.plot(x,fI[0:730]) -# plt.xlim([0,729]) -# # ax.plot(x,fI[15:745]) -# # plt.xlim([15,744]) -# plt.title("Light Limitation") -# plt.xlabel("Time [d]") -# plt.savefig("bfm_fI.jpg") - -# fig, ax = plt.subplots() -# ax.plot(x,gpp[0:730]) -# plt.xlim([0,729]) -# # ax.plot(x,gpp[15:745]) -# # plt.xlim([15,744]) -# plt.title("Phyto GPP") -# plt.xlabel("Time [d]") -# plt.savefig("bfm_gpp.jpg") - -# fig, ax = plt.subplots() -# ax.plot(x,exu[0:730]) -# plt.xlim([0,729]) -# # ax.plot(x,exu[15:745]) -# # plt.xlim([15,744]) -# plt.title("Phyto Exudation") -# plt.xlabel("Time [d]") -# plt.savefig("bfm_exu.jpg") - -# fig, ax = plt.subplots() -# ax.plot(x,o2[0:730]) -# plt.xlim([0,729]) -# # ax.plot(x,o2[15:745]) -# # plt.xlim([15,744]) -# plt.title("Oxygen") -# plt.xlabel("Time [d]") -# plt.ylabel("mmol O / m3") -# plt.savefig("bfm_o2.jpg") - -# fig, ax = plt.subplots() -# ax.plot(x,nh4[0:730]) -# plt.xlim([0,729]) -# # ax.plot(x,nh4[15:745]) -# # plt.xlim([15,744]) -# plt.title("Ammonium") -# plt.xlabel("Time [d]") -# plt.ylabel("mmol N / m3") -# plt.savefig("bfm_nh4.jpg") - -# fig, ax = plt.subplots() -# ax.plot(x,no3[0:730]) -# plt.xlim([0,729]) -# # ax.plot(x,no3[15:745]) -# # plt.xlim([15,744]) -# plt.title("Nitrate") -# plt.xlabel("Time [d]") -# plt.ylabel("mmol N / m3") -# plt.savefig("bfm_no3.jpg") - -# fig, ax = plt.subplots() -# ax.plot(x,po4[0:730]) -# plt.xlim([0,729]) -# # ax.plot(x,po4[15:745]) -# # plt.xlim([15,744]) -# plt.title("Phosphate") -# plt.xlabel("Time [d]") -# plt.ylabel("mmol P / m3") -# plt.savefig("bfm_po4.jpg") - -# fig, ax = plt.subplots() -# ax.plot(x,pl[0:730]) -# plt.xlim([0,729]) -# # ax.plot(x,pl[15:745]) -# # plt.xlim([15,744]) -# plt.title("Phyto Chl") -# plt.xlabel("Time [d]") -# plt.ylabel("mg Chl / m3") -# plt.savefig("bfm_pl.jpg") - -# fig, ax = plt.subplots() -# ax.plot(x,pc[0:730]) -# plt.xlim([0,729]) -# # ax.plot(x,pc[15:745]) -# # plt.xlim([15,744]) -# plt.title("Phyto Carbon") -# plt.xlabel("Time [d]") -# plt.ylabel("mg C / m3") -# plt.savefig("bfm_pc.jpg") - -# fig, ax = plt.subplots() -# ax.plot(x,pn[0:730]) -# plt.xlim([0,729]) -# # ax.plot(x,pn[15:745]) -# # plt.xlim([15,744]) -# plt.title("Phyto Nitrate") -# plt.xlabel("Time [d]") -# plt.ylabel("mmol N / m3") -# plt.savefig("bfm_pn.jpg") - -# fig, ax = plt.subplots() -# ax.plot(x,pp[0:730]) -# plt.xlim([0,729]) -# # ax.plot(x,pp[15:745]) -# # plt.xlim([15,744]) -# plt.title("Phyto Phosphate") -# plt.xlabel("Time [d]") -# plt.ylabel("mmol P / m3") -# plt.savefig("bfm_pp.jpg") - -# fig, ax = plt.subplots() -# ax.plot(x,resz[0:730]) -# plt.xlim([0,729]) -# # ax.plot(x,pp[15:745]) -# # plt.xlim([15,744]) -# plt.title("Zoo Respiration") -# plt.xlabel("Time [d]") -# plt.ylabel("mg C / m3") -# plt.savefig("bfm_resz.jpg") - -# fig, ax = plt.subplots() -# ax.plot(x,zc[0:730]) -# plt.xlim([0,729]) -# # ax.plot(x,pc[15:745]) -# # plt.xlim([15,744]) -# plt.title("Zoo Carbon") -# plt.xlabel("Time [d]") -# plt.ylabel("mg C / m3") -# plt.savefig("bfm_zc.jpg") - -# fig, ax = plt.subplots() -# ax.plot(x,pomc[0:730]) -# plt.xlim([0,729]) -# # ax.plot(x,pc[15:745]) -# # plt.xlim([15,744]) -# plt.title("POM Carbon") -# plt.xlabel("Time [d]") -# plt.ylabel("mg C / m3") -# plt.savefig("bfm_pomc.jpg") - -# fig, ax = plt.subplots() -# ax.plot(x,pomn[0:730]) -# plt.xlim([0,729]) -# # ax.plot(x,pc[15:745]) -# # plt.xlim([15,744]) -# plt.title("POM Nitrogen") -# plt.xlabel("Time [d]") -# plt.ylabel("mmol N / m3") -# plt.savefig("bfm_pomn.jpg") - -# fig, ax = plt.subplots() -# ax.plot(x,pomp[0:730]) -# plt.xlim([0,729]) -# # ax.plot(x,pc[15:745]) -# # plt.xlim([15,744]) -# plt.title("POM Phosphorus") -# plt.xlabel("Time [d]") -# plt.ylabel("mmol P / m3") -# plt.savefig("bfm_pomp.jpg") - -# fig, ax = plt.subplots() -# ax.plot(x,domc[0:730]) -# plt.xlim([0,729]) -# # ax.plot(x,pc[15:745]) -# # plt.xlim([15,744]) -# plt.title("DOM Carbon") -# plt.xlabel("Time [d]") -# plt.ylabel("mg C / m3") -# plt.savefig("bfm_domc.jpg") - -# fig, ax = plt.subplots() -# ax.plot(x,domn[0:730]) -# plt.xlim([0,729]) -# # ax.plot(x,pc[15:745]) -# # plt.xlim([15,744]) -# plt.title("DOM Nitrogen") -# plt.xlabel("Time [d]") -# plt.ylabel("mmol N / m3") -# plt.savefig("bfm_domn.jpg") - -# fig, ax = plt.subplots() -# ax.plot(x,domp[0:730]) -# plt.xlim([0,729]) -# # ax.plot(x,pc[15:745]) -# # plt.xlim([15,744]) -# plt.title("DOM Phosphorus") -# plt.xlabel("Time [d]") -# plt.ylabel("mmol P / m3") -# plt.savefig("bfm_domp.jpg") \ No newline at end of file diff --git a/bfm17/bfm_temp.jpg b/bfm17/bfm_temp.jpg deleted file mode 100644 index f9ef2e6..0000000 Binary files a/bfm17/bfm_temp.jpg and /dev/null differ diff --git a/bfm17/bfm_zc.jpg b/bfm17/bfm_zc.jpg deleted file mode 100644 index d8e7224..0000000 Binary files a/bfm17/bfm_zc.jpg and /dev/null differ diff --git a/bfm50.yaml b/bfm50.yaml new file mode 100644 index 0000000..07c8e8a --- /dev/null +++ b/bfm50.yaml @@ -0,0 +1,1363 @@ +base_element: c + +tracers: + o2: + long_name: oxygen + type: inorganic + composition: + o: 300. # [mmol O / m^3] + parameters: + temperature_regulation: + temp_limited: False + + co2: + long_name: carbon dioxide + type: inorganic + composition: + c: 27060. + parameters: + + + ta: + long_name: total alkalinity + type: inorganic + composition: + eq: 2660. + parameters: + calc_alkalinity: False + + po4: + long_name: phosphate + type: inorganic + composition: + p: 1. # [mmol P / m^3] + parameters: + temperature_regulation: + temp_limited: False + + no3: + long_name: nitrate + type: inorganic + composition: + n: 5. # [mmol N / m^3] + parameters: + temperature_regulation: + temp_limited: True + function: q10 + coefficient: 2.367 + base_temp: 20. + + nh4: + long_name: ammonium + type: inorganic + composition: + n: 1. # [mmol N / m^3] + parameters: + temperature_regulation: + temp_limited: True + function: q10 + coefficient: 2.367 + base_temp: 20. + + n2: + long_name: nitrogen sink + type: inorganic + composition: + n: 200. + parameters: + temperature_regulation: + temp_limited: + + si04: + long_name: silicate + type: inorganic + composition: + si: 8. + parameters: + temperature_regulation: + temp_limited: True + function: q10 + coefficient: 1.49 + base_temp: 20. + + hs: + long_name: reduction equivalents + type: inorganic + composition: + s: 1. # [mmol S / m^3] + parameters: + temperature_regulation: + temp_limited: False + + bac1: + long_name: bacteria + type: bacteria + composition: + c: 1. + n: 0.0167 + p: 1.85E-03 + parameters: + temperature_regulation: + temp_limited: True + function: q10 + coefficient: 2.95 + base_temp: 20. + + phyto1: + long_name: diatoms + type: phytoplankton + composition: + c: 1. + n: 0.0126 + p: 7.86E-04 + chl: 0.025 + si: 0.01 + parameters: + light_attenuation: + max_photo_rate: + cell_quota: + constituents: ["c","n","p","chl","si"] + max: [1., 0.252, 1.572E-03, 0.025, 0.01] + min: [1., 4.193E-03, 1.8E-04, 0.025, 0.] + opt: [1., 0.0126, 7.86E-04, 0.025, 0.01] + nutrient_limitation: + colimitation: minimum + include: ["no3","po4","si04"] + no3: + type: internal + function: droop + nh4_inihibited: True + nh4: + type: external + function: monod + half_sat: + po4: + type: internal + function: droop + sio4: + type: + function: + temperature_regulation: + temp_limited: True + function: q10 + coefficient: 2. + base_temp: 20. + + phyto2: + long_name: nanoflagellates + type: phytoplankton + composition: + c: 1. + n: 0.0126 + p: 7.86E-04 + chl: 0.015 + parameters: + cell_quota: + constituents: ["c","n","p","chl"] + max: [1., 0.252, 1.572E-03, 0.015] + min: [1., 4.193E-03, 1.8E-04, 0.015] + opt: [1., 0.0126, 7.86E-04, 0.015] + nutrient_limitation: + colimitation: minimum + include: ["no3","po4"] + no3: + type: internal + function: droop + nh4_inihibited: True + nh4: + type: external + function: monod + half_sat: + po4: + type: internal + function: droop + primary_production: + eps_PAR: + light_attenuation: 0.03 + max_photo_rate: + initial_PI_slope: + temperature_regulation: + temp_limited: True + function: q10 + coefficient: 2. + base_temp: 20. + + phyto3: + long_name: picophytoplankton + type: phytoplankton + composition: + c: 1. + n: 0.0126 + p: 7.86E-04 + chl: 0.02 + parameters: + cell_quota: + constituents: ["c","n","p","chl"] + max: [1., 0.252, 1.572E-03, 0.02] + min: [1., 4.193E-03, 1.8E-04, 0.02] + opt: [1., 0.0126, 7.86E-04, 0.02] + nutrient_limitation: + colimitation: minimum + include: ["no3","po4"] + no3: + type: internal + function: droop + nh4_inihibited: True + nh4: + type: external + function: monod + half_sat: + po4: + type: internal + function: droop + primary_production: + eps_PAR: + light_attenuation: 0.03 + max_photo_rate: + initial_PI_slope: + temperature_regulation: + temp_limited: True + function: q10 + coefficient: 2. + base_temp: 20. + + phyto4: + long_name: large phytoplankton + type: phytoplankton + composition: + c: 1. + n: 0.0126 + p: 7.86E-04 + chl: 0.02 + parameters: + cell_quota: + constituents: ["c","n","p","chl"] + max: [1., 0.0252, 1.572E-03, 0.02] + min: [1., 6.87E-03, 4.29E-04, 0.02] + opt: [1., 0.0126, 7.86E-04, 0.02] + nutrient_limitation: + colimitation: minimum + include: ["no3","po4"] + no3: + type: internal + function: droop + nh4_inihibited: True + nh4: + type: external + function: monod + half_sat: + po4: + type: internal + function: droop + primary_production: + eps_PAR: + light_attenuation: 0.03 + max_photo_rate: + initial_PI_slope: + temperature_regulation: + temp_limited: True + function: q10 + coefficient: 2. + base_temp: 20. + + mesozoo1: + long_name: carnivorous mesozooplankton + type: zooplankton + composition: + c: 1. + n: 0.015 + p: 1.67E-03 + parameters: + cell_quota: + constituents: ["c","n","p"] + max: + min: + opt: + grazing: + assimilation_efficiency: + ingestion_efficiency: + max_grazing_rate: + grazing_preferences: + phyto1: 0. + phyto2: 0. + phyto3: 0. + phyto4: 0. + mesozoo1: 1. + mesozoo2: 1. + microzoo1: 0. + microzoo2: 0. + temperature_regulation: + temp_limited: True + function: q10 + coefficient: 2. + base_temp: 20. + oxygen_inhibition: + oxygen_limited: + function: + half_sat: + + mesozoo2: + long_name: omnivorous mesozooplankton + type: zooplankton + composition: + c: 1. + n: 0.015 + p: 1.67E-03 + parameters: + cell_quota: + constituents: ["c","n","p"] + max: + min: + opt: + grazing: + assimilation_efficiency: + ingestion_efficiency: + max_grazing_rate: + grazing_preferences: + phyto1: 1. + phyto2: 0. + phyto3: 0. + phyto4: 0.7 + mesozoo1: 0. + mesozoo2: 1. + microzoo1: 1. + microzoo2: 0. + temperature_regulation: + temp_limited: True + function: q10 + coefficient: 2. + base_temp: 20. + oxygen_inhibition: + oxygen_limited: + function: + half_sat: + + microzoo1: + long_name: microzooplankton + type: zooplankton + composition: + c: 1. + n: 0.0167 + p: 1.85E-03 + parameters: + cell_quota: + constituents: ["c","n","p"] + max: + min: + opt: + grazing: + assimilation_efficiency: + ingestion_efficiency: + max_grazing_rate: + grazing_preferences: + bac1: 0.1 + phyto1: 0.2 + phyto2: 1. + phyto3: 0.1 + phyto4: 0. + microzoo1: 1. + microzoo2: 0.8 + temperature_regulation: + temp_limited: True + function: q10 + coefficient: 2. + base_temp: 20. + oxygen_inhibition: + oxygen_limited: + function: + half_sat: + + microzoo2: + long_name: heterotrophic flagellates + type: zooplankton + composition: + c: 1. + n: 0.0167 + p: 1.85E-03 + parameters: + cell_quota: + constituents: ["c","n","p"] + max: + min: + opt: + grazing: + assimilation_efficiency: + ingestion_efficiency: + max_grazing_rate: + grazing_preferences: + bac1: 0.9 + phyto1: 0. + phyto2: 0.1 + phyto3: 1. + phyto4: 0.0 + microzoo1: 0. + microzoo2: 0.2 + temperature_regulation: + temp_limited: True + function: q10 + coefficient: 2. + base_temp: 20. + oxygen_inhibition: + oxygen_limited: + function: + half_sat: + + dom1: + long_name: labile dissolved organic matter + type: detritus + composition: + c: 1. + n: 0.0126 + p: 7.862E-04 + parameters: + light_attenuation: + + dom2: + long_name: semi-labile dissolved organic matter + type: detritus + composition: + c: 0.1 + parameters: + light_attenuation: + + dom3: + long_name: semi-refractory dissolved organic matter + type: detritus + composition: + c: 1. + parameters: + light_attenuation: + + pom1: + long_name: particulate organic matter + type: detritus + composition: + c: 1. + n: 0.0126 + p: 7.862E-04 + si: 0.0145 + parameters: + light_attenuation: + +# ------------------------------------------------------------------------------------------------- +reactions: + # --------------------------------------------------- + # Bacteria + # --------------------------------------------------- + - type: mortality + consumed: + bac1: [c,n,p] + produced: + dom1: [c,n,p] + parameters: + mortality_rate: + mortality_rate_oxy: + partition: + + - type: mortality + consumed: + bac1: [c,n,p] + produced: + pom1: [c,n,p] + parameters: + mortality_rate: + partition: + + - type: respiration + consumed: + bac1: [c] + o2: [o] + produced: + co2: [c] + parameters: + oxygen_limited: True + respiration_rate: + convert_o2: 1/12 + + + - type: uptake + consumed: + dom1: [c,n,p] + produced: + bac1: [c,n,p] + parameters: + strategy: independent + + - type: uptake + consumed: + pom1: [c,n,p] + produced: + bac1: [c,n,p] + parameters: + strategy: independent + + # --------------------------------------------------- + # Phytoplankton + # --------------------------------------------------- + # Phyto1 + - type: chlorophyll_synthesis + consumed: + produced: + phyto1: [chl] + parameters: + eps_PAR: 0.4 # [-] + initial_PI_slope: + chl_quota: # [mg Chl / mg C] + chl_relax_rate: # [(mg C . m^2) / (mg Chl μE)] + optimal_Epar_Ek: + max_photo_rate: # [1/d] + light_attenuation_water: 0.0435 # [1/m] + light_attenuation_phyto: 0.03 # [m^2 / mg Chl] + + - type: exudation + consumed: + phyto1: [c] + produced: + dom2: [c] + parameters: + method: photosynthesis + + - type: gross_primary_production + consumed: + phyto1: [c] + o2: [o] + produced: + co2: [c] + parameters: + convert_o2: 1/12 + + - type: lysis + consumed: + phyto1: [c,n,p] + produced: + dom1: [c,n,p] + parameters: + method: cell_quota + extra_lysis_rate: # [1/d] + half_sat_stress_lysis: + half_sat_extra_lysis: + max_stress_lysis_rate: # [1/d] + om_type: dissolved + + - type: lysis + consumed: + phyto1: [c,n,p,si] + produced: + pom1: [c,n,p,si] + parameters: + method: cell_quota + extra_lysis_rate: # [1/d] + half_sat_stress_lysis: + half_sat_extra_lysis: + max_stress_lysis_rate: # [1/d] + om_type: particulate + + - type: photosynthesis + consumed: + phyto1: [c] + produced: + parameters: + eps_PAR: 0.4 + light_limitation: platt + light_location: integrated + light_attenuation_water: 0.0435 + initial_PI_slope: 1.38E-05 + max_photo_rate: 2.5 + + - type: respiration + consumed: + phyto1: [c] + o2: [o] + produced: + co2: [c] + parameters: + activity_respiration_frac: # [-] + basal_respiration_rate: # [1/d] + convert_o2: 1/12 # [mmol O2 / unit base_element] + + - type: uptake + consumed: + no3: [n] + nh4: [n] + produced: + phyto1: [n] + dom1: [n] + parameters: + + - type: uptake + consumed: + po4: [p] + produced: + phyto1: [p] + dom1: [p] + parameters: + + - type: uptake + consumed: + si04: [si] + produced: + phyto1: [si] + parameters: + + # Phyto2 + - type: chlorophyll_synthesis + consumed: + produced: + phyto2: [chl] + parameters: + eps_PAR: 0.4 # [-] + initial_PI_slope: + chl_quota: # [mg Chl / mg C] + chl_relax_rate: # [(mg C . m^2) / (mg Chl μE)] + optimal_Epar_Ek: + max_photo_rate: # [1/d] + light_attenuation_water: 0.0435 # [1/m] + light_attenuation_phyto: 0.03 # [m^2 / mg Chl] + + - type: exudation + consumed: + phyto2: [c] + produced: + dom2: [c] + parameters: + method: photosynthesis + + - type: gross_primary_production + consumed: + phyto2: [c] + o2: [o] + produced: + co2: [c] + parameters: + convert_o2: 1/12 + + - type: lysis + consumed: + phyto2: [c,n,p] + produced: + dom1: [c,n,p] + parameters: + method: cell_quota + extra_lysis_rate: # [1/d] + half_sat_stress_lysis: + half_sat_extra_lysis: + max_stress_lysis_rate: # [1/d] + om_type: dissolved + + - type: lysis + consumed: + phyto2: [c,n,p] + produced: + pom1: [c,n,p] + parameters: + method: cell_quota + extra_lysis_rate: # [1/d] + half_sat_stress_lysis: + half_sat_extra_lysis: + max_stress_lysis_rate: # [1/d] + om_type: particulate + + - type: photosynthesis + consumed: + phyto2: [c] + produced: + parameters: + eps_PAR: 0.4 + light_limitation: platt + light_location: integrated + light_attenuation_water: 0.0435 + initial_PI_slope: 1.38E-05 + max_photo_rate: 1.6 + + - type: respiration + consumed: + phyto2: [c] + o2: [o] + produced: + co2: [c] + parameters: + activity_respiration_frac: 0.05 # [-] + basal_respiration_rate: 0.05 # [1/d] + convert_o2: 1/12 # [mmol O2 / unit base_element] + + - type: uptake + consumed: + no3: [n] + nh4: [n] + produced: + phyto2: [n] + dom1: [n] + parameters: + + - type: uptake + consumed: + po4: [p] + produced: + phyto2: [p] + dom1: [p] + parameters: + + # Phyto3 + - type: chlorophyll_synthesis + consumed: + produced: + phyto3: [chl] + parameters: + eps_PAR: 0.4 # [-] + initial_PI_slope: + chl_quota: # [mg Chl / mg C] + chl_relax_rate: # [(mg C . m^2) / (mg Chl μE)] + optimal_Epar_Ek: + max_photo_rate: # [1/d] + light_attenuation_water: 0.0435 # [1/m] + light_attenuation_phyto: 0.03 # [m^2 / mg Chl] + + - type: exudation + consumed: + phyto3: [c] + produced: + dom2: [c] + parameters: + method: photosynthesis + + - type: gross_primary_production + consumed: + phyto3: [c] + o2: [o] + produced: + co2: [c] + parameters: + convert_o2: 1/12 + + - type: lysis + consumed: + phyto3: [c,n,p] + produced: + dom1: [c,n,p] + parameters: + method: cell_quota + extra_lysis_rate: # [1/d] + half_sat_stress_lysis: + half_sat_extra_lysis: + max_stress_lysis_rate: # [1/d] + om_type: dissolved + + - type: lysis + consumed: + phyto3: [c,n,p] + produced: + pom1: [c,n,p] + parameters: + method: cell_quota + extra_lysis_rate: # [1/d] + half_sat_stress_lysis: + half_sat_extra_lysis: + max_stress_lysis_rate: # [1/d] + om_type: particulate + + - type: photosynthesis + consumed: + phyto3: [c] + produced: + parameters: + eps_PAR: 0.4 + light_limitation: platt + light_location: integrated + light_attenuation_water: 0.0435 + initial_PI_slope: 1.38E-05 + max_photo_rate: 1.02 + + - type: respiration + consumed: + phyto3: [c] + o2: [o] + produced: + co2: [c] + parameters: + activity_respiration_frac: 0.05 # [-] + basal_respiration_rate: 0.05 # [1/d] + convert_o2: 1/12 # [mmol O2 / unit base_element] + + - type: uptake + consumed: + no3: [n] + nh4: [n] + produced: + phyto3: [n] + dom1: [n] + parameters: + + - type: uptake + consumed: + po4: [p] + produced: + phyto3: [p] + dom1: [p] + parameters: + + # Phyto4 + - type: chlorophyll_synthesis + consumed: + produced: + phyto1: [chl] + parameters: + eps_PAR: 0.4 # [-] + initial_PI_slope: + chl_quota: # [mg Chl / mg C] + chl_relax_rate: # [(mg C . m^2) / (mg Chl μE)] + optimal_Epar_Ek: + max_photo_rate: # [1/d] + light_attenuation_water: 0.0435 # [1/m] + light_attenuation_phyto: 0.03 # [m^2 / mg Chl] + + - type: exudation + consumed: + phyto4: [c] + produced: + dom2: [c] + parameters: + method: photosynthesis + + - type: gross_primary_production + consumed: + phyto4: [c] + o2: [o] + produced: + co2: [c] + parameters: + convert_o2: 1/12 + + - type: lysis + consumed: + phyto4: [c,n,p] + produced: + dom1: [c,n,p] + parameters: + method: cell_quota + extra_lysis_rate: # [1/d] + half_sat_stress_lysis: + half_sat_extra_lysis: + max_stress_lysis_rate: # [1/d] + om_type: dissolved + + - type: lysis + consumed: + phyto4: [c,n,p] + produced: + pom1: [c,n,p] + parameters: + method: cell_quota + extra_lysis_rate: # [1/d] + half_sat_stress_lysis: + half_sat_extra_lysis: + max_stress_lysis_rate: # [1/d] + om_type: particulate + + - type: photosynthesis + consumed: + phyto4: [c] + produced: + parameters: + eps_PAR: 0.4 + light_limitation: platt + light_location: integrated + light_attenuation_water: 0.0435 + initial_PI_slope: 1.38E-05 + max_photo_rate: 0.83 + + - type: respiration + consumed: + phyto4: [c] + o2: [o] + produced: + co2: [c] + parameters: + activity_respiration_frac: 0.05 # [-] + basal_respiration_rate: 0.05 # [1/d] + convert_o2: 1/12 # [mmol O2 / unit base_element] + + - type: uptake + consumed: + no3: [n] + nh4: [n] + produced: + phyto4: [n] + dom1: [n] + parameters: + + - type: uptake + consumed: + po4: [p] + produced: + phyto4: [p] + dom1: [p] + parameters: + + # --------------------------------------------------- + # Zooplankton + # --------------------------------------------------- + # Mesozoo1 + - type: grazing + consumed: + mesozoo1: [c,n,p] + produced: + mesozoo1: [c,n,p] + parameters: + function: holling-2 + feeding_threshold: # [mg C / m^3] + max_grazing_rate: # [1/d] + half_sat_grazing: # [mg C / m^3] + + - type: grazing + consumed: + mesozoo2: [c,n,p] + produced: + mesozoo1: [c,n,p] + parameters: + function: holling-2 + feeding_threshold: # [mg C / m^3] + max_grazing_rate: # [1/d] + half_sat_grazing: # [mg C / m^3] + + - type: excretion + consumed: + mesozoo1: [n] + produced: + nh4: [n] + parameters: + function: grazing + + - type: excretion + consumed: + mesozoo1: [p] + produced: + po4: [p] + parameters: + function: grazing + + - type: excretion + consumed: + mesozoo1: [c,n,p] + produced: + dom1: [c,n,p] + parameters: + function: grazing + partition: [] # c,n,p [-] + + - type: excretion + consumed: + mesozoo1: [c,n,p] + produced: + pom1: [c,n,p] + parameters: + function: grazing + partition: [] # c,n,p [-] + + - type: mortality + consumed: + mesozoo1: [c,n,p] + produced: + dom1: [c,n,p] + parameters: + + - type: mortality + consumed: + mesozoo1: [c,n,p] + produced: + pom1: [c,n,p] + parameters: + + - type: respiration + consumed: + mesozoo1: [c] + o2: [o] + produced: + co2: [c] + parameters: + respiration_rate: + convert_o2: 1/12 + + # Mesozoo2 + - type: grazing + consumed: + phyto1: [c,n,p,chl,si] + produced: + mesozoo2: [c,n,p] + parameters: + function: holling-2 + feeding_threshold: # [mg C / m^3] + max_grazing_rate: # [1/d] + half_sat_grazing: # [mg C / m^3] + + - type: grazing + consumed: + phyto4: [c,n,p,chl] + produced: + mesozoo2: [c,n,p] + parameters: + function: holling-2 + feeding_threshold: # [mg C / m^3] + max_grazing_rate: # [1/d] + half_sat_grazing: # [mg C / m^3] + + - type: grazing + consumed: + mesozoo2: [c,n,p] + produced: + mesozoo2: [c,n,p] + parameters: + function: holling-2 + feeding_threshold: # [mg C / m^3] + max_grazing_rate: # [1/d] + half_sat_grazing: # [mg C / m^3] + + - type: grazing + consumed: + microzoo1: [c,n,p] + produced: + mesozoo2: [c,n,p] + parameters: + function: holling-2 + feeding_threshold: # [mg C / m^3] + max_grazing_rate: # [1/d] + half_sat_grazing: # [mg C / m^3] + + - type: excretion + consumed: + mesozoo2: [n] + produced: + nh4: [n] + parameters: + function: grazing + + - type: excretion + consumed: + mesozoo2: [p] + produced: + po4: [p] + parameters: + function: grazing + + - type: excretion + consumed: + mesozoo2: [c,n,p] + produced: + dom1: [c,n,p] + parameters: + function: grazing + partition: [] # c,n,p [-] + + - type: excretion + consumed: + mesozoo2: [c,n,p] + produced: + pom1: [c,n,p] + parameters: + function: grazing + partition: [] # c,n,p [-] + + - type: mortality + consumed: + mesozoo2: [c,n,p] + produced: + dom1: [c,n,p] + parameters: + + - type: mortality + consumed: + mesozoo2: [c,n,p] + produced: + pom1: [c,n,p] + parameters: + + - type: respiration + consumed: + mesozoo2: [c] + o2: [o] + produced: + co2: [c] + parameters: + respiration_rate: + convert_o2: 1/12 + + # Microzoo1 + - type: grazing + consumed: + bac1: [c,n,p] + produced: + microzoo1: [c,n,p] + parameters: + function: holling-2 + feeding_threshold: # [mg C / m^3] + max_grazing_rate: # [1/d] + half_sat_grazing: # [mg C / m^3] + + - type: grazing + consumed: + phyto1: [c,n,p,chl,si] + produced: + microzoo1: [c,n,p] + parameters: + function: holling-2 + feeding_threshold: # [mg C / m^3] + max_grazing_rate: # [1/d] + half_sat_grazing: # [mg C / m^3] + + - type: grazing + consumed: + phyto2: [c,n,p,chl] + produced: + mesozoo2: [c,n,p] + parameters: + function: holling-2 + feeding_threshold: # [mg C / m^3] + max_grazing_rate: # [1/d] + half_sat_grazing: # [mg C / m^3] + + - type: grazing + consumed: + phyto3: [c,n,p,chl] + produced: + mesozoo2: [c,n,p] + parameters: + function: holling-2 + feeding_threshold: # [mg C / m^3] + max_grazing_rate: # [1/d] + half_sat_grazing: # [mg C / m^3] + + - type: grazing + consumed: + microzoo1: [c,n,p] + produced: + microzoo1: [c,n,p] + parameters: + function: holling-2 + feeding_threshold: # [mg C / m^3] + max_grazing_rate: # [1/d] + half_sat_grazing: # [mg C / m^3] + + - type: grazing + consumed: + microzoo2: [c,n,p] + produced: + microzoo1: [c,n,p] + parameters: + function: holling-2 + feeding_threshold: # [mg C / m^3] + max_grazing_rate: # [1/d] + half_sat_grazing: # [mg C / m^3] + + - type: excretion + consumed: + microzoo1: [n] + produced: + nh4: [n] + parameters: + function: grazing + + - type: excretion + consumed: + microzoo1: [p] + produced: + po4: [p] + parameters: + function: grazing + + - type: excretion + consumed: + microzoo1: [c,n,p] + produced: + dom1: [c,n,p] + parameters: + function: grazing + partition: [] # c,n,p [-] + + - type: excretion + consumed: + microzoo1: [c,n,p] + produced: + pom1: [c,n,p] + parameters: + function: grazing + partition: [] # c,n,p [-] + + - type: mortality + consumed: + microzoo1: [c,n,p] + produced: + dom1: [c,n,p] + parameters: + + - type: mortality + consumed: + microzoo1: [c,n,p] + produced: + pom1: [c,n,p] + parameters: + + - type: respiration + consumed: + microzoo1: [c] + o2: [o] + produced: + co2: [c] + parameters: + respiration_rate: + convert_o2: 1/12 + + # Microzoo2 + - type: grazing + consumed: + bac1: [c,n,p] + produced: + microzoo2: [c,n,p] + parameters: + function: holling-2 + feeding_threshold: # [mg C / m^3] + max_grazing_rate: # [1/d] + half_sat_grazing: # [mg C / m^3] + + - type: grazing + consumed: + phyto2: [c,n,p,chl] + produced: + microzoo2: [c,n,p] + parameters: + function: holling-2 + feeding_threshold: # [mg C / m^3] + max_grazing_rate: # [1/d] + half_sat_grazing: # [mg C / m^3] + + - type: grazing + consumed: + phyto3: [c,n,p,chl] + produced: + microzoo2: [c,n,p] + parameters: + function: holling-2 + feeding_threshold: # [mg C / m^3] + max_grazing_rate: # [1/d] + half_sat_grazing: # [mg C / m^3] + + - type: grazing + consumed: + microzoo2: [c,n,p] + produced: + microzoo2: [c,n,p] + parameters: + function: holling-2 + feeding_threshold: # [mg C / m^3] + max_grazing_rate: # [1/d] + half_sat_grazing: # [mg C / m^3] + + - type: excretion + consumed: + microzoo2: [n] + produced: + nh4: [n] + parameters: + function: grazing + + - type: excretion + consumed: + microzoo2: [p] + produced: + po4: [p] + parameters: + function: grazing + + - type: excretion + consumed: + microzoo2: [c,n,p] + produced: + dom1: [c,n,p] + parameters: + function: grazing + partition: [] # c,n,p [-] + + - type: excretion + consumed: + microzoo2: [c,n,p] + produced: + pom1: [c,n,p] + parameters: + function: grazing + partition: [] # c,n,p [-] + + - type: mortality + consumed: + microzoo2: [c,n,p] + produced: + dom1: [c,n,p] + parameters: + + - type: mortality + consumed: + microzoo2: [c,n,p] + produced: + pom1: [c,n,p] + parameters: + + - type: respiration + consumed: + microzoo2: [c] + o2: [o] + produced: + co2: [c] + parameters: + respiration_rate: + convert_o2: 1/12 + + - type: + consumed: + produced: + parameters: + - type: + consumed: + produced: + parameters: + - type: + consumed: + produced: + parameters: + - type: + consumed: + produced: + parameters: + - type: + consumed: + produced: + parameters: + - type: + consumed: + produced: + parameters: + - type: + consumed: + produced: + parameters: + - type: + consumed: + produced: + parameters: + - type: + consumed: + produced: + parameters: + - type: + consumed: + produced: + parameters: + - type: + consumed: + produced: + parameters: + - type: + consumed: + produced: + parameters: + + + + + \ No newline at end of file diff --git a/cobalt.yaml b/cobalt.yaml new file mode 100644 index 0000000..77bc5d8 --- /dev/null +++ b/cobalt.yaml @@ -0,0 +1,836 @@ +base_element: n + +tracers: + o2: + long_name: oxygen + composition: + o: + parameters: + + no3: + long_name: nitrate + type: inorganic + composition: + n: # [mmol N / m^3] + parameters: + temp_limited: True + + nh4: + long_name: ammonium + type: inorganic + composition: + n: # [mmol N / m^3] + parameters: + temp_limited: True + + po4: + long_name: phosphate + type: inorganic + composition: + p: # [mmol P / m^3] + parameters: + temp_limited: False + + sio4: + long_name: silicate + type: inorganic + composition: + si: + parameters: + temp_limited: False + + fe: + long_name: dissolved iron + type: inorganic + composition: + fe: # [nmol Fe / m^3] + parameters: + temp_limited: False + + bac1: + long_name: bacteria + type: bacteria + composition: + n: + parameters: + nutrient_limitation: + colimitation: minimum # mininimum, product, or sum of all limiting nutrients + dom1: + element: n + type: external + function: monod + # half_sat: 5.E-07 # [mol / kg] + half_sat: 0.5 # [mmol / m^3] + oxygen_inhibition: + function: monod + # half_sat: 8.E-06 # [mol O2 / kg] + half_sat: 8.E-06 # [mmol / m^3] + exponent: 4. # optional: Hill exponent for Monod function (default to 1. if not included) + # min_o2: 0.8E-06 # optional: minimum oxygen concentration for determining aerobic/anerobic respiration [mol O2 / kg] + min_o2: 0.8E-06 # [mmol / m^3] + temperature_regulation: + function: eppley # arrhenius, eppley, or q10 + coefficient: 0.063 + base_temp: 0. # optional: exp(coefficient * (T - base_temp)) (default to 0. if not included) + + phyto1: + long_name: small phytoplankton + type: phytoplankton + composition: + n: # [mmol N / m^3] + fe: # [nmol Fe / m^3] + parameters: + light_attenuation: 0.03 + cell_quota: + constituents: [n,fe] + min: + max: + opt: + nutrient_limitation: + colimitation: minimum # mininimum, product, or sum of all limiting nutrients + no3: + type: external # internal (cell quota for nutrient w.r.t. base element) or external (availability of nutrient in system using half-saturation constant) + function: monod # monod or droop nutrient limitation types + nh4_inihibited: True # flag to include additional scaling for no3 limitation + # half_sat: 0.5 # μmol / kg + half_sat: 0.5 # [mmol / m^3] + exponent: 1. # optional: Hill exponent for Monod function (default to 1. if not included) + nh4: + type: external # internal (cell quota for nutrient w.r.t. base element) or external (availability of nutrient in system using half-saturation constant) + function: monod + # half_sat: 0.01 # μmol / kg + half_sat: 0.01 # [mmol / m^3] + exponent: 1. # optional: Hill exponent for Monod function (default to 1. if not included) + po4: + type: external # internal (cell quota for nutrient w.r.t. base element) or external (availability of nutrient in system using half-saturation constant) + function: monod + # half_sat: 0.05 # μmol / kg + half_sat: 0.05 # [mmol / m^3] + exponent: 1. # optional: Hill exponent for Monod function (default to 1. if not included) + fe: + type: internal # internal (cell quota for nutrient w.r.t. base element) or external (availability of nutrient in system using half-saturation constant) + function: monod + # half_sat: 0.1 # nmol / kg + half_sat: 0.1 # [μmol / m^3] + exponent: 2. # optional: Hill exponent for Monod function (default to 1. if not included) + temperature_regulation: + temp_limited: True + function: eppley # arrhenius, eppley, or q10 + coefficient: 0.063 + base_temp: 0. # optional: exp(coefficient * (T - base_temp)) (default to 0. if not included) + + phyto2: + long_name: large phytoplankton + type: phytoplankton + composition: + n: # [mmol N / m^3] + fe: # [nmol Fe / m^3] + si: # [mmol Si / m^3] + parameters: + light_attenuation: 0.03 + cell_quota: + constituents: [n,fe,si] + min: + max: + opt: + nutrient_limitation: + colimitation: + nutrients: # nutrients to be included in colimitation calculation + type: minimum # mininimum, product, or sum of all limiting nutrients + no3: + type: external # internal (cell quota for nutrient w.r.t. base element) or external (availability of nutrient in system using half-saturation constant) + function: monod # monod or droop nutrient limitation types + nh4_inihibited: True # flag to include additional scaling for no3 limitation + # half_sat: 2.5 # μmol / kg + half_sat: 2.5 # [mmol / m^3] + exponent: 1. # optional: Hill exponent for Monod function (default to 1. if not included) + nh4: + type: external # internal (cell quota for nutrient w.r.t. base element) or external (availability of nutrient in system using half-saturation constant) + function: monod + # half_sat: 0.05 # μmol / kg + half_sat: 0.05 # [mmol / m^3] + exponent: 1. # optional: Hill exponent for Monod function (default to 1. if not included) + po4: + type: external # internal (cell quota for nutrient w.r.t. base element) or external (availability of nutrient in system using half-saturation constant) + function: monod + # half_sat: 0.05 # μmol / kg + half_sat: 0.05 # [mmol / m^3] + exponent: 1. # optional: Hill exponent for Monod function (default to 1. if not included) + fe: + type: internal # internal (cell quota for nutrient w.r.t. base element) or external (availability of nutrient in system using half-saturation constant) + function: monod + # half_sat: 0.5 # nmol / kg + half_sat: 0.5 # [μmol / m^3] + exponent: 2. # optional: Hill exponent for Monod function (default to 1. if not included) + si: + type: external + function: monod + # half_sat: 2. # μmol / kg + half_sat: 2. # [mmol / m^3] + exponent: 1. # optional: Hill exponent for Monod function (default to 1. if not included) + temperature_regulation: + temp_limited: True + function: eppley # arrhenius, eppley, or q10 + coefficient: 0.063 + base_temp: 0. # optional: exp(coefficient * (T - base_temp)) (default to 0. if not included) + + phyto3: + long_name: diazotrophs + type: phytoplankton + composition: + n: # [mmol N / m^3] + fe: # [nmol Fe / m^3] + parameters: + light_attenuation: 0.03 + cell_quota: + constituents: [n,fe] + min: + max: + opt: + nutrient_limitation: + colimitation: + nutrients: # nutrients to be included in colimitation calculation + type: minimum # mininimum, product, or sum of all limiting nutrients + no3: + type: external # internal (cell quota for nutrient w.r.t. base element) or external (availability of nutrient in system using half-saturation constant) + function: monod # monod or droop nutrient limitation types + nh4_inihibited: True # flag to include additional scaling for no3 limitation + # half_sat: 5. # μmol / kg + half_sat: 5. # [mmol / m^3] + exponent: 1. # optional: Hill exponent for Monod function (default to 1. if not included) + nh4: + type: external # internal (cell quota for nutrient w.r.t. base element) or external (availability of nutrient in system using half-saturation constant) + function: monod + # half_sat: 0.1 # μmol / kg + half_sat: 0.1 # [mmol / m^3] + exponent: 1. # optional: Hill exponent for Monod function (default to 1. if not included) + po4: + type: external # internal (cell quota for nutrient w.r.t. base element) or external (availability of nutrient in system using half-saturation constant) + function: monod + # half_sat: 0.05 # μmol / kg + half_sat: 0.05 # [mmol / m^3] + exponent: 1. # optional: Hill exponent for Monod function (default to 1. if not included) + fe: + type: internal # internal (cell quota for nutrient w.r.t. base element) or external (availability of nutrient in system using half-saturation constant) + function: monod + # half_sat: 0.5 # nmol / kg + half_sat: 0.5 # [μmol / m^3] + exponent: 2. # optional: Hill exponent for Monod function (default to 1. if not included) + oxygen_inhibition: + # half_sat: 30 # μmol / kg + half_sat: 30 # [mmol / m^3] + exponent: 4. # optional: Hill exponent for Monod function (default to 1. if not included) + temperature_regulation: + temp_limited: True + function: eppley # arrhenius, eppley, or q10 + coefficient: 0.063 + base_temp: 0. # optional: exp(coefficient * (T - base_temp)) (default to 0. if not included) + + zoo1: + long_name: small zooplankton + type: zooplankton + composition: + n: # [mmol N / m^3] + parameters: + grazing_preferences: + bac1: 0.25 # [-] + phyto1: 1. # [-] + phyto2: 0. # [-] + phyto3: 0. # [-] + zoo1: 0. # [-] + zoo2: 0. # [-] + zoo3: 0. # [-] + temperature_regulation: + function: eppley # arrhenius, eppley, or q10 + coefficient: 0.063 + base_temp: 0. # optional: exp(coefficient * (T - base_temp)) (default to 0. if not included) + + zoo2: + long_name: medium zooplankton + type: zooplankton + composition: + n: # [mmol N / m^3] + parameters: + grazing_preferences: + bac1: 0. # [-] + phyto1: 0. # [-] + phyto2: 1. # [-] + phyto3: 1. # [-] + zoo1: 1. # [-] + zoo2: 0. # [-] + zoo3: 0. # [-] + temperature_regulation: + function: eppley # arrhenius, eppley, or q10 + coefficient: 0.063 + base_temp: 0. # optional: exp(coefficient * (T - base_temp)) (default to 0. if not included) + + zoo3: + long_name: large zooplankton + type: zooplankton + composition: + n: # [mmol N / m^3] + parameters: + grazing_preferences: + bac1: 0. # [-] + phyto1: 0. # [-] + phyto2: 1. # [-] + phyto3: 1. # [-] + zoo1: 0. # [-] + zoo2: 1. # [-] + zoo3: 0. # [-] + temperature_regulation: + function: eppley # arrhenius, eppley, or q10 + coefficient: 0.063 + base_temp: 0. # optional: exp(coefficient * (T - base_temp)) (default to 0. if not included) + + dom1: + long_name: labile dissolved organic matter + type: detritus + composition: + n: # [mmol N / m^3] + p: # [mmol P / m^3] + parameters: + light_attenuation: 0. + + dom2: + long_name: semi-labile dissolved organic matter + type: detritus + composition: + n: # [mmol N / m^3] + p: # [mmol P / m^3] + parameters: + light_attenuation: 0. + + dom3: + long_name: semi-refractory dissolved organic matter + type: detritus + composition: + n: # [mmol N / m^3] + p: # [mmol P / m^3] + parameters: + light_attenuation: 0. + + pom1: + long_name: particulate organic matter + type: detritus + composition: + n: # [mmol N / m^3] + p: # [mmol P / m^3] + fe: # [nmol Fe / m^3] + caco3: # [mmol C / m^3] + si: # [mmol Si / m^3] + parameters: + light_attenuation: 0. + +# ------------------------------------------------------------------------------------------------- +parameters: + constants: + einstein_to_watts: 0.217 + + environment: + PAR_frac: 0.45 + light_attenuation_water: 0.04 # [1/m] + latitude: 0.0 + forcing: seasonal_cycling + seasonal: + summer_mld: 10. # [m] + winter_mld: 40. # [m] + summer_salt: 36.5 # [psu] + winter_salt: 37. # [psu] + summer_sun: 300.0 # [W / m^2] + winter_sun: 20.0 # [W / m^2] + summer_temp: 30. # [°C] + winter_temp: 10. # [°C] + summer_wind: 2. # [m/s] + winter_wind: 6. # [m/s] + temp_dependence: + type: arrhenius + ref_temp: 303.15 # [K] + coeff: -4000. + light_dependence: + PAR_frac: 0.45 # [-] + light_attenuation_water: 0.04 # [1/m] + + # temp_dependence: + # type: q10 + # base_temp: 10. + + simulation: + latitude: 0.0 + num_days: 730 + timestep: 360.0 + + water_column: + column_depth: 5.0 + num_layers: 1 + +# ------------------------------------------------------------------------------------------------- +reactions: + + # Nutrient Uptake/Release - Bacteria + - type: uptake_release + consumed: + nh4: [n] + produced: + bac1: [n] + parameters: + + - type: uptake_release + consumed: + po4: [p] + produced: + bac1: [p] + parameters: + + # Nutrient Uptake - Small Phytoplankton (phyto1) + - type: uptake + consumed: + no3: [n] + nh4: [n] + produced: + phyto1: [n] + parameters: + basis: growth + # "growth": uptake rate calculated using growth rate + # "nutrient": uptake rate calculated based on nutrient availability + # "constant": multiplier added to growth formulation + strategy: independent + # "coupled": uptake based on that of different nutrients + # "independent": uptake calculated independent of other nutrients + + - type: uptake + consumed: + po4: [p] + produced: + phyto1: [p] + parameters: + basis: growth + # "growth": uptake rate calculated using growth rate + # "nutrient": uptake rate calculated based on nutrient availability + # "constant": multiplier added to growth formulation + strategy: coupled + # "coupled": uptake based on that of different nutrients + # "independent": uptake calculated independent of other nutrients + coupled_uptake: + link: ["no3","nh4"] + method: sum + convert_uptake: 0.0625 + + - type: uptake + consumed: + fe: [fe] + produced: + phyto1: [fe] + parameters: + basis: constant + # "growth": uptake rate calculated using growth rate + # "nutrient": uptake rate calculated based on nutrient availability + # "constant": multiplier added to growth formulation + strategy: independent + # "coupled": uptake based on that of different nutrients + # "independent": uptake calculated independent of other nutrients + constant: 1.125 + convert_uptake: 15.E-06 # [mmol O2 / mmol base_element] + + # Nutrient Uptake - Large Phytoplankton (phyto2) + - type: uptake + consumed: + no3: [n] + nh4: [n] + produced: + phyto2: [n] + parameters: + basis: growth + # "growth": uptake rate calculated using growth rate + # "nutrient": uptake rate calculated based on nutrient availability + # "constant": multiplier added to growth formulation + strategy: independent + # "coupled": uptake based on that of different nutrients + # "independent": uptake calculated independent of other nutrients + + - type: uptake + consumed: + po4: [p] + produced: + phyto2: [p] + parameters: + basis: constant + # "growth": uptake rate calculated using growth rate + # "nutrient": uptake rate calculated based on nutrient availability + # "constant": multiplier added to growth formulation + strategy: independent + # "coupled": uptake based on that of different nutrients + # "independent": uptake calculated independent of other nutrients + coupled_uptake: + link: ["no3","nh4"] + method: sum + convert_uptake: 0.0625 + + - type: uptake + consumed: + fe: [fe] + produced: + phyto2: [fe] + parameters: + basis: constant + # "growth": uptake rate calculated using growth rate + # "nutrient": uptake rate calculated based on nutrient availability + # "constant": multiplier added to growth formulation + strategy: independent + # "coupled": uptake based on that of different nutrients + # "independent": uptake calculated independent of other nutrients + constant: 1.25 + convert_uptake: 15.E-06 # [mmol O2 / mmol base_element] + + - type: uptake + consumed: + sio4: [si] + produced: + phyto2: [si] + parameters: + basis: constant + # "growth": uptake rate calculated using growth rate + # "nutrient": uptake rate calculated based on nutrient availability + # "constant": multiplier added to growth formulation + strategy: coupled + # "coupled": uptake based on that of different nutrients + # "independent": uptake calculated independent of other nutrients + coupled_uptake: + link: ["no3","nh4"] # nutrients who's uptake is dependent of + method: sum # Only include if multiple nutrients are linked the total uptake calculated by + # "sum": sum of upake rates of linked nutrients + # "minimum": minimum uptake rate + # "maximum": maximum uptake rate + # "product": product of uptake rates of linked nutrients + convert_uptake: 5. # conversion factor between nutrient being consumed and the linked nutrient [mmol consumed / mmol linked] + + # Nutrient Uptake - Diazotrophs (phyto3) + - type: uptake + consumed: + no3: [n] + nh4: [n] + produced: + phyto3: [n] + parameters: + basis: growth + # "growth": uptake rate calculated using growth rate + # "nutrient": uptake rate calculated based on nutrient availability + # "constant": multiplier added to growth formulation + strategy: independent + # "coupled": uptake based on that of different nutrients + # "independent": uptake calculated independent of other nutrients + + - type: uptake + consumed: + po4: [p] + produced: + phyto3: [p] + parameters: + basis: constant + # "growth": uptake rate calculated using growth rate + # "nutrient": uptake rate calculated based on nutrient availability + # "constant": multiplier added to growth formulation + strategy: independent + # "coupled": uptake based on that of different nutrients + # "independent": uptake calculated independent of other nutrients + coupled_uptake: + link: ["no3","nh4"] + method: sum + convert_uptake: 0.025 + + - type: uptake + consumed: + fe: [fe] + produced: + phyto3: [fe] + parameters: + basis: constant + # "growth": uptake rate calculated using growth rate + # "nutrient": uptake rate calculated based on nutrient availability + # "constant": multiplier added to growth formulation + strategy: independent + # "coupled": uptake based on that of different nutrients + # "independent": uptake calculated independent of other nutrients + constant: 1.25 + convert_uptake: 15.E-06 # [mmol O2 / mmol base_element] + + # Photosynthesis + - type: photosynthesis + consumed: + phyto1: [n] + produced: + parameters: + eps_PAR: 0.4 + light_limitation: geider + light_location: integrated + initial_PI_slope: 2.4E-05 # [(mg C . m^2) / (mg Chl μE)] + max_photo_rate: 1.125 # [1/d] + theta_max: 0.03 # [g Chl / g C] + theta_min: 0.002 # [g Chl / g C] + + - type: photosynthesis + consumed: + phyto2: [n] + produced: + parameters: + eps_PAR: 0.4 + light_limitation: geider + light_location: integrated + initial_PI_slope: 0.8E-05 # [(mg C . m^2) / (mg Chl μE)] + max_photo_rate: 1.25 # [1/d] + theta_max: 0.05 # [g Chl / g C] + theta_min: 0.002 # [g Chl / g C] + + - type: photosynthesis + consumed: + phyto3: [n] + produced: + parameters: + eps_PAR: 0.4 + light_limitation: geider + light_location: integrated + initial_PI_slope: 0.8E-05 # [(mg C . m^2) / (mg Chl μE)] + max_photo_rate: 0.5 # [1/d] + theta_max: 0.03 # [g Chl / g C] + theta_min: 0.002 # [g Chl / g C] + + # Phytoplankton Respiration + - type: respiration + consumed: + phyto1: [n] + o2: [o] + produced: + nh4: [n] + parameters: + activity_respiration_frac: 0.05 # [-] + basal_respiration_rate: 0.03 # [1/d] + convert_o2: 7.375 # [mmol O2 / mmol base_element] -- Ammonia production 16*NH4+ + 106*CO2 + 62*H2O <-> C106H172O38N16 + 118*O2 + 16*H+ + + - type: respiration + consumed: + phyto2: [n] + o2: [o] + produced: + nh4: [n] + parameters: + activity_respiration_frac: 0.05 # [-] + basal_respiration_rate: 0.05 # [1/d] + convert_o2: 7.375 # [mmol O2 / mmol base_element] -- Ammonia production 16*NH4+ + 106*CO2 + 62*H2O <-> C106H172O38N16 + 118*O2 + 16*H+ + + - type: respiration + consumed: + phyto3: [n] + o2: [o] + produced: + nh4: [n] + parameters: + activity_respiration_frac: 0.05 # [-] + basal_respiration_rate: 0.05 # [1/d] + convert_o2: 7.375 # [mmol O2 / mmol base_element] -- Ammonia production 16*NH4+ + 106*CO2 + 62*H2O <-> C106H172O38N16 + 118*O2 + 16*H+ + + # Bacteria Respiration + - type: respiration + consumed: + bac1: [n] + o2: [o] + produced: + nh4: [n] + parameters: + activity_respiration_frac: # [-] + anoxic_respiration_frac: # [-] + + respiration_rate: 0.0075 # [1/d] + convert_o2: 7.375 # [mmol O2 / mmol base_element] -- Ammonia production 16*NH4+ + 106*CO2 + 62*H2O <-> C106H172O38N16 + 118*O2 + 16*H+ + # min_o2: 0.8E-06 # [mol O2 / kg] -- minimum oxygen concentration for determining aerobic/anerobic respiration + min_o2: 0.8 # [mol O2 / kg] -- minimum oxygen concentration for determining aerobic/anerobic respiration + + # Phytoplankton Exudation + - type: exudation + consumed: + phyto1: [n] + produced: + dom1: [n] + parameters: + method: uptake # options: uptake, photosynthesis + # "photosynthesis": exudation rate calculated using excreted fraction of photosynthesis + # "uptake": exudation rate calculated using constant excreted fraction of nutrient uptake + excreted_fraction: 0.13 + + - type: exudation + consumed: + phyto2: [n] + produced: + dom1: [n] + parameters: + method: uptake # options: uptake, photosynthesis + # "photosynthesis": exudation rate calculated using excreted fraction of photosynthesis + # "uptake": exudation rate calculated using constant excreted fraction of nutrient uptake + excreted_fraction: 0.13 + + - type: exudation + consumed: + phyto3: [n] + produced: + dom1: [n] + parameters: + method: uptake # options: uptake, photosynthesis + # "photosynthesis": exudation rate calculated using excreted fraction of photosynthesis + # "uptake": exudation rate calculated using constant excreted fraction of nutrient uptake + excreted_fraction: 0.13 + + # Phytoplankton Lysis + - type: lysis + consumed: + phyto1: [n] + produced: + dom1: [n] + parameters: + partition: + method: constant # options: constant, cell_quota + # "constant": lysis rate calculated as a quadratic loss term using constant rate + # "cell_quota": lysis rate calculated using intracellular nutrient quota to release excess + lysis_rate: 0.2E06 # [mmol N / m^3] + + - type: lysis + consumed: + phyto1: [n] + produced: + dom2: [n] + parameters: + partition: + method: constant # options: constant, cell_quota + # "constant": lysis rate calculated as a quadratic loss term using constant rate + # "cell_quota": lysis rate calculated using intracellular nutrient quota to release excess + lysis_rate: 0.2E06 # [mmol N / m^3] + + - type: lysis + consumed: + phyto1: [n] + produced: + dom3: [n] + parameters: + partition: + method: constant # options: constant, cell_quota + # "constant": lysis rate calculated as a quadratic loss term using constant rate + # "cell_quota": lysis rate calculated using intracellular nutrient quota to release excess + lysis_rate: 0.2E06 # [mmol N / m^3] + + - type: lysis + consumed: + phyto1: + produced: + dom1: [p] + parameters: + partition: + method: constant # options: constant, cell_quota + # "constant": lysis rate calculated as a quadratic loss term using constant rate + # "cell_quota": lysis rate calculated using intracellular nutrient quota to release excess + lysis_rate: 0.2E06 # [mmol N / m^3] + convert_lysis: 0.0625 + + - type: lysis + consumed: + phyto1: + produced: + dom2: [p] + parameters: + partition: + method: constant # options: constant, cell_quota + # "constant": lysis rate calculated as a quadratic loss term using constant rate + # "cell_quota": lysis rate calculated using intracellular nutrient quota to release excess + lysis_rate: 0.2E06 # [mmol N / m^3] + convert_lysis: 0.0625 + + - type: lysis + consumed: + phyto1: + produced: + dom3: [p] + parameters: + partition: + method: constant # options: constant, cell_quota + # "constant": lysis rate calculated as a quadratic loss term using constant rate + # "cell_quota": lysis rate calculated using intracellular nutrient quota to release excess + lysis_rate: 0.2E06 # [mmol N / m^3] + convert_lysis: 0.0625 + + - type: lysis + consumed: + phyto1: [fe] + produced: + pom1: [fe] + parameters: + partition: + method: constant # options: constant, cell_quota + # "constant": lysis rate calculated as a quadratic loss term using constant rate + # "cell_quota": lysis rate calculated using intracellular nutrient quota to release excess + lysis_rate: 0.2E06 # [mmol N / m^3] + convert_lysis: cell_quota + + # Phytoplankton Aggregation + - type: aggregation + consumed: + phyto1: [n] + produced: + pom1: [n] + parameters: + aggregation_loss: 0.1E06 + aggregation_frac: 0.25 + max_photo_rate: 1.125 + + - type: aggregation + consumed: + phyto1: + produced: + pom1: [p] + parameters: + aggregation_loss: 0.1E06 + aggregation_frac: 0.25 + max_photo_rate: 1.125 + convert_aggregation: 0.0625 + + - type: aggregation + consumed: + phyto1: + produced: + pom1: [fe] + parameters: + aggregation_loss: 0.1E06 + aggregation_frac: 0.25 + max_photo_rate: 1.125 + convert_aggregation: cell_quota + + - type: aggregation + consumed: + phyto3: [n] + produced: + pom1: [n] + parameters: + aggregation_loss: 0.1E06 + aggregation_frac: 0.25 + max_photo_rate: 0.5 + + - type: aggregation + consumed: + phyto3: + produced: + pom1: [p] + parameters: + aggregation_loss: 0.1E06 + aggregation_frac: 0.25 + max_photo_rate: 0.5 + convert_aggregation: 0.025 + + - type: aggregation + consumed: + phyto3: + produced: + pom1: [fe] + parameters: + aggregation_loss: 0.1E06 + aggregation_frac: 0.25 + max_photo_rate: 0.5 + convert_aggregation: cell_quota + + + diff --git a/concentration.npz b/concentration.npz new file mode 100644 index 0000000..9ebfddd Binary files /dev/null and b/concentration.npz differ diff --git a/dakota.in b/dakota.in new file mode 100644 index 0000000..4359578 --- /dev/null +++ b/dakota.in @@ -0,0 +1,31 @@ +environment + tabular_data + tabular_data_file = 'None' + +method + sampling + samples = 100 + seed = 12345 + sample_type = lhs + +model + single + +variables + continuous_design = 54 + initial_point 0.06 0.06 0.06 0.05 1e-06 1e-07 1.3e-07 0.0001 10.0 0.01 0.25 0.00687 0.0126 0.0025 0.000429 0.000786 0.016 0.016 0.016 0.05 0.03 1.6 0.0 1.52e-05 0.4 0.0 0.0 0.1 0.05 0.05 0.05 1.5 1.0 0.025 1.0 0.0025 0.01258 0.0007862 0.25 0.5 1.0 1e-06 50.0 2.0 200.0 0.5 [1e-06, 0.0] 0.25 0.02 0.05 0.0001 0.1 1.0 1.0 + lower_bounds None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None + upper_bounds None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None + descriptors 'pom1d.relaxation_velocities.nrt_o2' 'pom1d.relaxation_velocities.nrt_po4' 'pom1d.relaxation_velocities.nrt_no3' 'pom1d.relaxation_velocities.nrt_nh4' 'pom1d.background_diffusion.umol' 'pom1d.background_diffusion.umolt' 'pom1d.background_diffusion.umols' 'pom1d.background_diffusion.umolbgc' 'bfm17_new_structure.nh4.nitrification.half_sat_oxygen' 'bfm17_new_structure.nh4.nitrification.nitrification_rate' 'bfm17_new_structure.phyto1.cell_quota.n.max' 'bfm17_new_structure.phyto1.cell_quota.n.min' 'bfm17_new_structure.phyto1.cell_quota.n.opt' 'bfm17_new_structure.phyto1.cell_quota.p.max' 'bfm17_new_structure.phyto1.cell_quota.p.min' 'bfm17_new_structure.phyto1.cell_quota.p.opt' 'bfm17_new_structure.phyto1.cell_quota.chl.max' 'bfm17_new_structure.phyto1.cell_quota.chl.min' 'bfm17_new_structure.phyto1.cell_quota.chl.opt' 'bfm17_new_structure.phyto1.exudation.excreted_fraction' 'bfm17_new_structure.phyto1.growth.light_attenuation' 'bfm17_new_structure.phyto1.growth.max_photo_rate' 'bfm17_new_structure.phyto1.growth.chl_relax_rate' 'bfm17_new_structure.phyto1.growth.initial_PI_slope' 'bfm17_new_structure.phyto1.growth.eps_PAR' 'bfm17_new_structure.phyto1.lysis.extra_lysis_rate' 'bfm17_new_structure.phyto1.lysis.half_sat_extra_lysis' 'bfm17_new_structure.phyto1.lysis.half_sat_stress_lysis' 'bfm17_new_structure.phyto1.lysis.max_stress_lysis_rate' 'bfm17_new_structure.phyto1.respiration.activity_respiration_frac' 'bfm17_new_structure.phyto1.respiration.basal_respiration_rate' 'bfm17_new_structure.phyto1.uptake.n.half_sat_nh4_preference' 'bfm17_new_structure.phyto1.uptake.n.luxury_storage' 'bfm17_new_structure.phyto1.uptake.n.specific_affinity' 'bfm17_new_structure.phyto1.uptake.p.luxury_storage' 'bfm17_new_structure.phyto1.uptake.p.specific_affinity' 'bfm17_new_structure.zoo1.cell_quota.n.opt' 'bfm17_new_structure.zoo1.cell_quota.p.opt' 'bfm17_new_structure.zoo1.excretion.assimilation_efficiency' 'bfm17_new_structure.zoo1.excretion.ingestion_efficiency' 'bfm17_new_structure.zoo1.grazing.grazing_preferences.phyto1' 'bfm17_new_structure.zoo1.grazing.grazing_preferences.zoo1' 'bfm17_new_structure.zoo1.grazing.feeding_threshold' 'bfm17_new_structure.zoo1.grazing.max_grazing_rate' 'bfm17_new_structure.zoo1.grazing.half_sat_grazing' 'bfm17_new_structure.zoo1.mortality.half_sat_oxygen' 'bfm17_new_structure.zoo1.mortality.mortality_rate' 'bfm17_new_structure.zoo1.mortality.mortality_rate_oxy' 'bfm17_new_structure.zoo1.respiration.respiration_rate' 'bfm17_new_structure.dom1.remineralization.remineralization_rate' 'bfm17_new_structure.pom1.light_attenuation' 'bfm17_new_structure.pom1.remineralization.remineralization_rate' 'bfm17_new_structure.pom1.sinking.sinking_rate' 'bfm17_new_structure.pom1.sinking.burial_velocity' + +interface + fork + analysis_driver = 'None' + asynchronous + evaluation_concurrency = '8' + +responses + num_objective_functions = 1 + no_gradients + no_hessians + diff --git a/dakota.yaml b/dakota.yaml new file mode 100644 index 0000000..3f06b46 --- /dev/null +++ b/dakota.yaml @@ -0,0 +1,306 @@ +dakota: + environment: + tabular_data_file: + + method: + type: sampling + samples: 100 + seed: 12345 + sample_type: lhs + + model: + type: single + + interface: + analysis_driver: + fork: True + asynchronous: True + evaluation_concurrency: 8 + + responses: + num_objective_functions: 1 + gradients: False + hessians: False + + +parameters: +# ------------------------------------------------------------------------------------------------- +# POM1D Parameters +# ------------------------------------------------------------------------------------------------- + pom1d: + relaxation_velocities: + nrt_o2: + lower: + initial: 0.06 + upper: + nrt_po4: + lower: + initial: 0.06 + upper: + nrt_no3: + lower: + initial: 0.06 + upper: + nrt_nh4: + lower: + initial: 0.05 + upper: + + background_diffusion: + umol: + lower: + initial: 1.E-06 + upper: + umolt: + lower: + initial: 1.E-07 + upper: + umols: + lower: + initial: 1.3E-07 + upper: + umolbgc: + lower: + initial: 1.E-04 + upper: + +# ------------------------------------------------------------------------------------------------- +# BGC Parameters +# ------------------------------------------------------------------------------------------------- + bfm17_new_structure: + # ------------------------------------------------------------------------------------------------- + # Nutrients + # ------------------------------------------------------------------------------------------------- + nh4: + nitrification: + half_sat_oxygen: + lower: + initial: 10. + upper: + nitrification_rate: + lower: + initial: 0.01 + upper: + + # ------------------------------------------------------------------------------------------------- + # Phytoplankton + # ------------------------------------------------------------------------------------------------- + phyto1: + cell_quota: + n: + max: + lower: + initial: 0.25 + upper: + min: + lower: + initial: 6.87E-03 + upper: + opt: + lower: + initial: 1.26E-02 + upper: + p: + max: + lower: + initial: 2.5E-03 + upper: + min: + lower: + initial: 4.29E-04 + upper: + opt: + lower: + initial: 7.86E-04 + upper: + chl: + max: + lower: + initial: 0.016 + upper: + min: + lower: + initial: 0.016 + upper: + opt: + lower: + initial: 0.016 + upper: + exudation: + excreted_fraction: + lower: + initial: 0.05 + upper: + growth: + light_attenuation: + lower: + initial: 0.03 + upper: + max_photo_rate: + lower: + initial: 1.6 + upper: + chl_relax_rate: + lower: + initial: 0. + upper: + initial_PI_slope: + lower: + initial: 1.52E-05 + upper: + eps_PAR: + lower: + initial: 0.4 + upper: + lysis: + extra_lysis_rate: + lower: + initial: 0. + upper: + half_sat_extra_lysis: + lower: + initial: 0. + upper: + half_sat_stress_lysis: + lower: + initial: 0.1 + upper: + max_stress_lysis_rate: + lower: + initial: 0.05 + upper: + respiration: + activity_respiration_frac: + lower: + initial: 0.05 + upper: + basal_respiration_rate: + lower: + initial: 0.05 + upper: + uptake: + n: + half_sat_nh4_preference: + lower: + initial: 1.5 + upper: + luxury_storage: + lower: + initial: 1. + upper: + specific_affinity: + lower: + initial: 0.025 + upper: + p: + luxury_storage: + lower: + initial: 1. + upper: + specific_affinity: + lower: + initial: 0.0025 + upper: + + # ------------------------------------------------------------------------------------------------- + # Zooplankton + # ------------------------------------------------------------------------------------------------- + zoo1: + cell_quota: + n: + opt: + lower: + initial: 1.258E-02 + upper: + p: + opt: + lower: + initial: 7.862E-04 + upper: + excretion: + assimilation_efficiency: + lower: + initial: 0.25 + upper: + ingestion_efficiency: + lower: + initial: 0.5 + upper: + grazing: + grazing_preferences: + phyto1: + lower: + initial: 1. + upper: + zoo1: + lower: + initial: 1.E-06 + upper: + feeding_threshold: + lower: + initial: 50. + upper: + max_grazing_rate: + lower: + initial: 2.0 + upper: + half_sat_grazing: + lower: + initial: 200. + upper: + mortality: + half_sat_oxygen: + lower: + initial: 0.5 + upper: + mortality_rate: + linear: + lower: + initial: 1.E-06 + upper: + quadratic: + lower: + initial: 0. + upper: + mortality_rate_oxy: + lower: + initial: 0.25 + upper: + respiration: + respiration_rate: + lower: + initial: 0.02 + upper: + + # ------------------------------------------------------------------------------------------------- + # Dissolved Organic Matter + # ------------------------------------------------------------------------------------------------- + dom1: + remineralization: + remineralization_rate: + lower: + initial: 0.05 + upper: + + # ------------------------------------------------------------------------------------------------- + # Particulate Organic Matter + # ------------------------------------------------------------------------------------------------- + pom1: + light_attenuation: + lower: + initial: 1.E-04 + upper: + remineralization: + remineralization_rate: + lower: + initial: 0.1 + upper: + sinking: + sinking_rate: + lower: + initial: 1. + upper: + burial_velocity: + lower: + initial: 1. + upper: \ No newline at end of file diff --git a/functions/bgc_rate_eqns.py b/functions/bgc_rate_eqns.py index bb72dc0..d7504f0 100644 --- a/functions/bgc_rate_eqns.py +++ b/functions/bgc_rate_eqns.py @@ -6,55 +6,8 @@ from setup.initialize import coordinate_system -def bgc_rate_eqns(iter, base_element, parameters, tracers): -# def bgc_rate_eqns(iter): - -# from globe import base_element, parameters, tracers - # Extract model paramters - environmental_parameters = parameters["environment"] - simulation_parameters = parameters["simulation"] - water_column_parameters = parameters["water_column"] - water_column_parameters = coordinate_system(parameters["water_column"]) - - # Seasonal cycling for temperature, salinity, radiation, and mixed layer depth - temperature = seasonal_cycling.get_temperature(simulation_parameters["time"][iter], environmental_parameters["winter_temp"], environmental_parameters["summer_temp"]) - surface_PAR = seasonal_cycling.get_sunlight(simulation_parameters["time"][iter], environmental_parameters["winter_sun"], environmental_parameters["summer_sun"],environmental_parameters["latitude"]) - # surface_PAR = seasonal_cycling.get_irrad(simulation_parameters["time"][iter], environmental_parameters["winter_sun"], environmental_parameters["summer_sun"]) - mixed_layer_depth = seasonal_cycling.get_mixed_layer_depth(simulation_parameters["time"][iter], environmental_parameters["winter_mld"], environmental_parameters["summer_mld"]) - salinity = seasonal_cycling.get_salinity(simulation_parameters["time"][iter], environmental_parameters["winter_salt"], environmental_parameters["summer_salt"]) - wind = seasonal_cycling.get_wind(simulation_parameters["time"][iter], environmental_parameters["winter_wind"], environmental_parameters["summer_wind"]) +def bgc_rate_eqns(iter, base_element, physical, pom1d, tracers): - # Clear previous rates - for key in tracers: - tracers[key].d_dt = np.zeros_like(tracers[key].d_dt) - - if iter == 360: - x=1 - - # Calculate bgc rates - for key in tracers: - if tracers[key].type == "bacteria": - pass - elif tracers[key].type == "detritus": - tracers[key].detritus(iter, base_element, tracers) - elif tracers[key].type == "inorganic": - tracers[key].inorg(iter, base_element, environmental_parameters["base_temp"], water_column_parameters["z"], water_column_parameters["dz"], mixed_layer_depth, surface_PAR, temperature, salinity, wind, tracers) - elif tracers[key].type == "phytoplankton": - tracers[key].phyto(iter, base_element, environmental_parameters["base_temp"], environmental_parameters["light_attenuation_water"], water_column_parameters["z"], water_column_parameters["dz"], mixed_layer_depth, surface_PAR, temperature, tracers) - # fI, irrad, nut_lim = tracers[key].phyto(iter, base_element, environmental_parameters["base_temp"], environmental_parameters["light_attenuation_water"], water_column_parameters["z"], water_column_parameters["dz"], mixed_layer_depth, surface_PAR, temperature, tracers) - # fI, irrad = tracers[key].phyto(iter, base_element, environmental_parameters["base_temp"], water_column_parameters["z"], water_column_parameters["dz"], mixed_layer_depth, surface_PAR, temperature, tracers) - elif tracers[key].type == "zooplankton": - tracers[key].zoo(iter, base_element, environmental_parameters["base_temp"], temperature, tracers) - # rsp = tracers[key].zoo(iter, base_element, environmental_parameters["base_temp"], temperature, tracers) - - # Apply rates to tracer concentrations - for key in tracers: - tracers[key].conc[...,iter+1] = tracers[key].conc[...,iter] + simulation_parameters["timestep"] * tracers[key].d_dt / 86400 # Convert rates from 1/d to 1/s - - # Set minimum of zero for tracer concentrations - for key in tracers: - tracers[key].conc[...,iter+1] = np.maximum(1E-20*np.ones_like(tracers[key].conc[...,iter+1]),tracers[key].conc[...,iter+1]) - # Update concentration ratios for key in tracers: if tracers[key].type in ["bacteria", "detritus","phytoplankton","zooplankton"]: @@ -62,61 +15,51 @@ def bgc_rate_eqns(iter, base_element, parameters, tracers): index = tracers[key].composition.index(base_element) # Calculate concentration ratios - concentration_ratio(iter+1, index, tracers[key]) - # return fI, irrad, nut_lim - - -def bgc_rate_eqns_solveivp(time, concentration): - from test_globe import base_element, parameters, tracers, tracer_indices - # Extract model paramters - environmental_parameters = parameters["environment"] - simulation_parameters = parameters["simulation"] - water_column_parameters = parameters["water_column"] - water_column_parameters = coordinate_system(parameters["water_column"]) - - # Seasonal cycling for temperature, salinity, radiation, and mixed layer depth - temperature = seasonal_cycling.get_temperature(time, environmental_parameters["winter_temp"], environmental_parameters["summer_temp"]) - # surface_PAR = seasonal_cycling.get_sunlight(simulation_parameters["time"][iter], environmental_parameters["winter_sun"], environmental_parameters["summer_sun"]) - surface_PAR = seasonal_cycling.get_irrad(time, environmental_parameters["winter_sun"], environmental_parameters["summer_sun"]) - mixed_layer_depth = seasonal_cycling.get_mixed_layer_depth(time, environmental_parameters["winter_mld"], environmental_parameters["summer_mld"]) - salinity = seasonal_cycling.get_salinity(time, environmental_parameters["winter_salt"], environmental_parameters["summer_salt"]) - wind = seasonal_cycling.get_wind(time, environmental_parameters["winter_wind"], environmental_parameters["summer_wind"]) + concentration_ratio(iter, index, tracers[key]) - # temperature = seasonal_cycling.get_temperature(simulation_parameters["time"][iter], environmental_parameters["winter_temp"], environmental_parameters["summer_temp"]) - # # surface_PAR = seasonal_cycling.get_sunlight(simulation_parameters["time"][iter], environmental_parameters["winter_sun"], environmental_parameters["summer_sun"]) - # surface_PAR = seasonal_cycling.get_irrad(simulation_parameters["time"][iter], environmental_parameters["winter_sun"], environmental_parameters["summer_sun"]) - # mixed_layer_depth = seasonal_cycling.get_mixed_layer_depth(simulation_parameters["time"][iter], environmental_parameters["winter_mld"], environmental_parameters["summer_mld"]) - # salinity = seasonal_cycling.get_salinity(simulation_parameters["time"][iter], environmental_parameters["winter_salt"], environmental_parameters["summer_salt"]) - # wind = seasonal_cycling.get_wind(simulation_parameters["time"][iter], environmental_parameters["winter_wind"], environmental_parameters["summer_wind"]) - - # Clip concentration matrix - for i in range(0,len(concentration)): - concentration[i] = np.maximum(1E-20,concentration[i]) + if physical["environment"]["forcing"] == "seasonal": + # Seasonal cycling + physical["bgc_phys_vars"]["temperature"] = seasonal_cycling.get_temperature(physical["simulation"]["time"][iter], physical["environment"]["seasonal_cycling"]["winter_temp"], physical["environment"]["seasonal_cycling"]["summer_temp"]) + physical["bgc_phys_vars"]["surface_PAR"] = seasonal_cycling.get_sunlight(physical["simulation"]["time"][iter], physical["environment"]["seasonal_cycling"]["winter_sun"], physical["environment"]["seasonal_cycling"]["summer_sun"],physical["environment"]["seasonal_cycling"]["latitude"]) + physical["bgc_phys_vars"]["mld"] = seasonal_cycling.get_mixed_layer_depth(physical["simulation"]["time"][iter], physical["environment"]["seasonal_cycling"]["winter_mld"], physical["environment"]["seasonal_cycling"]["summer_mld"]) + physical["bgc_phys_vars"]["salinity"] = seasonal_cycling.get_salinity(physical["simulation"]["time"][iter], physical["environment"]["seasonal_cycling"]["winter_salt"], physical["environment"]["seasonal_cycling"]["summer_salt"]) + physical["bgc_phys_vars"]["wind"] = seasonal_cycling.get_wind(physical["simulation"]["time"][iter], physical["environment"]["seasonal_cycling"]["winter_wind"], physical["environment"]["seasonal_cycling"]["summer_wind"]) + physical["bgc_phys_vars"]["z"] = physical["vertical_grid"]["z"] # Clear previous rates for key in tracers: tracers[key].d_dt = np.zeros_like(tracers[key].d_dt) - + # Calculate bgc rates for key in tracers: if tracers[key].type == "bacteria": - pass + tracers[key].bac(iter, base_element, physical, tracers) elif tracers[key].type == "detritus": - tracers[key].detritus(concentration, tracer_indices, base_element, tracers) + tracers[key].detritus(iter, base_element, physical, tracers) elif tracers[key].type == "inorganic": - tracers[key].inorg(concentration, tracer_indices, base_element, environmental_parameters["base_temp"], water_column_parameters["z"], water_column_parameters["dz"], mixed_layer_depth, surface_PAR, temperature, salinity, wind, tracers) + tracers[key].inorg(iter, base_element, physical, tracers) elif tracers[key].type == "phytoplankton": - tracers[key].phyto(concentration, tracer_indices, base_element, environmental_parameters["base_temp"], water_column_parameters["z"], water_column_parameters["dz"], mixed_layer_depth, surface_PAR, temperature, tracers) - # fI, irrad = tracers[key].phyto(iter, base_element, environmental_parameters["base_temp"], water_column_parameters["z"], water_column_parameters["dz"], mixed_layer_depth, surface_PAR, temperature, tracers) + tracers[key].phyto(iter, base_element, physical, tracers) elif tracers[key].type == "zooplankton": - tracers[key].zoo(concentration, tracer_indices, base_element, environmental_parameters["base_temp"], temperature, tracers) - # rsp = tracers[key].zoo(iter, base_element, environmental_parameters["base_temp"], temperature, tracers) + tracers[key].zoo(iter, base_element, physical, tracers) - # Update rate of change matrix - dt = np.zeros_like(concentration) - for t in tracers: - indices = tracer_indices[t] - for i in range(len(indices)): - dt[indices[i]] = tracers[t].d_dt[i,...] + # Convert rates fro 1/d to 1/s + for key in tracers: + tracers[key].d_dt /= physical["simulation"]["sec_per_day"] + + # # Apply rates to tracer concentrations + # for key in tracers: + # tracers[key].conc[...,iter+1] = tracers[key].conc[...,iter] + physical["simulation"]["dt"] * tracers[key].d_dt / physical["simulation"]["sec_per_day"] # Convert rates from 1/d to 1/s + + # # Set minimum of zero for tracer concentrations + # for key in tracers: + # tracers[key].conc[...,iter+1] = np.maximum(1E-20*np.ones_like(tracers[key].conc[...,iter+1]),tracers[key].conc[...,iter+1]) + + # # Update concentration ratios + # for key in tracers: + # if tracers[key].type in ["bacteria", "detritus","phytoplankton","zooplankton"]: + # # Get index of base element + # index = tracers[key].composition.index(base_element) - return dt \ No newline at end of file + # # Calculate concentration ratios + # concentration_ratio(iter+1, index, tracers[key]) diff --git a/functions/calculate_averages.py b/functions/calculate_averages.py new file mode 100644 index 0000000..d78072c --- /dev/null +++ b/functions/calculate_averages.py @@ -0,0 +1,43 @@ +import numpy as np + +def average(concentration, physical, type): + + timesteps_per_day = 86400/physical["simulation"]["dt"] + day = 0 + month = 0 + + if type == 'concentration': + daily = np.zeros((concentration.shape[0],concentration.shape[1],physical["simulation"]["days"])) + monthly = np.zeros((concentration.shape[0],concentration.shape[1],physical["simulation"]["months"])) + + for i in range(0,physical["simulation"]["iters"]-1): + daily[:,:,day] += concentration[:,:,i] # add to day tally + + if (i != 0) & ((i+1) % timesteps_per_day == 0): # take average at the end of day + daily[:,:,day] = daily[:,:,day]/timesteps_per_day + monthly[:,:,month] += daily[:,:,day] # add to month tally + + if (day != 0) & ((day+1) % 30 == 0): + monthly[:,:,month] = monthly[:,:,month]/30 # take average at the end of month + month += 1 # move to next month + + day += 1 # move to next day + + else: + daily = np.zeros((concentration.shape[0],physical["simulation"]["days"])) + monthly = np.zeros((concentration.shape[0],physical["simulation"]["months"])) + + for i in range(0,physical["simulation"]["iters"]-1): + daily[:,day] += concentration[:,i] # add to day tally + + if (i != 0) & ((i+1) % timesteps_per_day == 0): # take average at the end of day + daily[:,day] = daily[:,day]/timesteps_per_day + monthly[:,month] += daily[:,day] # add to month tally + + if (day != 0) & ((day+1) % 30 == 0): + monthly[:,month] = monthly[:,month]/30 # take average at the end of month + month += 1 # move to next month + + day += 1 # move to next day + + return daily, monthly \ No newline at end of file diff --git a/functions/other_functions.py b/functions/other_functions.py index 3e4a5d8..8293574 100644 --- a/functions/other_functions.py +++ b/functions/other_functions.py @@ -1,85 +1,98 @@ import numpy as np import sys -# def light_attenuation(parameters, phyto): -# """ -# Definition:: Calculates light attenuation factor for photosynthesis -# Beer's Law attenuation coefficient -# """ -# k_PAR = parameters["light_attenuation_water"] + parameters["light_attenuation_phyto"] * phyto - -# return k_PAR - - def light_attenuation(abbrev, iter, base_element, light_attenuation_water, tracers): """ Definition:: Calculates light attenuation factor for photosynthesis Beer's Law attenuation coefficient """ - k_PAR = light_attenuation_water * np.ones_like(tracers[abbrev].conc[0][iter]) + k_PAR = light_attenuation_water * np.ones_like(tracers[abbrev].conc[0,:,iter]) for key in tracers: if tracers[key].type == "detritus": base_index = tracers[key].composition.index(base_element) - k_PAR += tracers[key].light_attenuation * np.array(tracers[key].conc[base_index][iter]) + k_PAR += tracers[key].light_attenuation * np.array(tracers[key].conc[base_index,:,iter]) if tracers[key].type == "phytoplankton": # Light attenuation coefficient for phytoplankton is calculated using chl if available if "chl" in tracers[key].composition: chl_index = tracers[key].composition.index("chl") - k_PAR += tracers[key].light_attenuation * np.array(tracers[key].conc[chl_index][iter]) + k_PAR += tracers[key].light_attenuation * np.array(tracers[key].conc[chl_index,:,iter]) else: base_index = tracers[key].composition.index(base_element) - k_PAR += tracers[key].light_attenuation * np.array(tracers[key].conc[base_index][iter]) + k_PAR += tracers[key].light_attenuation * np.array(tracers[key].conc[base_index,:,iter]) return k_PAR # def light_limitation(parameters, dz, irrad, k_PAR, mixed_layer_depth, surface_PAR, Vm, pl_pc): -def light_limitation(parameters, dz, irrad, k_PAR, pl_pc, Vm): +def light_limitation(phyto, iter, parameters, dz, irrad, k_PAR, Vm): """ k_PAR = Light Attenuation Coefficient surface_PAR = Photosynthetically Active Radiation (PAR) at watetr surface (z = 0) """ + # ------------------------------------------------------------------------------------------------- + # Monod + # ------------------------------------------------------------------------------------------------- if parameters["light_limitation"] == "monod": # Heinle & Slawig (2013) light_limitation = irrad / (parameters["half_sat_light"] + irrad + 1E-20) - elif parameters["light_limitation"] == "platt": # Jassby and Platt (1976) - # *86400 to convert from [1/s] to [1/d] + # ------------------------------------------------------------------------------------------------- + # Geider et al. (1997) / Jassby and Platt (1976) + # ------------------------------------------------------------------------------------------------- + elif parameters["light_limitation"] in ["geider","platt"]: + + # Calculate irradiance at depth if parameters["light_location"] == "top": - r = np.maximum(1E-20*np.ones_like(irrad), irrad) * 86400 - elif parameters["light_location"] == "middle": - r = np.maximum(1E-20*np.ones_like(irrad), irrad) * np.exp( -k_PAR * dz/2) * 86400 - elif parameters["light_location"] == "integrated": - r = irrad / (k_PAR * dz) * (1. - np.exp(-k_PAR*dz)) - - irr = np.maximum(1E-20*np.ones_like(r), r*86400) - exp = pl_pc * ( parameters["initial_PI_slope"] / Vm ) * irr + irrad_at_depth = np.maximum(1E-20*np.ones_like(irrad), irrad) * 86400 # *86400 to convert from [1/s] to [1/d] + elif parameters["light_location"] == "middle": # Lazzari et al. (2012) + irrad_at_depth = np.maximum(1E-20*np.ones_like(irrad), irrad) * np.exp( -k_PAR * dz/2) * 86400 # *86400 to convert from [1/s] to [1/d] + elif parameters["light_location"] == "integrated": # Vichi et al. (2007) + r = irrad / (k_PAR * dz) * (1. - np.exp(-k_PAR*dz)) + irrad_at_depth = np.maximum(1E-20*np.ones_like(r), r*86400) # *86400 to convert from [1/s] to [1/d] + + # Calculate Chl:C ratio using either ... + if {"c","chl"}.issubset(phyto.composition): # Carbon and Chlorophyll concentrations (if preselt) + # if "c" in self.composition and "chl" in self.composition: + carbon_index = phyto.composition.index("c") + pc = phyto.conc[carbon_index,:,iter] + chl_index = phyto.composition.index("chl") + pl = phyto.conc[chl_index,:,iter] + pl_pc = pl / pc # Chl:C ratio (used in light limitation) + else: # Geider et al. (1997) Dynamic Model + if "theta_min" not in parameters: parameters["theta_min"] = 0. + pl_pc = (parameters["theta_max"] - parameters["theta_min"]) / ( 1. + ( ( parameters["theta_max"] * parameters["initial_PI_slope"] * irrad_at_depth ) / + ( 2 * Vm * phyto.temp_regulation_factor * phyto.nutrient_limitation_factor + 1.E-20 ) ) ) \ + + parameters["theta_min"] + + # Calculate exponent for light limitation + exp = pl_pc * ( parameters["initial_PI_slope"] / Vm ) * irrad_at_depth # Stays like this for Platt + if parameters["light_limitation"] == "geider": # scale by temperature and nutrient limitation factors for Geider + exp = exp / ( phyto.temp_regulation_factor * phyto.nutrient_limitation_factor ) light_limitation = 1. - np.exp(-exp) - elif parameters["light_limitation"] == "smith": # Smith (1936) + # ------------------------------------------------------------------------------------------------- + # Smith (1936) + # ------------------------------------------------------------------------------------------------- + elif parameters["light_limitation"] == "smith": # Evans & Parslow (1985) formulation num = Vm * parameters["initial_PI_slope"] * irrad den = np.sqrt((Vm**2) + ((parameters["initial_PI_slope"]*irrad)**2)) light_limitation = num/(den + 1E-20) - # Anderson et al. (2015) formulation - # coeff = Vm / ( k_PAR * mixed_layer_depth ) - # numerator = ( parameters["initial_PI_slope"] * surface_PAR ) + np.sqrt( ( Vm ** 2 ) + ( ( parameters["initial_PI_slope"] * surface_PAR) ** 2 ) ) - # denominator = ( parameters["initial_PI_slope"] * irrad ) + np.sqrt( ( Vm ** 2 ) + ( ( parameters["initial_PI_slope"] * irrad ) ** 2 ) ) - - # light_limitation = coeff * np.log(numerator/denominator) - - return exp, irr, light_limitation + return exp, irrad_at_depth, light_limitation def max_growth_rate(parameters, temperature): """ - Defiition:: Calculates the temperature-dependent maximum phytoplankton grwoth rate, Vm + Defiition:: Calculates the temperature-dependent maximum phytoplankton growth rate, Vm """ - Vm = parameters["a"] * ( parameters["b"] ** ( parameters["c"] * temperature ) ) + if parameters["type"] == "base_b": + Vm = parameters["a"] * ( parameters["b"] ** ( parameters["c"] * temperature ) ) + elif parameters["type"] == "standard": + Vm = parameters["base_growth_rate"] * np.exp(parameters["eppley_coeff"] * temperature) return Vm @@ -90,8 +103,14 @@ def irradiance(eps_PAR, surface_PAR, depth, k_PAR): eps_PAR = fraction of photosynthetically available radiation 0.217 = conversion from Einstein to Watts """ - # irradiance = ( surface_PAR * eps_PAR / 0.217) * np.exp( -k_PAR * depth) - irradiance = surface_PAR * eps_PAR / 0.217 + + # irradiance = surface_PAR * eps_PAR / 0.217 + + irradiance = np.zeros(len(depth)) + irradiance[0] = surface_PAR * eps_PAR / 0.217 + if len(depth) > 1: + for i in range(1,len(depth)): + irradiance[i] = irradiance[i-1] * np.exp(-1. * k_PAR[i-1] * depth[i-1]) return irradiance @@ -104,6 +123,13 @@ def nutrient_limitation(nutrient, half_sat): return nutrient_limitation_factor + +def monod(nutrient, half_sat, exponent): + + limitation_factor = np.power(nutrient, exponent) / ( np.power(nutrient, exponent) + np.power(half_sat, exponent) + 1.E-20) + + return limitation_factor + # def nutrient_limitation(self, tracers): # """ # Definition:: Calculates the limitation factor a nutrient using the Michaelis-Menten formulation @@ -117,12 +143,23 @@ def nutrient_limitation(nutrient, half_sat): # return fN -def temperature_regulation(base_temp, temperature, q10): +# def temperature_dependence(base_temp, temperature, q10, tracer): +def temperature_dependence(temperature, tracer): + """ Definition:: Calculates temperature regulating factor """ - # temp_regulating_factor = np.exp( np.log(q10) * (temperature - base_temp) / base_temp ) - temp_regulating_factor = q10**((temperature-base_temp)/base_temp) + if tracer.temperature_regulation["function"] == "arrhenius": + # Convert temperature from Celsius to Kelvin (+273.15) + # Universal gas constant (R = 8.314 J mol-1 K-1) + temp_regulating_factor = tracer.temperature_regulation["coefficient"] * np.exp(-tracer.temperature_regulation["activation_energy"] / (8.314 * (temperature+273.15)) ) + + elif tracer.temperature_regulation["function"] == "eppley": + temp_regulating_factor = np.exp(tracer.temperature_regulation["coefficient"] * temperature) + + elif tracer.temperature_regulation["function"] == "q10": + # temp_regulating_factor = q10**((temperature-base_temp)/base_temp) + temp_regulating_factor = tracer.temperature_regulation["coefficient"]**((temperature - tracer.temperature_regulation["base_temp"]) / tracer.temperature_regulation["base_temp"]) return temp_regulating_factor @@ -131,16 +168,20 @@ def concentration_ratio(iter, index, tracer): """ Definition:: Calculates concentration ratio of elements in tracer composition to its base element """ - for const in range(0,len(tracer.conc[...,iter])): - tracer.conc_ratio[const] = tracer.conc[const,iter] / (tracer.conc[index,iter] + 1E-20) + # for const in range(0,len(tracer.conc[...,iter])): + # tracer.conc_ratio[const] = tracer.conc[const,iter] / (tracer.conc[index,iter] + 1E-20) + + for const in range(0,len(tracer.conc)): + tracer.conc_ratio[const] = tracer.conc[const,:,iter] / (tracer.conc[index,:,iter] + 1E-20) + # Detritus may be initialized to zero, fix concentration ratio - if iter == 0 and tracer.type == 'detritus': - if tracer.conc[const,iter] == 0.: - tracer.conc_ratio[const] = 1. # Initialize first concentration ratio for detritus to 1 + # if iter == 0 and tracer.type == 'detritus': + # if tracer.conc[const,:,iter].all() == np.zeros(len(tracer.conc[const,:,iter])): + # tracer.conc_ratio[const,:] = 1. # Initialize first concentration ratio for detritus to 1 # Concentration ratio of base element is alway 1 - tracer.conc_ratio[index] = 1. + tracer.conc_ratio[index,:] = 1. def concentration_ratio_solveivp(concentration, index, indices, tracer): @@ -215,16 +256,23 @@ def tracer_elements(base_element, reaction, tracers): return c, p, ec, ep, ic, ip +# def on_off(parameter): + +# x = len(parameter) +# if + def switch(parameter): - # x = len(parameter) - # x = np.shape(parameter)[0] - # switch = np.zeros(x) - # for i in range(0,x): - # if parameter[i] > 0.0: - # switch[i] = 1.0 - - if parameter > 0.: switch = 1. - else: switch = 0. + + x = len(parameter) + if x > 1: + x = np.shape(parameter)[0] + switch = np.zeros(x) + for i in range(0,x): + if parameter[i] > 0.0: + switch[i] = 1.0 + else: + if parameter > 0.: switch = 1. + else: switch = 0. return switch \ No newline at end of file diff --git a/functions/rates.py b/functions/rates.py index 68bf266..b01ebea 100644 --- a/functions/rates.py +++ b/functions/rates.py @@ -1,5 +1,5 @@ import numpy as np -from functions.other_functions import light_attenuation, light_limitation, max_growth_rate, irradiance, nutrient_limitation, temperature_regulation +from functions.other_functions import light_attenuation, light_limitation, max_growth_rate, irradiance, nutrient_limitation, temperature_dependence def egestion(parameters, grazing_rates): diff --git a/generic_COBALT.F90 b/generic_COBALT.F90 new file mode 100644 index 0000000..aa22749 --- /dev/null +++ b/generic_COBALT.F90 @@ -0,0 +1,13255 @@ + +! Charles Stock +! +! +! +! This module contains the generic version of the COBALT 2.0 model: "Carbon Ocean +! Biogeochemistry and Lower Trophics". COBALT augments the foodweb dynamics +! in TOPAZ to enable anaylisis of the energy flow through the planktonic +! foodweb and improve the mechanistic resolution of foodweb dynamics that +! influence biogeochemical processes. +! +! +! COBALT simulates the biogeochemical cycling of carbon, nitrogen, +! phosphorous, iron, silica, calcium carbonate, and lithogenic +! material in the ocean. The code is built upon the TOPAZ code +! developed by John Dunne. The primary changes to TOPAZ are: +! +! 1) the addition of three zooplankton groups +! 2) The addition of bacteria +! 3) The expansion of the dissolved organic nitrogen and +! phosphorous groups to include three types each: labile, +! semi-labile, and refractory +! 4) Constant Stoichiometry by plankton functional type +! +! The primary COBALT reference is: +! +! Stock, CA, Dunne, JP, John, JG. 2014. Global-scale carbon and energy +! flows through the marine planktonic food web: An analysis with a' +! coupled physical-biological model. Progress in Oceanography 120, 1-18. +! +! Version 2.0 has a number of refinements: +! +! 1) Ammonia uptake parameters are now based on the "high-affinity" +! settings from Paulot et al., 2015; GBC; 29(8) +! 2) Phytoplankton aggregation is initiated only when growth rates +! fall below 1/4 maximum values +! 3) The default parameterization has elevated N:P ratios for both +! diazotrophs and small phytoplankton +! 4) Remineralization of sinking detritus is now based on the temperature +! and oxygen dependences described in Laufkotter et al., 2017; O2 +! dependence of other aerobic processes have also been adjusted +! for consistency. +! 5) The default carbon chemistry routine is now MOCSY +! 6) The iron scavenging has been re-tuned to new atmospheric (Ginoux-AM4), +! sediment, river and hydrothermal vent (Tagliabue) sources. +! +! parameterizations used herein, as well as definitions for all variables +! and parameters. The 33 model state variables are: +! +! alk: alkalinity +! cadet_arag: calcium carbonate detritus (aragonite) +! cadet_calc: calcium carbonate detritus (calcite) +! dic: dissolved inorganic carbon +! fed: dissolved iron +! fedi: diazotroph iron +! felg: large phytoplankton iron +! fedet: iron detritus +! fesm: small phytoplankton iron +! ldon: labile dissolved organic nitrogen +! ldop: labile dissolved organic phosphorous +! lith: lithogenic aluminosilicate particles +! lithdet: lithogenic detritus +! nbact: bacteria +! ndet: nitrogen detritus +! ndi: diazotroph nitrogen +! nlg: large phyto nitrogen +! nsm: high-light adapted small phyto nitrogen +! nh4: ammonia +! no3: nitrate +! o2: oxygen +! pdet: phosphorous detritus +! po4: phosphate +! srdon: semi-refractory dissolved organic nitrogen +! (decays over years to decades) +! srdop: semi-refractory dissolved organic phosphorous +! (decays over years to decades) +! sldon: semi-labile dissolved organic nitrogen +! (decays on monthly time scales) +! sldop: semi-labile dissolved organic phosphorous +! (decays on monthly time scales) +! sidet: silica detritus +! silg: large phyto silica +! sio4: silicate +! nsmz: small zooplankton nitrogen +! nmdz: medium zooplankton nitrogen +! nlgz: large zooplankton nitrogen +! +! +! +! +! If true, then simulate radiocarbon. Includes 2 prognostic tracers, DI14C +! and DO14C. Requires that do_carbon = .true. Note that 14C is not taken up +! by CaCO3 at the current time, but cycles only through the soft tissue. +! This is a mistake that will be fixed later. +! +! +! +! Defines the carbon equilibration method. Default is 'ocmip2' which uses +! the FMS_ocmip2_co2calc routine. The other option is 'mocsy', which uses +! the set of routines authored by J. Orr. See reference at: +! http://ocmip5.ipsl.jussieu.fr/mocsy/index.html +! +! +! +! +! +! +! +! Stock, Charles A., John P Dunne, and Jasmin G John, 2014: Global-scale carbon and energy flows +! through the marine food web: an analysis with a coupled physical-biological mode. Progress in Oceanography, +! 120, DOI:10.1016/j.pocean.2013.07.001. +! +! Stock, Charles A., and John P Dunne, 2010: Controls on the ratio of mesozooplankton production to +! primary production in marine ecosystems. Deep-Sea Research, Part I, 57(1), DOI:10.1016/j.dsr.2009.10.006. +! +! Dunne, John P., Jasmin G John, Elena Shevliakova, Ronald J Stouffer, John P Krasting, Sergey Malyshev, +! P C D Milly, Lori T Sentman, Alistair Adcroft, William F Cooke, Krista A Dunne, Stephen M Griffies, +! Robert Hallberg, Matthew J Harrison, Hiram Levy II, Andrew T Wittenberg, Peter Phillipps, and Niki Zadeh, +! 2013: GFDL's ESM2 global coupled climate-carbon Earth System Models Part II: Carbon system formulation and +! baseline simulation characteristics. Journal of Climate, 26(7), DOI:10.1175/JCLI-D-12-00150.1. +! +! +! +! +!---------------------------------------------------------------- + +module generic_COBALT + + use coupler_types_mod, only: coupler_2d_bc_type + use field_manager_mod, only: fm_string_len, fm_path_name_len + use mpp_mod, only: mpp_clock_id, mpp_clock_begin, mpp_clock_end + use mpp_mod, only: CLOCK_COMPONENT, CLOCK_SUBCOMPONENT, CLOCK_MODULE + use mpp_mod, only: input_nml_file, mpp_error, stdlog, NOTE, WARNING, FATAL, stdout, mpp_chksum + use time_manager_mod, only: time_type + use fm_util_mod, only: fm_util_start_namelist, fm_util_end_namelist + use constants_mod, only: WTMCO2, WTMO2,WTMN,rdgas,wtmair + use data_override_mod, only: data_override + use fms_mod, only: write_version_number, FATAL, WARNING, stdout, stdlog,mpp_pe,mpp_root_pe + use fms_mod, only: check_nml_error + + use g_tracer_utils, only : g_tracer_type,g_tracer_start_param_list,g_tracer_end_param_list + use g_tracer_utils, only : g_tracer_add,g_tracer_add_param, g_tracer_set_files + use g_tracer_utils, only : g_tracer_set_values,g_tracer_get_pointer + use g_tracer_utils, only : g_tracer_get_common,g_tracer_set_common + use g_tracer_utils, only : g_tracer_coupler_set,g_tracer_coupler_get + use g_tracer_utils, only : g_tracer_get_values + use g_tracer_utils, only : g_diag_type, g_diag_field_add + use g_tracer_utils, only : register_diag_field=>g_register_diag_field + use g_tracer_utils, only : g_send_data, is_root_pe + + use FMS_ocmip2_co2calc_mod, only : FMS_ocmip2_co2calc, CO2_dope_vector + + implicit none ; private +!----------------------------------------------------------------------- + character(len=128) :: version = '$Id: generic_COBALT.F90,v 20.0.2.1.2.1 2014/09/29 16:40:08 Niki.Zadeh Exp $' + character(len=128) :: tag = '$Name: bugfix_nnz $' +!----------------------------------------------------------------------- + + character(len=fm_string_len), parameter :: mod_name = 'generic_COBALT' + character(len=fm_string_len), parameter :: package_name = 'generic_cobalt' + + public do_generic_COBALT + public generic_COBALT_register + public generic_COBALT_init + public generic_COBALT_register_diag + public generic_COBALT_update_from_coupler + public generic_COBALT_update_from_source + public generic_COBALT_update_from_bottom + public generic_COBALT_set_boundary_values + public generic_COBALT_end + public as_param_cobalt + + !The following variables for using this module + ! are overwritten by generic_tracer_nml namelist + logical, save :: do_generic_COBALT = .false. + character(len=10), save :: as_param_cobalt = 'gfdl_cmip6' + + real, parameter :: sperd = 24.0 * 3600.0 + real, parameter :: spery = 365.25 * sperd + real, parameter :: epsln=1.0e-30 + real,parameter :: missing_value1=-1.0e+10 + real, parameter :: missing_value_diag=-1.0e+10 + + real, parameter :: vb_nh3 = 25. + + logical :: do_nh3_diag + +! Namelist Options + + character(len=10) :: co2_calc = 'ocmip2' ! other option is 'mocsy' + logical :: do_14c = .false. + logical :: debug = .false. + logical :: do_nh3_atm_ocean_exchange = .false. + real :: k_nh4_small = 1.e-8 + real :: k_nh4_diazo = 1.e-7 + real :: k_nh4_large = 5.e-8 + real :: k_no3_small = 5.e-7 + real :: k_no3_diazo = 5.0e-6 + real :: k_no3_large = 2.5e-6 + real :: o2_min_nit= 0.01e-6 + real :: k_o2_nit = 3.9e-6 + real :: irr_inhibit = 10. + real :: gamma_nitrif= 3.5e6 !month(-1) + real :: k_nh3_nitrif= 3.1e-9 !mol/kg + real :: imbalance_tolerance=1.0e-10 !the tolerance for non-conservation in C,N,P,Sc,Fe + + integer :: scheme_no3_nh4_lim = 2 !1-Frost and Franzen (1992) + !2-O'Neill + + integer :: scheme_nitrif = 3 !1-default COBALT + !2-update with no temperature dependence + !3-update with temperature dependence + +namelist /generic_COBALT_nml/ do_14c, co2_calc, debug, do_nh3_atm_ocean_exchange, scheme_nitrif, & + k_nh4_small,k_nh4_large,k_nh4_diazo,scheme_no3_nh4_lim,k_no3_small,k_no3_large,k_no3_diazo, & + o2_min_nit,k_o2_nit,irr_inhibit,k_nh3_nitrif,gamma_nitrif,imbalance_tolerance + + ! Declare phytoplankton, zooplankton and cobalt variable types, which contain + ! the vast majority of all variables used in this module. + + type phytoplankton + real :: alpha, & + fe_2_n_max, & + p_2_n_static, & + k_fe_2_n, & + k_fed, & + k_nh4, & + k_no3, & + k_po4, & + k_sio4, & + P_C_max, & + si_2_n_max, & + si_2_n_static, & + thetamax, & + bresp, & + agg, & + frac_mu_agg, & + vir, & + exu + real, ALLOCATABLE, dimension(:,:) :: & + jprod_n_100, & + jprod_n_new_100, & + jprod_n_n2_100, & + jzloss_n_100, & + jaggloss_n_100, & + jvirloss_n_100, & + jexuloss_n_100, & + f_n_100, & + juptake_fe_100, & + juptake_po4_100, & + juptake_sio4_100, & + nlim_bw_100, & + plim_bw_100, & + def_fe_bw_100, & + irrlim_bw_100 + real, ALLOCATABLE, dimension(:,:,:) :: & + def_fe , & + def_p , & + f_fe , & + f_n , & + felim , & + irrlim , & + jzloss_fe , & + jzloss_n , & + jzloss_p , & + jzloss_sio2 , & + jaggloss_fe , & + jaggloss_n , & + jaggloss_p , & + jaggloss_sio2,& + agg_lim ,& + jvirloss_fe , & + jvirloss_n , & + jvirloss_p , & + jvirloss_sio2,& + jexuloss_fe , & + jexuloss_n , & + jexuloss_p , & + jhploss_fe , & + jhploss_n , & + jhploss_p , & + jhploss_sio2, & + juptake_n2 , & + juptake_fe , & + juptake_nh4 , & + juptake_no3 , & + juptake_po4 , & + juptake_sio4, & + jprod_n , & + liebig_lim , & + mu , & + f_mu_mem , & + mu_mix , & + nh4lim , & + no3lim , & + po4lim , & + o2lim , & + q_fe_2_n , & + q_p_2_n , & + silim , & + q_si_2_n , & + theta + integer :: & + id_def_fe = -1, & + id_def_p = -1, & + id_felim = -1, & + id_irrlim = -1, & + id_jzloss_fe = -1, & + id_jzloss_n = -1, & + id_jzloss_p = -1, & + id_jzloss_sio2 = -1, & + id_jaggloss_fe = -1, & + id_jaggloss_n = -1, & + id_jaggloss_p = -1, & + id_jaggloss_sio2= -1, & + id_agg_lim = -1, & + id_jvirloss_fe = -1, & + id_jvirloss_n = -1, & + id_jvirloss_p = -1, & + id_jvirloss_sio2= -1, & + id_jexuloss_n = -1, & + id_jexuloss_p = -1, & + id_jexuloss_fe = -1, & + id_jhploss_fe = -1, & + id_jhploss_n = -1, & + id_jhploss_p = -1, & + id_jhploss_sio2 = -1, & + id_juptake_n2 = -1, & + id_juptake_fe = -1, & + id_juptake_nh4 = -1, & + id_juptake_no3 = -1, & + id_juptake_po4 = -1, & + id_juptake_sio4 = -1, & + id_jprod_n = -1, & + id_liebig_lim = -1, & + id_mu = -1, & + id_f_mu_mem = -1, & + id_mu_mix = -1, & + id_nh4lim = -1, & + id_no3lim = -1, & + id_po4lim = -1, & + id_o2lim = -1, & + id_q_fe_2_n = -1, & + id_q_p_2_n = -1, & + id_silim = -1, & + id_q_si_2_n = -1, & + id_theta = -1, & + id_jprod_n_100 = -1, & + id_jprod_n_new_100 = -1, & + id_jprod_n_n2_100 = -1, & + id_jzloss_n_100 = -1, & + id_jaggloss_n_100 = -1, & + id_jvirloss_n_100 = -1, & + id_jexuloss_n_100 = -1, & + id_f_n_100 = -1, & + id_sfc_f_n = -1, & + id_sfc_chl = -1, & + id_sfc_def_fe = -1, & + id_sfc_felim = -1, & + id_sfc_q_fe_2_n = -1, & + id_sfc_nh4lim = -1, & + id_sfc_no3lim = -1, & + id_sfc_po4lim = -1, & + id_sfc_irrlim = -1, & + id_sfc_theta = -1, & + id_sfc_mu = -1 + end type phytoplankton + + type zooplankton + real :: & + imax, & ! maximum ingestion rate (sec-1) + ki, & ! half-sat for ingestion (moles N m-3) + gge_max, & ! max gross growth efficiciency (approached as i >> bresp, dimensionless) + nswitch, & ! switching parameter (dimensionless) + mswitch, & ! switching parameter (dimensionless) + bresp, & ! basal respiration rate (sec-1) + ktemp, & ! temperature dependence of zooplankton rates (C-1) + phi_det, & ! fraction of ingested N to detritus + phi_ldon, & ! fraction of ingested N/P to labile don + phi_sldon, & ! fraction of ingested N/P to semi-labile don + phi_srdon, & ! fraction of ingested N/P to semi-refractory don + phi_ldop, & ! fraction of ingested N/P to labile dop + phi_sldop, & ! fraction of ingested N/P to semi-labile dop + phi_srdop, & ! fraction of ingested N/P to semi-refractory dop + phi_nh4, & ! fraction of ingested N to nh4 due to ingestion-related metabolism + phi_po4, & ! fraction of ingested N to po4 due to ingestion-related metabolism + q_p_2_n, & ! p:n ratio of zooplankton + ipa_smp, & ! innate prey availability of low-light adapt. small phytos + ipa_lgp, & ! innate prey availability of large phytoplankton + ipa_diaz, & ! innate prey availability of diazotrophs + ipa_smz, & ! innate prey availability of small zooplankton + ipa_mdz, & ! innate prey availability of large zooplankton + ipa_lgz, & ! innate prey availability of x-large zooplankton + ipa_det, & ! innate prey availability of detritus + ipa_bact ! innate prey availability for bacteria + real, ALLOCATABLE, dimension(:,:) :: & + jprod_n_100, & + jingest_n_100, & + jzloss_n_100, & + jhploss_n_100, & + jprod_ndet_100, & + jprod_don_100, & + jremin_n_100, & + f_n_100 + real, ALLOCATABLE, dimension(:,:,:) :: & + f_n, & ! zooplankton biomass + jzloss_n, & ! Losses of n due to consumption by other zooplankton groups + jzloss_p, & ! Losses of p due to consumption by other zooplankton groups + jhploss_n, & ! Losses of n due to consumption by unresolved higher preds + jhploss_p, & ! Losses of p due to consumption by unresolved higher preds + jingest_n, & ! Total ingestion of n + jingest_p, & ! Total ingestion of p + jingest_sio2, & ! Total ingestion of silicate + jingest_fe, & ! Total ingestion of iron + jprod_ndet, & ! production of nitrogen detritus by zooplankton group + jprod_pdet, & ! production of phosphorous detritus by zooplankton group + jprod_ldon, & ! production of labile dissolved organic N by zooplankton group + jprod_ldop, & ! production of labile dissolved organic P by zooplankton group + jprod_srdon, & ! production of semi-refractory dissolved organic N by zooplankton group + jprod_srdop, & ! production of semi-refractory dissolved organic P by zooplankton group + jprod_sldon, & ! production of semi-labile dissolved organic N by zooplankton group + jprod_sldop, & ! production of semi-labile dissolved organic P by zooplankton group + jprod_fed, & ! production of dissolved iron + jprod_fedet, & ! production of iron detritus + jprod_sidet, & ! production of silica detritus + jprod_sio4, & ! production of silicate via rapid dissolution at surface + jprod_po4, & ! phosphate production by zooplankton + jprod_nh4, & ! ammonia production by zooplankton + jprod_n, & ! zooplankton production + o2lim, & ! oxygen limitation of zooplankton activity + temp_lim ! Temperature limitation + integer :: & + id_jzloss_n = -1, & + id_jzloss_p = -1, & + id_jhploss_n = -1, & + id_jhploss_p = -1, & + id_jingest_n = -1, & + id_jingest_p = -1, & + id_jingest_sio2 = -1, & + id_jingest_fe = -1, & + id_jprod_ndet = -1, & + id_jprod_pdet = -1, & + id_jprod_ldon = -1, & + id_jprod_ldop = -1, & + id_jprod_srdon = -1, & + id_jprod_srdop = -1, & + id_jprod_sldon = -1, & + id_jprod_sldop = -1, & + id_jprod_fed = -1, & + id_jprod_fedet = -1, & + id_jprod_sidet = -1, & + id_jprod_sio4 = -1, & + id_jprod_po4 = -1, & + id_jprod_nh4 = -1, & + id_jprod_n = -1, & + id_o2lim = -1, & + id_temp_lim = -1, & + id_jprod_n_100 = -1, & + id_jingest_n_100 = -1, & + id_jzloss_n_100 = -1, & + id_jhploss_n_100 = -1, & + id_jprod_ndet_100 = -1, & + id_jprod_don_100 = -1, & + id_jremin_n_100 = -1, & + id_f_n_100 = -1 + end type zooplankton + + type bacteria + real :: & + mu_max, & ! maximum bacterial growth rate (sec-1) + k_ldon, & ! half-sat for nitrogen-limited growth (mmoles N m-3) + gge_max, & ! max gross growth efficiciency (dimensionless) + bresp, & ! basal respiration rate (sec-1) + ktemp, & ! temperature dependence of bacterial rates (C-1) + vir, & ! virus-driven loss rate for bacteria (sec-1 mmole N m-3) + q_p_2_n ! p:n ratio for bacteria + real, ALLOCATABLE, dimension(:,:) :: & + jprod_n_100, & + jzloss_n_100, & + jvirloss_n_100, & + jremin_n_100, & + juptake_ldon_100, & + f_n_100 + real, ALLOCATABLE, dimension(:,:,:) :: & + f_n, & ! bacteria biomass + jzloss_n, & ! Losses of n due to consumption by zooplankton + jzloss_p, & ! Losses of p due to consumption by zooplankton + jhploss_n, & ! Losses of n due to consumption by unresolved higher preds + jhploss_p, & ! Losses of p due to consumption by unresolved higher preds + jvirloss_n , & ! nitrogen losses via viruses + jvirloss_p , & ! phosphorous losses via viruses + juptake_ldon, & ! Total uptake of ldon + juptake_ldop, & ! Total uptake of sldon + jprod_nh4, & ! production of ammonia bacteria + jprod_po4, & ! production of phosphate by bacteria + jprod_n, & ! bacterial production + ldonlim, & ! limitation due to organic substrate + o2lim, & ! limitation due to oxygen + temp_lim ! Temperature limitation + integer :: & + id_jzloss_n = -1, & + id_jzloss_p = -1, & + id_jhploss_n = -1, & + id_jhploss_p = -1, & + id_jvirloss_n = -1, & + id_jvirloss_p = -1, & + id_juptake_ldon = -1, & + id_juptake_ldop = -1, & + id_jprod_nh4 = -1, & + id_jprod_po4 = -1, & + id_jprod_n = -1, & + id_temp_lim = -1, & + id_o2lim = -1, & + id_ldonlim = -1, & + id_jprod_n_100 = -1, & + id_jzloss_n_100 = -1, & + id_jvirloss_n_100 = -1, & + id_jremin_n_100 = -1, & + id_juptake_ldon_100 = -1, & + id_f_n_100 + end type bacteria + + integer, parameter :: NUM_PHYTO = 3 + ! + ! Array allocations and flux calculations assume that phyto(1) is the + ! only phytoplankton group cabable of nitrogen uptake by N2 fixation while phyto(2:NUM_PHYTO) + ! are only cabable of nitrgen uptake by NH4 and NO3 uptake + ! + integer, parameter :: DIAZO = 1 + integer, parameter :: LARGE = 2 + integer, parameter :: SMALL = 3 + type(phytoplankton), dimension(NUM_PHYTO) :: phyto + + ! define three zooplankton types + integer, parameter :: NUM_ZOO = 3 + type(zooplankton), dimension(NUM_ZOO) :: zoo + + type(bacteria), dimension(1) :: bact + + integer, parameter :: NUM_PREY = 8 + + type generic_COBALT_type + + logical :: & + init, & ! If tracers should be initializated + force_update_fluxes,& ! If OCMIP2 tracers fluxes should be updated every coupling timesteps + ! when update_from_source is not called every coupling timesteps + ! as is the case with MOM6 THERMO_SPANS_COUPLING option + p_2_n_static, & ! If P:N is fixed in phytoplankton + cased_steady, & ! steady state approximation for cased + tracer_debug + + real :: & + atm_co2_flux, & + c_2_n, & + ca_2_n_arag, & + ca_2_n_calc, & + caco3_sat_max, & + doc_background, & + fe_2_n_upt_fac, & + fe_2_n_sed, & + ffe_sed_max, & + ffe_geotherm_ratio,& + ffe_iceberg_ratio,& + fe_coast, & + felig_2_don, & + felig_bkg , & + gamma_cadet_arag, & + gamma_cadet_calc, & + gamma_irr_mem, & + gamma_mu_mem, & + gamma_ndet, & + gamma_nitrif, & + k_nh3_nitrif, & + gamma_sidet, & + gamma_srdon, & + gamma_srdop, & + gamma_sldon, & + gamma_sldop, & + kappa_sidet, & + irr_inhibit, & + k_n_inhib_di, & + k_o2, & + k_o2_nit, & + kappa_eppley, & + kappa_remin, & + remin_ramp_scale, & + kfe_eq_lig_hl, & + kfe_eq_lig_ll, & + alpha_fescav, & + beta_fescav, & + io_fescav, & + remin_eff_fedet, & + half_life_14c, & + lambda_14c, & + k_lith, & + phi_lith, & + alk_2_n_denit, & + n_2_n_denit, & + k_no3_denit, & + z_burial, & + phi_surfresp_cased, & + phi_deepresp_cased, & + alpha_cased, & + beta_cased, & + gamma_cased, & + Co_cased, & + o2_min, & + o2_min_nit, & + o2_2_nfix, & + o2_2_nh4, & + o2_2_no3, & + o2_2_nitrif, & + o2_inhib_di_pow, & + o2_inhib_di_sat, & + rpcaco3, & + rplith, & + rpsio2, & + thetamin, & + thetamin_nolim, & + vir_ktemp, & + lysis_phi_ldon, & + lysis_phi_srdon, & + lysis_phi_sldon, & + lysis_phi_ldop, & + lysis_phi_srdop, & + lysis_phi_sldop, & + wsink, & + z_sed, & + zeta, & + refuge_conc, & + imax_hp, & ! unresolved higher pred. max ingestion rate + ki_hp, & ! unresolved higher pred. half-sat + ktemp_hp, & ! temperature dependence for higher predators + coef_hp, & ! scaling between unresolved preds and available prey + nswitch_hp, & ! higher predator switching behavior + mswitch_hp, & ! higher predator switching behavior + hp_ipa_smp, & ! innate prey availability of small phytos to hp's + hp_ipa_lgp, & ! " " " " " " " " " large phytos to hp's + hp_ipa_diaz, & ! " " " " " " " " " diazotrophs to hp's + hp_ipa_bact, & ! " " " " " " " " " bacteria to hp's + hp_ipa_smz, & ! " " " " " " " " " small zooplankton to hp's + hp_ipa_mdz, & ! " " " " " " " " " medium zooplankton to hp's + hp_ipa_lgz, & ! " " " " " " " " " large zooplankton to hp's + hp_ipa_det, & ! " " " " " " " " " detritus to hp's + hp_phi_det ! fraction of ingested N to detritus + + real, dimension(3) :: total_atm_co2 + + real :: htotal_scale_lo, htotal_scale_hi, htotal_in + real :: Rho_0, a_0, a_1, a_2, a_3, a_4, a_5, b_0, b_1, b_2, b_3, c_0 + real :: a1_co2, a2_co2, a3_co2, a4_co2, a5_co2 + real :: a1_o2, a2_o2, a3_o2, a4_o2, a5_o2 + + logical, dimension(:,:), ALLOCATABLE :: & + mask_z_sat_arag,& + mask_z_sat_calc + + real, dimension(:,:,:), ALLOCATABLE :: & + f_alk,& ! Other prognostic variables + f_cadet_arag,& + f_cadet_calc,& + f_dic,& + f_fed,& + f_fedet,& + f_ldon,& + f_ldop,& + f_lith,& + f_lithdet,& + f_ndet,& + f_nh4,& + f_no3,& + f_o2,& + f_pdet,& + f_po4,& + f_srdon,& + f_srdop,& + f_sldon,& + f_sldop,& + f_sidet,& + f_silg,& + f_sio4,& + co3_sol_arag,& + co3_sol_calc,& + f_chl,& + f_nh3,& + f_co3_ion,& + f_htotal,& + f_irr_mem,& + f_cased,& + f_cadet_arag_btf,& + f_cadet_calc_btf,& + f_fedet_btf, & + f_lithdet_btf, & + f_ndet_btf,& + f_pdet_btf,& + f_sidet_btf,& + jnbact,& + jndi,& + jnsm,& + jnlg,& + jnsmz,& + jnmdz,& + jnlgz,& + jalk,& + jalk_plus_btm,& + jcadet_arag,& + jcadet_calc,& + jdic,& + jdic_plus_btm,& + jdin_plus_btm,& + jfed,& + jfed_plus_btm,& + jfedi,& + jfelg,& + jfesm,& + jfedet,& + jldon,& + jldop,& + jlith,& + jlithdet,& + jndet,& + jnh4,& + jnh4_plus_btm,& + jno3,& + jno3_plus_btm,& + jo2,& + jo2_plus_btm,& + jpdet,& + jpo4,& + jpo4_plus_btm,& + jsrdon,& + jsrdop,& + jsldon,& + jsldop,& + jsidet,& + jsilg,& + jsio4,& + jsio4_plus_btm,& + jprod_ndet,& + jprod_pdet,& + jprod_ldon,& + jprod_ldop,& + jprod_sldon,& + jprod_sldop,& + jprod_srdon,& + jprod_srdop,& + jprod_fed,& + jprod_fedet,& + jprod_sidet,& + jprod_sio4, & + jprod_lithdet,& + jprod_cadet_arag,& + jprod_cadet_calc,& + jprod_nh4,& + jprod_nh4_plus_btm,& + jprod_po4,& + det_jzloss_n,& + det_jzloss_p,& + det_jzloss_fe,& + det_jzloss_si,& + det_jhploss_n,& + det_jhploss_p,& + det_jhploss_fe,& + det_jhploss_si,& + jdiss_cadet_arag,& + jdiss_cadet_arag_plus_btm,& + jdiss_cadet_calc,& + jdiss_cadet_calc_plus_btm,& + jdiss_sidet,& + jremin_ndet,& + jremin_pdet,& + jremin_fedet,& + jfe_ads,& + jfe_coast,& + kfe_eq_lig,& + feprime,& + ligand,& + fe_sol,& + expkT,& + expkreminT,& + hp_temp_lim,& + hp_o2lim,& + hp_jingest_n,& + hp_jingest_p,& + hp_jingest_fe,& + hp_jingest_sio2,& + irr_inst,& + irr_mix,& + jno3denit_wc,& + jo2resp_wc, & + jnitrif,& + omega_arag,& + omega_calc,& + omegaa,& + omegac,& + tot_layer_int_c,& + tot_layer_int_fe,& + tot_layer_int_n,& + tot_layer_int_p,& + tot_layer_int_si,& + tot_layer_int_o2,& + tot_layer_int_alk,& + total_filter_feeding,& + nlg_diatoms,& + q_si_2_n_lg_diatoms,& + zt, & + zm, & + c14_2_n,& + f_di14c,& + f_do14c,& + fpo14c,& + j14c_decay_dic,& + j14c_decay_doc,& + j14c_reminp,& + jdi14c,& + jdo14c, & +!============================================================================================================== +! JGJ 2016/08/08 CMIP6 Ocnbgc +! CAS: added tot_layer_int_dic + dissoc, & + o2sat, & + remoc, & + tot_layer_int_doc,& + tot_layer_int_poc,& + tot_layer_int_dic + +!============================================================================================================== + + real, dimension(:,:), ALLOCATABLE :: & + b_alk,b_dic,b_fed,b_nh4,b_no3,b_o2,b_po4,b_sio4,b_di14c,& ! bottom flux terms + co2_csurf,pco2_csurf,co2_alpha,c14o2_csurf,c14o2_alpha,& + nh3_csurf,nh3_alpha,pnh3_csurf,& + fcadet_arag_btm,& + fcadet_calc_btm,& + ffedet_btm,& + flithdet_btm,& + fpdet_btm,& + fndet_btm,& + fsidet_btm,& + fcased_burial,& + fcased_redis,& + fcased_redis_surfresp,& + cased_redis_coef,& + cased_redis_delz,& + ffe_sed,& + ffe_geotherm,& + ffe_iceberg,& + fnfeso4red_sed,& + fno3denit_sed,& + fnoxic_sed,& + frac_burial,& + fndet_burial,& + fpdet_burial,& + jprod_allphytos_100,& + jprod_diat_100,& + htotallo, htotalhi,& + hp_jingest_n_100,& + hp_jremin_n_100,& + hp_jprod_ndet_100,& + jprod_lithdet_100,& + jprod_sidet_100,& + jprod_cadet_calc_100,& + jprod_cadet_arag_100,& + jprod_mesozoo_200, & + jremin_ndet_100, & + f_ndet_100, & + f_don_100, & + f_silg_100, & + f_mesozoo_200, & + fndet_100, & + fpdet_100, & + fsidet_100, & + fcadet_calc_100, & + fcadet_arag_100, & + ffedet_100, & + flithdet_100, & + btm_temp, & + btm_o2, & + btm_htotal, & + btm_co3_ion, & + btm_co3_sol_arag, & + btm_co3_sol_calc, & + cased_2d, & + o2min, & + z_o2min, & + z_sat_arag,& + z_sat_calc,& +!============================================================================================================== +! JGJ 2016/08/08 CMIP6 Ocnbgc + f_alk_int_100, & + f_dic_int_100, & + f_din_int_100, & + f_fed_int_100, & + f_po4_int_100, & + f_sio4_int_100, & + jalk_100, & + jdic_100, & + jdin_100, & + jfed_100, & + jpo4_100, & + jsio4_100, & + jprod_ptot_100, & + wc_vert_int_c,& + wc_vert_int_dic,& + wc_vert_int_doc,& + wc_vert_int_poc,& + wc_vert_int_n,& + wc_vert_int_p,& + wc_vert_int_fe,& + wc_vert_int_si,& + wc_vert_int_o2,& + wc_vert_int_alk,& + wc_vert_int_jdiss_sidet,& + wc_vert_int_jdiss_cadet,& + wc_vert_int_jo2resp,& + wc_vert_int_jprod_cadet,& + wc_vert_int_jno3denit,& + wc_vert_int_jnitrif,& + wc_vert_int_juptake_nh4,& + wc_vert_int_jprod_nh4,& + wc_vert_int_juptake_no3,& + wc_vert_int_nfix +!============================================================================================================== + + real, dimension(:,:,:,:), pointer :: & + p_alk,& + p_cadet_arag,& + p_cadet_calc,& + p_dic,& + p_di14c,& + p_do14c,& + p_fed,& + p_fedet,& + p_fedi,& + p_felg,& + p_fesm,& + p_ldon,& + p_ldop,& + p_lith,& + p_lithdet,& + p_nbact,& + p_ndet,& + p_ndi,& + p_nlg,& + p_nsm,& + p_nh4,& + p_no3,& + p_o2,& + p_pdet,& + p_po4,& + p_srdon,& + p_srdop,& + p_sldon,& + p_sldop,& + p_sidet,& + p_silg,& + p_sio4,& + p_nsmz,& + p_nmdz,& + p_nlgz + + real, dimension (:,:), allocatable :: & + runoff_flux_alk,& + runoff_flux_dic,& + runoff_flux_di14c,& + runoff_flux_lith,& + runoff_flux_fed,& + runoff_flux_no3,& + runoff_flux_ldon,& + runoff_flux_sldon,& + runoff_flux_srdon,& + runoff_flux_ndet,& + runoff_flux_po4,& + runoff_flux_ldop,& + runoff_flux_sldop,& + runoff_flux_srdop,& + dry_fed, wet_fed,& + dry_lith, wet_lith,& + dry_no3, wet_no3,& + dry_nh4, wet_nh4,& + dry_po4, wet_po4, & + stf_gas_dic,& + stf_gas_o2,& + deltap_dic,& + deltap_o2 + + integer :: nkml + character(len=fm_string_len) :: file + character(len=fm_string_len) :: ice_restart_file + character(len=fm_string_len) :: ocean_restart_file,IC_file + + integer :: & + id_ndi = -1, & + id_nlg = -1, & + id_nsm = -1, & + id_nsmz = -1, & + id_nmdz = -1, & + id_nlgz = -1, & + id_nbact = -1, & + id_alk = -1, & + id_cadet_arag = -1, & + id_cadet_calc = -1, & + id_dic = -1, & + id_fed = -1, & + id_fedet = -1, & + id_fedi = -1, & + id_felg = -1, & + id_fesm = -1, & + id_ldon = -1, & + id_ldop = -1, & + id_lith = -1, & + id_lithdet = -1, & + id_ndet = -1, & + id_nh4 = -1, & + id_no3 = -1, & + id_o2 = -1, & + id_pdet = -1, & + id_po4 = -1, & + id_srdop = -1, & + id_srdon = -1, & + id_sldon = -1, & + id_sldop = -1, & + id_sidet = -1, & + id_silg = -1, & + id_sio4 = -1, & + id_co3_sol_arag = -1, & + id_co3_sol_calc = -1, & + id_dep_dry_fed = -1, & + id_dep_dry_nh4 = -1, & + id_dep_dry_no3 = -1, & + id_dep_dry_po4 = -1, & + id_dep_wet_fed = -1, & + id_dep_wet_nh4 = -1, & + id_dep_wet_no3 = -1, & + id_dep_wet_po4 = -1, & + id_dep_wet_lith = -1, & + id_dep_dry_lith = -1, & + id_omega_arag = -1, & + id_omega_calc = -1, & + id_chl = -1, & + id_co3_ion = -1, & + id_htotal = -1, & + id_irr_mem = -1, & + id_cased = -1, & + id_cadet_arag_btf = -1, & + id_cadet_calc_btf = -1, & + id_fedet_btf = -1, & + id_lithdet_btf = -1, & + id_ndet_btf = -1, & + id_pdet_btf = -1, & + id_sidet_btf = -1, & + id_jfed = -1, & + id_jprod_ndet = -1, & + id_jprod_pdet = -1, & + id_jprod_sldon = -1, & + id_jprod_ldon = -1, & + id_jprod_srdon = -1, & + id_jprod_sldop = -1, & + id_jprod_ldop = -1, & + id_jprod_srdop = -1, & + id_jprod_fed = -1, & + id_jprod_fedet = -1, & + id_jprod_sidet = -1, & + id_jprod_sio4 = -1, & + id_jprod_lithdet = -1, & + id_jprod_cadet_arag = -1, & + id_jprod_cadet_calc = -1, & + id_jprod_po4 = -1, & + id_jprod_nh4 = -1, & + id_jprod_nh4_plus_btm = -1, & + id_det_jzloss_n = -1, & + id_det_jzloss_p = -1, & + id_det_jzloss_fe = -1, & + id_det_jzloss_si = -1, & + id_det_jhploss_n = -1, & + id_det_jhploss_p = -1, & + id_det_jhploss_fe = -1, & + id_det_jhploss_si = -1, & + id_jdiss_sidet = -1, & + id_jdiss_cadet_arag = -1, & + id_jdiss_cadet_arag_plus_btm = -1, & + id_jdiss_cadet_calc = -1, & + id_jdiss_cadet_calc_plus_btm = -1, & + id_jremin_ndet = -1, & + id_jremin_pdet = -1, & + id_jremin_fedet = -1, & + id_jfe_ads = -1, & + id_jfe_coast = -1, & + id_kfe_eq_lig = -1, & + id_feprime = -1, & + id_ligand = -1, & + id_fe_sol = -1, & + id_expkT = -1, & + id_expkreminT = -1, & + id_hp_temp_lim = -1, & + id_hp_o2lim = -1, & + id_hp_jingest_n = -1, & + id_hp_jingest_p = -1, & + id_hp_jingest_fe = -1, & + id_hp_jingest_sio2 = -1, & + id_irr_inst = -1, & + id_irr_mix = -1, & + id_jalk = -1, & + id_jalk_plus_btm = -1, & + id_jdic = -1, & + id_jdic_plus_btm = -1, & + id_jnh4 = -1, & + id_jndet = -1, & + id_jnh4_plus_btm = -1, & + id_jno3denit_wc = -1, & + id_jo2resp_wc = -1, & + id_jnitrif = -1, & + id_co2_csurf = -1, & + id_pco2_csurf = -1, & + id_co2_alpha = -1, & + id_nh3_csurf = -1, & + id_nh3_alpha = -1, & + id_fcadet_arag = -1, & + id_fcadet_calc = -1, & + id_ffedet = -1, & + id_fndet = -1, & + id_fpdet = -1, & + id_fsidet = -1, & + id_flithdet = -1, & + id_fcadet_arag_btm = -1, & + id_fcadet_calc_btm = -1, & + id_ffedet_btm = -1, & + id_flithdet_btm = -1, & + id_fndet_btm = -1, & + id_fpdet_btm = -1, & + id_fsidet_btm = -1, & + id_fcased_burial = -1, & + id_fcased_redis = -1, & + id_fcased_redis_surfresp = -1, & + id_cased_redis_coef = -1, & + id_cased_redis_delz = -1, & + id_ffe_sed = -1, & + id_ffe_geotherm = -1, & + id_ffe_iceberg = -1, & + id_fnfeso4red_sed= -1, & + id_fno3denit_sed = -1, & + id_fnoxic_sed = -1, & + id_frac_burial = -1, & + id_fndet_burial = -1, & + id_fpdet_burial = -1, & + id_nphyto_tot = -1, & + id_no3_in_source = -1, & + id_pco2surf = -1, & + id_pnh3surf = -1, & + id_sfc_alk = -1, & + id_sfc_cadet_arag= -1, & + id_sfc_cadet_calc= -1, & + id_sfc_dic = -1, & + id_sfc_fed = -1, & + id_sfc_ldon = -1, & + id_sfc_sldon = -1, & + id_sfc_srdon = -1, & + id_sfc_no3 = -1, & + id_sfc_nh4 = -1, & + id_sfc_po4 = -1, & + id_sfc_sio4 = -1, & + id_sfc_htotal = -1, & + id_sfc_o2 = -1, & + id_sfc_chl = -1, & + id_sfc_irr = -1, & + id_sfc_irr_mem = -1, & + id_sfc_temp = -1, & + id_btm_temp = -1, & + id_btm_o2 = -1, & + id_btm_htotal = -1, & + id_btm_co3_sol_arag = -1, & + id_btm_co3_sol_calc = -1, & + id_btm_co3_ion = -1, & + id_cased_2d = -1, & + id_sfc_co3_ion = -1, & + id_sfc_co3_sol_arag = -1, & + id_sfc_co3_sol_calc = -1, & + id_runoff_flux_alk = -1, & + id_runoff_flux_dic = -1, & + id_runoff_flux_di14c = -1, & + id_runoff_flux_fed = -1, & + id_runoff_flux_lith = -1, & + id_runoff_flux_no3 = -1, & + id_runoff_flux_ldon = -1, & + id_runoff_flux_sldon = -1, & + id_runoff_flux_srdon = -1, & + id_runoff_flux_ndet = -1, & + id_runoff_flux_po4 = -1, & + id_runoff_flux_ldop = -1, & + id_runoff_flux_sldop = -1, & + id_runoff_flux_srdop = -1, & + id_tot_layer_int_c = -1, & + id_tot_layer_int_fe = -1, & + id_tot_layer_int_n = -1, & + id_tot_layer_int_p = -1, & + id_tot_layer_int_si = -1, & + id_tot_layer_int_o2 = -1, & + id_tot_layer_int_alk = -1, & + id_wc_vert_int_c = -1, & + id_wc_vert_int_dic = -1, & + id_wc_vert_int_doc = -1, & + id_wc_vert_int_poc = -1, & + id_wc_vert_int_n = -1, & + id_wc_vert_int_p = -1, & + id_wc_vert_int_fe = -1, & + id_wc_vert_int_si = -1, & + id_wc_vert_int_o2 = -1, & + id_wc_vert_int_alk = -1, & + id_wc_vert_int_jdiss_sidet = -1, & + id_wc_vert_int_jdiss_cadet = -1, & + id_wc_vert_int_jo2resp = -1, & + id_wc_vert_int_jprod_cadet = -1, & + id_wc_vert_int_jno3denit = -1, & + id_wc_vert_int_jnitrif = -1, & + id_wc_vert_int_juptake_nh4 = -1, & + id_wc_vert_int_jprod_nh4 = -1, & + id_wc_vert_int_juptake_no3 = -1, & + id_wc_vert_int_nfix = -1, & + id_total_filter_feeding = -1,& + id_nlg_diatoms = -1, & + id_jprod_allphytos_100 = -1, & + id_jprod_diat_100 = -1, & + id_q_si_2_n_lg_diatoms = -1, & + id_hp_jingest_n_100 = -1, & + id_hp_jremin_n_100 = -1, & + id_hp_jprod_ndet_100 = -1, & + id_jprod_lithdet_100 = -1, & + id_jprod_sidet_100 = -1, & + id_jprod_cadet_calc_100 = -1, & + id_jprod_cadet_arag_100 = -1, & + id_jprod_mesozoo_200 = -1, & + id_jremin_ndet_100 = -1, & + id_f_ndet_100 = -1, & + id_f_don_100 = -1, & + id_f_silg_100 = -1, & + id_f_mesozoo_200 = -1, & + id_fndet_100 = -1, & + id_fpdet_100 = -1, & + id_ffedet_100 = -1, & + id_fcadet_calc_100 = -1, & + id_fcadet_arag_100 = -1, & + id_flithdet_100 = -1, & + id_fsidet_100 = -1, & + id_o2min = -1, & + id_z_o2min = -1, & + id_z_sat_arag = -1, & ! Depth of Aragonite saturation + id_z_sat_calc = -1, & ! Depth of Calcite saturation + id_b_di14c = -1, & ! Bottom flux of DI14C + id_c14_2_n = -1, & ! DI14C to PO4 uptake ratio + id_c14o2_csurf = -1, & ! Surface water 14CO2* + id_c14o2_alpha = -1, & ! Surface water 14CO2* solubility + id_fpo14c = -1, & ! PO14C sinking flux + id_j14c_decay_dic= -1, & ! Radioactive decay of DI14C + id_j14c_decay_doc= -1, & ! Radioactive decay of DO14C + id_j14c_reminp = -1, & ! 14C particle remineralization layer integral + id_jdi14c = -1, & ! DI14C source layer integral + id_jdo14c = -1, & ! Semilabile DO14C source layer integral + id_di14c = -1, & ! Dissolved inorganic radiocarbon Prognostic tracer + id_do14c = -1, & ! Dissolved organic radiocarbon Prognostic tracer + id_f_alk_int_100 = -1, & + id_f_dic_int_100 = -1, & + id_f_din_int_100 = -1, & + id_f_fed_int_100 = -1, & + id_f_po4_int_100 = -1, & + id_f_sio4_int_100 = -1, & + id_jo2_plus_btm = -1, & + id_jalk_100 = -1, & + id_jdic_100 = -1, & + id_jdin_100 = -1, & + id_jfed_100 = -1, & + id_jpo4_100 = -1, & + id_jsio4_100 = -1, & +!============================================================================================================== +! JGJ 2016/08/08 CMIP6 OcnBgchem + id_thetao = -1, & ! for testing + id_dissic = -1, & + id_dissicnat = -1, & + id_dissicabio = -1, & + id_dissi14cabio = -1, & + id_dissoc = -1, & + id_phyc = -1, & + id_zooc = -1, & + id_bacc = -1, & + id_detoc = -1, & + id_calc = -1, & + id_arag = -1, & + id_phydiat = -1, & + id_phydiaz = -1, & + id_phypico = -1, & + id_phymisc = -1, & + id_zmicro = -1, & + id_zmeso = -1, & + id_talk = -1, & + id_talknat = -1, & + id_ph = -1, & + id_phnat = -1, & + id_phabio = -1, & + id_o2_cmip = -1, & + id_o2sat = -1, & + id_no3_cmip = -1, & + id_pka_nh3 = -1, & + id_nh4_cmip = -1, & + id_po4_cmip = -1, & + id_dfe = -1, & + id_si = -1, & + id_chl_cmip = -1, & + id_chldiat = -1, & + id_chldiaz = -1, & + id_chlpico = -1, & + id_chlmisc = -1, & + id_poc = -1, & + id_pon = -1, & + id_pop = -1, & + id_bfe = -1, & + id_bsi = -1, & + id_phyn = -1, & + id_phyp = -1, & + id_phyfe = -1, & + id_physi = -1, & + id_co3 = -1, & + id_co3nat = -1, & + id_co3abio = -1, & + id_co3satcalc = -1, & + id_co3satarag = -1, & + id_pp = -1, & + id_pnitrate = -1, & + id_pphosphate = -1, & + id_pbfe = -1, & + id_pbsi = -1, & + id_parag = -1, & + id_pcalc = -1, & + id_expc = -1, & + id_expn = -1, & + id_expp = -1, & + id_expfe = -1, & + id_expsi = -1, & + id_expcalc = -1, & + id_exparag = -1, & + id_remoc = -1, & + id_dcalc = -1, & + id_darag = -1, & + id_ppdiat = -1, & + id_ppdiaz = -1, & + id_pppico = -1, & + id_ppmisc = -1, & + id_bddtdic = -1, & + id_bddtdin = -1, & + id_bddtdip = -1, & + id_bddtdife = -1, & + id_bddtdisi = -1, & + id_bddtalk = -1, & + id_fescav = -1, & + id_fediss = -1, & + id_graz = -1, & + id_dissicos = -1, & + id_dissicnatos = -1, & + id_dissicabioos = -1, & + id_dissi14cabioos = -1, & + id_dissocos = -1, & + id_phycos = -1, & + id_zoocos = -1, & + id_baccos = -1, & + id_detocos = -1, & + id_calcos = -1, & + id_aragos = -1, & + id_phydiatos = -1, & + id_phydiazos = -1, & + id_phypicoos = -1, & + id_phymiscos = -1, & + id_zmicroos = -1, & + id_zmesoos = -1, & + id_talkos = -1, & + id_talknatos = -1, & + id_phos = -1, & + id_phnatos = -1, & + id_phabioos = -1, & + id_o2os = -1, & + id_o2satos = -1, & + id_no3os = -1, & + id_nh4os = -1, & + id_po4os = -1, & + id_dfeos = -1, & + id_sios = -1, & + id_chlos = -1, & + id_chldiatos = -1, & + id_chldiazos = -1, & + id_chlpicoos = -1, & + id_chlmiscos = -1, & + id_ponos = -1, & + id_popos = -1, & + id_bfeos = -1, & + id_bsios = -1, & + id_phynos = -1, & + id_phypos = -1, & + id_phyfeos = -1, & + id_physios = -1, & + id_co3os = -1, & + id_co3natos = -1, & + id_co3abioos = -1, & + id_co3satcalcos = -1, & + id_co3sataragos = -1, & + id_limndiat = -1, & + id_limndiaz = -1, & + id_limnpico = -1, & + id_limnmisc = -1, & + id_limirrdiat = -1, & + id_limirrdiaz = -1, & + id_limirrpico = -1, & + id_limirrmisc = -1, & + id_limfediat = -1, & + id_limfediaz = -1, & + id_limfepico = -1, & + id_limfemisc = -1, & + id_limpdiat = -1, & + id_limpdiaz = -1, & + id_limppico = -1, & + id_limpmisc = -1, & + id_intpp = -1, & + id_intppnitrate = -1, & + id_intppdiat = -1, & + id_intppdiaz = -1, & + id_intpppico = -1, & + id_intppmisc = -1, & + id_intpbn = -1, & + id_intpbp = -1, & + id_intpbfe = -1, & + id_intpbsi = -1, & + id_intpcalcite = -1, & + id_intparag = -1, & + id_epc100 = -1, & + id_epn100 = -1, & + id_epp100 = -1, & + id_epfe100 = -1, & + id_epsi100 = -1, & + id_epcalc100 = -1, & + id_eparag100 = -1, & + id_intdic = -1, & + id_intdoc = -1, & + id_intpoc = -1, & + id_spco2 = -1, & + id_spco2nat = -1, & + id_spco2abio = -1, & + id_dpco2 = -1, & + id_dpco2nat = -1, & + id_dpco2abio = -1, & + id_dpo2 = -1, & + id_fgco2 = -1, & + id_fgco2nat = -1, & + id_fgco2abio = -1, & + id_fg14co2abio = -1, & + id_fgo2 = -1, & + id_icfriver = -1, & + id_fric = -1, & + id_ocfriver = -1, & + id_froc = -1, & + id_intpn2 = -1, & + id_fsn = -1, & + id_frn = -1, & + id_fsfe = -1, & + id_frfe = -1, & +! id_o2min = -1, & ! previously defined + id_zo2min = -1, & + id_zsatcalc = -1, & + id_zsatarag = -1, & + id_fddtdic = -1, & + id_fddtdin = -1, & + id_fddtdip = -1, & + id_fddtdife = -1, & + id_fddtdisi = -1, & + id_fddtalk = -1, & + id_fbddtdic = -1, & + id_fbddtdin = -1, & + id_fbddtdip = -1, & + id_fbddtdife = -1, & + id_fbddtdisi = -1, & + id_fbddtalk = -1 + +!============================================================================================================== + end type generic_COBALT_type + + !An auxiliary type for storing varible names + type, public :: vardesc + character(len=fm_string_len) :: name ! The variable name in a NetCDF file. + character(len=fm_string_len) :: longname ! The long name of that variable. + character(len=1) :: hor_grid ! The hor. grid: u, v, h, q, or 1. + character(len=1) :: z_grid ! The vert. grid: L, i, or 1. + character(len=1) :: t_grid ! The time description: s, a, m, or 1. + character(len=fm_string_len) :: units ! The dimensions of the variable. + character(len=1) :: mem_size ! The size in memory: d or f. + end type vardesc + + type(generic_COBALT_type) :: cobalt + + type(CO2_dope_vector) :: CO2_dope_vec + + ! identification numbers for mpp clocks + integer :: id_clock_carbon_calculations + integer :: id_clock_phyto_growth + integer :: id_clock_bacteria_growth + integer :: id_clock_zooplankton_calculations + integer :: id_clock_other_losses + integer :: id_clock_production_loop + integer :: id_clock_ballast_loops + integer :: id_clock_source_sink_loop1 + integer :: id_clock_source_sink_loop2 + integer :: id_clock_source_sink_loop3 + integer :: id_clock_source_sink_loop4 + integer :: id_clock_source_sink_loop5 + integer :: id_clock_source_sink_loop6 + integer :: id_clock_cobalt_send_diagnostics + integer :: id_clock_cobalt_calc_diagnostics + +contains + + subroutine generic_COBALT_register(tracer_list) + type(g_tracer_type), pointer :: tracer_list +integer :: ioun +integer :: ierr +integer :: io_status +integer :: stdoutunit,stdlogunit +!----------------------------------------------------------------------- +! local parameters +!----------------------------------------------------------------------- +! + character(len=fm_string_len), parameter :: sub_name = 'generic_COBALT_register' +character(len=256), parameter :: error_header = & + '==>Error from ' // trim(mod_name) // '(' // trim(sub_name) // '): ' +character(len=256), parameter :: warn_header = & + '==>Warning from ' // trim(mod_name) // '(' // trim(sub_name) // '): ' +character(len=256), parameter :: note_header = & + '==>Note from ' // trim(mod_name) // '(' // trim(sub_name) // '): ' + + + +! provide for namelist over-ride +! This needs to go before the add_tracers in order to allow the namelist +! settings to switch tracers on and off. +! +stdoutunit=stdout();stdlogunit=stdlog() + +read (input_nml_file, nml=generic_COBALT_nml, iostat=io_status) +ierr = check_nml_error(io_status,'generic_COBALT_nml') + +write (stdoutunit,'(/)') +write (stdoutunit, generic_COBALT_nml) +write (stdlogunit, generic_COBALT_nml) + + if (do_14c) then + write (stdoutunit,*) trim(note_header), 'Simulating radiocarbon' + endif + + if (trim(co2_calc) == 'ocmip2') then + write (stdoutunit,*) trim(note_header), 'Using FMS OCMIP2 CO2 routine' + else if (trim(co2_calc) == 'mocsy') then + write (stdoutunit,*) trim(note_header), 'Using Mocsy CO2 routine' + else + call mpp_error(FATAL,"Unknown co2_calc option specified in generic_COBALT_nml") + endif + !Specify all prognostic and diagnostic tracers of this modules. + call user_add_tracers(tracer_list) + + end subroutine generic_COBALT_register + + ! + ! + ! Initialize the generic COBALT module + ! + ! + ! This subroutine: + ! Adds all the COBALT Tracers to the list of generic Tracers + ! passed to it via utility subroutine g_tracer_add(). + ! + ! Adds all the parameters used by this module via utility + ! subroutine g_tracer_add_param(). + ! + ! Allocates all work arrays used in the module. + ! + ! + ! + ! Pointer to the head of generic tracer list. + ! + ! + subroutine generic_COBALT_init(tracer_list, force_update_fluxes) + type(g_tracer_type), pointer :: tracer_list + logical ,intent(in) :: force_update_fluxes + + character(len=fm_string_len), parameter :: sub_name = 'generic_COBALT_init' + + !There are situations where the column_physics (update_from_source) is not called every timestep + ! such as when MOM6 THERMO_SPANS_COUPLING=True , yet we want the fluxes to be updated every timestep + ! In that case we can force an update by setting the namelist generic_tracer_nml:force_update_fluxes=.true. + cobalt%force_update_fluxes = force_update_fluxes + + if (do_nh3_atm_ocean_exchange .or. scheme_nitrif.eq.2 .or. scheme_nitrif.eq.3) then + do_nh3_diag=.true. + else + do_nh3_diag=.false. + end if + + !Specify and initialize all parameters used by this package + call user_add_params + + !Allocate all the private work arrays used by this module. + call user_allocate_arrays + + id_clock_carbon_calculations = mpp_clock_id('(Cobalt: carbon calcs)' ,grain=CLOCK_MODULE) + id_clock_phyto_growth = mpp_clock_id('(Cobalt: phytoplankton growth calcs)',grain=CLOCK_MODULE) + id_clock_bacteria_growth = mpp_clock_id('(Cobalt: bacteria growth calcs)',grain=CLOCK_MODULE) + id_clock_zooplankton_calculations = mpp_clock_id('(Cobalt: zooplankton calculations)',grain=CLOCK_MODULE) + id_clock_other_losses = mpp_clock_id('(Cobalt: other losses)',grain=CLOCK_MODULE) + id_clock_production_loop = mpp_clock_id('(Cobalt: production loop)',grain=CLOCK_MODULE) + id_clock_ballast_loops = mpp_clock_id('(Cobalt: ballasting loops)',grain=CLOCK_MODULE) + id_clock_source_sink_loop1 = mpp_clock_id('(Cobalt: source/sink loop 1)',grain=CLOCK_MODULE) + id_clock_source_sink_loop2 = mpp_clock_id('(Cobalt: source/sink loop 2)',grain=CLOCK_MODULE) + id_clock_source_sink_loop3 = mpp_clock_id('(Cobalt: source/sink loop 3)',grain=CLOCK_MODULE) + id_clock_source_sink_loop4 = mpp_clock_id('(Cobalt: source/sink loop 4)',grain=CLOCK_MODULE) + id_clock_source_sink_loop5 = mpp_clock_id('(Cobalt: source/sink loop 5)',grain=CLOCK_MODULE) + id_clock_source_sink_loop6 = mpp_clock_id('(Cobalt: source/sink loop 6)',grain=CLOCK_MODULE) + id_clock_cobalt_send_diagnostics = mpp_clock_id('(Cobalt: send diagnostics)',grain=CLOCK_MODULE) + id_clock_cobalt_calc_diagnostics = mpp_clock_id('(Cobalt: calculate diagnostics)',grain=CLOCK_MODULE) + + end subroutine generic_COBALT_init + + ! Register diagnostic fields to be used in this module. + ! Note that the tracer fields are automatically registered in user_add_tracers + ! User adds only diagnostics for fields that are not a member of g_tracer_type + ! + subroutine generic_COBALT_register_diag(diag_list) + type(g_diag_type), pointer :: diag_list + type(vardesc) :: vardesc_temp + integer :: isc,iec,jsc,jec,isd,ied,jsd,jed,nk,ntau, axes(3), axesTi(3) + type(time_type):: init_time + character(len=fm_string_len) :: cmor_field_name + character(len=fm_string_len) :: cmor_long_name + character(len=fm_string_len) :: cmor_units + character(len=fm_string_len) :: cmor_standard_name +! real :: conversion + + + call g_tracer_get_common(isc,iec,jsc,jec,isd,ied,jsd,jed,nk,ntau,axes=axes,init_time=init_time) + + ! The following vardesc types contain a package of metadata about each tracer, + ! including, in order, the following elements: name; longname; horizontal + ! staggering ('h') for collocation with thickness points ; vertical staggering + ! ('L') for a layer variable ; temporal staggering ('s' for snapshot) ; units ; + ! and precision in non-restart output files ('f' for 32-bit float or 'd' for + ! 64-bit doubles). For most tracers, only the name, longname and units should + ! be changed. + + + ! Register the diagnostics for the various phytoplankton + ! + ! Register Limitation Diagnostics + ! + vardesc_temp = vardesc("def_fe_Di","Diaz. Phyto. Fe Deficiency",'h','L','s','dimensionless','f') + phyto(DIAZO)%id_def_fe = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("def_fe_Lg","Large Phyto. Fe Deficiency",'h','L','s','dimensionless','f') + phyto(LARGE)%id_def_fe = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("def_fe_Sm","Small Phyto. Fe Deficiency",'h','L','s','dimensionless','f') + phyto(SMALL)%id_def_fe = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("felim_Di","Diaz. Phyto. Fed uptake Limitation",'h','L','s','dimensionless','f') + phyto(DIAZO)%id_felim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("felim_Lg","Large Phyto. Fed uptake Limitation",'h','L','s','dimensionless','f') + phyto(LARGE)%id_felim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("felim_Sm","Small Phyto. Fed uptake Limitation",'h','L','s','dimensionless','f') + phyto(SMALL)%id_felim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("irrlim_Di","Diaz. Phyto. Light Limitation",'h','L','s','dimensionless','f') + phyto(DIAZO)%id_irrlim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("irrlim_Lg","Large Phyto. Light Limitation",'h','L','s','dimensionless','f') + phyto(LARGE)%id_irrlim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("irrlim_Sm","Small Phyto. Light Limitation",'h','L','s','dimensionless','f') + phyto(SMALL)%id_irrlim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("theta_Di","Diaz. Phyto. Chl:C",'h','L','s','g Chl (g C)-1','f') + phyto(DIAZO)%id_theta = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("theta_Lg","Large Phyto. Chl:C",'h','L','s','g Chl (g C)-1','f') + phyto(LARGE)%id_theta = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("theta_Sm","Small Phyto. Chl:C",'h','L','s','g Chl (g C)-1','f') + phyto(SMALL)%id_theta = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("mu_Di","Diaz. Phyto. Overall Growth Rate",'h','L','s','s-1','f') + phyto(DIAZO)%id_mu = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("mu_Lg","Large Phyto. Overall Growth Rate",'h','L','s','s-1','f') + phyto(LARGE)%id_mu = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("mu_Sm","Small Phyto. Growth Rate",'h','L','s','s-1','f') + phyto(SMALL)%id_mu = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("mu_mem_Di","Diaz. Phyto. Growth Memory",'h','L','s','s-1','f') + phyto(DIAZO)%id_f_mu_mem = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("mu_mem_Lg","Large Phyto. Growth memory",'h','L','s','s-1','f') + phyto(LARGE)%id_f_mu_mem = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("mu_mem_Sm","Small Phyto. Growth Memory",'h','L','s','s-1','f') + phyto(SMALL)%id_f_mu_mem = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("mu_mix_Di","Diaz. Phyto. ML ave",'h','L','s','s-1','f') + phyto(DIAZO)%id_mu_mix = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("mu_mix_Lg","Large Phyto. ML ave",'h','L','s','s-1','f') + phyto(LARGE)%id_mu_mix = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("mu_mix_Sm","Small Phyto. ML ave",'h','L','s','s-1','f') + phyto(SMALL)%id_mu_mix = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("nh4lim_Lg","Ammonia Limitation of Large Phyto",'h','L','s','dimensionless','f') + phyto(LARGE)%id_nh4lim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("nh4lim_Sm","Ammonia Limitation of Small Phyto",'h','L','s','dimensionless','f') + phyto(SMALL)%id_nh4lim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("nh4lim_Di","Ammonia Limitation of Diazo",'h','L','s','dimensionless','f') + phyto(DIAZO)%id_nh4lim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("no3lim_Lg","Nitrate Limitation of Large Phyto",'h','L','s','dimensionless','f') + phyto(LARGE)%id_no3lim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("no3lim_Sm","Nitrate Limitation of Small Phyto",'h','L','s','dimensionless','f') + phyto(SMALL)%id_no3lim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("no3lim_Di","Ammonia Limitation of Diazo",'h','L','s','dimensionless','f') + phyto(DIAZO)%id_no3lim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("po4lim_Di","Phosphate Limitation of Diaz. Phyto",'h','L','s','dimensionless','f') + phyto(DIAZO)%id_po4lim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("po4lim_Lg","Phosphate Limitation of Large Phyto",'h','L','s','dimensionless','f') + phyto(LARGE)%id_po4lim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("po4lim_Sm","Phosphate Limitation of Small Phyto",'h','L','s','dimensionless','f') + phyto(SMALL)%id_po4lim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("o2lim_Di","Oxygen Limitation of Diaz. Phyto",'h','L','s','dimensionless','f') + phyto(DIAZO)%id_o2lim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("q_fe_2_n_Di","Fe:N ratio of Diaz. Phyto",'h','L','s','mol Fe/mol N','f') + phyto(DIAZO)%id_q_fe_2_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("q_fe_2_n_Lg","Fe:N ratio of Large Phyto",'h','L','s','mol Fe/mol N','f') + phyto(LARGE)%id_q_fe_2_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("q_fe_2_n_Sm","Fe:N ratio of Small Phyto",'h','L','s','mol Fe/mol N','f') + phyto(SMALL)%id_q_fe_2_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("silim_Lg","SiO4 Limitation of Large Phyto",'h','L','s','dimensionless','f') + phyto(LARGE)%id_silim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("q_si_2_n_Lg","Si:N ratio of Large Phyto",'h','L','s','mol Si/mol N','f') + phyto(LARGE)%id_q_si_2_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Register diagnostics for phytoplankton loss terms: zooplankton + ! CAS: loss diagnostics simplified to just N + + vardesc_temp = vardesc("jzloss_n_Di","Diazotroph nitrogen loss to zooplankton layer integral",& + 'h','L','s','mol N m-2 s-1','f') + phyto(DIAZO)%id_jzloss_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jzloss_n_Lg","Large phyto nitrogen loss to zooplankton layer integral",& + 'h','L','s','mol N m-2 s-1','f') + phyto(LARGE)%id_jzloss_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jzloss_n_Sm","Small phyto nitrogen loss to zooplankton layer integral",& + 'h','L','s','mol N m-2 s-1','f') + phyto(SMALL)%id_jzloss_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Register diagnostics for phytoplankton loss terms: aggregation + ! + + vardesc_temp = vardesc("jaggloss_n_Di","Diazotroph nitrogen loss to aggregation layer integral",& + 'h','L','s','mol N m-2 s-1','f') + phyto(DIAZO)%id_jaggloss_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jaggloss_n_Lg","Large phyto nitrogen loss to aggregation layer integral",& + 'h','L','s','mol N m-2 s-1','f') + phyto(LARGE)%id_jaggloss_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jaggloss_n_Sm","Small phyto nitrogen loss to aggregation layer integral",& + 'h','L','s','mol N m-2 s-1','f') + phyto(SMALL)%id_jaggloss_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("agg_lim_Di","Diazotroph aggregation limitation",& + 'h','L','s','dimensionless','f') + phyto(DIAZO)%id_agg_lim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("agg_lim_Lg","Large phyto aggregation limitation",& + 'h','L','s','dimensionless','f') + phyto(LARGE)%id_agg_lim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("agg_lim_Sm","Small phyto aggregation limitation",& + 'h','L','s','dimensionless','f') + phyto(SMALL)%id_agg_lim= register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + + ! + ! Register diagnostics for phytoplankton loss terms: viruses + ! + + vardesc_temp = vardesc("jvirloss_n_Di","Diazotroph nitrogen loss to viruses layer integral",& + 'h','L','s','mol N m-2 s-1','f') + phyto(DIAZO)%id_jvirloss_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jvirloss_n_Lg","Large phyto nitrogen loss to viruses layer integral",& + 'h','L','s','mol N m-2 s-1','f') + phyto(LARGE)%id_jvirloss_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jvirloss_n_Sm","Small phyto nitrogen loss to viruses layer integral",& + 'h','L','s','mol N m-2 s-1','f') + phyto(SMALL)%id_jvirloss_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Register diagnostics for phytoplankton exudation + ! + vardesc_temp = vardesc("jexuloss_n_Di","Diazotroph nitrogen loss via exudation",& + 'h','L','s','mol N m-2 s-1','f') + phyto(DIAZO)%id_jexuloss_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jexuloss_n_Lg","Large phyto nitrogen loss via exudation",& + 'h','L','s','mol N m-2 s-1','f') + phyto(LARGE)%id_jexuloss_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jexuloss_n_Sm","Small phyto nitrogen loss via exudation",& + 'h','L','s','mol N m-2 s-1','f') + phyto(SMALL)%id_jexuloss_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Register dynamic silicate diagnostics + ! + vardesc_temp = vardesc("nlg_diatoms","large phytoplankton nitrogen from diatoms",& + 'h','L','s','mol kg-1','f') + cobalt%id_nlg_diatoms = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("q_si_2_n_lg_diatoms","Si:N ratio in large diatoms",& + 'h','L','s','mol Si mol N','f') + cobalt%id_q_si_2_n_lg_diatoms = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Register Phytoplankton Production Diagnostics + ! + + vardesc_temp = vardesc("juptake_n2_Di","Nitrogen fixation layer integral",'h','L','s','mol N m-2 s-1','f') + phyto(DIAZO)%id_juptake_n2 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("juptake_fe_Di","Diaz. phyto. Fed uptake layer integral",'h','L','s','mol Fe m-2 s-1','f') + phyto(DIAZO)%id_juptake_fe = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("juptake_fe_Lg","Large phyto. Fed uptake layer integral",'h','L','s','mol Fe m-2 s-1','f') + phyto(LARGE)%id_juptake_fe = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("juptake_fe_Sm","Small phyto. Fed uptake layer integral",& + 'h','L','s','mol Fe m-2 s-1','f') + phyto(SMALL)%id_juptake_fe = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("juptake_nh4_Di","Diaz. phyto. NH4 uptake layer integral",'h','L','s','mol NH4 m-2 s-1','f') + phyto(DIAZO)%id_juptake_nh4 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("juptake_nh4_Lg","Large phyto. NH4 uptake layer integral",'h','L','s','mol NH4 m-2 s-1','f') + phyto(LARGE)%id_juptake_nh4 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("juptake_nh4_Sm","Small phyto. NH4 uptake layer integral",& + 'h','L','s','mol NH4 m-2 s-1','f') + phyto(SMALL)%id_juptake_nh4 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("juptake_no3_Di","Diaz. phyto. NO3 uptake layer integral",'h','L','s','mol NO3 m-2 s-1','f') + phyto(DIAZO)%id_juptake_no3 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("juptake_no3_Lg","Large phyto. NO3 uptake layer integral",'h','L','s','mol NO3 m-2 s-1','f') + phyto(LARGE)%id_juptake_no3 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("juptake_no3_Sm","Small phyto. NO3 uptake layer integral",& + 'h','L','s','mol NO3 m-2 s-1','f') + phyto(SMALL)%id_juptake_no3 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("juptake_po4_Di","Diaz. phyto. PO4 uptake layer integral",'h','L','s','mol PO4 m-2 s-1','f') + phyto(DIAZO)%id_juptake_po4 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("juptake_po4_Lg","Large phyto. PO4 uptake layer integral",'h','L','s','mol PO4 m-2 s-1','f') + phyto(LARGE)%id_juptake_po4 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("juptake_po4_Sm","Small phyto. PO4 uptake layer integral",& + 'h','L','s','mol PO4 m-2 s-1','f') + phyto(SMALL)%id_juptake_po4 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("juptake_sio4_Lg","Large phyto. SiO4 uptake layer integral",'h','L','s','mol m-2 s-1','f') + phyto(LARGE)%id_juptake_sio4 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_ndi","Diazotroph Nitrogen production layer integral",'h','L','s','mol m-2 s-1','f') + phyto(DIAZO)%id_jprod_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_nsmp","Small phyto. Nitrogen production layer integral",'h','L','s','mol m-2 s-1','f') + phyto(SMALL)%id_jprod_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_nlgp","Large phyto. Nitrogen production layer integral",'h','L','s','mol m-2 s-1','f') + phyto(LARGE)%id_jprod_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Register zooplankton diagnostics, starting with losses of zooplankton to ingestion by zooplankton + ! + + vardesc_temp = vardesc("jzloss_n_Smz","Small zooplankton nitrogen loss to zooplankton layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(1)%id_jzloss_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jzloss_n_Mdz","Medium-sized zooplankton nitrogen loss to zooplankton layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(2)%id_jzloss_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jzloss_n_Lgz","Large zooplankton nitrogen loss to zooplankton layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(3)%id_jzloss_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Register diagnostics for zooplankton loss terms: higher predators + ! + + vardesc_temp = vardesc("jhploss_n_Smz","Small zooplankton nitrogen loss to higher predators layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(1)%id_jhploss_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jhploss_n_Mdz","Medium-sized zooplankton nitrogen loss to higher predators layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(2)%id_jhploss_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jhploss_n_Lgz","Large zooplankton nitrogen loss to higher predators layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(3)%id_jhploss_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Register zooplankton ingestion rates + ! + + vardesc_temp = vardesc("jingest_n_Smz","Ingestion of nitrogen by small zooplankton, layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(1)%id_jingest_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jingest_n_Mdz","Ingestion of nitrogen by medium-sized zooplankton, layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(2)%id_jingest_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jingest_n_Lgz","Ingestion of nitrogen by large zooplankton, layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(3)%id_jingest_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jingest_p_Smz","Ingestion of phosphorous by small zooplankton, layer integral", & + 'h','L','s','mol P m-2 s-1','f') + zoo(1)%id_jingest_p = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jingest_p_Mdz","Ingestion of phosphorous by medium-sized zooplankton, layer integral",& + 'h','L','s','mol P m-2 s-1','f') + zoo(2)%id_jingest_p = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jingest_p_Lgz","Ingestion of phosphorous by large zooplankton, layer integral",& + 'h','L','s','mol P m-2 s-1','f') + zoo(3)%id_jingest_p = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jingest_sio2_Smz","Ingestion of sio2 by small zooplankton, layer integral",& + 'h','L','s','mol SiO2 m-2 s-1','f') + zoo(1)%id_jingest_sio2 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jingest_sio2_Mdz","Ingestion of sio2 by medium-sized zooplankton, layer integral",& + 'h','L','s','mol SiO2 m-2 s-1','f') + zoo(2)%id_jingest_sio2 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jingest_sio2_Lgz","Ingestion of sio2 by large zooplankton, layer integral",& + 'h','L','s','mol SiO2 m-2 s-1','f') + zoo(3)%id_jingest_sio2 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jingest_fe_Smz","Ingestion of Fe by small zooplankton, layer integral",& + 'h','L','s','mol Fe m-2 s-1','f') + zoo(1)%id_jingest_fe = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jingest_fe_Mdz","Ingestion of Fe by medium-sized zooplankton, layer integral",& + 'h','L','s','mol Fe m-2 s-1','f') + zoo(2)%id_jingest_fe = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jingest_fe_Lgz","Ingestion of Fe by large zooplankton, layer integral",& + 'h','L','s','mol Fe m-2 s-1','f') + zoo(3)%id_jingest_fe = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Register detrital production terms for zooplankton + ! + + vardesc_temp = vardesc("jprod_ndet_Smz","Production of nitrogen detritus by small zooplankton, layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(1)%id_jprod_ndet = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_ndet_Mdz","Production of nitrogen detritus by medium zooplankton, layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(2)%id_jprod_ndet = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_ndet_Lgz","Production of nitrogen detritus by large zooplankton, layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(3)%id_jprod_ndet = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_pdet_Smz","Production of phosphorous detritus by small zooplankton, layer integral",& + 'h','L','s','mol P m-2 s-1','f') + zoo(1)%id_jprod_pdet = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_pdet_Mdz","Production of phosphorous detritus by medium zooplankton, layer integral",& + 'h','L','s','mol P m-2 s-1','f') + zoo(2)%id_jprod_pdet = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_pdet_Lgz","Production of phosphorous detritus by large zooplankton, layer integral",& + 'h','L','s','mol P m-2 s-1','f') + zoo(3)%id_jprod_pdet = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_sidet_Smz","Production of opal detritus by small zooplankton, layer integral",& + 'h','L','s','mol SiO2 m-2 s-1','f') + zoo(1)%id_jprod_sidet = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_sidet_Mdz","Production of opal detritus by medium zooplankton, layer integral",& + 'h','L','s','mol SiO2 m-2 s-1','f') + zoo(2)%id_jprod_sidet = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_sidet_Lgz","Production of opal detritus by large zooplankton, layer integral",& + 'h','L','s','mol SiO2 m-2 s-1','f') + zoo(3)%id_jprod_sidet = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_sio4_Smz","Production of sio4 through grazing/dissolution, layer integral",& + 'h','L','s','mol SiO4 m-2 s-1','f') + zoo(1)%id_jprod_sio4 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_sio4_Mdz","Production of sio4 through grazing/dissolution, layer integral",& + 'h','L','s','mol SiO4 m-2 s-1','f') + zoo(2)%id_jprod_sio4 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_sio4_Lgz","Production of sio4 through grazing/dissolution, layer integral",& + 'h','L','s','mol SiO4 m-2 s-1','f') + zoo(3)%id_jprod_sio4 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_fedet_Smz","Production of iron detritus by small zooplankton, layer integral",& + 'h','L','s','mol Fe m-2 s-1','f') + zoo(1)%id_jprod_fedet = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_fedet_Mdz","Production of iron detritus by medium zooplankton, layer integral",& + 'h','L','s','mol Fe m-2 s-1','f') + zoo(2)%id_jprod_fedet = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_fedet_Lgz","Production of iron detritus by large zooplankton, layer integral",& + 'h','L','s','mol Fe m-2 s-1','f') + zoo(3)%id_jprod_fedet = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Register dissolved organic/inorganic production terms for zooplankton + ! + ! Labile dissolved organic nitrogen + vardesc_temp = vardesc("jprod_ldon_Smz","Production of labile dissolved organic nitrogen by small zooplankton, layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(1)%id_jprod_ldon = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_ldon_Mdz","Production of labile dissolved organic nitrogen by medium zooplankton, layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(2)%id_jprod_ldon = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_ldon_Lgz","Production of labile dissolved organic nitrogen by large zooplankton, layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(3)%id_jprod_ldon = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! Labile dissolved organic phosphorous + vardesc_temp = vardesc("jprod_ldop_Smz","Production of labile dissolved organic phosphorous by small zooplankton, layer integral",& + 'h','L','s','mol P m-2 s-1','f') + zoo(1)%id_jprod_ldop = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_ldop_Mdz","Production of labile dissolved organic phosphorous by medium zooplankton, layer integral",& + 'h','L','s','mol P m-2 s-1','f') + zoo(2)%id_jprod_ldop = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_ldop_Lgz","Production of labile dissolved organic phosphorous by large zooplankton, layer integral",& + 'h','L','s','mol P m-2 s-1','f') + zoo(3)%id_jprod_ldop = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! Refractory dissolved organic nitrogen + vardesc_temp = vardesc("jprod_srdon_Smz","Production of semi-refractory dissolved organic nitrogen by small zooplankton, layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(1)%id_jprod_srdon = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_srdon_Mdz","Production of semi-refractory dissolved organic nitrogen by medium zooplankton, layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(2)%id_jprod_srdon = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_srdon_Lgz","Production of semi-refractory dissolved organic nitrogen by large zooplankton, layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(3)%id_jprod_srdon = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! Labile dissolved organic phosphorous + vardesc_temp = vardesc("jprod_srdop_Smz","Production of semi-refractory dissolved organic phosphorous by small zooplankton, layer integral",& + 'h','L','s','mol P m-2 s-1','f') + zoo(1)%id_jprod_srdop = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_srdop_Mdz","Production of semi-refractory dissolved organic phosphorous by medium zooplankton, layer integral",& + 'h','L','s','mol P m-2 s-1','f') + zoo(2)%id_jprod_srdop = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_srdop_Lgz","Production of semi-refractory dissolved organic phosphorous by large zooplankton, layer integral",& + 'h','L','s','mol P m-2 s-1','f') + zoo(3)%id_jprod_srdop = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! semi-labile dissolved organic nitrogen + vardesc_temp = vardesc("jprod_sldon_Smz","Production of semi-labile dissolved organic nitrogen by small zooplankton, layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(1)%id_jprod_sldon = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_sldon_Mdz","Production of semi-labile dissolved organic nitrogen by medium zooplankton, layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(2)%id_jprod_sldon = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_sldon_Lgz","Production of semi-labile dissolved organic nitrogen by large zooplankton, layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(3)%id_jprod_sldon = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! semi-labile dissolved organic phosphorous + vardesc_temp = vardesc("jprod_sldop_Smz","Production of semi-labile dissolved organic phosphorous by small zooplankton, layer integral",& + 'h','L','s','mol P m-2 s-1','f') + zoo(1)%id_jprod_sldop = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_sldop_Mdz","Production of semi-labile dissolved organic phosphorous by medium zooplankton, layer integral",& + 'h','L','s','mol P m-2 s-1','f') + zoo(2)%id_jprod_sldop = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_sldop_Lgz","Production of semi-labile dissolved organic phosphorous by large zooplankton, layer integral",& + 'h','L','s','mol P m-2 s-1','f') + zoo(3)%id_jprod_sldop = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! dissolved iron + vardesc_temp = vardesc("jprod_fed_Smz","Production of dissolved iron by small zooplankton, layer integral",& + 'h','L','s','mol Fe m-2 s-1','f') + zoo(1)%id_jprod_fed = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_fed_Mdz","Production of dissolved iron by medium-sized zooplankton, layer integral",& + 'h','L','s','mol Fe m-2 s-1','f') + zoo(2)%id_jprod_fed = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_fed_Lgz","Production of dissolved iron by large zooplankton, layer integral",& + 'h','L','s','mol Fe m-2 s-1','f') + zoo(3)%id_jprod_fed = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! phosphate + vardesc_temp = vardesc("jprod_po4_Smz","Production of phosphate by small zooplankton, layer integral",& + 'h','L','s','mol PO4 m-2 s-1','f') + zoo(1)%id_jprod_po4 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_po4_Mdz","Production of phosphate by medium-sized zooplankton, layer integral",& + 'h','L','s','mol PO4 m-2 s-1','f') + zoo(2)%id_jprod_po4 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_po4_Lgz","Production of phosphate by large zooplankton, layer integral",& + 'h','L','s','mol PO4 m-2 s-1','f') + zoo(3)%id_jprod_po4 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! ammonia + vardesc_temp = vardesc("jprod_nh4_Smz","Production of ammonia by small zooplankton, layer integral",& + 'h','L','s','mol NH4 m-2 s-1','f') + zoo(1)%id_jprod_nh4 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_nh4_Mdz","Production of ammonia by medium-sized zooplankton, layer integral",& + 'h','L','s','mol NH4 m-2 s-1','f') + zoo(2)%id_jprod_nh4 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_nh4_Lgz","Production of ammonia by large zooplankton, layer integral",& + 'h','L','s','mol NH4 m-2 s-1','f') + zoo(3)%id_jprod_nh4 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Register zooplankton production terms + ! + + vardesc_temp = vardesc("jprod_nsmz","Production of new biomass (nitrogen) by small zooplankton, layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(1)%id_jprod_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_nmdz","Production of new biomass (nitrogen) by medium-sized zooplankton, layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(2)%id_jprod_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_nlgz","Production of new biomass (nitrogen) by large zooplankton, layer integral",& + 'h','L','s','mol N m-2 s-1','f') + zoo(3)%id_jprod_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("o2lim_Smz","Oxygen limitation of small zooplankton",'h','L','s','dimensionless','f') + zoo(1)%id_o2lim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("o2lim_Mdz","Oxygen limitation of medium-sized zooplankton",'h','L','s','dimensionless','f') + zoo(2)%id_o2lim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("o2lim_Lgz","Oxygen limitation of large zooplankton",'h','L','s','dimensionless','f') + zoo(3)%id_o2lim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("temp_lim_Smz","Temperature limitation of small zooplankton",'h','L','s','dimensionless','f') + zoo(1)%id_temp_lim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("temp_lim_Mdz","Temperature limitation of medium-sized zooplankton",'h','L','s','dimensionless','f') + zoo(2)%id_temp_lim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("temp_lim_Lgz","Temperature limitation of large zooplankton",'h','L','s','dimensionless','f') + zoo(3)%id_temp_lim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Register bacterial diagnostics, starting with losses of bacteria to ingestion by zooplankton + ! CAS: limit loss terms to N + + vardesc_temp = vardesc("jzloss_n_Bact","Bacterial nitrogen loss to zooplankton layer integral",& + 'h','L','s','mol N m-2 s-1','f') + bact(1)%id_jzloss_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Register diagnostics for bacteria loss terms: viruses + ! + + vardesc_temp = vardesc("jvirloss_n_Bact","Bacterial nitrogen loss to viruses layer integral",& + 'h','L','s','mol N m-2 s-1','f') + bact(1)%id_jvirloss_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Register bacterial uptake terms + ! + + vardesc_temp = vardesc("juptake_ldon","Bacterial uptake of labile dissolved organic nitrogen",'h','L','s','mol N m-2 s-1','f') + bact(1)%id_juptake_ldon = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("juptake_ldop","Bacterial uptake of labile dissolved organic phosphorous",'h','L','s','mol P m-2 s-1','f') + bact(1)%id_juptake_ldop = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Register dissolved inorganic production terms for bacteria + ! + ! phosphate + vardesc_temp = vardesc("jprod_po4_Bact","Production of phosphate by bacteria, layer integral",& + 'h','L','s','mol PO4 m-2 s-1','f') + bact(1)%id_jprod_po4 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! ammonia + vardesc_temp = vardesc("jprod_nh4_Bact","Production of ammonia by bacteria, layer integral",& + 'h','L','s','mol NH4 m-2 s-1','f') + bact(1)%id_jprod_nh4 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Register bacterial production terms + ! + + vardesc_temp = vardesc("jprod_nbact","Production of new biomass (nitrogen) by bacteria, layer integral",& + 'h','L','s','mol N m-2 s-1','f') + bact(1)%id_jprod_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("o2lim_Bact","Oxygen limitation of bacteria",'h','L','s','dimensionless','f') + bact(1)%id_o2lim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("ldonlim_Bact","ldon limitation of bacteria",'h','L','s','dimensionless','f') + bact(1)%id_ldonlim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("temp_lim_Bact","Temperature limitation of bacteria",'h','L','s','dimensionless','f') + bact(1)%id_temp_lim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Register general COBALT diagnostics + ! + + vardesc_temp = vardesc("co3_sol_arag","Carbonate Ion Solubility for Aragonite",'h','L','s','mol kg-1','f') + cobalt%id_co3_sol_arag = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("co3_sol_calc","Carbonate Ion Solubility for Calcite",'h','L','s','mol kg-1','f') + cobalt%id_co3_sol_calc = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("omega_arag","Carbonate Ion Saturation State for Aragonite",'h','L','s','mol kg-1','f') + cobalt%id_omega_arag = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("omega_calc","Carbonate Ion Saturation State for Calcite",'h','L','s','mol kg-1','f') + cobalt%id_omega_calc = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! A few overall production diagnostics + ! + + vardesc_temp = vardesc("jprod_cadet_arag","Aragonite CaCO3 production layer integral",'h','L','s','mol m-2 s-1','f') + cobalt%id_jprod_cadet_arag = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_cadet_calc","Calcite CaCO3 production layer integral",'h','L','s','mol m-2 s-1','f') + cobalt%id_jprod_cadet_calc = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_lithdet","Lithogenic detritus production layer integral",'h','L','s','g m-2 s-1','f') + cobalt%id_jprod_lithdet = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_sidet","opal detritus production layer integral",'h','L','s','mol SiO2 m-2 s-1','f') + cobalt%id_jprod_sidet = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_sio4","sio4 production layer integral",'h','L','s','mol SiO2 m-2 s-1','f') + cobalt%id_jprod_sio4 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_fedet","Detrital Fedet production layer integral",'h','L','s','mol Fe m-2 s-1','f') + cobalt%id_jprod_fedet = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_ndet","Detrital PON production layer integral",'h','L','s','mol N m-2 s-1','f') + cobalt%id_jprod_ndet = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_pdet","Detrital phosphorus production layer integral",'h','L','s','mol P m-2 s-1','f') + cobalt%id_jprod_pdet = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_ldon","labile dissolved organic nitrogen production layer integral",& + 'h','L','s','mol N m-2 s-1','f') + cobalt%id_jprod_ldon = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_ldop","labile dissolved organic phosphorous production layer integral",& + 'h','L','s','mol P m-2 s-1','f') + cobalt%id_jprod_ldop = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_srdon","refractory dissolved organic nitrogen production layer integral",& + 'h','L','s','mol N m-2 s-1','f') + cobalt%id_jprod_srdon = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_srdop","refractory dissolved organic phosphorous production layer integral",& + 'h','L','s','mol P m-2 s-1','f') + cobalt%id_jprod_srdop = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_sldon","semi-labile dissolved organic nitrogen production layer integral",& + 'h','L','s','mol N m-2 s-1','f') + cobalt%id_jprod_sldon = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_sldop","semi-labile dissolved organic phosphorous production layer integral",& + 'h','L','s','mol P m-2 s-1','f') + cobalt%id_jprod_sldop = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_po4","phosphate production layer integral",& + 'h','L','s','mol PO4 m-2 s-1','f') + cobalt%id_jprod_po4 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_nh4","NH4 production layer integral",'h','L','s','mol NH4 m-2 s-1','f') + cobalt%id_jprod_nh4 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + +! CAS added a "plus_btm" version of jprod_nh4 to use for the remoc CMIP variable + vardesc_temp = vardesc("jprod_nh4_plus_btm","NH4 production layer integral plus bottom fluxes",'h','L','s','mol NH4 m-2 s-1','f') + cobalt%id_jprod_nh4_plus_btm = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! loss diagnostics: detrital loss terms + ! + + vardesc_temp = vardesc("det_jzloss_n","nitrogen detritus loss to zooplankton layer integral",& + 'h','L','s','mol N m-2 s-1','f') + cobalt%id_det_jzloss_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("det_jhploss_n","nitrogen detritus loss to higher predators layer integral",& + 'h','L','s','mol N m-2 s-1','f') + cobalt%id_det_jhploss_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Loss diagnostics: dissolution and remineralization + ! + + vardesc_temp = vardesc("jdiss_sidet","SiO2 detritus dissolution, layer integral",& + 'h','L','s','mol m-2 s-1','f') + cobalt%id_jdiss_sidet = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jdiss_cadet_arag","CaCO3 detritus dissolution, layer integral", & + 'h','L','s','mol CaCO3 m-2 s-1','f') + cobalt%id_jdiss_cadet_arag = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! CAS added diagnostic including bottom fluxes for cmip5 + vardesc_temp = vardesc("jdiss_cadet_arag_plus_btm","CaCO3 detritus dissolution plus bottom dissolution, layer integral", & + 'h','L','s','mol CaCO3 m-2 s-1','f') + cobalt%id_jdiss_cadet_arag_plus_btm = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jdiss_cadet_calc","CaCO3 detritus dissolution, layer integral", & + 'h','L','s','mol CaCO3 m-2 s-1','f') + cobalt%id_jdiss_cadet_calc = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! CAS added diagnostic including bottom fluxes for cmip5 + vardesc_temp = vardesc("jdiss_cadet_calc_plus_btm","CaCO3 detritus dissolution plus bottom dissolution, layer integral", & + 'h','L','s','mol CaCO3 m-2 s-1','f') + cobalt%id_jdiss_cadet_calc_plus_btm = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jremin_ndet","Nitrogen detritus remineralization, layer integral",& + 'h','L','s','mol N m-2 s-1','f') + cobalt%id_jremin_ndet = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jremin_pdet","Phosphorous detritus remineralization, layer integral",& + 'h','L','s','mol P m-2 s-1','f') + cobalt%id_jremin_pdet = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jremin_fedet","Iron detritus remineralization, layer integral",& + 'h','L','s','mol m-2 s-1','f') + cobalt%id_jremin_fedet = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! iron cycling diagnostics + ! + + vardesc_temp = vardesc("jprod_fed","dissolved iron production layer integral",& + 'h','L','s','mol Fe m-2 s-1','f') + cobalt%id_jprod_fed = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jfed","Dissolved Iron Change layer integral",'h','L','s','mol Fe m-2 s-1','f') + cobalt%id_jfed = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jfe_ads","Iron adsorption layer integral",'h','L','s','mol Fe m-2 s-1','f') + cobalt%id_jfe_ads = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jfe_coast","Coastal iron efflux layer integral",'h','L','s','mol Fe m-2 s-1','f') + cobalt%id_jfe_coast = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("kfe_eq_lig","Effective ligand binding strength",'h','L','s','kg mol-1','f') + cobalt%id_kfe_eq_lig = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("feprime","Free iron concentration",'h','L','s','mol kg-1','f') + cobalt%id_feprime = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("ligand","ligand concentration",'h','L','s','mol kg-1','f') + cobalt%id_ligand = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("fe_sol","iron solubility",'h','L','s','mol kg-1','f') + cobalt%id_fe_sol = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Temperature limitation diagnostics + ! + + vardesc_temp = vardesc("expkT","Eppley temperature limitation factor",'h','L','s','dimensionless','f') + cobalt%id_expkT = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("expkreminT","Detritus remineralization temperature limitation factor",'h','L','s','dimensionless','f') + cobalt%id_expkreminT = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("hp_o2lim","Oxygen limitation of higher predators",'h','L','s','dimensionless','f') + cobalt%id_hp_o2lim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("hp_temp_lim","Temperature limitation of higher predators",'h','L','s','dimensionless','f') + cobalt%id_hp_temp_lim = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Some additional light field diagnostics + ! + + vardesc_temp = vardesc("irr_inst","Instantaneous Light",'h','L','s','W m-2','f') + cobalt%id_irr_inst = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("irr_mix","Light averaged over mixing layer",'h','L','s','W m-2','f') + cobalt%id_irr_mix = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Nitrification/Denitrification diagnostics + ! + + vardesc_temp = vardesc("jnitrif","Nitrification layer integral",'h','L','s','mol m-2 s-1','f') + cobalt%id_jnitrif = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jno3denit_wc","Water column Denitrification layer integral",'h','L','s','mol m-2 s-1','f') + cobalt%id_jno3denit_wc = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Track total aerobic respiration in the water column + ! + + vardesc_temp = vardesc("jo2resp_wc","Water column aerobic respiration layer integral",'h','L','s','mol m-2 s-1','f') + cobalt%id_jo2resp_wc = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Some useful total layer integrals + ! + + vardesc_temp = vardesc("nphyto_tot","Total NO3: Di+Lg+Sm",'h','L','s','mol m-2 s-1','f') + cobalt%id_nphyto_tot = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("tot_layer_int_c","Total Carbon (DIC+OC+IC) boxwise layer integral",'h','L','s','mol m-2','f') + cobalt%id_tot_layer_int_c = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("tot_layer_int_fe","Total Iron (Fed_OFe) boxwise layer integral",'h','L','s','mol m-2','f') + cobalt%id_tot_layer_int_fe = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("tot_layer_int_n","Total Nitrogen (NO3+NH4+ON) boxwise layer integral",'h','L','s','mol m-2','f') + cobalt%id_tot_layer_int_n = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("tot_layer_int_p","Total Phosphorus (PO4+OP) boxwise layer integral",'h','L','s','mol m-2','f') + cobalt%id_tot_layer_int_p = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("tot_layer_int_si","Total Silicon (SiO4+SiO2) boxwise layer integral",'h','L','s','mol m-2','f') + cobalt%id_tot_layer_int_si = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("tot_layer_int_o2","Total oxygen boxwise layer integral",'h','L','s','mol m-2','f') + cobalt%id_tot_layer_int_o2 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("tot_layer_int_alk","Total alkalinity boxwise layer integral",'h','L','s','mol m-2','f') + cobalt%id_tot_layer_int_alk = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("total_filter_feeding","Total filter feeding by large organisms",'h','L','s','mol N m-2 s-1','f') + cobalt%id_total_filter_feeding = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Save river, depositon and bulk elemental fluxes + ! + + vardesc_temp = vardesc("dep_dry_fed","Dry Deposition of Iron to the ocean",'h','1','s','mol m-2 s-1','f') + cobalt%id_dep_dry_fed = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("dep_dry_lith","Dry Deposition of Lithogenic Material",'h','1','s','g m-2 s-1','f') + cobalt%id_dep_dry_lith = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("dep_dry_nh4","Dry Deposition of Ammonia to the ocean",'h','1','s','mol m-2 s-1','f') + cobalt%id_dep_dry_nh4 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("dep_dry_no3","Dry Deposition of Nitrate to the ocean",'h','1','s','mol m-2 s-1','f') + cobalt%id_dep_dry_no3 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("dep_dry_po4","Dry Deposition of Phosphate to the ocean",'h','1','s','mol m-2 s-1','f') + cobalt%id_dep_dry_po4 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("dep_wet_fed","Wet Deposition of Iron to the ocean",'h','1','s','mol m-2 s-1','f') + cobalt%id_dep_wet_fed = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("dep_wet_lith","Wet Deposition of Lithogenic Material",'h','1','s','g m-2 s-1','f') + cobalt%id_dep_wet_lith = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("dep_wet_nh4","Wet Deposition of Ammonia to the ocean",'h','1','s','mol m-2 s-1','f') + cobalt%id_dep_wet_nh4 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("dep_wet_no3","Wet Deposition of Nitrate to the ocean",'h','1','s','mol m-2 s-1','f') + cobalt%id_dep_wet_no3 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("dep_wet_po4","Wet Deposition of Phosphate to the ocean",'h','1','s','mol m-2 s-1','f') + cobalt%id_dep_wet_po4 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("pka_nh3","pKa of NH3",'h','1','s','','f') + cobalt%id_pka_nh3 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("runoff_flux_alk","Alkalinity runoff flux to the ocean",'h','1','s','mol m-2 s-1','f') + cobalt%id_runoff_flux_alk = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("runoff_flux_dic","Dissolved Inorganic Carbon runoff flux to the ocean",'h','1','s','mol m-2 s-1','f') + cobalt%id_runoff_flux_dic = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("runoff_flux_di14c","Dissolved Inorganic Carbon 14 runoff flux to the ocean",'h','1','s','mol m-2 s-1','f') + cobalt%id_runoff_flux_di14c = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("runoff_flux_fed","Iron runoff flux to the ocean",'h','1','s','mol m-2 s-1','f') + cobalt%id_runoff_flux_fed = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("runoff_flux_lith","Lithogenic runoff flux to the ocean",'h','1','s','g m-2 s-1','f') + cobalt%id_runoff_flux_lith = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("runoff_flux_no3","Nitrate runoff flux to the ocean",'h','1','s','mol m-2 s-1','f') + cobalt%id_runoff_flux_no3 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("runoff_flux_ldon","LDON runoff flux to the ocean",'h','1','s','mol m-2 s-1','f') + cobalt%id_runoff_flux_ldon = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("runoff_flux_sldon","SLDON runoff flux to the ocean",'h','1','s','mol m-2 s-1','f') + cobalt%id_runoff_flux_sldon = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("runoff_flux_srdon","SRDON runoff flux to the ocean",'h','1','s','mol m-2 s-1','f') + cobalt%id_runoff_flux_srdon = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("runoff_flux_ndet","NDET runoff flux to the ocean",'h','1','s','mol m-2 s-1','f') + cobalt%id_runoff_flux_ndet = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("runoff_flux_po4","PO4 runoff flux to the ocean",'h','1','s','mol m-2 s-1','f') + cobalt%id_runoff_flux_po4 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("runoff_flux_ldop","LDOP runoff flux to the ocean",'h','1','s','mol m-2 s-1','f') + cobalt%id_runoff_flux_ldop = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("runoff_flux_sldop","SLDOP runoff flux to the ocean",'h','1','s','mol m-2 s-1','f') + cobalt%id_runoff_flux_sldop = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("runoff_flux_srdop","SRDOP runoff flux to the ocean",'h','1','s','mol m-2 s-1','f') + cobalt%id_runoff_flux_srdop = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! 3D sinking information + ! CAS: Note that passing 1D axesTi acts as a flag to save the tracers on the grid interface + ! as per the note from Niki in the Bling code where saving on interfaces was developed: + ! Niki: The register_diag_field interface needs to be extended to take the MOM6 axes_grp as argument + ! instead of this integer array axes_grp%handle + ! Currently the actual MOM6 diag axes is chosen to be T or Tl based on the size of the axes argument, 2 or 3. + ! The actual values of these axes argument are not used, only their size is checked to determine the diag axes! + ! This is not correct since axesTi and axesTl are both of size 3, likewise there are many axes of size 2. + ! To accomodate axesTi with the least amount of code modification we can set and check for an input array of size 1. + + vardesc_temp = vardesc("fcadet_arag","CaCO3 sinking flux",'h','1','s','mol m-2 s-1','f') + axesTi(:)=0 + cobalt%id_fcadet_arag = register_diag_field(package_name, vardesc_temp%name, axesTi(1:1),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("fcadet_calc","CaCO3 sinking flux",'h','1','s','mol m-2 s-1','f') + cobalt%id_fcadet_calc = register_diag_field(package_name, vardesc_temp%name, axesTi(1:1),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("ffedet","fedet sinking flux",'h','1','s','mol m-2 s-1','f') + cobalt%id_ffedet = register_diag_field(package_name, vardesc_temp%name, axesTi(1:1),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("flithdet","lithdet sinking flux",'h','1','s','g m-2 s-1','f') + cobalt%id_flithdet = register_diag_field(package_name, vardesc_temp%name, axesTi(1:1),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("fndet","ndet sinking flux",'h','1','s','mol m-2 s-1','f') + cobalt%id_fndet = register_diag_field(package_name, vardesc_temp%name, axesTi(1:1),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("fpdet","pdet sinking flux",'h','1','s','mol m-2 s-1','f') + cobalt%id_fpdet = register_diag_field(package_name, vardesc_temp%name, axesTi(1:1),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("fsidet","sidet sinking flux",'h','1','s','mol m-2 s-1','f') + cobalt%id_fsidet = register_diag_field(package_name, vardesc_temp%name, axesTi(1:1),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! 2D sinking, bottom source/sink and burial diagnostics + ! + + vardesc_temp = vardesc("fcadet_arag_btm","CaCO3 sinking flux at bottom",'h','1','s','mol m-2 s-1','f') + cobalt%id_fcadet_arag_btm = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("fcadet_calc_btm","CaCO3 sinking flux at bottom",'h','1','s','mol m-2 s-1','f') + cobalt%id_fcadet_calc_btm = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("fcased_burial","CaCO3 permanent burial flux",'h','1','s','mol m-2 s-1','f') + cobalt%id_fcased_burial = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("fcased_redis","CaCO3 redissolution from sediments",'h','1','s','mol m-2 s-1','f') + cobalt%id_fcased_redis = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("fcased_redis_surfresp","CaCO3 redissolution rom sediments, surfresp",'h','1','s','mol m-2 s-1','f') + cobalt%id_fcased_redis_surfresp = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("cased_redis_coef","CaCO3 redissolution from sediments, deepresp coefficient, ",'h','1','s','s-1','f') + cobalt%id_cased_redis_coef = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("cased_redis_delz","CaCO3 redissolution from sediments, effective depth",'h','1','s','none (0-1)','f') + cobalt%id_cased_redis_delz = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("ffedet_btm","fedet sinking flux burial",'h','1','s','mol m-2 s-1','f') + cobalt%id_ffedet_btm = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("ffe_sed","Sediment iron efflux",'h','1','s','mol m-2 s-1','f') + cobalt%id_ffe_sed = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("ffe_geotherm","Geothermal iron efflux",'h','1','s','mol m-2 s-1','f') + cobalt%id_ffe_geotherm = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("ffe_iceberg","iceberg iron efflux",'h','1','s','mol m-2 s-1','f') + cobalt%id_ffe_iceberg = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("flithdet_btm","Lithogenic detrital sinking flux burial",'h','1','s','g m-2 s-1','f') + cobalt%id_flithdet_btm = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("fndet_btm","ndet sinking flux to bottom",'h','1','s','mol m-2 s-1','f') + cobalt%id_fndet_btm = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("fnfeso4red_sed","Sediment Ndet Fe and SO4 reduction flux",'h','1','s','mol m-2 s-1','f') + cobalt%id_fnfeso4red_sed = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("fno3denit_sed","Sediment denitrification flux",'h','1','s','mol m-2 s-1','f') + cobalt%id_fno3denit_sed = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("fnoxic_sed","Sediment oxic Ndet remineralization flux",'h','1','s','mol m-2 s-1','f') + cobalt%id_fnoxic_sed = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("fpdet_btm","pdet sinking flux to bottom",'h','1','s','mol m-2 s-1','f') + cobalt%id_fpdet_btm = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("fsidet_btm","sidet sinking flux to bottom",'h','1','s','mol m-2 s-1','f') + cobalt%id_fsidet_btm = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("frac_burial","fraction of organic matter buried",'h','1','s','dimensionless','f') + cobalt%id_frac_burial = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("fndet_burial","ndet burial flux",'h','1','s','mol m-2 s-1','f') + cobalt%id_fndet_burial = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("fpdet_burial","pdet burial flux",'h','1','s','mol m-2 s-1','f') + cobalt%id_fpdet_burial = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Surface Diagnostics + ! + + vardesc_temp = vardesc("pco2surf","Oceanic pCO2",'h','1','s','uatm','f') + cobalt%id_pco2surf = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("pnh3surf","Oceanic pNH3",'h','1','s','uatm','f') + cobalt%id_pnh3surf = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_alk","Surface Alkalinity",'h','1','s','eq kg-1','f') + cobalt%id_sfc_alk = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_cadet_arag","Surface Detrital Aragonite",'h','1','s','mol kg-1','f') + cobalt%id_sfc_cadet_arag = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_cadet_calc","Surface Detrital Calcite",'h','1','s','mol kg-1','f') + cobalt%id_sfc_cadet_calc = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_dic","Surface Dissolved Inorganic Carbon",'h','1','s','mol kg-1','f') + cobalt%id_sfc_dic = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_fed","Surface Dissolved Iron",'h','1','s','mol kg-1','f') + cobalt%id_sfc_fed = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_ldon","Surface Labile Dissolved Organic Nitrogen",'h','1','s','mol kg-1','f') + cobalt%id_sfc_ldon = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_sldon","Surface semi-labile Dissolved Organic Nitrogen",'h','1','s','mol kg-1','f') + cobalt%id_sfc_sldon = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_srdon","Surface semi-refractory Dissolved Organic Nitrogen",'h','1','s','mol kg-1','f') + cobalt%id_sfc_srdon = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_no3","Surface NO3",'h','1','s','mol kg-1','f') + cobalt%id_sfc_no3 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_nh4","Surface NH4",'h','1','s','mol kg-1','f') + cobalt%id_sfc_nh4 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_po4","Surface PO4",'h','1','s','mol kg-1','f') + cobalt%id_sfc_po4 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_sio4","Surface SiO4",'h','1','s','mol kg-1','f') + cobalt%id_sfc_sio4 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_htotal","Surface Htotal",'h','1','s','mol kg-1','f') + cobalt%id_sfc_htotal = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_o2","Surface Oxygen",'h','1','s','mol kg-1','f') + cobalt%id_sfc_o2 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_chl","Surface Chl",'h','1','s','ug kg-1','f') + cobalt%id_sfc_chl = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_irr","Surface Irradiance",'h','1','s','W m-2','f') + cobalt%id_sfc_irr = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_irr_mem","Surface Irradiance memory",'h','1','s','W m-2','f') + cobalt%id_sfc_irr_mem = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_temp","Surface Temperature",'h','1','s','deg C','f') + cobalt%id_sfc_temp = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("btm_temp","Bottom Temperature",'h','1','s','deg C','f') + cobalt%id_btm_temp = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("btm_o2","Bottom Oxygen",'h','1','s','mol kg-1','f') + cobalt%id_btm_o2 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("btm_htotal","Bottom Htotal",'h','1','s','mol kg-1','f') + cobalt%id_btm_htotal = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("btm_co3_ion","Bottom Carbonate Ion",'h','1','s','mol kg-1','f') + cobalt%id_btm_co3_ion = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("btm_co3_sol_arag","Bottom Aragonite Solubility",'h','1','s','mol kg-1','f') + cobalt%id_btm_co3_sol_arag = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("btm_co3_sol_calc","Bottom Calcite Solubility",'h','1','s','mol kg-1','f') + cobalt%id_btm_co3_sol_calc = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("cased_2d","calcium carbonite in sediment",'h','1','s','mol m-3','f') + cobalt%id_cased_2d = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_co3_ion","Surface Carbonate Ion",'h','1','s','mol kg-1','f') + cobalt%id_sfc_co3_ion = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_co3_sol_arag","Surface Carbonate Ion Solubility for Aragonite",'h','1','s','mol kg-1','f') + cobalt%id_sfc_co3_sol_arag = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_co3_sol_calc","Surface Carbonate Ion Solubility for Calcite ",'h','1','s','mol kg-1','f') + cobalt%id_sfc_co3_sol_calc = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_nsmp","Surface small phyto. nitrogen",'h','1','s','mol kg-1','f') + phyto(SMALL)%id_sfc_f_n = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_nlgp","Surface large phyto. nitrogen",'h','1','s','mol kg-1','f') + phyto(LARGE)%id_sfc_f_n = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_ndi","Surface diazotroph nitrogen",'h','1','s','mol kg-1','f') + phyto(DIAZO)%id_sfc_f_n = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_chl_smp","Surface small phyto. chlorophyll",'h','1','s','ug kg-1','f') + phyto(SMALL)%id_sfc_chl = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_chl_lgp","Surface large phyto. chlorophyll",'h','1','s','ug kg-1','f') + phyto(LARGE)%id_sfc_chl = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_chl_di","Surface diazotroph chlorophyll",'h','1','s','mol kg-1','f') + phyto(DIAZO)%id_sfc_chl = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_def_fe_smp","Surface small phyto. iron deficiency",'h','1','s','dimensionsless','f') + phyto(SMALL)%id_sfc_def_fe = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_def_fe_lgp","Surface large phyto. iron deficiency",'h','1','s','dimensionless','f') + phyto(LARGE)%id_sfc_def_fe = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_def_fe_di","Surface diazotroph iron deficiency",'h','1','s','dimensionless','f') + phyto(DIAZO)%id_sfc_def_fe = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_felim_smp","Surface small phyto. iron uptake limitation",'h','1','s','dimensionsless','f') + phyto(SMALL)%id_sfc_felim = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_felim_lgp","Surface large phyto. iron uptake limitation",'h','1','s','dimensionless','f') + phyto(LARGE)%id_sfc_felim = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_felim_di","Surface diazotroph iron uptake limitation",'h','1','s','dimensionless','f') + phyto(DIAZO)%id_sfc_felim = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_q_fe_2_n_di","Surface diazotroph iron:nitrogen",'h','1','s','moles Fe (moles N)-1','f') + phyto(DIAZO)%id_sfc_q_fe_2_n = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_q_fe_2_n_smp","Surface small phyto. iron:nitrogen",'h','1','s','moles Fe (moles N)-1','f') + phyto(SMALL)%id_sfc_q_fe_2_n = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_q_fe_2_n_lgp","Surface large phyto. iron:nitrogen",'h','1','s','moles Fe (moles N)-1','f') + phyto(LARGE)%id_sfc_q_fe_2_n = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_irrlim_smp","Surface small phyto. light limitation",'h','1','s','dimensionsless','f') + phyto(SMALL)%id_sfc_irrlim = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_irrlim_lgp","Surface large phyto. light limitation",'h','1','s','dimensionless','f') + phyto(LARGE)%id_sfc_irrlim = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_irrlim_di","Surface diazotroph light limitation",'h','1','s','dimensionless','f') + phyto(DIAZO)%id_sfc_irrlim = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_theta_smp","Surface small phyto. Chl:C",'h','1','s','g Chl (g C)-1','f') + phyto(SMALL)%id_sfc_theta = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_theta_lgp","Surface large phyto. Chl:C",'h','1','s','g Chl (g C)-1','f') + phyto(LARGE)%id_sfc_theta = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_theta_di","Surface diazotroph Chl:C",'h','1','s','g Chl (g C)-1','f') + phyto(DIAZO)%id_sfc_theta = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_mu_smp","Surface small phyto. Chl:C",'h','1','s','sec-1','f') + phyto(SMALL)%id_sfc_mu = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_mu_lgp","Surface large phyto. Chl:C",'h','1','s','sec-1','f') + phyto(LARGE)%id_sfc_mu = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_mu_di","Surface diazotroph growth rate",'h','1','s','sec-1','f') + phyto(DIAZO)%id_sfc_mu = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_no3lim_smp","Surface small phyto. nitrate limitation",'h','1','s','dimensionsless','f') + phyto(SMALL)%id_sfc_no3lim = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_no3lim_lgp","Surface large phyto. nitrate limitation",'h','1','s','dimensionless','f') + phyto(LARGE)%id_sfc_no3lim = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_no3lim_di","Surface diazotroph nitrate limitation",'h','1','s','dimensionless','f') + phyto(DIAZO)%id_sfc_no3lim = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_nh4lim_smp","Surface small phyto. ammonia limitation",'h','1','s','dimensionsless','f') + phyto(SMALL)%id_sfc_nh4lim = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_nh4lim_lgp","Surface large phyto. ammonia limitation",'h','1','s','dimensionless','f') + phyto(LARGE)%id_sfc_nh4lim = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_nh4lim_di","Surface diazotroph ammonia limitation",'h','1','s','dimensionless','f') + phyto(DIAZO)%id_sfc_nh4lim = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_po4lim_smp","Surface small phyto. phosphate limitation",'h','1','s','dimensionsless','f') + phyto(SMALL)%id_sfc_po4lim = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_po4lim_lgp","Surface large phyto. phosphate limitation",'h','1','s','dimensionless','f') + phyto(LARGE)%id_sfc_po4lim = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("sfc_po4lim_di","Surface diazotroph phosphate limitation",'h','1','s','dimensionless','f') + phyto(DIAZO)%id_sfc_po4lim = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! 100m integrated fluxes + ! + + vardesc_temp = vardesc("jprod_allphytos_100","Total Nitrogen prim. prod. integral in upper 100m",'h','1','s','mol m-2 s-1','f') + cobalt%id_jprod_allphytos_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + +! CAS: Added diagnostic for diatom NPP in top 100m for CMIP + vardesc_temp = vardesc("jprod_diat_100","Diatom prim. prod. integral in upper 100m",'h','1','s','mol m-2 s-1','f') + cobalt%id_jprod_diat_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_ndi_100","Diazotroph nitrogen prim. prod. integral in upper 100m",'h','1','s','mol m-2 s-1','f') + phyto(DIAZO)%id_jprod_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_nsmp_100","Small phyto. nitrogen prim. prod. integral in upper 100m",'h','1','s','mol m-2 s-1','f') + phyto(SMALL)%id_jprod_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_nlgp_100","Large phyto. nitrogen prim. prod. integral in upper 100m",'h','1','s','mol m-2 s-1','f') + phyto(LARGE)%id_jprod_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_ndi_new_100","Diazotroph new (NO3-based) prim. prod. integral in upper 100m",'h','1','s','mol m-2 s-1','f') + phyto(DIAZO)%id_jprod_n_new_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_nsmp_new_100","Small phyto. new (NO3-based) prim. prod. integral in upper 100m",'h','1','s','mol m-2 s-1','f') + phyto(SMALL)%id_jprod_n_new_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_nlgp_new_100","Large phyto. new (NO3-based) prim. prod. integral in upper 100m",'h','1','s','mol m-2 s-1','f') + phyto(LARGE)%id_jprod_n_new_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_ndi_n2_100","Diazotroph nitrogen fixation in upper 100m",'h','1','s','mol m-2 s-1','f') + phyto(DIAZO)%id_jprod_n_n2_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jzloss_ndi_100","Diazotroph nitrogen loss to zooplankton integral in upper 100m",'h','1','s','mol m-2 s-1','f') + phyto(DIAZO)%id_jzloss_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jzloss_nsmp_100","Small phyto. nitrogen loss to zooplankton integral in upper 100m",'h','1','s','mol m-2 s-1','f') + phyto(SMALL)%id_jzloss_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jzloss_nlgp_100","Large phyto. nitrogen loss to zooplankton integral in upper 100m",'h','1','s','mol m-2 s-1','f') + phyto(LARGE)%id_jzloss_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jaggloss_nsmp_100","Small phyto. nitrogen aggregation loss integral in upper 100m",'h','1','s','mol m-2 s-1','f') + phyto(SMALL)%id_jaggloss_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jaggloss_nlgp_100","Large phyto. nitrogen aggregation loss integral in upper 100m",'h','1','s','mol m-2 s-1','f') + phyto(LARGE)%id_jaggloss_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jvirloss_nsmp_100","Small phyto. nitrogen virus loss integral in upper 100m",'h','1','s','mol m-2 s-1','f') + phyto(SMALL)%id_jvirloss_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jexuloss_ndi_100","Diazotroph nitrogen exudation loss integral in upper 100m",'h','1','s','mol m-2 s-1','f') + phyto(DIAZO)%id_jexuloss_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jexuloss_nsmp_100","Small phyto. nitrogen exudation loss integral in upper 100m",'h','1','s','mol m-2 s-1','f') + phyto(SMALL)%id_jexuloss_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jexuloss_nlgp_100","Large phyto. nitrogen exudation loss integral in upper 100m",'h','1','s','mol m-2 s-1','f') + phyto(LARGE)%id_jexuloss_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_nsmz_100","Small zooplankton nitrogen prod. integral in upper 100m",'h','1','s','mol m-2 s-1','f') + zoo(1)%id_jprod_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_nmdz_100","Medium zooplankton nitrogen prod. integral in upper 100m",'h','1','s','mol m-2 s-1','f') + zoo(2)%id_jprod_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_nlgz_100","Large zooplankton nitrogen prod. integral in upper 100m",'h','1','s','mol m-2 s-1','f') + zoo(3)%id_jprod_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jingest_n_nsmz_100","Small zooplankton nitrogen ingestion integral in upper 100m",'h','1','s','mol m-2 s-1','f') + zoo(1)%id_jingest_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jingest_n_nmdz_100","Medium zooplankton nitrogen ingestion integral in upper 100m",'h','1','s','mol m-2 s-1','f') + zoo(2)%id_jingest_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jingest_n_nlgz_100","Large zooplankton nitrogen ingestion integral in upper 100m",'h','1','s','mol m-2 s-1','f') + zoo(3)%id_jingest_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jzloss_nsmz_100","Small zooplankton nitrogen loss to zooplankton integral in upper 100m",'h','1','s','mol m-2 s-1','f') + zoo(1)%id_jzloss_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jzloss_nmdz_100","Medium zooplankton nitrogen loss to zooplankton integral in upper 100m",'h','1','s','mol m-2 s-1','f') + zoo(2)%id_jzloss_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jhploss_nmdz_100","Medium zooplankton nitrogen loss to higher preds. integral in upper 100m",'h','1','s','mol m-2 s-1','f') + zoo(2)%id_jhploss_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jhploss_nlgz_100","Large zooplankton nitrogen loss to higher preds. integral in upper 100m",'h','1','s','mol m-2 s-1','f') + zoo(3)%id_jhploss_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_ndet_nmdz_100","Medium zooplankton nitrogen detritus prod. integral in upper 100m",'h','1','s','mol m-2 s-1','f') + zoo(2)%id_jprod_ndet_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_ndet_nlgz_100","Large zooplankton nitrogen detritus prod. integral in upper 100m",'h','1','s','mol m-2 s-1','f') + zoo(3)%id_jprod_ndet_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_don_nsmz_100","Small zooplankton dissolved org. nitrogen prod. integral in upper 100m",'h','1','s','mol m-2 s-1','f') + zoo(1)%id_jprod_don_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_don_nmdz_100","Medium zooplankton dissolved org. nitrogen prod. integral in upper 100m",'h','1','s','mol m-2 s-1','f') + zoo(2)%id_jprod_don_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jremin_n_nsmz_100","Small zooplankton nitrogen remineralization integral in upper 100m",'h','1','s','mol m-2 s-1','f') + zoo(1)%id_jremin_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jremin_n_nmdz_100","Medium zooplankton nitrogen remineralization integral in upper 100m",'h','1','s','mol m-2 s-1','f') + zoo(2)%id_jremin_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jremin_n_nlgz_100","Large zooplankton nitrogen remineralization integral in upper 100m",'h','1','s','mol m-2 s-1','f') + zoo(3)%id_jremin_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jremin_n_hp_100","Higher predator nitrogen remineralization integral in upper 100m",'h','1','s','mol m-2 s-1','f') + cobalt%id_hp_jremin_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jingest_n_hp_100","Higher predator ingestion of nitrogen integral in upper 100m",'h','1','s','mol m-2 s-1','f') + cobalt%id_hp_jingest_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_ndet_hp_100","Higher predator nitrogen detritus prod. integral in upper 100m",'h','1','s','mol m-2 s-1','f') + cobalt%id_hp_jprod_ndet_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_nbact_100","Bacteria nitrogen prod. integral in upper 100m",'h','1','s','mol m-2 s-1','f') + bact(1)%id_jprod_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jzloss_nbact_100","Bacteria nitrogen loss to zooplankton integral in upper 100m",'h','1','s','mol m-2 s-1','f') + bact(1)%id_jzloss_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jvirloss_nbact_100","Bacteria nitrogen loss to viruses integral in upper 100m",'h','1','s','mol m-2 s-1','f') + bact(1)%id_jvirloss_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jremin_n_nbact_100","Bacteria nitrogen remineralization integral in upper 100m",'h','1','s','mol m-2 s-1','f') + bact(1)%id_jremin_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("juptake_ldon_nbact_100","Bacterial uptake of labile dissolved org. nitrogen in upper 100m",'h','1','s','mol m-2 s-1','f') + bact(1)%id_juptake_ldon_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_lithdet_100","Lithogenic detritus production integral in upper 100m",'h','1','s','mol m-2 s-1','f') + cobalt%id_jprod_lithdet_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_sidet_100","Silica detritus production integral in upper 100m",'h','1','s','mol m-2 s-1','f') + cobalt%id_jprod_sidet_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_cadet_calc_100","Calcite detritus production integral in upper 100m",'h','1','s','mol m-2 s-1','f') + cobalt%id_jprod_cadet_calc_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_cadet_arag_100","Aragonite detritus production integral in upper 100m",'h','1','s','mol m-2 s-1','f') + cobalt%id_jprod_cadet_arag_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jremin_ndet_100","Remineralization of nitrogen detritus integral in upper 100m",'h','1','s','mol m-2 s-1','f') + cobalt%id_jremin_ndet_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jprod_mesozoo_200","Mesozooplankton Production, 200m integration",'h','1','s','mol m-2 s-1','f') + cobalt%id_jprod_mesozoo_200 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Water column integrated fluxes + ! + + vardesc_temp = vardesc("wc_vert_int_jdiss_sidet","Water column silica dissolution vertical integral",'h','1','s','mol m-2 s-1','f') + cobalt%id_wc_vert_int_jdiss_sidet = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("wc_vert_int_jdiss_cadet","Water column calcium carbonate dissolution vertical integral",'h','1','s','mol m-2 s-1','f') + cobalt%id_wc_vert_int_jdiss_cadet = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("wc_vert_int_jo2resp","Water column oxygen respired vertical integral",'h','1','s','mol m-2 s-1','f') + cobalt%id_wc_vert_int_jo2resp = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("wc_vert_int_jprod_cadet","Water column calcium carbonate production vertical integral",'h','1','s','mol m-2 s-1','f') + cobalt%id_wc_vert_int_jprod_cadet = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("wc_vert_int_jno3denit","Water column denitrification vertical integral",'h','1','s','mol m-2 s-1','f') + cobalt%id_wc_vert_int_jno3denit = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("wc_vert_int_jnitrif","Water column nitrification vertical integral",'h','1','s','mol m-2 s-1','f') + cobalt%id_wc_vert_int_jnitrif = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("wc_vert_int_juptake_nh4"," Water column ammonia based NPP vertical integral",'h','1','s','mol m-2 s-1','f') + cobalt%id_wc_vert_int_juptake_nh4 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("wc_vert_int_jprod_nh4"," Water column ammonia production vertical integral",'h','1','s','mol m-2 s-1','f') + cobalt%id_wc_vert_int_jprod_nh4 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("wc_vert_int_juptake_no3","Water column nitrate based NPP, vertical integral",'h','1','s','mol m-2 s-1','f') + cobalt%id_wc_vert_int_juptake_no3 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("wc_vert_int_nfix","Nitrogen fixation vertical integral",'h','1','s','mol N m-2 s-1','f') + cobalt%id_wc_vert_int_nfix = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! 100m integrated biomass + ! + + vardesc_temp = vardesc("nsmp_100","Small phytoplankton nitrogen biomass in upper 100m",'h','1','s','mol m-2','f') + phyto(SMALL)%id_f_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("nlgp_100","Large phytoplankton nitrogen biomass in upper 100m",'h','1','s','mol m-2','f') + phyto(LARGE)%id_f_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("ndi_100","Diazotroph nitrogen biomass in upper 100m",'h','1','s','mol m-2','f') + phyto(DIAZO)%id_f_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("nsmz_100","Small zooplankton nitrogen biomass in upper 100m",'h','1','s','mol m-2','f') + zoo(1)%id_f_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("nmdz_100","Medium zooplankton nitrogen biomass in upper 100m",'h','1','s','mol m-2','f') + zoo(2)%id_f_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("nlgz_100","Large zooplankton nitrogen biomass in upper 100m",'h','1','s','mol m-2','f') + zoo(3)%id_f_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("nbact_100","Bacterial nitrogen biomass in upper 100m",'h','1','s','mol m-2','f') + bact(1)%id_f_n_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("silgp_100","Large phytoplankton silicon biomass in upper 100m",'h','1','s','mol m-2','f') + cobalt%id_f_silg_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("ndet_100","Nitrogen detritus biomass in upper 100m",'h','1','s','mol m-2','f') + cobalt%id_f_ndet_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("don_100","Dissolved organic nitrogen (sr+sl+l) in upper 100m",'h','1','s','mol m-2','f') + cobalt%id_f_don_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("mesozoo_200","Mesozooplankton biomass, 200m integral",'h','1','s','mol m-2','f') + cobalt%id_f_mesozoo_200 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Water column integrated tracers + ! + + vardesc_temp = vardesc("wc_vert_int_c","Total carbon (DIC+OC+IC) vertical integral",'h','1','s','mol m-2','f') + cobalt%id_wc_vert_int_c = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("wc_vert_int_dic","Dissolved inorganic carbon vertical integral",'h','1','s','mol m-2','f') + cobalt%id_wc_vert_int_dic = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("wc_vert_int_doc","Total dissolved organic carbon vertical integral",'h','1','s','mol m-2','f') + cobalt%id_wc_vert_int_doc = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("wc_vert_int_poc","Total particulate organic carbon vertical integral",'h','1','s','mol m-2','f') + cobalt%id_wc_vert_int_poc = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("wc_vert_int_n","Total nitrogen vertical integral",'h','1','s','mol m-2','f') + cobalt%id_wc_vert_int_n = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("wc_vert_int_p","Total phosphorus vertical integral",'h','1','s','mol m-2','f') + cobalt%id_wc_vert_int_p = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("wc_vert_int_fe","Total iron vertical integral",'h','1','s','mol m-2','f') + cobalt%id_wc_vert_int_fe = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("wc_vert_int_si","Total silicon vertical integral",'h','1','s','mol m-2','f') + cobalt%id_wc_vert_int_si = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("wc_vert_int_o2","Total oxygen vertical integral",'h','1','s','mol m-2','f') + cobalt%id_wc_vert_int_o2 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("wc_vert_int_alk","Total alkalinity vertical integral",'h','1','s','mol m-2','f') + cobalt%id_wc_vert_int_alk = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! sinking flux = 100m + ! + + vardesc_temp = vardesc("fndet_100","Nitrogen detritus sinking flux @ 100m",'h','1','s','mol m-2 s-1','f') + cobalt%id_fndet_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("fpdet_100","Phosphorous detritus sinking flux @ 100m",'h','1','s','mol m-2 s-1','f') + cobalt%id_fpdet_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("ffedet_100","Iron detritus sinking flux @ 100m",'h','1','s','mol m-2 s-1','f') + cobalt%id_ffedet_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("fsidet_100","Silicon detritus sinking flux @ 100m",'h','1','s','mol m-2 s-1','f') + cobalt%id_fsidet_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("fcadet_calc_100","Calcite detritus sinking flux @ 100m",'h','1','s','mol m-2 s-1','f') + cobalt%id_fcadet_calc_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("fcadet_arag_100","Aragonite detritus sinking flux @ 100m",'h','1','s','mol m-2 s-1','f') + cobalt%id_fcadet_arag_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("flithdet_100","Lithogenic detritus sinking flux @ 100m",'h','1','s','mol m-2 s-1','f') + cobalt%id_flithdet_100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! Oxygen minima (value and location + ! + + vardesc_temp = vardesc("o2min","Minimum Oxygen",'h','1','s','mol kg-1','f') + cobalt%id_o2min = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("z_o2min","Depth of Oxygen minimum",'h','1','s','m','f') + cobalt%id_z_o2min = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + ! + ! Calcite and aragonite saturation depths + ! + + vardesc_temp = vardesc("z_sat_arag","Depth of Aragonite Saturation",'h','1','s','m','f') + cobalt%id_z_sat_arag = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + mask_variant=.TRUE.) + + vardesc_temp = vardesc("z_sat_calc","Depth of Calcite Saturation",'h','1','s','m','f') + cobalt%id_z_sat_calc = register_diag_field(package_name, vardesc_temp%name, axes(1:2),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + mask_variant=.TRUE.) + + if (do_14c) then !<> + + + ! + ! Additional diagnostics added for debugging jgj 2015/10/26 + ! + + vardesc_temp = vardesc("jalk","Alkalinity source layer integral",'h','L','s','eq m-2 s-1','f') + cobalt%id_jalk = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jalk_plus_btm","Alkalinity source plus btm layer integral",'h','L','s','eq m-2 s-1','f') + cobalt%id_jalk_plus_btm = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jdic","Dissolved Inorganic Carbon source layer integral",'h','L','s','mol m-2 s-1','f') + cobalt%id_jdic = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jdic_plus_btm","Dissolved Inorganic Carbon source plus btm layer integral",'h','L','s','mol m-2 s-1','f') + cobalt%id_jdic_plus_btm = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jnh4","NH4 source layer integral",'h','L','s','mol m-2 s-1','f') + cobalt%id_jnh4 = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jndet","NDET source layer integral",'h','L','s','mol m-2 s-1','f') + cobalt%id_jndet = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jnh4_plus_btm","NH4 source plus btm layer integral",'h','L','s','mol m-2 s-1','f') + cobalt%id_jnh4_plus_btm = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + + vardesc_temp = vardesc("jo2_plus_btm","O2 source plus btm layer integral",'h','L','s','mol m-2 s-1','f') + cobalt%id_jo2_plus_btm = register_diag_field(package_name, vardesc_temp%name, axes(1:3),& + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1) + +!============================================================================================================== +! 2016/07/05 jgj register and send temperature as a test + + vardesc_temp = vardesc("temp","Potential Temperature",'h','L','s','Celsius','f') + cobalt%id_thetao = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="thetao", cmor_units="C", & + cmor_standard_name="sea_water_potential_temperature", & + cmor_long_name ="Sea Water Potential Temperature") + +!============================================================================================================== +! JGJ 2016/08/08 CMIP6 OcnBgchem Oyr/Omon/day: Marine Biogeochemical Fields + + vardesc_temp = vardesc("dissic_raw","Dissolved Inorganic Carbon Concentration",'h','L','s','mol m-3','f') + cobalt%id_dissic = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="dissic", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_dissolved_inorganic_carbon_in_sea_water", & + cmor_long_name="Dissolved Inorganic Carbon Concentration") + + vardesc_temp = vardesc("dissicnat_raw","Natural Dissolved Inorganic Carbon Concentration",'h','L','s','mol m-3','f') + cobalt%id_dissicnat = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="dissicnat", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_dissolved_inorganic_carbon_natural_analogue_in_sea_water", & + cmor_long_name="Natural Dissolved Inorganic Carbon Concentration") + + vardesc_temp = vardesc("dissicabio_raw","Abiotic Dissolved Inorganic Carbon Concentration",'h','L','s','mol m-3','f') + cobalt%id_dissicabio = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="dissicabio", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_dissolved_inorganic_carbon_abiotic_analogue_in_sea_water", & + cmor_long_name="Abiotic Dissolved Inorganic Carbon Concentration") + +! 2017/12/28 jgj Updated standard name in data request: added _abiotic_analogue + vardesc_temp = vardesc("dissi14cabio_raw","Abiotic Dissolved Inorganic 14Carbon Concentration",'h','L','s','mol m-3','f') + cobalt%id_dissi14cabio = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="dissi14cabio", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_dissolved_inorganic_carbon14_abiotic_analogue_in_sea_water", & + cmor_long_name="Abiotic Dissolved Inorganic 14Carbon Concentration") + + vardesc_temp = vardesc("dissoc_raw","Dissolved Organic Carbon Concentration",'h','L','s','mol m-3','f') + cobalt%id_dissoc = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="dissoc", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_dissolved_organic_carbon_in_sea_water", & + cmor_long_name="Dissolved Organic Carbon Concentration") + + vardesc_temp = vardesc("phyc_raw","Phytoplankton Carbon Concentration",'h','L','s','mol m-3','f') + cobalt%id_phyc = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="phyc", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_phytoplankton_expressed_as_carbon_in_sea_water", & + cmor_long_name="Phytoplankton Carbon Concentration") + + vardesc_temp = vardesc("zooc_raw","Zooplankton Carbon Concentration",'h','L','s','mol m-3','f') + cobalt%id_zooc = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="zooc", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_zooplankton_expressed_as_carbon_in_sea_water", & + cmor_long_name="Zooplankton Carbon Concentration") + + vardesc_temp = vardesc("bacc_raw","Bacterial Carbon Concentration",'h','L','s','mol m-3','f') + cobalt%id_bacc = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="bacc", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_bacteria_expressed_as_carbon_in_sea_water", & + cmor_long_name="Bacterial Carbon Concentration") + + vardesc_temp = vardesc("detoc_raw","Detrital Organic Carbon Concentration",'h','L','s','mol m-3','f') + cobalt%id_detoc = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="detoc", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_organic_detritus_expressed_as_carbon_in_sea_water", & + cmor_long_name="Detrital Organic Carbon Concentration") + + vardesc_temp = vardesc("calc_raw","Calcite Concentration",'h','L','s','mol m-3','f') + cobalt%id_calc = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="calc", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_calcite_expressed_as_carbon_in_sea_water", & + cmor_long_name="Calcite Concentration") + + vardesc_temp = vardesc("arag_raw","Aragonite Concentration",'h','L','s','mol m-3','f') + cobalt%id_arag = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="arag", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_aragonite_expressed_as_carbon_in_sea_water", & + cmor_long_name="Aragonite Concentration") + + vardesc_temp = vardesc("phydiat_raw","Mole Concentration of Diatoms expressed as Carbon in sea water",'h','L','s','mol m-3','f') + cobalt%id_phydiat = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="phydiat", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_diatoms_expressed_as_carbon_in_sea_water", & + cmor_long_name="Mole Concentration of Diatoms expressed as Carbon in sea water") + + vardesc_temp = vardesc("phydiaz_raw","Mole Concentration of Diazotrophs expressed as Carbon in sea water",'h','L','s','mol m-3','f') + cobalt%id_phydiaz = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="phydiaz", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_diazotrophs_expressed_as_carbon_in_sea_water", & + cmor_long_name="Mole Concentration of Diazotrophs expressed as Carbon in sea water") + + vardesc_temp = vardesc("phypico_raw","Mole Concentration of Picophytoplankton expressed as Carbon in sea water",'h','L','s','mol m-3','f') + cobalt%id_phypico = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="phypico", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_picophytoplankton_expressed_as_carbon_in_sea_water", & + cmor_long_name="Mole Concentration of Picophytoplankton expressed as Carbon in sea water") + + vardesc_temp = vardesc("phymisc_raw","Mole Concentration of Miscellaneous Phytoplankton expressed as Carbon in sea water",'h','L','s','mol m-3','f') + cobalt%id_phymisc = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="phymisc", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_miscellaneous_phytoplankton_expressed_as_carbon_in_sea_water", & + cmor_long_name="Mole Concentration of Miscellaneous Phytoplankton expressed as Carbon in sea water") + + vardesc_temp = vardesc("zmicro_raw","Mole Concentration of Microzooplankton expressed as Carbon in sea water",'h','L','s','mol m-3','f') + cobalt%id_zmicro = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="zmicro", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_microzooplankton_expressed_as_carbon_in_sea_water", & + cmor_long_name="Mole Concentration of Microzooplankton expressed as Carbon in sea water") + + vardesc_temp = vardesc("zmeso_raw","Mole Concentration of Mesozooplankton expressed as Carbon in sea water",'h','L','s','mol m-3','f') + cobalt%id_zmeso = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="zmeso", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_mesozooplankton_expressed_as_carbon_in_sea_water", & + cmor_long_name="Mole Concentration of Mesozooplankton expressed as Carbon in sea water") + + vardesc_temp = vardesc("talk_raw","Total Alkalinity",'h','L','s','mol m-3','f') + cobalt%id_talk = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="talk", cmor_units="mol m-3", & + cmor_standard_name="sea_water_alkalinity_expressed_as_mole_equivalent", & + cmor_long_name="Total Alkalinity") + + vardesc_temp = vardesc("talknat_raw","Natural Total Alkalinity",'h','L','s','mol m-3','f') + cobalt%id_talknat = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="talknat", cmor_units="mol m-3", & + cmor_standard_name="sea_water_alkalinity_natural_analogue_expressed_as_mole_equivalent", & + cmor_long_name="Natural Total Alkalinity") + + vardesc_temp = vardesc("ph_raw","pH",'h','L','s','1','f') + cobalt%id_ph = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="ph", cmor_units="1", & + cmor_standard_name="sea_water_ph_reported_on_total_scale", & + cmor_long_name="pH") + + vardesc_temp = vardesc("phnat_raw","Natural pH",'h','L','s','1','f') + cobalt%id_phnat = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="phnat", cmor_units="1", & + cmor_standard_name="sea_water_ph_natural_analogue_reported_on_total_scale", & + cmor_long_name="Natural pH") + + vardesc_temp = vardesc("phabio_raw","Abiotic pH",'h','L','s','1','f') + cobalt%id_phabio = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="phabio", cmor_units="1", & + cmor_standard_name="sea_water_ph_abiotic_analogue_reported_on_total_scale", & + cmor_long_name="Abiotic pH") + +!! same name in model and CMOR, but different units - use _cmip for now + vardesc_temp = vardesc("o2_raw","Dissolved Oxygen Concentration",'h','L','s','mol m-3','f') + cobalt%id_o2_cmip = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="o2_cmip", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_dissolved_molecular_oxygen_in_sea_water", & + cmor_long_name="Dissolved Oxygen Concentration") + + vardesc_temp = vardesc("o2sat_raw","Dissolved Oxygen Concentration at Saturation",'h','L','s','mol m-3','f') + cobalt%id_o2sat = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="o2sat", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_dissolved_molecular_oxygen_in_sea_water_at_saturation", & + cmor_long_name="Dissolved Oxygen Concentration at Saturation") + +!! same name in model and CMOR, but different units - use _cmip for now + vardesc_temp = vardesc("no3_raw","Dissolved Nitrate Concentration",'h','L','s','mol m-3','f') + cobalt%id_no3_cmip = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="no3_cmip", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_nitrate_in_sea_water", & + cmor_long_name="Dissolved Nitrate Concentration") + +!! same name in model and CMOR, but different units - use for now + vardesc_temp = vardesc("nh4_raw","Dissolved Ammonium Concentration",'h','L','s','mol m-3','f') + cobalt%id_nh4_cmip = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="nh4_cmip", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_ammonium_in_sea_water", & + cmor_long_name="Dissolved Ammonium Concentration") + +!! same name in model and CMOR, but different units - use _cmip for now + vardesc_temp = vardesc("po4_raw","Total Dissolved Inorganic Phosphorus Concentration",'h','L','s','mol m-3','f') + cobalt%id_po4_cmip = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="po4_cmip", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_dissolved_inorganic_phosphorus_in_sea_water", & + cmor_long_name="Total Dissolved Inorganic Phosphorus Concentration") + + vardesc_temp = vardesc("dfe_raw","Dissolved Iron Concentration",'h','L','s','mol m-3','f') + cobalt%id_dfe = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="dfe", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_dissolved_iron_in_sea_water", & + cmor_long_name="Dissolved Iron Concentration") + + vardesc_temp = vardesc("si_raw","Total Dissolved Inorganic Silicon Concentration",'h','L','s','mol m-3','f') + cobalt%id_si = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="si", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_dissolved_inorganic_silicon_in_sea_water", & + cmor_long_name="Total Dissolved Inorganic Silicon Concentration") + +!! same name in model and CMOR, but different units - use _cmip for now + vardesc_temp = vardesc("chl_raw","Mass Concentration of Total Phytoplankton expressed as Chlorophyll in sea water",'h','L','s','kg m-3','f') + cobalt%id_chl_cmip = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="chl_cmip", cmor_units="kg m-3", & + cmor_standard_name="mass_concentration_of_phytoplankton_expressed_as_chlorophyll_in_sea_water", & + cmor_long_name="Mass Concentration of Total Phytoplankton expressed as Chlorophyll in sea water") + + vardesc_temp = vardesc("chldiat_raw","Mass Concentration of Diatoms expressed as Chlorophyll in sea water",'h','L','s','kg m-3','f') + cobalt%id_chldiat = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="chldiat", cmor_units="kg m-3", & + cmor_standard_name="mass_concentration_of_diatoms_expressed_as_chlorophyll_in_sea_water", & + cmor_long_name="Mass Concentration of Diatoms expressed as Chlorophyll in sea water") + + vardesc_temp = vardesc("chldiaz_raw","Mass Concentration of Diazotrophs expressed as Chlorophyll in sea water",'h','L','s','kg m-3','f') + cobalt%id_chldiaz = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="chldiaz", cmor_units="kg m-3", & + cmor_standard_name="mass_concentration_of_diazotrophs_expressed_as_chlorophyll_in_sea_water", & + cmor_long_name="Mass Concentration of Diazotrophs expressed as Chlorophyll in sea water") + + vardesc_temp = vardesc("chlpico_raw","Mass Concentration of Picophytoplankton expressed as Chlorophyll in sea water",'h','L','s','kg m-3','f') + cobalt%id_chlpico = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="chlpico", cmor_units="kg m-3", & + cmor_standard_name="mass_concentration_of_picophytoplankton_expressed_as_chlorophyll_in_sea_water", & + cmor_long_name="Mass Concentration of Picophytoplankton expressed as Chlorophyll in sea water") + + vardesc_temp = vardesc("chlmisc_raw","Mass Concentration of Other Phytoplankton expressed as Chlorophyll in sea water",'h','L','s','kg m-3','f') + cobalt%id_chlmisc = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="chlmisc", cmor_units="kg m-3", & + cmor_standard_name="mass_concentration_of_miscellaneous_phytoplankton_expressed_as_chlorophyll_in_sea_water", & + cmor_long_name="Mass Concentration of Other Phytoplankton expressed as Chlorophyll in sea water") + +! 2017/11/27 not in data request + vardesc_temp = vardesc("poc_raw","Mole Concentration of Particulate Organic Matter expressed as Carbon in sea water",'h','L','s','mol m-3','f') + cobalt%id_poc = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="poc", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_particulate_organic_matter_expressed_as_carbon_in_sea_water", & + cmor_long_name="Mole Concentration of Particulate Organic Matter expressed as Carbon in sea water") + + vardesc_temp = vardesc("pon_raw","Mole Concentration of Particulate Organic Matter expressed as Nitrogen in sea water",'h','L','s','mol N m-3','f') + cobalt%id_pon = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="pon", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_particulate_organic_matter_expressed_as_nitrogen_in_sea_water", & + cmor_long_name="Mole Concentration of Particulate Organic Matter expressed as Nitrogen in sea water") + + vardesc_temp = vardesc("pop_raw","Mole Concentration of Particulate Organic Matter expressed as Phosphorus in sea water",'h','L','s','mol m-3','f') + cobalt%id_pop = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="pop", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_particulate_organic_matter_expressed_as_phosphorus_in_sea_water", & + cmor_long_name="Mole Concentration of Particulate Organic Matter expressed as Phosphorus in sea water") + + vardesc_temp = vardesc("bfe_raw","Mole Concentration of Particulate Organic Matter expressed as Iron in sea water",'h','L','s','mol m-3','f') + cobalt%id_bfe = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="bfe", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_particulate_organic_matter_expressed_as_iron_in_sea_water", & + cmor_long_name="Mole Concentration of Particulate Organic Matter expressed as Iron in sea water") + +! CHECK3: +! 2017/12/28 jgj Updated standard name in data request to include _organic + vardesc_temp = vardesc("bsi_raw","Mole Concentration of Particulate Organic Matter expressed as Silicon in sea water",'h','L','s','mol m-3','f') + cobalt%id_bsi = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="bsi", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_particulate_organic_matter_expressed_as_silicon_in_sea_water", & + cmor_long_name="Mole Concentration of Particulate Organic Matter expressed as Silicon in sea water") + + vardesc_temp = vardesc("phyn_raw","Mole Concentration of Total Phytoplankton expressed as Nitrogen in sea water",'h','L','s','mol m-3','f') + cobalt%id_phyn = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="phyn", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_phytoplankton_expressed_as_nitrogen_in_sea_water", & + cmor_long_name="Mole Concentration of Total Phytoplankton expressed as Nitrogen in sea water") + + vardesc_temp = vardesc("phyp_raw","Mole Concentration of Total Phytoplankton expressed as Phosphorus in sea water",'h','L','s','mol m-3','f') + cobalt%id_phyp = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="phyp", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_phytoplankton_expressed_as_phosphorus_in_sea_water", & + cmor_long_name="Mole Concentration of Total Phytoplankton expressed as Phosphorus in sea water") + + vardesc_temp = vardesc("phyfe_raw","Mole Concentration of Total Phytoplankton expressed as Iron in sea water",'h','L','s','mol m-3','f') + cobalt%id_phyfe = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="phyfe", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_phytoplankton_expressed_as_iron_in_sea_water", & + cmor_long_name="Mole Concentration of Total Phytoplankton expressed as Iron in sea water") + + vardesc_temp = vardesc("physi_raw","Mole Concentration of Total Phytoplankton expressed as Silicon in sea water",'h','L','s','mol m-3','f') + cobalt%id_physi = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="physi", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_phytoplankton_expressed_as_silicon_in_sea_water", & + cmor_long_name="Mole Concentration of Total Phytoplankton expressed as Silicon in sea water") + + vardesc_temp = vardesc("co3_raw","Carbonate Ion Concentration",'h','L','s','mol m-3','f') + cobalt%id_co3 = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="co3", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_carbonate_expressed_as_carbon_in_sea_water", & + cmor_long_name="Carbonate Ion Concentration") + + vardesc_temp = vardesc("co3nat_raw","Natural Carbonate Ion Concentration",'h','L','s','mol m-3','f') + cobalt%id_co3nat = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="co3nat", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_carbonate_natural_analogue_expressed_as_carbon_in_sea_water", & + cmor_long_name="Natural Carbonate Ion Concentration") + + vardesc_temp = vardesc("co3abio_raw","Abiotic Carbonate Ion Concentration",'h','L','s','mol m-3','f') + cobalt%id_co3abio = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="co3abio", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_carbonate_abiotic_analogue_expressed_as_carbon_in_sea_water", & + cmor_long_name="Abiotic Carbonate Ion Concentration") + +! 2018/01/17 jgj Updated standard name in data request to match equivalent surface variable + vardesc_temp = vardesc("co3satcalc_raw","Mole Concentration of Carbonate Ion in Equilibrium with Pure Calcite in sea water",'h','L','s','mol m-3','f') + cobalt%id_co3satcalc = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="co3satcalc", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_carbonate_expressed_as_carbon_at_equilibrium_with_pure_calcite_in_sea_water", & + cmor_long_name="Mole Concentration of Carbonate Ion in Equilibrium with Pure Calcite in sea water") + +! 2018/01/17 jgj Updated standard name in data request to match equivalent surface variable + vardesc_temp = vardesc("co3satarag_raw","Mole Concentration of Carbonate Ion in Equilibrium with Pure Aragonite in sea water",'h','L','s','mol m-3','f') + cobalt%id_co3satarag = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="co3satarag", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_carbonate_expressed_as_carbon_at_equilibrium_with_pure_aragonite_in_sea_water", & + cmor_long_name="Mole Concentration of Carbonate Ion in Equilibrium with Pure Aragonite in sea water") + +!------------------------------------------------------------------------------------------------------------------ +! 3-D rates +! CHECK3: all GFDL and CMOR units + + vardesc_temp = vardesc("pp_raw","Primary Carbon Production by Total Phytoplankton",'h','L','s','mol m-3 s-1','f') + cobalt%id_pp = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="pp", cmor_units="mol m-3 s-1", & + cmor_standard_name="tendency_of_mole_concentration_of_particulate_organic_matter_expressed_as_carbon_in_sea_water_due_to_net_primary_production", & + cmor_long_name="Primary Carbon Production by Total Phytoplankton") + + vardesc_temp = vardesc("pnitrate_raw","Primary Carbon Production by Phytoplankton due to Nitrate Uptake Alone",'h','L','s','mol m-3 s-1','f') + cobalt%id_pnitrate = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="pnitrate", cmor_units="mol m-3 s-1", & + cmor_standard_name="tendency_of_mole_concentration_of_particulate_organic_matter_expressed_as_carbon_in_sea_water_due_to_nitrate_utilization", & + cmor_long_name="Primary Carbon Production by Phytoplankton due to Nitrate Uptake Alone") + +! Not requested +! vardesc_temp = vardesc("pphosphate_raw","Primary Carbon Production by Phytoplankton due to Phosphorus",'h','L','s','mol m-3 s-1','f') +! cobalt%id_pphosphate = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & +! init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & +! cmor_field_name="pphosphate", cmor_units="mol m-3 s-1", & +! cmor_standard_name="tendency_of_mole_concentration_of_particulate_organic_matter_expressed_as_carbon_in_sea_water_due_to_phosphorus", & +! cmor_long_name="Primary Carbon Production by Phytoplankton due to Phosphorus") + + vardesc_temp = vardesc("pbfe_raw","Biogenic Iron Production",'h','L','s','mol m-3 s-1','f') + cobalt%id_pbfe = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="pbfe", cmor_units="mol m-3 s-1", & + cmor_standard_name="tendency_of_mole_concentration_of_iron_in_sea_water_due_to_biological_production", & + cmor_long_name="Biogenic Iron Production") + + vardesc_temp = vardesc("pbsi_raw","Biogenic Silicon Production",'h','L','s','mol m-3 s-1','f') + cobalt%id_pbsi = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="pbsi", cmor_units="mol m-3 s-1", & + cmor_standard_name="tendency_of_mole_concentration_of_silicon_in_sea_water_due_to_biological_production", & + cmor_long_name="Biogenic Silicon Production") + +! Oyr only + vardesc_temp = vardesc("pcalc_raw","Calcite Production",'h','L','s','mol m-3 s-1','f') + cobalt%id_pcalc = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="pcalc", cmor_units="mol m-3 s-1", & + cmor_standard_name="tendency_of_mole_concentration_of_calcite_expressed_as_carbon_in_sea_water_due_to_biological_production", & + cmor_long_name="Calcite Production") + +! Oyr only + vardesc_temp = vardesc("parag_raw","Aragonite Production",'h','L','s','mol m-3 s-1','f') + cobalt%id_parag = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="parag", cmor_units="mol m-3 s-1", & + cmor_standard_name="tendency_of_mole_concentration_of_aragonite_expressed_as_carbon_in_sea_water_due_to_biological_production", & + cmor_long_name="Aragonite Production") + +! CHECK3: +! 2017/08/04 jgj: CMOR requires positive down, area:areacello, volume:volcello + vardesc_temp = vardesc("expc_raw","Sinking Particulate Organic Carbon Flux",'h','L','s','mol m-2 s-1','f') + cobalt%id_expc = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="expc", cmor_units="mol m-2 s-1", & + cmor_standard_name="sinking_mole_flux_of_particulate_organic_matter_expressed_as_carbon_in_sea_water", & + cmor_long_name="Sinking Particulate Organic Carbon Flux") + + vardesc_temp = vardesc("expn_raw","Sinking Particulate Organic Nitrogen Flux",'h','L','s','mol m-2 s-1','f') + cobalt%id_expn = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="expn", cmor_units="mol m-2 s-1", & + cmor_standard_name="sinking_mole_flux_of_particulate_organic_nitrogen_in_sea_water", & + cmor_long_name="Sinking Particulate Organic Nitrogen Flux") + + vardesc_temp = vardesc("expp_raw","Sinking Particulate Organic Phosphorus Flux",'h','L','s','mol m-2 s-1','f') + cobalt%id_expp = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="expp", cmor_units="mol m-2 s-1", & + cmor_standard_name="sinking_mole_flux_of_particulate_organic_phosphorus_in_sea_water", & + cmor_long_name="Sinking Particulate Organic Phosphorus Flux") + + vardesc_temp = vardesc("expfe_raw","Sinking Particulate Iron Flux",'h','L','s','mol m-2 s-1','f') + cobalt%id_expfe = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="expfe", cmor_units="mol m-2 s-1", & + cmor_standard_name="sinking_mole_flux_of_particulate_iron_in_sea_water", & + cmor_long_name="Sinking Particulate Iron Flux") + + vardesc_temp = vardesc("expsi_raw","Sinking Particulate Silicon Flux",'h','L','s','mol m-2 s-1','f') + cobalt%id_expsi = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="expsi", cmor_units="mol m-2 s-1", & + cmor_standard_name="sinking_mole_flux_of_particulate_silicon_in_sea_water", & + cmor_long_name="Sinking Particulate Silicon Flux") + + vardesc_temp = vardesc("expcalc_raw","Sinking Calcite Flux",'h','L','s','mol m-2 s-1','f') + cobalt%id_expcalc = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="expcalc", cmor_units="mol m-2 s-1", & + cmor_standard_name="sinking_mole_flux_of_calcite_expressed_as_carbon_in_sea_water", & + cmor_long_name="Sinking Calcite Flux") + + vardesc_temp = vardesc("exparag_raw","Sinking Aragonite Flux",'h','L','s','mol m-2 s-1','f') + cobalt%id_exparag = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="exparag", cmor_units="mol m-2 s-1", & + cmor_standard_name="sinking_mole_flux_of_aragonite_expressed_as_carbon_in_sea_water", & + cmor_long_name="Sinking Aragonite Flux") + + vardesc_temp = vardesc("remoc_raw","Remineralization of Organic Carbon",'h','L','s','mol m-3 s-1','f') + cobalt%id_remoc = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="remoc", cmor_units="mol m-3 s-1", & + cmor_standard_name="tendency_of_mole_concentration_of_particulate_organic matter_expressed_as_carbon_in_sea_water_due_to_remineralization", & + cmor_long_name="Remineralization of Organic Carbon") + + vardesc_temp = vardesc("dcalc_raw","Calcite Dissolution",'h','L','s','mol m-3 s-1','f') + cobalt%id_dcalc = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="dcalc", cmor_units="mol m-3 s-1", & + cmor_standard_name="tendency_of_mole_concentration_of_calcite_expressed_as_carbon_in_sea_water_due_to_dissolution", & + cmor_long_name="Calcite Dissolution") + + vardesc_temp = vardesc("darag_raw","Aragonite Dissolution",'h','L','s','mol m-3 s-1','f') + cobalt%id_darag = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="darag", cmor_units="mol m-3 s-1", & + cmor_standard_name="tendency_of_mole_concentration_of_aragonite_expressed_as_carbon_in_sea_water_due_to_dissolution", & + cmor_long_name="Aragonite Dissolution") + +! CHECK3: + vardesc_temp = vardesc("ppdiat_raw","Net Primary Organic Carbon Production by Diatoms",'h','L','s','mol m-3 s-1','f') + cobalt%id_ppdiat = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="ppdiat", cmor_units="mol m-3 s-1", & + cmor_standard_name="tendency_of_mole_concentration_of_particulate_organic_matter_expressed_as_carbon_in_sea_water_due_to_net_primary_production_by_diatoms", & + cmor_long_name="Net Primary Organic Carbon Production by Diatoms") + +! CHECK3: + vardesc_temp = vardesc("ppdiaz_raw","Net Primary Mole Productivity of Carbon by Diazotrophs",'h','L','s','mol m-3 s-1','f') + cobalt%id_ppdiaz = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="ppdiaz", cmor_units="mol m-3 s-1", & + cmor_standard_name="tendency_of_mole_concentration_of_particulate_organic_matter_expressed_as_carbon_in_sea_water_due_to_net_primary_production_by_diazotrophs", & + cmor_long_name="Net Primary Mole Productivity of Carbon by Diazotrophs") + +! CHECK3: + vardesc_temp = vardesc("pppico_raw","Net Primary Mole Productivity of Carbon by Picophytoplankton",'h','L','s','mol m-3 s-1','f') + cobalt%id_pppico = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="pppico", cmor_units="mol m-3 s-1", & + cmor_standard_name="tendency_of_mole_concentration_of_particulate_organic_matter_expressed_as_carbon_in_sea_water_due_to_net_primary_production_by_picophytoplankton", & + cmor_long_name="Net Primary Mole Productivity of Carbon by Picophytoplankton") + +! CHECK3: + vardesc_temp = vardesc("ppmisc_raw","Net Primary Organic Carbon Production by Other Phytoplankton",'h','L','s','mol m-3 s-1','f') + cobalt%id_ppmisc = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="ppmisc", cmor_units="mol m-3 s-1", & + cmor_standard_name="tendency_of_mole_concentration_of_particulate_organic_matter_expressed_as_carbon_in_sea_water_due_to_net_primary_production_by_miscellaneous_phytoplankton", & + cmor_long_name="Net Primary Organic Carbon Production by Other Phytoplankton") + + vardesc_temp = vardesc("bddtdic_raw","Rate of Change of Dissolved Inorganic Carbon due to Biological Activity",'h','L','s','mol m-3 s-1','f') + cobalt%id_bddtdic = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="bddtdic", cmor_units="mol m-3 s-1", & + cmor_standard_name="tendency_of_mole_concentration_of_dissolved_inorganic_carbon_in_sea_water_due_to_biological_processes", & + cmor_long_name="Rate of Change of Dissolved Inorganic Carbon due to Biological Activity") + + vardesc_temp = vardesc("bddtdin_raw","Rate of Change of Nitrogen Nutrient due to Biological Activity",'h','L','s','mol m-3 s-1','f') + cobalt%id_bddtdin = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="bddtdin", cmor_units="mol m-3 s-1", & + cmor_standard_name="tendency_of_mole_concentration_of_dissolved_inorganic_nitrogen_in_sea_water_due_to_biological_processes", & + cmor_long_name="Rate of Change of Nitrogen Nutrient due to Biological Activity") + + vardesc_temp = vardesc("bddtdip_raw","Rate of Change of Dissolved Phosphorus due to Biological Activity",'h','L','s','mol m-3 s-1','f') + cobalt%id_bddtdip = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="bddtdip", cmor_units="mol m-3 s-1", & + cmor_standard_name="tendency_of_mole_concentration_of_dissolved_inorganic_phosphorus_in_sea_water_due_to_biological_processes", & + cmor_long_name="Rate of Change of Dissolved Phosphorus due to Biological Activity") + + vardesc_temp = vardesc("bddtdife_raw","Rate of Change of Dissolved Inorganic Iron due to Biological Activity",'h','L','s','mol m-3 s-1','f') + cobalt%id_bddtdife = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="bddtdife", cmor_units="mol m-3 s-1", & + cmor_standard_name="tendency_of_mole_concentration_of_dissolved_inorganic_iron_in_sea_water_due_to_biological_processes", & + cmor_long_name="Rate of Change of Dissolved Inorganic Iron due to Biological Activity") + + vardesc_temp = vardesc("bddtdisi_raw","Rate of Change of Dissolved Inorganic Silicon due to Biological Activity",'h','L','s','mol m-3 s-1','f') + cobalt%id_bddtdisi = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="bddtdisi", cmor_units="mol m-3 s-1", & + cmor_standard_name="tendency_of_mole_concentration_of_dissolved_inorganic_silicon_in_sea_water_due_to_biological_processes", & + cmor_long_name="Rate of Change of Dissolved Inorganic Silicon due to Biological Activity") + + vardesc_temp = vardesc("bddtalk_raw","Rate of Change of Alkalinity due to Biological Activity",'h','L','s','mol m-3 s-1','f') + cobalt%id_bddtalk = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="bddtalk", cmor_units="mol m-3 s-1", & + cmor_standard_name="tendency_of_sea_water_alkalinity_expressed_as_mole_equivalent_due_to_biological_processes", & + cmor_long_name="Rate of Change of Alkalinity due to Biological Activity") + + vardesc_temp = vardesc("fescav_raw","Nonbiogenic Iron Scavenging",'h','L','s','mol m-3 s-1','f') + cobalt%id_fescav = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="fescav", cmor_units="mol m-3 s-1", & + cmor_standard_name="tendency_of_mole_concentration_of_dissolved_iron_in_sea_water_due_to_scavenging_by_inorganic_particles", & + cmor_long_name="Nonbiogenic Iron Scavenging") + + vardesc_temp = vardesc("fediss_raw","Particle Source of Dissolved Iron",'h','L','s','mol m-3 s-1','f') + cobalt%id_fediss = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="fediss", cmor_units="mol m-3 s-1", & + cmor_standard_name="tendency_of_mole_concentration_of_dissolved_iron_in_sea_water_due_to_dissolution_from_inorganic_particles", & + cmor_long_name="Particle Source of Dissolved Iron") + +! CHECK3: +! 2017/08/04 jgj: CMOR requires area:areacello, volume:volcello + vardesc_temp = vardesc("graz_raw","Total Grazing of Phytoplankton by Zooplankton",'h','L','s','mol m-3 s-1','f') + cobalt%id_graz = register_diag_field(package_name, vardesc_temp%name, axes(1:3), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="graz", cmor_units="mol m-3 s-1", & + cmor_standard_name="tendency_of_mole_concentration_of_particulate_organic_matter_expressed_as_carbon_in_sea_water_due_to_grazing_of_phytoplankton", & + cmor_long_name="Total Grazing of Phytoplankton by Zooplankton") + +!------------------------------------------------------------------------------------------------------------------ +! 3-D Limitation terms +! 2018/07/18 limitation terms are now 2-D + + vardesc_temp = vardesc("limndiat_raw","Nitrogen Limitation of Diatoms",'h','1','s','1','f') + cobalt%id_limndiat = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="limndiat", cmor_units="1", & + cmor_standard_name="nitrogen_growth_limitation_of_diatoms", & + cmor_long_name="Nitrogen Limitation of Diatoms") + + vardesc_temp = vardesc("limndiaz_raw","Nitrogen Limitation of Diazotrophs",'h','1','s','1','f') + cobalt%id_limndiaz = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="limndiaz", cmor_units="1", & + cmor_standard_name="nitrogen_growth_limitation_of_diazotrophs", & + cmor_long_name="Nitrogen Limitation of Diazotrophs") + + vardesc_temp = vardesc("limnpico_raw","Nitrogen Limitation of Picophytoplankton",'h','1','s','1','f') + cobalt%id_limnpico = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="limnpico", cmor_units="1", & + cmor_standard_name="nitrogen_growth_limitation_of_picophytoplankton", & + cmor_long_name="Nitrogen Limitation of Picophytoplankton") + + vardesc_temp = vardesc("limnmisc_raw","Nitrogen Limitation of Other Phytoplankton",'h','1','s','1','f') + cobalt%id_limnmisc = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="limnmisc", cmor_units="1", & + cmor_standard_name="nitrogen_growth_limitation_of_miscellaneous_phytoplankton", & + cmor_long_name="Nitrogen Limitation of Other Phytoplankton") + + vardesc_temp = vardesc("limirrdiat_raw","Irradiance Limitation of Diatoms",'h','1','s','1','f') + cobalt%id_limirrdiat = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="limirrdiat", cmor_units="1", & + cmor_standard_name="growth_limitation_of_diatoms_due_to_solar_irradiance", & + cmor_long_name="Irradiance Limitation of Diatoms") + + vardesc_temp = vardesc("limirrdiaz_raw","Irradiance Limitation of Diazotrophs",'h','1','s','1','f') + cobalt%id_limirrdiaz = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="limirrdiaz", cmor_units="1", & + cmor_standard_name="growth_limitation_of_diazotrophs_due_to_solar_irradiance", & + cmor_long_name="Irradiance Limitation of Diazotrophs") + + vardesc_temp = vardesc("limirrpico_raw","Irradiance Limitation of Picophytoplankton",'h','1','s','1','f') + cobalt%id_limirrpico = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="limirrpico", cmor_units="1", & + cmor_standard_name="growth_limitation_of_picophytoplankton_due_to_solar_irradiance", & + cmor_long_name="Irradiance Limitation of Picophytoplankton") + + vardesc_temp = vardesc("limirrmisc_raw","Irradiance Limitation of Other Phytoplankton",'h','1','s','1','f') + cobalt%id_limirrmisc = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="limirrmisc", cmor_units="1", & + cmor_standard_name="growth_limitation_of_miscellaneous_phytoplankton_due_to_solar_irradiance", & + cmor_long_name="Irradiance Limitation of Other Phytoplankton") + + vardesc_temp = vardesc("limfediat_raw","Iron Limitation of Diatoms",'h','1','s','1','f') + cobalt%id_limfediat = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="limfediat", cmor_units="1", & + cmor_standard_name="iron_growth_limitation_of_diatoms", & + cmor_long_name="Iron Limitation of Diatoms") + + vardesc_temp = vardesc("limfediaz_raw","Iron Limitation of Diazotrophs",'h','1','s','1','f') + cobalt%id_limfediaz = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="limfediaz", cmor_units="1", & + cmor_standard_name="iron_growth_limitation_of_diazotrophs", & + cmor_long_name="Iron Limitation of Diazotrophs") + + vardesc_temp = vardesc("limfepico_raw","Iron Limitation of Picophytoplankton",'h','1','s','1','f') + cobalt%id_limfepico = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="limfepico", cmor_units="1", & + cmor_standard_name="iron_growth_limitation_of_picophytoplankton", & + cmor_long_name="Iron Limitation of Picophytoplankton") + + vardesc_temp = vardesc("limfemisc_raw","Iron Limitation of Other Phytoplankton",'h','1','s','1','f') + cobalt%id_limfemisc = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="limfemisc", cmor_units="1", & + cmor_standard_name="iron_growth_limitation_of_miscellaneous_phytoplankton", & + cmor_long_name="Iron Limitation of Other Phytoplankton") + +!-- added by JGJ - not requested by CMIP6/OMIP + vardesc_temp = vardesc("limpdiat_raw","Phosphorus Limitation of Diatoms",'h','1','s','1','f') + cobalt%id_limpdiat = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="limpdiat", cmor_units="1", & + cmor_standard_name="phosphorus_growth_limitation_of_diatoms", & + cmor_long_name="Phosphorus Limitation of Diatoms") + + vardesc_temp = vardesc("limpdiaz_raw","Phosphorus Limitation of Diazotrophs",'h','1','s','1','f') + cobalt%id_limpdiaz = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="limpdiaz", cmor_units="1", & + cmor_standard_name="phosphorus_growth_limitation_of_diazotrophs", & + cmor_long_name="Phosphorus Limitation of Diazotrophs") + + vardesc_temp = vardesc("limppico_raw","Phosphorus Limitation of Picophytoplankton",'h','1','s','1','f') + cobalt%id_limppico = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="limppico", cmor_units="1", & + cmor_standard_name="phosphorus_growth_limitation_of_picophytoplankton", & + cmor_long_name="Phosphorus Limitation of Picophytoplankton") + + vardesc_temp = vardesc("limpmisc_raw","Phosphorus Limitation of Other Phytoplankton",'h','1','s','1','f') + cobalt%id_limpmisc = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="limpmisc", cmor_units="1", & + cmor_standard_name="phosphorus_growth_limitation_of_miscellaneous_phytoplankton", & + cmor_long_name="Phosphorus Limitation of Other Phytoplankton") + +!------------------------------------------------------------------------------------------------------------------ +! 2-D fields +! sfc tracers + + vardesc_temp = vardesc("dissicos_raw","Surface Dissolved Inorganic Carbon Concentration",'h','1','s','mol m-3','f') + cobalt%id_dissicos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="dissicos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_dissolved_inorganic_carbon_in_sea_water", & + cmor_long_name="Surface Dissolved Inorganic Carbon Concentration") + + vardesc_temp = vardesc("dissicnatos_raw","Surface Natural Dissolved Inorganic Carbon Concentration",'h','1','s','mol m-3','f') + cobalt%id_dissicnatos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="dissicnatos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_dissolved_inorganic_carbon_natural_analogue_in_sea_water", & + cmor_long_name="Surface Natural Dissolved Inorganic Carbon Concentration") + + vardesc_temp = vardesc("dissicabioos_raw","Surface Abiotic Dissolved Inorganic Carbon Concentration",'h','1','s','mol m-3','f') + cobalt%id_dissicabioos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="dissicabioos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_dissolved_inorganic_carbon_abiotic_analogue_in_sea_water", & + cmor_long_name="Surface Abiotic Dissolved Inorganic Carbon Concentration") + + vardesc_temp = vardesc("dissi14cabioos_raw","Surface Abiotic Dissolved Inorganic 14Carbon Concentration",'h','1','s','mol m-3','f') + cobalt%id_dissi14cabioos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="dissi14cabioos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_dissolved_inorganic_carbon14_abiotic_analogue_in_sea_water", & + cmor_long_name="Surface Abiotic Dissolved Inorganic 14Carbon Concentration") + + vardesc_temp = vardesc("dissocos_raw","Surface Dissolved Organic Carbon Concentration",'h','1','s','mol m-3','f') + cobalt%id_dissocos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="dissocos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_dissolved_organic_carbon_in_sea_water", & + cmor_long_name="Surface Dissolved Organic Carbon Concentration") + +! also in Oday (updated to match Omon long_name, standard_name) + vardesc_temp = vardesc("phycos_raw","Surface Phytoplankton Carbon Concentration",'h','1','s','mol m-3','f') + cobalt%id_phycos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="phycos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_phytoplankton_expressed_as_carbon_in_sea_water", & + cmor_long_name="Surface Phytoplankton Carbon Concentration") + + vardesc_temp = vardesc("zoocos_raw","Surface Zooplankton Carbon Concentration",'h','1','s','mol m-3','f') + cobalt%id_zoocos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="zoocos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_zooplankton_expressed_as_carbon_in_sea_water", & + cmor_long_name="Surface Zooplankton Carbon Concentration") + + vardesc_temp = vardesc("baccos_raw","Surface Bacterial Carbon Concentration",'h','1','s','mol m-3','f') + cobalt%id_baccos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="baccos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_bacteria_expressed_as_carbon_in_sea_water", & + cmor_long_name="Surface Bacterial Carbon Concentration") + + vardesc_temp = vardesc("detocos_raw","Surface Detrital Organic Carbon Concentration",'h','1','s','mol m-3','f') + cobalt%id_detocos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="detocos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_organic_detritus_expressed_as_carbon_in_sea_water", & + cmor_long_name="Surface Detrital Organic Carbon Concentration") + + vardesc_temp = vardesc("calcos_raw","Surface Calcite Concentration",'h','1','s','mol m-3','f') + cobalt%id_calcos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="calcos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_calcite_expressed_as_carbon_in_sea_water", & + cmor_long_name="Surface Calcite Concentration") + + vardesc_temp = vardesc("aragos_raw","Surface Aragonite Concentration",'h','1','s','mol m-3','f') + cobalt%id_aragos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="aragos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_aragonite_expressed_as_carbon_in_sea_water", & + cmor_long_name="Surface Aragonite Concentration") + + vardesc_temp = vardesc("phydiatos_raw","Surface Mole Concentration of Diatoms expressed as Carbon in sea water",'h','1','s','mol m-3','f') + cobalt%id_phydiatos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="phydiatos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_diatoms_expressed_as_carbon_in_sea_water", & + cmor_long_name="Surface Mole Concentration of Diatoms expressed as Carbon in sea water") + + vardesc_temp = vardesc("phydiazos_raw","Surface Mole Concentration of Diazotrophs expressed as Carbon in sea water",'h','1','s','mol m-3','f') + cobalt%id_phydiazos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="phydiazos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_diazotrophs_expressed_as_carbon_in_sea_water", & + cmor_long_name="Surface Mole Concentration of Diazotrophs expressed as Carbon in sea water") + + vardesc_temp = vardesc("phypicoos_raw","Surface Mole Concentration of Picophytoplankton expressed as Carbon in sea water",'h','1','s','mol m-3','f') + cobalt%id_phypicoos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="phypicoos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_picophytoplankton_expressed_as_carbon_in_sea_water", & + cmor_long_name="Surface Mole Concentration of Picophytoplankton expressed as Carbon in sea water") + + vardesc_temp = vardesc("phymiscos_raw","Surface Mole Concentration of Miscellaneous Phytoplankton expressed as Carbon in sea water",'h','1','s','mol m-3','f') + cobalt%id_phymiscos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="phymiscos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_miscellaneous_phytoplankton_expressed_as_carbon_in_sea_water", & + cmor_long_name="Surface Mole Concentration of Miscellaneous Phytoplankton expressed as Carbon in sea water") + + vardesc_temp = vardesc("zmicroos_raw","Surface Mole Concentration of Microzooplankton expressed as Carbon in sea water",'h','1','s','mol m-3','f') + cobalt%id_zmicroos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="zmicroos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_microzooplankton_expressed_as_carbon_in_sea_water", & + cmor_long_name="Surface Mole Concentration of Microzooplankton expressed as Carbon in sea water") + + vardesc_temp = vardesc("zmesoos_raw","Surface Mole Concentration of Mesozooplankton expressed as Carbon in sea water",'h','1','s','mol m-3','f') + cobalt%id_zmesoos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="zmesoos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_mesozooplankton_expressed_as_carbon_in_sea_water", & + cmor_long_name="Surface Mole Concentration of Mesozooplankton expressed as Carbon in sea water") + + vardesc_temp = vardesc("talkos_raw","Surface Total Alkalinity",'h','1','s','mol m-3','f') + cobalt%id_talkos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="talkos", cmor_units="mol m-3", & + cmor_standard_name="sea_water_alkalinity_expressed_as_mole_equivalent", & + cmor_long_name="Surface Total Alkalinity") + + vardesc_temp = vardesc("talknatos_raw","Surface Natural Total Alkalinity",'h','1','s','mol m-3','f') + cobalt%id_talknatos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="talknatos", cmor_units="mol m-3", & + cmor_standard_name="sea_water_alkalinity_natural_analogue_expressed_as_mole_equivalent", & + cmor_long_name="Surface Natural Total Alkalinity") + + vardesc_temp = vardesc("phos_raw","Surface pH",'h','1','s','1','f') + cobalt%id_phos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="phos", cmor_units="1", & + cmor_standard_name="sea_water_ph_reported_on_total_scale", & + cmor_long_name="Surface pH") + + vardesc_temp = vardesc("phnatos_raw","Surface Natural pH",'h','1','s','1','f') + cobalt%id_phnatos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="phnatos", cmor_units="1", & + cmor_standard_name="sea_water_ph_natural_analogue_reported_on_total_scale", & + cmor_long_name="Surface Natural pH") + + vardesc_temp = vardesc("phabioos_raw","Surface Abiotic pH",'h','1','s','1','f') + cobalt%id_phabioos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="phabioos", cmor_units="1", & + cmor_standard_name="sea_water_ph_abiotic_analogue_reported_on_total_scale", & + cmor_long_name="Surface Abiotic pH") + +!! jgj 2017/08/04 removed _cmip in cmor_field_name - update diag table + vardesc_temp = vardesc("o2os_raw","Surface Dissolved Oxygen Concentration",'h','1','s','mol m-3','f') + cobalt%id_o2os = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="o2os", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_dissolved_molecular_oxygen_in_sea_water", & + cmor_long_name="Surface Dissolved Oxygen Concentration") + +! CHECK3: need 3-D field + vardesc_temp = vardesc("o2satos_raw","Surface Dissolved Oxygen Concentration at Saturation",'h','1','s','mol m-3','f') + cobalt%id_o2satos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="o2satos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_dissolved_molecular_oxygen_in_sea_water_at_saturation", & + cmor_long_name="Surface Dissolved Oxygen Concentration at Saturation") + +!! jgj 2017/08/04 removed _cmip in cmor_field_name - update diag table + vardesc_temp = vardesc("no3os_raw","Surface Dissolved Nitrate Concentration",'h','1','s','mol m-3','f') + cobalt%id_no3os = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="no3os", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_nitrate_in_sea_water", & + cmor_long_name="Surface Dissolved Nitrate Concentration") + + vardesc_temp = vardesc("nh4os_raw","Surface Dissolved Ammonium Concentration",'h','1','s','mol m-3','f') + cobalt%id_nh4os = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="nh4os", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_ammonium_in_sea_water", & + cmor_long_name="Surface Dissolved Ammonium Concentration") + +! CHECK3: +! 2018/01/12 standard name is "surface_mole_concentration_of_dissolved_inorganic_phosphorous_in_sea_water" in data request +! 2018/01/17 removed surface_ from CF standard name in data request + vardesc_temp = vardesc("po4os_raw","Surface Total Dissolved Inorganic Phosphorus Concentration",'h','1','s','mol m-3','f') + cobalt%id_po4os = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="po4os", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_dissolved_inorganic_phosphorus_in_sea_water", & + cmor_long_name="Surface Total Dissolved Inorganic Phosphorus Concentration") + + vardesc_temp = vardesc("dfeos_raw","Surface Dissolved Iron Concentration",'h','1','s','mol m-3','f') + cobalt%id_dfeos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="dfeos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_dissolved_iron_in_sea_water", & + cmor_long_name="Surface Dissolved Iron Concentration") + + vardesc_temp = vardesc("sios_raw","Surface Total Dissolved Inorganic Silicon Concentration",'h','1','s','mol m-3','f') + cobalt%id_sios = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="sios", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_dissolved_inorganic_silicon_in_sea_water", & + cmor_long_name="Surface Total Dissolved Inorganic Silicon Concentration") + +!! jgj 2017/08/04 removed _cmip in cmor_field_name - update diag table +! also in Oday (updated to match Omon long_name, standard_name) + vardesc_temp = vardesc("chlos_raw","Surface Mass Concentration of Total Phytoplankton expressed as Chlorophyll in sea water",'h','1','s','kg m-3','f') + cobalt%id_chlos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="chlos", cmor_units="kg m-3", & + cmor_standard_name="mass_concentration_of_phytoplankton_expressed_as_chlorophyll_in_sea_water", & + cmor_long_name="Surface Mass Concentration of Total Phytoplankton expressed as Chlorophyll in sea water") + + vardesc_temp = vardesc("chldiatos_raw","Surface Mass Concentration of Diatoms expressed as Chlorophyll in sea water",'h','1','s','kg m-3','f') + cobalt%id_chldiatos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="chldiatos", cmor_units="kg m-3", & + cmor_standard_name="mass_concentration_of_diatoms_expressed_as_chlorophyll_in_sea_water", & + cmor_long_name="Surface Mass Concentration of Diatoms expressed as Chlorophyll in sea water") + + vardesc_temp = vardesc("chldiazos_raw","Surface Mass Concentration of Diazotrophs expressed as Chlorophyll in sea water",'h','1','s','kg m-3','f') + cobalt%id_chldiazos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="chldiazos", cmor_units="kg m-3", & + cmor_standard_name="mass_concentration_of_diazotrophs_expressed_as_chlorophyll_in_sea_water", & + cmor_long_name="Surface Mass Concentration of Diazotrophs expressed as Chlorophyll in sea water") + + vardesc_temp = vardesc("chlpicoos_raw","Surface Mass Concentration of Picophytoplankton expressed as Chlorophyll in sea water",'h','1','s','kg m-3','f') + cobalt%id_chlpicoos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="chlpicoos", cmor_units="kg m-3", & + cmor_standard_name="mass_concentration_of_picophytoplankton_expressed_as_chlorophyll_in_sea_water", & + cmor_long_name="Surface Mass Concentration of Picophytoplankton expressed as Chlorophyll in sea water") + + vardesc_temp = vardesc("chlmiscos_raw","Surface Mass Concentration of Other Phytoplankton expressed as Chlorophyll in sea water",'h','1','s','kg m-3','f') + cobalt%id_chlmiscos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="chlmiscos", cmor_units="kg m-3", & + cmor_standard_name="mass_concentration_of_miscellaneous_phytoplankton_expressed_as_chlorophyll_in_sea_water", & + cmor_long_name="Surface Mass Concentration of Other Phytoplankton expressed as Chlorophyll in sea water") + + vardesc_temp = vardesc("ponos_raw","Surface Mole Concentration of Particulate Organic Matter expressed as Nitrogen in sea water",'h','1','s','mol N m-3','f') + cobalt%id_ponos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="ponos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_particulate_organic_matter_expressed_as_nitrogen_in_sea_water", & + cmor_long_name="Surface Mole Concentration of Particulate Organic Matter expressed as Nitrogen in sea water") + + vardesc_temp = vardesc("popos_raw","Surface Mole Concentration of Particulate Organic Matter expressed as Phosphorus in sea water",'h','1','s','mol m-3','f') + cobalt%id_popos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="popos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_particulate_organic_matter_expressed_as_phosphorus_in_sea_water", & + cmor_long_name="Surface Mole Concentration of Particulate Organic Matter expressed as Phosphorus in sea water") + + vardesc_temp = vardesc("bfeos_raw","Surface Mole Concentration of Particulate Organic Matter expressed as Iron in sea water",'h','1','s','mol m-3','f') + cobalt%id_bfeos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="bfeos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_particulate_organic_matter_expressed_as_iron_in_sea_water", & + cmor_long_name="Surface Mole Concentration of Particulate Organic Matter expressed as Iron in sea water") + + vardesc_temp = vardesc("bsios_raw","Surface Mole Concentration of Particulate Organic Matter expressed as Silicon in sea water",'h','1','s','mol m-3','f') + cobalt%id_bsios = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="bsios", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_particulate_organic_matter_expressed_as_silicon_in_sea_water", & + cmor_long_name="Surface Mole Concentration of Particulate Organic Matter expressed as Silicon in sea water") + + vardesc_temp = vardesc("phynos_raw","Surface Mole Concentration of Phytoplankton Nitrogen in sea water",'h','1','s','mol m-3','f') + cobalt%id_phynos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="phynos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_phytoplankton_expressed_as_nitrogen_in_sea_water", & + cmor_long_name="Surface Mole Concentration of Phytoplankton Nitrogen in sea water") + + vardesc_temp = vardesc("phypos_raw","Surface Mole Concentration of Total Phytoplankton expressed as Phosphorus in sea water",'h','1','s','mol m-3','f') + cobalt%id_phypos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="phypos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_phytoplankton_expressed_as_phosphorus_in_sea_water", & + cmor_long_name="Surface Mole Concentration of Total Phytoplankton expressed as Phosphorus in sea water") + +! 2017/12/28 jgj Updated long name in data request: Surface Mole Concentration of Total Phytoplankton expressed as Iron in sea water + vardesc_temp = vardesc("phyfeos_raw","Surface Mole Concentration of Total Phytoplankton expressed as Iron in sea water",'h','1','s','mol m-3','f') + cobalt%id_phyfeos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="phyfeos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_phytoplankton_expressed_as_iron_in_sea_water", & + cmor_long_name="Surface Mole Concentration of Total Phytoplankton expressed as Iron in sea water") + + vardesc_temp = vardesc("physios_raw","Surface Mole Concentration of Total Phytoplankton expressed as Silicon in sea water",'h','1','s','mol m-3','f') + cobalt%id_physios = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="physios", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_phytoplankton_expressed_as_silicon_in_sea_water", & + cmor_long_name="Surface Mole Concentration of Total Phytoplankton expressed as Silicon in sea water") + + vardesc_temp = vardesc("co3os_raw","Surface Carbonate Ion Concentration",'h','1','s','mol m-3','f') + cobalt%id_co3os = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="co3os", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_carbonate_expressed_as_carbon_in_sea_water", & + cmor_long_name="Surface Carbonate Ion Concentration") + + vardesc_temp = vardesc("co3natos_raw","Surface Natural Carbonate Ion Concentration",'h','1','s','mol m-3','f') + cobalt%id_co3natos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="co3natos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_carbonate_ion_natural_analogue_in_sea_water", & + cmor_long_name="Surface Natural Carbonate Ion Concentration") + + vardesc_temp = vardesc("co3abioos_raw","Surface Abiotic Carbonate Ion Concentration",'h','1','s','mol m-3','f') + cobalt%id_co3abioos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="co3abioos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_carbonate_abiotic_analogue_expressed_as_carbon_in_sea_water", & + cmor_long_name="Surface Abiotic Carbonate Ion Concentration") + + vardesc_temp = vardesc("co3satcalcos_raw","Surface Mole Concentration of Carbonate Ion in Equilibrium with Pure Calcite in sea water",'h','1','s','mol m-3','f') + cobalt%id_co3satcalcos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="co3satcalcos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_carbonate_expressed_as_carbon_at_equilibrium_with_pure_calcite_in_sea_water", & + cmor_long_name="Surface Mole Concentration of Carbonate Ion in Equilibrium with Pure Calcite in sea water") + + vardesc_temp = vardesc("co3sataragos_raw","Surface Mole Concentration of Carbonate Ion in Equilibrium with Pure Aragonite in sea water",'h','1','s','mol m-3','f') + cobalt%id_co3sataragos = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="co3sataragos", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_carbonate_expressed_as_carbon_at_equilibrium_with_pure_aragonite_in_sea_water", & + cmor_long_name="Surface Mole Concentration of Carbonate Ion in Equilibrium with Pure Aragonite in sea water") + +!------------------------------------------------------------------------------------------------------------------ +! 2-D fields (from Omon) + + vardesc_temp = vardesc("intpp_raw","Primary Organic Carbon Production by All Types of Phytoplankton",'h','1','s','mol m-2 s-1','f') + cobalt%id_intpp = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="intpp", cmor_units="mol m-2 s-1", & + cmor_standard_name="net_primary_mole_productivity_of_biomass_expressed_as_carbon_by_phytoplankton", & + cmor_long_name="Primary Organic Carbon Production by All Types of Phytoplankton") + + vardesc_temp = vardesc("intppnitrate_raw","Primary Organic Carbon Production by Phytoplankton Based on Nitrate Uptake Alone",'h','1','s','mol m-2 s-1','f') + cobalt%id_intppnitrate = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="intppnitrate", cmor_units="mol m-2 s-1", & + cmor_standard_name="net_primary_mole_productivity_of_biomass_expressed_as_carbon_due_to_nitrate_utilization", & + cmor_long_name="Primary Organic Carbon Production by Phytoplankton Based on Nitrate Uptake Alone") + + vardesc_temp = vardesc("intppdiat_raw","Net Primary Organic Carbon Production by Diatoms",'h','1','s','mol m-2 s-1','f') + cobalt%id_intppdiat = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="intppdiat", cmor_units="mol m-2 s-1", & + cmor_standard_name="net_primary_mole_productivity_of_biomass_expressed_as_carbon_by_diatoms", & + cmor_long_name="Net Primary Organic Carbon Production by Diatoms") + + vardesc_temp = vardesc("intppdiaz_raw","Net Primary Mole Productivity of Carbon by Diazotrophs",'h','1','s','mol m-2 s-1','f') + cobalt%id_intppdiaz = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="intppdiaz", cmor_units="mol m-2 s-1", & + cmor_standard_name="net_primary_mole_productivity_of_biomass_expressed_as_carbon_by_diazotrophs", & + cmor_long_name="Net Primary Mole Productivity of Carbon by Diazotrophs") + + vardesc_temp = vardesc("intpppico_raw","Net Primary Mole Productivity of Carbon by Picophytoplankton",'h','1','s','mol m-2 s-1','f') + cobalt%id_intpppico = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="intpppico", cmor_units="mol m-2 s-1", & + cmor_standard_name="net_primary_mole_productivity_of_biomass_expressed_as_carbon_by_picophytoplankton", & + cmor_long_name="Net Primary Mole Productivity of Carbon by Picophytoplankton") + + vardesc_temp = vardesc("intppmisc_raw","Net Primary Organic Carbon Production by Other Phytoplankton",'h','1','s','mol m-2 s-1','f') + cobalt%id_intppmisc = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="intppmisc", cmor_units="mol m-2 s-1", & + cmor_standard_name="net_primary_mole_productivity_of_biomass_expressed_as_carbon_by_miscellaneous_phytoplankton", & + cmor_long_name="Net Primary Organic Carbon Production by Other Phytoplankton") + + vardesc_temp = vardesc("intpbn_raw","Nitrogen Production",'h','1','s','mol m-2 s-1','f') + cobalt%id_intpbn = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="intpbn", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_nitrogen_due_to_biological_production", & + cmor_long_name="Nitrogen Production") + + vardesc_temp = vardesc("intpbp_raw","Phosphorus Production",'h','1','s','mol m-2 s-1','f') + cobalt%id_intpbp = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="intpbp", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_phosphorus_due_to_biological_production", & + cmor_long_name="Phosphorus Production") + + vardesc_temp = vardesc("intpbfe_raw","Iron Production",'h','1','s','mol m-2 s-1','f') + cobalt%id_intpbfe = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="intpbfe", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_iron_due_to_biological_production", & + cmor_long_name="Iron Production") + + vardesc_temp = vardesc("intpbsi_raw","Silicon Production",'h','1','s','mol m-2 s-1','f') + cobalt%id_intpbsi = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="intpbsi", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_silicon_due_to_biological_production", & + cmor_long_name="Silicon Production") + + vardesc_temp = vardesc("intpcalcite_raw","Calcite Production",'h','1','s','mol m-2 s-1','f') + cobalt%id_intpcalcite = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="intpcalcite", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_calcite_expressed_as_carbon_due_to_biological_production", & + cmor_long_name="Calcite Production") + + vardesc_temp = vardesc("intparag_raw","Aragonite Production",'h','1','s','mol m-2 s-1','f') + cobalt%id_intparag = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="intparag", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_aragonite_expressed_as_carbon_due_to_biological_production", & + cmor_long_name="Aragonite Production") + +! CHECK3: these should be AT 100m - check how to output to conform to CMOR/data request + vardesc_temp = vardesc("epc100_raw","Downward Flux of Particulate Organic Carbon",'h','1','s','mol m-2 s-1','f') + cobalt%id_epc100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="epc100", cmor_units="mol m-2 s-1", & + cmor_standard_name="sinking_mole_flux_of_particulate_organic_matter_expressed_as_carbon_in_sea_water", & + cmor_long_name="Downward Flux of Particulate Organic Carbon") + + vardesc_temp = vardesc("epn100_raw","Downward Flux of Particulate Nitrogen",'h','1','s','mol m-2 s-1','f') + cobalt%id_epn100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="epn100", cmor_units="mol m-2 s-1", & + cmor_standard_name="sinking_mole_flux_of_particulate_organic_nitrogen_in_sea_water", & + cmor_long_name="Downward Flux of Particulate Nitrogen") + + vardesc_temp = vardesc("epp100_raw","Downward Flux of Particulate Phosphorus",'h','1','s','mol m-2 s-1','f') + cobalt%id_epp100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="epp100", cmor_units="mol m-2 s-1", & + cmor_standard_name="sinking_mole_flux_of_particulate_organic_phosphorus_in_sea_water", & + cmor_long_name="Downward Flux of Particulate Phosphorus") + + vardesc_temp = vardesc("epfe100_raw","Downward Flux of Particulate Iron",'h','1','s','mol m-2 s-1','f') + cobalt%id_epfe100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="epfe100", cmor_units="mol m-2 s-1", & + cmor_standard_name="sinking_mole_flux_of_particulate_iron_in_sea_water", & + cmor_long_name="Downward Flux of Particulate Iron") + + vardesc_temp = vardesc("epsi100_raw","Downward Flux of Particulate Silicon",'h','1','s','mol m-2 s-1','f') + cobalt%id_epsi100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="epsi100", cmor_units="mol m-2 s-1", & + cmor_standard_name="sinking_mole_flux_of_particulate_silicon_in_sea_water", & + cmor_long_name="Downward Flux of Particulate Silicon") + + vardesc_temp = vardesc("epcalc100_raw","Downward Flux of Calcite",'h','1','s','mol m-2 s-1','f') + cobalt%id_epcalc100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="epcalc100", cmor_units="mol m-2 s-1", & + cmor_standard_name="sinking_mole_flux_of_calcite_expressed_as_carbon_in_sea_water", & + cmor_long_name="Downward Flux of Calcite") + + vardesc_temp = vardesc("eparag100_raw","Downward Flux of Aragonite",'h','1','s','mol m-2 s-1','f') + cobalt%id_eparag100 = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="eparag100", cmor_units="mol m-2 s-1", & + cmor_standard_name="sinking_mole_flux_of_aragonite_expressed_as_carbon_in_sea_water", & + cmor_long_name="Downward Flux of Aragonite") + +! vertically integrated + vardesc_temp = vardesc("intdic_raw","Dissolved Inorganic Carbon Content",'h','1','s','kg m-2','f') + cobalt%id_intdic = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="intdic", cmor_units="kg m-2", & + cmor_standard_name="ocean_mass_content_of_dissolved_inorganic_carbon", & + cmor_long_name="Dissolved Inorganic Carbon Content") + + vardesc_temp = vardesc("intdoc_raw","Dissolved Organic Carbon Content",'h','1','s','kg m-2','f') + cobalt%id_intdoc = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="intdoc", cmor_units="kg m-2", & + cmor_standard_name="ocean_mass_content_of_dissolved_organic_carbon", & + cmor_long_name="Dissolved Organic Carbon Content") + + vardesc_temp = vardesc("intpoc_raw","Particulate Organic Carbon Content",'h','1','s','kg m-2','f') + cobalt%id_intpoc = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="intpoc", cmor_units="kg m-2", & + cmor_standard_name="ocean_mass_content_of_particulate_organic_matter_expressed_as_carbon", & + cmor_long_name="Particulate Organic Carbon Content") + + vardesc_temp = vardesc("spco2_raw","Surface Aqueous Partial Pressure of CO2",'h','1','s','Pa','f') + cobalt%id_spco2 = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="spco2", cmor_units="Pa", & + cmor_standard_name="surface_partial_pressure_of_carbon_dioxide_in_sea_water", & + cmor_long_name="Surface Aqueous Partial Pressure of CO2") + + vardesc_temp = vardesc("spco2nat_raw","Natural Surface Aqueous Partial Pressure of CO2",'h','1','s','Pa','f') + cobalt%id_spco2nat = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="spco2nat", cmor_units="Pa", & + cmor_standard_name="surface_partial_pressure_of_carbon_dioxide_natural_analogue_in_sea_water", & + cmor_long_name="Natural Surface Aqueous Partial Pressure of CO2") + + vardesc_temp = vardesc("spco2abio_raw","Abiotic Surface Aqueous Partial Pressure of CO2",'h','1','s','Pa','f') + cobalt%id_spco2abio = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="spco2abio", cmor_units="Pa", & + cmor_standard_name="surface_partial_pressure_of_carbon_dioxide_abiotic_analogue_in_sea_water", & + cmor_long_name="Abiotic Surface Aqueous Partial Pressure of CO2") + + vardesc_temp = vardesc("dpco2_raw","Delta PCO2",'h','1','s','Pa','f') + cobalt%id_dpco2 = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="dpco2", cmor_units="Pa", & + cmor_standard_name="surface_carbon_dioxide_partial_pressure_difference_between_sea_water_and_air", & + cmor_long_name="Delta PCO2") + + vardesc_temp = vardesc("dpco2nat_raw","Natural Delta PCO2",'h','1','s','Pa','f') + cobalt%id_dpco2nat = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="dpco2nat", cmor_units="Pa", & + cmor_standard_name="surface_carbon_dioxide_natural_analogue_partial_pressure_difference_between_sea_water_and_air", & + cmor_long_name="Natural Delta PCO2") + + vardesc_temp = vardesc("dpco2abio_raw","Abiotic Delta PCO2",'h','1','s','Pa','f') + cobalt%id_dpco2abio = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="dpco2abio", cmor_units="Pa", & + cmor_standard_name="surface_carbon_dioxide_abiotic_analogue_partial_pressure_difference_between_sea_water_and_air", & + cmor_long_name="Abiotic Delta PCO2") + + vardesc_temp = vardesc("dpo2_raw","Delta PO2",'h','1','s','Pa','f') + cobalt%id_dpo2 = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="dpo2", cmor_units="Pa", & + cmor_standard_name="surface_molecular_oxygen_partial_pressure_difference_between_sea_water_and_air", & + cmor_long_name="Delta PO2") + +! CHECK3: +! 2017/08/04 jgj: CMOR requires positive down, area:areacello + vardesc_temp = vardesc("fgco2_raw","Surface Downward Flux of Total CO2",'h','1','s','kg m-2 s-1','f') + cobalt%id_fgco2 = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="fgco2", cmor_units="kg m-2 s-1", & + cmor_standard_name="surface_downward_mass_flux_of_carbon_dioxide_expressed_as_carbon", & + cmor_long_name="Surface Downward Flux of Total CO2") + +! CHECK3: +! 2017/08/04 jgj: CMOR requires positive down, area:areacello + vardesc_temp = vardesc("fgco2nat_raw","Surface Downward Flux of Natural CO2",'h','1','s','kg m-2 s-1','f') + cobalt%id_fgco2nat = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="fgco2nat", cmor_units="kg m-2 s-1", & + cmor_standard_name="surface_downward_mass_flux_of_carbon_dioxide_natural_analogue_expressed_as_carbon", & + cmor_long_name="Surface Downward Flux of Natural CO2") + +! CHECK3: +! 2017/08/04 jgj: CMOR requires positive down, area:areacello + vardesc_temp = vardesc("fgco2abio_raw","Surface Downward Flux of Abiotic CO2",'h','1','s','kg m-2 s-1','f') + cobalt%id_fgco2abio = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="fgco2abio", cmor_units="kg m-2 s-1", & + cmor_standard_name="surface_downward_mass_flux_of_carbon_dioxide_abiotic_analogue_expressed_as_carbon", & + cmor_long_name="Surface Downward Flux of Abiotic CO2") + +! CHECK3: +! 2017/08/04 jgj: CMOR requires positive down, area:areacello + vardesc_temp = vardesc("fg14co2abio_raw","Surface Downward Flux of Abiotic 14CO2",'h','1','s','kg m-2 s-1','f') + cobalt%id_fg14co2abio = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="fg14co2abio", cmor_units="kg m-2 s-1", & + cmor_standard_name="surface_downward_mass_flux_of_carbon14_dioxide_abiotic_analogue_expressed_as_carbon", & + cmor_long_name="Surface Downward Flux of Abiotic 14CO2") + +! CHECK3: +! 2017/08/04 jgj: CMOR requires positive down, area:areacello + vardesc_temp = vardesc("fgo2_raw","Surface Downward Flux of O2",'h','1','s','mol m-2 s-1','f') + cobalt%id_fgo2 = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="fgo2", cmor_units="mol m-2 s-1", & + cmor_standard_name="surface_downward_mole_flux_of_molecular_oxygen", & + cmor_long_name="Surface Downward Flux of O2") + + vardesc_temp = vardesc("icfriver_raw","Flux of Inorganic Carbon Into Ocean Surface by Runoff",'h','1','s','mol m-2 s-1','f') + cobalt%id_icfriver = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="icfriver", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_inorganic_carbon_due_to_runoff_and_sediment_dissolution", & + cmor_long_name="Flux of Inorganic Carbon Into Ocean Surface by Runoff") + + vardesc_temp = vardesc("fric_raw","Downward Inorganic Carbon Flux at Ocean Bottom",'h','1','s','mol m-2 s-1','f') + cobalt%id_fric = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="fric", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_inorganic_carbon_due_to_sedimentation", & + cmor_long_name="Downward Inorganic Carbon Flux at Ocean Bottom") + + vardesc_temp = vardesc("ocfriver_raw","Flux of Organic Carbon Into Ocean Surface by Runoff",'h','1','s','mol m-2 s-1','f') + cobalt%id_ocfriver = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="ocfriver", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_organic_carbon_due_to_runoff_and_sediment_dissolution", & + cmor_long_name="Flux of Organic Carbon Into Ocean Surface by Runoff") + + vardesc_temp = vardesc("froc_raw","Downward Organic Carbon Flux at Ocean Bottom",'h','1','s','mol m-2 s-1','f') + cobalt%id_froc = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="froc", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_organic_carbon_due_to_sedimentation", & + cmor_long_name="Downward Organic Carbon Flux at Ocean Bottom") + + vardesc_temp = vardesc("intpn2_raw","Nitrogen Fixation Rate in Ocean",'h','1','s','mol m-2 s-1','f') + cobalt%id_intpn2 = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="intpn2", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_elemental_nitrogen_due_to_fixation", & + cmor_long_name="Nitrogen Fixation Rate in Ocean") + + vardesc_temp = vardesc("fsn_raw","Surface Downward Net Flux of Nitrogen",'h','1','s','mol m-2 s-1','f') + cobalt%id_fsn = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="fsn", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_elemental_nitrogen_due_to_deposition_and_fixation_and_runoff", & + cmor_long_name="Surface Downward Net Flux of Nitrogen") + + vardesc_temp = vardesc("frn_raw","Nitrogen Loss to Sediments and through Denitrification",'h','1','s','mol m-2 s-1','f') + cobalt%id_frn = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="frn", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_elemental_nitrogen_due_to_denitrification_and_sedimentation", & + cmor_long_name="Nitrogen Loss to Sediments and through Denitrification") + + vardesc_temp = vardesc("fsfe_raw","Surface Downward Net Flux of Iron",'h','1','s','mol m-2 s-1','f') + cobalt%id_fsfe = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="fsfe", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_iron_due_to_deposition_and_runoff_and_sediment_dissolution", & + cmor_long_name="Surface Downward Net Flux of Iron") + + vardesc_temp = vardesc("frfe_raw","Iron Loss to Sediments",'h','1','s','mol m-2 s-1','f') + cobalt%id_frfe = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="frfe", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_iron_due_to_sedimentation", & + cmor_long_name="Iron Loss to Sediments") + + vardesc_temp = vardesc("o2min_raw","Oxygen Minimum Concentration",'h','1','s','mol m-3','f') + cobalt%id_o2min = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="o2min", cmor_units="mol m-3", & + cmor_standard_name="mole_concentration_of_dissolved_molecular_oxygen_in_sea_water_at_shallowest_local_minimum_in_vertical_profile", & + cmor_long_name="Oxygen Minimum Concentration") + + vardesc_temp = vardesc("zo2min_raw","Depth of Oxygen Minimum Concentration",'h','1','s','m','f') + cobalt%id_zo2min = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="zo2min", cmor_units="m", & + cmor_standard_name="depth_at_shallowest_local_minimum_in_vertical_profile_of_mole_concentration_of_dissolved_molecular_oxygen_in_sea_water", & + cmor_long_name="Depth of Oxygen Minimum Concentration") + + vardesc_temp = vardesc("zsatcalc_raw","Calcite Saturation Depth",'h','1','s','m','f') + cobalt%id_zsatcalc = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="zsatcalc", cmor_units="m", & + cmor_standard_name="minimum_depth_of_calcite_undersaturation_in_sea_water", & + cmor_long_name="Calcite Saturation Depth") + + vardesc_temp = vardesc("zsatarag_raw","Aragonite Saturation Depth",'h','1','s','m','f') + cobalt%id_zsatarag = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="zsatarag", cmor_units="m", & + cmor_standard_name="minimum_depth_of_aragonite_undersaturation_in_sea_water", & + cmor_long_name="Aragonite Saturation Depth") + + vardesc_temp = vardesc("fddtdic_raw","Rate of Change of Net Dissolved Inorganic Carbon",'h','1','s','mol m-2 s-1','f') + cobalt%id_fddtdic = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="fddtdic", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_dissolved_inorganic_carbon", & + cmor_long_name="Rate of Change of Net Dissolved Inorganic Carbon") + + vardesc_temp = vardesc("fddtdin_raw","Rate of Change of Net Dissolved Inorganic Nitrogen",'h','1','s','mol m-2 s-1','f') + cobalt%id_fddtdin = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="fddtdin", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_dissolved_inorganic_nitrogen", & + cmor_long_name="Rate of Change of Net Dissolved Inorganic Nitrogen") + + vardesc_temp = vardesc("fddtdip_raw","Rate of Change of Net Dissolved Inorganic Phosphorus",'h','1','s','mol m-2 s-1','f') + cobalt%id_fddtdip = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="fddtdip", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_dissolved_inorganic_phosphorus", & + cmor_long_name="Rate of Change of Net Dissolved Inorganic Phosphorus") + + vardesc_temp = vardesc("fddtdife_raw","Rate of Change of Net Dissolved Inorganic Iron",'h','1','s','mol m-2 s-1','f') + cobalt%id_fddtdife = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="fddtdife", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_dissolved_inorganic_iron", & + cmor_long_name="Rate of Change of Net Dissolved Inorganic Iron") + + vardesc_temp = vardesc("fddtdisi_raw","Rate of Change of Net Dissolved Inorganic Silicon",'h','1','s','mol m-2 s-1','f') + cobalt%id_fddtdisi = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="fddtdisi", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_dissolved_inorganic_silicon", & + cmor_long_name="Rate of Change of Net Dissolved Inorganic Silicon") + + vardesc_temp = vardesc("fddtalk_raw","Rate of Change of Total Alkalinity",'h','1','s','mol m-2 s-1','f') + cobalt%id_fddtalk = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="fddtalk", cmor_units="mol m-2 s-1", & + cmor_standard_name="integral_wrt_depth_of_tendency_of_sea_water_alkalinity_expressed_as_mole_equivalent", & + cmor_long_name="Rate of Change of Total Alkalinity") + + vardesc_temp = vardesc("fbddtdic_raw","Rate of Change of Dissolved Inorganic Carbon due to Biological Activity",'h','1','s','mol m-2 s-1','f') + cobalt%id_fbddtdic = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="fbddtdic", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_dissolved_inorganic_carbon_due_to_biological_processes", & + cmor_long_name="Rate of Change of Dissolved Inorganic Carbon due to Biological Activity") + + vardesc_temp = vardesc("fbddtdin_raw","Rate of Change of Dissolved Inorganic Nitrogen due to Biological Activity",'h','1','s','mol m-2 s-1','f') + cobalt%id_fbddtdin = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="fbddtdin", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_dissolved_inorganic_nitrogen_due_to_biological_processes", & + cmor_long_name="Rate of Change of Dissolved Inorganic Nitrogen due to Biological Activity") + + vardesc_temp = vardesc("fbddtdip_raw","Rate of Change of Dissolved Inorganic Phosphorus due to Biological Activity",'h','1','s','mol m-2 s-1','f') + cobalt%id_fbddtdip = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="fbddtdip", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_dissolved_inorganic_phosphorus_due_to_biological_processes", & + cmor_long_name="Rate of Change of Dissolved Inorganic Phosphorus due to Biological Activity") + + vardesc_temp = vardesc("fbddtdife_raw","Rate of Change of Dissolved Inorganic Iron due to Biological Activity",'h','1','s','mol m-2 s-1','f') + cobalt%id_fbddtdife = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="fbddtdife", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_dissolved_inorganic_iron_due_to_biological_processes", & + cmor_long_name="Rate of Change of Dissolved Inorganic Iron due to Biological Activity") + + vardesc_temp = vardesc("fbddtdisi_raw","Rate of Change of Dissolved Inorganic Silicon due to Biological Activity",'h','1','s','mol m-2 s-1','f') + cobalt%id_fbddtdisi = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="fbddtdisi", cmor_units="mol m-2 s-1", & + cmor_standard_name="tendency_of_ocean_mole_content_of_dissolved_inorganic_silicon_due_to_biological_processes", & + cmor_long_name="Rate of Change of Dissolved Inorganic Silicon due to Biological Activity") + + vardesc_temp = vardesc("fbddtalk_raw","Rate of Change of Biological Alkalinity due to Biological Activity",'h','1','s','mol m-2 s-1','f') + cobalt%id_fbddtalk = register_diag_field(package_name, vardesc_temp%name, axes(1:2), & + init_time, vardesc_temp%longname,vardesc_temp%units, missing_value = missing_value1, & + cmor_field_name="fbddtalk", cmor_units="mol m-2 s-1", & + cmor_standard_name="integral_wrt_depth_of_tendency_of_sea_water_alkalinity_expressed_as_mole_equivalent_due_to_biological_processes", & + cmor_long_name="Rate of Change of Biological Alkalinity due to Biological Activity") + +!------------------------------------------------------------------------------------------------------------------ +! 2-D fields (from Oday) + +! previously defined above + + +!============================================================================================================== + + end subroutine generic_COBALT_register_diag + + ! + ! This is an internal sub, not a public interface. + ! Add all the parameters to be used in this module. + ! + subroutine user_add_params + + !Specify all parameters used in this modules. + !===============================@=============================== + !User adds one call for each parameter below! + !User also adds the definition of each parameter in generic_COBALT_params type + !============================================================== + + !============= + !Block Starts: g_tracer_add_param + !============= + !Add the known experimental parameters used for calculations + !in this module. + !All the g_tracer_add_param calls must happen between + !g_tracer_start_param_list and g_tracer_end_param_list calls. + !This implementation enables runtime overwrite via field_table. + + call g_tracer_start_param_list(package_name) + call g_tracer_add_param('init', cobalt%init, .false. ) + + call g_tracer_add_param('htotal_scale_lo', cobalt%htotal_scale_lo, 0.01) + call g_tracer_add_param('htotal_scale_hi', cobalt%htotal_scale_hi, 100.0) + + ! Rho_0 is used in the Boussinesq + ! approximation to calculations of pressure and + ! pressure gradients, in units of kg m-3. + call g_tracer_add_param('RHO_0', cobalt%Rho_0, 1035.0) + call g_tracer_add_param('NKML' , cobalt%nkml, 1) + !----------------------------------------------------------------------- + ! coefficients for O2 saturation + !----------------------------------------------------------------------- + call g_tracer_add_param('a_0', cobalt%a_0, 2.00907) + call g_tracer_add_param('a_1', cobalt%a_1, 3.22014) + call g_tracer_add_param('a_2', cobalt%a_2, 4.05010) + call g_tracer_add_param('a_3', cobalt%a_3, 4.94457) + call g_tracer_add_param('a_4', cobalt%a_4, -2.56847e-01) + call g_tracer_add_param('a_5', cobalt%a_5, 3.88767) + call g_tracer_add_param('b_0', cobalt%b_0, -6.24523e-03) + call g_tracer_add_param('b_1', cobalt%b_1, -7.37614e-03) + call g_tracer_add_param('b_2', cobalt%b_2, -1.03410e-02 ) + call g_tracer_add_param('b_3', cobalt%b_3, -8.17083e-03) + call g_tracer_add_param('c_0', cobalt%c_0, -4.88682e-07) + !----------------------------------------------------------------------- + ! Schmidt number coefficients + !----------------------------------------------------------------------- + if (trim(as_param_cobalt) == 'W92') then + ! Compute the Schmidt number of CO2 in seawater using the + ! formulation presented by Wanninkhof (1992, J. Geophys. Res., 97, + ! 7373-7382). + call g_tracer_add_param('a1_co2', cobalt%a1_co2, 2068.9) + call g_tracer_add_param('a2_co2', cobalt%a2_co2, -118.63) + call g_tracer_add_param('a3_co2', cobalt%a3_co2, 2.9311) + call g_tracer_add_param('a4_co2', cobalt%a4_co2, -0.027) + call g_tracer_add_param('a5_co2', cobalt%a5_co2, 0.0) ! Not used for W92 + ! Compute the Schmidt number of O2 in seawater using the + ! formulation proposed by Keeling et al. (1998, Global Biogeochem. + ! Cycles, 12, 141-163). + call g_tracer_add_param('a1_o2', cobalt%a1_o2, 1929.7) + call g_tracer_add_param('a2_o2', cobalt%a2_o2, -117.46) + call g_tracer_add_param('a3_o2', cobalt%a3_o2, 3.116) + call g_tracer_add_param('a4_o2', cobalt%a4_o2, -0.0306) + call g_tracer_add_param('a5_o2', cobalt%a5_o2, 0.0) ! Not used for W92 + if (is_root_pe()) call mpp_error(NOTE,'generic_cobalt: Using Schmidt number coefficients for W92') + else if ((trim(as_param_cobalt) == 'W14') .or. (trim(as_param_cobalt) == 'gfdl_cmip6')) then + ! Compute the Schmidt number of CO2 in seawater using the + ! formulation presented by Wanninkhof + ! (2014, Limnol. Oceanogr., 12, 351-362) + call g_tracer_add_param('a1_co2', cobalt%a1_co2, 2116.8) + call g_tracer_add_param('a2_co2', cobalt%a2_co2, -136.25) + call g_tracer_add_param('a3_co2', cobalt%a3_co2, 4.7353) + call g_tracer_add_param('a4_co2', cobalt%a4_co2, -0.092307) + call g_tracer_add_param('a5_co2', cobalt%a5_co2, 0.0007555) + ! Compute the Schmidt number of O2 in seawater using the + ! formulation presented by Wanninkhof + ! (2014, Limnol. Oceanogr., 12, 351-362) + call g_tracer_add_param('a1_o2', cobalt%a1_o2, 1920.4) + call g_tracer_add_param('a2_o2', cobalt%a2_o2, -135.6) + call g_tracer_add_param('a3_o2', cobalt%a3_o2, 5.2122) + call g_tracer_add_param('a4_o2', cobalt%a4_o2, -0.10939) + call g_tracer_add_param('a5_o2', cobalt%a5_o2, 0.00093777) + if (is_root_pe()) call mpp_error(NOTE,'generic_cobalt: Using Schmidt number coefficients for W14') + else + call mpp_error(FATAL,'generic_cobalt: unable to set Schmidt number coefficients for as_param '//trim(as_param_cobalt)) + endif + !----------------------------------------------------------------------- + ! Stoichiometry + !----------------------------------------------------------------------- + ! + ! + ! Values taken from OCMIP-II Biotic protocols after Anderson (1995) for an + ! organic material of C106H172O38N16(H3PO4) which gives an average oxidation state + ! for carbon of (3*16+2*38-172)/106 = -0.4528. These calculations ignore H3PO4. + ! + ! Nitrate Production: + ! 16*H+ + 16*NO3- + 106*CO2 + 78*H2O <-> C106H172O38N16 + 150*O2 + ! Effect is to increase alkalinity by 16 NO3 equivalents. + ! + ! Ammonia Production (and reverse for remineralization): + ! 16*NH4+ + 106*CO2 + 62*H2O <-> C106H172O38N16 + 118*O2 + 16*H+ + ! Effect is to decrease (increase for remineralization) alkalinity by 16 NH4 equivalents. + ! + ! N2 Production: + ! 8*N2 + 106*CO2 + 86*H2O <-> C106H172O38N16 + 130*O2 + ! No effect on alkalinity. + ! + ! Nitrification: + ! NH4+ + 2*O2 <-> NO3- + H2O + 2*H+ + ! Effect is to decrease alkalinity by 2 NH4 equivalents. + ! + ! Denitrification: + ! C106H172O38N16 + 472/5*NO3- + 552/5*H+ <-> 106*CO2 + 16*NH4+ + 236/5*N2 + 546/5*H2O + ! Effect is to increase alkalinity by 552/472 = 1.169 NO3 equivalents. + ! + call g_tracer_add_param('n_2_n_denit', cobalt%n_2_n_denit, 472.0/(5.0*16.0)) ! mol N NO3 mol N org-1 + call g_tracer_add_param('o2_2_nfix', cobalt%o2_2_nfix, 130.0/16.0) ! mol O2 mol N-1 +! call g_tracer_add_param('o2_2_nfix', cobalt%o2_2_nfix, (118.0+3.0/(5.0+3.0)*(150.0-118.0))/16.0) ! mol O2 mol N-1 + call g_tracer_add_param('o2_2_nh4', cobalt%o2_2_nh4, 118.0 / 16.0) ! mol O2 mol N-1 + call g_tracer_add_param('o2_2_nitrif', cobalt%o2_2_nitrif, 2.0) ! mol O2 mol N-1 + call g_tracer_add_param('o2_2_no3', cobalt%o2_2_no3, 150.0 / 16.0) ! mol O2 mol N-1 + ! + !----------------------------------------------------------------------- + ! Nutrient Limitation Parameters (phytoplankton) + !----------------------------------------------------------------------- + ! + call g_tracer_add_param('k_fed_Di', phyto(DIAZO)%k_fed, 5.0e-10) ! mol Fed kg-1 + call g_tracer_add_param('k_fed_Lg', phyto(LARGE)%k_fed, 5.0e-10) ! mol Fed kg-1 + call g_tracer_add_param('k_fed_Sm', phyto(SMALL)%k_fed, 1.0e-10) ! mol Fed kg-1 + call g_tracer_add_param('k_nh4_Lg', phyto(LARGE)%k_nh4, k_nh4_large) ! mol NH4 kg-1 + call g_tracer_add_param('k_nh4_Sm', phyto(SMALL)%k_nh4, k_nh4_small) ! mol NH4 kg-1 + call g_tracer_add_param('k_nh4_Di', phyto(DIAZO)%k_nh4, k_nh4_diazo) ! mol NH4 kg-1 + call g_tracer_add_param('k_no3_Lg', phyto(LARGE)%k_no3, k_no3_large) ! mol NO3 kg-1 + call g_tracer_add_param('k_no3_Sm', phyto(SMALL)%k_no3, k_no3_small) ! mol NO3 kg-1 + call g_tracer_add_param('k_no3_Di', phyto(DIAZO)%k_no3, k_no3_diazo) ! mol NO3 kg-1 + call g_tracer_add_param('k_po4_Di', phyto(DIAZO)%k_po4, 5.0e-8) ! mol PO4 kg-1 + call g_tracer_add_param('k_po4_Lg', phyto(LARGE)%k_po4, 5.0e-8) ! mol PO4 kg-1 + call g_tracer_add_param('k_po4_Sm', phyto(SMALL)%k_po4, 1.0e-8) ! mol PO4 kg-1 + call g_tracer_add_param('k_sio4_Lg',phyto(LARGE)%k_sio4, 2.0e-6) ! mol SiO4 kg-1 + call g_tracer_add_param('k_fe_2_n_Di', phyto(DIAZO)%k_fe_2_n, 12.0e-6 * 106.0 / 16.0) ! mol Fe mol N-1 + call g_tracer_add_param('k_fe_2_n_Lg', phyto(LARGE)%k_fe_2_n, 6.0e-6 * 106.0 / 16.0) ! mol Fe mol N-1 + call g_tracer_add_param('k_fe_2_n_Sm',phyto(SMALL)%k_fe_2_n, 3.0e-6*106.0/16.0) ! mol Fe mol N-1 + call g_tracer_add_param('fe_2_n_max_Sm',phyto(SMALL)%fe_2_n_max, 50.e-6*106.0/16.0) ! mol Fe mol N-1 + call g_tracer_add_param('fe_2_n_max_Lg', phyto(LARGE)%fe_2_n_max, 500.0e-6*106.0/16.0) ! mol Fe mol N-1 + call g_tracer_add_param('fe_2_n_max_Di', phyto(DIAZO)%fe_2_n_max, 500.0e-6*106.0/16.0) ! mol Fe mol N-1 + call g_tracer_add_param('fe_2_n_upt_fac', cobalt%fe_2_n_upt_fac, 15.0e-6) ! mol Fe mol N-1 + ! + !----------------------------------------------------------------------- + ! Phytoplankton light limitation/growth rate + !----------------------------------------------------------------------- + ! + call g_tracer_add_param('alpha_Di', phyto(DIAZO)%alpha, 0.8e-5 * 2.77e18 / 6.022e17) ! g C g Chl-1 m2 J-1 + call g_tracer_add_param('alpha_Lg', phyto(LARGE)%alpha, 0.8e-5 * 2.77e18 / 6.022e17) ! g C g Chl-1 m2 J-1 + call g_tracer_add_param('alpha_Sm', phyto(SMALL)%alpha, 2.4e-5*2.77e18/6.022e17) ! g C g Chl-1 m-2 J-1 + call g_tracer_add_param('kappa_eppley', cobalt%kappa_eppley, 0.063) ! deg C-1 + call g_tracer_add_param('P_C_max_Di', phyto(DIAZO)%P_C_max, 0.50/sperd) ! s-1 + ! Uncomment for "no mass change" check + ! call g_tracer_add_param('P_C_max_Di', phyto(DIAZO)%P_C_max, 0.01/sperd) ! s-1 + call g_tracer_add_param('P_C_max_Lg', phyto(LARGE)%P_C_max, 1.25/sperd) ! s-1 + call g_tracer_add_param('P_C_max_Sm', phyto(SMALL)%P_C_max, 1.25/sperd) ! s-1 + call g_tracer_add_param('thetamax_Di', phyto(DIAZO)%thetamax, 0.03) ! g Chl g C-1 + call g_tracer_add_param('thetamax_Lg', phyto(LARGE)%thetamax, 0.05) ! g Chl g C-1 + call g_tracer_add_param('thetamax_Sm', phyto(SMALL)%thetamax, 0.03) ! g Chl g C-1 + call g_tracer_add_param('bresp_Di', phyto(DIAZO)%bresp,0.05/sperd) ! sec-1 + call g_tracer_add_param('bresp_Lg', phyto(LARGE)%bresp,0.05/sperd) ! sec-1 + call g_tracer_add_param('bresp_Sm', phyto(SMALL)%bresp,0.03/sperd) ! sec-1 + call g_tracer_add_param('thetamin', cobalt%thetamin, 0.002) ! g Chl g C-1 + call g_tracer_add_param('thetamin_nolim', cobalt%thetamin_nolim, 0.0) ! g Chl g C-1 + call g_tracer_add_param('zeta', cobalt%zeta, 0.05) ! dimensionless + call g_tracer_add_param('gamma_irr_mem', cobalt%gamma_irr_mem, 1.0 / sperd) ! s-1 + call g_tracer_add_param('gamma_mu_mem', cobalt%gamma_mu_mem, 1.0 / sperd) ! s-1 + call g_tracer_add_param('refuge_conc', cobalt%refuge_conc, 1.0e-9) ! moles N kg-1 + ! + !----------------------------------------------------------------------- + ! Nitrogen fixation inhibition parameters + !----------------------------------------------------------------------- + ! + call g_tracer_add_param('o2_inhib_Di_pow', cobalt%o2_inhib_Di_pow, 4.0) ! mol O2-1 m3 + call g_tracer_add_param('o2_inhib_Di_sat', cobalt%o2_inhib_Di_sat, 3.0e-4) ! mol O2 kg-1 + ! + !----------------------------------------------------------------------- + ! Other stoichiometry + !----------------------------------------------------------------------- + ! + call g_tracer_add_param('p_2_n_static', cobalt%p_2_n_static, .true. ) + call g_tracer_add_param('c_2_n', cobalt%c_2_n, 106.0 / 16.0) + call g_tracer_add_param('alk_2_n_denit', cobalt%alk_2_n_denit, 552.0/472.0) ! eq. alk mol NO3-1 + call g_tracer_add_param('p_2_n_static_Di', phyto(DIAZO)%p_2_n_static,1.0/40.0 ) ! mol P mol N-1 + call g_tracer_add_param('p_2_n_static_Lg', phyto(LARGE)%p_2_n_static,1.0/12.0 ) ! mol P mol N-1 + call g_tracer_add_param('p_2_n_static_Sm', phyto(SMALL)%p_2_n_static,1.0/20.0 ) ! mol P mol N-1 + call g_tracer_add_param('si_2_n_static_Lg', phyto(LARGE)%si_2_n_static, 2.0) ! mol Si mol N-1 + call g_tracer_add_param('si_2_n_max_Lg', phyto(LARGE)%si_2_n_max, 3.0) ! mol Si mol N-1 + call g_tracer_add_param('ca_2_n_arag', cobalt%ca_2_n_arag, 0.030 * 106.0 / 16.0) ! mol Ca mol N-1 + call g_tracer_add_param('ca_2_n_calc', cobalt%ca_2_n_calc, 0.013 * 106.0 / 16.0) ! mol Ca mol N-1 + call g_tracer_add_param('caco3_sat_max', cobalt%caco3_sat_max,10.0) ! dimensionless + ! + !----------------------------------------------------------------------- + ! Zooplankton Stoichiometry - presently static + !----------------------------------------------------------------------- + ! + call g_tracer_add_param('q_p_2_n_smz',zoo(1)%q_p_2_n, 1.0/18.0) ! mol P mol N-1 + call g_tracer_add_param('q_p_2_n_mdz',zoo(2)%q_p_2_n, 1.0/16.0) ! mol P mol N-1 + call g_tracer_add_param('q_p_2_n_lgz',zoo(3)%q_p_2_n, 1.0/16.0) ! mol P mol N-1 + ! + !----------------------------------------------------------------------- + ! Bacteria Stoichiometry - presently static + !----------------------------------------------------------------------- + ! + call g_tracer_add_param('q_p_2_n_bact',bact(1)%q_p_2_n, 1.0/16.0) ! mol P mol N-1 + ! + ! + !----------------------------------------------------------------------- + ! Phytoplankton aggregation + !----------------------------------------------------------------------- + ! + call g_tracer_add_param('agg_Sm',phyto(SMALL)%agg,0.1*1e6 / sperd) ! s-1 (mole N kg)-1 + call g_tracer_add_param('agg_Di',phyto(DIAZO)%agg, 0.0 / sperd) ! s-1 (mole N kg)-1 + call g_tracer_add_param('agg_Lg',phyto(LARGE)%agg,0.3*1e6/ sperd) ! s-1 (mole N kg)-1 + call g_tracer_add_param('frac_mu_agg_Sm',phyto(SMALL)%frac_mu_agg,0.25) ! none + call g_tracer_add_param('frac_mu_agg_Di',phyto(DIAZO)%frac_mu_agg,0.25) ! none + call g_tracer_add_param('frac_mu_agg_Lg',phyto(LARGE)%frac_mu_agg,0.25) ! none + ! + !----------------------------------------------------------------------- + ! Phytoplankton and bacterial losses to viruses + !----------------------------------------------------------------------- + ! + call g_tracer_add_param('vir_Sm',phyto(SMALL)%vir, 0.20*1e6/sperd ) ! s-1 (mole N kg)-1 + call g_tracer_add_param('vir_Di',phyto(DIAZO)%vir, 0.0 ) ! s-1 (mole N kg)-1 + call g_tracer_add_param('vir_Lg',phyto(LARGE)%vir, 0.0 ) ! s-1 (mole N kg)-1 + call g_tracer_add_param('vir_Bact',bact(1)%vir, 0.20*1e6/sperd) ! s-1 (mole N kg)-1 + call g_tracer_add_param('ktemp_vir',cobalt%vir_ktemp, 0.063) ! C-1 + ! + !----------------------------------------------------------------------- + ! Phytoplankton losses to exudation + !----------------------------------------------------------------------- + ! + call g_tracer_add_param('exu_Sm',phyto(SMALL)%exu, 0.13) ! dimensionless (fraction of NPP) + call g_tracer_add_param('exu_Di',phyto(DIAZO)%exu, 0.13) ! dimensionless (fraction of NPP) + call g_tracer_add_param('exu_Lg',phyto(LARGE)%exu, 0.13) ! dimensionless (fraction of NPP) + ! + !----------------------------------------------------------------------- + ! Zooplankton ingestion parameterization and temperature dependence + !----------------------------------------------------------------------- + ! + call g_tracer_add_param('imax_smz',zoo(1)%imax, 0.9*1.42 / sperd) ! s-1 + call g_tracer_add_param('imax_mdz',zoo(2)%imax, 0.57 / sperd) ! s-1 + call g_tracer_add_param('imax_lgz',zoo(3)%imax, 0.23 / sperd) ! s-1 + call g_tracer_add_param('ki_smz',zoo(1)%ki, 1.25e-6) ! moles N kg-1 + call g_tracer_add_param('ki_mdz',zoo(2)%ki, 1.25e-6) ! moles N kg-1 + call g_tracer_add_param('ki_lgz',zoo(3)%ki, 1.25e-6) ! moles N kg-1 + call g_tracer_add_param('ktemp_smz',zoo(1)%ktemp, 0.063) ! C-1 + call g_tracer_add_param('ktemp_mdz',zoo(2)%ktemp, 0.063) ! C-1 + call g_tracer_add_param('ktemp_lgz',zoo(3)%ktemp, 0.063) ! C-1 + ! + !----------------------------------------------------------------------- + ! Bacterial growth and uptake parameters + !----------------------------------------------------------------------- + ! + call g_tracer_add_param('mu_max_bact',bact(1)%mu_max, 1.0/sperd ) ! s-1 + call g_tracer_add_param('k_ldon_bact', bact(1)%k_ldon, 5.0e-7) ! mol ldon kg-1 + call g_tracer_add_param('ktemp_bact', bact(1)%ktemp, 0.063) ! C-1 + call g_tracer_add_param('gge_max_bact',bact(1)%gge_max,0.4) ! dimensionless + call g_tracer_add_param('bresp_bact',bact(1)%bresp, 0.0075/sperd) ! s-1 + ! + !----------------------------------------------------------------------- + ! Zooplankton switching and prey preference parameters + !----------------------------------------------------------------------- + ! + ! parameters controlling the extent of biomass-based switching between + ! multiple prey options + call g_tracer_add_param('nswitch_smz',zoo(1)%nswitch, 2.0) ! dimensionless + call g_tracer_add_param('nswitch_mdz',zoo(2)%nswitch, 2.0) ! dimensionless + call g_tracer_add_param('nswitch_lgz',zoo(3)%nswitch, 2.0) ! dimensionless + call g_tracer_add_param('mswitch_smz',zoo(1)%mswitch, 2.0) ! dimensionless + call g_tracer_add_param('mswitch_mdz',zoo(2)%mswitch, 2.0) ! dimensionless + call g_tracer_add_param('mswitch_lgz',zoo(3)%mswitch, 2.0) ! dimensionless + ! innate prey availability for small zooplankton + call g_tracer_add_param('smz_ipa_smp',zoo(1)%ipa_smp, 1.0) ! dimensionless + call g_tracer_add_param('smz_ipa_lgp',zoo(1)%ipa_lgp, 0.0) ! dimensionless + call g_tracer_add_param('smz_ipa_diaz',zoo(1)%ipa_diaz,0.0) ! dimensionless + call g_tracer_add_param('smz_ipa_smz',zoo(1)%ipa_smz, 0.0) ! dimensionless + call g_tracer_add_param('smz_ipa_mdz',zoo(1)%ipa_mdz, 0.0) ! dimensionless + call g_tracer_add_param('smz_ipa_lgz',zoo(1)%ipa_lgz, 0.0) ! dimensionless + call g_tracer_add_param('smz_ipa_bact',zoo(1)%ipa_bact,0.25) ! dimensionless + call g_tracer_add_param('smz_ipa_det',zoo(1)%ipa_det, 0.0) ! dimensionless + ! innate prey availability for large zooplankton + call g_tracer_add_param('mdz_ipa_smp',zoo(2)%ipa_smp, 0.0) ! dimensionless + call g_tracer_add_param('mdz_ipa_lgp',zoo(2)%ipa_lgp, 1.0) ! dimensionless + call g_tracer_add_param('mdz_ipa_diaz',zoo(2)%ipa_diaz,1.0) ! dimensionless + call g_tracer_add_param('mdz_ipa_smz',zoo(2)%ipa_smz, 1.0) ! dimensionless + call g_tracer_add_param('mdz_ipa_mdz',zoo(2)%ipa_mdz, 0.0) ! dimensionless + call g_tracer_add_param('mdz_ipa_lgz',zoo(2)%ipa_lgz, 0.0) ! dimensionless + call g_tracer_add_param('mdz_ipa_bact',zoo(2)%ipa_bact, 0.0) ! dimensionless + call g_tracer_add_param('mdz_ipa_det',zoo(2)%ipa_det, 0.0) ! dimensionless + ! innate prey availability large predatory zooplankton/krill + call g_tracer_add_param('lgz_ipa_smp',zoo(3)%ipa_smp, 0.0) ! dimensionless + call g_tracer_add_param('lgz_ipa_lgp',zoo(3)%ipa_lgp, 1.0) ! dimensionless + call g_tracer_add_param('lgz_ipa_diaz',zoo(3)%ipa_diaz, 1.0) ! dimensionless + call g_tracer_add_param('lgz_ipa_smz',zoo(3)%ipa_smz, 0.0) ! dimensionless + call g_tracer_add_param('lgz_ipa_mdz',zoo(3)%ipa_mdz, 1.0) ! dimensionless + call g_tracer_add_param('lgz_ipa_lgz',zoo(3)%ipa_lgz, 0.0) ! dimensionless + call g_tracer_add_param('lgz_ipa_bact',zoo(3)%ipa_bact, 0.0) ! dimensionless + call g_tracer_add_param('lgz_ipa_det',zoo(3)%ipa_det, 0.0) ! dimensionless + ! + !---------------------------------------------------------------------- + ! Zooplankton bioenergetics + !---------------------------------------------------------------------- + ! + call g_tracer_add_param('gge_max_smz',zoo(1)%gge_max, 0.4) ! dimensionless + call g_tracer_add_param('gge_max_mdz',zoo(2)%gge_max, 0.4) ! dimensionless + call g_tracer_add_param('gge_max_lgz',zoo(3)%gge_max, 0.4) ! dimensionless + call g_tracer_add_param('bresp_smz',zoo(1)%bresp, 0.9*0.020 / sperd) ! s-1 + call g_tracer_add_param('bresp_mdz',zoo(2)%bresp, 0.008 / sperd) ! s-1 + call g_tracer_add_param('bresp_lgz',zoo(3)%bresp, 0.0032 / sperd) ! s-1 + ! + !---------------------------------------------------------------------- + ! Bacterial bioenergetics + !---------------------------------------------------------------------- + ! + call g_tracer_add_param('gge_max_bact',bact(1)%gge_max,0.4) ! dimensionless + call g_tracer_add_param('bresp_bact',bact(1)%bresp, 0.0075/sperd) ! s-1 + ! + !---------------------------------------------------------------------- + ! Partitioning of zooplankton ingestion to other compartments + !---------------------------------------------------------------------- + ! + call g_tracer_add_param('phi_det_smz',zoo(1)%phi_det, 0.10) ! dimensionless + call g_tracer_add_param('phi_det_mdz',zoo(2)%phi_det, 0.20) ! dimensionless + call g_tracer_add_param('phi_det_lgz',zoo(3)%phi_det, 0.30) ! dimensionless + call g_tracer_add_param('phi_ldon_smz',zoo(1)%phi_ldon, 0.7*0.20) ! dimensionless + call g_tracer_add_param('phi_ldon_mdz',zoo(2)%phi_ldon, 0.7*0.10) ! dimensionless + call g_tracer_add_param('phi_ldon_lgz',zoo(3)%phi_ldon, 0.7*0.0) ! dimensionless + call g_tracer_add_param('phi_ldop_smz',zoo(1)%phi_ldop, 0.65*0.20) ! dimensionless + call g_tracer_add_param('phi_ldop_mdz',zoo(2)%phi_ldop, 0.65*0.10) ! dimensionless + call g_tracer_add_param('phi_ldop_lgz',zoo(3)%phi_ldop, 0.65*0.0) ! dimensionless + call g_tracer_add_param('phi_srdon_smz',zoo(1)%phi_srdon, 0.1*0.20) ! dimensionless + call g_tracer_add_param('phi_srdon_mdz',zoo(2)%phi_srdon, 0.1*0.10) ! dimensionless + call g_tracer_add_param('phi_srdon_lgz',zoo(3)%phi_srdon, 0.1*0.0) ! dimensionless + call g_tracer_add_param('phi_srdop_smz',zoo(1)%phi_srdop, 0.15*0.20) ! dimensionless + call g_tracer_add_param('phi_srdop_mdz',zoo(2)%phi_srdop, 0.15*0.10) ! dimensionless + call g_tracer_add_param('phi_srdop_lgz',zoo(3)%phi_srdop, 0.15*0.0) ! dimensionless + call g_tracer_add_param('phi_sldon_smz',zoo(1)%phi_sldon, 0.2*0.20) ! dimensionless + call g_tracer_add_param('phi_sldon_mdz',zoo(2)%phi_sldon, 0.2*0.10) ! dimensionless + call g_tracer_add_param('phi_sldon_lgz',zoo(3)%phi_sldon, 0.2*0.0) ! dimensionless + call g_tracer_add_param('phi_sldop_smz',zoo(1)%phi_sldop, 0.2*0.20) ! dimensionless + call g_tracer_add_param('phi_sldop_mdz',zoo(2)%phi_sldop, 0.2*0.10) ! dimensionless + call g_tracer_add_param('phi_sldop_lgz',zoo(3)%phi_sldop, 0.2*0.0) ! dimensionless + call g_tracer_add_param('phi_nh4_smz',zoo(1)%phi_nh4, 0.30) ! dimensionless + call g_tracer_add_param('phi_nh4_mdz',zoo(2)%phi_nh4, 0.30) ! dimensionless + call g_tracer_add_param('phi_nh4_lgz',zoo(3)%phi_nh4, 0.30) ! dimensionless + call g_tracer_add_param('phi_po4_smz',zoo(1)%phi_po4, 0.30) ! dimensionless + call g_tracer_add_param('phi_po4_mdz',zoo(2)%phi_po4, 0.30) ! dimensionless + call g_tracer_add_param('phi_po4_lgz',zoo(3)%phi_po4, 0.30) ! dimensionless + ! + !---------------------------------------------------------------------- + ! Partitioning of viral losses to various dissolved pools + !---------------------------------------------------------------------- + ! + call g_tracer_add_param('phi_ldon_vir',cobalt%lysis_phi_ldon, 0.7) ! dimensionless + call g_tracer_add_param('phi_srdon_vir',cobalt%lysis_phi_srdon, 0.1) ! dimensionless + call g_tracer_add_param('phi_sldon_vir',cobalt%lysis_phi_sldon, 0.2) ! dimensionless + call g_tracer_add_param('phi_ldop_vir',cobalt%lysis_phi_ldop, 0.65) ! dimensionless + call g_tracer_add_param('phi_srdop_vir',cobalt%lysis_phi_srdop, 0.15) ! dimensionless + call g_tracer_add_param('phi_sldop_vir',cobalt%lysis_phi_sldop, 0.20) ! dimensionless + ! + !---------------------------------------------------------------------- + ! Parameters for unresolved higher predators + !---------------------------------------------------------------------- + ! + call g_tracer_add_param('imax_hp', cobalt%imax_hp, 0.09/sperd) ! s-1 + call g_tracer_add_param('ki_hp', cobalt%ki_hp, 1.25e-6) ! mol N kg-1 + call g_tracer_add_param('coef_hp', cobalt%coef_hp, 2.0) ! dimensionless + call g_tracer_add_param('ktemp_hp', cobalt%ktemp_hp, 0.063) ! C-1 + call g_tracer_add_param('nswitch_hp', cobalt%nswitch_hp, 2.0) ! dimensionless + call g_tracer_add_param('mswitch_hp', cobalt%mswitch_hp, 2.0) ! dimensionless + call g_tracer_add_param('hp_ipa_smp', cobalt%hp_ipa_smp, 0.0) ! dimensionless + call g_tracer_add_param('hp_ipa_lgp', cobalt%hp_ipa_lgp, 0.0) ! dimensionless + call g_tracer_add_param('hp_ipa_diaz', cobalt%hp_ipa_diaz, 0.0) ! dimensionless + call g_tracer_add_param('hp_ipa_smz', cobalt%hp_ipa_smz, 0.0) ! dimensionless + call g_tracer_add_param('hp_ipa_mdz', cobalt%hp_ipa_mdz, 1.0) ! dimensionless + call g_tracer_add_param('hp_ipa_lgz', cobalt%hp_ipa_lgz, 1.0) ! dimensionless + call g_tracer_add_param('hp_ipa_bact', cobalt%hp_ipa_bact,0.0) ! dimensionless + call g_tracer_add_param('hp_ipa_det', cobalt%hp_ipa_det, 0.0) ! dimensionless + call g_tracer_add_param('hp_phi_det', cobalt%hp_phi_det, 0.35) ! dimensionless + ! + !---------------------------------------------------------------------- + ! Iron chemistry + !---------------------------------------------------------------------- + ! + call g_tracer_add_param('felig_bkg', cobalt%felig_bkg, 0.5e-9) ! mol Fe kg-1 + call g_tracer_add_param('felig_2_don', cobalt%felig_2_don, 0.5e-3) ! mol lig mol N-1 + call g_tracer_add_param('fe_2_n_sed', cobalt%fe_2_n_sed, 100.0e-5 * 106 / 16) ! mol Fe mol N-1 + call g_tracer_add_param('ffe_sed_max', cobalt%ffe_sed_max, 170.0/1.0e6/sperd) ! mol Fe m-2 s-1 + call g_tracer_add_param('ffe_geotherm_ratio', cobalt%ffe_geotherm_ratio,2.0e-12) ! mol Fe m-2 s-1 (watt m-2)-1 + call g_tracer_add_param('ffe_iceberg_ratio', cobalt%ffe_iceberg_ratio,1.0e-7) ! mol Fe kg-1 ice melt + call g_tracer_add_param('fe_coast', cobalt%fe_coast,0.0 ) ! mol Fe m kg-1 s-1 + call g_tracer_add_param('alpha_fescav',cobalt%alpha_fescav, 0.0/spery) ! sec-1 + call g_tracer_add_param('beta_fescav',cobalt%beta_fescav, 2.5e9/spery ) ! sec-1 (mole ndet kg-1)-1 + call g_tracer_add_param('remin_eff_fedet',cobalt%remin_eff_fedet, 0.25) ! unitless + call g_tracer_add_param('io_fescav',cobalt%io_fescav, 10.0 ) ! watts m-2 + call g_tracer_add_param('kfe_eq_lig_ll',cobalt%kfe_eq_lig_ll, 1.0e12) ! mol lig-1 kg + call g_tracer_add_param('kfe_eq_lig_hl',cobalt%kfe_eq_lig_hl, 1.0e9) ! mol lig-1 kg + + ! Radiocarbon + call g_tracer_add_param('half_life_14c', cobalt%half_life_14c, 5730.0 ) ! s + call g_tracer_add_param('lambda_14c', cobalt%lambda_14c, log(2.0) / (cobalt%half_life_14c * spery)) ! s-1 + !------------------------------------------------------------------------- + ! Remineralization + !------------------------------------------------------------------------- + ! + call g_tracer_add_param('k_o2', cobalt%k_o2, 8.0e-6) ! mol O2 kg-1 + call g_tracer_add_param('o2_min', cobalt%o2_min, 0.8e-6 ) ! mol O2 kg-1 + call g_tracer_add_param('k_o2_nit', cobalt%k_o2_nit, k_o2_nit) ! mol O2 kg-1 + call g_tracer_add_param('o2_min_nit', cobalt%o2_min_nit, o2_min_nit ) ! mol O2 kg-1 + call g_tracer_add_param('kappa_remin', cobalt%kappa_remin, 0.063 ) ! deg C-1 + call g_tracer_add_param('remin_ramp_scale', cobalt%remin_ramp_scale, 50.0 ) ! m + call g_tracer_add_param('rpcaco3', cobalt%rpcaco3, 0.070/12.0*16.0/106.0*100.0) ! mol N mol Ca-1 + call g_tracer_add_param('rplith', cobalt%rplith, 0.065/12.0*16.0/106.0) ! mol N g lith-1 + call g_tracer_add_param('rpsio2', cobalt%rpsio2, 0.026/12.0*16.0/106.0*60.0) ! mol N mol Si-1 + call g_tracer_add_param('gamma_ndet', cobalt%gamma_ndet, cobalt%wsink / 350.0 ) ! s-1 + call g_tracer_add_param('gamma_cadet_arag',cobalt%gamma_cadet_arag,cobalt%wsink/760.0) ! s-1 + call g_tracer_add_param('gamma_cadet_calc',cobalt%gamma_cadet_calc,cobalt%wsink/1343.0) ! s-1 + call g_tracer_add_param('kappa_sidet', cobalt%kappa_sidet, 0.063 ) ! deg C -1 + call g_tracer_add_param('gamma_sidet', cobalt%gamma_sidet, cobalt%wsink / 1.0e4 ) ! s-1 + call g_tracer_add_param('phi_lith' , cobalt%phi_lith, 0.002) ! dimensionless + call g_tracer_add_param('k_lith', cobalt%k_lith, 0.5/spery ) ! s-1 + call g_tracer_add_param('z_sed', cobalt%z_sed, 0.1 ) ! m + call g_tracer_add_param('k_no3_denit',cobalt%k_no3_denit,1.0e-6) ! mol NO3 kg-1 + call g_tracer_add_param('z_burial',cobalt%z_burial,50.0) ! m + ! + !----------------------------------------------------------------------- + ! Calcium carbonate in sediments (see Dunne et al., 2012, GBC, 26) + !----------------------------------------------------------------------- + ! + call g_tracer_add_param('cased_steady', cobalt%cased_steady, .false. ) + call g_tracer_add_param('phi_surfresp_cased',cobalt%phi_surfresp_cased,0.14307) ! none + call g_tracer_add_param('phi_deepresp_cased',cobalt%phi_deepresp_cased,4.1228) ! none + call g_tracer_add_param('alpha_cased',cobalt%alpha_cased,2.7488) ! none + call g_tracer_add_param('beta_cased',cobalt%beta_cased,-2.2185) ! none + call g_tracer_add_param('gamma_cased',cobalt%gamma_cased,0.03607/spery) ! sec-1 + call g_tracer_add_param('Co_cased',cobalt%Co_cased,8.1e3) ! moles m-3 + ! + !----------------------------------------------------------------------- + ! Dissolved Organic Material + !----------------------------------------------------------------------- + ! + call g_tracer_add_param('gamma_srdon', cobalt%gamma_srdon, 1.0 / (10.0 * spery)) ! s-1 + call g_tracer_add_param('gamma_srdop', cobalt%gamma_srdop, 1.0 / (4.0 * spery)) ! s-1 + call g_tracer_add_param('gamma_sldon', cobalt%gamma_sldon, 1.0 / (90 * sperd)) ! s-1 + call g_tracer_add_param('gamma_sldop', cobalt%gamma_sldop, 1.0 / (90 * sperd)) ! s-1 + ! 2016/08/24 jgj add parameter for background dissolved organic material + ! For the oceanic carbon budget, a constant 42 uM of dissolved organic + ! carbon is added to represent the refractory component. + ! For the oceanic nitrogen budget, a constant 2 uM of dissolved organic + ! nitrogen is added to represent the refractory component. + ! 2016/09/22 jgj changed background DOC to 4.0e-5 per agreement with CAS, JPD + ! + call g_tracer_add_param('doc_background', cobalt%doc_background, 4.0e-5) ! uM + + !--------------------------------------------------------------------- + ! + !----------------------------------------------------------------------- + ! Nitrification + !----------------------------------------------------------------------- + ! + call g_tracer_add_param('gamma_nitrif', cobalt%gamma_nitrif, gamma_nitrif / (30.0 * sperd)) ! s-1 + call g_tracer_add_param('gamma_nitrif', cobalt%k_nh3_nitrif, k_nh3_nitrif ) ! moles kg-1 + call g_tracer_add_param('irr_inhibit', cobalt%irr_inhibit, irr_inhibit) ! W m-2 + ! + !----------------------------------------------------------------------- + ! Miscellaneous + !----------------------------------------------------------------------- + ! + call g_tracer_add_param('tracer_debug', cobalt%tracer_debug, .false.) + + call g_tracer_end_param_list(package_name) + !=========== + !Block Ends: g_tracer_add_param + !=========== + + end subroutine user_add_params + + subroutine user_add_tracers(tracer_list) + type(g_tracer_type), pointer :: tracer_list + character(len=fm_string_len), parameter :: sub_name = 'user_add_tracers' + real :: as_coeff_cobalt + + if ((trim(as_param_cobalt) == 'W92') .or. (trim(as_param_cobalt) == 'gfdl_cmip6')) then + ! Air-sea gas exchange coefficient presented in OCMIP2 protocol. + ! Value is 0.337 cm/hr in units of m/s. + as_coeff_cobalt=9.36e-7 + else + ! Value is 0.251 cm/hr in units of m/s + as_coeff_cobalt=6.972e-7 + endif + + ! + !Add here only the parameters that are required at the time of registeration + !(to make flux exchanging Ocean tracers known for all PE's) + ! + call g_tracer_start_param_list(package_name) + ! + call g_tracer_add_param('htotal_in', cobalt%htotal_in, 1.0e-08) + ! + ! Sinking velocity of detritus: a value of 20 m d-1 is consistent with a characteristic sinking + ! velocity of 100 m d-1 of marine aggregates and a disaggregation rate constant + ! of 5 d-1 in the surface ocean (Clegg and Whitfield, 1992; Dunne, 1999). Alternatively, 100 m d-1 + ! is more in line with the deep water synthesis of Berelson (2002; Particel settling rates increase + ! with depth in the ocean, DSR-II, 49, 237-252). + ! + call g_tracer_add_param('wsink', cobalt%wsink, 100.0 / sperd) ! m s-1 + + call g_tracer_add_param('ice_restart_file' , cobalt%ice_restart_file , 'ice_cobalt.res.nc') + call g_tracer_add_param('ocean_restart_file' , cobalt%ocean_restart_file , 'ocean_cobalt.res.nc' ) + call g_tracer_add_param('IC_file' , cobalt%IC_file , '') + ! + call g_tracer_end_param_list(package_name) + + ! Set Restart files + call g_tracer_set_files(ice_restart_file = cobalt%ice_restart_file,& + ocean_restart_file = cobalt%ocean_restart_file ) + + !All tracer fields shall be registered for diag output. + + !===================================================== + !Specify all prognostic tracers of this modules. + !===================================================== + !User adds one call for each prognostic tracer below! + !User should specify if fluxes must be extracted from boundary + !by passing one or more of the following methods as .true. + !and provide the corresponding parameters array + !methods: flux_gas,flux_runoff,flux_wetdep,flux_drydep + ! + !Pass an init_value arg if the tracers should be initialized to a nonzero value everywhere + !otherwise they will be initialized to zero. + ! + !=========================================================== + !Prognostic Tracers + !=========================================================== + ! + ! + ! ALK (Total carbonate alkalinity) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'alk', & + longname = 'Alkalinity', & + units = 'mol/kg', & + prog = .true., & + flux_runoff= .true., & + flux_param = (/ 1.0e-03 /), & + flux_bottom= .true. ) + ! + ! Aragonite (Sinking detrital/particulate CaCO3) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'cadet_arag', & + longname = 'Detrital CaCO3', & + units = 'mol/kg', & + prog = .true., & + sink_rate = cobalt%wsink, & + btm_reservoir = .true. ) + ! + ! Calcite + ! + call g_tracer_add(tracer_list,package_name,& + name = 'cadet_calc', & + longname = 'Detrital CaCO3', & + units = 'mol/kg', & + prog = .true., & + sink_rate = cobalt%wsink, & + btm_reservoir = .true. ) + ! + ! DIC (Dissolved inorganic carbon) + ! + call g_tracer_add(tracer_list,package_name, & + name = 'dic', & + longname = 'Dissolved Inorganic Carbon', & + units = 'mol/kg', & + prog = .true., & + flux_gas = .true., & + flux_gas_name = 'co2_flux', & + flux_gas_type = 'air_sea_gas_flux_generic', & + flux_gas_molwt = WTMCO2, & + flux_gas_param = (/ as_coeff_cobalt, 9.7561e-06 /), & + ! Uncomment for "no mass change" check + ! flux_gas_param = (/ 0.0, 0.0 /), & + flux_gas_restart_file = 'ocean_cobalt_airsea_flux.res.nc', & + flux_runoff= .true., & + flux_param = (/12.011e-03 /), & + flux_bottom= .true., & + init_value = 0.001 ) + ! + ! Dissolved Fe (assumed to be all available to phytoplankton) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'fed', & + longname = 'Dissolved Iron', & + units = 'mol/kg', & + prog = .true., & + flux_runoff= .true., & + flux_wetdep= .true., & + flux_drydep= .true., & + flux_param = (/ 55.847e-03 /), & + flux_bottom= .true. ) + ! + ! Fedet (Sinking detrital/particulate iron) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'fedet', & + longname = 'Detrital Iron', & + units = 'mol/kg', & + prog = .true., & + sink_rate = cobalt%wsink, & + btm_reservoir = .true. ) + ! + ! Diazotroph Fe (Iron in N2-fixing phytoplankton for variable Fe:N ratios) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'fedi', & + longname = 'Diazotroph Iron', & + units = 'mol/kg', & + prog = .true. ) + ! + ! Large Fe (Iron in large phytoplankton to allow for variable Fe:N ratios) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'felg', & + longname = 'Large Phytoplankton Iron', & + units = 'mol/kg', & + prog = .true. ) + ! + ! Small Fe + ! + call g_tracer_add(tracer_list,package_name,& + name = 'fesm', & + longname = 'Small Phytoplankton Iron', & + units = 'mol/kg', & + prog = .true. ) + ! + ! LDON (Labile dissolved organic nitrogen) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'ldon', & + flux_runoff= .true., & + longname = 'labile DON', & + units = 'mol/kg', & + prog = .true., & + flux_param = (/ 1.0e-3 /) ) + ! + ! LDOP (Labile dissolved organic phosphorous) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'ldop', & + flux_runoff= .true., & + longname = 'labile DOP', & + units = 'mol/kg', & + prog = .true., & + flux_param = (/ 1.0e-3 /) ) + ! + ! LITH (Lithogenic aluminosilicate particles) + ! + call g_tracer_add(tracer_list,package_name, & + name = 'lith', & + longname = 'Lithogenic Aluminosilicate', & + units = 'g/kg', & + prog = .true., & +! const_init_value = 0.0 , & + flux_runoff= .true., & + flux_wetdep= .true., & + flux_drydep= .true., & + flux_param = (/ 1.0e-03 /) ) + ! + ! LITHdet (Detrital Lithogenic aluminosilicate particles) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'lithdet', & + longname = 'lithdet', & + units = 'g/kg', & + prog = .true., & + sink_rate = cobalt%wsink, & + btm_reservoir = .true. ) + ! + ! NBact: Bacteria nitrogen + ! + call g_tracer_add(tracer_list,package_name,& + name = 'nbact', & + longname = 'bacterial', & + units = 'mol/kg', & + prog = .true. ) + ! + ! Ndet (Sinking detrital/particulate Nitrogen) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'ndet', & + longname = 'ndet', & + flux_runoff= .true., & + units = 'mol/kg', & + prog = .true., & + sink_rate = cobalt%wsink,& + btm_reservoir = .true., & + flux_param = (/ 1.0e-3 /) ) + ! + ! NDi (assumed to be facultative N2-fixers, with a variable N:P ratio + ! + call g_tracer_add(tracer_list,package_name,& + name = 'ndi', & + longname = 'Diazotroph Nitrogen', & + units = 'mol/kg', & + prog = .true. ) + + ! + ! NLg (assumed to be a dynamic combination of diatoms and other + ! eukaryotes all effectively greater than 5 um in diameter, + ! and having a fixed C:N ratio) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'nlg', & + longname = 'Large Phytoplankton Nitrogen', & + units = 'mol/kg', & + prog = .true. ) + ! + ! NSm (Nitrogen in picoplankton and nanoplankton + ! ~less than 5 um in diameter and having a fixed C:N:P ratio) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'nsm', & + longname = 'Small Phytoplankton Nitrogen', & + units = 'mol/kg', & + prog = .true. ) + ! + ! NH4 + ! + if (do_nh3_atm_ocean_exchange) then + if (mpp_root_pe().eq.mpp_pe()) then + write(*,*) 'setting up nh3 tracer (ocean)' + end if + call g_tracer_add(tracer_list,package_name,& + name = 'nh4', & + longname = 'Ammonia', & + units = 'mol/kg', & + prog = .true., & + flux_wetdep= .true., & + flux_drydep= .true., & + flux_gas = .true., & +! implementation='duce', & + implementation='johnson', & + flux_gas_name = 'nh3_flux', & + flux_gas_type = 'air_sea_gas_flux_generic', & + flux_gas_molwt = WTMN, & + flux_gas_param = (/ 17.,vb_nh3 /), & + flux_gas_restart_file = 'ocean_cobalt_airsea_flux.res.nc', & + flux_param = (/ WTMN*1.e-3/), & + flux_bottom= .true., & + init_value = 0.001 ) + else + if (mpp_root_pe().eq.mpp_pe()) then + write(*,*) 'setting up nh3 tracer (ocean - no exchange)' + end if + + call g_tracer_add(tracer_list,package_name,& + name = 'nh4', & + longname = 'Ammonia', & + units = 'mol/kg', & + prog = .true., & + flux_wetdep= .true., & + flux_drydep= .true., & + flux_param = (/ 14.0067e-03 /), & + flux_bottom= .true. ) + end if + ! + ! NO3 + ! + call g_tracer_add(tracer_list,package_name,& + name = 'no3', & + longname = 'Nitrate', & + units = 'mol/kg', & + prog = .true., & + flux_runoff= .true., & + flux_wetdep= .true., & + flux_drydep= .true., & + flux_param = (/ 14.0067e-03 /), & + flux_bottom= .true. ) + ! + ! O2 + ! + call g_tracer_add(tracer_list,package_name, & + name = 'o2', & + longname = 'Oxygen', & + units = 'mol/kg', & + prog = .true., & + flux_gas = .true., & + flux_gas_name = 'o2_flux', & + flux_gas_type = 'air_sea_gas_flux_generic', & + flux_gas_molwt = WTMO2, & + flux_gas_param = (/ as_coeff_cobalt, 9.7561e-06 /), & + flux_gas_restart_file = 'ocean_cobalt_airsea_flux.res.nc', & + flux_bottom= .true. ) + ! + ! Pdet (Sinking detrital/particulate Phosphorus) + ! + call g_tracer_add(tracer_list,package_name, & + name = 'pdet', & + longname = 'Detrital Phosphorus', & + units = 'mol/kg', & + prog = .true., & + sink_rate = cobalt%wsink, & + btm_reservoir = .true. ) + ! + ! PO4 + ! + call g_tracer_add(tracer_list,package_name,& + name = 'po4', & + longname = 'Phosphate', & + flux_runoff= .true., & + units = 'mol/kg', & + prog = .true., & + flux_wetdep= .true., & + flux_drydep= .true., & + flux_bottom= .true., & + flux_param = (/ 1.0e-3 /) ) + ! + ! SRDON (Semi-Refractory dissolved organic nitrogen) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'srdon', & + longname = 'Semi-Refractory DON', & + flux_runoff= .true., & + units = 'mol/kg', & + prog = .true., & + flux_param = (/ 1.0e-3 /) ) + ! + ! SRDOP (Semi-Refractory dissolved organic phosphorus) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'srdop', & + longname = 'Semi-Refractory DOP', & + flux_runoff= .true., & + units = 'mol/kg', & + prog = .true., & + flux_param = (/ 1.0e-3 /) ) + ! + ! SLDON (Semilabile dissolved organic nitrogen) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'sldon', & + longname = 'Semilabile DON', & + flux_runoff= .true., & + units = 'mol/kg', & + prog = .true., & + flux_param = (/ 1.0e-3 /) ) + ! + ! SLDOP (Semilabile dissolved organic phosphorus) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'sldop', & + longname = 'Semilabile DOP', & + flux_runoff= .true., & + units = 'mol/kg', & + prog = .true., & + flux_param = (/ 1.0e-3 /) ) + ! + ! Sidet (Sinking detrital/particulate Silicon) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'sidet', & + longname = 'Detrital Silicon', & + units = 'mol/kg', & + prog = .true., & + sink_rate = cobalt%wsink, & + btm_reservoir = .true. ) + ! + ! SiLg (Silicon in large phytoplankton for variable Si:N ratios + ! + call g_tracer_add(tracer_list,package_name,& + name = 'silg', & + longname = 'Large Phytoplankton Silicon', & + units = 'mol/kg', & + prog = .true. ) + ! + ! SiO4 + ! + call g_tracer_add(tracer_list,package_name,& + name = 'sio4', & + longname = 'Silicate', & + units = 'mol/kg', & + prog = .true., & + flux_bottom= .true. ) + + ! + ! Small zooplankton N + ! + call g_tracer_add(tracer_list,package_name,& + name = 'nsmz', & + longname = 'Small Zooplankton Nitrogen', & + units = 'mol/kg', & + prog = .true. ) + + ! + ! Medium-sized zooplankton N + ! + call g_tracer_add(tracer_list,package_name,& + name = 'nmdz', & + longname = 'Medium-sized zooplankton Nitrogen', & + units = 'mol/kg', & + prog = .true. ) + + ! + ! Large zooplankton N (Pred zoo + krill) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'nlgz', & + longname = 'large Zooplankton Nitrogen', & + units = 'mol/kg', & + prog = .true. ) + + if (do_14c) then !<> + + !=========================================================== + !Diagnostic Tracers + !=========================================================== + ! + ! Cased (CaCO3 in sediments) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'cased', & + longname = 'Sediment CaCO3', & + units = 'mol m-3', & + prog = .false. ) + ! + ! Chl (Chlorophyll) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'chl', & + longname = 'Chlorophyll', & + units = 'ug kg-1', & + prog = .false., & + init_value = 0.08 ) + ! + ! CO3_ion (Carbonate ion) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'co3_ion', & + longname = 'Carbonate ion', & + units = 'mol/kg', & + prog = .false. ) + ! + ! cadet_arag_btf (Aragonite flux to sediments) + ! + call g_tracer_add(tracer_list,package_name, & + name = 'cadet_arag_btf', & + longname = 'aragonite flux to Sediments', & + units = 'mol m-2 s-1', & + prog = .false. ) + ! + ! cadet_calc_btf (Calcite flux to sediments) + ! + call g_tracer_add(tracer_list,package_name, & + name = 'cadet_calc_btf', & + longname = 'calcite flux to Sediments', & + units = 'mol m-2 s-1', & + prog = .false. ) + ! + ! lithdet_btf (Lithogenic flux to sediments) + ! + call g_tracer_add(tracer_list,package_name, & + name = 'lithdet_btf', & + longname = 'Lith flux to Sediments', & + units = 'g m-2 s-1', & + prog = .false. ) + ! + ! ndet_btf (N flux to sediments) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'ndet_btf', & + longname = 'N flux to Sediments', & + units = 'mol m-2 s-1', & + prog = .false. ) + ! + ! pdet_btf (P flux to sediments) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'pdet_btf', & + longname = 'P flux to Sediments', & + units = 'mol m-2 s-1', & + prog = .false. ) + ! + ! sidet_btf (SiO2 flux to sediments) + ! + call g_tracer_add(tracer_list,package_name, & + name = 'sidet_btf', & + longname = 'SiO2 flux to Sediments', & + units = 'mol m-2 s-1', & + prog = .false. ) + ! + ! fedet_btf (Fe flux to sediments) + ! (only used in "no mass change" check) + call g_tracer_add(tracer_list,package_name, & + name = 'fedet_btf', & + longname = 'Fe flux to Sediments', & + units = 'mol m-2 s-1', & + prog = .false. ) + ! + ! htotal (H+ ion concentration) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'htotal', & + longname = 'H+ ion concentration', & + units = 'mol/kg', & + prog = .false., & + init_value = cobalt%htotal_in ) + ! + ! Irr_mem (Irradiance Memory) + ! + call g_tracer_add(tracer_list,package_name,& + name = 'irr_mem', & + longname = 'Irradiance memory', & + units = 'Watts/m^2', & + prog = .false. ) + + call g_tracer_add(tracer_list,package_name,& + name = 'mu_mem_ndi', & + longname = 'Growth memory', & + units = 'sec-1', & + prog = .false. ) + + call g_tracer_add(tracer_list,package_name,& + name = 'mu_mem_nlg', & + longname = 'Growth memory', & + units = 'sec-1', & + prog = .false. ) + + call g_tracer_add(tracer_list,package_name,& + name = 'mu_mem_nsm', & + longname = 'Growth memory', & + units = 'sec-1', & + prog = .false. ) + + if (do_nh3_atm_ocean_exchange .or. scheme_nitrif.eq.2 .or. scheme_nitrif.eq.3) then + call g_tracer_add(tracer_list,package_name,& + name = 'nh3', & + longname = 'NH3', & + units = 'mol/kg', & + prog = .false., & + init_value = 1.e-10 ) + end if + + end subroutine user_add_tracers + + + ! + ! + ! Modify the values obtained from the coupler if necessary. + ! + ! + ! Some tracer fields need to be modified after values are obtained from the coupler. + ! This subroutine is the place for specific tracer manipulations. + ! + ! + ! + ! Pointer to the head of generic tracer list. + ! + ! + subroutine generic_COBALT_update_from_coupler(tracer_list) + type(g_tracer_type), pointer :: tracer_list + + character(len=fm_string_len), parameter :: sub_name = 'generic_COBALT_update_from_copler' + + real, dimension(:,:) ,pointer :: stf_alk,dry_no3,wet_no3 + + ! + ! NO3 has deposition, river flux, and negative deposition contribution to alkalinity + ! + call g_tracer_get_pointer(tracer_list,'no3','drydep',dry_no3) + call g_tracer_get_pointer(tracer_list,'no3','wetdep',wet_no3) + + call g_tracer_get_pointer(tracer_list,'alk','stf',stf_alk) + + stf_alk = stf_alk - dry_no3 - wet_no3 ! update 'tracer%stf' thru pointer + + return + end subroutine generic_COBALT_update_from_coupler + + ! + ! + ! + ! Set values of bottom fluxes and reservoirs + ! + ! + ! + ! + ! This routine calculates bottom fluxes for tracers with bottom reservoirs. + ! It is called near the end of the time step, meaning that the fluxes + ! calculated pertain to the next time step. + ! + ! + ! + ! + ! + ! + ! + ! + ! Time step increment + ! + ! + ! Time step index to be used for %field + ! + ! + ! + subroutine generic_COBALT_update_from_bottom(tracer_list, dt, tau, model_time) + type(g_tracer_type), pointer :: tracer_list + real, intent(in) :: dt + integer, intent(in) :: tau + type(time_type), intent(in) :: model_time + integer :: isc,iec, jsc,jec,isd,ied,jsd,jed,nk,ntau + logical :: used + real, dimension(:,:,:),pointer :: grid_tmask + real, dimension(:,:,:),pointer :: temp_field + + call g_tracer_get_common(isc,iec,jsc,jec,isd,ied,jsd,jed,nk,ntau,grid_tmask=grid_tmask) + + ! + ! The bottom reservoirs of aragonite and calcite are immediately redistributed to the + ! water column as a bottom flux (btf) where they impact the alkalinity and DIC + ! + call g_tracer_get_values(tracer_list,'cadet_arag','btm_reservoir',cobalt%fcadet_arag_btm,isd,jsd) + cobalt%fcadet_arag_btm = cobalt%fcadet_arag_btm/dt + call g_tracer_get_pointer(tracer_list,'cadet_arag_btf','field',temp_field) + temp_field(:,:,1) = cobalt%fcadet_arag_btm(:,:) + call g_tracer_set_values(tracer_list,'cadet_arag','btm_reservoir',0.0) + if (cobalt%id_fcadet_arag_btm .gt. 0) & + used = g_send_data(cobalt%id_fcadet_arag_btm,cobalt%fcadet_arag_btm, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + + call g_tracer_get_values(tracer_list,'cadet_calc','btm_reservoir',cobalt%fcadet_calc_btm,isd,jsd) + cobalt%fcadet_calc_btm = cobalt%fcadet_calc_btm/dt + call g_tracer_get_pointer(tracer_list,'cadet_calc_btf','field',temp_field) + temp_field(:,:,1) = cobalt%fcadet_calc_btm(:,:) + call g_tracer_set_values(tracer_list,'cadet_calc','btm_reservoir',0.0) + if (cobalt%id_fcadet_calc_btm .gt. 0) & + used = g_send_data(cobalt%id_fcadet_calc_btm, cobalt%fcadet_calc_btm, & + model_time, rmask = grid_tmask(:,:,1), & + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + ! + ! Iron is buried, but can re-enter the water column in association with + ! organic matter degradation (see ffe_sed in update_from_source) + ! + call g_tracer_get_values(tracer_list,'fedet','btm_reservoir',cobalt%ffedet_btm,isd,jsd) + cobalt%ffedet_btm = cobalt%ffedet_btm/dt + ! uncomment for "no mass change check" + !call g_tracer_get_pointer(tracer_list,'fedet_btf','field',temp_field) + !temp_field(:,:,1) = cobalt%ffedet_btm(:,:) + call g_tracer_set_values(tracer_list,'fedet','btm_reservoir',0.0) + if (cobalt%id_ffedet_btm .gt. 0) & + used = g_send_data(cobalt%id_ffedet_btm, cobalt%ffedet_btm, & + model_time, rmask = grid_tmask(:,:,1), & + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + ! + ! Lithogenic material is buried + ! + call g_tracer_get_values(tracer_list,'lithdet','btm_reservoir',cobalt%flithdet_btm,isd,jsd) + cobalt%flithdet_btm = cobalt%flithdet_btm /dt + call g_tracer_get_pointer(tracer_list,'lithdet_btf','field',temp_field) + temp_field(:,:,1) = cobalt%flithdet_btm(:,:) + call g_tracer_set_values(tracer_list,'lithdet','btm_reservoir',0.0) + if (cobalt%id_flithdet_btm .gt. 0) & + used = g_send_data(cobalt%id_flithdet_btm, cobalt%flithdet_btm, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + ! + ! N, P, and Si detritus that hits the bottom is re-entered as a bottom source of + ! nh4, po4, and SiO4 respectively + ! + call g_tracer_get_values(tracer_list,'ndet','btm_reservoir',cobalt%fndet_btm,isd,jsd) + cobalt%fndet_btm = cobalt%fndet_btm/dt + call g_tracer_get_pointer(tracer_list,'ndet_btf','field',temp_field) + temp_field(:,:,1) = cobalt%fndet_btm(:,:) + call g_tracer_set_values(tracer_list,'ndet','btm_reservoir',0.0) + if (cobalt%id_fndet_btm .gt. 0) & + used = g_send_data(cobalt%id_fndet_btm,cobalt%fndet_btm, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + + call g_tracer_get_values(tracer_list,'pdet','btm_reservoir',cobalt%fpdet_btm,isd,jsd) + cobalt%fpdet_btm = cobalt%fpdet_btm/dt + call g_tracer_get_pointer(tracer_list,'pdet_btf','field',temp_field) + temp_field(:,:,1) = cobalt%fpdet_btm(:,:) + call g_tracer_set_values(tracer_list,'pdet','btm_reservoir',0.0) + if (cobalt%id_fpdet_btm .gt. 0) & + used = g_send_data(cobalt%id_fpdet_btm,cobalt%fpdet_btm, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + + call g_tracer_get_values(tracer_list,'sidet','btm_reservoir',cobalt%fsidet_btm,isd,jsd) + cobalt%fsidet_btm = cobalt%fsidet_btm/dt + call g_tracer_get_pointer(tracer_list,'sidet_btf','field',temp_field) + temp_field(:,:,1) = cobalt%fsidet_btm(:,:) + call g_tracer_set_values(tracer_list,'sidet','btm_reservoir',0.0) + if (cobalt%id_fsidet_btm .gt. 0) & + used = g_send_data(cobalt%id_fsidet_btm, cobalt%fsidet_btm, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + + end subroutine generic_COBALT_update_from_bottom + + ! + ! + ! Update tracer concentration fields due to the source/sink contributions. + ! + ! + ! This is the subroutine to contain most of the biogeochemistry for calculating the + ! interaction of tracers with each other and with outside forcings. + ! + ! + ! + ! Pointer to the head of generic tracer list. + ! + ! + ! Lower bounds of x and y extents of input arrays on data domain + ! + ! + ! Ocean temperature + ! + ! + ! Ocean salinity + ! + ! + ! Ocean layer thickness (meters) + ! + ! + ! Ocean opacity + ! + ! + ! Shortwave peneteration + ! + ! + ! + ! + ! + ! Grid area + ! + ! + ! Time step index of %field + ! + ! + ! Time step increment + ! + ! + subroutine generic_COBALT_update_from_source(tracer_list,Temp,Salt,rho_dzt,dzt,hblt_depth,& + ilb,jlb,tau,dt,grid_dat,model_time,nbands,max_wavelength_band,sw_pen_band,opacity_band,internal_heat,frunoff) + + type(g_tracer_type), pointer :: tracer_list + real, dimension(ilb:,jlb:,:), intent(in) :: Temp,Salt,rho_dzt,dzt + real, dimension(ilb:,jlb:), intent(in) :: hblt_depth + integer, intent(in) :: ilb,jlb,tau + real, intent(in) :: dt + real, dimension(ilb:,jlb:), intent(in) :: grid_dat + type(time_type), intent(in) :: model_time + + integer, intent(in) :: nbands + real, dimension(:), intent(in) :: max_wavelength_band + real, dimension(:,ilb:,jlb:), intent(in) :: sw_pen_band + real, dimension(:,ilb:,jlb:,:), intent(in) :: opacity_band + real, dimension(ilb:,jlb:), intent(in) :: internal_heat + real, dimension(ilb:,jlb:), intent(in) :: frunoff + + character(len=fm_string_len), parameter :: sub_name = 'generic_COBALT_update_from_source' + integer :: isc,iec, jsc,jec,isd,ied,jsd,jed,nk,ntau, i, j, k , kblt, m, n, k_100, k_200, kbot + real, dimension(:,:,:) ,pointer :: grid_tmask + integer, dimension(:,:),pointer :: mask_coast,grid_kmt + ! + !------------------------------------------------------------------------ + ! Local Variables + !------------------------------------------------------------------------ + ! + logical :: used, first + integer :: nb + real :: r_dt + real :: feprime_temp + real :: juptake_di_tot2nterm + real :: log_btm_flx + real :: P_C_m + real :: p_lim_nhet + real :: TK, PRESS, PKSPA, PKSPC + real :: tmp_hblt, tmp_irrad, tmp_irrad_ML,tmp_opacity,tmp_mu_ML + real :: drho_dzt + real, dimension(:), Allocatable :: tmp_irr_band + real, dimension(:,:), Allocatable :: rho_dzt_100, rho_dzt_200 + real, dimension(:,:,:), Allocatable :: z_remin_ramp + real,dimension(1:NUM_ZOO,1:NUM_PREY) :: ipa_matrix,pa_matrix,ingest_matrix + real,dimension(1:NUM_PREY) :: hp_ipa_vec,hp_pa_vec,hp_ingest_vec + real,dimension(1:NUM_PREY) :: prey_vec,prey_p2n_vec,prey_fe2n_vec,prey_si2n_vec + real,dimension(1:NUM_ZOO) :: tot_prey + real :: tot_prey_hp, sw_fac_denom, assim_eff + real :: bact_uptake_ratio, vmax_bact, growth_ratio + real :: fpoc_btm, log_fpoc_btm + real :: fe_salt + real :: sal,tt,tkb,ts,ts2,ts3,ts4,ts5 + + logical :: phos_nh3_override + + real, dimension(:,:,:), Allocatable :: pre_totn, net_srcn, post_totn + real, dimension(:,:,:), Allocatable :: pre_totp, post_totp + real, dimension(:,:,:), Allocatable :: pre_totsi, post_totsi + real, dimension(:,:,:), Allocatable :: pre_totfe, net_srcfe, post_totfe + real, dimension(:,:,:), Allocatable :: pre_totc, net_srcc, post_totc + real, dimension(:,:), Allocatable :: pka_nh3,phos_nh3_exchange + + real :: tr,ltr + real :: imbal + integer :: stdoutunit, imbal_flag, outunit + + + r_dt = 1.0 / dt + + call g_tracer_get_common(isc,iec,jsc,jec,isd,ied,jsd,jed,nk,ntau,& + grid_tmask=grid_tmask,grid_mask_coast=mask_coast,grid_kmt=grid_kmt) + + call mpp_clock_begin(id_clock_carbon_calculations) + !Get necessary fields + call g_tracer_get_values(tracer_list,'htotal','field', cobalt%f_htotal,isd,jsd,ntau=1) + call g_tracer_get_values(tracer_list,'po4' ,'field', cobalt%f_po4,isd,jsd,ntau=tau) + call g_tracer_get_values(tracer_list,'sio4' ,'field', cobalt%f_sio4,isd,jsd,ntau=tau) + call g_tracer_get_values(tracer_list,'alk' ,'field', cobalt%f_alk,isd,jsd,ntau=tau) + call g_tracer_get_values(tracer_list,'dic' ,'field', cobalt%f_dic ,isd,jsd,ntau=tau) + if (do_nh3_diag) then + allocate(pka_nh3(isd:ied,jsd:jed)) + pka_nh3 = 0. + call g_tracer_get_values(tracer_list,'nh4' ,'field', cobalt%f_nh4 ,isd,jsd,ntau=tau) + end if + allocate(phos_nh3_exchange(isd:ied,jsd:jed)) + + !--------------------------------------------------------------------- + !Calculate co3_ion + !Also calculate co2 fluxes csurf and alpha for the next round of exchange + !--------------------------------------------------------------------- + + cobalt%zt = 0.0 + cobalt%zm = 0.0 + do j = jsc, jec ; do i = isc, iec !{ + cobalt%zt(i,j,1) = dzt(i,j,1) + cobalt%zm(i,j,1) = 0.5*dzt(i,j,1) + enddo; enddo !} i,j + + do k = 2, nk ; do j = jsc, jec ; do i = isc, iec !{ + cobalt%zt(i,j,k) = cobalt%zt(i,j,k-1) + dzt(i,j,k) + cobalt%zm(i,j,k) = cobalt%zm(i,j,k-1) + dzt(i,j,k) + enddo; enddo ; enddo !} i,j,k + + k=1 + do j = jsc, jec ; do i = isc, iec !{ + cobalt%htotallo(i,j) = cobalt%htotal_scale_lo * cobalt%f_htotal(i,j,k) + cobalt%htotalhi(i,j) = cobalt%htotal_scale_hi * cobalt%f_htotal(i,j,k) + enddo; enddo ; !} i, j + + + call FMS_ocmip2_co2calc(CO2_dope_vec,grid_tmask(:,:,k),& + Temp(:,:,k), Salt(:,:,k), & + cobalt%f_dic(:,:,k), & + cobalt%f_po4(:,:,k), & + cobalt%f_sio4(:,:,k), & + cobalt%f_alk(:,:,k), & + cobalt%htotallo, cobalt%htotalhi,& + !InOut + cobalt%f_htotal(:,:,k), & + !Optional In + co2_calc=trim(co2_calc), & + zt=cobalt%zt(:,:,k), & + !OUT + co2star=cobalt%co2_csurf(:,:), alpha=cobalt%co2_alpha(:,:), & + pCO2surf=cobalt%pco2_csurf(:,:), & + co3_ion=cobalt%f_co3_ion(:,:,k), & + omega_arag=cobalt%omegaa(:,:,k), & + omega_calc=cobalt%omegac(:,:,k)) + + do k = 2, nk + do j = jsc, jec ; do i = isc, iec !{ + cobalt%htotallo(i,j) = cobalt%htotal_scale_lo * cobalt%f_htotal(i,j,k) + cobalt%htotalhi(i,j) = cobalt%htotal_scale_hi * cobalt%f_htotal(i,j,k) + enddo; enddo ; !} i, j + + call FMS_ocmip2_co2calc(CO2_dope_vec,grid_tmask(:,:,k),& + Temp(:,:,k), Salt(:,:,k), & + cobalt%f_dic(:,:,k), & + cobalt%f_po4(:,:,k), & + cobalt%f_sio4(:,:,k), & + cobalt%f_alk(:,:,k), & + cobalt%htotallo, cobalt%htotalhi,& + !InOut + cobalt%f_htotal(:,:,k), & + !Optional In + co2_calc=trim(co2_calc), & + zt=cobalt%zt(:,:,k), & + !OUT + co3_ion=cobalt%f_co3_ion(:,:,k), & + omega_arag=cobalt%omegaa(:,:,k), & + omega_calc=cobalt%omegac(:,:,k)) + enddo + + call g_tracer_set_values(tracer_list,'htotal','field',cobalt%f_htotal ,isd,jsd,ntau=1) + call g_tracer_set_values(tracer_list,'co3_ion','field',cobalt%f_co3_ion ,isd,jsd,ntau=1) + call g_tracer_set_values(tracer_list,'dic','alpha',cobalt%co2_alpha ,isd,jsd) + call g_tracer_set_values(tracer_list,'dic','csurf',cobalt%co2_csurf ,isd,jsd) + + call mpp_clock_end(id_clock_carbon_calculations) + + if (do_nh3_atm_ocean_exchange) then + !to override pH used for ocean nh3 exchange + call data_override('OCN', 'phos_nh3_exchange', phos_nh3_exchange(isc:iec,jsc:jec), model_time,override=phos_nh3_override) + + do j = jsc, jec ; do i = isc, iec + pka_nh3(i,j) = calc_pka_nh3(temp(i,j,1),salt(i,j,1))*grid_tmask(i,j,1) + tr = 298.15/(temp(i,j,1)+273.15)-1. + ltr = -tr+log(298.15/(temp(i,j,1)+273.15)) + !f1p + !henry's constant is from Mark Jacobson's book "Fundamental of Atmospheric Modeling". + !I used this expression to be consistent with the cloud chemistry module + !the units are in mol/kg/atm. However it's probably in mol/kg(pure water)/atm + !the density of pure water is ~997 kg/m3 (25C). alpha will then be scaled by the density of seawater, which for some reason I don't quite understand is always set to 1035. + !to be consistent, I am scaling the Jacobson's number by 997/1035. This decreases the solubility of NH3 by less than 4%. For references, Sander's estimate is alpha(298.15)=0.59*101.63=59.96 mol/kg(pure water)/atm, about 4% greater than Jacobson's. + cobalt%nh3_alpha(i,j) = 5.76e1*exp(13.79*tr-5.39*ltr)*997. + !apply salinity correction + cobalt%nh3_alpha(i,j) = cobalt%nh3_alpha(i,j)/saltout_correction(101325./(1.e-3*rdgas*wtmair*(temp(i,j,1)+273.15)*cobalt%nh3_alpha(i,j)),vb_nh3,salt(i,j,1))* 1./cobalt%Rho_0 !mol/kg/atm + if (phos_nh3_override) then + cobalt%nh3_csurf(i,j) = cobalt%f_nh4(i,j,1)/(1.+10**(pka_nh3(i,j)-max(min(phos_nh3_exchange(i,j),11.),3.))) !in mol/kg + else + cobalt%nh3_csurf(i,j) = cobalt%f_nh4(i,j,1)/(1.+10**(pka_nh3(i,j)+log10(min(max(cobalt%f_htotal(i,j,1),1e-11),1e-3)))) !in mol/kg + end if + cobalt%pnh3_csurf(i,j) = cobalt%nh3_csurf(i,j)/cobalt%nh3_alpha(i,j)*1.e6 !in uatm + enddo; enddo ; ! + + call g_tracer_set_values(tracer_list,'nh4','alpha',cobalt%nh3_alpha ,isd,jsd) + call g_tracer_set_values(tracer_list,'nh4','csurf',cobalt%nh3_csurf ,isd,jsd) + end if + + if (do_14c) then !<> + + !--------------------------------------------------------------------- + ! Get positive tracer concentrations + !--------------------------------------------------------------------- + + call mpp_clock_begin(id_clock_phyto_growth) + + call g_tracer_get_values(tracer_list,'cadet_arag','field',cobalt%f_cadet_arag ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'cadet_calc','field',cobalt%f_cadet_calc ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'fed' ,'field',cobalt%f_fed ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'fedet' ,'field',cobalt%f_fedet ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'ldon' ,'field',cobalt%f_ldon ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'ldop' ,'field',cobalt%f_ldop ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'lith' ,'field',cobalt%f_lith ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'lithdet','field',cobalt%f_lithdet ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'ndet' ,'field',cobalt%f_ndet ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'nh4' ,'field',cobalt%f_nh4 ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'no3' ,'field',cobalt%f_no3 ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'o2' ,'field',cobalt%f_o2 ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'pdet' ,'field',cobalt%f_pdet ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'po4' ,'field',cobalt%f_po4 ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'srdon' ,'field',cobalt%f_srdon ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'srdop' ,'field',cobalt%f_srdop ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'sldon' ,'field',cobalt%f_sldon ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'sldop' ,'field',cobalt%f_sldop ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'sidet' ,'field',cobalt%f_sidet ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'sio4' ,'field',cobalt%f_sio4 ,isd,jsd,ntau=tau,positive=.true.) +! + ! phytoplankton fields + ! + call g_tracer_get_values(tracer_list,'fedi' ,'field',phyto(DIAZO)%f_fe(:,:,:) ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'felg' ,'field',phyto(LARGE)%f_fe(:,:,:) ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'fesm','field',phyto(SMALL)%f_fe(:,:,:),isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'ndi' ,'field',phyto(DIAZO)%f_n(:,:,:) ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'nlg' ,'field',phyto(LARGE)%f_n(:,:,:) ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'nsm' ,'field',phyto(SMALL)%f_n(:,:,:),isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'silg' ,'field',cobalt%f_silg ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'mu_mem_ndi' ,'field',phyto(DIAZO)%f_mu_mem,isd,jsd,ntau=1) + call g_tracer_get_values(tracer_list,'mu_mem_nlg' ,'field',phyto(LARGE)%f_mu_mem,isd,jsd,ntau=1) + call g_tracer_get_values(tracer_list,'mu_mem_nsm' ,'field',phyto(SMALL)%f_mu_mem,isd,jsd,ntau=1) + ! + ! zooplankton fields + ! + call g_tracer_get_values(tracer_list,'nsmz' ,'field',zoo(1)%f_n(:,:,:) ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'nmdz' ,'field',zoo(2)%f_n(:,:,:) ,isd,jsd,ntau=tau,positive=.true.) + call g_tracer_get_values(tracer_list,'nlgz' ,'field',zoo(3)%f_n(:,:,:) ,isd,jsd,ntau=tau,positive=.true.) + ! + ! bacteria + ! + call g_tracer_get_values(tracer_list,'nbact' ,'field',bact(1)%f_n(:,:,:) ,isd,jsd,ntau=tau,positive=.true.) + ! + ! diagnostic tracers that are passed between time steps (except chlorophyll) + ! + call g_tracer_get_values(tracer_list,'cased' ,'field',cobalt%f_cased ,isd,jsd,ntau=1) + call g_tracer_get_values(tracer_list,'co3_ion','field',cobalt%f_co3_ion ,isd,jsd,ntau=1,positive=.true.) + call g_tracer_get_values(tracer_list,'cadet_arag_btf','field',cobalt%f_cadet_arag_btf,isd,jsd,ntau=1) + call g_tracer_get_values(tracer_list,'cadet_calc_btf','field',cobalt%f_cadet_calc_btf,isd,jsd,ntau=1) + call g_tracer_get_values(tracer_list,'lithdet_btf','field',cobalt%f_lithdet_btf,isd,jsd,ntau=1) + call g_tracer_get_values(tracer_list,'ndet_btf','field',cobalt%f_ndet_btf,isd,jsd,ntau=1) + call g_tracer_get_values(tracer_list,'pdet_btf','field',cobalt%f_pdet_btf,isd,jsd,ntau=1) + call g_tracer_get_values(tracer_list,'sidet_btf','field',cobalt%f_sidet_btf,isd,jsd,ntau=1) + ! uncomment for "no mass change" test + !call g_tracer_get_values(tracer_list,'fedet_btf','field',cobalt%f_fedet_btf,isd,jsd,ntau=1) + call g_tracer_get_values(tracer_list,'irr_mem','field',cobalt%f_irr_mem ,isd,jsd,ntau=1) + + ! zero out cumulative COBALT-wide production diagnostics + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec + cobalt%jprod_fed(i,j,k) = 0.0 + cobalt%jprod_fedet(i,j,k) = 0.0 + cobalt%jprod_ndet(i,j,k) = 0.0 + cobalt%jprod_pdet(i,j,k) = 0.0 + cobalt%jprod_sldon(i,j,k) = 0.0 + cobalt%jprod_ldon(i,j,k) = 0.0 + cobalt%jprod_srdon(i,j,k) = 0.0 + cobalt%jprod_sldop(i,j,k) = 0.0 + cobalt%jprod_ldop(i,j,k) = 0.0 + cobalt%jprod_srdop(i,j,k) = 0.0 + cobalt%jprod_sidet(i,j,k) = 0.0 + cobalt%jprod_sio4(i,j,k) = 0.0 + cobalt%jprod_po4(i,j,k) = 0.0 + cobalt%jprod_nh4(i,j,k) = 0.0 + cobalt%jno3denit_wc(i,j,k) = 0.0 +! added jgj - do we need it every timestep + cobalt%jremin_ndet(i,j,k) = 0.0 + cobalt%jo2resp_wc(i,j,k) = 0.0 + enddo; enddo ; enddo !} i,j,k +! +!----------------------------------------------------------------------------------- +! 1: Phytoplankton growth and nutrient uptake calculations +!----------------------------------------------------------------------------------- +! + ! + !----------------------------------------------------------------------------------- + ! 1.1: Nutrient Limitation Calculations + !----------------------------------------------------------------------------------- + ! + ! Calculate iron cell quota + ! + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec + do n = 1,NUM_PHYTO !{ + phyto(n)%q_fe_2_n(i,j,k) = max(0.0, phyto(n)%f_fe(i,j,k)/ & + max(epsln,phyto(n)%f_n(i,j,k))) + phyto(n)%q_p_2_n(i,j,k) = phyto(n)%p_2_n_static + enddo !} n + ! + ! N limitation with NH4 inhibition after Frost and Franzen (1992) + ! (Note nitrate does not limit diazotroph growth but uptake limitation is used + ! to determine nitrogen fixation versus facultative no3/nh4 uptake, see Sec. 1.3) + do n= 1, NUM_PHYTO !{ + if (scheme_no3_nh4_lim .eq. 1) then + phyto(n)%no3lim(i,j,k) = cobalt%f_no3(i,j,k) / & + ( (phyto(n)%k_no3+cobalt%f_no3(i,j,k)) * (1.0 + cobalt%f_nh4(i,j,k)/phyto(n)%k_nh4) ) + phyto(n)%nh4lim(i,j,k) = cobalt%f_nh4(i,j,k) / (phyto(n)%k_nh4 + cobalt%f_nh4(i,j,k)) + elseif (scheme_no3_nh4_lim .eq. 2) then + phyto(n)%no3lim(i,j,k) = cobalt%f_no3(i,j,k) / & + ( phyto(n)%k_no3+cobalt%f_no3(i,j,k)+phyto(n)%k_no3/phyto(n)%k_nh4*cobalt%f_nh4(i,j,k) ) + phyto(n)%nh4lim(i,j,k) = cobalt%f_nh4(i,j,k) / & + ( phyto(n)%k_nh4+cobalt%f_nh4(i,j,k)+phyto(n)%k_nh4/phyto(n)%k_no3*cobalt%f_no3(i,j,k) ) + end if + enddo !} n + ! + ! O2 inhibition term for diazotrophs + ! + n = DIAZO + phyto(n)%o2lim(i,j,k) = (1.0 - cobalt%f_o2(i,j,k)**cobalt%o2_inhib_Di_pow / & + (cobalt%f_o2(i,j,k)**cobalt%o2_inhib_Di_pow+cobalt%o2_inhib_Di_sat**cobalt%o2_inhib_Di_pow)) + ! + ! SiO4, PO4 and Fe uptake limitation with Michaelis-Mentin + ! + phyto(LARGE)%silim(i,j,k) = cobalt%f_sio4(i,j,k) / (phyto(LARGE)%k_sio4 + cobalt%f_sio4(i,j,k)) + do n= 1, NUM_PHYTO !{ + phyto(n)%po4lim(i,j,k) = cobalt%f_po4(i,j,k) / (phyto(n)%k_po4 + cobalt%f_po4(i,j,k)) + phyto(n)%felim(i,j,k) = cobalt%f_fed(i,j,k) / (phyto(n)%k_fed + cobalt%f_fed(i,j,k)) + phyto(n)%def_fe(i,j,k) = phyto(n)%q_fe_2_n(i,j,k)**2.0 / (phyto(n)%k_fe_2_n**2.0 + & + phyto(n)%q_fe_2_n(i,j,k)**2.0) + enddo !} n + enddo; enddo ; enddo !} i,j,k + ! + ! Calculate nutrient limitation based on the most limiting nutrient (liebig_lim) + ! + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + n=DIAZO + phyto(n)%liebig_lim(i,j,k) = phyto(n)%o2lim(i,j,k)* & + min(phyto(n)%po4lim(i,j,k), phyto(n)%def_fe(i,j,k)) + do n= 2, NUM_PHYTO !{ + phyto(n)%liebig_lim(i,j,k) = min(phyto(n)%no3lim(i,j,k)+phyto(n)%nh4lim(i,j,k),& + phyto(n)%po4lim(i,j,k), phyto(n)%def_fe(i,j,k)) + enddo !} n + enddo; enddo ; enddo !} i,j,k + ! + !----------------------------------------------------------------------- + ! 1.2: Light Limitation/Growth Calculations + !----------------------------------------------------------------------- + ! + ! Create relevant light fields based on incident radiation and opacity + ! information passed from the ocean code + ! + allocate(tmp_irr_band(nbands)) + do j = jsc, jec ; do i = isc, iec !{ + + do nb=1,nbands !{ + if (max_wavelength_band(nb) .lt. 710.0) then !{ + tmp_irr_band(nb) = sw_pen_band(nb,i,j) + else + tmp_irr_band(nb) = 0.0 + endif !} + enddo !} + + kblt = 0 ; tmp_irrad_ML = 0.0 ; tmp_hblt = 0.0 + do k = 1, nk !{ + tmp_irrad = 0.0 + do nb=1,nbands !{ + tmp_opacity = opacity_band(nb,i,j,k) + tmp_irrad = tmp_irrad + max(0.0,tmp_irr_band(nb) * exp(-tmp_opacity * dzt(i,j,k) * 0.5)) + ! Change tmp_irr_band from being the value at the middle of layer k to the value + ! at the bottom of layer k. + tmp_irr_band(nb) = tmp_irr_band(nb) * exp(-tmp_opacity * dzt(i,j,k)) + enddo !} + cobalt%irr_inst(i,j,k) = tmp_irrad * grid_tmask(i,j,k) + cobalt%irr_mix(i,j,k) = tmp_irrad * grid_tmask(i,j,k) + if ((k == 1) .or. (tmp_hblt .lt. hblt_depth(i,j))) then !{ + kblt = kblt+1 + tmp_irrad_ML = tmp_irrad_ML + cobalt%irr_mix(i,j,k) * dzt(i,j,k) + tmp_hblt = tmp_hblt + dzt(i,j,k) + endif !} + enddo !} k-loop + cobalt%irr_mix(i,j,1:kblt) = tmp_irrad_ML / max(1.0e-6,tmp_hblt) + enddo; enddo !} i,j + + deallocate(tmp_irr_band) + ! + ! Calculate the temperature limitation (expkT) and the time integrated + ! irradiance (f_irr_mem) to which the Chl:C ratio responds (~24 hours) + ! + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + cobalt%expkT(i,j,k) = exp(cobalt%kappa_eppley * Temp(i,j,k)) + cobalt%f_irr_mem(i,j,k) = (cobalt%f_irr_mem(i,j,k) + (cobalt%irr_mix(i,j,k) - & + cobalt%f_irr_mem(i,j,k)) * min(1.0,cobalt%gamma_irr_mem * dt)) * grid_tmask(i,j,k) + enddo; enddo ; enddo !} i,j,k + + !nh3 + if (do_nh3_diag) then + cobalt%f_nh3(:,:,:) = 0. + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + cobalt%f_nh3(i,j,k) = cobalt%f_nh4(i,j,k)/(1.+10**(calc_pka_nh3(temp(i,j,k),salt(i,j,k))+log10(min(max(cobalt%f_htotal(i,j,1),1e-10),1e-5)))) * grid_tmask(i,j,k) + enddo; enddo ; enddo !} i,j,k + end if + + + ! + ! Phytoplankton growth rate calculation based on Geider et al. (1997) + ! + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + cobalt%f_chl(i,j,k) = 0.0 + + do n = 1, NUM_PHYTO !{ + P_C_m = max(phyto(n)%liebig_lim(i,j,k)*phyto(n)%P_C_max*cobalt%expkT(i,j,k),epsln) + phyto(n)%theta(i,j,k) = (phyto(n)%thetamax-cobalt%thetamin) / (1.0 + & + phyto(n)%thetamax*phyto(n)%alpha*cobalt%f_irr_mem(i,j,k)*0.5 / & + P_C_m) + cobalt%thetamin + cobalt%f_chl(i,j,k) = cobalt%f_chl(i,j,k)+cobalt%c_2_n*12.0e6*phyto(n)%theta(i,j,k)* & + phyto(n)%f_n(i,j,k) + phyto(n)%irrlim(i,j,k) = (1.0-exp(-phyto(n)%alpha*cobalt%irr_inst(i,j,k)* & + phyto(n)%theta(i,j,k)/P_C_m)) + + ! calculate the growth rate + phyto(n)%mu(i,j,k) = P_C_m / (1.0 + cobalt%zeta) * phyto(n)%irrlim(i,j,k) - & + cobalt%expkT(i,j,k)*phyto(n)%bresp* & + phyto(n)%f_n(i,j,k)/(cobalt%refuge_conc + phyto(n)%f_n(i,j,k)) + + ! calculate net production by phytoplankton group + phyto(n)%jprod_n(i,j,k) = phyto(n)%mu(i,j,k)*phyto(n)%f_n(i,j,k) + + phyto(n)%mu_mix(i,j,k) = phyto(n)%mu(i,j,k) + + enddo !} n + + enddo; enddo ; enddo !} i,j,k + + do j = jsc, jec ; do i = isc, iec ; do n = 1,NUM_PHYTO !{ + kblt = 0 ; tmp_mu_ML = 0.0 ; tmp_hblt = 0.0 + do k = 1, nk !{ + if ((k == 1) .or. (tmp_hblt .lt. hblt_depth(i,j))) then !{ + kblt = kblt+1 + tmp_mu_ML = tmp_mu_ML + phyto(n)%mu_mix(i,j,k) * dzt(i,j,k) + tmp_hblt = tmp_hblt + dzt(i,j,k) + endif !} + enddo !} k-loop + phyto(n)%mu_mix(i,j,1:kblt) = tmp_mu_ML / max(epsln,tmp_hblt) + enddo; enddo; enddo !} i,j,n + + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec; do n = 1,NUM_PHYTO !{ + phyto(n)%f_mu_mem(i,j,k) = phyto(n)%f_mu_mem(i,j,k) + (phyto(n)%mu_mix(i,j,k) - & + phyto(n)%f_mu_mem(i,j,k))*min(1.0,cobalt%gamma_mu_mem*dt)*grid_tmask(i,j,k) + enddo; enddo ; enddo; enddo !} i,j,k,n + + !----------------------------------------------------------------------- + ! 1.3: Nutrient uptake calculations + !----------------------------------------------------------------------- + ! + ! Uptake of nitrate and ammonia + ! + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + n = DIAZO + phyto(n)%juptake_n2(i,j,k) = max(0.0,(1.0 - phyto(n)%no3lim(i,j,k) - phyto(n)%nh4lim(i,j,k))* & + phyto(n)%mu(i,j,k)*phyto(n)%f_n(i,j,k)) + phyto(n)%juptake_nh4(i,j,k) = max(0.0,phyto(n)%nh4lim(i,j,k)* phyto(n)%mu(i,j,k)*phyto(n)%f_n(i,j,k)) + phyto(n)%juptake_no3(i,j,k) = max(0.0,phyto(n)%no3lim(i,j,k)* phyto(n)%mu(i,j,k)*phyto(n)%f_n(i,j,k)) + ! uncomment for "no mass change" test (next 2 lines) + ! phyto(n)%juptake_nh4(i,j,k) = phyto(n)%juptake_nh4(i,j,k) + phyto(n)%juptake_n2(i,j,k) + ! phyto(n)%juptake_n2(i,j,k) = 0.0 + + ! If growth is negative, results in net respiration and production of nh4, aerobic loss in all cases + cobalt%jprod_nh4(i,j,k) = cobalt%jprod_nh4(i,j,k) - min(0.0,phyto(n)%mu(i,j,k)*phyto(n)%f_n(i,j,k)) + cobalt%jo2resp_wc(i,j,k) = cobalt%jo2resp_wc(i,j,k) - min(0.0,phyto(n)%mu(i,j,k)*phyto(n)%f_n(i,j,k))*cobalt%o2_2_nh4 + do n = 2, NUM_PHYTO !{ + phyto(n)%juptake_no3(i,j,k) = max( 0.0, phyto(n)%mu(i,j,k)*phyto(n)%f_n(i,j,k)* & + phyto(n)%no3lim(i,j,k)/(phyto(n)%no3lim(i,j,k)+phyto(n)%nh4lim(i,j,k)+epsln) ) + phyto(n)%juptake_nh4(i,j,k) = max( 0.0, phyto(n)%mu(i,j,k)*phyto(n)%f_n(i,j,k)* & + phyto(n)%nh4lim(i,j,k)/(phyto(n)%no3lim(i,j,k)+phyto(n)%nh4lim(i,j,k)+epsln) ) + cobalt%jprod_nh4(i,j,k) = cobalt%jprod_nh4(i,j,k) - min(0.0,phyto(n)%mu(i,j,k)*phyto(n)%f_n(i,j,k)) + cobalt%jo2resp_wc(i,j,k) = cobalt%jo2resp_wc(i,j,k) - min(0.0,phyto(n)%mu(i,j,k)*phyto(n)%f_n(i,j,k))*cobalt%o2_2_nh4 + enddo !} n + enddo; enddo ; enddo !} i,j,k + ! + ! Phosphorous uptake + ! + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + n=DIAZO + phyto(n)%juptake_po4(i,j,k) = (phyto(n)%juptake_n2(i,j,k)+phyto(n)%juptake_nh4(i,j,k) + & + phyto(n)%juptake_no3(i,j,k))*phyto(n)%p_2_n_static + cobalt%jprod_po4(i,j,k) = cobalt%jprod_po4(i,j,k) - & + min(0.0,phyto(n)%mu(i,j,k)*phyto(n)%f_n(i,j,k))*phyto(n)%p_2_n_static + do n = 2, NUM_PHYTO + phyto(n)%juptake_po4(i,j,k) = (phyto(n)%juptake_no3(i,j,k)+ & + phyto(n)%juptake_nh4(i,j,k)) * phyto(n)%p_2_n_static + cobalt%jprod_po4(i,j,k) = cobalt%jprod_po4(i,j,k) - & + min(0.0,phyto(n)%mu(i,j,k)*phyto(n)%f_n(i,j,k))*phyto(n)%p_2_n_static + enddo !} n + enddo; enddo ; enddo !} i,j,k + ! + ! Iron uptake + ! + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + do n = 1, NUM_PHYTO !{ + if (phyto(n)%q_fe_2_n(i,j,k).lt.phyto(n)%fe_2_n_max) then + phyto(n)%juptake_fe(i,j,k) = phyto(n)%P_C_max*cobalt%expkT(i,j,k)*phyto(n)%f_n(i,j,k)* & + phyto(n)%felim(i,j,k)*cobalt%fe_2_n_upt_fac + else + phyto(n)%juptake_fe(i,j,k) = 0.0 + endif + enddo !} n + enddo; enddo ; enddo !} i,j,k + ! + ! Silicate uptake + ! + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + cobalt%nlg_diatoms(i,j,k)=phyto(LARGE)%f_n(i,j,k)*phyto(LARGE)%silim(i,j,k) + cobalt%q_si_2_n_lg_diatoms(i,j,k)= cobalt%f_silg(i,j,k)/ & + (cobalt%nlg_diatoms(i,j,k) + epsln) + phyto(LARGE)%juptake_sio4(i,j,k) = & + max(phyto(LARGE)%juptake_no3(i,j,k)+phyto(LARGE)%juptake_nh4(i,j,k),0.0)*phyto(LARGE)%silim(i,j,k)* & + phyto(LARGE)%silim(i,j,k)*phyto(LARGE)%si_2_n_max + + ! Note that this is si_2_n in large phytoplankton pool, not in diatoms themselves (q_si_2_n_lg_diatoms) + phyto(LARGE)%q_si_2_n(i,j,k) = cobalt%f_silg(i,j,k)/(phyto(LARGE)%f_n(i,j,k)+epsln) + + enddo; enddo ; enddo !} i,j,k +! + call mpp_clock_end(id_clock_phyto_growth) +! +!----------------------------------------------------------------------- +! 2: Bacterial Growth and Uptake Calculations +!----------------------------------------------------------------------- +! + ! + ! calculate an effective maximum ldon uptake rate (at 0 deg. C) for bacteria + ! from specified values of bact(1)%gge_max, bact(1)%mu_max and bact(1)%bresp + ! + + call mpp_clock_begin(id_clock_bacteria_growth) + vmax_bact = (1.0/bact(1)%gge_max)*(bact(1)%mu_max + bact(1)%bresp) + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + bact(1)%temp_lim(i,j,k) = exp(bact(1)%ktemp*Temp(i,j,k)) + bact(1)%ldonlim(i,j,k) = cobalt%f_ldon(i,j,k)/(bact(1)%k_ldon + cobalt%f_ldon(i,j,k)) + bact(1)%o2lim(i,j,k) = max(cobalt%f_o2(i,j,k),cobalt%o2_min)/ & + (cobalt%k_o2 + max(cobalt%f_o2(i,j,k),cobalt%o2_min)) + bact(1)%juptake_ldon(i,j,k) = vmax_bact*bact(1)%temp_lim(i,j,k)*bact(1)%ldonlim(i,j,k)* & + bact(1)%o2lim(i,j,k)*bact(1)%f_n(i,j,k) + bact_uptake_ratio = ( cobalt%f_ldop(i,j,k)/max(cobalt%f_ldon(i,j,k),epsln) ) + bact(1)%juptake_ldop(i,j,k) = bact(1)%juptake_ldon(i,j,k)*bact_uptake_ratio + ! calculate bacteria production if N-limited, adjust down if P-limited + bact(1)%jprod_n(i,j,k) = bact(1)%gge_max*bact(1)%juptake_ldon(i,j,k) - & + bact(1)%f_n(i,j,k)/(cobalt%refuge_conc + bact(1)%f_n(i,j,k)) * & + bact(1)%temp_lim(i,j,k)*bact(1)%bresp*bact(1)%f_n(i,j,k) + bact(1)%jprod_n(i,j,k) = min(bact(1)%jprod_n(i,j,k), & + bact(1)%juptake_ldop(i,j,k)/bact(1)%q_p_2_n) + ! remineralization of oragnic N to nh4 = difference between uptake and production + bact(1)%jprod_nh4(i,j,k) = bact(1)%juptake_ldon(i,j,k) - max(bact(1)%jprod_n(i,j,k),0.0) + cobalt%jprod_nh4(i,j,k) = cobalt%jprod_nh4(i,j,k) + bact(1)%jprod_nh4(i,j,k) + + if (cobalt%f_o2(i,j,k) .gt. cobalt%o2_min) then !{ + ! aerobic remineralization, nh4 production, o2 respired + cobalt%jo2resp_wc(i,j,k) = cobalt%jo2resp_wc(i,j,k) + bact(1)%jprod_nh4(i,j,k)*cobalt%o2_2_nh4 + else + ! low o2 leads to water column denitrification. nh4 is created, but no o2 is used + cobalt%jno3denit_wc(i,j,k) = cobalt%jno3denit_wc(i,j,k) + & + bact(1)%jprod_nh4(i,j,k)*cobalt%n_2_n_denit + ! uncomment for "no mass change" test + ! cobalt%jno3denit_wc(i,j,k) = 0.0 + endif !} + + ! produce phosphate at the same rate regardless of whether aerobic/anaerobic + bact(1)%jprod_po4(i,j,k) = bact(1)%juptake_ldop(i,j,k) - max(bact(1)%jprod_n(i,j,k)*bact(1)%q_p_2_n,0.0) + cobalt%jprod_po4(i,j,k) = cobalt%jprod_po4(i,j,k) + bact(1)%jprod_po4(i,j,k) + + enddo; enddo ; enddo !} i,j,k +! + call mpp_clock_end(id_clock_bacteria_growth) +! +!----------------------------------------------------------------------- +! 3: Plankton foodweb dynamics +!----------------------------------------------------------------------- +! + ! + ! 3.1 Plankton foodweb dynamics: consumption by zooplankton and higher predators + ! + + call mpp_clock_begin(id_clock_zooplankton_calculations) + + ! + ! Set-up local matrices for calculating zooplankton ingestion of + ! multiple prey types. The rows are consumers (i.e., NUM_ZOO zooplankton + ! groups), the columns are food sources (i.e., NUM_PREY potential food sources) + ! + ! ipa_matrix = the innate prey availability matrix + ! pa_matrix = prey availability matrix after accounting for switching + ! ingest_matrix = ingestion matrix + ! tot_prey = total prey available to predator m + ! + ! The definition of predator-prey matrices is intended to allow for + ! efficient experimentation with predator-prey interconnections. + ! However, we are still working to reduce the runtime required to + ! include this feature. The matrix structures are thus included, + ! but the standard COBALT interactions have been hard-coded. This + ! makes the code faster, but adding consumer-resource linkages + ! requires new code rather than just changing parameters + ! + ! Note that the primary ingestion calculations allow for variable + ! stoichiometry. + ! + + do m = 1,NUM_ZOO !{ + ipa_matrix(m,1) = zoo(m)%ipa_diaz + ipa_matrix(m,2) = zoo(m)%ipa_lgp + ipa_matrix(m,3) = zoo(m)%ipa_smp + ipa_matrix(m,4) = zoo(m)%ipa_bact + ipa_matrix(m,5) = zoo(m)%ipa_smz + ipa_matrix(m,6) = zoo(m)%ipa_mdz + ipa_matrix(m,7) = zoo(m)%ipa_lgz + ipa_matrix(m,8) = zoo(m)%ipa_det + tot_prey(m) = 0.0 + do n = 1,NUM_PREY !{ + pa_matrix(m,n) = 0.0 + ingest_matrix(m,n) = 0.0 + enddo !} n + enddo !} m + + ! + ! Set-up local matrices for calculating higher predator ingestion + ! of multiple prey types + ! + + hp_ipa_vec(1) = cobalt%hp_ipa_diaz + hp_ipa_vec(2) = cobalt%hp_ipa_lgp + hp_ipa_vec(3) = cobalt%hp_ipa_smp + hp_ipa_vec(4) = cobalt%hp_ipa_bact + hp_ipa_vec(5) = cobalt%hp_ipa_smz + hp_ipa_vec(6) = cobalt%hp_ipa_mdz + hp_ipa_vec(7) = cobalt%hp_ipa_lgz + hp_ipa_vec(8) = cobalt%hp_ipa_det + tot_prey_hp = 0.0 + do n = 1,NUM_PREY !{ + hp_pa_vec(n) = 0.0 + hp_ingest_vec(n) = 0.0 + enddo !} n + + ! + ! Set all static stoichiometric ratios outside k,j,i loop + ! + + prey_p2n_vec(1) = phyto(DIAZO)%p_2_n_static + prey_p2n_vec(2) = phyto(LARGE)%p_2_n_static + prey_p2n_vec(3) = phyto(SMALL)%p_2_n_static + prey_p2n_vec(4) = bact(1)%q_p_2_n + prey_p2n_vec(5) = zoo(1)%q_p_2_n + prey_p2n_vec(6) = zoo(2)%q_p_2_n + prey_p2n_vec(7) = zoo(3)%q_p_2_n + + prey_fe2n_vec(4) = 0.0 + prey_fe2n_vec(5) = 0.0 + prey_fe2n_vec(6) = 0.0 + prey_fe2n_vec(7) = 0.0 + + prey_si2n_vec(1) = 0.0 + prey_si2n_vec(3) = 0.0 + prey_si2n_vec(4) = 0.0 + prey_si2n_vec(5) = 0.0 + prey_si2n_vec(6) = 0.0 + prey_si2n_vec(7) = 0.0 + + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec; !{ + + ! + ! 3.1.1: Calculate zooplankton ingestion fluxes + ! + + ! Calculate the temperature and oxygen limitations, no ingestion + ! in low o2 environments + do m = 1,3 !{ + zoo(m)%temp_lim(i,j,k) = exp(zoo(m)%ktemp*Temp(i,j,k)) + zoo(m)%o2lim(i,j,k) = max((cobalt%f_o2(i,j,k) - cobalt%o2_min),0.0)/ & + (cobalt%k_o2 + max(cobalt%f_o2(i,j,k)-cobalt%o2_min,0.0)) + enddo !} m + cobalt%hp_temp_lim(i,j,k) = exp(cobalt%ktemp_hp*Temp(i,j,k)) + cobalt%hp_o2lim(i,j,k) = max((cobalt%f_o2(i,j,k) - cobalt%o2_min),0.0)/ & + (cobalt%k_o2 + max(cobalt%f_o2(i,j,k)-cobalt%o2_min,0.0)) + + ! Prey vectors for ingestion and loss calculations + ! (note: ordering of phytoplankton must be consistent with + ! DIAZO, LARGE, SMALL ordering inherited from TOPAZ) + ! + prey_vec(1) = max(phyto(DIAZO)%f_n(i,j,k) - cobalt%refuge_conc,0.0) + prey_vec(2) = max(phyto(LARGE)%f_n(i,j,k) - cobalt%refuge_conc,0.0) + prey_vec(3) = max(phyto(SMALL)%f_n(i,j,k) - cobalt%refuge_conc,0.0) + prey_vec(4) = max(bact(1)%f_n(i,j,k) - cobalt%refuge_conc,0.0) + prey_vec(5) = max(zoo(1)%f_n(i,j,k) - cobalt%refuge_conc,0.0) + prey_vec(6) = max(zoo(2)%f_n(i,j,k) - cobalt%refuge_conc,0.0) + prey_vec(7) = max(zoo(3)%f_n(i,j,k) - cobalt%refuge_conc,0.0) + prey_vec(8) = max(cobalt%f_ndet(i,j,k) - cobalt%refuge_conc,0.0) + ! + ! Set dynamic stoichiometric rations inside k,j,i loop + prey_p2n_vec(8) = cobalt%f_pdet(i,j,k)/(cobalt%f_ndet(i,j,k)+epsln) + prey_fe2n_vec(1) = phyto(DIAZO)%q_fe_2_n(i,j,k) + prey_fe2n_vec(2) = phyto(LARGE)%q_fe_2_n(i,j,k) + prey_fe2n_vec(3) = phyto(SMALL)%q_fe_2_n(i,j,k) + prey_fe2n_vec(8) = cobalt%f_fedet(i,j,k)/(cobalt%f_ndet(i,j,k)+epsln) + prey_si2n_vec(2) = phyto(LARGE)%q_si_2_n(i,j,k) + prey_si2n_vec(8) = cobalt%f_sidet(i,j,k)/(cobalt%f_ndet(i,j,k)+epsln) + + ! + ! Calculate zooplankton ingestion + ! + ! Small zooplankton (m = 1) consuming small phytoplankton (3) and + ! bacteria (4). sw_fac_denom is the denominator of the abundance- + ! based switching factor, tot_prey is the total available prey + ! after accounting for switching. + ! + ! CAS: speed up code by using integer "switch" terms and sqrt? + + m = 1 + sw_fac_denom = (ipa_matrix(m,3)*prey_vec(3))**zoo(m)%nswitch + & + (ipa_matrix(m,4)*prey_vec(4))**zoo(m)%nswitch + pa_matrix(m,3) = ipa_matrix(m,3)* & + ( (ipa_matrix(m,3)*prey_vec(3))**zoo(m)%nswitch / & + (sw_fac_denom+epsln) )**(1.0/zoo(m)%mswitch) + pa_matrix(m,4) = ipa_matrix(m,4)* & + ( (ipa_matrix(m,4)*prey_vec(4))**zoo(m)%nswitch / & + (sw_fac_denom+epsln) )**(1.0/zoo(m)%mswitch) + tot_prey(m) = pa_matrix(m,3)*prey_vec(3) + pa_matrix(m,4)*prey_vec(4) + ingest_matrix(m,3) = zoo(m)%temp_lim(i,j,k)*zoo(m)%o2lim(i,j,k)*zoo(m)%imax* & + pa_matrix(m,3)*prey_vec(3)*zoo(m)%f_n(i,j,k)/(zoo(m)%ki+tot_prey(m)) + ingest_matrix(m,4) = zoo(m)%temp_lim(i,j,k)*zoo(m)%o2lim(i,j,k)*zoo(m)%imax* & + pa_matrix(m,4)*prey_vec(4)*zoo(m)%f_n(i,j,k)/(zoo(m)%ki+tot_prey(m)) + zoo(m)%jingest_n(i,j,k) = ingest_matrix(m,3) + ingest_matrix(m,4) + zoo(m)%jingest_p(i,j,k) = ingest_matrix(m,3)*prey_p2n_vec(3) + & + ingest_matrix(m,4)*prey_p2n_vec(4) + zoo(m)%jingest_fe(i,j,k) = ingest_matrix(m,3)*prey_fe2n_vec(3) + + ! + ! Medium zooplankton (m = 2) consuming diazotrophs (1), large + ! phytoplankton (2), and small zooplankton (5) + ! + + m = 2 + sw_fac_denom = (ipa_matrix(m,1)*prey_vec(1))**zoo(m)%nswitch + & + (ipa_matrix(m,2)*prey_vec(2))**zoo(m)%nswitch + & + (ipa_matrix(m,5)*prey_vec(5))**zoo(m)%nswitch + pa_matrix(m,1) = ipa_matrix(m,1)* & + ( (ipa_matrix(m,1)*prey_vec(1))**zoo(m)%nswitch / & + (sw_fac_denom+epsln) )**(1.0/zoo(m)%mswitch) + pa_matrix(m,2) = ipa_matrix(m,2)* & + ( (ipa_matrix(m,2)*prey_vec(2))**zoo(m)%nswitch / & + (sw_fac_denom+epsln) )**(1.0/zoo(m)%mswitch) + pa_matrix(m,5) = ipa_matrix(m,5)* & + ( (ipa_matrix(m,5)*prey_vec(5))**zoo(m)%nswitch / & + (sw_fac_denom+epsln) )**(1.0/zoo(m)%mswitch) + tot_prey(m) = pa_matrix(m,1)*prey_vec(1) + pa_matrix(m,2)*prey_vec(2) + & + pa_matrix(m,5)*prey_vec(5) + ingest_matrix(m,1) = zoo(m)%temp_lim(i,j,k)*zoo(m)%o2lim(i,j,k)*zoo(m)%imax* & + pa_matrix(m,1)*prey_vec(1)*zoo(m)%f_n(i,j,k)/(zoo(m)%ki+tot_prey(m)) + ingest_matrix(m,2) = zoo(m)%temp_lim(i,j,k)*zoo(m)%o2lim(i,j,k)*zoo(m)%imax* & + pa_matrix(m,2)*prey_vec(2)*zoo(m)%f_n(i,j,k)/(zoo(m)%ki+tot_prey(m)) + ingest_matrix(m,5) = zoo(m)%temp_lim(i,j,k)*zoo(m)%o2lim(i,j,k)*zoo(m)%imax* & + pa_matrix(m,5)*prey_vec(5)*zoo(m)%f_n(i,j,k)/(zoo(m)%ki+tot_prey(m)) + zoo(m)%jingest_n(i,j,k) = ingest_matrix(m,1) + ingest_matrix(m,2) + & + ingest_matrix(m,5) + zoo(m)%jingest_p(i,j,k) = ingest_matrix(m,1)*prey_p2n_vec(1) + & + ingest_matrix(m,2)*prey_p2n_vec(2) + & + ingest_matrix(m,5)*prey_p2n_vec(5) + zoo(m)%jingest_fe(i,j,k) = ingest_matrix(m,1)*prey_fe2n_vec(1) + & + ingest_matrix(m,2)*prey_fe2n_vec(2) + zoo(m)%jingest_sio2(i,j,k) = ingest_matrix(m,2)*prey_si2n_vec(2) + + ! + ! Large zooplankton (m = 3) consuming diazotrophs (2), large phytoplankton (2) + ! and medium zooplankton (6) + ! + + m = 3 + sw_fac_denom = (ipa_matrix(m,1)*prey_vec(1))**zoo(m)%nswitch + & + (ipa_matrix(m,2)*prey_vec(2))**zoo(m)%nswitch + & + (ipa_matrix(m,6)*prey_vec(6))**zoo(m)%nswitch + pa_matrix(m,1) = ipa_matrix(m,1)* & + ( (ipa_matrix(m,1)*prey_vec(1))**zoo(m)%nswitch / & + (sw_fac_denom+epsln) )**(1.0/zoo(m)%mswitch) + pa_matrix(m,2) = ipa_matrix(m,2)* & + ( (ipa_matrix(m,2)*prey_vec(2))**zoo(m)%nswitch / & + (sw_fac_denom+epsln) )**(1.0/zoo(m)%mswitch) + pa_matrix(m,6) = ipa_matrix(m,6)* & + ( (ipa_matrix(m,6)*prey_vec(6))**zoo(m)%nswitch / & + (sw_fac_denom+epsln) )**(1.0/zoo(m)%mswitch) + tot_prey(m) = pa_matrix(m,1)*prey_vec(1) + pa_matrix(m,2)*prey_vec(2) + & + pa_matrix(m,6)*prey_vec(6) + ingest_matrix(m,1) = zoo(m)%temp_lim(i,j,k)*zoo(m)%o2lim(i,j,k)*zoo(m)%imax* & + pa_matrix(m,1)*prey_vec(1)*zoo(m)%f_n(i,j,k)/(zoo(m)%ki+tot_prey(m)) + ingest_matrix(m,2) = zoo(m)%temp_lim(i,j,k)*zoo(m)%o2lim(i,j,k)*zoo(m)%imax* & + pa_matrix(m,2)*prey_vec(2)*zoo(m)%f_n(i,j,k)/(zoo(m)%ki+tot_prey(m)) + ingest_matrix(m,6) = zoo(m)%temp_lim(i,j,k)*zoo(m)%o2lim(i,j,k)*zoo(m)%imax* & + pa_matrix(m,6)*prey_vec(6)*zoo(m)%f_n(i,j,k)/(zoo(m)%ki+tot_prey(m)) + zoo(m)%jingest_n(i,j,k) = ingest_matrix(m,1) + ingest_matrix(m,2) + & + ingest_matrix(m,6) + zoo(m)%jingest_p(i,j,k) = ingest_matrix(m,1)*prey_p2n_vec(1) + & + ingest_matrix(m,2)*prey_p2n_vec(2) + & + ingest_matrix(m,6)*prey_p2n_vec(6) + zoo(m)%jingest_fe(i,j,k) = ingest_matrix(m,1)*prey_fe2n_vec(1) + & + ingest_matrix(m,2)*prey_fe2n_vec(2) + zoo(m)%jingest_sio2(i,j,k) = ingest_matrix(m,2)*prey_si2n_vec(2) + + cobalt%total_filter_feeding(i,j,k) = ingest_matrix(2,1) + ingest_matrix(2,2) + & + ingest_matrix(2,3) + ingest_matrix(3,1) + ingest_matrix(3,2) + & + ingest_matrix(3,3) + + ! + ! Calculate losses to zooplankton + ! + + do n = 1,NUM_PHYTO + phyto(n)%jzloss_n(i,j,k) = 0.0 + enddo + + do m = 1,NUM_ZOO !{ + phyto(DIAZO)%jzloss_n(i,j,k) = phyto(DIAZO)%jzloss_n(i,j,k) + ingest_matrix(m,DIAZO) + phyto(LARGE)%jzloss_n(i,j,k) = phyto(LARGE)%jzloss_n(i,j,k) + ingest_matrix(m,LARGE) + phyto(SMALL)%jzloss_n(i,j,k) = phyto(SMALL)%jzloss_n(i,j,k) + ingest_matrix(m,SMALL) + enddo !} m + + do n = 1,NUM_PHYTO !{ + phyto(n)%jzloss_p(i,j,k) = phyto(n)%jzloss_n(i,j,k)*prey_p2n_vec(n) + phyto(n)%jzloss_fe(i,j,k) = phyto(n)%jzloss_n(i,j,k)*prey_fe2n_vec(n) + phyto(n)%jzloss_sio2(i,j,k) = phyto(n)%jzloss_n(i,j,k)*prey_si2n_vec(n) + enddo !} n + + ! + ! losses of bacteria to zooplankton + ! + + bact(1)%jzloss_n(i,j,k) = 0.0 + do m = 1,NUM_ZOO !{ + bact(1)%jzloss_n(i,j,k) = bact(1)%jzloss_n(i,j,k) + ingest_matrix(m,4) + enddo !} m + bact(1)%jzloss_p(i,j,k) = bact(1)%jzloss_n(i,j,k)*prey_p2n_vec(4) + + ! + ! losses of zooplankton to zooplankton + ! + + do n = 1,NUM_ZOO !{ + zoo(n)%jzloss_n(i,j,k) = 0.0 + do m = 1,NUM_ZOO !{ + zoo(n)%jzloss_n(i,j,k) = zoo(n)%jzloss_n(i,j,k) + ingest_matrix(m,NUM_PHYTO+1+n) + enddo !} m + zoo(n)%jzloss_p(i,j,k) = zoo(n)%jzloss_n(i,j,k)*prey_p2n_vec(NUM_PHYTO+1+n) + enddo !} n + + ! + ! 3.1.2 Calculate ingestion by higher predators + ! + + ! The higher-predator ingestion calculations mirror those used for zooplankton + ! + sw_fac_denom = (hp_ipa_vec(6)*prey_vec(6))**cobalt%nswitch_hp + & + (hp_ipa_vec(7)*prey_vec(7))**cobalt%nswitch_hp + hp_pa_vec(6) = hp_ipa_vec(6)* & + ( (hp_ipa_vec(6)*prey_vec(6))**cobalt%nswitch_hp / & + (sw_fac_denom+epsln) )**(1.0/cobalt%mswitch_hp) + hp_pa_vec(7) = hp_ipa_vec(7)* & + ( (hp_ipa_vec(7)*prey_vec(7))**cobalt%nswitch_hp / & + (sw_fac_denom+epsln) )**(1.0/cobalt%mswitch_hp) + tot_prey_hp = hp_pa_vec(6)*prey_vec(6) + hp_pa_vec(7)*prey_vec(7) + hp_ingest_vec(6) = cobalt%hp_temp_lim(i,j,k)*cobalt%hp_o2lim(i,j,k)*cobalt%imax_hp* & + hp_pa_vec(6)*prey_vec(6)*tot_prey_hp**(cobalt%coef_hp-1.0)/ & + (cobalt%ki_hp+tot_prey_hp) + hp_ingest_vec(7) = cobalt%hp_temp_lim(i,j,k)*cobalt%hp_o2lim(i,j,k)*cobalt%imax_hp* & + hp_pa_vec(7)*prey_vec(7)*tot_prey_hp**(cobalt%coef_hp-1.0)/ & + (cobalt%ki_hp+tot_prey_hp) + cobalt%hp_jingest_n(i,j,k) = hp_ingest_vec(6) + hp_ingest_vec(7) + cobalt%hp_jingest_p(i,j,k) = hp_ingest_vec(6)*prey_p2n_vec(6) + & + hp_ingest_vec(7)*prey_p2n_vec(7) + ! + ! Calculate losses to higher predators + ! + + do n = 1,NUM_ZOO !{ + zoo(n)%jhploss_n(i,j,k) = hp_ingest_vec(NUM_PHYTO+1+n) + zoo(n)%jhploss_p(i,j,k) = zoo(n)%jhploss_n(i,j,k)*prey_p2n_vec(NUM_PHYTO+1+n) + enddo !} n + + enddo; enddo; enddo !} i,j,k + call mpp_clock_end(id_clock_zooplankton_calculations) + + ! + ! 3.2: Plankton foodweb dynamics: Other mortality and loss terms + ! + + call mpp_clock_begin(id_clock_other_losses) + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec; !{ + + ! + ! 3.2.1 Calculate losses of phytoplankton to aggregation + ! + + do n = 1,NUM_PHYTO !{ + growth_ratio = min(phyto(n)%f_mu_mem(i,j,k)/ & + (phyto(n)%frac_mu_agg*phyto(n)%P_C_max*cobalt%expkT(i,j,k)),1.0) + phyto(n)%agg_lim(i,j,k) = (1.0-growth_ratio)**2 + phyto(n)%jaggloss_n(i,j,k) = phyto(n)%agg_lim(i,j,k)*phyto(n)%agg*phyto(n)%f_n(i,j,k)**2.0 + phyto(n)%jaggloss_p(i,j,k) = phyto(n)%jaggloss_n(i,j,k)*phyto(n)%q_p_2_n(i,j,k) + phyto(n)%jaggloss_fe(i,j,k) = phyto(n)%jaggloss_n(i,j,k)*phyto(n)%q_fe_2_n(i,j,k) + phyto(n)%jaggloss_sio2(i,j,k) = phyto(n)%jaggloss_n(i,j,k)*phyto(n)%q_si_2_n(i,j,k) + enddo !} n + + ! + ! 3.2.2 Calculate phytoplankton and bacterial losses to viruses + ! + + do n = 1,NUM_PHYTO !{ + phyto(n)%jvirloss_n(i,j,k) = bact(1)%temp_lim(i,j,k)*phyto(n)%vir*phyto(n)%f_n(i,j,k)**2.0 + phyto(n)%jvirloss_p(i,j,k) = phyto(n)%jvirloss_n(i,j,k)*phyto(n)%q_p_2_n(i,j,k) + phyto(n)%jvirloss_fe(i,j,k) = phyto(n)%jvirloss_n(i,j,k)*phyto(n)%q_fe_2_n(i,j,k) + phyto(n)%jvirloss_sio2(i,j,k) = phyto(n)%jvirloss_n(i,j,k)*phyto(n)%q_si_2_n(i,j,k) + enddo !} n + + bact(1)%jvirloss_n(i,j,k) = bact(1)%temp_lim(i,j,k)*bact(1)%vir*bact(1)%f_n(i,j,k)**2.0 + bact(1)%jvirloss_p(i,j,k) = bact(1)%jvirloss_n(i,j,k)*bact(1)%q_p_2_n + + ! + ! 3.2.3 Calculate losses to exudation + ! + + n = DIAZO + phyto(n)%jexuloss_n(i,j,k) = phyto(n)%exu*max(phyto(n)%juptake_no3(i,j,k)+ & + phyto(n)%juptake_nh4(i,j,k)+phyto(n)%juptake_n2(i,j,k),0.0) + phyto(n)%jexuloss_p(i,j,k) = phyto(n)%exu*max(phyto(n)%juptake_po4(i,j,k),0.0) + phyto(n)%jexuloss_fe(i,j,k) = phyto(n)%exu*max(phyto(n)%juptake_fe(i,j,k),0.0) + do n = 2,NUM_PHYTO !{ + phyto(n)%jexuloss_n(i,j,k) = phyto(n)%exu*max(phyto(n)%juptake_no3(i,j,k)+phyto(n)%juptake_nh4(i,j,k),0.0) + phyto(n)%jexuloss_p(i,j,k) = phyto(n)%exu*max(phyto(n)%juptake_po4(i,j,k),0.0) + phyto(n)%jexuloss_fe(i,j,k) = phyto(n)%exu*max(phyto(n)%juptake_fe(i,j,k),0.0) + enddo + + enddo; enddo; enddo !} i,j,k + call mpp_clock_end(id_clock_other_losses) + + ! + ! 3.3: Plankton foodweb dynamics: Production calculations + ! + + call mpp_clock_begin(id_clock_production_loop) + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + + ! + ! 3.3.1: Calculate the production of detritus and dissolved organic material + ! + ! + ! Production of detritus and dissolved organic material from zooplankton egestion + ! + + do m = 1,NUM_ZOO + zoo(m)%jprod_ndet(i,j,k) = zoo(m)%phi_det*zoo(m)%jingest_n(i,j,k) + zoo(m)%jprod_pdet(i,j,k) = zoo(m)%phi_det*zoo(m)%jingest_p(i,j,k) + zoo(m)%jprod_sldon(i,j,k) = zoo(m)%phi_sldon*zoo(m)%jingest_n(i,j,k) + zoo(m)%jprod_ldon(i,j,k) = zoo(m)%phi_ldon*zoo(m)%jingest_n(i,j,k) + zoo(m)%jprod_srdon(i,j,k) = zoo(m)%phi_srdon*zoo(m)%jingest_n(i,j,k) + zoo(m)%jprod_sldop(i,j,k) = zoo(m)%phi_sldop*zoo(m)%jingest_p(i,j,k) + zoo(m)%jprod_ldop(i,j,k) = zoo(m)%phi_ldop*zoo(m)%jingest_p(i,j,k) + zoo(m)%jprod_srdop(i,j,k) = zoo(m)%phi_srdop*zoo(m)%jingest_p(i,j,k) + zoo(m)%jprod_fedet(i,j,k) = zoo(m)%phi_det*zoo(m)%jingest_fe(i,j,k) + zoo(m)%jprod_sidet(i,j,k) = zoo(m)%phi_det*zoo(m)%jingest_sio2(i,j,k) + + + ! augment cumulative production with zooplankton terms + cobalt%jprod_ndet(i,j,k) = cobalt%jprod_ndet(i,j,k) + zoo(m)%jprod_ndet(i,j,k) + cobalt%jprod_pdet(i,j,k) = cobalt%jprod_pdet(i,j,k) + zoo(m)%jprod_pdet(i,j,k) + cobalt%jprod_sldon(i,j,k) = cobalt%jprod_sldon(i,j,k) + zoo(m)%jprod_sldon(i,j,k) + cobalt%jprod_ldon(i,j,k) = cobalt%jprod_ldon(i,j,k) + zoo(m)%jprod_ldon(i,j,k) + cobalt%jprod_srdon(i,j,k) = cobalt%jprod_srdon(i,j,k) + zoo(m)%jprod_srdon(i,j,k) + cobalt%jprod_sldop(i,j,k) = cobalt%jprod_sldop(i,j,k) + zoo(m)%jprod_sldop(i,j,k) + cobalt%jprod_ldop(i,j,k) = cobalt%jprod_ldop(i,j,k) + zoo(m)%jprod_ldop(i,j,k) + cobalt%jprod_srdop(i,j,k) = cobalt%jprod_srdop(i,j,k) + zoo(m)%jprod_srdop(i,j,k) + cobalt%jprod_fedet(i,j,k) = cobalt%jprod_fedet(i,j,k) + zoo(m)%jprod_fedet(i,j,k) + cobalt%jprod_sidet(i,j,k) = cobalt%jprod_sidet(i,j,k) + zoo(m)%jprod_sidet(i,j,k) + enddo !} m + + ! + ! Production of detritus and dissolved organic material from higher predator egestion + ! (did not track individual terms, just add to cumulative total) + ! + + cobalt%jprod_ndet(i,j,k) = cobalt%jprod_ndet(i,j,k) + cobalt%hp_phi_det*cobalt%hp_jingest_n(i,j,k) + cobalt%jprod_pdet(i,j,k) = cobalt%jprod_pdet(i,j,k) + cobalt%hp_phi_det*cobalt%hp_jingest_p(i,j,k) + cobalt%jprod_fedet(i,j,k) = cobalt%jprod_fedet(i,j,k) + cobalt%hp_phi_det*cobalt%hp_jingest_fe(i,j,k) + cobalt%jprod_sidet(i,j,k) = cobalt%jprod_sidet(i,j,k) + cobalt%hp_phi_det*cobalt%hp_jingest_sio2(i,j,k) + + ! + ! Sources from phytoplankton aggregation + ! + + do m = 1,NUM_PHYTO + cobalt%jprod_ndet(i,j,k) = cobalt%jprod_ndet(i,j,k) + phyto(m)%jaggloss_n(i,j,k) + cobalt%jprod_pdet(i,j,k) = cobalt%jprod_pdet(i,j,k) + phyto(m)%jaggloss_p(i,j,k) + cobalt%jprod_fedet(i,j,k) = cobalt%jprod_fedet(i,j,k) + phyto(m)%jaggloss_fe(i,j,k) + cobalt%jprod_sidet(i,j,k) = cobalt%jprod_sidet(i,j,k) + phyto(m)%jaggloss_sio2(i,j,k) + enddo !} m + + ! + ! Sources from viral lysis of phytoplankton (0 in default formulation) and exudation + ! + + do m = 1,NUM_PHYTO + cobalt%jprod_ldon(i,j,k) = cobalt%jprod_ldon(i,j,k) + cobalt%lysis_phi_ldon*phyto(m)%jvirloss_n(i,j,k) + & + phyto(m)%jexuloss_n(i,j,k) + cobalt%jprod_sldon(i,j,k) = cobalt%jprod_sldon(i,j,k) + cobalt%lysis_phi_sldon*phyto(m)%jvirloss_n(i,j,k) + cobalt%jprod_srdon(i,j,k) = cobalt%jprod_srdon(i,j,k) + cobalt%lysis_phi_srdon*phyto(m)%jvirloss_n(i,j,k) + cobalt%jprod_ldop(i,j,k) = cobalt%jprod_ldop(i,j,k) + cobalt%lysis_phi_ldop*phyto(m)%jvirloss_p(i,j,k) + & + phyto(m)%jexuloss_p(i,j,k) + cobalt%jprod_sldop(i,j,k) = cobalt%jprod_sldop(i,j,k) + cobalt%lysis_phi_sldop*phyto(m)%jvirloss_p(i,j,k) + cobalt%jprod_srdop(i,j,k) = cobalt%jprod_srdop(i,j,k) + cobalt%lysis_phi_srdop*phyto(m)%jvirloss_p(i,j,k) + cobalt%jprod_fed(i,j,k) = cobalt%jprod_fed(i,j,k) + phyto(m)%jvirloss_fe(i,j,k) + & + phyto(m)%jexuloss_fe(i,j,k) + cobalt%jprod_sio4(i,j,k) = cobalt%jprod_sio4(i,j,k) + phyto(m)%jvirloss_sio2(i,j,k) + enddo !} m + + + ! + ! Sources of dissolved organic material from viral lysis due to bacteria + ! + + cobalt%jprod_ldon(i,j,k) = cobalt%jprod_ldon(i,j,k) + cobalt%lysis_phi_ldon*bact(1)%jvirloss_n(i,j,k) + cobalt%jprod_sldon(i,j,k) = cobalt%jprod_sldon(i,j,k) + cobalt%lysis_phi_sldon*bact(1)%jvirloss_n(i,j,k) + cobalt%jprod_srdon(i,j,k) = cobalt%jprod_srdon(i,j,k) + cobalt%lysis_phi_srdon*bact(1)%jvirloss_n(i,j,k) + cobalt%jprod_ldop(i,j,k) = cobalt%jprod_ldop(i,j,k) + cobalt%lysis_phi_ldop*bact(1)%jvirloss_p(i,j,k) + cobalt%jprod_sldop(i,j,k) = cobalt%jprod_sldop(i,j,k) + cobalt%lysis_phi_sldop*bact(1)%jvirloss_p(i,j,k) + cobalt%jprod_srdop(i,j,k) = cobalt%jprod_srdop(i,j,k) + cobalt%lysis_phi_srdop*bact(1)%jvirloss_p(i,j,k) + + ! + ! Sources of dissolved organic material from bacterial mortality (metabolic costs higher than food uptake). + ! These conditions are assumed to lead to a lysis-like redistribution of bacteria organic matter. + ! + + cobalt%jprod_ldon(i,j,k) = cobalt%jprod_ldon(i,j,k) - cobalt%lysis_phi_ldon* & + min(bact(1)%jprod_n(i,j,k),0.0) + cobalt%jprod_sldon(i,j,k) = cobalt%jprod_sldon(i,j,k) - cobalt%lysis_phi_sldon* & + min(bact(1)%jprod_n(i,j,k),0.0) + cobalt%jprod_srdon(i,j,k) = cobalt%jprod_srdon(i,j,k) - cobalt%lysis_phi_srdon* & + min(bact(1)%jprod_n(i,j,k),0.0) + cobalt%jprod_ldop(i,j,k) = cobalt%jprod_ldop(i,j,k) - cobalt%lysis_phi_ldop* & + min(bact(1)%jprod_n(i,j,k)*bact(1)%q_p_2_n,0.0) + cobalt%jprod_sldop(i,j,k) = cobalt%jprod_sldop(i,j,k) - cobalt%lysis_phi_sldop* & + min(bact(1)%jprod_n(i,j,k)*bact(1)%q_p_2_n,0.0) + cobalt%jprod_srdop(i,j,k) = cobalt%jprod_srdop(i,j,k) - cobalt%lysis_phi_srdop* & + min(bact(1)%jprod_n(i,j,k)*bact(1)%q_p_2_n,0.0) + ! + ! 3.3.2: Zooplankton production and excretion calculations + ! + + do m = 1,NUM_ZOO + assim_eff = 1.0-zoo(m)%phi_det-zoo(m)%phi_ldon-zoo(m)%phi_sldon-zoo(m)%phi_srdon + zoo(m)%jprod_n(i,j,k) = zoo(m)%gge_max*zoo(m)%jingest_n(i,j,k) - & + zoo(m)%f_n(i,j,k)/(cobalt%refuge_conc + zoo(m)%f_n(i,j,k))* & + zoo(m)%temp_lim(i,j,k)*zoo(m)%bresp*zoo(m)%f_n(i,j,k) + zoo(m)%jprod_n(i,j,k) = min(zoo(m)%jprod_n(i,j,k), & + assim_eff*zoo(m)%jingest_p(i,j,k)/zoo(m)%q_p_2_n) + + ! + ! Ingested material that does not go to zooplankton production, detrital production + ! or production of dissolved organic material is excreted as nh4 or po4. If production + ! is negative, zooplankton are lost to large detritus + ! + if (zoo(m)%jprod_n(i,j,k) .gt. 0.0) then + zoo(m)%jprod_nh4(i,j,k) = zoo(m)%jingest_n(i,j,k) - zoo(m)%jprod_ndet(i,j,k) - & + zoo(m)%jprod_n(i,j,k) - zoo(m)%jprod_ldon(i,j,k) - & + zoo(m)%jprod_sldon(i,j,k) - zoo(m)%jprod_srdon(i,j,k) + zoo(m)%jprod_po4(i,j,k) = zoo(m)%jingest_p(i,j,k) - zoo(m)%jprod_pdet(i,j,k) - & + zoo(m)%jprod_n(i,j,k)*zoo(m)%q_p_2_n - zoo(m)%jprod_ldop(i,j,k) - & + zoo(m)%jprod_sldop(i,j,k) - zoo(m)%jprod_srdop(i,j,k) + else + ! None of the ingestion material goes to zooplankton production + zoo(m)%jprod_nh4(i,j,k) = zoo(m)%jingest_n(i,j,k) - zoo(m)%jprod_ndet(i,j,k) - & + zoo(m)%jprod_ldon(i,j,k) - zoo(m)%jprod_sldon(i,j,k) - & + zoo(m)%jprod_srdon(i,j,k) + zoo(m)%jprod_po4(i,j,k) = zoo(m)%jingest_p(i,j,k) - zoo(m)%jprod_pdet(i,j,k) - & + zoo(m)%jprod_ldop(i,j,k) - zoo(m)%jprod_sldop(i,j,k) - & + zoo(m)%jprod_srdop(i,j,k) + + ! The negative production (i.e., mortality) is lost to large detritus. Update values + ! for zooplankton and for total. + + zoo(m)%jprod_ndet(i,j,k) = zoo(m)%jprod_ndet(i,j,k) - zoo(m)%jprod_n(i,j,k) + zoo(m)%jprod_pdet(i,j,k) = zoo(m)%jprod_pdet(i,j,k) - zoo(m)%jprod_n(i,j,k)*zoo(m)%q_p_2_n + cobalt%jprod_ndet(i,j,k) = cobalt%jprod_ndet(i,j,k) - zoo(m)%jprod_n(i,j,k) + cobalt%jprod_pdet(i,j,k) = cobalt%jprod_pdet(i,j,k) - zoo(m)%jprod_n(i,j,k)*zoo(m)%q_p_2_n + endif + + ! cumulative production of inorganic nutrients + cobalt%jprod_nh4(i,j,k) = cobalt%jprod_nh4(i,j,k) + zoo(m)%jprod_nh4(i,j,k) + cobalt%jprod_po4(i,j,k) = cobalt%jprod_po4(i,j,k) + zoo(m)%jprod_po4(i,j,k) + cobalt%jo2resp_wc(i,j,k) = cobalt%jo2resp_wc(i,j,k) + zoo(m)%jprod_nh4(i,j,k)*cobalt%o2_2_nh4 + + ! + ! Any ingested iron that is not allocated to detritus is routed back to the + ! dissolved pool. + ! + zoo(m)%jprod_fed(i,j,k) = (1.0 - zoo(m)%phi_det)*zoo(m)%jingest_fe(i,j,k) + cobalt%jprod_fed(i,j,k) = cobalt%jprod_fed(i,j,k) + zoo(m)%jprod_fed(i,j,k) + ! + ! Any ingested opal that is not allocated to detritus is assumed to undergo + ! rapid dissolution to dissolved silica + ! + zoo(m)%jprod_sio4(i,j,k) = (1.0 - zoo(m)%phi_det)*zoo(m)%jingest_sio2(i,j,k) + cobalt%jprod_sio4(i,j,k) = cobalt%jprod_sio4(i,j,k) + zoo(m)%jprod_sio4(i,j,k) + + enddo !} m + + ! + ! Excretion by higher predators + ! + cobalt%jprod_fed(i,j,k) = cobalt%jprod_fed(i,j,k) + (1.0-cobalt%hp_phi_det)*cobalt%hp_jingest_fe(i,j,k) + cobalt%jprod_sio4(i,j,k) = cobalt%jprod_sio4(i,j,k) + (1.0-cobalt%hp_phi_det)*cobalt%hp_jingest_sio2(i,j,k) + cobalt%jprod_nh4(i,j,k) = cobalt%jprod_nh4(i,j,k) + (1.0-cobalt%hp_phi_det)*cobalt%hp_jingest_n(i,j,k) + cobalt%jprod_po4(i,j,k) = cobalt%jprod_po4(i,j,k) + (1.0-cobalt%hp_phi_det)*cobalt%hp_jingest_p(i,j,k) + cobalt%jo2resp_wc(i,j,k) = cobalt%jo2resp_wc(i,j,k) + (1.0-cobalt%hp_phi_det)*cobalt%hp_jingest_n(i,j,k)*cobalt%o2_2_nh4 + + enddo; enddo ; enddo !} i,j,k + call mpp_clock_end(id_clock_production_loop) + + call mpp_clock_begin(id_clock_ballast_loops) +! +! +!------------------------------------------------------------------------------------ +! 4: Production of calcium carbonate (Calcite and Aragonite) and lithogenic material +!------------------------------------------------------------------------------------ +! + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + + ! + ! 4.1: Calculate aragonite and calcite saturation states + ! + if (trim(co2_calc) == "ocmip2") then + TK = Temp(i,j,k) + 273.15 + PRESS = 0.1016 * cobalt%zt(i,j,k) + 1.013 + PKSPA = 171.945 + 0.077993 * TK - 2903.293 / TK - 71.595 * log10(TK) - (-0.068393 + 1.7276e-3 * & + TK + 88.135 / TK) * sqrt(max(epsln, Salt(i,j,k))) + 0.10018 * max(epsln, Salt(i,j,k)) - & + 5.9415e-3 * max(epsln, Salt(i,j,k))**(1.5) - 0.02 - (48.76 - 2.8 - 0.5304 * Temp(i,j,k)) * & + (PRESS - 1.013) / (191.46 * TK) + (1e-3 * (11.76 - 0.3692 * Temp(i,j,k))) * (PRESS - 1.013) *& + (PRESS - 1.013) / (382.92 * TK) + cobalt%co3_sol_arag(i,j,k) = 10**(-PKSPA) / (2.937d-4 * max(5.0, Salt(i,j,k))) + cobalt%omega_arag(i,j,k) = cobalt%f_co3_ion(i,j,k) / cobalt%co3_sol_arag(i,j,k) + PKSPC = 171.9065 + 0.077993 * TK - 2839.319 / TK - 71.595 * log10(TK) - (-0.77712 + 2.8426e-3 * & + TK + 178.34 / TK) * sqrt(max(epsln, Salt(i,j,k))) + 0.07711 * max(epsln, Salt(i,j,k)) - & + 4.1249e-3 * max(epsln, Salt(i,j,k))**(1.5) - 0.02 - (48.76 - 0.5304 * Temp(i,j,k)) * & + (PRESS - 1.013) / (191.46 * TK) + (1e-3 * (11.76 - 0.3692 * Temp(i,j,k))) * (PRESS - 1.013) *& + (PRESS - 1.013) / (382.92 * TK) + cobalt%co3_sol_calc(i,j,k) = 10**(-PKSPC) / (2.937d-4 * max(5.0, Salt(i,j,k))) + cobalt%omega_calc(i,j,k) = cobalt%f_co3_ion(i,j,k) / cobalt%co3_sol_calc(i,j,k) + else if (trim(co2_calc) == "mocsy") then + cobalt%omega_arag(i,j,k) = cobalt%omegaa(i,j,k) ! from Mocsy + cobalt%omega_calc(i,j,k) = cobalt%omegac(i,j,k) ! from Mocsy + cobalt%co3_sol_arag(i,j,k) = cobalt%f_co3_ion(i,j,k) / max(cobalt%omega_arag(i,j,k),epsln) + cobalt%co3_sol_calc(i,j,k) = cobalt%f_co3_ion(i,j,k) / max(cobalt%omega_calc(i,j,k),epsln) + else + call mpp_error(FATAL,"Unable to compute aragonite and calcite saturation states") + endif + + enddo; enddo ; enddo !} i,j,k + + ! + ! 4.2: Calculate the production rate of aragonite and calcite detritus + ! + + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + cobalt%jprod_cadet_arag(i,j,k) = (zoo(2)%jzloss_n(i,j,k)*zoo(3)%phi_det + & + (zoo(2)%jhploss_n(i,j,k) + zoo(3)%jhploss_n(i,j,k))*cobalt%hp_phi_det)* & + cobalt%ca_2_n_arag*min(cobalt%caco3_sat_max, max(0.0,cobalt%omega_arag(i,j,k) - 1.0)) + epsln + cobalt%jprod_cadet_calc(i,j,k) = (zoo(1)%jzloss_n(i,j,k)*zoo(2)%phi_det + & + phyto(SMALL)%jzloss_n(i,j,k)*zoo(1)%phi_det + phyto(LARGE)%jzloss_n(i,j,k)*zoo(3)%phi_det + & + phyto(SMALL)%jaggloss_n(i,j,k) + phyto(LARGE)%jaggloss_n(i,j,k))*cobalt%ca_2_n_calc* & + min(cobalt%caco3_sat_max, max(0.0, cobalt%omega_calc(i,j,k) - 1.0)) + epsln + enddo; enddo ; enddo !} i,j,k + + ! + ! 4.3: Lithogenic detritus production (repackaged from f_lith during filter feeding) + ! + + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + cobalt%jprod_lithdet(i,j,k)=( cobalt%total_filter_feeding(i,j,k)/ & + ( phyto(LARGE)%f_n(i,j,k) + phyto(DIAZO)%f_n(i,j,k) + epsln ) * & + cobalt%phi_lith + cobalt%k_lith ) * cobalt%f_lith(i,j,k) + enddo; enddo ; enddo !} i,j,k + +! +!--------------------------------------------------------------------------------------------------------- +! 5: Detrital dissolution and remineralization calculation +!--------------------------------------------------------------------------------------------------------- +! + + ! + ! 5.1: Dissolution of aragonite, calcite and opal detrital particles + ! + + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + cobalt%jdiss_cadet_arag(i,j,k) = cobalt%gamma_cadet_arag * & + max(0.0, 1.0 - cobalt%omega_arag(i,j,k)) * cobalt%f_cadet_arag(i,j,k) + cobalt%jdiss_cadet_calc(i,j,k) = cobalt%gamma_cadet_calc * & + max(0.0, 1.0 - cobalt%omega_calc(i,j,k)) * cobalt%f_cadet_calc(i,j,k) + !cobalt%jdiss_sidet(i,j,k) = cobalt%gamma_sidet * cobalt%f_sidet(i,j,k) + cobalt%jdiss_sidet(i,j,k) = cobalt%gamma_sidet * exp(cobalt%kappa_sidet * & + Temp(i,j,k)) * cobalt%f_sidet(i,j,k) + cobalt%jprod_sio4(i,j,k) = cobalt%jprod_sio4(i,j,k) + cobalt%jdiss_sidet(i,j,k) + enddo; enddo ; enddo !} i,j,k + + ! + ! 5.2: Remineralization of nitrogen, phosphorous and iron detritus accounting for oxygen + ! and mineral protection + ! + + ! Calculate the depth for scaling of remineralization near the surface + allocate(z_remin_ramp(isd:ied,jsd:jed,1:nk)); z_remin_ramp = 0.0 + z_remin_ramp(:,:,1) = dzt(:,:,1) + do k = 2,nk !{ + z_remin_ramp(:,:,k) = z_remin_ramp(:,:,k-1) + dzt(:,:,k) + enddo !}k +! +!--------------------------------------------------------------------------------------------------------- + + do k=1,nk ; do j=jsc,jec ; do i=isc,iec !{ + cobalt%expkreminT(i,j,k) = exp(cobalt%kappa_remin * Temp(i,j,k)) + !cobalt%jno3denit_wc(i,j,k) = 0.0 + ! + ! Under oxic conditions + ! + if (cobalt%f_o2(i,j,k) .gt. cobalt%o2_min) then !{ + cobalt%jremin_ndet(i,j,k) = cobalt%gamma_ndet * cobalt%expkreminT(i,j,k) * & + z_remin_ramp(i,j,k)/(z_remin_ramp(i,j,k) + cobalt%remin_ramp_scale) * cobalt%f_o2(i,j,k) / & + ( cobalt%k_o2 + cobalt%f_o2(i,j,k) )*max( 0.0, cobalt%f_ndet(i,j,k) - & + cobalt%rpcaco3*(cobalt%f_cadet_arag(i,j,k) + cobalt%f_cadet_calc(i,j,k)) - & + cobalt%rplith*cobalt%f_lithdet(i,j,k) - cobalt%rpsio2*cobalt%f_sidet(i,j,k) ) + cobalt%jprod_nh4(i,j,k) = cobalt%jprod_nh4(i,j,k) + cobalt%jremin_ndet(i,j,k) + cobalt%jo2resp_wc(i,j,k) = cobalt%jo2resp_wc(i,j,k) + cobalt%jremin_ndet(i,j,k)*cobalt%o2_2_nh4 + ! + ! Under sub-oxic conditions + ! + else !}{ + cobalt%jremin_ndet(i,j,k) = cobalt%gamma_ndet * cobalt%o2_min / & + (cobalt%k_o2 + cobalt%o2_min)* & + cobalt%f_no3(i,j,k) / (phyto(SMALL)%k_no3 + cobalt%f_no3(i,j,k))* & + max(0.0, cobalt%f_ndet(i,j,k) - & + cobalt%rpcaco3*(cobalt%f_cadet_arag(i,j,k) + cobalt%f_cadet_calc(i,j,k)) - & + cobalt%rplith*cobalt%f_lithdet(i,j,k) - cobalt%rpsio2*cobalt%f_sidet(i,j,k) ) + cobalt%jno3denit_wc(i,j,k) = cobalt%jno3denit_wc(i,j,k) + cobalt%jremin_ndet(i,j,k) * cobalt%n_2_n_denit + ! uncomment for "no mass change" test + ! cobalt%jno3denit_wc(i,j,k) = 0.0 + ! using TOPAZ stoichiometry, denitrification produces ammonia. + cobalt%jprod_nh4(i,j,k) = cobalt%jprod_nh4(i,j,k) + cobalt%jremin_ndet(i,j,k) + endif !} + ! + ! P and Fe assumed to be protected similarly to N + ! + cobalt%jremin_pdet(i,j,k) = cobalt%jremin_ndet(i,j,k)/(cobalt%f_ndet(i,j,k) + epsln)* & + cobalt%f_pdet(i,j,k) + cobalt%jprod_po4(i,j,k) = cobalt%jprod_po4(i,j,k) + cobalt%jremin_pdet(i,j,k) + cobalt%jremin_fedet(i,j,k) = cobalt%jremin_ndet(i,j,k) / (cobalt%f_ndet(i,j,k) + epsln) * & + cobalt%remin_eff_fedet*cobalt%f_fedet(i,j,k) + cobalt%jprod_fed(i,j,k) = cobalt%jprod_fed(i,j,k) + cobalt%jremin_fedet(i,j,k) + enddo; enddo; enddo !} i,j,k + + deallocate(z_remin_ramp) +! +! +!-------------------------------------------------------------------------------------------- +! 6: Nitrification +!-------------------------------------------------------------------------------------------- +! + + ! + ! Nitrification + ! + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + cobalt%jnitrif(i,j,k) = 0.0 + if (scheme_nitrif .eq. 2 .or. scheme_nitrif .eq. 3) then + if (cobalt%f_o2(i,j,k) .gt. cobalt%o2_min_nit) then !{ + cobalt%jnitrif(i,j,k) = cobalt%gamma_nitrif * & + cobalt%f_nh3(i,j,k)/(cobalt%f_nh3(i,j,k)+cobalt%k_nh3_nitrif) * & + (1.-cobalt%f_irr_mem(i,j,k)/(cobalt%irr_inhibit+cobalt%f_irr_mem(i,j,k))) * & + cobalt%f_o2(i,j,k)/(cobalt%k_o2_nit+cobalt%f_o2(i,j,k)) * cobalt%f_nh4(i,j,k)**2 + + if (scheme_nitrif .eq. 3) then + cobalt%jnitrif(i,j,k) = cobalt%jnitrif(i,j,k)*cobalt%expkT(i,j,k) + end if + end if + elseif (scheme_nitrif .eq. 1) then + if (cobalt%f_o2(i,j,k) .gt. cobalt%o2_min) then !{ + cobalt%jnitrif(i,j,k) = cobalt%gamma_nitrif * cobalt%expkT(i,j,k) * cobalt%f_nh4(i,j,k) * & + phyto(SMALL)%nh4lim(i,j,k) * (1.0 - cobalt%f_irr_mem(i,j,k) / & + (cobalt%irr_inhibit + cobalt%f_irr_mem(i,j,k))) * cobalt%f_o2(i,j,k) / & + ( cobalt%k_o2 + cobalt%f_o2(i,j,k) ) + end if + end if + cobalt%jo2resp_wc(i,j,k) = cobalt%jo2resp_wc(i,j,k) + cobalt%jnitrif(i,j,k)*cobalt%o2_2_nitrif + enddo; enddo; enddo !} i,j,k + +! +! +!-------------------------------------------------------------------------------------------- +! 7: Iron +!-------------------------------------------------------------------------------------------- +! + + ! + ! Iron scavenging and coastal sources + ! + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + cobalt%kfe_eq_lig(i,j,k) = min(cobalt%kfe_eq_lig_ll, 10.0**( log10(cobalt%kfe_eq_lig_hl) + & + max(0.0,log10(cobalt%io_fescav/max(epsln,cobalt%irr_inst(i,j,k)))) ) ) + + cobalt%ligand(i,j,k) = cobalt%felig_bkg + cobalt%felig_2_don*(cobalt%f_sldon(i,j,k) + & + cobalt%f_srdon(i,j,k) + cobalt%f_ldon(i,j,k)) + feprime_temp = 1.0 + cobalt%kfe_eq_lig(i,j,k) * (cobalt%ligand(i,j,k) - cobalt%f_fed(i,j,k)) + cobalt%feprime(i,j,k) = (-feprime_temp + (feprime_temp * feprime_temp + 4.0 * cobalt%kfe_eq_lig(i,j,k) * & + cobalt%f_fed(i,j,k))**(0.5)) / (2.0 * max(epsln,cobalt%kfe_eq_lig(i,j,k))) + + fe_salt = 19.922*Salt(i,j,k)/(1000.0 - 1.005*Salt(i,j,k)) + cobalt%fe_sol(i,j,k) = 10**(-10.53 + 322.5/(Temp(i,j,k)+273.15) - 2.524*sqrt(fe_salt) + & + 2.921*fe_salt) + + ! + ! Iron adsorption to detrital particles + ! + if (cobalt%feprime(i,j,k).lt.cobalt%fe_sol(i,j,k)) then + cobalt%jfe_ads(i,j,k) = cobalt%alpha_fescav*cobalt%feprime(i,j,k) + & + cobalt%beta_fescav*cobalt%feprime(i,j,k)*cobalt%f_ndet(i,j,k) + else + cobalt%jfe_ads(i,j,k) = 10.0*(cobalt%alpha_fescav*cobalt%feprime(i,j,k) + & + cobalt%beta_fescav*cobalt%feprime(i,j,k)*cobalt%f_ndet(i,j,k)) + endif + ! uncomment if running "no mass change" test + !cobalt%jfe_ads(i,j,k) = 0.0 + + enddo; enddo; enddo !} i,j,k + +! +!------------------------------------------------------------------------------------------------- +! 8: Sedimentary/coastal fluxes/transformations +!------------------------------------------------------------------------------------------------- +! + + ! + ! Coastal iron input (default is 0) + ! + !do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + ! cobalt%jfe_coast(i,j,k) = cobalt%fe_coast * mask_coast(i,j) * grid_tmask(i,j,k) / & + ! sqrt(grid_dat(i,j)) + ! ! uncomment if running "no mass change" test + ! !cobalt%jfe_coast(i,j,k) = 0.0 + !enddo; enddo; enddo !} i,j,k + + do j = jsc, jec; do i = isc, iec !{ + k = grid_kmt(i,j) + if (k .gt. 0) then !{ + ! + ! Nitrogen flux from the sediments + ! + if (cobalt%f_ndet_btf(i,j,1) .gt. 0.0) then !{ + ! fpoc_bottom in mmoles C m-2 day-1 for burial relationship + fpoc_btm = cobalt%f_ndet_btf(i,j,1)*cobalt%c_2_n*sperd*1000.0 + !cobalt%frac_burial(i,j) = (0.013 + 0.53*fpoc_btm**2.0)/((7.0+fpoc_btm)**2.0) + cobalt%frac_burial(i,j) = 0.013 + 0.53*fpoc_btm**2.0/((7.0+fpoc_btm)**2.0) * & + cobalt%zt(i,j,k) / (cobalt%z_burial + cobalt%zt(i,j,k)) + ! uncomment for "no mass change" test + !cobalt%frac_burial(i,j) = 0.0 + cobalt%fndet_burial(i,j) = cobalt%frac_burial(i,j)*cobalt%f_ndet_btf(i,j,1) + cobalt%fpdet_burial(i,j) = cobalt%frac_burial(i,j)*cobalt%f_pdet_btf(i,j,1) + ! fpoc_bottom in micromoles C cm-2 day-1 for denitrification relationship, cap at 43 + ! to prevent anomalous extrapolation of the relationship + log_fpoc_btm = log(min(43.0,0.1*fpoc_btm)) + cobalt%fno3denit_sed(i,j) = min(cobalt%f_no3(i,j,k)*cobalt%Rho_0*r_dt, & + min((cobalt%f_ndet_btf(i,j,1)-cobalt%fndet_burial(i,j))*cobalt%n_2_n_denit, & + 10.0**(-0.9543+0.7662*log_fpoc_btm - 0.235*log_fpoc_btm**2.0)/(cobalt%c_2_n*sperd*100.0)* & + cobalt%n_2_n_denit*cobalt%f_no3(i,j,k)/(cobalt%k_no3_denit + cobalt%f_no3(i,j,k)))) * & + cobalt%zt(i,j,k) / (cobalt%z_burial + cobalt%zt(i,j,k)) + ! uncomment "no mass change" test + !cobalt%fno3denit_sed(i,j) = 0.0 + if (cobalt%f_o2(i,j,k) .gt. cobalt%o2_min) then !{ + cobalt%fnoxic_sed(i,j) = max(0.0, min(cobalt%f_o2(i,j,k)*cobalt%Rho_0*r_dt*(1.0/cobalt%o2_2_nh4), & + cobalt%f_ndet_btf(i,j,1) - cobalt%fndet_burial(i,j) - & + cobalt%fno3denit_sed(i,j)/cobalt%n_2_n_denit)) + else + cobalt%fnoxic_sed(i,j) = 0.0 + endif !} + cobalt%fno3denit_sed(i,j) = cobalt%fno3denit_sed(i,j) + & + min(cobalt%f_no3(i,j,k)*cobalt%Rho_0*r_dt-cobalt%fno3denit_sed(i,j), & + (cobalt%f_ndet_btf(i,j,1)-cobalt%fnoxic_sed(i,j)-cobalt%fndet_burial(i,j) - & + cobalt%fno3denit_sed(i,j)/cobalt%n_2_n_denit)*cobalt%n_2_n_denit) + cobalt%fnfeso4red_sed(i,j) = max(0.0, cobalt%f_ndet_btf(i,j,1)-cobalt%fnoxic_sed(i,j)- & + cobalt%fndet_burial(i,j)-cobalt%fno3denit_sed(i,j)/cobalt%n_2_n_denit) + else + cobalt%fnfeso4red_sed(i,j) = 0.0 + cobalt%fno3denit_sed(i,j) = 0.0 + cobalt%fnoxic_sed(i,j) = 0.0 + endif !} + + ! iron from sediment (Elrod) + !cobalt%ffe_sed(i,j) = cobalt%fe_2_n_sed * cobalt%f_ndet_btf(i,j,1) + ! iron from sediment (Dale) + cobalt%ffe_sed(i,j) = cobalt%ffe_sed_max * tanh( (cobalt%f_ndet_btf(i,j,1)*cobalt%c_2_n*sperd*1.0e3)/ & + max(cobalt%f_o2(i,j,k)*1.0e6,epsln) ) + + cobalt%ffe_geotherm(i,j) = cobalt%ffe_geotherm_ratio*internal_heat(i,j)*4184.0/dt + ! default for icebergs: 40 nanomoles fe dissolved per kg of icemelt + ! sediments: Raiswell et al., 2008: 0.5 kg sed per m-3 of iceberg; 0.1% mean Fe, 5-10% soluble + ! ~500 nanomoles Fe per kg-1 icemelt + cobalt%ffe_iceberg(i,j) = cobalt%ffe_iceberg_ratio*max(frunoff(i,j),0.0) + cobalt%jprod_fed(i,j,1) = cobalt%jprod_fed(i,j,1) + cobalt%ffe_iceberg(i,j)/rho_dzt(i,j,1) + + ! + ! Calcium carbonate flux and burial + ! 2015/11/18 JGJ: fix from JPD to cap the absolute cased dissolution rate to 10 mmol m-2 d-1 + ! Calcite cycling in the sediments is based on the model of Dunne et al., 2012. + ! + ! phi_surfresp_cased = 0.14307 ! const for enhanced diss., surf sed respiration (dimensionless) + ! phi_deepresp_cased = 4.1228 ! const for enhanced diss., deep sed respiration (dimensionless) + ! alpha_cased = 2.7488 ! exponent controlling non-linearity of deep dissolution + ! beta_cased = -2.2185 ! exponent controlling non-linearity of effective thickness + ! gamma_cased = 0.03607/spery ! dissolution rate constant + ! Co_cased = 8.1e3 ! moles CaCo3 m-3 for pure calcite sediment with porosity = 0.7 + ! + ! if cased_steady is true, burial is calculated from Dunne's eq. (2) assuming dcased/dt = 0. + ! This ensures that all the calcite bottom flux is partitioned between burial and redissolution. + ! The steady state cased value of cased is calculated to reflect the changing bottom conditions. + ! This influences the the partitioning of burial and redissolution over time, but there are + ! no alkalinity changes/drifts associated with the long-term evolution of cased + ! + ! If cased_steady is false, calcite is partitioned between dissolution, burial and evolving + ! cased as described in Dunne et al. (2012). The multi-century scale evolution of cased + ! impacts alkalinity, but care must to ensure that cased starts in equilibrium with the + ! mean ocean state to avoid unrealistic drifts. + + ! Enhanced dissolution by fast respiration near the sediment surface, proportional + ! to organic flux, moles Ca m-2 s-1, limited to a max 1/2 the instantaneous calcite flux + cobalt%fcased_redis_surfresp(i,j)=min(0.5*cobalt%f_cadet_calc_btf(i,j,1), & + cobalt%phi_surfresp_cased*cobalt%f_ndet_btf(i,j,1)*cobalt%c_2_n) + ! Ca-specific dissolution coeficient, depends on calcite saturation state and is enhanced by + ! respiration deep in the sediment (s-1), non-linearity controlled by alpha_cased + cobalt%cased_redis_coef(i,j) = cobalt%gamma_cased*max(0.0,1.0-cobalt%omega_calc(i,j,k)+ & + cobalt%phi_deepresp_cased*cobalt%f_ndet_btf(i,j,1)*cobalt%c_2_n*spery)**cobalt%alpha_cased + ! Effective thickness term that enhances burial of calcite when total sediment accumulation is high + ! dimensionless value between 0 and 1 + cobalt%cased_redis_delz(i,j) = max(1.0, & + cobalt%f_lithdet_btf(i,j,1)*spery+cobalt%f_cadet_calc_btf(i,j,1)*100.0*spery)**cobalt%beta_cased + ! calculate the sediment redissolution rate (moles Ca m-2 sec-1). This calculation is subject to + ! three limiters: a) a maximum of 1/2 of the total cased over one time step; b) a maximum of 0.01 + ! moles Ca per day; and c) a minimum of 0.0 + cobalt%fcased_redis(i,j) = max(0.0, min(0.01/sperd, min(0.5*cobalt%f_cased(i,j,1)*r_dt, & + cobalt%fcased_redis_surfresp(i,j)+cobalt%cased_redis_coef(i,j)*cobalt%cased_redis_delz(i,j)*cobalt%f_cased(i,j,1))) ) + ! + ! Old expression + ! + !cobalt%fcased_redis(i,j) = max(0.0, min(0.01/sperd,min(0.5 * cobalt%f_cased(i,j,1) * r_dt, min(0.5 * & + ! cobalt%f_cadet_calc_btf(i,j,1), 0.14307 * cobalt%f_ndet_btf(i,j,1) * cobalt%c_2_n) + & + ! 0.03607 / spery * max(0.0, 1.0 - cobalt%omega_calc(i,j,k) + & + ! 4.1228 * cobalt%f_ndet_btf(i,j,1) * cobalt%c_2_n * spery)**(2.7488) * & + ! max(1.0, cobalt%f_lithdet_btf(i,j,1) * spery + cobalt%f_cadet_calc_btf(i,j,1) * 100.0 * & + ! spery)**(-2.2185) * cobalt%f_cased(i,j,1))))*grid_tmask(i,j,k) + + if (cobalt%cased_steady) then + cobalt%fcased_burial(i,j) = cobalt%f_cadet_calc_btf(i,j,1) - cobalt%fcased_redis(i,j) + cobalt%f_cased(i,j,1) = cobalt%fcased_burial(i,j)*cobalt%Co_cased/cobalt%f_cadet_calc_btf(i,j,1) + else + cobalt%fcased_burial(i,j) = max(0.0, cobalt%f_cadet_calc_btf(i,j,1) * cobalt%f_cased(i,j,1) / & + cobalt%Co_cased) + cobalt%f_cased(i,j,1) = cobalt%f_cased(i,j,1) + (cobalt%f_cadet_calc_btf(i,j,1) - & + cobalt%fcased_redis(i,j) - cobalt%fcased_burial(i,j)) / cobalt%z_sed * dt * & + grid_tmask(i,j,k) + endif + + ! uncomment for "no mass change" test (next 3 lines) + !cobalt%fcased_redis(i,j) = cobalt%f_cadet_calc_btf(i,j,1) + !cobalt%fcased_burial(i,j) = 0.0 + !cobalt%f_cased(i,j,1) = cobalt%f_cased(i,j,1) + ! + ! Bottom flux boundaries passed to the vertical mixing routine + ! + + cobalt%b_alk(i,j) = - 2.0*(cobalt%fcased_redis(i,j)+cobalt%f_cadet_arag_btf(i,j,1)) - & + cobalt%fnoxic_sed(i,j) - cobalt%fno3denit_sed(i,j)*cobalt%alk_2_n_denit + cobalt%b_dic(i,j) = - cobalt%fcased_redis(i,j) - cobalt%f_cadet_arag_btf(i,j,1) - & + (cobalt%f_ndet_btf(i,j,1) - cobalt%fndet_burial(i,j)) * cobalt%c_2_n + ! uncomment for "no mass change" test (next 2 lines) + !cobalt%b_dic(i,j) = - cobalt%f_cadet_calc_btf(i,j,1) - cobalt%f_cadet_arag_btf(i,j,1) - & + ! (cobalt%f_ndet_btf(i,j,1) - cobalt%fndet_burial(i,j)) * cobalt%c_2_n + cobalt%b_fed(i,j) = - cobalt%ffe_sed(i,j) - cobalt%ffe_geotherm(i,j) + ! uncomment for "no mass change" test (next line) + !cobalt%b_fed(i,j) = - cobalt%f_fedet_btf(i,j,1) + cobalt%b_nh4(i,j) = - cobalt%f_ndet_btf(i,j,1) + cobalt%fndet_burial(i,j) + cobalt%b_no3(i,j) = cobalt%fno3denit_sed(i,j) + cobalt%b_o2(i,j) = cobalt%o2_2_nh4 * (cobalt%fnoxic_sed(i,j) + cobalt%fnfeso4red_sed(i,j)) + cobalt%b_po4(i,j) = - cobalt%f_pdet_btf(i,j,1) + cobalt%fpdet_burial(i,j) + cobalt%b_sio4(i,j)= - cobalt%f_sidet_btf(i,j,1) + + endif !} + enddo; enddo !} i, j + + do k = 2, nk ; do j = jsc, jec ; do i = isc, iec !{ + cobalt%f_cased(i,j,k) = 0.0 + enddo; enddo ; enddo !} i,j,k + + call mpp_clock_end(id_clock_ballast_loops) + + call g_tracer_set_values(tracer_list,'alk', 'btf', cobalt%b_alk ,isd,jsd) + call g_tracer_set_values(tracer_list,'dic', 'btf', cobalt%b_dic ,isd,jsd) + call g_tracer_set_values(tracer_list,'fed', 'btf', cobalt%b_fed ,isd,jsd) + call g_tracer_set_values(tracer_list,'nh4', 'btf', cobalt%b_nh4 ,isd,jsd) + call g_tracer_set_values(tracer_list,'no3', 'btf', cobalt%b_no3 ,isd,jsd) + call g_tracer_set_values(tracer_list,'o2', 'btf', cobalt%b_o2 ,isd,jsd) + call g_tracer_set_values(tracer_list,'po4', 'btf', cobalt%b_po4 ,isd,jsd) + call g_tracer_set_values(tracer_list,'sio4', 'btf', cobalt%b_sio4,isd,jsd) +! + call mpp_clock_begin(id_clock_source_sink_loop1) +! +!----------------------------------------------------------------------- +! 8: Source/sink calculations +!----------------------------------------------------------------------- +! + ! + !------------------------------------------------------------------- + ! 4.1: Update the prognostics tracer fields via their pointers. + !------------------------------------------------------------------- + ! + call g_tracer_get_pointer(tracer_list,'alk' ,'field',cobalt%p_alk ) + call g_tracer_get_pointer(tracer_list,'cadet_arag','field',cobalt%p_cadet_arag) + call g_tracer_get_pointer(tracer_list,'cadet_calc','field',cobalt%p_cadet_calc) + call g_tracer_get_pointer(tracer_list,'dic' ,'field',cobalt%p_dic ) + call g_tracer_get_pointer(tracer_list,'fed' ,'field',cobalt%p_fed ) + call g_tracer_get_pointer(tracer_list,'fedi' ,'field',cobalt%p_fedi ) + call g_tracer_get_pointer(tracer_list,'felg' ,'field',cobalt%p_felg ) + call g_tracer_get_pointer(tracer_list,'fesm' ,'field',cobalt%p_fesm ) + call g_tracer_get_pointer(tracer_list,'fedet' ,'field',cobalt%p_fedet ) + call g_tracer_get_pointer(tracer_list,'ldon' ,'field',cobalt%p_ldon ) + call g_tracer_get_pointer(tracer_list,'ldop' ,'field',cobalt%p_ldop ) + call g_tracer_get_pointer(tracer_list,'lith' ,'field',cobalt%p_lith ) + call g_tracer_get_pointer(tracer_list,'lithdet','field',cobalt%p_lithdet) + call g_tracer_get_pointer(tracer_list,'nbact' ,'field',cobalt%p_nbact ) + call g_tracer_get_pointer(tracer_list,'ndet' ,'field',cobalt%p_ndet ) + call g_tracer_get_pointer(tracer_list,'ndi' ,'field',cobalt%p_ndi ) + call g_tracer_get_pointer(tracer_list,'nlg' ,'field',cobalt%p_nlg ) + call g_tracer_get_pointer(tracer_list,'nsm' ,'field',cobalt%p_nsm ) + call g_tracer_get_pointer(tracer_list,'nh4' ,'field',cobalt%p_nh4 ) + call g_tracer_get_pointer(tracer_list,'no3' ,'field',cobalt%p_no3 ) + call g_tracer_get_pointer(tracer_list,'o2' ,'field',cobalt%p_o2 ) + call g_tracer_get_pointer(tracer_list,'pdet' ,'field',cobalt%p_pdet ) + call g_tracer_get_pointer(tracer_list,'po4' ,'field',cobalt%p_po4 ) + call g_tracer_get_pointer(tracer_list,'srdon' ,'field',cobalt%p_srdon ) + call g_tracer_get_pointer(tracer_list,'srdop' ,'field',cobalt%p_srdop ) + call g_tracer_get_pointer(tracer_list,'sldon' ,'field',cobalt%p_sldon ) + call g_tracer_get_pointer(tracer_list,'sldop' ,'field',cobalt%p_sldop ) + call g_tracer_get_pointer(tracer_list,'sidet' ,'field',cobalt%p_sidet ) + call g_tracer_get_pointer(tracer_list,'silg' ,'field',cobalt%p_silg ) + call g_tracer_get_pointer(tracer_list,'sio4' ,'field',cobalt%p_sio4 ) + call g_tracer_get_pointer(tracer_list,'nsmz' ,'field',cobalt%p_nsmz ) + call g_tracer_get_pointer(tracer_list,'nmdz' ,'field',cobalt%p_nmdz ) + call g_tracer_get_pointer(tracer_list,'nlgz' ,'field',cobalt%p_nlgz ) + + if (do_14c) then + call g_tracer_get_pointer(tracer_list,'di14c','field',cobalt%p_di14c) + call g_tracer_get_pointer(tracer_list,'do14c','field',cobalt%p_do14c) + endif + + ! CAS calculate total N and P before source/sink + ! calculate internal sources (those not applied as air-sea or benthos + ! exchanges) to close the balance + allocate(pre_totn(isc:iec,jsc:jec,1:nk)) + allocate(pre_totc(isc:iec,jsc:jec,1:nk)) + allocate(net_srcn(isc:iec,jsc:jec,1:nk)) + allocate(pre_totp(isc:iec,jsc:jec,1:nk)) + allocate(pre_totfe(isc:iec,jsc:jec,1:nk)) + allocate(net_srcfe(isc:iec,jsc:jec,1:nk)) + allocate(pre_totsi(isc:iec,jsc:jec,1:nk)) + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + pre_totn(i,j,k) = (cobalt%p_no3(i,j,k,tau) + cobalt%p_nh4(i,j,k,tau) + & + cobalt%p_ndi(i,j,k,tau) + cobalt%p_nlg(i,j,k,tau) + & + cobalt%p_nsm(i,j,k,tau) + cobalt%p_nbact(i,j,k,tau) + & + cobalt%p_ldon(i,j,k,tau) + cobalt%p_sldon(i,j,k,tau) + & + cobalt%p_srdon(i,j,k,tau) + cobalt%p_ndet(i,j,k,tau) + & + cobalt%p_nsmz(i,j,k,tau) + cobalt%p_nmdz(i,j,k,tau) + & + cobalt%p_nlgz(i,j,k,tau))*grid_tmask(i,j,k) + net_srcn(i,j,k) = (phyto(DIAZO)%juptake_n2(i,j,k) - cobalt%jno3denit_wc(i,j,k))* & + dt*grid_tmask(i,j,k) + pre_totc(i,j,k) = (cobalt%p_dic(i,j,k,tau) + & + cobalt%p_cadet_arag(i,j,k,tau) + cobalt%p_cadet_calc(i,j,k,tau) + & + cobalt%c_2_n*(cobalt%p_ndi(i,j,k,tau) + cobalt%p_nlg(i,j,k,tau) + & + cobalt%p_nsm(i,j,k,tau) + cobalt%p_nbact(i,j,k,tau) + & + cobalt%p_ldon(i,j,k,tau) + cobalt%p_sldon(i,j,k,tau) + & + cobalt%p_srdon(i,j,k,tau) + cobalt%p_ndet(i,j,k,tau) + & + cobalt%p_nsmz(i,j,k,tau) + cobalt%p_nmdz(i,j,k,tau) + & + cobalt%p_nlgz(i,j,k,tau)))*grid_tmask(i,j,k) + pre_totp(i,j,k) = (cobalt%p_po4(i,j,k,tau) + cobalt%p_ndi(i,j,k,tau)*phyto(1)%p_2_n_static + & + cobalt%p_nlg(i,j,k,tau)*phyto(2)%p_2_n_static + & + cobalt%p_nsm(i,j,k,tau)*phyto(3)%p_2_n_static + & + cobalt%p_ldop(i,j,k,tau) + cobalt%p_sldop(i,j,k,tau) + & + cobalt%p_srdop(i,j,k,tau) + cobalt%p_pdet(i,j,k,tau) + & + cobalt%p_nsmz(i,j,k,tau)*zoo(1)%q_p_2_n + & + cobalt%p_nmdz(i,j,k,tau)*zoo(2)%q_p_2_n + & + cobalt%p_nlgz(i,j,k,tau)*zoo(3)%q_p_2_n + & + bact(1)%q_p_2_n*cobalt%p_nbact(i,j,k,tau))*grid_tmask(i,j,k) + pre_totfe(i,j,k) = (cobalt%p_fed(i,j,k,tau) + cobalt%p_fedi(i,j,k,tau) + & + cobalt%p_felg(i,j,k,tau) + cobalt%p_fesm(i,j,k,tau) + & + cobalt%p_fedet(i,j,k,tau))*grid_tmask(i,j,k) + net_srcfe(i,j,k) = cobalt%jfe_coast(i,j,k)*dt*grid_tmask(i,j,k) + pre_totsi(i,j,k) = (cobalt%p_sio4(i,j,k,tau) + cobalt%p_silg(i,j,k,tau) + & + cobalt%p_sidet(i,j,k,tau))*grid_tmask(i,j,k) + enddo; enddo ; enddo !} i,j,k + do j = jsc, jec ; do i = isc, iec !{ + net_srcfe(i,j,1) = net_srcfe(i,j,1)+cobalt%ffe_iceberg(i,j)*dt*grid_tmask(i,j,1)/rho_dzt(i,j,1) + enddo; enddo + + if (cobalt%id_no3_in_source .gt. 0) & + used = g_send_data(cobalt%id_no3_in_source, cobalt%f_no3, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + call mpp_clock_end(id_clock_source_sink_loop1) + ! + !----------------------------------------------------------------------- + ! 4.2: Source sink calculations + !----------------------------------------------------------------------- + ! + ! Phytoplankton Nitrogen and Phosphorus + ! + call mpp_clock_begin(id_clock_source_sink_loop2) + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + ! + ! Diazotrophic Phytoplankton Nitrogen + ! + cobalt%jndi(i,j,k) = phyto(DIAZO)%mu(i,j,k)*phyto(DIAZO)%f_n(i,j,k) - & + phyto(DIAZO)%jzloss_n(i,j,k) - & + phyto(DIAZO)%jhploss_n(i,j,k) - phyto(DIAZO)%jaggloss_n(i,j,k) - & + phyto(DIAZO)%jvirloss_n(i,j,k) - phyto(DIAZO)%jexuloss_n(i,j,k) + cobalt%p_ndi(i,j,k,tau) = cobalt%p_ndi(i,j,k,tau) + cobalt%jndi(i,j,k)*dt*grid_tmask(i,j,k) + ! + ! Large Phytoplankton Nitrogen + ! + cobalt%jnlg(i,j,k) = phyto(LARGE)%mu(i,j,k)*phyto(LARGE)%f_n(i,j,k) - & + phyto(LARGE)%jzloss_n(i,j,k) - phyto(LARGE)%jhploss_n(i,j,k) - & + phyto(LARGE)%jaggloss_n(i,j,k) - phyto(LARGE)%jvirloss_n(i,j,k) - & + phyto(LARGE)%jexuloss_n(i,j,k) + cobalt%p_nlg(i,j,k,tau) = cobalt%p_nlg(i,j,k,tau) + cobalt%jnlg(i,j,k)*dt*grid_tmask(i,j,k) + ! + ! Small Phytoplankton Nitrogen + ! + cobalt%jnsm(i,j,k) = phyto(SMALL)%mu(i,j,k)*phyto(SMALL)%f_n(i,j,k) - & + phyto(SMALL)%jzloss_n(i,j,k) - phyto(SMALL)%jhploss_n(i,j,k) - & + phyto(SMALL)%jaggloss_n(i,j,k) - phyto(SMALL)%jvirloss_n(i,j,k) - & + phyto(SMALL)%jexuloss_n(i,j,k) + cobalt%p_nsm(i,j,k,tau) = cobalt%p_nsm(i,j,k,tau) + cobalt%jnsm(i,j,k)*dt*grid_tmask(i,j,k) + enddo; enddo ; enddo !} i,j,k +! + call mpp_clock_end(id_clock_source_sink_loop2) + ! + ! Phytoplankton Silicon and Iron + ! + call mpp_clock_begin(id_clock_source_sink_loop3) + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + ! + ! Large Phytoplankton Silicon + ! + cobalt%jsilg(i,j,k) = phyto(LARGE)%juptake_sio4(i,j,k) - & + phyto(LARGE)%jzloss_sio2(i,j,k) - phyto(LARGE)%jhploss_sio2(i,j,k) - & + phyto(LARGE)%jaggloss_sio2(i,j,k) - phyto(LARGE)%jvirloss_sio2(i,j,k) + cobalt%p_silg(i,j,k,tau) = cobalt%p_silg(i,j,k,tau) + cobalt%jsilg(i,j,k)*dt*grid_tmask(i,j,k) + ! + ! Diazotrophic Phytoplankton Iron + ! + cobalt%jfedi(i,j,k) = phyto(DIAZO)%juptake_fe(i,j,k) - & + phyto(DIAZO)%jzloss_fe(i,j,k) - & + phyto(DIAZO)%jhploss_fe(i,j,k) - phyto(DIAZO)%jaggloss_fe(i,j,k) - & + phyto(DIAZO)%jvirloss_fe(i,j,k) - phyto(DIAZO)%jexuloss_fe(i,j,k) + cobalt%p_fedi(i,j,k,tau) = cobalt%p_fedi(i,j,k,tau) + cobalt%jfedi(i,j,k)*dt*grid_tmask(i,j,k) + ! + ! Large Phytoplankton Iron + ! + cobalt%jfelg(i,j,k) = phyto(LARGE)%juptake_fe(i,j,k) - & + phyto(LARGE)%jzloss_fe(i,j,k) - & + phyto(LARGE)%jhploss_fe(i,j,k) - phyto(LARGE)%jaggloss_fe(i,j,k) - & + phyto(LARGE)%jvirloss_fe(i,j,k) - phyto(LARGE)%jexuloss_fe(i,j,k) + cobalt%p_felg(i,j,k,tau) = cobalt%p_felg(i,j,k,tau) + cobalt%jfelg(i,j,k)*dt*grid_tmask(i,j,k) + ! + ! Small Phytoplankton Iron + ! + cobalt%jfesm(i,j,k) = phyto(SMALL)%juptake_fe(i,j,k) - & + phyto(SMALL)%jzloss_fe(i,j,k) - & + phyto(SMALL)%jhploss_fe(i,j,k) - phyto(SMALL)%jaggloss_fe(i,j,k) - & + phyto(SMALL)%jvirloss_fe(i,j,k) - phyto(SMALL)%jexuloss_fe(i,j,k) + cobalt%p_fesm(i,j,k,tau) = cobalt%p_fesm(i,j,k,tau) + cobalt%jfesm(i,j,k)*dt*grid_tmask(i,j,k) + ! + ! Bacteria + ! + cobalt%jnbact(i,j,k) = bact(1)%jprod_n(i,j,k) - bact(1)%jzloss_n(i,j,k) - & + bact(1)%jvirloss_n(i,j,k) - bact(1)%jhploss_n(i,j,k) + cobalt%p_nbact(i,j,k,tau) = cobalt%p_nbact(i,j,k,tau) + cobalt%jnbact(i,j,k)*dt*grid_tmask(i,j,k) + enddo; enddo ; enddo !} i,j,k + + call mpp_clock_end(id_clock_source_sink_loop3) + ! + ! Zooplankton + ! + call mpp_clock_begin(id_clock_source_sink_loop4) + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + ! + ! Small zooplankton + ! + cobalt%jnsmz(i,j,k) = zoo(1)%jprod_n(i,j,k) - zoo(1)%jzloss_n(i,j,k) - & + zoo(1)%jhploss_n(i,j,k) + cobalt%p_nsmz(i,j,k,tau) = cobalt%p_nsmz(i,j,k,tau) + cobalt%jnsmz(i,j,k)*dt*grid_tmask(i,j,k) + ! + ! Medium zooplankton + ! + cobalt%jnmdz(i,j,k) = zoo(2)%jprod_n(i,j,k) - zoo(2)%jzloss_n(i,j,k) - & + zoo(2)%jhploss_n(i,j,k) + cobalt%p_nmdz(i,j,k,tau) = cobalt%p_nmdz(i,j,k,tau) + cobalt%jnmdz(i,j,k)*dt*grid_tmask(i,j,k) + ! + ! Large zooplankton + ! + cobalt%jnlgz(i,j,k) = zoo(3)%jprod_n(i,j,k) - zoo(3)%jzloss_n(i,j,k) - & + zoo(3)%jhploss_n(i,j,k) + cobalt%p_nlgz(i,j,k,tau) = cobalt%p_nlgz(i,j,k,tau) + cobalt%jnlgz(i,j,k)*dt*grid_tmask(i,j,k) + enddo; enddo ; enddo !} i,j,k +! + call mpp_clock_end(id_clock_source_sink_loop4) + ! + ! NO3 + ! + call mpp_clock_begin(id_clock_source_sink_loop5) + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + cobalt%jno3(i,j,k) = cobalt%jnitrif(i,j,k) - phyto(DIAZO)%juptake_no3(i,j,k) - & + phyto(LARGE)%juptake_no3(i,j,k) - phyto(SMALL)%juptake_no3(i,j,k) - & + cobalt%jno3denit_wc(i,j,k) + cobalt%p_no3(i,j,k,tau) = cobalt%p_no3(i,j,k,tau) + cobalt%jno3(i,j,k)*dt*grid_tmask(i,j,k) + enddo; enddo ; enddo !} i,j,k + ! + ! Other nutrients + ! + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + ! + ! NH4 + ! + cobalt%jnh4(i,j,k) = cobalt%jprod_nh4(i,j,k) - phyto(DIAZO)%juptake_nh4(i,j,k) - & + phyto(LARGE)%juptake_nh4(i,j,k) - phyto(SMALL)%juptake_nh4(i,j,k) - & + cobalt%jnitrif(i,j,k) + cobalt%p_nh4(i,j,k,tau) = cobalt%p_nh4(i,j,k,tau) + cobalt%jnh4(i,j,k) * dt * grid_tmask(i,j,k) + ! + ! PO4 + ! + cobalt%jpo4(i,j,k) = cobalt%jprod_po4(i,j,k) - phyto(DIAZO)%juptake_po4(i,j,k) - & + phyto(LARGE)%juptake_po4(i,j,k) - phyto(SMALL)%juptake_po4(i,j,k) + cobalt%p_po4(i,j,k,tau) = cobalt%p_po4(i,j,k,tau) + cobalt%jpo4(i,j,k) * dt * grid_tmask(i,j,k) + ! + ! SiO4 + ! + cobalt%jsio4(i,j,k) = cobalt%jprod_sio4(i,j,k) - phyto(LARGE)%juptake_sio4(i,j,k) + cobalt%p_sio4(i,j,k,tau) = cobalt%p_sio4(i,j,k,tau) + cobalt%jsio4(i,j,k) * dt * grid_tmask(i,j,k) + enddo; enddo ; enddo !} i,j,k + + ! 2016/06/13 JGJ: keep original Fed calculation + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + ! + ! Fed + ! use original code to compute jprod_fed, jfed and p_fed + ! + cobalt%jprod_fed(i,j,k) = cobalt%jprod_fed(i,j,k) + cobalt%jfe_coast(i,j,k) + cobalt%jfed(i,j,k) = cobalt%jprod_fed(i,j,k) - phyto(DIAZO)%juptake_fe(i,j,k) - & + phyto(LARGE)%juptake_fe(i,j,k) - phyto(SMALL)%juptake_fe(i,j,k) - & + cobalt%jfe_ads(i,j,k) + cobalt%p_fed(i,j,k,tau) = cobalt%p_fed(i,j,k,tau) + cobalt%jfed(i,j,k) * dt * grid_tmask(i,j,k) + enddo; enddo; enddo !} i,j,k + + call mpp_clock_end(id_clock_source_sink_loop5) + ! + !----------------------------------------------------------------------- + ! Detrital Components + !----------------------------------------------------------------------- + ! + call mpp_clock_begin(id_clock_source_sink_loop6) + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + ! + ! Cadet_arag + ! + cobalt%jcadet_arag(i,j,k) = cobalt%jprod_cadet_arag(i,j,k) - cobalt%jdiss_cadet_arag(i,j,k) + cobalt%p_cadet_arag(i,j,k,tau) = cobalt%p_cadet_arag(i,j,k,tau) + cobalt%jcadet_arag(i,j,k)*dt*grid_tmask(i,j,k) + ! + ! Cadet_calc + ! + cobalt%jcadet_calc(i,j,k) = cobalt%jprod_cadet_calc(i,j,k) - cobalt%jdiss_cadet_calc(i,j,k) + cobalt%p_cadet_calc(i,j,k,tau) = cobalt%p_cadet_calc(i,j,k,tau) + cobalt%jcadet_calc(i,j,k)*dt*grid_tmask(i,j,k) + ! + ! Lithdet + ! + cobalt%jlithdet(i,j,k) = cobalt%jprod_lithdet(i,j,k) + cobalt%p_lithdet(i,j,k,tau) = cobalt%p_lithdet(i,j,k,tau) + cobalt%jlithdet(i,j,k) * dt * & + grid_tmask(i,j,k) + ! + ! Ndet + ! + cobalt%jndet(i,j,k) = cobalt%jprod_ndet(i,j,k) - cobalt%jremin_ndet(i,j,k) - & + cobalt%det_jzloss_n(i,j,k) - cobalt%det_jhploss_n(i,j,k) + cobalt%p_ndet(i,j,k,tau) = cobalt%p_ndet(i,j,k,tau) + cobalt%jndet(i,j,k)*dt*grid_tmask(i,j,k) + ! + ! Pdet + ! + cobalt%jpdet(i,j,k) = cobalt%jprod_pdet(i,j,k) - cobalt%jremin_pdet(i,j,k) - & + cobalt%det_jzloss_p(i,j,k) - cobalt%det_jhploss_p(i,j,k) + cobalt%p_pdet(i,j,k,tau) = cobalt%p_pdet(i,j,k,tau) + cobalt%jpdet(i,j,k)*dt*grid_tmask(i,j,k) + ! + ! Sidet + ! + cobalt%jsidet(i,j,k) = cobalt%jprod_sidet(i,j,k) - & + cobalt%jdiss_sidet(i,j,k) - cobalt%det_jzloss_si(i,j,k) - & + cobalt%det_jhploss_si(i,j,k) + cobalt%p_sidet(i,j,k,tau) = cobalt%p_sidet(i,j,k,tau) + cobalt%jsidet(i,j,k)*dt*grid_tmask(i,j,k) + enddo; enddo ; enddo !} i,j,k + + ! 2016/06/13 JGJ: keep original jfedet calculation + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + ! + ! Fedet + ! use original code to compute fedet + ! + cobalt%jprod_fedet(i,j,k) = cobalt%jprod_fedet(i,j,k) + cobalt%jfe_ads(i,j,k) + cobalt%jfedet(i,j,k) = cobalt%jprod_fedet(i,j,k) - & + cobalt%jremin_fedet(i,j,k) - cobalt%det_jzloss_fe(i,j,k) - & + cobalt%det_jhploss_fe(i,j,k) + cobalt%p_fedet(i,j,k,tau) = cobalt%p_fedet(i,j,k,tau) + cobalt%jfedet(i,j,k)*dt*grid_tmask(i,j,k) + enddo; enddo; enddo !} i,j,k + ! + ! Dissolved Organic Matter + ! + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + ! + ! Labile Dissolved Organic Nitrogen + ! + cobalt%jldon(i,j,k) = cobalt%jprod_ldon(i,j,k) + & + cobalt%gamma_sldon*cobalt%f_sldon(i,j,k) + & + cobalt%gamma_srdon*cobalt%f_srdon(i,j,k) - bact(1)%juptake_ldon(i,j,k) + cobalt%p_ldon(i,j,k,tau) = cobalt%p_ldon(i,j,k,tau) + cobalt%jldon(i,j,k)*dt* & + grid_tmask(i,j,k) + ! + ! Labile Dissolved Organic Phosphorous + ! + cobalt%jldop(i,j,k) = cobalt%jprod_ldop(i,j,k) + & + cobalt%gamma_sldop*cobalt%f_sldop(i,j,k) + & + cobalt%gamma_srdop*cobalt%f_srdop(i,j,k) - bact(1)%juptake_ldop(i,j,k) + cobalt%p_ldop(i,j,k,tau) = cobalt%p_ldop(i,j,k,tau) + cobalt%jldop(i,j,k)*dt* & + grid_tmask(i,j,k) + ! + ! Semilabile Dissolved Organic Nitrogen + ! + cobalt%jsldon(i,j,k) = cobalt%jprod_sldon(i,j,k) - & + cobalt%gamma_sldon*cobalt%f_sldon(i,j,k) + cobalt%p_sldon(i,j,k,tau) = cobalt%p_sldon(i,j,k,tau) + cobalt%jsldon(i,j,k) * dt * & + grid_tmask(i,j,k) + ! + ! Semilabile dissolved organic phosphorous + ! + cobalt%jsldop(i,j,k) = cobalt%jprod_sldop(i,j,k) - & + cobalt%gamma_sldop*cobalt%f_sldop(i,j,k) + cobalt%p_sldop(i,j,k,tau) = cobalt%p_sldop(i,j,k,tau) + cobalt%jsldop(i,j,k) * dt * & + grid_tmask(i,j,k) + ! + ! Refractory Dissolved Organic Nitrogen + ! + cobalt%jsrdon(i,j,k) = cobalt%jprod_srdon(i,j,k) - cobalt%gamma_srdon * cobalt%f_srdon(i,j,k) + cobalt%p_srdon(i,j,k,tau) = cobalt%p_srdon(i,j,k,tau) + cobalt%jsrdon(i,j,k) * dt * & + grid_tmask(i,j,k) + ! + ! Refractory dissolved organic phosphorous + ! + cobalt%jsrdop(i,j,k) = cobalt%jprod_srdop(i,j,k) - cobalt%gamma_srdop * cobalt%f_srdop(i,j,k) + cobalt%p_srdop(i,j,k,tau) = cobalt%p_srdop(i,j,k,tau) + cobalt%jsrdop(i,j,k) * dt * & + grid_tmask(i,j,k) + enddo; enddo ; enddo !} i,j,k + ! + ! O2 + ! + do k = 1, nk ; do j =jsc, jec ; do i = isc, iec !{ + cobalt%jo2(i,j,k) = (cobalt%o2_2_no3 * (phyto(DIAZO)%juptake_no3(i,j,k) + & + phyto(LARGE)%juptake_no3(i,j,k) + phyto(SMALL)%juptake_no3(i,j,k)) + & + cobalt%o2_2_nh4 * & + (phyto(DIAZO)%juptake_nh4(i,j,k) + phyto(LARGE)%juptake_nh4(i,j,k) + & + phyto(SMALL)%juptake_nh4(i,j,k) + & + phyto(DIAZO)%juptake_n2(i,j,k))) * grid_tmask(i,j,k) + cobalt%jo2(i,j,k) = cobalt%jo2(i,j,k) - cobalt%jo2resp_wc(i,j,k) + cobalt%p_o2(i,j,k,tau) = cobalt%p_o2(i,j,k,tau) + cobalt%jo2(i,j,k) * dt * grid_tmask(i,j,k) + enddo; enddo ; enddo !} i,j,k + ! + ! The Carbon system + ! + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + ! + ! Alkalinity + ! CAS: remove o2 removal via nitrification from the total o2 respired + ! to isolate the change in alkalinity due to organic matter remineralization + ! + cobalt%jalk(i,j,k) = 2.0 * (cobalt%jdiss_cadet_arag(i,j,k) + & + cobalt%jdiss_cadet_calc(i,j,k) - cobalt%jprod_cadet_arag(i,j,k) - & + cobalt%jprod_cadet_calc(i,j,k)) + phyto(DIAZO)%juptake_no3(i,j,k) + & + phyto(LARGE)%juptake_no3(i,j,k) + phyto(SMALL)%juptake_no3(i,j,k) + & + (cobalt%jo2resp_wc(i,j,k)-cobalt%jnitrif(i,j,k)*cobalt%o2_2_nitrif)/cobalt%o2_2_nh4 + & + cobalt%alk_2_n_denit*cobalt%jno3denit_wc(i,j,k) - & + phyto(DIAZO)%juptake_nh4(i,j,k) - phyto(LARGE)%juptake_nh4(i,j,k) - & + phyto(SMALL)%juptake_nh4(i,j,k) - 2.0 * cobalt%jnitrif(i,j,k) + + cobalt%p_alk(i,j,k,tau) = cobalt%p_alk(i,j,k,tau) + cobalt%jalk(i,j,k) * dt * grid_tmask(i,j,k) + ! + ! Dissolved Inorganic Carbon + ! + cobalt%jdic(i,j,k) =(cobalt%c_2_n * (cobalt%jno3(i,j,k) + & + cobalt%jnh4(i,j,k) + cobalt%jno3denit_wc(i,j,k) - phyto(DIAZO)%juptake_n2(i,j,k)) + & + cobalt%jdiss_cadet_arag(i,j,k) + cobalt%jdiss_cadet_calc(i,j,k) - & + cobalt%jprod_cadet_arag(i,j,k) - cobalt%jprod_cadet_calc(i,j,k)) + cobalt%p_dic(i,j,k,tau) = cobalt%p_dic(i,j,k,tau) + cobalt%jdic(i,j,k) * dt * grid_tmask(i,j,k) + enddo; enddo ; enddo !} i,j,k +! + + if (do_14c) then !<> + ! + !----------------------------------------------------------------------- + ! Lithogenic aluminosilicate particulates + !----------------------------------------------------------------------- + ! + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + cobalt%p_lith(i,j,k,tau) = cobalt%p_lith(i,j,k,tau) - cobalt%jlithdet(i,j,k) * dt * & + grid_tmask(i,j,k) + enddo; enddo ; enddo !} i,j,k + call mpp_clock_end(id_clock_source_sink_loop6) + call mpp_clock_begin(id_clock_cobalt_calc_diagnostics) + ! + !Set the diagnostics tracer fields. + ! + call g_tracer_set_values(tracer_list,'cased', 'field',cobalt%f_cased ,isd,jsd,ntau=1) + call g_tracer_set_values(tracer_list,'chl', 'field',cobalt%f_chl ,isd,jsd,ntau=1) + if (do_nh3_diag) call g_tracer_set_values(tracer_list,'nh3', 'field',cobalt%f_nh3 ,isd,jsd,ntau=1) + call g_tracer_set_values(tracer_list,'co3_ion','field',cobalt%f_co3_ion ,isd,jsd,ntau=1) + call g_tracer_set_values(tracer_list,'irr_mem' ,'field',cobalt%f_irr_mem ,isd,jsd,ntau=1) + call g_tracer_set_values(tracer_list,'mu_mem_ndi' ,'field',phyto(DIAZO)%f_mu_mem ,isd,jsd,ntau=1) + call g_tracer_set_values(tracer_list,'mu_mem_nlg' ,'field',phyto(LARGE)%f_mu_mem ,isd,jsd,ntau=1) + call g_tracer_set_values(tracer_list,'mu_mem_nsm' ,'field',phyto(SMALL)%f_mu_mem ,isd,jsd,ntau=1) + + ! CAS calculate totals after source/sinks have been applied + imbal_flag = 0; + stdoutunit = stdout(); + allocate(post_totn(isc:iec,jsc:jec,1:nk)) + allocate(post_totc(isc:iec,jsc:jec,1:nk)) + allocate(post_totp(isc:iec,jsc:jec,1:nk)) + allocate(post_totsi(isc:iec,jsc:jec,1:nk)) + allocate(post_totfe(isc:iec,jsc:jec,1:nk)) + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec !{ + post_totn(i,j,k) = (cobalt%p_no3(i,j,k,tau) + cobalt%p_nh4(i,j,k,tau) + & + cobalt%p_ndi(i,j,k,tau) + cobalt%p_nlg(i,j,k,tau) + & + cobalt%p_nsm(i,j,k,tau) + cobalt%p_nbact(i,j,k,tau) + & + cobalt%p_ldon(i,j,k,tau) + cobalt%p_sldon(i,j,k,tau) + & + cobalt%p_srdon(i,j,k,tau) + cobalt%p_ndet(i,j,k,tau) + & + cobalt%p_nsmz(i,j,k,tau) + cobalt%p_nmdz(i,j,k,tau) + & + cobalt%p_nlgz(i,j,k,tau))*grid_tmask(i,j,k) + imbal = (post_totn(i,j,k) - pre_totn(i,j,k) - net_srcn(i,j,k))*86400.0/dt*1.03e6 + if (abs(imbal).gt.imbalance_tolerance) then + call mpp_error(FATAL,& + '==>biological source/sink imbalance (generic_COBALT_update_from_source): Nitrogen') + endif + + post_totc(i,j,k) = (cobalt%p_dic(i,j,k,tau) + & + cobalt%p_cadet_arag(i,j,k,tau) + cobalt%p_cadet_calc(i,j,k,tau) + & + cobalt%c_2_n*(cobalt%p_ndi(i,j,k,tau) + cobalt%p_nlg(i,j,k,tau) + & + cobalt%p_nsm(i,j,k,tau) + cobalt%p_nbact(i,j,k,tau) + & + cobalt%p_ldon(i,j,k,tau) + cobalt%p_sldon(i,j,k,tau) + & + cobalt%p_srdon(i,j,k,tau) + cobalt%p_ndet(i,j,k,tau) + & + cobalt%p_nsmz(i,j,k,tau) + cobalt%p_nmdz(i,j,k,tau) + & + cobalt%p_nlgz(i,j,k,tau)))*grid_tmask(i,j,k) + imbal = (post_totc(i,j,k) - pre_totc(i,j,k))*86400.0/dt*1.03e6 + if (abs(imbal).gt.imbalance_tolerance) then + call mpp_error(FATAL,& + '==>biological source/sink imbalance (generic_COBALT_update_from_source): Carbon') + endif + + post_totp(i,j,k) = (cobalt%p_po4(i,j,k,tau) + cobalt%p_ndi(i,j,k,tau)*phyto(1)%p_2_n_static + & + cobalt%p_nlg(i,j,k,tau)*phyto(2)%p_2_n_static + & + cobalt%p_nsm(i,j,k,tau)*phyto(3)%p_2_n_static + & + cobalt%p_ldop(i,j,k,tau) + cobalt%p_sldop(i,j,k,tau) + & + cobalt%p_srdop(i,j,k,tau) + cobalt%p_pdet(i,j,k,tau) + & + cobalt%p_nsmz(i,j,k,tau)*zoo(1)%q_p_2_n + & + cobalt%p_nmdz(i,j,k,tau)*zoo(2)%q_p_2_n + & + cobalt%p_nlgz(i,j,k,tau)*zoo(3)%q_p_2_n + & + bact(1)%q_p_2_n*cobalt%p_nbact(i,j,k,tau))*grid_tmask(i,j,k) + imbal = (post_totp(i,j,k) - pre_totp(i,j,k))*86400.0/dt*1.03e6 + if (abs(imbal).gt.imbalance_tolerance) then + call mpp_error(FATAL,& + '==>biological source/sink imbalance (generic_COBALT_update_from_source): Phosphorus') + endif + + post_totfe(i,j,k) = (cobalt%p_fed(i,j,k,tau) + cobalt%p_fedi(i,j,k,tau) + & + cobalt%p_felg(i,j,k,tau) + cobalt%p_fesm(i,j,k,tau) + & + cobalt%p_fedet(i,j,k,tau))*grid_tmask(i,j,k) + imbal = (post_totfe(i,j,k) - pre_totfe(i,j,k) - net_srcfe(i,j,k))*86400.0/dt*1.03e6 + if (abs(imbal).gt.imbalance_tolerance) then + call mpp_error(FATAL,& + '==>biological source/sink imbalance (generic_COBALT_update_from_source): Iron') + endif + + post_totsi(i,j,k) = (cobalt%p_sio4(i,j,k,tau) + cobalt%p_silg(i,j,k,tau) + & + cobalt%p_sidet(i,j,k,tau))*grid_tmask(i,j,k) + imbal = (post_totsi(i,j,k) - pre_totsi(i,j,k))*86400.0/dt*1.03e6 + if (abs(imbal).gt.imbalance_tolerance) then + call mpp_error(FATAL,& + '==>biological source/sink imbalance (generic_COBALT_update_from_source): Silica') + endif + enddo; enddo ; enddo !} i,j,k + + + ! + ! + !----------------------------------------------------------------------- + ! Save variables for diagnostics + !----------------------------------------------------------------------- + ! + + do j = jsc, jec ; do i = isc, iec !{ + if (grid_kmt(i,j) .gt. 0) then !{ + cobalt%o2min(i,j)=cobalt%p_o2(i,j,1,tau) + cobalt%z_o2min(i,j)=cobalt%zt(i,j,1) + cobalt%z_sat_arag(i,j)=missing_value1 + cobalt%z_sat_calc(i,j)=missing_value1 + cobalt%mask_z_sat_arag(i,j) = .FALSE. + cobalt%mask_z_sat_calc(i,j) = .FALSE. + if (cobalt%omega_arag(i,j,1) .le. 1.0) cobalt%z_sat_arag(i,j)=0.0 + if (cobalt%omega_calc(i,j,1) .le. 1.0) cobalt%z_sat_calc(i,j)=0.0 + endif !} + enddo ; enddo !} i,j,k + do j = jsc, jec ; do i = isc, iec !{ + first = .true. + do k = 2, nk + if (k .le. grid_kmt(i,j) .and. first) then !{ + if (cobalt%p_o2(i,j,k,tau) .lt. cobalt%p_o2(i,j,k-1,tau)) then + cobalt%o2min(i,j)=cobalt%p_o2(i,j,k,tau) + cobalt%z_o2min(i,j)=cobalt%zt(i,j,k) + else + first = .false. + endif !} + endif !} + enddo; + enddo ; enddo !} i,j + + do k = 2, nk ; do j = jsc, jec ; do i = isc, iec !{ + if (k .le. grid_kmt(i,j)) then !{ + if (cobalt%omega_arag(i,j,k) .le. 1.0 .and. cobalt%z_sat_arag(i,j) .lt. 0.0) then + cobalt%z_sat_arag(i,j)=cobalt%zt(i,j,k) + cobalt%mask_z_sat_arag(i,j) = .TRUE. + endif + if (cobalt%omega_calc(i,j,k) .le. 1.0 .and. cobalt%z_sat_calc(i,j) .lt. 0.0) then + cobalt%z_sat_calc(i,j)=cobalt%zt(i,j,k) + cobalt%mask_z_sat_calc(i,j) = .TRUE. + endif + endif !} + enddo; enddo ; enddo !} i,j,k + + ! O2 saturation + do k = 1, nk ; do j = jsc, jec ; do i = isc, iec + sal = min(42.0,max(0.0,Salt(i,j,k))) + tt = 298.15 - min(40.0,max(0.0,Temp(i,j,k))) + tkb = 273.15 + min(40.0,max(0.0,Temp(i,j,k))) + ts = log(tt / tkb) + ts2 = ts * ts + ts3 = ts2 * ts + ts4 = ts3 * ts + ts5 = ts4 * ts + + !The atmospheric code needs solubilities in units of mol/m3/atm + cobalt%o2sat(i,j,k) = (1000.0/22391.6) * grid_tmask(i,j,1) * & !convert from ml/l to mol m-3 + exp( cobalt%a_0 + cobalt%a_1*ts + cobalt%a_2*ts2 + cobalt%a_3*ts3 + cobalt%a_4*ts4 + cobalt%a_5*ts5 + & + (cobalt%b_0 + cobalt%b_1*ts + cobalt%b_2*ts2 + cobalt%b_3*ts3 + cobalt%c_0*sal)*sal) + + enddo; enddo ; enddo !} i,j,k + + ! + !--------------------------------------------------------------------- + ! Calculate total carbon = Dissolved Inorganic Carbon + Phytoplankton Carbon + ! + Dissolved Organic Carbon (including refractory) + Heterotrophic Biomass + ! + Detrital Orgainc and Inorganic Carbon + ! For the oceanic carbon budget, a constant 42 uM of dissolved organic + ! carbon is added to represent the refractory component. + ! For the oceanic nitrogen budget, a constant 2 uM of dissolved organic + ! nitrogen is added to represent the refractory component. + !--------------------------------------------------------------------- + ! + cobalt%tot_layer_int_c(:,:,:) = (cobalt%p_dic(:,:,:,tau) + cobalt%doc_background + cobalt%p_cadet_arag(:,:,:,tau) +& + cobalt%p_cadet_calc(:,:,:,tau) + cobalt%c_2_n * (cobalt%p_ndi(:,:,:,tau) + cobalt%p_nlg(:,:,:,tau) + & + cobalt%p_nsm(:,:,:,tau) + cobalt%p_nbact(:,:,:,tau) + & + cobalt%p_ldon(:,:,:,tau) + cobalt%p_sldon(:,:,:,tau) + cobalt%p_srdon(:,:,:,tau) + & + cobalt%p_ndet(:,:,:,tau) + cobalt%p_nsmz(:,:,:,tau) + cobalt%p_nmdz(:,:,:,tau) + & + cobalt%p_nlgz(:,:,:,tau))) * rho_dzt(:,:,:) + + cobalt%tot_layer_int_fe(:,:,:) = (cobalt%p_fed(:,:,:,tau) + cobalt%p_fedi(:,:,:,tau) + & + cobalt%p_felg(:,:,:,tau) + cobalt%p_fesm(:,:,:,tau) + & + cobalt%p_fedet(:,:,:,tau)) * rho_dzt(:,:,:) + + cobalt%tot_layer_int_n(:,:,:) = (cobalt%p_no3(:,:,:,tau) + & + cobalt%p_nh4(:,:,:,tau) + cobalt%p_ndi(:,:,:,tau) + cobalt%p_nlg(:,:,:,tau) + & + cobalt%p_nsm(:,:,:,tau) + cobalt%p_nbact(:,:,:,tau) + & + cobalt%p_ldon(:,:,:,tau) + cobalt%p_sldon(:,:,:,tau) + cobalt%p_srdon(:,:,:,tau) + cobalt%p_ndet(:,:,:,tau) + & + cobalt%p_nsmz(:,:,:,tau) + cobalt%p_nmdz(:,:,:,tau) + cobalt%p_nlgz(:,:,:,tau)) * & + rho_dzt(:,:,:) + + cobalt%tot_layer_int_p(:,:,:) = (cobalt%p_po4(:,:,:,tau) + & + cobalt%p_ndi(:,:,:,tau)*phyto(1)%p_2_n_static + & + cobalt%p_nlg(:,:,:,tau)*phyto(2)%p_2_n_static + & + cobalt%p_nsm(:,:,:,tau)*phyto(3)%p_2_n_static + & + cobalt%p_ldop(:,:,:,tau) + cobalt%p_sldop(:,:,:,tau) + & + cobalt%p_srdop(:,:,:,tau) + cobalt%p_pdet(:,:,:,tau) + & + bact(1)%q_p_2_n*cobalt%p_nbact(:,:,:,tau) + zoo(1)%q_p_2_n*cobalt%p_nsmz(:,:,:,tau) + & + zoo(2)%q_p_2_n*cobalt%p_nmdz(:,:,:,tau) + zoo(3)%q_p_2_n*cobalt%p_nlgz(:,:,:,tau)) & + * rho_dzt(:,:,:) + + cobalt%tot_layer_int_si(:,:,:) = (cobalt%p_sio4(:,:,:,tau) + cobalt%p_silg(:,:,:,tau) + & + cobalt%p_sidet(:,:,:,tau)) * rho_dzt(:,:,:) + + cobalt%tot_layer_int_o2(:,:,:) = cobalt%p_o2(:,:,:,tau)*rho_dzt(:,:,:) + + cobalt%tot_layer_int_alk(:,:,:) = cobalt%p_alk(:,:,:,tau)*rho_dzt(:,:,:) + +! CHECK3 + !add background of 42 uM (as in other parts of cobalt)- may need to change to 3.8e-5 per JPD + cobalt%tot_layer_int_dic(:,:,:) = cobalt%p_dic(:,:,:,tau)*rho_dzt(:,:,:) + +! CHECK3 +! CAS: spreadsheet indicates no background, should we remove it? + cobalt%tot_layer_int_doc(:,:,:) = cobalt%doc_background + cobalt%c_2_n * (cobalt%p_ldon(:,:,:,tau) + cobalt%p_sldon(:,:,:,tau) + & + cobalt%p_srdon(:,:,:,tau)) * rho_dzt(:,:,:) + + cobalt%tot_layer_int_poc(:,:,:) = (cobalt%p_ndi(:,:,:,tau) + cobalt%p_nlg(:,:,:,tau) + cobalt%p_nsm(:,:,:,tau) + & + cobalt%p_nbact(:,:,:,tau) + cobalt%p_ndet(:,:,:,tau) + cobalt%p_nsmz(:,:,:,tau) + cobalt%p_nmdz(:,:,:,tau) + & + cobalt%p_nlgz(:,:,:,tau))*cobalt%c_2_n*rho_dzt(:,:,:) + + + ! + !--------------------------------------------------------------------- + ! calculate water column vertical integrals for diagnostics + !--------------------------------------------------------------------- + ! + do j = jsc, jec ; do i = isc, iec !{ + cobalt%wc_vert_int_c(i,j) = 0.0 + cobalt%wc_vert_int_dic(i,j) = 0.0 + cobalt%wc_vert_int_doc(i,j) = 0.0 + cobalt%wc_vert_int_poc(i,j) = 0.0 + cobalt%wc_vert_int_n(i,j) = 0.0 + cobalt%wc_vert_int_p(i,j) = 0.0 + cobalt%wc_vert_int_fe(i,j) = 0.0 + cobalt%wc_vert_int_si(i,j) = 0.0 + cobalt%wc_vert_int_o2(i,j) = 0.0 + cobalt%wc_vert_int_alk(i,j) = 0.0 + cobalt%wc_vert_int_jdiss_sidet(i,j) = 0.0 + cobalt%wc_vert_int_jdiss_cadet(i,j) = 0.0 + cobalt%wc_vert_int_jo2resp(i,j) = 0.0 + cobalt%wc_vert_int_jprod_cadet(i,j) = 0.0 + cobalt%wc_vert_int_jno3denit(i,j) = 0.0 + cobalt%wc_vert_int_jnitrif(i,j) = 0.0 + cobalt%wc_vert_int_juptake_nh4(i,j) = 0.0 + cobalt%wc_vert_int_jprod_nh4(i,j) = 0.0 + cobalt%wc_vert_int_juptake_no3(i,j) = 0.0 + cobalt%wc_vert_int_nfix(i,j) = 0.0 + enddo; enddo !} i,j + do j = jsc, jec ; do i = isc, iec ; do k = 1, nk !{ + cobalt%wc_vert_int_c(i,j) = cobalt%wc_vert_int_c(i,j) + cobalt%tot_layer_int_c(i,j,k)*grid_tmask(i,j,k) + cobalt%wc_vert_int_dic(i,j) = cobalt%wc_vert_int_dic(i,j) + cobalt%tot_layer_int_dic(i,j,k)*grid_tmask(i,j,k) + cobalt%wc_vert_int_doc(i,j) = cobalt%wc_vert_int_doc(i,j) + cobalt%tot_layer_int_doc(i,j,k)*grid_tmask(i,j,k) + cobalt%wc_vert_int_poc(i,j) = cobalt%wc_vert_int_poc(i,j) + cobalt%tot_layer_int_poc(i,j,k)*grid_tmask(i,j,k) + cobalt%wc_vert_int_n(i,j) = cobalt%wc_vert_int_n(i,j) + cobalt%tot_layer_int_n(i,j,k)*grid_tmask(i,j,k) + cobalt%wc_vert_int_p(i,j) = cobalt%wc_vert_int_p(i,j) + cobalt%tot_layer_int_p(i,j,k)*grid_tmask(i,j,k) + cobalt%wc_vert_int_fe(i,j) = cobalt%wc_vert_int_fe(i,j) + cobalt%tot_layer_int_fe(i,j,k)*grid_tmask(i,j,k) + cobalt%wc_vert_int_si(i,j) = cobalt%wc_vert_int_si(i,j) + cobalt%tot_layer_int_si(i,j,k)*grid_tmask(i,j,k) + cobalt%wc_vert_int_o2(i,j) = cobalt%wc_vert_int_o2(i,j) + cobalt%tot_layer_int_o2(i,j,k)*grid_tmask(i,j,k) + cobalt%wc_vert_int_alk(i,j) = cobalt%wc_vert_int_alk(i,j) + cobalt%tot_layer_int_alk(i,j,k)*grid_tmask(i,j,k) + cobalt%wc_vert_int_jdiss_sidet(i,j) = cobalt%wc_vert_int_jdiss_sidet(i,j) + & + cobalt%jdiss_sidet(i,j,k) * rho_dzt(i,j,k) * grid_tmask(i,j,k) + cobalt%wc_vert_int_jdiss_cadet(i,j) = cobalt%wc_vert_int_jdiss_cadet(i,j) + & + (cobalt%jdiss_cadet_calc(i,j,k)+cobalt%jdiss_cadet_arag(i,j,k))*rho_dzt(i,j,k)*grid_tmask(i,j,k) + cobalt%wc_vert_int_jo2resp(i,j) = cobalt%wc_vert_int_jo2resp(i,j) + & + cobalt%jo2resp_wc(i,j,k) * rho_dzt(i,j,k) * grid_tmask(i,j,k) + cobalt%wc_vert_int_jprod_cadet(i,j) = cobalt%wc_vert_int_jprod_cadet(i,j) + & + (cobalt%jprod_cadet_calc(i,j,k)+cobalt%jprod_cadet_arag(i,j,k))*rho_dzt(i,j,k)*grid_tmask(i,j,k) + cobalt%wc_vert_int_jno3denit(i,j) = cobalt%wc_vert_int_jno3denit(i,j) + & + cobalt%jno3denit_wc(i,j,k) * rho_dzt(i,j,k) * grid_tmask(i,j,k) + cobalt%wc_vert_int_jnitrif(i,j) = cobalt%wc_vert_int_jnitrif(i,j) + & + cobalt%jnitrif(i,j,k) * rho_dzt(i,j,k) * grid_tmask(i,j,k) + cobalt%wc_vert_int_juptake_nh4(i,j) = cobalt%wc_vert_int_juptake_nh4(i,j) + & + (phyto(1)%juptake_nh4(i,j,k)+phyto(2)%juptake_nh4(i,j,k)+phyto(3)%juptake_nh4(i,j,k))* & + rho_dzt(i,j,k) * grid_tmask(i,j,k) + + cobalt%wc_vert_int_jprod_nh4(i,j) = cobalt%wc_vert_int_jprod_nh4(i,j) + & + cobalt%jprod_nh4(i,j,k)*rho_dzt(i,j,k) * grid_tmask(i,j,k) + + cobalt%wc_vert_int_juptake_no3(i,j) = cobalt%wc_vert_int_juptake_no3(i,j) + & + (phyto(1)%juptake_no3(i,j,k)+phyto(2)%juptake_no3(i,j,k)+phyto(3)%juptake_no3(i,j,k))* & + rho_dzt(i,j,k) * grid_tmask(i,j,k) + cobalt%wc_vert_int_nfix(i,j) = cobalt%wc_vert_int_nfix(i,j) + phyto(DIAZO)%juptake_n2(i,j,k) *& + rho_dzt(i,j,k) * grid_tmask(i,j,k) + enddo; enddo; enddo !} i,j,k + ! + !--------------------------------------------------------------------- + ! Add external bottom fluxes to specific rates + !--------------------------------------------------------------------- + ! + do j = jsc, jec ; do i = isc, iec ; do k = 1, nk !{ + ! CAS added calcite and aragonite redisolution terms + cobalt%jdiss_cadet_calc_plus_btm(i,j,k) = cobalt%jdiss_cadet_calc(i,j,k) + cobalt%jdiss_cadet_arag_plus_btm(i,j,k) = cobalt%jdiss_cadet_arag(i,j,k) + ! CAS added a jprod_nh4_plus_btm for remoc CMIP variable + cobalt%jprod_nh4_plus_btm(i,j,k) = cobalt%jprod_nh4(i,j,k) + cobalt%jalk_plus_btm(i,j,k) = cobalt%jalk(i,j,k) + cobalt%jdic_plus_btm(i,j,k) = cobalt%jdic(i,j,k) + cobalt%jfed_plus_btm(i,j,k) = cobalt%jfed(i,j,k) + cobalt%jnh4_plus_btm(i,j,k) = cobalt%jnh4(i,j,k) + cobalt%jno3_plus_btm(i,j,k) = cobalt%jno3(i,j,k) + cobalt%jo2_plus_btm(i,j,k) = cobalt%jo2(i,j,k) + cobalt%jpo4_plus_btm(i,j,k) = cobalt%jpo4(i,j,k) + cobalt%jsio4_plus_btm(i,j,k) = cobalt%jsio4(i,j,k) + cobalt%jdin_plus_btm(i,j,k) = cobalt%jno3(i,j,k) + cobalt%jnh4(i,j,k) + enddo; enddo; enddo !} i,j,k + + do j = jsc, jec ; do i = isc, iec !{ + k = grid_kmt(i,j) + if (k .gt. 0) then !{ + + ! CAS added calcite and aragonite redissolution terms + cobalt%jdiss_cadet_calc_plus_btm(i,j,k) = cobalt%jdiss_cadet_calc(i,j,k) + & + cobalt%fcased_redis(i,j) / rho_dzt(i,j,k) + cobalt%jdiss_cadet_arag_plus_btm(i,j,k) = cobalt%jdiss_cadet_arag(i,j,k) + & + cobalt%f_cadet_arag_btf(i,j,1) / rho_dzt(i,j,k) + + ! CAS added for remoc calculation + cobalt%jprod_nh4_plus_btm(i,j,k) = cobalt%jprod_nh4(i,j,k) + (cobalt%f_ndet_btf(i,j,1) - cobalt%fndet_burial(i,j)) / rho_dzt(i,j,k) + + + cobalt%jalk_plus_btm(i,j,k) = cobalt%jalk(i,j,k) + & + (2.0 * (cobalt%fcased_redis(i,j) + cobalt%f_cadet_arag_btf(i,j,1)) + & + cobalt%f_ndet_btf(i,j,1) + cobalt%alk_2_n_denit * cobalt%fno3denit_sed(i,j)) / rho_dzt(i,j,k) +! updated + cobalt%jdic_plus_btm(i,j,k) = cobalt%jdic(i,j,k) + & + (cobalt%fcased_redis(i,j) + cobalt%f_cadet_arag_btf(i,j,1) + & + ((cobalt%f_ndet_btf(i,j,1) - cobalt%fndet_burial(i,j)) * cobalt%c_2_n)) / rho_dzt(i,j,k) + +! CAS is ffe_sed a biogenic source or is it similar to coast/atmosphere source? + cobalt%jfed_plus_btm(i,j,k) = cobalt%jfed(i,j,k) + (cobalt%ffe_sed(i,j)+cobalt%ffe_geotherm(i,j)) / rho_dzt(i,j,k) +! updated +! CAS: fixed parentheses (commented out old for comparison, think rho_dzt should only divide bottom fluxes) + cobalt%jnh4_plus_btm(i,j,k) = cobalt%jnh4(i,j,k) + (cobalt%f_ndet_btf(i,j,1) - cobalt%fndet_burial(i,j)) / rho_dzt(i,j,k) +! cobalt%jnh4_plus_btm(i,j,k) = (cobalt%jnh4(i,j,k) + cobalt%f_ndet_btf(i,j,1) - cobalt%fndet_burial(i,j)) / rho_dzt(i,j,k) + +! NOTE: should fno3denit_sed be SUBTRACTED ? +! CAS: yes, I think so, I've made the change + cobalt%jno3_plus_btm(i,j,k) = cobalt%jno3(i,j,k) - cobalt%fno3denit_sed(i,j) / rho_dzt(i,j,k) + + cobalt%jo2_plus_btm(i,j,k) = cobalt%jo2(i,j,k) + & + (cobalt%o2_2_nh4 * (cobalt%fnoxic_sed(i,j) + cobalt%fnfeso4red_sed(i,j))) / rho_dzt(i,j,k) + +! updated +! CAS: fixed parentheses to not include jpo4 as in jnh4 example above + cobalt%jpo4_plus_btm(i,j,k) = cobalt%jpo4(i,j,k) + (cobalt%f_pdet_btf(i,j,1) - cobalt%fpdet_burial(i,j)) / rho_dzt(i,j,k) + + cobalt%jsio4_plus_btm(i,j,k) = cobalt%jsio4(i,j,k) + cobalt%f_sidet_btf(i,j,1) / rho_dzt(i,j,k) + + cobalt%jdin_plus_btm(i,j,k) = cobalt%jno3_plus_btm(i,j,k) + cobalt%jnh4_plus_btm(i,j,k) + + endif !} + enddo; enddo !} i, j + +! +! CHECK3 Remineralization of Organic Carbon, remoc=(jprod_nh4*c_2_n/dht) for k=1,kbot-1 + (jprod_nh4+f_ndet_btf-fndet_burial)*c_2_n/dht for k=kbot +! CAS: will code this up when I address denitrification issue + do j = jsc, jec ; do i = isc, iec !{ + kbot = grid_kmt(i,j) + if (kbot .gt. 0) then !{ + do k = 1, kbot-1 !{ + cobalt%remoc(i,j,k) = cobalt%jprod_nh4(i,j,k) * cobalt%c_2_n / dzt(i,j,k) + enddo !} k + cobalt%remoc(i,j,kbot) = (cobalt%jprod_nh4(i,j,kbot) + cobalt%f_ndet_btf(i,j,1) - cobalt%fndet_burial(i,j)) * cobalt%c_2_n / dzt(i,j,kbot) + endif !} + enddo; enddo !} i, j + + +!**************************************************************************************************** + + allocate(rho_dzt_100(isc:iec,jsc:jec)) + ! + !--------------------------------------------------------------------- + ! calculate upper 100 m vertical integrals + !--------------------------------------------------------------------- + ! + do j = jsc, jec ; do i = isc, iec !{ + rho_dzt_100(i,j) = rho_dzt(i,j,1) + cobalt%f_alk_int_100(i,j) = cobalt%p_alk(i,j,1,tau) * rho_dzt(i,j,1) + cobalt%f_dic_int_100(i,j) = cobalt%p_dic(i,j,1,tau) * rho_dzt(i,j,1) + cobalt%f_din_int_100(i,j) = (cobalt%p_no3(i,j,1,tau) + cobalt%p_nh4(i,j,1,tau)) * rho_dzt(i,j,1) + cobalt%f_fed_int_100(i,j) = cobalt%p_fed(i,j,1,tau) * rho_dzt(i,j,1) + cobalt%f_po4_int_100(i,j) = cobalt%p_po4(i,j,1,tau) * rho_dzt(i,j,1) + cobalt%f_sio4_int_100(i,j) = cobalt%p_sio4(i,j,1,tau) * rho_dzt(i,j,1) + cobalt%jalk_100(i,j) = cobalt%jalk(i,j,1) * rho_dzt(i,j,1) + cobalt%jdic_100(i,j) = cobalt%jdic(i,j,1) * rho_dzt(i,j,1) + cobalt%jdin_100(i,j) = (cobalt%jno3(i,j,1) + cobalt%jnh4(i,j,1)) * rho_dzt(i,j,1) + cobalt%jfed_100(i,j) = cobalt%jfed(i,j,1) * rho_dzt(i,j,1) + cobalt%jpo4_100(i,j) = cobalt%jpo4(i,j,1) * rho_dzt(i,j,1) + cobalt%jsio4_100(i,j) = cobalt%jsio4(i,j,1) * rho_dzt(i,j,1) +! cobalt%jprod_ptot_100(i,j) = (phyto(DIAZO)%jprod_po4(i,j,1) + phyto(LARGE)%jprod_po4(i,j,1) + & +! phyto(SMALL)%jprod_po4(i,j,1)) * rho_dzt(i,j,1) +! previously computed in COBALT + cobalt%jprod_ptot_100(i,j) = cobalt%jprod_po4(i,j,1) * rho_dzt(i,j,1) + do n = 1, NUM_PHYTO !{ + phyto(n)%jprod_n_100(i,j) = phyto(n)%jprod_n(i,j,1) * rho_dzt(i,j,1) + phyto(n)%jprod_n_new_100(i,j) = phyto(n)%juptake_no3(i,j,1) * rho_dzt(i,j,1) + phyto(n)%jzloss_n_100(i,j) = phyto(n)%jzloss_n(i,j,1) * rho_dzt(i,j,1) + phyto(n)%jexuloss_n_100(i,j) = phyto(n)%jexuloss_n(i,j,1) * rho_dzt(i,j,1) + phyto(n)%f_n_100(i,j) = phyto(n)%f_n(i,j,1) * rho_dzt(i,j,1) +! added juptake_fe_100 + phyto(n)%juptake_fe_100(i,j) = phyto(n)%juptake_fe(i,j,1) * rho_dzt(i,j,1) +! CAS: added juptake_po4_100 + phyto(n)%juptake_po4_100(i,j) = phyto(n)%juptake_po4(i,j,1) * rho_dzt(i,j,1) + enddo !} n + phyto(DIAZO)%jprod_n_n2_100(i,j) = phyto(DIAZO)%juptake_n2(i,j,1) * rho_dzt(i,j,1) + phyto(SMALL)%jvirloss_n_100(i,j) = phyto(SMALL)%jvirloss_n(i,j,1) * rho_dzt(i,j,1) + phyto(SMALL)%jaggloss_n_100(i,j) = phyto(SMALL)%jaggloss_n(i,j,1) * rho_dzt(i,j,1) + phyto(LARGE)%jaggloss_n_100(i,j) = phyto(LARGE)%jaggloss_n(i,j,1) * rho_dzt(i,j,1) +! CAS: added diagnotistic for depth integrated diatom production + cobalt%jprod_diat_100(i,j) = phyto(LARGE)%jprod_n(i,j,1)*phyto(LARGE)%silim(i,j,1)*rho_dzt(i,j,1) +! added juptake_sio4_100 (large only) + phyto(LARGE)%juptake_sio4_100(i,j) = phyto(LARGE)%juptake_sio4(i,j,1) * rho_dzt(i,j,1) + do n = 1, NUM_ZOO !{ + zoo(n)%jprod_n_100(i,j) = zoo(n)%jprod_n(i,j,1) * rho_dzt(i,j,1) + zoo(n)%jingest_n_100(i,j) = zoo(n)%jingest_n(i,j,1) * rho_dzt(i,j,1) + zoo(n)%jremin_n_100(i,j) = zoo(n)%jprod_nh4(i,j,1) * rho_dzt(i,j,1) + zoo(n)%f_n_100(i,j) = zoo(n)%f_n(i,j,1) * rho_dzt(i,j,1) + enddo !} n + + do n = 1,2 !{ + zoo(n)%jzloss_n_100(i,j) = zoo(n)%jzloss_n(i,j,1) * rho_dzt(i,j,1) + zoo(n)%jprod_don_100(i,j) = (zoo(n)%jprod_ldon(i,j,1) + zoo(n)%jprod_sldon(i,j,1) + & + zoo(n)%jprod_srdon(i,j,1)) * rho_dzt(i,j,1) + enddo !} n + + do n = 2,3 !{ + zoo(n)%jhploss_n_100(i,j) = zoo(n)%jhploss_n(i,j,1) * rho_dzt(i,j,1) + zoo(n)%jprod_ndet_100(i,j) = zoo(n)%jprod_ndet(i,j,1) * rho_dzt(i,j,1) + enddo !} n + + cobalt%hp_jingest_n_100(i,j) = cobalt%hp_jingest_n(i,j,1)*rho_dzt(i,j,1) + cobalt%hp_jremin_n_100(i,j) = cobalt%hp_jingest_n(i,j,1)*rho_dzt(i,j,1)*(1.0-cobalt%hp_phi_det) + cobalt%hp_jprod_ndet_100(i,j) = cobalt%hp_jingest_n(i,j,1)*rho_dzt(i,j,1)*cobalt%hp_phi_det + + bact(1)%jprod_n_100(i,j) = bact(1)%jprod_n(i,j,1) * rho_dzt(i,j,1) + bact(1)%jzloss_n_100(i,j) = bact(1)%jzloss_n(i,j,1) * rho_dzt(i,j,1) + bact(1)%jvirloss_n_100(i,j) = bact(1)%jvirloss_n(i,j,1) * rho_dzt(i,j,1) + bact(1)%jremin_n_100(i,j) = bact(1)%jprod_nh4(i,j,1) * rho_dzt(i,j,1) + bact(1)%juptake_ldon_100(i,j) = bact(1)%juptake_ldon(i,j,1) * rho_dzt(i,j,1) + bact(1)%f_n_100(i,j) = bact(1)%f_n(i,j,1) * rho_dzt(i,j,1) + + cobalt%jprod_lithdet_100(i,j) = cobalt%jprod_lithdet(i,j,1) * rho_dzt(i,j,1) + cobalt%jprod_sidet_100(i,j) = cobalt%jprod_sidet(i,j,1) * rho_dzt(i,j,1) + cobalt%jprod_cadet_calc_100(i,j) = cobalt%jprod_cadet_calc(i,j,1) * rho_dzt(i,j,1) + cobalt%jprod_cadet_arag_100(i,j) = cobalt%jprod_cadet_arag(i,j,1) * rho_dzt(i,j,1) + cobalt%jremin_ndet_100(i,j) = cobalt%jremin_ndet(i,j,1) * rho_dzt(i,j,1) + + cobalt%f_ndet_100(i,j) = cobalt%f_ndet(i,j,1)*rho_dzt(i,j,1) + cobalt%f_don_100(i,j) = (cobalt%f_ldon(i,j,1)+cobalt%f_sldon(i,j,1)+cobalt%f_srdon(i,j,1))* & + rho_dzt(i,j,1) + cobalt%f_silg_100(i,j) = cobalt%f_silg(i,j,1)*rho_dzt(i,j,1) + + cobalt%fndet_100(i,j) = cobalt%f_ndet(i,j,1) * cobalt%Rho_0 * cobalt%wsink + cobalt%fpdet_100(i,j) = cobalt%f_pdet(i,j,1) * cobalt%Rho_0 * cobalt%wsink + cobalt%ffedet_100(i,j) = cobalt%f_fedet(i,j,1) * cobalt%Rho_0 * cobalt%wsink + cobalt%flithdet_100(i,j) = cobalt%f_lithdet(i,j,1) * cobalt%Rho_0 * cobalt%wsink + cobalt%fsidet_100(i,j) = cobalt%f_sidet(i,j,1) * cobalt%Rho_0 * cobalt%wsink + cobalt%fcadet_arag_100(i,j) = cobalt%f_cadet_arag(i,j,1) * cobalt%Rho_0 * cobalt%wsink + cobalt%fcadet_calc_100(i,j) = cobalt%f_cadet_calc(i,j,1) * cobalt%Rho_0 * cobalt%wsink + enddo; enddo !} i,j + + do j = jsc, jec ; do i = isc, iec ; !{ + k_100 = 1 + do k = 2, grid_kmt(i,j) !{ + if (rho_dzt_100(i,j) .lt. cobalt%Rho_0 * 100.0) then + k_100 = k + rho_dzt_100(i,j) = rho_dzt_100(i,j) + rho_dzt(i,j,k) + cobalt%f_alk_int_100(i,j) = cobalt%f_alk_int_100(i,j) + cobalt%p_alk(i,j,k,tau) * rho_dzt(i,j,k) + cobalt%f_dic_int_100(i,j) = cobalt%f_dic_int_100(i,j) + cobalt%p_dic(i,j,k,tau) * rho_dzt(i,j,k) + cobalt%f_din_int_100(i,j) = cobalt%f_din_int_100(i,j) + (cobalt%p_no3(i,j,k,tau) + & + cobalt%p_nh4(i,j,k,tau)) * rho_dzt(i,j,k) + cobalt%f_fed_int_100(i,j) = cobalt%f_fed_int_100(i,j) + cobalt%p_fed(i,j,k,tau) * rho_dzt(i,j,k) + cobalt%f_po4_int_100(i,j) = cobalt%f_po4_int_100(i,j) + cobalt%p_po4(i,j,k,tau) * rho_dzt(i,j,k) + cobalt%f_sio4_int_100(i,j) = cobalt%f_sio4_int_100(i,j) + cobalt%p_sio4(i,j,k,tau) * rho_dzt(i,j,k) + cobalt%jalk_100(i,j) = cobalt%jalk_100(i,j) + cobalt%jalk(i,j,k) * rho_dzt(i,j,k) + cobalt%jdic_100(i,j) = cobalt%jdic_100(i,j) + cobalt%jdic(i,j,k) * rho_dzt(i,j,k) + cobalt%jdin_100(i,j) = cobalt%jdin_100(i,j) + (cobalt%jno3(i,j,k) + cobalt%jnh4(i,j,k)) * rho_dzt(i,j,k) + cobalt%jfed_100(i,j) = cobalt%jfed_100(i,j) + cobalt%jfed(i,j,k) * rho_dzt(i,j,k) + cobalt%jpo4_100(i,j) = cobalt%jpo4_100(i,j) + cobalt%jpo4(i,j,k) * rho_dzt(i,j,k) + cobalt%jsio4_100(i,j) = cobalt%jsio4_100(i,j) + cobalt%jsio4(i,j,k) * rho_dzt(i,j,k) +! cobalt%jprod_ptot_100(i,j) = cobalt%jprod_ptot_100(i,j) + (phyto(DIAZO)%jprod_po4(i,j,k) & +! + phyto(LARGE)%jprod_po4(i,j,k) + phyto(SMALL)%jprod_po4(i,j,k)) * rho_dzt(i,j,k) +! previously computed in COBALT + cobalt%jprod_ptot_100(i,j) = cobalt%jprod_ptot_100(i,j) + cobalt%jprod_po4(i,j,k) * rho_dzt(i,j,k) + + do n = 1, NUM_PHYTO !{ + phyto(n)%jprod_n_100(i,j) = phyto(n)%jprod_n_100(i,j) + phyto(n)%jprod_n(i,j,k)* & + rho_dzt(i,j,k) + phyto(n)%jprod_n_new_100(i,j) = phyto(n)%jprod_n_new_100(i,j) + phyto(n)%juptake_no3(i,j,k)* & + rho_dzt(i,j,k) + phyto(n)%jzloss_n_100(i,j) = phyto(n)%jzloss_n_100(i,j) + phyto(n)%jzloss_n(i,j,k)* & + rho_dzt(i,j,k) + phyto(n)%jexuloss_n_100(i,j) = phyto(n)%jexuloss_n_100(i,j) + phyto(n)%jexuloss_n(i,j,k)* & + rho_dzt(i,j,k) + phyto(n)%f_n_100(i,j) = phyto(n)%f_n_100(i,j) + phyto(n)%f_n(i,j,k)*rho_dzt(i,j,k) +! added juptake_fe_100 + phyto(n)%juptake_fe_100(i,j) = phyto(n)%juptake_fe_100(i,j) + phyto(n)%juptake_fe(i,j,k)*rho_dzt(i,j,k) +! CAS: added juptake_po4_100 + phyto(n)%juptake_po4_100(i,j) = phyto(n)%juptake_po4_100(i,j) + phyto(n)%juptake_po4(i,j,k)*rho_dzt(i,j,k) + enddo !} n + phyto(DIAZO)%jprod_n_n2_100(i,j) = phyto(DIAZO)%jprod_n_n2_100(i,j) + & + phyto(DIAZO)%juptake_n2(i,j,k)*rho_dzt(i,j,k) + phyto(SMALL)%jvirloss_n_100(i,j) = phyto(SMALL)%jvirloss_n_100(i,j) + & + phyto(SMALL)%jvirloss_n(i,j,k)*rho_dzt(i,j,k) + phyto(SMALL)%jaggloss_n_100(i,j) = phyto(SMALL)%jaggloss_n_100(i,j) + & + phyto(SMALL)%jaggloss_n(i,j,k)*rho_dzt(i,j,k) + phyto(LARGE)%jaggloss_n_100(i,j) = phyto(LARGE)%jaggloss_n_100(i,j) + & + phyto(LARGE)%jaggloss_n(i,j,k)*rho_dzt(i,j,k) +! CAS: added diagnotistic for depth integrated diatom production + cobalt%jprod_diat_100(i,j) = cobalt%jprod_diat_100(i,j) + & + phyto(LARGE)%jprod_n(i,j,k)*phyto(LARGE)%silim(i,j,k)*rho_dzt(i,j,k) +! added juptake_sio4_100 (large only) + phyto(LARGE)%juptake_sio4_100(i,j) = phyto(LARGE)%juptake_sio4_100(i,j) + & + phyto(LARGE)%juptake_sio4(i,j,k)*rho_dzt(i,j,k) + + do n = 1, NUM_ZOO !{ + zoo(n)%jprod_n_100(i,j) = zoo(n)%jprod_n_100(i,j) + zoo(n)%jprod_n(i,j,k)* & + rho_dzt(i,j,k) + zoo(n)%jingest_n_100(i,j) = zoo(n)%jingest_n_100(i,j) + zoo(n)%jingest_n(i,j,k)* & + rho_dzt(i,j,k) + zoo(n)%jremin_n_100(i,j) = zoo(n)%jremin_n_100(i,j) + zoo(n)%jprod_nh4(i,j,k)* & + rho_dzt(i,j,k) + zoo(n)%f_n_100(i,j) = zoo(n)%f_n_100(i,j) + zoo(n)%f_n(i,j,k)*rho_dzt(i,j,k) + enddo !} n + + do n = 1,2 !{ + zoo(n)%jzloss_n_100(i,j) = zoo(n)%jzloss_n_100(i,j) + zoo(n)%jzloss_n(i,j,k)* & + rho_dzt(i,j,k) + zoo(n)%jprod_don_100(i,j) = zoo(n)%jprod_don_100(i,j) + (zoo(n)%jprod_ldon(i,j,k) + & + zoo(n)%jprod_sldon(i,j,k) + zoo(n)%jprod_srdon(i,j,k))*rho_dzt(i,j,k) + enddo !} n + + do n = 2,3 !{ + zoo(n)%jhploss_n_100(i,j) = zoo(n)%jhploss_n_100(i,j) + zoo(n)%jhploss_n(i,j,k)* & + rho_dzt(i,j,k) + zoo(n)%jprod_ndet_100(i,j) = zoo(n)%jprod_ndet_100(i,j) + zoo(n)%jprod_ndet(i,j,k)* & + rho_dzt(i,j,k) + enddo !} n + + cobalt%hp_jingest_n_100(i,j) = cobalt%hp_jingest_n_100(i,j) + cobalt%hp_jingest_n(i,j,k)* & + rho_dzt(i,j,k) + cobalt%hp_jremin_n_100(i,j) = cobalt%hp_jremin_n_100(i,j) + cobalt%hp_jingest_n(i,j,k)* & + (1.0-cobalt%hp_phi_det)*rho_dzt(i,j,k) + cobalt%hp_jprod_ndet_100(i,j) = cobalt%hp_jprod_ndet_100(i,j) + cobalt%hp_jingest_n(i,j,k)* & + cobalt%hp_phi_det*rho_dzt(i,j,k) + + bact(1)%jprod_n_100(i,j) = bact(1)%jprod_n_100(i,j) + bact(1)%jprod_n(i,j,k) * rho_dzt(i,j,k) + bact(1)%jzloss_n_100(i,j) = bact(1)%jzloss_n_100(i,j) + bact(1)%jzloss_n(i,j,k) * rho_dzt(i,j,k) + bact(1)%jvirloss_n_100(i,j) = bact(1)%jvirloss_n_100(i,j) + bact(1)%jvirloss_n(i,j,k) * rho_dzt(i,j,k) + bact(1)%jremin_n_100(i,j) = bact(1)%jremin_n_100(i,j) + bact(1)%jprod_nh4(i,j,k) * rho_dzt(i,j,k) + bact(1)%juptake_ldon_100(i,j) = bact(1)%juptake_ldon_100(i,j) + bact(1)%juptake_ldon(i,j,k) * rho_dzt(i,j,k) + bact(1)%f_n_100(i,j) = bact(1)%f_n_100(i,j) + bact(1)%f_n(i,j,k)*rho_dzt(i,j,k) + + cobalt%jprod_lithdet_100(i,j) = cobalt%jprod_lithdet_100(i,j) + cobalt%jprod_lithdet(i,j,k) * rho_dzt(i,j,k) + cobalt%jprod_sidet_100(i,j) = cobalt%jprod_sidet_100(i,j) + cobalt%jprod_sidet(i,j,k) * rho_dzt(i,j,k) + cobalt%jprod_cadet_calc_100(i,j) = cobalt%jprod_cadet_calc_100(i,j) + cobalt%jprod_cadet_calc(i,j,k) * rho_dzt(i,j,k) + cobalt%jprod_cadet_arag_100(i,j) = cobalt%jprod_cadet_arag_100(i,j) + cobalt%jprod_cadet_arag(i,j,k) * rho_dzt(i,j,k) + cobalt%jremin_ndet_100(i,j) = cobalt%jremin_ndet_100(i,j) + cobalt%jremin_ndet(i,j,k) * rho_dzt(i,j,k) + cobalt%f_ndet_100(i,j) = cobalt%f_ndet_100(i,j) + cobalt%f_ndet(i,j,k)*rho_dzt(i,j,k) + cobalt%f_don_100(i,j) = cobalt%f_don_100(i,j) + (cobalt%f_ldon(i,j,k) + cobalt%f_sldon(i,j,k) + & + cobalt%f_srdon(i,j,k))*rho_dzt(i,j,k) + cobalt%f_silg_100(i,j) = cobalt%f_silg_100(i,j) + cobalt%f_silg(i,j,k)*rho_dzt(i,j,k) + + cobalt%fndet_100(i,j) = cobalt%f_ndet(i,j,k) * cobalt%Rho_0 * cobalt%wsink + cobalt%fpdet_100(i,j) = cobalt%f_pdet(i,j,k) * cobalt%Rho_0 * cobalt%wsink + cobalt%ffedet_100(i,j) = cobalt%f_fedet(i,j,k) * cobalt%Rho_0 * cobalt%wsink + cobalt%flithdet_100(i,j) = cobalt%f_lithdet(i,j,k) * cobalt%Rho_0 * cobalt%wsink + cobalt%fsidet_100(i,j) = cobalt%f_sidet(i,j,k) * cobalt%Rho_0 * cobalt%wsink + cobalt%fcadet_arag_100(i,j) = cobalt%f_cadet_arag(i,j,k) * cobalt%Rho_0 * cobalt%wsink + cobalt%fcadet_calc_100(i,j) = cobalt%f_cadet_calc(i,j,k) * cobalt%Rho_0 * cobalt%wsink + + endif + enddo !} k + + if (k_100 .gt. 1 .and. k_100 .lt. grid_kmt(i,j)) then + drho_dzt = cobalt%Rho_0 * 100.0 - rho_dzt_100(i,j) + cobalt%f_alk_int_100(i,j) = cobalt%f_alk_int_100(i,j) + cobalt%p_alk(i,j,k_100,tau) * drho_dzt + cobalt%f_dic_int_100(i,j) = cobalt%f_dic_int_100(i,j) + cobalt%p_dic(i,j,k_100,tau) * drho_dzt + cobalt%f_din_int_100(i,j) = cobalt%f_din_int_100(i,j) + (cobalt%p_no3(i,j,k_100,tau) + & + cobalt%p_nh4(i,j,k_100,tau)) * drho_dzt + cobalt%f_fed_int_100(i,j) = cobalt%f_fed_int_100(i,j) + cobalt%p_fed(i,j,k_100,tau) * drho_dzt + cobalt%f_po4_int_100(i,j) = cobalt%f_po4_int_100(i,j) + cobalt%p_po4(i,j,k_100,tau) * drho_dzt + cobalt%f_sio4_int_100(i,j) = cobalt%f_sio4_int_100(i,j) + cobalt%p_sio4(i,j,k_100,tau) * drho_dzt + cobalt%jalk_100(i,j) = cobalt%jalk_100(i,j) + cobalt%jalk(i,j,k_100) * drho_dzt + cobalt%jdic_100(i,j) = cobalt%jdic_100(i,j) + cobalt%jdic(i,j,k_100) * drho_dzt + cobalt%jdin_100(i,j) = cobalt%jdin_100(i,j) + (cobalt%jno3(i,j,k_100) + cobalt%jnh4(i,j,k_100)) * drho_dzt + cobalt%jfed_100(i,j) = cobalt%jfed_100(i,j) + cobalt%jfed(i,j,k_100) * drho_dzt + cobalt%jpo4_100(i,j) = cobalt%jpo4_100(i,j) + cobalt%jpo4(i,j,k_100) * drho_dzt + cobalt%jsio4_100(i,j) = cobalt%jsio4_100(i,j) + cobalt%jsio4(i,j,k_100) * drho_dzt +! cobalt%jprod_ptot_100(i,j) = cobalt%jprod_ptot_100(i,j) + & +! (phyto(DIAZO)%jprod_po4(i,j,k_100) + phyto(LARGE)%jprod_po4(i,j,k_100) + & +! phyto(SMALL)%jprod_po4(i,j,k_100)) * drho_dzt +! previously computed in COBALT + cobalt%jprod_ptot_100(i,j) = cobalt%jprod_ptot_100(i,j) + cobalt%jprod_po4(i,j,k_100) * drho_dzt + + do n = 1, NUM_PHYTO !{ + phyto(n)%jprod_n_100(i,j) = phyto(n)%jprod_n_100(i,j) + phyto(n)%jprod_n(i,j,k_100)* & + drho_dzt + phyto(n)%jprod_n_new_100(i,j) = phyto(n)%jprod_n_new_100(i,j) + phyto(n)%juptake_no3(i,j,k_100)* & + drho_dzt + phyto(n)%jzloss_n_100(i,j) = phyto(n)%jzloss_n_100(i,j) + phyto(n)%jzloss_n(i,j,k_100)* & + drho_dzt + phyto(n)%jexuloss_n_100(i,j) = phyto(n)%jexuloss_n_100(i,j) + phyto(n)%jexuloss_n(i,j,k_100)* & + drho_dzt + phyto(n)%f_n_100(i,j) = phyto(n)%f_n_100(i,j) + phyto(n)%f_n(i,j,k_100)*drho_dzt +! added juptake_fe_100 + phyto(n)%juptake_fe_100(i,j) = phyto(n)%juptake_fe_100(i,j) + phyto(n)%juptake_fe(i,j,k_100)*drho_dzt +! CAS: added juptake_po4_100 + phyto(n)%juptake_po4_100(i,j) = phyto(n)%juptake_po4_100(i,j) + phyto(n)%juptake_po4(i,j,k_100)*drho_dzt + enddo !} n + phyto(DIAZO)%jprod_n_n2_100(i,j) = phyto(DIAZO)%jprod_n_n2_100(i,j) + & + phyto(DIAZO)%juptake_n2(i,j,k_100)*drho_dzt + phyto(SMALL)%jvirloss_n_100(i,j) = phyto(SMALL)%jvirloss_n_100(i,j) + & + phyto(SMALL)%jvirloss_n(i,j,k_100)*drho_dzt + phyto(SMALL)%jaggloss_n_100(i,j) = phyto(SMALL)%jaggloss_n_100(i,j) + & + phyto(SMALL)%jaggloss_n(i,j,k_100)*drho_dzt + phyto(LARGE)%jaggloss_n_100(i,j) = phyto(LARGE)%jaggloss_n_100(i,j) + & + phyto(LARGE)%jaggloss_n(i,j,k_100)*drho_dzt +! CAS: added diagnotistic for depth integrated diatom production + cobalt%jprod_diat_100(i,j) = cobalt%jprod_diat_100(i,j) + & + phyto(LARGE)%jprod_n(i,j,k_100)*phyto(LARGE)%silim(i,j,k_100)*drho_dzt +! added juptake_sio4_100 (large only) + phyto(LARGE)%juptake_sio4_100(i,j) = phyto(LARGE)%juptake_sio4_100(i,j) + & + phyto(LARGE)%juptake_sio4(i,j,k_100)*drho_dzt + + do n = 1, NUM_ZOO !{ + zoo(n)%jprod_n_100(i,j) = zoo(n)%jprod_n_100(i,j) + zoo(n)%jprod_n(i,j,k_100)* & + drho_dzt + zoo(n)%jingest_n_100(i,j) = zoo(n)%jingest_n_100(i,j) + zoo(n)%jingest_n(i,j,k_100)* & + drho_dzt + zoo(n)%jremin_n_100(i,j) = zoo(n)%jremin_n_100(i,j) + zoo(n)%jprod_nh4(i,j,k_100)* & + drho_dzt + zoo(n)%f_n_100(i,j) = zoo(n)%f_n_100(i,j) + zoo(n)%f_n(i,j,k_100)*drho_dzt + enddo !} n + + do n = 1,2 !{ + zoo(n)%jzloss_n_100(i,j) = zoo(n)%jzloss_n_100(i,j) + zoo(n)%jzloss_n(i,j,k_100)* & + drho_dzt + zoo(n)%jprod_don_100(i,j) = zoo(n)%jprod_don_100(i,j) + (zoo(n)%jprod_ldon(i,j,k_100) + & + zoo(n)%jprod_sldon(i,j,k_100) + zoo(n)%jprod_srdon(i,j,k_100))*drho_dzt + enddo !} n + + do n = 2,3 !{ + zoo(n)%jhploss_n_100(i,j) = zoo(n)%jhploss_n_100(i,j) + zoo(n)%jhploss_n(i,j,k_100)* & + drho_dzt + zoo(n)%jprod_ndet_100(i,j) = zoo(n)%jprod_ndet_100(i,j) + zoo(n)%jprod_ndet(i,j,k_100)* & + drho_dzt + enddo !} n + + cobalt%hp_jingest_n_100(i,j) = cobalt%hp_jingest_n_100(i,j) + cobalt%hp_jingest_n(i,j,k_100)* & + drho_dzt + cobalt%hp_jremin_n_100(i,j) = cobalt%hp_jremin_n_100(i,j) + cobalt%hp_jingest_n(i,j,k_100)* & + (1.0-cobalt%hp_phi_det)*drho_dzt + cobalt%hp_jprod_ndet_100(i,j) = cobalt%hp_jprod_ndet_100(i,j) + cobalt%hp_jingest_n(i,j,k_100)* & + cobalt%hp_phi_det*drho_dzt + + bact(1)%jprod_n_100(i,j) = bact(1)%jprod_n_100(i,j) + bact(1)%jprod_n(i,j,k_100)* & + drho_dzt + bact(1)%jzloss_n_100(i,j) = bact(1)%jzloss_n_100(i,j) + bact(1)%jzloss_n(i,j,k_100)* & + drho_dzt + bact(1)%jvirloss_n_100(i,j) = bact(1)%jvirloss_n_100(i,j) + bact(1)%jvirloss_n(i,j,k_100)* & + drho_dzt + bact(1)%jremin_n_100(i,j) = bact(1)%jremin_n_100(i,j) + bact(1)%jprod_nh4(i,j,k_100)* & + drho_dzt + bact(1)%juptake_ldon_100(i,j) = bact(1)%juptake_ldon_100(i,j) + bact(1)%juptake_ldon(i,j,k_100)* & + drho_dzt + bact(1)%f_n_100(i,j) = bact(1)%f_n_100(i,j) + bact(1)%f_n(i,j,k_100)*drho_dzt + + cobalt%jprod_lithdet_100(i,j) = cobalt%jprod_lithdet_100(i,j) + cobalt%jprod_lithdet(i,j,k_100)* & + drho_dzt + cobalt%jprod_sidet_100(i,j) = cobalt%jprod_sidet_100(i,j) + cobalt%jprod_sidet(i,j,k_100)* & + drho_dzt + cobalt%jprod_cadet_calc_100(i,j) = cobalt%jprod_cadet_calc_100(i,j) + cobalt%jprod_cadet_calc(i,j,k_100)* & + drho_dzt + cobalt%jprod_cadet_arag_100(i,j) = cobalt%jprod_cadet_arag_100(i,j) + cobalt%jprod_cadet_arag(i,j,k_100)* & + drho_dzt + cobalt%jremin_ndet_100(i,j) = cobalt%jremin_ndet_100(i,j) + cobalt%jremin_ndet(i,j,k_100)* & + drho_dzt + + cobalt%f_ndet_100(i,j) = cobalt%f_ndet_100(i,j) + cobalt%f_ndet(i,j,k_100)*drho_dzt + cobalt%f_don_100(i,j) = cobalt%f_don_100(i,j) + (cobalt%f_ldon(i,j,k_100) + cobalt%f_sldon(i,j,k_100) + & + cobalt%f_srdon(i,j,k_100))*drho_dzt + cobalt%f_silg_100(i,j) = cobalt%f_silg_100(i,j) + cobalt%f_silg(i,j,k_100)*drho_dzt + + cobalt%fndet_100(i,j) = cobalt%f_ndet(i,j,k_100) * cobalt%Rho_0 * cobalt%wsink + cobalt%fpdet_100(i,j) = cobalt%f_pdet(i,j,k_100) * cobalt%Rho_0 * cobalt%wsink + cobalt%ffedet_100(i,j) = cobalt%f_fedet(i,j,k_100) * cobalt%Rho_0 * cobalt%wsink + cobalt%flithdet_100(i,j) = cobalt%f_lithdet(i,j,k_100) * cobalt%Rho_0 * cobalt%wsink + cobalt%fsidet_100(i,j) = cobalt%f_sidet(i,j,k_100) * cobalt%Rho_0 * cobalt%wsink + cobalt%fcadet_arag_100(i,j) = cobalt%f_cadet_arag(i,j,k_100) * cobalt%Rho_0 * cobalt%wsink + cobalt%fcadet_calc_100(i,j) = cobalt%f_cadet_calc(i,j,k_100) * cobalt%Rho_0 * cobalt%wsink + endif + + cobalt%jprod_allphytos_100(i,j) = phyto(SMALL)%jprod_n_100(i,j) + phyto(LARGE)%jprod_n_100(i,j) + & + phyto(DIAZO)%jprod_n_100(i,j) + enddo ; enddo !} i,j + + ! + ! Calculate biomass-weighted nutrient and light limitation terms for CMIP6 + ! Note: these need to be done after other 100m integrals because the biomass + ! weighting requires the pre-calculated 100m biomass + ! + do j = jsc, jec ; do i = isc, iec !{ + rho_dzt_100(i,j) = rho_dzt(i,j,1) + do n = 1,NUM_PHYTO + phyto(n)%nlim_bw_100(i,j) = (phyto(n)%no3lim(i,j,1)+phyto(n)%nh4lim(i,j,1))* & + phyto(n)%f_n(i,j,1)*rho_dzt(i,j,1)/(phyto(n)%f_n_100(i,j)+epsln) + phyto(n)%plim_bw_100(i,j) = phyto(n)%po4lim(i,j,1)* & + phyto(n)%f_n(i,j,1)*rho_dzt(i,j,1)/(phyto(n)%f_n_100(i,j)+epsln) + phyto(n)%def_fe_bw_100(i,j) = phyto(n)%def_fe(i,j,1)* & + phyto(n)%f_n(i,j,1)*rho_dzt(i,j,1)/(phyto(n)%f_n_100(i,j)+epsln) + phyto(n)%irrlim_bw_100(i,j) = phyto(n)%irrlim(i,j,1)* & + phyto(n)%f_n(i,j,1)*rho_dzt(i,j,1)/(phyto(n)%f_n_100(i,j)+epsln) + enddo !} n + enddo; enddo !} i, j + + do j = jsc, jec ; do i = isc, iec ; !{ + k_100 = 1 + do k = 2, grid_kmt(i,j) !{ + if (rho_dzt_100(i,j) .lt. cobalt%Rho_0 * 100.0) then + k_100 = k + rho_dzt_100(i,j) = rho_dzt_100(i,j) + rho_dzt(i,j,k) + do n = 1,NUM_PHYTO + phyto(n)%nlim_bw_100(i,j) = phyto(n)%nlim_bw_100(i,j) + & + (phyto(n)%no3lim(i,j,k)+phyto(n)%nh4lim(i,j,k))* & + phyto(n)%f_n(i,j,k)*rho_dzt(i,j,k)/phyto(n)%f_n_100(i,j) + phyto(n)%plim_bw_100(i,j) = phyto(n)%plim_bw_100(i,j) + phyto(n)%po4lim(i,j,k)* & + phyto(n)%f_n(i,j,k)*rho_dzt(i,j,k)/phyto(n)%f_n_100(i,j) + phyto(n)%def_fe_bw_100(i,j) = phyto(n)%def_fe_bw_100(i,j) + phyto(n)%def_fe(i,j,k)* & + phyto(n)%f_n(i,j,k)*rho_dzt(i,j,k)/phyto(n)%f_n_100(i,j) + phyto(n)%irrlim_bw_100(i,j) = phyto(n)%irrlim_bw_100(i,j) + phyto(n)%irrlim(i,j,k)* & + phyto(n)%f_n(i,j,k)*rho_dzt(i,j,k)/phyto(n)%f_n_100(i,j) + enddo + endif + enddo !} k + + if (k_100 .gt. 1 .and. k_100 .lt. grid_kmt(i,j)) then + drho_dzt = cobalt%Rho_0 * 100.0 - rho_dzt_100(i,j) + do n = 1,NUM_PHYTO + phyto(n)%nlim_bw_100(i,j) = phyto(n)%nlim_bw_100(i,j) + & + (phyto(n)%no3lim(i,j,k_100)+phyto(n)%nh4lim(i,j,k_100))* & + phyto(n)%f_n(i,j,k_100)*drho_dzt/phyto(n)%f_n_100(i,j) + phyto(n)%plim_bw_100(i,j) = phyto(n)%plim_bw_100(i,j) + phyto(n)%po4lim(i,j,k_100)* & + phyto(n)%f_n(i,j,k_100)*drho_dzt/phyto(n)%f_n_100(i,j) + phyto(n)%def_fe_bw_100(i,j) = phyto(n)%def_fe_bw_100(i,j) + phyto(n)%def_fe(i,j,k_100)* & + phyto(n)%f_n(i,j,k_100)*drho_dzt/phyto(n)%f_n_100(i,j) + phyto(n)%irrlim_bw_100(i,j) = phyto(n)%irrlim_bw_100(i,j) + phyto(n)%irrlim(i,j,k_100)* & + phyto(n)%f_n(i,j,k_100)*drho_dzt/phyto(n)%f_n_100(i,j) + enddo + endif + enddo; enddo !} i, j + deallocate(rho_dzt_100) + + do j = jsc, jec ; do i = isc, iec ; !{ + if (grid_kmt(i,j) .gt. 0) then !{ + cobalt%btm_temp(i,j) = TEMP(i,j,grid_kmt(i,j)) + cobalt%btm_o2(i,j) = cobalt%f_o2(i,j,grid_kmt(i,j)) + cobalt%btm_htotal(i,j) = cobalt%f_htotal(i,j,grid_kmt(i,j)) + cobalt%btm_co3_sol_arag(i,j) = cobalt%co3_sol_arag(i,j,grid_kmt(i,j)) + cobalt%btm_co3_sol_calc(i,j) = cobalt%co3_sol_calc(i,j,grid_kmt(i,j)) + cobalt%btm_co3_ion(i,j) = cobalt%f_co3_ion(i,j,grid_kmt(i,j)) + cobalt%cased_2d(i,j) = cobalt%f_cased(i,j,1) + endif + enddo; enddo !} i, j + + ! + !--------------------------------------------------------------------- + ! calculate upper 200m vertical integrals for mesozooplankton + ! quantities for comparison with COPEPOD database + !--------------------------------------------------------------------- + ! + allocate(rho_dzt_200(isc:iec,jsc:jec)) + do j = jsc, jec ; do i = isc, iec !{ + rho_dzt_200(i,j) = rho_dzt(i,j,1) + cobalt%jprod_mesozoo_200(i,j) = (zoo(2)%jprod_n(i,j,1) + zoo(3)%jprod_n(i,j,1))*rho_dzt(i,j,1) + cobalt%f_mesozoo_200(i,j) = (zoo(2)%f_n(i,j,1)+zoo(3)%f_n(i,j,1))*rho_dzt(i,j,1) + enddo; enddo !} i,j + + do j = jsc, jec ; do i = isc, iec ; !{ + k_200 = 1 + do k = 2, grid_kmt(i,j) !{ + if (rho_dzt_200(i,j) .lt. cobalt%Rho_0 * 200.0) then + k_200 = k + rho_dzt_200(i,j) = rho_dzt_200(i,j) + rho_dzt(i,j,k) + cobalt%jprod_mesozoo_200(i,j) = cobalt%jprod_mesozoo_200(i,j) + & + (zoo(2)%jprod_n(i,j,k) + zoo(3)%jprod_n(i,j,k))*rho_dzt(i,j,k) + cobalt%f_mesozoo_200(i,j) = cobalt%f_mesozoo_200(i,j) + & + (zoo(2)%f_n(i,j,k)+zoo(3)%f_n(i,j,k))*rho_dzt(i,j,k) + endif + enddo !} k + + if (k_200 .gt. 1 .and. k_200 .lt. grid_kmt(i,j)) then + drho_dzt = cobalt%Rho_0 * 200.0 - rho_dzt_200(i,j) + cobalt%jprod_mesozoo_200(i,j) = cobalt%jprod_mesozoo_200(i,j) + & + (zoo(2)%jprod_n(i,j,k_200) + zoo(3)%jprod_n(i,j,k_200))*drho_dzt + cobalt%f_mesozoo_200(i,j) = cobalt%f_mesozoo_200(i,j) + & + (zoo(2)%f_n(i,j,k_200)+zoo(3)%f_n(i,j,k_200))*drho_dzt + endif + enddo ; enddo !} i,j + + call g_tracer_get_values(tracer_list,'alk','runoff_tracer_flux',cobalt%runoff_flux_alk,isd,jsd) + call g_tracer_get_values(tracer_list,'dic','runoff_tracer_flux',cobalt%runoff_flux_dic,isd,jsd) + if (do_14c) then !{ + call g_tracer_get_values(tracer_list,'di14c','runoff_tracer_flux',cobalt%runoff_flux_di14c,isd,jsd) + endif !} + call g_tracer_get_values(tracer_list,'fed','runoff_tracer_flux',cobalt%runoff_flux_fed,isd,jsd) + call g_tracer_get_values(tracer_list,'fed','drydep',cobalt%dry_fed,isd,jsd) + call g_tracer_get_values(tracer_list,'fed','wetdep',cobalt%wet_fed,isd,jsd) + call g_tracer_get_values(tracer_list,'lith','runoff_tracer_flux',cobalt%runoff_flux_lith,isd,jsd) + call g_tracer_get_values(tracer_list,'lith','drydep',cobalt%dry_lith,isd,jsd) + call g_tracer_get_values(tracer_list,'lith','wetdep',cobalt%wet_lith,isd,jsd) + call g_tracer_get_values(tracer_list,'no3','runoff_tracer_flux',cobalt%runoff_flux_no3,isd,jsd) + call g_tracer_get_values(tracer_list,'no3','drydep',cobalt%dry_no3,isd,jsd) + call g_tracer_get_values(tracer_list,'no3','wetdep',cobalt%wet_no3,isd,jsd) + call g_tracer_get_values(tracer_list,'nh4','drydep',cobalt%dry_nh4,isd,jsd) + call g_tracer_get_values(tracer_list,'nh4','wetdep',cobalt%wet_nh4,isd,jsd) + call g_tracer_get_values(tracer_list,'po4','drydep',cobalt%dry_po4,isd,jsd) + call g_tracer_get_values(tracer_list,'po4','wetdep',cobalt%wet_po4,isd,jsd) + call g_tracer_get_values(tracer_list,'ldon','runoff_tracer_flux',cobalt%runoff_flux_ldon,isd,jsd) + call g_tracer_get_values(tracer_list,'sldon','runoff_tracer_flux',cobalt%runoff_flux_sldon,isd,jsd) + call g_tracer_get_values(tracer_list,'srdon','runoff_tracer_flux',cobalt%runoff_flux_srdon,isd,jsd) + call g_tracer_get_values(tracer_list,'ndet','runoff_tracer_flux',cobalt%runoff_flux_ndet,isd,jsd) + call g_tracer_get_values(tracer_list,'po4','runoff_tracer_flux',cobalt%runoff_flux_po4,isd,jsd) + call g_tracer_get_values(tracer_list,'ldop','runoff_tracer_flux',cobalt%runoff_flux_ldop,isd,jsd) + call g_tracer_get_values(tracer_list,'sldop','runoff_tracer_flux',cobalt%runoff_flux_sldop,isd,jsd) + call g_tracer_get_values(tracer_list,'srdop','runoff_tracer_flux',cobalt%runoff_flux_srdop,isd,jsd) +! JGJ: Added for CMIP6 + call g_tracer_get_values(tracer_list,'dic','stf_gas',cobalt%stf_gas_dic,isd,jsd) + call g_tracer_get_values(tracer_list,'o2','stf_gas',cobalt%stf_gas_o2,isd,jsd) + call g_tracer_get_values(tracer_list,'dic','deltap',cobalt%deltap_dic,isd,jsd) + call g_tracer_get_values(tracer_list,'o2','deltap',cobalt%deltap_o2,isd,jsd) + + +!--------------------------------------------------------------------- +! Add vertical integrals for diagnostics +!--------------------------------------------------------------------- +! + + call mpp_clock_end(id_clock_cobalt_calc_diagnostics) + call mpp_clock_begin(id_clock_cobalt_send_diagnostics) + + if (cobalt%id_pka_nh3 .gt. 0) then + used = g_send_data(cobalt%id_pka_nh3, pka_nh3, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + end if + if (allocated(pka_nh3)) deallocate(pka_nh3) + deallocate(phos_nh3_exchange) +! +!--------------------------------------------------------------------- +! +! Send phytoplankton diagnostic data + + do n= 1, NUM_PHYTO + if (phyto(n)%id_def_fe .gt. 0) & + used = g_send_data(phyto(n)%id_def_fe, phyto(n)%def_fe, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_felim .gt. 0) & + used = g_send_data(phyto(n)%id_felim, phyto(n)%felim, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_irrlim .gt. 0) & + used = g_send_data(phyto(n)%id_irrlim, phyto(n)%irrlim, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_jzloss_n .gt. 0) & + used = g_send_data(phyto(n)%id_jzloss_n, phyto(n)%jzloss_n*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_jaggloss_n .gt. 0) & + used = g_send_data(phyto(n)%id_jaggloss_n, phyto(n)%jaggloss_n*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_jvirloss_n .gt. 0) & + used = g_send_data(phyto(n)%id_jvirloss_n, phyto(n)%jvirloss_n*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_jexuloss_n .gt. 0) & + used = g_send_data(phyto(n)%id_jexuloss_n, phyto(n)%jexuloss_n*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_juptake_fe .gt. 0) & + used = g_send_data(phyto(n)%id_juptake_fe, phyto(n)%juptake_fe*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_juptake_nh4 .gt. 0) & + used = g_send_data(phyto(n)%id_juptake_nh4, phyto(n)%juptake_nh4*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_juptake_no3 .gt. 0) & + used = g_send_data(phyto(n)%id_juptake_no3, phyto(n)%juptake_no3*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_juptake_po4 .gt. 0) & + used = g_send_data(phyto(n)%id_juptake_po4, phyto(n)%juptake_po4*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_juptake_sio4 .gt. 0) & + used = g_send_data(phyto(n)%id_juptake_sio4, phyto(n)%juptake_sio4*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_juptake_n2 .gt. 0) & + used = g_send_data(phyto(n)%id_juptake_n2, phyto(n)%juptake_n2*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_jprod_n .gt. 0) & + used = g_send_data(phyto(n)%id_jprod_n, phyto(n)%jprod_n*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_liebig_lim .gt. 0) & + used = g_send_data(phyto(n)%id_liebig_lim,phyto(n)%liebig_lim, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_mu .gt. 0) & + used = g_send_data(phyto(n)%id_mu, phyto(n)%mu, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_nh4lim .gt. 0) & + used = g_send_data(phyto(n)%id_nh4lim, phyto(n)%nh4lim, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_no3lim .gt. 0) & + used = g_send_data(phyto(n)%id_no3lim, phyto(n)%no3lim, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_po4lim .gt. 0) & + used = g_send_data(phyto(n)%id_po4lim, phyto(n)%po4lim, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_o2lim .gt. 0) & + used = g_send_data(phyto(n)%id_o2lim, phyto(n)%o2lim, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_q_fe_2_n .gt. 0) & + used = g_send_data(phyto(n)%id_q_fe_2_n, phyto(n)%q_fe_2_n, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_silim .gt. 0) & + used = g_send_data(phyto(n)%id_silim, phyto(n)%silim, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_q_si_2_n .gt. 0) & + used = g_send_data(phyto(n)%id_q_si_2_n, phyto(n)%q_si_2_n, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_theta .gt. 0) & + used = g_send_data(phyto(n)%id_theta, phyto(n)%theta, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_f_mu_mem .gt. 0) & + used = g_send_data(phyto(n)%id_f_mu_mem, phyto(n)%f_mu_mem, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_mu_mix .gt. 0) & + used = g_send_data(phyto(n)%id_mu_mix, phyto(n)%mu_mix, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (phyto(n)%id_agg_lim .gt. 0) & + used = g_send_data(phyto(n)%id_agg_lim, phyto(n)%agg_lim, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + enddo + !-------------------------------------------------------------------------------------- + ! Send bacterial diagnostic data + ! + + if (bact(1)%id_jzloss_n .gt. 0) & + used = g_send_data(bact(1)%id_jzloss_n, bact(1)%jzloss_n*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (bact(1)%id_jvirloss_n .gt. 0) & + used = g_send_data(bact(1)%id_jvirloss_n, bact(1)%jvirloss_n*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (bact(1)%id_juptake_ldon .gt. 0) & + used = g_send_data(bact(1)%id_juptake_ldon, bact(1)%juptake_ldon*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (bact(1)%id_juptake_ldop .gt. 0) & + used = g_send_data(bact(1)%id_juptake_ldop, bact(1)%juptake_ldop*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (bact(1)%id_jprod_nh4 .gt. 0) & + used = g_send_data(bact(1)%id_jprod_nh4, bact(1)%jprod_nh4*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (bact(1)%id_jprod_po4 .gt. 0) & + used = g_send_data(bact(1)%id_jprod_po4, bact(1)%jprod_po4*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (bact(1)%id_jprod_n .gt. 0) & + used = g_send_data(bact(1)%id_jprod_n, bact(1)%jprod_n*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (bact(1)%id_o2lim .gt. 0) & + used = g_send_data(bact(1)%id_o2lim, bact(1)%o2lim, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (bact(1)%id_ldonlim .gt. 0) & + used = g_send_data(bact(1)%id_ldonlim, bact(1)%ldonlim, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (bact(1)%id_temp_lim .gt. 0) & + used = g_send_data(bact(1)%id_temp_lim, bact(1)%temp_lim, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + !-------------------------------------------------------------------------------------- + ! Send zooplankton diagnostic data + ! + + do n= 1, NUM_ZOO + if (zoo(n)%id_jzloss_n .gt. 0) & + used = g_send_data(zoo(n)%id_jzloss_n, zoo(n)%jzloss_n*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (zoo(n)%id_jhploss_n .gt. 0) & + used = g_send_data(zoo(n)%id_jhploss_n, zoo(n)%jhploss_n*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (zoo(n)%id_jingest_n .gt. 0) & + used = g_send_data(zoo(n)%id_jingest_n, zoo(n)%jingest_n*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (zoo(n)%id_jingest_p .gt. 0) & + used = g_send_data(zoo(n)%id_jingest_p, zoo(n)%jingest_p*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (zoo(n)%id_jingest_sio2 .gt. 0) & + used = g_send_data(zoo(n)%id_jingest_sio2, zoo(n)%jingest_sio2*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (zoo(n)%id_jingest_fe .gt. 0) & + used = g_send_data(zoo(n)%id_jingest_fe, zoo(n)%jingest_fe*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (zoo(n)%id_jprod_ndet .gt. 0) & + used = g_send_data(zoo(n)%id_jprod_ndet, zoo(n)%jprod_ndet*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (zoo(n)%id_jprod_pdet .gt. 0) & + used = g_send_data(zoo(n)%id_jprod_pdet, zoo(n)%jprod_pdet*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (zoo(n)%id_jprod_ldon .gt. 0) & + used = g_send_data(zoo(n)%id_jprod_ldon, zoo(n)%jprod_ldon*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (zoo(n)%id_jprod_ldop .gt. 0) & + used = g_send_data(zoo(n)%id_jprod_ldop, zoo(n)%jprod_ldop*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (zoo(n)%id_jprod_sldon .gt. 0) & + used = g_send_data(zoo(n)%id_jprod_sldon, zoo(n)%jprod_sldon*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (zoo(n)%id_jprod_sldop .gt. 0) & + used = g_send_data(zoo(n)%id_jprod_sldop, zoo(n)%jprod_sldop*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (zoo(n)%id_jprod_srdon .gt. 0) & + used = g_send_data(zoo(n)%id_jprod_srdon, zoo(n)%jprod_srdon*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (zoo(n)%id_jprod_srdop .gt. 0) & + used = g_send_data(zoo(n)%id_jprod_srdop, zoo(n)%jprod_srdop*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (zoo(n)%id_jprod_fed .gt. 0) & + used = g_send_data(zoo(n)%id_jprod_fed, zoo(n)%jprod_fed*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (zoo(n)%id_jprod_fedet .gt. 0) & + used = g_send_data(zoo(n)%id_jprod_fedet, zoo(n)%jprod_fedet*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (zoo(n)%id_jprod_sidet .gt. 0) & + used = g_send_data(zoo(n)%id_jprod_sidet, zoo(n)%jprod_sidet*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (zoo(n)%id_jprod_sio4 .gt. 0) & + used = g_send_data(zoo(n)%id_jprod_sio4, zoo(n)%jprod_sio4*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (zoo(n)%id_jprod_po4 .gt. 0) & + used = g_send_data(zoo(n)%id_jprod_po4, zoo(n)%jprod_po4*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (zoo(n)%id_jprod_nh4 .gt. 0) & + used = g_send_data(zoo(n)%id_jprod_nh4, zoo(n)%jprod_nh4*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (zoo(n)%id_jprod_n .gt. 0) & + used = g_send_data(zoo(n)%id_jprod_n, zoo(n)%jprod_n*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (zoo(n)%id_o2lim .gt. 0) & + used = g_send_data(zoo(n)%id_o2lim, zoo(n)%o2lim, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (zoo(n)%id_temp_lim .gt. 0) & + used = g_send_data(zoo(n)%id_temp_lim, zoo(n)%temp_lim, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + enddo + ! + ! Production diagnostics + ! + if (cobalt%id_jprod_cadet_arag .gt. 0) & + used = g_send_data(cobalt%id_jprod_cadet_arag, cobalt%jprod_cadet_arag * rho_dzt, & + model_time, rmask = grid_tmask(:,:,:),& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jprod_cadet_calc .gt. 0) & + used = g_send_data(cobalt%id_jprod_cadet_calc, cobalt%jprod_cadet_calc * rho_dzt, & + model_time, rmask = grid_tmask(:,:,:),& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jprod_ndet .gt. 0) & + used = g_send_data(cobalt%id_jprod_ndet, cobalt%jprod_ndet*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + !if (cobalt%id_jprod_pdet .gt. 0) & + ! used = g_send_data(cobalt%id_jprod_pdet, cobalt%jprod_pdet*rho_dzt, & + ! model_time, rmask = grid_tmask,& + ! is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + !if (cobalt%id_jprod_srdon .gt. 0) & + ! used = g_send_data(cobalt%id_jprod_srdon, cobalt%jprod_srdon*rho_dzt, & + ! model_time, rmask = grid_tmask,& + ! is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + !if (cobalt%id_jprod_sldon .gt. 0) & + ! used = g_send_data(cobalt%id_jprod_sldon, cobalt%jprod_sldon*rho_dzt, & + ! model_time, rmask = grid_tmask,& + ! is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + !if (cobalt%id_jprod_ldon .gt. 0) & + ! used = g_send_data(cobalt%id_jprod_ldon, cobalt%jprod_ldon*rho_dzt, & + ! model_time, rmask = grid_tmask,& + ! is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + !if (cobalt%id_jprod_srdop .gt. 0) & + ! used = g_send_data(cobalt%id_jprod_srdop, cobalt%jprod_srdop*rho_dzt, & + ! model_time, rmask = grid_tmask,& + ! is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + !if (cobalt%id_jprod_sldop .gt. 0) & + ! used = g_send_data(cobalt%id_jprod_sldop, cobalt%jprod_sldop*rho_dzt, & + ! model_time, rmask = grid_tmask,& + ! is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + !if (cobalt%id_jprod_ldop .gt. 0) & + ! used = g_send_data(cobalt%id_jprod_ldop, cobalt%jprod_ldop*rho_dzt, & + ! model_time, rmask = grid_tmask,& + ! is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jprod_nh4 .gt. 0) & + used = g_send_data(cobalt%id_jprod_nh4, cobalt%jprod_nh4*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jprod_nh4_plus_btm .gt. 0) & + used = g_send_data(cobalt%id_jprod_nh4_plus_btm, cobalt%jprod_nh4_plus_btm*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jprod_po4 .gt. 0) & + used = g_send_data(cobalt%id_jprod_po4, cobalt%jprod_po4*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jprod_fed .gt. 0) & + used = g_send_data(cobalt%id_jprod_fed, cobalt%jprod_fed*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jprod_fedet .gt. 0) & + used = g_send_data(cobalt%id_jprod_fedet, cobalt%jprod_fedet*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jprod_sidet .gt. 0) & + used = g_send_data(cobalt%id_jprod_sidet, cobalt%jprod_sidet*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jprod_sio4 .gt. 0) & + used = g_send_data(cobalt%id_jprod_sio4, cobalt%jprod_sio4*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jprod_lithdet .gt. 0) & + used = g_send_data(cobalt%id_jprod_lithdet, cobalt%jprod_lithdet*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jdiss_cadet_arag .gt. 0) & + used = g_send_data(cobalt%id_jdiss_cadet_arag, cobalt%jdiss_cadet_arag*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jdiss_cadet_calc .gt. 0) & + used = g_send_data(cobalt%id_jdiss_cadet_calc, cobalt%jdiss_cadet_calc*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jdiss_sidet .gt. 0) & + used = g_send_data(cobalt%id_jdiss_sidet, cobalt%jdiss_sidet*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jremin_ndet .gt. 0) & + used = g_send_data(cobalt%id_jremin_ndet, cobalt%jremin_ndet*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jremin_pdet .gt. 0) & + used = g_send_data(cobalt%id_jremin_pdet, cobalt%jremin_pdet*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jremin_fedet .gt. 0) & + used = g_send_data(cobalt%id_jremin_fedet, cobalt%jremin_fedet*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jfed .gt. 0) & + used = g_send_data(cobalt%id_jfed, cobalt%jfed*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jfe_ads .gt. 0) & + used = g_send_data(cobalt%id_jfe_ads, cobalt%jfe_ads*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jfe_coast .gt. 0) & + used = g_send_data(cobalt%id_jfe_coast, cobalt%jfe_coast*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_kfe_eq_lig .gt. 0) & + used = g_send_data(cobalt%id_kfe_eq_lig, log10(cobalt%kfe_eq_lig+epsln), & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_feprime .gt. 0) & + used = g_send_data(cobalt%id_feprime, cobalt%feprime, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_ligand .gt. 0) & + used = g_send_data(cobalt%id_ligand, cobalt%ligand, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_fe_sol .gt. 0) & + used = g_send_data(cobalt%id_fe_sol, cobalt%fe_sol, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_expkT .gt. 0) & + used = g_send_data(cobalt%id_expkT, cobalt%expkT, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_expkreminT .gt. 0) & + used = g_send_data(cobalt%id_expkreminT, cobalt%expkreminT, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_hp_o2lim .gt. 0) & + used = g_send_data(cobalt%id_hp_o2lim, cobalt%hp_o2lim, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_hp_temp_lim .gt. 0) & + used = g_send_data(cobalt%id_hp_temp_lim, cobalt%hp_temp_lim, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_irr_inst .gt. 0) & + used = g_send_data(cobalt%id_irr_inst, cobalt%irr_inst, & + model_time, rmask = grid_tmask(:,:,:),& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_irr_mix .gt. 0) & + used = g_send_data(cobalt%id_irr_mix, cobalt%irr_mix, & + model_time, rmask = grid_tmask(:,:,:),& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jno3denit_wc .gt. 0) & + used = g_send_data(cobalt%id_jno3denit_wc, cobalt%jno3denit_wc*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jo2resp_wc .gt. 0) & + used = g_send_data(cobalt%id_jo2resp_wc, cobalt%jo2resp_wc*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jnitrif .gt. 0) & + used = g_send_data(cobalt%id_jnitrif, cobalt%jnitrif*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_tot_layer_int_c .gt. 0) & + used = g_send_data(cobalt%id_tot_layer_int_c, cobalt%tot_layer_int_c,& + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_tot_layer_int_fe .gt. 0) & + used = g_send_data(cobalt%id_tot_layer_int_fe,cobalt%tot_layer_int_fe,& + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_tot_layer_int_n .gt. 0) & + used = g_send_data(cobalt%id_tot_layer_int_n,cobalt%tot_layer_int_n,& + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_tot_layer_int_p .gt. 0) & + used = g_send_data(cobalt%id_tot_layer_int_p,cobalt%tot_layer_int_p,& + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_tot_layer_int_si .gt. 0) & + used = g_send_data(cobalt%id_tot_layer_int_si,cobalt%tot_layer_int_si,& + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_tot_layer_int_o2 .gt. 0) & + used = g_send_data(cobalt%id_tot_layer_int_o2,cobalt%tot_layer_int_o2,& + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_tot_layer_int_alk .gt. 0) & + used = g_send_data(cobalt%id_tot_layer_int_alk,cobalt%tot_layer_int_alk,& + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_total_filter_feeding .gt. 0) & + used = g_send_data(cobalt%id_total_filter_feeding,cobalt%total_filter_feeding,& + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_nlg_diatoms.gt. 0) & + used = g_send_data(cobalt%id_nlg_diatoms,cobalt%nlg_diatoms,& + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_q_si_2_n_lg_diatoms.gt. 0) & + used = g_send_data(cobalt%id_q_si_2_n_lg_diatoms,cobalt%q_si_2_n_lg_diatoms,& + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_co2_csurf .gt. 0) & + used = g_send_data(cobalt%id_co2_csurf, cobalt%co2_csurf, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_pco2_csurf .gt. 0) & + used = g_send_data(cobalt%id_pco2_csurf, cobalt%pco2_csurf, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_co2_alpha .gt. 0) & + used = g_send_data(cobalt%id_co2_alpha, cobalt%co2_alpha, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_nh3_csurf .gt. 0) & + used = g_send_data(cobalt%id_nh3_csurf, cobalt%nh3_csurf, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_nh3_alpha .gt. 0) & + used = g_send_data(cobalt%id_nh3_alpha, cobalt%nh3_alpha, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_fcadet_arag_btm .gt. 0) & + used = g_send_data(cobalt%id_fcadet_arag_btm, cobalt%fcadet_arag_btm, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_fcadet_calc_btm .gt. 0) & + used = g_send_data(cobalt%id_fcadet_calc_btm, cobalt%fcadet_calc_btm, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_ffedet_btm .gt. 0) & + used = g_send_data(cobalt%id_ffedet_btm, cobalt%ffedet_btm, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_fndet_btm .gt. 0) & + used = g_send_data(cobalt%id_fndet_btm, cobalt%fndet_btm, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_fpdet_btm .gt. 0) & + used = g_send_data(cobalt%id_fpdet_btm, cobalt%fpdet_btm, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_fsidet_btm .gt. 0) & + used = g_send_data(cobalt%id_fsidet_btm, cobalt%fsidet_btm, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_flithdet_btm .gt. 0) & + used = g_send_data(cobalt%id_flithdet_btm, cobalt%flithdet_btm, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_fcased_burial .gt. 0) & + used = g_send_data(cobalt%id_fcased_burial, cobalt%fcased_burial, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_fcased_redis .gt. 0) & + used = g_send_data(cobalt%id_fcased_redis, cobalt%fcased_redis, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_fcased_redis_surfresp .gt. 0) & + used = g_send_data(cobalt%id_fcased_redis_surfresp,cobalt%fcased_redis_surfresp, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_cased_redis_coef .gt. 0) & + used = g_send_data(cobalt%id_cased_redis_coef, cobalt%cased_redis_coef, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_cased_redis_delz .gt. 0) & + used = g_send_data(cobalt%id_cased_redis_delz, cobalt%cased_redis_delz, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_ffe_sed .gt. 0) & + used = g_send_data(cobalt%id_ffe_sed, cobalt%ffe_sed, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_ffe_geotherm .gt. 0) & + used = g_send_data(cobalt%id_ffe_geotherm, cobalt%ffe_geotherm, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_ffe_iceberg .gt. 0) & + used = g_send_data(cobalt%id_ffe_iceberg, cobalt%ffe_iceberg, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_fnfeso4red_sed .gt. 0) & + used = g_send_data(cobalt%id_fnfeso4red_sed,cobalt%fnfeso4red_sed, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_fno3denit_sed .gt. 0) & + used = g_send_data(cobalt%id_fno3denit_sed, cobalt%fno3denit_sed, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_fnoxic_sed .gt. 0) & + used = g_send_data(cobalt%id_fnoxic_sed, cobalt%fnoxic_sed, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_frac_burial .gt. 0) & + used = g_send_data(cobalt%id_frac_burial, cobalt%frac_burial, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_fndet_burial .gt. 0) & + used = g_send_data(cobalt%id_fndet_burial, cobalt%fndet_burial, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_fpdet_burial .gt. 0) & + used = g_send_data(cobalt%id_fpdet_burial, cobalt%fpdet_burial, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_co3_sol_arag .gt. 0) & + used = g_send_data(cobalt%id_co3_sol_arag, cobalt%co3_sol_arag, & + model_time, rmask = grid_tmask(:,:,:),& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_co3_sol_calc .gt. 0) & + used = g_send_data(cobalt%id_co3_sol_calc, cobalt%co3_sol_calc, & + model_time, rmask = grid_tmask(:,:,:),& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_omega_arag .gt. 0) & + used = g_send_data(cobalt%id_omega_arag, cobalt%omega_arag, & + model_time, rmask = grid_tmask(:,:,:),& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_omega_calc .gt. 0) & + used = g_send_data(cobalt%id_omega_calc, cobalt%omega_calc, & + model_time, rmask = grid_tmask(:,:,:),& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_fcadet_arag .gt. 0) & + used = g_send_data(cobalt%id_fcadet_arag, cobalt%p_cadet_arag(:,:,:,tau) * cobalt%Rho_0 * & + cobalt%wsink * grid_tmask(:,:,:), & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_fcadet_calc .gt. 0) & + used = g_send_data(cobalt%id_fcadet_calc, cobalt%p_cadet_calc(:,:,:,tau) * cobalt%Rho_0 * & + cobalt%wsink*grid_tmask(:,:,:), & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_ffedet .gt. 0) & + used = g_send_data(cobalt%id_ffedet, cobalt%p_fedet(:,:,:,tau) * cobalt%Rho_0 * & + cobalt%wsink * grid_tmask(:,:,:),& + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_flithdet .gt. 0) & + used = g_send_data(cobalt%id_flithdet, cobalt%p_lithdet(:,:,:,tau) * cobalt%Rho_0 * & + cobalt%wsink * grid_tmask(:,:,:),& + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_fndet .gt. 0) & + used = g_send_data(cobalt%id_fndet, cobalt%p_ndet(:,:,:,tau) * cobalt%Rho_0 * & + cobalt%wsink * grid_tmask(:,:,:),& + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_fpdet .gt. 0) & + used = g_send_data(cobalt%id_fpdet, cobalt%p_pdet(:,:,:,tau) * cobalt%Rho_0 * & + cobalt%wsink * grid_tmask(:,:,:),& + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_fsidet .gt. 0) & + used = g_send_data(cobalt%id_fsidet, cobalt%p_sidet(:,:,:,tau) * cobalt%Rho_0 * & + cobalt%wsink *grid_tmask(:,:,:),& + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_nphyto_tot .gt. 0) & + used = g_send_data(cobalt%id_nphyto_tot, (cobalt%p_ndi(:,:,:,tau) + & + cobalt%p_nlg(:,:,:,tau) + cobalt%p_nsm(:,:,:,tau)), & + model_time, rmask = grid_tmask(:,:,:),& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + ! +! Radiocarbon fields +! + if (cobalt%id_b_di14c .gt. 0) & + used = g_send_data(cobalt%id_b_di14c, cobalt%b_di14c, & + model_time, rmask = grid_tmask(:,:,1), & + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_c14_2_n .gt. 0) & + used = g_send_data(cobalt%id_c14_2_n, cobalt%c14_2_n, & + model_time, rmask = grid_tmask, & + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_c14o2_csurf .gt. 0) & + used = g_send_data(cobalt%id_c14o2_csurf, cobalt%c14o2_csurf, & + model_time, rmask = grid_tmask(:,:,1), & + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_c14o2_alpha .gt. 0) & + used = g_send_data(cobalt%id_c14o2_alpha, cobalt%c14o2_alpha, & + model_time, rmask = grid_tmask(:,:,1), & + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_fpo14c .gt. 0) & + used = g_send_data(cobalt%id_fpo14c, cobalt%fpo14c, & + model_time, rmask = grid_tmask, & + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_j14c_decay_dic .gt. 0) & + used = g_send_data(cobalt%id_j14c_decay_dic, cobalt%j14c_decay_dic*rho_dzt, & + model_time, rmask = grid_tmask, & + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_j14c_decay_doc .gt. 0) & + used = g_send_data(cobalt%id_j14c_decay_doc, cobalt%j14c_decay_doc*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_j14c_reminp .gt. 0) & + used = g_send_data(cobalt%id_j14c_reminp, cobalt%j14c_reminp*rho_dzt, & + model_time, rmask = grid_tmask, & + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jdi14c .gt. 0) & + used = g_send_data(cobalt%id_jdi14c, cobalt%jdi14c*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + if (cobalt%id_jdo14c .gt. 0) & + used = g_send_data(cobalt%id_jdo14c, cobalt%jdo14c*rho_dzt, & + model_time, rmask = grid_tmask, & + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + ! + ! 2D COBALT fields + ! + if (cobalt%id_pco2surf .gt. 0) & + used = g_send_data(cobalt%id_pco2surf, cobalt%pco2_csurf, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_pnh3surf .gt. 0) & + used = g_send_data(cobalt%id_pnh3surf, cobalt%pnh3_csurf, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_sfc_alk .gt. 0) & + used = g_send_data(cobalt%id_sfc_alk, cobalt%p_alk(:,:,1,tau), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_sfc_cadet_arag .gt. 0) & + used = g_send_data(cobalt%id_sfc_cadet_arag,cobalt%p_cadet_arag(:,:,1,tau), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_sfc_cadet_calc .gt. 0) & + used = g_send_data(cobalt%id_sfc_cadet_calc,cobalt%p_cadet_calc(:,:,1,tau), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_sfc_dic .gt. 0) & + used = g_send_data(cobalt%id_sfc_dic, cobalt%p_dic(:,:,1,tau), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_sfc_fed .gt. 0) & + used = g_send_data(cobalt%id_sfc_fed, cobalt%p_fed(:,:,1,tau), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_sfc_ldon .gt. 0) & + used = g_send_data(cobalt%id_sfc_ldon, cobalt%p_ldon(:,:,1,tau), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_sfc_sldon .gt. 0) & + used = g_send_data(cobalt%id_sfc_sldon, cobalt%p_sldon(:,:,1,tau), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_sfc_srdon .gt. 0) & + used = g_send_data(cobalt%id_sfc_srdon, cobalt%p_srdon(:,:,1,tau), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_sfc_no3 .gt. 0) & + used = g_send_data(cobalt%id_sfc_no3, cobalt%p_no3(:,:,1,tau), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_sfc_nh4 .gt. 0) & + used = g_send_data(cobalt%id_sfc_nh4, cobalt%p_nh4(:,:,1,tau), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_sfc_po4 .gt. 0) & + used = g_send_data(cobalt%id_sfc_po4, cobalt%p_po4(:,:,1,tau), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_sfc_sio4 .gt. 0) & + used = g_send_data(cobalt%id_sfc_sio4, cobalt%p_sio4(:,:,1,tau), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_sfc_htotal .gt. 0) & + used = g_send_data(cobalt%id_sfc_htotal, cobalt%f_htotal(:,:,1), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_sfc_o2 .gt. 0) & + used = g_send_data(cobalt%id_sfc_o2, cobalt%p_o2(:,:,1,tau), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_sfc_chl .gt. 0) & + used = g_send_data(cobalt%id_sfc_chl, cobalt%f_chl(:,:,1), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_sfc_irr .gt. 0) & + used = g_send_data(cobalt%id_sfc_irr, cobalt%irr_inst(:,:,1), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_sfc_irr_mem .gt. 0) & + used = g_send_data(cobalt%id_sfc_irr_mem, cobalt%f_irr_mem(:,:,1), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_sfc_temp .gt. 0) & + used = g_send_data(cobalt%id_sfc_temp, Temp(:,:,1), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_btm_temp .gt. 0) & + used = g_send_data(cobalt%id_btm_temp, cobalt%btm_temp, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_btm_o2 .gt. 0) & + used = g_send_data(cobalt%id_btm_o2, cobalt%btm_o2, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_btm_htotal .gt. 0) & + used = g_send_data(cobalt%id_btm_htotal, cobalt%btm_htotal, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_cased_2d .gt. 0) & + used = g_send_data(cobalt%id_cased_2d, cobalt%cased_2d, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_sfc_co3_ion .gt. 0) & + used = g_send_data(cobalt%id_sfc_co3_ion, cobalt%f_co3_ion(:,:,1), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_sfc_co3_sol_arag .gt. 0) & + used = g_send_data(cobalt%id_sfc_co3_sol_arag, cobalt%co3_sol_arag(:,:,1), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_sfc_co3_sol_calc .gt. 0) & + used = g_send_data(cobalt%id_sfc_co3_sol_calc, cobalt%co3_sol_calc(:,:,1), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_btm_co3_ion .gt. 0) & + used = g_send_data(cobalt%id_btm_co3_ion, cobalt%btm_co3_ion, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_btm_co3_sol_arag .gt. 0) & + used = g_send_data(cobalt%id_btm_co3_sol_arag, cobalt%btm_co3_sol_arag, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + if (cobalt%id_btm_co3_sol_calc .gt. 0) & + used = g_send_data(cobalt%id_btm_co3_sol_calc, cobalt%btm_co3_sol_calc, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc,ie_in=iec, je_in=jec) + + do n= 1, NUM_PHYTO + if (phyto(n)%id_sfc_f_n .gt. 0) & + used = g_send_data(phyto(n)%id_sfc_f_n, phyto(n)%f_n(:,:,1), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (phyto(n)%id_sfc_chl .gt. 0) & + used = g_send_data(phyto(n)%id_sfc_chl, cobalt%c_2_n * 12.0e6 * & + phyto(n)%theta(:,:,1) * phyto(n)%f_n(:,:,1), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (phyto(n)%id_sfc_def_fe .gt. 0) & + used = g_send_data(phyto(n)%id_sfc_def_fe, phyto(n)%def_fe(:,:,1), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (phyto(n)%id_sfc_felim .gt. 0) & + used = g_send_data(phyto(n)%id_sfc_felim, phyto(n)%felim(:,:,1), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (phyto(n)%id_sfc_irrlim .gt. 0) & + used = g_send_data(phyto(n)%id_sfc_irrlim, phyto(n)%irrlim(:,:,1), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (phyto(n)%id_sfc_theta .gt. 0) & + used = g_send_data(phyto(n)%id_sfc_theta, phyto(n)%theta(:,:,1), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (phyto(n)%id_sfc_mu .gt. 0) & + used = g_send_data(phyto(n)%id_sfc_mu, phyto(n)%mu(:,:,1), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (phyto(n)%id_sfc_po4lim .gt. 0) & + used = g_send_data(phyto(n)%id_sfc_po4lim, phyto(n)%po4lim(:,:,1), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (phyto(n)%id_sfc_q_fe_2_n .gt. 0) & + used = g_send_data(phyto(n)%id_sfc_q_fe_2_n, phyto(n)%q_fe_2_n(:,:,1), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (phyto(n)%id_sfc_nh4lim .gt. 0) & + used = g_send_data(phyto(n)%id_sfc_nh4lim, phyto(n)%nh4lim(:,:,1), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (phyto(n)%id_sfc_no3lim .gt. 0) & + used = g_send_data(phyto(n)%id_sfc_no3lim, phyto(n)%no3lim(:,:,1), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + enddo + + ! + ! Save river, depositon and bulk elemental fluxes + ! + if (cobalt%id_dep_dry_fed .gt. 0) & + used = g_send_data(cobalt%id_dep_dry_fed, cobalt%dry_fed, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_dep_dry_lith .gt. 0) & + used = g_send_data(cobalt%id_dep_dry_lith, cobalt%dry_lith, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_dep_dry_nh4 .gt. 0) & + used = g_send_data(cobalt%id_dep_dry_nh4, cobalt%dry_nh4, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_dep_dry_no3 .gt. 0) & + used = g_send_data(cobalt%id_dep_dry_no3, cobalt%dry_no3, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_dep_dry_po4 .gt. 0) & + used = g_send_data(cobalt%id_dep_dry_po4, cobalt%dry_po4, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_dep_wet_fed .gt. 0) & + used = g_send_data(cobalt%id_dep_wet_fed, cobalt%wet_fed, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_dep_wet_lith .gt. 0) & + used = g_send_data(cobalt%id_dep_wet_lith, cobalt%wet_lith, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_dep_wet_nh4 .gt. 0) & + used = g_send_data(cobalt%id_dep_wet_nh4, cobalt%wet_nh4, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_dep_wet_no3 .gt. 0) & + used = g_send_data(cobalt%id_dep_wet_no3, cobalt%wet_no3, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_dep_wet_po4 .gt. 0) & + used = g_send_data(cobalt%id_dep_wet_po4, cobalt%wet_po4, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_runoff_flux_alk .gt. 0) & + used = g_send_data(cobalt%id_runoff_flux_alk, cobalt%runoff_flux_alk, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_runoff_flux_dic .gt. 0) & + used = g_send_data(cobalt%id_runoff_flux_dic, cobalt%runoff_flux_dic, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_runoff_flux_di14c .gt. 0) & + used = g_send_data(cobalt%id_runoff_flux_di14c, cobalt%runoff_flux_di14c, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_runoff_flux_fed .gt. 0) & + used = g_send_data(cobalt%id_runoff_flux_fed, cobalt%runoff_flux_fed, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_runoff_flux_lith .gt. 0) & + used = g_send_data(cobalt%id_runoff_flux_lith, cobalt%runoff_flux_lith, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_runoff_flux_no3 .gt. 0) & + used = g_send_data(cobalt%id_runoff_flux_no3, cobalt%runoff_flux_no3, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_runoff_flux_ldon .gt. 0) & + used = g_send_data(cobalt%id_runoff_flux_ldon, cobalt%runoff_flux_ldon, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_runoff_flux_sldon .gt. 0) & + used = g_send_data(cobalt%id_runoff_flux_sldon, cobalt%runoff_flux_sldon, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_runoff_flux_srdon .gt. 0) & + used = g_send_data(cobalt%id_runoff_flux_srdon, cobalt%runoff_flux_srdon, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_runoff_flux_ndet .gt. 0) & + used = g_send_data(cobalt%id_runoff_flux_ndet, cobalt%runoff_flux_ndet, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_runoff_flux_po4 .gt. 0) & + used = g_send_data(cobalt%id_runoff_flux_po4, cobalt%runoff_flux_po4, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_runoff_flux_ldop .gt. 0) & + used = g_send_data(cobalt%id_runoff_flux_ldop, cobalt%runoff_flux_ldop, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_runoff_flux_sldop .gt. 0) & + used = g_send_data(cobalt%id_runoff_flux_sldop, cobalt%runoff_flux_sldop, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_runoff_flux_srdop .gt. 0) & + used = g_send_data(cobalt%id_runoff_flux_srdop, cobalt%runoff_flux_srdop, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + ! + ! Save 100m integral fluxes + ! + if (cobalt%id_jprod_allphytos_100 .gt. 0) & + used = g_send_data(cobalt%id_jprod_allphytos_100, cobalt%jprod_allphytos_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_jprod_diat_100 .gt. 0) & + used = g_send_data(cobalt%id_jprod_diat_100, cobalt%jprod_diat_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + do n= 1, NUM_PHYTO !{ + if (phyto(n)%id_jprod_n_100 .gt. 0) & + used = g_send_data(phyto(n)%id_jprod_n_100, phyto(n)%jprod_n_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (phyto(n)%id_jprod_n_new_100 .gt. 0) & + used = g_send_data(phyto(n)%id_jprod_n_new_100, phyto(n)%jprod_n_new_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (phyto(n)%id_jzloss_n_100 .gt. 0) & + used = g_send_data(phyto(n)%id_jzloss_n_100, phyto(n)%jzloss_n_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (phyto(n)%id_jexuloss_n_100 .gt. 0) & + used = g_send_data(phyto(n)%id_jexuloss_n_100, phyto(n)%jexuloss_n_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (phyto(n)%id_f_n_100 .gt. 0) & + used = g_send_data(phyto(n)%id_f_n_100, phyto(n)%f_n_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + enddo !} n + if (phyto(DIAZO)%id_jprod_n_n2_100 .gt. 0) & + used = g_send_data(phyto(DIAZO)%id_jprod_n_n2_100, phyto(DIAZO)%jprod_n_n2_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (phyto(SMALL)%id_jvirloss_n_100 .gt. 0) & + used = g_send_data(phyto(SMALL)%id_jvirloss_n_100, phyto(SMALL)%jvirloss_n_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (phyto(SMALL)%id_jaggloss_n_100 .gt. 0) & + used = g_send_data(phyto(SMALL)%id_jaggloss_n_100, phyto(SMALL)%jaggloss_n_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (phyto(LARGE)%id_jaggloss_n_100 .gt. 0) & + used = g_send_data(phyto(LARGE)%id_jaggloss_n_100, phyto(LARGE)%jaggloss_n_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + do n= 1, NUM_ZOO !{ + if (zoo(n)%id_jprod_n_100 .gt. 0) & + used = g_send_data(zoo(n)%id_jprod_n_100, zoo(n)%jprod_n_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (zoo(n)%id_jingest_n_100 .gt. 0) & + used = g_send_data(zoo(n)%id_jingest_n_100, zoo(n)%jingest_n_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (zoo(n)%id_jremin_n_100 .gt. 0) & + used = g_send_data(zoo(n)%id_jremin_n_100, zoo(n)%jremin_n_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (zoo(n)%id_f_n_100 .gt. 0) & + used = g_send_data(zoo(n)%id_f_n_100, zoo(n)%f_n_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + enddo !} n + + do n= 1,2 !{ + if (zoo(n)%id_jzloss_n_100 .gt. 0) & + used = g_send_data(zoo(n)%id_jzloss_n_100, zoo(n)%jzloss_n_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (zoo(n)%id_jprod_don_100 .gt. 0) & + used = g_send_data(zoo(n)%id_jprod_don_100, zoo(n)%jprod_don_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + enddo !} n + + do n= 2,3 !{ + if (zoo(n)%id_jhploss_n_100 .gt. 0) & + used = g_send_data(zoo(n)%id_jhploss_n_100, zoo(n)%jhploss_n_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (zoo(n)%id_jprod_ndet_100 .gt. 0) & + used = g_send_data(zoo(n)%id_jprod_ndet_100, zoo(n)%jprod_ndet_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + enddo !} n + + if (cobalt%id_hp_jingest_n_100 .gt. 0) & + used = g_send_data(cobalt%id_hp_jingest_n_100, cobalt%hp_jingest_n_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_hp_jremin_n_100 .gt. 0) & + used = g_send_data(cobalt%id_hp_jremin_n_100, cobalt%hp_jremin_n_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_hp_jprod_ndet_100 .gt. 0) & + used = g_send_data(cobalt%id_hp_jprod_ndet_100, cobalt%hp_jprod_ndet_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (bact(1)%id_jprod_n_100 .gt. 0) & + used = g_send_data(bact(1)%id_jprod_n_100, bact(1)%jprod_n_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (bact(1)%id_jzloss_n_100 .gt. 0) & + used = g_send_data(bact(1)%id_jzloss_n_100, bact(1)%jzloss_n_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (bact(1)%id_jvirloss_n_100 .gt. 0) & + used = g_send_data(bact(1)%id_jvirloss_n_100, bact(1)%jvirloss_n_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (bact(1)%id_jremin_n_100 .gt. 0) & + used = g_send_data(bact(1)%id_jremin_n_100, bact(1)%jremin_n_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (bact(1)%id_juptake_ldon_100 .gt. 0) & + used = g_send_data(bact(1)%id_juptake_ldon_100, bact(1)%juptake_ldon_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (bact(1)%id_f_n_100 .gt. 0) & + used = g_send_data(bact(1)%id_f_n_100, bact(1)%f_n_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_jprod_lithdet_100 .gt. 0) & + used = g_send_data(cobalt%id_jprod_lithdet_100, cobalt%jprod_lithdet_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_jprod_sidet_100 .gt. 0) & + used = g_send_data(cobalt%id_jprod_sidet_100, cobalt%jprod_sidet_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_jprod_cadet_calc_100 .gt. 0) & + used = g_send_data(cobalt%id_jprod_cadet_calc_100, cobalt%jprod_cadet_calc_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_jprod_cadet_arag_100 .gt. 0) & + used = g_send_data(cobalt%id_jprod_cadet_arag_100, cobalt%jprod_cadet_arag_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_jremin_ndet_100 .gt. 0) & + used = g_send_data(cobalt%id_jremin_ndet_100, cobalt%jremin_ndet_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_jprod_mesozoo_200 .gt. 0) & + used = g_send_data(cobalt%id_jprod_mesozoo_200, cobalt%jprod_mesozoo_200, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_f_ndet_100 .gt. 0) & + used = g_send_data(cobalt%id_f_ndet_100, cobalt%f_ndet_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_f_don_100 .gt. 0) & + used = g_send_data(cobalt%id_f_don_100, cobalt%f_don_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_f_silg_100 .gt. 0) & + used = g_send_data(cobalt%id_f_silg_100, cobalt%f_silg_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_f_mesozoo_200 .gt. 0) & + used = g_send_data(cobalt%id_f_mesozoo_200, cobalt%f_mesozoo_200, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_fndet_100 .gt. 0) & + used = g_send_data(cobalt%id_fndet_100, cobalt%fndet_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_fpdet_100 .gt. 0) & + used = g_send_data(cobalt%id_fpdet_100, cobalt%fpdet_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_fsidet_100 .gt. 0) & + used = g_send_data(cobalt%id_fsidet_100, cobalt%fsidet_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_flithdet_100 .gt. 0) & + used = g_send_data(cobalt%id_flithdet_100, cobalt%flithdet_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_fcadet_calc_100 .gt. 0) & + used = g_send_data(cobalt%id_fcadet_calc_100, cobalt%fcadet_calc_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_fcadet_arag_100 .gt. 0) & + used = g_send_data(cobalt%id_fcadet_arag_100, cobalt%fcadet_arag_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_ffedet_100 .gt. 0) & + used = g_send_data(cobalt%id_ffedet_100, cobalt%ffedet_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + ! + !--------------------------------------------------------------------- + ! Save water column vertical integrals + !--------------------------------------------------------------------- + ! + if (cobalt%id_wc_vert_int_c .gt. 0) & + used = g_send_data(cobalt%id_wc_vert_int_c, cobalt%wc_vert_int_c, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_wc_vert_int_dic .gt. 0) & + used = g_send_data(cobalt%id_wc_vert_int_dic, cobalt%wc_vert_int_dic, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_wc_vert_int_doc .gt. 0) & + used = g_send_data(cobalt%id_wc_vert_int_doc, cobalt%wc_vert_int_doc, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_wc_vert_int_poc .gt. 0) & + used = g_send_data(cobalt%id_wc_vert_int_poc, cobalt%wc_vert_int_poc, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_wc_vert_int_n .gt. 0) & + used = g_send_data(cobalt%id_wc_vert_int_n, cobalt%wc_vert_int_n, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_wc_vert_int_p .gt. 0) & + used = g_send_data(cobalt%id_wc_vert_int_p, cobalt%wc_vert_int_p, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_wc_vert_int_fe .gt. 0) & + used = g_send_data(cobalt%id_wc_vert_int_fe, cobalt%wc_vert_int_fe, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_wc_vert_int_si .gt. 0) & + used = g_send_data(cobalt%id_wc_vert_int_si, cobalt%wc_vert_int_si, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_wc_vert_int_o2 .gt. 0) & + used = g_send_data(cobalt%id_wc_vert_int_o2, cobalt%wc_vert_int_o2, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_wc_vert_int_alk .gt. 0) & + used = g_send_data(cobalt%id_wc_vert_int_alk, cobalt%wc_vert_int_alk, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_wc_vert_int_jdiss_sidet .gt. 0) & + used = g_send_data(cobalt%id_wc_vert_int_jdiss_sidet, cobalt%wc_vert_int_jdiss_sidet, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_wc_vert_int_jdiss_cadet .gt. 0) & + used = g_send_data(cobalt%id_wc_vert_int_jdiss_cadet, cobalt%wc_vert_int_jdiss_cadet, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_wc_vert_int_jo2resp .gt. 0) & + used = g_send_data(cobalt%id_wc_vert_int_jo2resp, cobalt%wc_vert_int_jo2resp, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_wc_vert_int_jprod_cadet .gt. 0) & + used = g_send_data(cobalt%id_wc_vert_int_jprod_cadet, cobalt%wc_vert_int_jprod_cadet, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_wc_vert_int_jno3denit .gt. 0) & + used = g_send_data(cobalt%id_wc_vert_int_jno3denit, cobalt%wc_vert_int_jno3denit, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_wc_vert_int_jnitrif .gt. 0) & + used = g_send_data(cobalt%id_wc_vert_int_jnitrif, cobalt%wc_vert_int_jnitrif, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_wc_vert_int_juptake_nh4 .gt. 0) & + used = g_send_data(cobalt%id_wc_vert_int_juptake_nh4, cobalt%wc_vert_int_juptake_nh4, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_wc_vert_int_jprod_nh4 .gt. 0) & + used = g_send_data(cobalt%id_wc_vert_int_jprod_nh4, cobalt%wc_vert_int_jprod_nh4, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_wc_vert_int_juptake_no3 .gt. 0) & + used = g_send_data(cobalt%id_wc_vert_int_juptake_no3, cobalt%wc_vert_int_juptake_no3, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_wc_vert_int_nfix .gt. 0) & + used = g_send_data(cobalt%id_wc_vert_int_nfix, cobalt%wc_vert_int_nfix, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + ! + !--------------------------------------------------------------------- + ! Save CaCO3 saturation and O2 minimum depths + !--------------------------------------------------------------------- + ! + if (cobalt%id_o2min .gt. 0) & + used = g_send_data(cobalt%id_o2min, cobalt%o2min, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_z_o2min .gt. 0) & + used = g_send_data(cobalt%id_z_o2min, cobalt%z_o2min, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_z_sat_arag .gt. 0) & + used = g_send_data(cobalt%id_z_sat_arag, cobalt%z_sat_arag, & + model_time, mask = cobalt%mask_z_sat_arag, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + if (cobalt%id_z_sat_calc .gt. 0) & + used = g_send_data(cobalt%id_z_sat_calc, cobalt%z_sat_calc, & + model_time, mask = cobalt%mask_z_sat_calc, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + ! + !--------------------------------------------------------------------- + ! Send additional diagnostics jgj 2015/10/26 + !--------------------------------------------------------------------- + ! + + if (cobalt%id_jalk .gt. 0) & + used = g_send_data(cobalt%id_jalk, cobalt%jalk*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_jalk_plus_btm .gt. 0) & + used = g_send_data(cobalt%id_jalk_plus_btm, cobalt%jalk_plus_btm*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_jdic .gt. 0) & + used = g_send_data(cobalt%id_jdic, cobalt%jdic*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_jdic_plus_btm .gt. 0) & + used = g_send_data(cobalt%id_jdic_plus_btm, cobalt%jdic_plus_btm*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_jnh4 .gt. 0) & + used = g_send_data(cobalt%id_jnh4, cobalt%jnh4*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_jndet .gt. 0) & + used = g_send_data(cobalt%id_jndet, cobalt%jndet*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_jnh4_plus_btm .gt. 0) & + used = g_send_data(cobalt%id_jnh4_plus_btm, cobalt%jnh4_plus_btm*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_jo2_plus_btm .gt. 0) & + used = g_send_data(cobalt%id_jo2_plus_btm, cobalt%jo2_plus_btm*rho_dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +!============================================================================================================== +! 2016/07/05 jgj send temperature as a test + + if (cobalt%id_thetao .gt. 0) & + used = g_send_data(cobalt%id_thetao, Temp, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +!============================================================================================================== +! JGJ 2016/08/08 CMIP6 OcnBgchem Oyr and Omon: 3-D Marine Biogeochemical Tracer Fields +! + if (cobalt%id_dissic .gt. 0) & + used = g_send_data(cobalt%id_dissic, cobalt%p_dic(:,:,:,tau) * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! PENDING: +! if (cobalt%id_dissicnat .gt. 0) & +! used = g_send_data(cobalt%id_dissicnat, & +! model_time, rmask = grid_tmask,& +! is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! if (cobalt%id_dissicabio .gt. 0) +! used = g_send_data(cobalt%id_dissicabio, & +! model_time, rmask = grid_tmask,& +! is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! if (cobalt%id_dissi14cabio .gt. 0) +! used = g_send_data(cobalt%id_dissi14cabio, & +! model_time, rmask = grid_tmask,& +! is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! CHECK3: + ! CAS comment on spreadsheet implies that this is the explicitly represented + ! pool and suggests that we shouldn't add the background + cobalt%dissoc(:,:,:) = cobalt%doc_background + & + cobalt%c_2_n * (cobalt%p_ldon(:,:,:,tau) + cobalt%p_sldon(:,:,:,tau) + cobalt%p_srdon(:,:,:,tau) ) + + if (cobalt%id_dissoc .gt. 0) & + used = g_send_data(cobalt%id_dissoc, cobalt%dissoc * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_phyc .gt. 0) & + used = g_send_data(cobalt%id_phyc, (cobalt%p_nlg(:,:,:,tau) + cobalt%p_nsm(:,:,:,tau) + & + cobalt%p_ndi(:,:,:,tau)) * cobalt%c_2_n * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_zooc .gt. 0) & + used = g_send_data(cobalt%id_zooc, (cobalt%p_nlgz(:,:,:,tau) + cobalt%p_nsmz(:,:,:,tau) + & + cobalt%p_nmdz(:,:,:,tau)) * cobalt%c_2_n * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_bacc .gt. 0) & + used = g_send_data(cobalt%id_bacc, cobalt%p_nbact(:,:,:,tau) * cobalt%c_2_n * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_detoc .gt. 0) & + used = g_send_data(cobalt%id_detoc, cobalt%p_ndet(:,:,:,tau) * cobalt%c_2_n * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_calc .gt. 0) & + used = g_send_data(cobalt%id_calc, cobalt%p_cadet_calc(:,:,:,tau) * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_arag .gt. 0) & + used = g_send_data(cobalt%id_arag, cobalt%p_cadet_arag(:,:,:,tau) * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_phydiat.gt. 0) & + used = g_send_data(cobalt%id_phydiat, cobalt%nlg_diatoms * cobalt%c_2_n * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_phydiaz.gt. 0) & + used = g_send_data(cobalt%id_phydiaz, cobalt%p_ndi(:,:,:,tau) * cobalt%c_2_n * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_phypico.gt. 0) & + used = g_send_data(cobalt%id_phypico, cobalt%p_nsm(:,:,:,tau) * cobalt%c_2_n * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_phymisc.gt. 0) & + used = g_send_data(cobalt%id_phymisc, (cobalt%p_nlg(:,:,:,tau)-cobalt%nlg_diatoms) * cobalt%c_2_n * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_zmicro.gt. 0) & + used = g_send_data(cobalt%id_zmicro, cobalt%p_nsmz(:,:,:,tau) * cobalt%c_2_n * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_zmeso.gt. 0) & + used = g_send_data(cobalt%id_zmeso, (cobalt%p_nlgz(:,:,:,tau)+cobalt%p_nmdz(:,:,:,tau)) * cobalt%c_2_n * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_talk .gt. 0) & + used = g_send_data(cobalt%id_talk, cobalt%p_alk(:,:,:,tau) * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! PENDING: +! if (cobalt%id_talknat .gt. 0) & +! used = g_send_data(cobalt%id_talknat, +! model_time, rmask = grid_tmask,& +! is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! CHECK3: this is using ntau=1 + if (cobalt%id_ph .gt. 0) & + used = g_send_data(cobalt%id_ph, log10(cobalt%f_htotal+epsln) * (-1.0), & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! PENDING: +! if (cobalt%id_phnat .gt. 0) & +! used = g_send_data(cobalt%id_phnat, log10(cobalt%f_htotal+epsln) * -1.0, & +! model_time, rmask = grid_tmask,& +! is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! PENDING: +! if (cobalt%id_phabio .gt. 0) & +! used = g_send_data(cobalt%id_phabio, log10(cobalt%f_htotal+epsln) * -1.0, & +! model_time, rmask = grid_tmask,& +! is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_o2_cmip.gt. 0) & + used = g_send_data(cobalt%id_o2_cmip, cobalt%p_o2(:,:,:,tau) * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_o2sat .gt. 0) & + used = g_send_data(cobalt%id_o2sat, cobalt%o2sat, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_no3_cmip.gt. 0) & + used = g_send_data(cobalt%id_no3_cmip, cobalt%p_no3(:,:,:,tau) * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_nh4_cmip.gt. 0) & + used = g_send_data(cobalt%id_nh4_cmip, cobalt%p_nh4(:,:,:,tau) * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_po4_cmip.gt. 0) & + used = g_send_data(cobalt%id_po4_cmip, cobalt%p_po4(:,:,:,tau) * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_dfe .gt. 0) & + used = g_send_data(cobalt%id_dfe, cobalt%p_fed(:,:,:,tau) * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_si .gt. 0) & + used = g_send_data(cobalt%id_si, cobalt%p_sio4(:,:,:,tau) * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_chl_cmip.gt. 0) & + used = g_send_data(cobalt%id_chl_cmip, cobalt%f_chl * cobalt%Rho_0 / 1e9, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_chldiat .gt. 0) & + used = g_send_data(cobalt%id_chldiat, phyto(LARGE)%theta * cobalt%nlg_diatoms * cobalt%c_2_n * cobalt%Rho_0 * 12e-3, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_chldiaz .gt. 0) & + used = g_send_data(cobalt%id_chldiaz, phyto(DIAZO)%theta * cobalt%p_ndi(:,:,:,tau) * cobalt%c_2_n * cobalt%Rho_0 * 12e-3, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_chlpico .gt. 0) & + used = g_send_data(cobalt%id_chlpico, phyto(SMALL)%theta * cobalt%p_nsm(:,:,:,tau) * cobalt%c_2_n * cobalt%Rho_0 * 12e-3, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_chlmisc .gt. 0) & + used = g_send_data(cobalt%id_chlmisc, phyto(LARGE)%theta * (cobalt%p_nlg(:,:,:,tau)-cobalt%nlg_diatoms) * & + cobalt%c_2_n * cobalt%Rho_0 * 12e-3, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_poc .gt. 0) & + used = g_send_data(cobalt%id_poc, (cobalt%p_ndi(:,:,:,tau) + cobalt%p_nlg(:,:,:,tau) + & + cobalt%p_nsm(:,:,:,tau) + cobalt%p_nbact(:,:,:,tau) + cobalt%p_ndet(:,:,:,tau) + & + cobalt%p_nsmz(:,:,:,tau) + cobalt%p_nmdz(:,:,:,tau) + cobalt%p_nlgz(:,:,:,tau)) * cobalt%Rho_0 * cobalt%c_2_n, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_pon .gt. 0) & + used = g_send_data(cobalt%id_pon, (cobalt%p_ndi(:,:,:,tau) + cobalt%p_nlg(:,:,:,tau) + & + cobalt%p_nsm(:,:,:,tau) + cobalt%p_nbact(:,:,:,tau) + cobalt%p_ndet(:,:,:,tau) + & + cobalt%p_nsmz(:,:,:,tau) + cobalt%p_nmdz(:,:,:,tau) + cobalt%p_nlgz(:,:,:,tau)) * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! CAS: added bacteria and more general accomodation of static but different p_2_n ratios + if (cobalt%id_pop .gt. 0) & + used = g_send_data(cobalt%id_pop, (phyto(DIAZO)%p_2_n_static * cobalt%p_ndi(:,:,:,tau) + & + phyto(LARGE)%p_2_n_static * cobalt%p_nlg(:,:,:,tau) + phyto(SMALL)%p_2_n_static * cobalt%p_nsm(:,:,:,tau) + & + cobalt%p_pdet(:,:,:,tau) + zoo(1)%q_p_2_n * cobalt%p_nsmz(:,:,:,tau) + zoo(2)%q_p_2_n * cobalt%p_nmdz(:,:,:,tau) + & + zoo(3)%q_p_2_n * cobalt%p_nlgz(:,:,:,tau) + bact(1)%q_p_2_n * cobalt%p_nbact(:,:,:,tau)) * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! CAS: old code, delete once satisfied with new code above +! if (cobalt%id_pop .gt. 0) & +! used = g_send_data(cobalt%id_pop, ((phyto(DIAZO)%p_2_n_static * cobalt%p_ndi(:,:,:,tau)) + cobalt%p_nlg(:,:,:,tau) + & +! cobalt%p_nsm(:,:,:,tau) + cobalt%p_nbact(:,:,:,tau) + cobalt%p_pdet(:,:,:,tau) + & +! cobalt%p_nsmz(:,:,:,tau) + cobalt%p_nmdz(:,:,:,tau) + cobalt%p_nlgz(:,:,:,tau)) * cobalt%Rho_0, & +! model_time, rmask = grid_tmask,& +! is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_bfe .gt. 0) & + used = g_send_data(cobalt%id_bfe, (cobalt%p_fedi(:,:,:,tau) + cobalt%p_felg(:,:,:,tau) + cobalt%p_fesm(:,:,:,tau) + & + cobalt%p_fedet(:,:,:,tau)) * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_bsi .gt. 0) & + used = g_send_data(cobalt%id_bsi, (cobalt%p_silg(:,:,:,tau) + cobalt%p_sidet(:,:,:,tau)) * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_phyn .gt. 0) & + used = g_send_data(cobalt%id_phyn, (cobalt%p_nlg(:,:,:,tau) + cobalt%p_nsm(:,:,:,tau) + & + cobalt%p_ndi(:,:,:,tau)) * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! CAS: added p_2_n ratios for other phyto groups + if (cobalt%id_phyp .gt. 0) & + used = g_send_data(cobalt%id_phyp, (phyto(DIAZO)%p_2_n_static * cobalt%p_ndi(:,:,:,tau) + & + phyto(LARGE)%p_2_n_static * cobalt%p_nlg(:,:,:,tau) + phyto(SMALL)%p_2_n_static * cobalt%p_nsm(:,:,:,tau) )* & + cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_phyfe .gt. 0) & + used = g_send_data(cobalt%id_phyfe, (cobalt%p_fedi(:,:,:,tau) + cobalt%p_felg(:,:,:,tau) + & + cobalt%p_fesm(:,:,:,tau)) * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_physi .gt. 0) & + used = g_send_data(cobalt%id_physi, cobalt%p_silg(:,:,:,tau) * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_co3 .gt. 0) & + used = g_send_data(cobalt%id_co3, cobalt%f_co3_ion * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! PENDING: +! if (cobalt%id_co3nat .gt. 0) & +! used = g_send_data(cobalt%id_co3nat, cobalt%f_co3_ion * cobalt%Rho_0, & +! model_time, rmask = grid_tmask,& +! is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! PENDING: +! if (cobalt%id_co3abio .gt. 0) & +! used = g_send_data(cobalt%id_co3abio, cobalt%f_co3_ion * cobalt%Rho_0, & +! model_time, rmask = grid_tmask,& +! is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_co3satcalc .gt. 0) & + used = g_send_data(cobalt%id_co3satcalc, cobalt%co3_sol_calc * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_co3satarag .gt. 0) & + used = g_send_data(cobalt%id_co3satarag, cobalt%co3_sol_arag * cobalt%Rho_0, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +!============================================================================================================== +! JGJ 2016/08/08 CMIP6 OcnBgchem Oyr: Marine Biogeochemical 3-D Fields: Rates of Production and Removal +! +! CHECK3: using dzt for layer thickness +! Maybe just use cobalt%Rho_0 instead of rho_dzt / dzt in production terms +! +! also in Omon + if (cobalt%id_pp .gt. 0) & + used = g_send_data(cobalt%id_pp, (phyto(DIAZO)%jprod_n + phyto(LARGE)%jprod_n + & + phyto(SMALL)%jprod_n) * rho_dzt * cobalt%c_2_n / dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! also in Omon + if (cobalt%id_pnitrate .gt. 0) & + used = g_send_data(cobalt%id_pnitrate, (phyto(DIAZO)%juptake_no3 + phyto(LARGE)%juptake_no3 + & + phyto(SMALL)%juptake_no3) * rho_dzt * cobalt%c_2_n / dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! Not requested +! if (cobalt%id_pphosphate .gt. 0) & +! used = g_send_data(cobalt%id_pphosphate, (phyto(DIAZO)%juptake_po4 + phyto(LARGE)%juptake_po4 + & +! phyto(SMALL)%juptake_po4) * rho_dzt * cobalt%c_2_n / dzt, & +! model_time, rmask = grid_tmask,& +! is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! also in Omon + if (cobalt%id_pbfe .gt. 0) & + used = g_send_data(cobalt%id_pbfe, (phyto(DIAZO)%juptake_fe + phyto(LARGE)%juptake_fe + & + phyto(SMALL)%juptake_fe) * rho_dzt / dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! also in Omon + if (cobalt%id_pbsi .gt. 0) & + used = g_send_data(cobalt%id_pbsi, phyto(LARGE)%juptake_sio4 * rho_dzt / dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_pcalc .gt. 0) & + used = g_send_data(cobalt%id_pcalc, cobalt%jprod_cadet_calc * rho_dzt / dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_parag .gt. 0) & + used = g_send_data(cobalt%id_parag, cobalt%jprod_cadet_arag * rho_dzt / dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! also in Omon + if (cobalt%id_expc .gt. 0) & + used = g_send_data(cobalt%id_expc, cobalt%p_ndet(:,:,:,tau) * cobalt%Rho_0 *cobalt%wsink * cobalt%c_2_n, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_expn .gt. 0) & + used = g_send_data(cobalt%id_expn, cobalt%p_ndet(:,:,:,tau) * cobalt%Rho_0 *cobalt%wsink, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_expp .gt. 0) & + used = g_send_data(cobalt%id_expp, cobalt%p_pdet(:,:,:,tau) * cobalt%Rho_0 *cobalt%wsink, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_expfe .gt. 0) & + used = g_send_data(cobalt%id_expfe, cobalt%p_fedet(:,:,:,tau) * cobalt%Rho_0 *cobalt%wsink, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_expsi .gt. 0) & + used = g_send_data(cobalt%id_expsi, cobalt%p_sidet(:,:,:,tau) * cobalt%Rho_0 *cobalt%wsink, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_expcalc .gt. 0) & + used = g_send_data(cobalt%id_expcalc, cobalt%p_cadet_calc(:,:,:,tau) * cobalt%Rho_0 * cobalt%wsink, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_exparag .gt. 0) & + used = g_send_data(cobalt%id_exparag, cobalt%p_cadet_arag(:,:,:,tau) * cobalt%Rho_0 * cobalt%wsink, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! CAS: added jprod_nh4_plus_btm to calculate + if (cobalt%id_remoc .gt. 0) & + used = g_send_data(cobalt%id_remoc, cobalt%jprod_nh4_plus_btm*cobalt%c_2_n*rho_dzt/dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! CAS: added redissolution from sediment + if (cobalt%id_dcalc .gt. 0) & + used = g_send_data(cobalt%id_dcalc, cobalt%jdiss_cadet_calc_plus_btm*rho_dzt/dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! CAS: added redissolution from sediment + if (cobalt%id_darag .gt. 0) & + used = g_send_data(cobalt%id_darag, cobalt%jdiss_cadet_arag_plus_btm*rho_dzt/dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! CAS: fixed unit conversion on production from all groups by adding *rho_dzt, + if (cobalt%id_ppdiat .gt. 0) & + used = g_send_data(cobalt%id_ppdiat, phyto(LARGE)%jprod_n * phyto(LARGE)%silim * rho_dzt * cobalt%c_2_n / dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_ppdiaz .gt. 0) & + used = g_send_data(cobalt%id_ppdiaz, phyto(DIAZO)%jprod_n * rho_dzt * cobalt%c_2_n / dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_pppico .gt. 0) & + used = g_send_data(cobalt%id_pppico, phyto(SMALL)%jprod_n * rho_dzt * cobalt%c_2_n / dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_ppmisc .gt. 0) & + used = g_send_data(cobalt%id_ppmisc, (phyto(LARGE)%jprod_n * (1 - phyto(LARGE)%silim)) * rho_dzt * cobalt%c_2_n / dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! CHECK3 _z regridding +! CAS fixed conversion for all bddt terms + if (cobalt%id_bddtdic .gt. 0) & + used = g_send_data(cobalt%id_bddtdic, cobalt%jdic_plus_btm * rho_dzt / dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_bddtdin .gt. 0) & + used = g_send_data(cobalt%id_bddtdin, cobalt%jdin_plus_btm * rho_dzt / dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_bddtdip .gt. 0) & + used = g_send_data(cobalt%id_bddtdip, cobalt%jpo4_plus_btm * rho_dzt / dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_bddtdife .gt. 0) & + used = g_send_data(cobalt%id_bddtdife, cobalt%jfed_plus_btm * rho_dzt / dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_bddtdisi .gt. 0) & + used = g_send_data(cobalt%id_bddtdisi, cobalt%jsio4_plus_btm * rho_dzt / dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + + if (cobalt%id_bddtalk .gt. 0) & + used = g_send_data(cobalt%id_bddtalk, cobalt%jalk_plus_btm * rho_dzt / dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! CAS: fixed conversion + if (cobalt%id_fescav .gt. 0) & + used = g_send_data(cobalt%id_fescav, cobalt%jfe_ads * rho_dzt / dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! CAS: fixed conversion + if (cobalt%id_fediss .gt. 0) & + used = g_send_data(cobalt%id_fediss, cobalt%jremin_fedet * rho_dzt / dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +! also in Omon +! CAS: fixed conversion + if (cobalt%id_graz .gt. 0) & + used = g_send_data(cobalt%id_graz, (phyto(DIAZO)%jzloss_n + phyto(LARGE)%jzloss_n + & + phyto(SMALL)%jzloss_n) * cobalt%c_2_n * rho_dzt / dzt, & + model_time, rmask = grid_tmask,& + is_in=isc, js_in=jsc, ks_in=1,ie_in=iec, je_in=jec, ke_in=nk) + +!============================================================================================================== +! JGJ 2016/08/08 CMIP6 OcnBgchem Omon: Marine Biogeochemical 2-D Surface Fields +! Identical to Oyr 3-D Tracer fields but for surface only + + if (cobalt%id_dissicos .gt. 0) & + used = g_send_data(cobalt%id_dissicos, cobalt%p_dic(:,:,1,tau) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! PENDING: +! if (cobalt%id_dissicnatos .gt. 0) & +! used = g_send_data(cobalt%id_dissicnatos, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! if (cobalt%id_dissicabioos .gt. 0) +! used = g_send_data(cobalt%id_dissicabioos, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! if (cobalt%id_dissi14cabioos .gt. 0) +! used = g_send_data(cobalt%id_dissi14cabioos, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_dissocos .gt. 0) & + used = g_send_data(cobalt%id_dissocos, cobalt%dissoc(:,:,1) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_phycos .gt. 0) & + used = g_send_data(cobalt%id_phycos, (cobalt%p_nlg(:,:,1,tau) + cobalt%p_nsm(:,:,1,tau) + & + cobalt%p_ndi(:,:,1,tau)) * cobalt%c_2_n * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_zoocos .gt. 0) & + used = g_send_data(cobalt%id_zoocos, (cobalt%p_nlgz(:,:,1,tau) + cobalt%p_nsmz(:,:,1,tau) + & + cobalt%p_nmdz(:,:,1,tau)) * cobalt%c_2_n * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_baccos .gt. 0) & + used = g_send_data(cobalt%id_baccos, cobalt%p_nbact(:,:,1,tau) * cobalt%c_2_n * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_detocos .gt. 0) & + used = g_send_data(cobalt%id_detocos, cobalt%p_ndet(:,:,1,tau) * cobalt%c_2_n * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_calcos .gt. 0) & + used = g_send_data(cobalt%id_calcos, cobalt%p_cadet_calc(:,:,1,tau) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_aragos .gt. 0) & + used = g_send_data(cobalt%id_aragos, cobalt%p_cadet_arag(:,:,1,tau) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_phydiatos.gt. 0) & + used = g_send_data(cobalt%id_phydiatos, cobalt%nlg_diatoms(:,:,1) * cobalt%c_2_n * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_phydiazos.gt. 0) & + used = g_send_data(cobalt%id_phydiazos, cobalt%p_ndi(:,:,1,tau) * cobalt%c_2_n * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_phypicoos.gt. 0) & + used = g_send_data(cobalt%id_phypicoos, cobalt%p_nsm(:,:,1,tau) * cobalt%c_2_n * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_phymiscos.gt. 0) & + used = g_send_data(cobalt%id_phymiscos, (cobalt%p_nlg(:,:,1,tau)-cobalt%nlg_diatoms(:,:,1)) * cobalt%c_2_n * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_zmicroos.gt. 0) & + used = g_send_data(cobalt%id_zmicroos, cobalt%p_nsmz(:,:,1,tau) * cobalt%c_2_n * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_zmesoos.gt. 0) & + used = g_send_data(cobalt%id_zmesoos, (cobalt%p_nlgz(:,:,1,tau)+cobalt%p_nmdz(:,:,1,tau)) * cobalt%c_2_n * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_talkos .gt. 0) & + used = g_send_data(cobalt%id_talkos, cobalt%p_alk(:,:,1,tau) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! PENDING: +! if (cobalt%id_talknatos .gt. 0) & +! used = g_send_data(cobalt%id_talknatos, +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CHECK3: this is using ntau=1 + if (cobalt%id_phos .gt. 0) & + used = g_send_data(cobalt%id_phos, log10(cobalt%f_htotal(:,:,1)+epsln) * (-1.0), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! PENDING: +! if (cobalt%id_phnatos .gt. 0) & +! used = g_send_data(cobalt%id_phnatos, log10(cobalt%f_htotal(:,:,1)+epsln) * -1.0, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! PENDING: +! if (cobalt%id_phabioos .gt. 0) & +! used = g_send_data(cobalt%id_phabioos, log10(cobalt%f_htotal(:,:,1)+epsln) * -1.0, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_o2os .gt. 0) & + used = g_send_data(cobalt%id_o2os, cobalt%p_o2(:,:,1,tau) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_o2satos .gt. 0) & + used = g_send_data(cobalt%id_o2satos, cobalt%o2sat(:,:,1), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_no3os .gt. 0) & + used = g_send_data(cobalt%id_no3os, cobalt%p_no3(:,:,1,tau) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_nh4os.gt. 0) & + used = g_send_data(cobalt%id_nh4os, cobalt%p_nh4(:,:,1,tau) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_po4os.gt. 0) & + used = g_send_data(cobalt%id_po4os, cobalt%p_po4(:,:,1,tau) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_dfeos .gt. 0) & + used = g_send_data(cobalt%id_dfeos, cobalt%p_fed(:,:,1,tau) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_sios .gt. 0) & + used = g_send_data(cobalt%id_sios, cobalt%p_sio4(:,:,1,tau) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_chlos .gt. 0) & + used = g_send_data(cobalt%id_chlos, cobalt%f_chl(:,:,1) * cobalt%Rho_0 / 1e9, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_chldiatos .gt. 0) & + used = g_send_data(cobalt%id_chldiatos, phyto(LARGE)%theta(:,:,1) * cobalt%nlg_diatoms(:,:,1) * cobalt%c_2_n * cobalt%Rho_0 * 12e3, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_chldiazos .gt. 0) & + used = g_send_data(cobalt%id_chldiazos, phyto(DIAZO)%theta(:,:,1) * cobalt%p_ndi(:,:,1,tau) * cobalt%c_2_n * cobalt%Rho_0 * 12e3, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_chlpicoos .gt. 0) & + used = g_send_data(cobalt%id_chlpicoos, phyto(SMALL)%theta(:,:,1) * cobalt%p_nsm(:,:,1,tau) * cobalt%c_2_n * cobalt%Rho_0 * 12e3, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_chlmiscos .gt. 0) & + used = g_send_data(cobalt%id_chlmiscos, phyto(LARGE)%theta(:,:,1) * (cobalt%p_nlg(:,:,1,tau)-cobalt%nlg_diatoms(:,:,1)) * & + cobalt%c_2_n * cobalt%Rho_0 * 12e3, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_ponos .gt. 0) & + used = g_send_data(cobalt%id_ponos, (cobalt%p_ndi(:,:,1,tau) + cobalt%p_nlg(:,:,1,tau) + & + cobalt%p_nsm(:,:,1,tau) + cobalt%p_nbact(:,:,1,tau) + cobalt%p_ndet(:,:,1,tau) + & + cobalt%p_nsmz(:,:,1,tau) + cobalt%p_nmdz(:,:,1,tau) + cobalt%p_nlgz(:,:,1,tau)) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_popos .gt. 0) & + used = g_send_data(cobalt%id_popos, ((phyto(DIAZO)%p_2_n_static * cobalt%p_ndi(:,:,1,tau)) + cobalt%p_nlg(:,:,1,tau) + & + cobalt%p_nsm(:,:,1,tau) + cobalt%p_nbact(:,:,1,tau) + cobalt%p_pdet(:,:,1,tau) + & + cobalt%p_nsmz(:,:,1,tau) + cobalt%p_nmdz(:,:,1,tau) + cobalt%p_nlgz(:,:,1,tau)) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_bfeos .gt. 0) & + used = g_send_data(cobalt%id_bfeos, (cobalt%p_fedi(:,:,1,tau) + cobalt%p_felg(:,:,1,tau) + cobalt%p_fesm(:,:,1,tau) + & + cobalt%p_fedet(:,:,1,tau)) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_bsios .gt. 0) & + used = g_send_data(cobalt%id_bsios, (cobalt%p_silg(:,:,1,tau) + cobalt%p_sidet(:,:,1,tau)) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_phynos .gt. 0) & + used = g_send_data(cobalt%id_phynos, (cobalt%p_nlg(:,:,1,tau) + cobalt%p_nsm(:,:,1,tau) + & + cobalt%p_ndi(:,:,1,tau)) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_phypos .gt. 0) & + used = g_send_data(cobalt%id_phypos, ((phyto(DIAZO)%p_2_n_static * cobalt%p_ndi(:,:,1,tau)) + cobalt%p_nlg(:,:,1,tau) + & + cobalt%p_nsm(:,:,1,tau)) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_phyfeos .gt. 0) & + used = g_send_data(cobalt%id_phyfeos, (cobalt%p_fedi(:,:,1,tau) + cobalt%p_felg(:,:,1,tau) + & + cobalt%p_fesm(:,:,1,tau)) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_physios .gt. 0) & + used = g_send_data(cobalt%id_physios, cobalt%p_silg(:,:,1,tau) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_co3os .gt. 0) & + used = g_send_data(cobalt%id_co3os, cobalt%f_co3_ion(:,:,1) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_co3natos .gt. 0) & + used = g_send_data(cobalt%id_co3natos, cobalt%f_co3_ion(:,:,1) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_co3abioos .gt. 0) & + used = g_send_data(cobalt%id_co3abioos, cobalt%f_co3_ion(:,:,1) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_co3satcalcos .gt. 0) & + used = g_send_data(cobalt%id_co3satcalcos, cobalt%co3_sol_calc(:,:,1) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_co3sataragos .gt. 0) & + used = g_send_data(cobalt%id_co3sataragos, cobalt%co3_sol_arag(:,:,1) * cobalt%Rho_0, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + +!============================================================================================================== +! JGJ 2016/08/08 CMIP6 OcnBgchem Omon: 3-D Marine Biogeochemical 3-D Fields +! Tracers and rates above +! Limitation terms below +! 2018/07/18 changed to 2d terms +! + if (cobalt%id_limndiat .gt. 0) & + used = g_send_data(cobalt%id_limndiat, phyto(LARGE)%nlim_bw_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CAS, JGJ: not outputting/serving limndiaz because diazotrophs are not N limited +! if (cobalt%id_limndiaz .gt. 0) & +! used = g_send_data(cobalt%id_limndiaz, phyto(DIAZO)%nlim_bw_100, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_limnpico .gt. 0) & + used = g_send_data(cobalt%id_limnpico, phyto(SMALL)%nlim_bw_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_limnmisc .gt. 0) & + used = g_send_data(cobalt%id_limnmisc, phyto(LARGE)%nlim_bw_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_limirrdiat .gt. 0) & + used = g_send_data(cobalt%id_limirrdiat, phyto(LARGE)%irrlim_bw_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_limirrdiaz .gt. 0) & + used = g_send_data(cobalt%id_limirrdiaz, phyto(DIAZO)%irrlim_bw_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_limirrpico .gt. 0) & + used = g_send_data(cobalt%id_limirrpico, phyto(SMALL)%irrlim_bw_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_limirrmisc .gt. 0) & + used = g_send_data(cobalt%id_limirrmisc, phyto(LARGE)%irrlim_bw_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_limfediat .gt. 0) & + used = g_send_data(cobalt%id_limfediat, phyto(LARGE)%def_fe_bw_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_limfediaz .gt. 0) & + used = g_send_data(cobalt%id_limfediaz, phyto(DIAZO)%def_fe_bw_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_limfepico .gt. 0) & + used = g_send_data(cobalt%id_limfepico, phyto(SMALL)%def_fe_bw_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_limfemisc .gt. 0) & + used = g_send_data(cobalt%id_limfemisc, phyto(LARGE)%def_fe_bw_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! added by JGJ, not requested for CMIP6 + if (cobalt%id_limpdiat .gt. 0) & + used = g_send_data(cobalt%id_limpdiat, phyto(LARGE)%plim_bw_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_limpdiaz .gt. 0) & + used = g_send_data(cobalt%id_limpdiaz, phyto(DIAZO)%plim_bw_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_limppico .gt. 0) & + used = g_send_data(cobalt%id_limppico, phyto(SMALL)%plim_bw_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_limpmisc .gt. 0) & + used = g_send_data(cobalt%id_limpmisc, phyto(LARGE)%plim_bw_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +!============================================================================================================== +! JGJ 2016/08/08 CMIP6 OcnBgchem Omon: Marine Biogeochemical 2-D Fields + + if (cobalt%id_intpp .gt. 0) & + used = g_send_data(cobalt%id_intpp, cobalt%jprod_allphytos_100 * cobalt%c_2_n, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_intppnitrate .gt. 0) & + used = g_send_data(cobalt%id_intppnitrate, (phyto(DIAZO)%jprod_n_new_100 + phyto(LARGE)%jprod_n_new_100 + & + phyto(SMALL)%jprod_n_new_100) * cobalt%c_2_n, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CAS: defined jprod_diat_100, removed extra "p" from individual group production + if (cobalt%id_intppdiat .gt. 0) & + used = g_send_data(cobalt%id_intppdiat, cobalt%jprod_diat_100 * cobalt%c_2_n, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_intppdiaz .gt. 0) & + used = g_send_data(cobalt%id_intppdiaz, phyto(DIAZO)%jprod_n_100 * cobalt%c_2_n, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_intpppico .gt. 0) & + used = g_send_data(cobalt%id_intpppico, phyto(SMALL)%jprod_n_100 * cobalt%c_2_n, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CAS: can now use jprod_diat_100 to back out misc production + if (cobalt%id_intppmisc .gt. 0) & + used = g_send_data(cobalt%id_intppmisc, (phyto(LARGE)%jprod_n_100 - cobalt%jprod_diat_100) * cobalt%c_2_n, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CAS: I think this is fine + if (cobalt%id_intpbn .gt. 0) & + used = g_send_data(cobalt%id_intpbn, cobalt%jprod_allphytos_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CAS: I've added juptake_po4_100 in a manner analagous to iron below + if (cobalt%id_intpbp .gt. 0) & + used = g_send_data(cobalt%id_intpbp, (phyto(DIAZO)%juptake_po4_100 + phyto(LARGE)%juptake_po4_100 + & + phyto(SMALL)%juptake_po4_100), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_intpbfe .gt. 0) & + used = g_send_data(cobalt%id_intpbfe, (phyto(DIAZO)%juptake_fe_100 + phyto(LARGE)%juptake_fe_100 + & + phyto(SMALL)%juptake_fe_100), & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_intpbsi .gt. 0) & + used = g_send_data(cobalt%id_intpbsi, phyto(LARGE)%juptake_sio4_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_intpcalcite .gt. 0) & + used = g_send_data(cobalt%id_intpcalcite, cobalt%jprod_cadet_calc_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_intparag .gt. 0) & + used = g_send_data(cobalt%id_intparag, cobalt%jprod_cadet_arag_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CHECK3: should be AT 100m + if (cobalt%id_epc100 .gt. 0) & + used = g_send_data(cobalt%id_epc100, cobalt%fndet_100 * cobalt%c_2_n, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CHECK3: should be AT 100m + if (cobalt%id_epn100 .gt. 0) & + used = g_send_data(cobalt%id_epn100, cobalt%fndet_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CHECK3: should be AT 100m + if (cobalt%id_epp100 .gt. 0) & + used = g_send_data(cobalt%id_epp100, cobalt%fpdet_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CHECK3: should be AT 100m + if (cobalt%id_epfe100 .gt. 0) & + used = g_send_data(cobalt%id_epfe100, cobalt%ffedet_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CHECK3: should be AT 100m + if (cobalt%id_epsi100 .gt. 0) & + used = g_send_data(cobalt%id_epsi100, cobalt%fsidet_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CHECK3: should be AT 100m + if (cobalt%id_epcalc100 .gt. 0) & + used = g_send_data(cobalt%id_epcalc100, cobalt%fcadet_calc_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CHECK3: should be AT 100m + if (cobalt%id_eparag100 .gt. 0) & + used = g_send_data(cobalt%id_eparag100, cobalt%fcadet_arag_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CAS: should be wc_vert_int_dic?, *12e-3 to go from moles C m-2 to kg C m-2 + if (cobalt%id_intdic .gt. 0) & + used = g_send_data(cobalt%id_intdic, cobalt%wc_vert_int_dic*12e-3, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CAS: added wc_vert_int_doc, *12e-3 to go from moles C m-2 to kg C m-2 + if (cobalt%id_intdoc .gt. 0) & + used = g_send_data(cobalt%id_intdoc, cobalt%wc_vert_int_doc*12e-3, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CAS: added wc_vert_int_poc, *12e-3 to go from moles C m-2 to kg C m-2 + if (cobalt%id_intpoc .gt. 0) & + used = g_send_data(cobalt%id_intpoc, cobalt%wc_vert_int_poc*12e-3, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_spco2 .gt. 0) & + used = g_send_data(cobalt%id_spco2, cobalt%pco2_csurf * 0.1013, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! PENDING: +! if (cobalt%id_spco2nat .gt. 0) & +! used = g_send_data(cobalt%id_spco2nat, cobalt%pco2_csurf * 0.1013, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! PENDING: +! if (cobalt%id_spco2abio .gt. 0) & +! used = g_send_data(cobalt%id_spco2abio, cobalt%pco2_csurf * 0.1013, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CHECK3: + if (cobalt%id_dpco2 .gt. 0) & + used = g_send_data(cobalt%id_dpco2, cobalt%deltap_dic * 0.1013, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! PENDING: +! if (cobalt%id_dpco2nat .gt. 0) & +! used = g_send_data(cobalt%id_dpco2nat, cobalt%dic_deltap * 0.1013, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! PENDING: +! if (cobalt%id_dpco2abio .gt. 0) & +! used = g_send_data(cobalt%id_dpco2abio, cobalt%dic_deltap * 0.1013, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_dpo2 .gt. 0) & + used = g_send_data(cobalt%id_dpo2, cobalt%deltap_o2 * 0.1013, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_fgco2 .gt. 0) & + used = g_send_data(cobalt%id_fgco2, cobalt%stf_gas_dic * 12e-3, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! PENDING: +! if (cobalt%id_fgco2nat .gt. 0) & +! used = g_send_data(cobalt%id_fgco2nat, +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! if (cobalt%id_fgco2abio .gt. 0) & +! used = g_send_data(cobalt%id_fgco2abio, +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! if (cobalt%id_fg14co2abio .gt. 0) & +! used = g_send_data(cobalt%id_fg14co2abio, +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_fgo2 .gt. 0) & + used = g_send_data(cobalt%id_fgo2, cobalt%stf_gas_o2, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CAS: fcased_redis is accounted for elsewhere, just keep runoff + if (cobalt%id_icfriver .gt. 0) & + used = g_send_data(cobalt%id_icfriver, cobalt%runoff_flux_dic, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CAS: Updated based on 7/19 e-mails with jpd and jgj + if (cobalt%id_fric .gt. 0) & + used = g_send_data(cobalt%id_fric, cobalt%fcadet_calc_btm - cobalt%fcased_redis, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CAS: organic nitrogen runoff from rivers*c_2_n ratio + if (cobalt%id_ocfriver .gt. 0) & + used = g_send_data(cobalt%id_ocfriver, cobalt%c_2_n* & + (cobalt%runoff_flux_ldon+cobalt%runoff_flux_sldon+cobalt%runoff_flux_srdon),& + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CAS: equal to ndet burial*c_2_n + if (cobalt%id_froc .gt. 0) & + used = g_send_data(cobalt%id_froc,cobalt%c_2_n*cobalt%fndet_burial, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_intpn2 .gt. 0) & + used = g_send_data(cobalt%id_intpn2, cobalt%wc_vert_int_nfix, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CHECK3: TOPAZ: nongas_source_n=runoff_flux_no3+runoff_flux_nh4 +dry_no3+wet_no3+dry_nh4+wet_nh4+wc_vert_int_nfix +! CAS: should we include 1) don fluxes from rivers? 2) nh4 fluxes from rivers? 3) nh4 deposition? + if (cobalt%id_fsn .gt. 0) & + used = g_send_data(cobalt%id_fsn, cobalt%runoff_flux_no3 + cobalt%dry_no3 + cobalt%wet_no3 + cobalt%wc_vert_int_nfix, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CHECK3: TOPAZ: fno3denit_tot=fno3denit_sed+wc_vert_int_jno3denit, where wc_vert_int_jno3denit=jno3denit_wc*rho_dzt (sum over k) +! CAS: added burial + if (cobalt%id_frn .gt. 0) & + used = g_send_data(cobalt%id_frn, cobalt%fno3denit_sed + cobalt%wc_vert_int_jno3denit + cobalt%fndet_burial, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CHECK3: TOPAZ: nongas_source_fe=runoff_flux_fed+wc_vert_int_jfe_coast+dry_fed+wet_fed+ffe_sed, where wc_vert_int_jfe_coast=jfe_coast*rho_dzt (sum over k) + if (cobalt%id_fsfe .gt. 0) & + used = g_send_data(cobalt%id_fsfe, cobalt%runoff_flux_fed + cobalt%dry_fed + cobalt%wet_fed + & + cobalt%ffe_sed+cobalt%ffe_geotherm, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_frfe .gt. 0) & + used = g_send_data(cobalt%id_frfe, cobalt%ffedet_btm, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! revise calculation if providing to CMIP6 +! 2016/08/15 - we will not be providing o2min, zo2min +! if (cobalt%id_o2min .gt. 0) & +! used = g_send_data(cobalt%id_o2min, cobalt%o2min, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! if (cobalt%id_zo2min .gt. 0) & +! used = g_send_data(cobalt%id_zo2min, cobalt%zo2min, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! 2016/08/15 - we will not be providing zsatcalc, zsatarag + if (cobalt%id_zsatcalc .gt. 0) & + used = g_send_data(cobalt%id_zsatcalc, cobalt%z_sat_calc, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_zsatarag .gt. 0) & + used = g_send_data(cobalt%id_zsatarag, cobalt%z_sat_arag, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! 2016/08/15 - we will not be providing these fields +! CHECK: rate was computed offline for TOPAZ by saving a reference history file, dividing by secs_per_month and differencing monthly averages +! can we compute rates in the code this time? +! if (cobalt%id_fddtdic .gt. 0) & +! used = g_send_data(cobalt%id_fddtdic, cobalt%f_dic_int_100, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CHECK: rate was computed offline for TOPAZ by saving a reference history file, dividing by secs_per_month and differencing monthly averages +! can we compute rates in the code this time? +! if (cobalt%id_fddtdin .gt. 0) & +! used = g_send_data(cobalt%id_fddtdin, cobalt%f_din_int_100, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CHECK: rate was computed offline for TOPAZ by saving a reference history file, dividing by secs_per_month and differencing monthly averages +! can we compute rates in the code this time? +! if (cobalt%id_fddtdip .gt. 0) & +! used = g_send_data(cobalt%id_fddtdip, cobalt%f_po4_int_100, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CHECK: rate was computed offline for TOPAZ by saving a reference history file, dividing by secs_per_month and differencing monthly averages +! can we compute rates in the code this time? +! if (cobalt%id_fddtdife .gt. 0) & +! used = g_send_data(cobalt%id_fddtdife, cobalt%f_fed_int_100, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CHECK: rate was computed offline for TOPAZ by saving a reference history file, dividing by secs_per_month and differencing monthly averages +! can we compute rates in the code this time? +! if (cobalt%id_fddtdisi .gt. 0) & +! used = g_send_data(cobalt%id_fddtdisi, cobalt%f_sio4_int_100, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! CHECK: rate was computed offline for TOPAZ by saving a reference history file, dividing by secs_per_month and differencing monthly averages +! can we compute rates in the code this time? +! if (cobalt%id_fddtalk .gt. 0) & +! used = g_send_data(cobalt%id_fddtalk, cobalt%f_alk_int_100, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! if (cobalt%id_fbddtdic .gt. 0) & +! used = g_send_data(cobalt%id_fbddtdic, cobalt%jdic_100, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! if (cobalt%id_fbddtdin .gt. 0) & +! used = g_send_data(cobalt%id_fbddtdin, cobalt%jdin_100, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! if (cobalt%id_fbddtdip .gt. 0) & +! used = g_send_data(cobalt%id_fbddtdip, cobalt%jpo4_100, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! if (cobalt%id_fbddtdife .gt. 0) & +! used = g_send_data(cobalt%id_fbddtdife, cobalt%jfed_100, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! if (cobalt%id_fbddtdisi .gt. 0) & +! used = g_send_data(cobalt%id_fbddtdisi, cobalt%jsio4_100, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +! if (cobalt%id_fbddtalk .gt. 0) & +! used = g_send_data(cobalt%id_fbddtalk, cobalt%jalk_100, & +! model_time, rmask = grid_tmask(:,:,1),& +! is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +!============================================================================================================== +! JGJ 2016/08/08 CMIP6 OcnBgchem day: Marine Biogeochemical daily fields +! chlos and phycos - in Omon and Oday +!============================================================================================================== +! 2016/08/15 JGJ: 100m integrals w/o CMOR conversion + + if (cobalt%id_f_dic_int_100 .gt. 0) & + used = g_send_data(cobalt%id_f_dic_int_100, cobalt%f_dic_int_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_f_din_int_100 .gt. 0) & + used = g_send_data(cobalt%id_f_din_int_100, cobalt%f_din_int_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_f_po4_int_100 .gt. 0) & + used = g_send_data(cobalt%id_f_po4_int_100, cobalt%f_po4_int_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_f_fed_int_100 .gt. 0) & + used = g_send_data(cobalt%id_f_fed_int_100, cobalt%f_fed_int_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_f_sio4_int_100 .gt. 0) & + used = g_send_data(cobalt%id_f_sio4_int_100, cobalt%f_sio4_int_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_f_alk_int_100 .gt. 0) & + used = g_send_data(cobalt%id_f_alk_int_100, cobalt%f_alk_int_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_jdic_100 .gt. 0) & + used = g_send_data(cobalt%id_jdic_100, cobalt%jdic_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_jdin_100 .gt. 0) & + used = g_send_data(cobalt%id_jdin_100, cobalt%jdin_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_jpo4_100 .gt. 0) & + used = g_send_data(cobalt%id_jpo4_100, cobalt%jpo4_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_jfed_100 .gt. 0) & + used = g_send_data(cobalt%id_jfed_100, cobalt%jfed_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_jsio4_100 .gt. 0) & + used = g_send_data(cobalt%id_jsio4_100, cobalt%jsio4_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + + if (cobalt%id_jalk_100 .gt. 0) & + used = g_send_data(cobalt%id_jalk_100, cobalt%jalk_100, & + model_time, rmask = grid_tmask(:,:,1),& + is_in=isc, js_in=jsc, ie_in=iec, je_in=jec) + +!============================================================================================================== + + call mpp_clock_end(id_clock_cobalt_send_diagnostics) + + end subroutine generic_COBALT_update_from_source + + + ! + ! + ! Calculate and set coupler values at the surface / bottom + ! + ! + ! + ! + ! + ! + ! Pointer to the head of generic tracer list. + ! + ! + ! Lower bounds of x and y extents of input arrays on data domain + ! + ! + ! Sea Surface Temperature + ! + ! + ! Sea Surface Salinity + ! + ! + ! Ocean density + ! + ! + ! Time step index of %field + ! + ! + + !User must provide the calculations for these boundary values. + subroutine generic_COBALT_set_boundary_values(tracer_list,SST,SSS,rho,ilb,jlb,tau,dzt,model_time) + type(g_tracer_type), pointer :: tracer_list + real, dimension(ilb:,jlb:), intent(in) :: SST, SSS + real, dimension(ilb:,jlb:,:,:), intent(in) :: rho + integer, intent(in) :: ilb,jlb,tau + real, dimension(ilb:,jlb:,:), optional, intent(in) :: dzt + type(time_type), intent(in) :: model_time + integer :: isc,iec, jsc,jec,isd,ied,jsd,jed,nk,ntau , i, j + real :: sal,ST,o2_saturation + real :: tt,tk,ts,ts2,ts3,ts4,ts5 + real, dimension(:,:,:) ,pointer :: grid_tmask + real, dimension(:,:,:,:), pointer :: o2_field,dic_field,po4_field,sio4_field,alk_field,di14c_field,nh4_field + real, dimension(:,:,:), ALLOCATABLE :: htotal_field,co3_ion_field + real, dimension(:,:), ALLOCATABLE :: co2_alpha,co2_csurf,co2_sc_no,o2_alpha,o2_csurf,o2_sc_no,nh3_alpha,nh3_csurf,nh3_sc_no,phos_nh3_exchange + real, dimension(:,:), ALLOCATABLE :: c14o2_alpha,c14o2_csurf + real :: pka_nh3,tr,ltr + + logical :: phos_nh3_override + + character(len=fm_string_len), parameter :: sub_name = 'generic_COBALT_set_boundary_values' + + ! + ! + !Get the necessary properties + ! + call g_tracer_get_common(isc,iec,jsc,jec,isd,ied,jsd,jed,nk,ntau,grid_tmask=grid_tmask) + + call g_tracer_get_pointer(tracer_list,'o2' ,'field', o2_field) + + allocate(co2_alpha(isd:ied, jsd:jed)); co2_alpha=0.0 + allocate(co2_csurf(isd:ied, jsd:jed)); co2_csurf=0.0 + allocate(co2_sc_no(isd:ied, jsd:jed)); co2_sc_no=0.0 + allocate(nh3_alpha(isd:ied, jsd:jed)); nh3_alpha=0.0 + allocate(nh3_csurf(isd:ied, jsd:jed)); nh3_csurf=0.0 + allocate(nh3_sc_no(isd:ied, jsd:jed)); nh3_sc_no=0.0 + !for nh3 ph emission override + allocate(phos_nh3_exchange(isd:ied, jsd:jed)); phos_nh3_exchange=0.0 + allocate(c14o2_alpha(isd:ied, jsd:jed)); c14o2_alpha=0.0 + allocate(c14o2_csurf(isd:ied, jsd:jed)); c14o2_csurf=0.0 + allocate(o2_alpha(isd:ied, jsd:jed)); o2_alpha=0.0 + allocate(o2_csurf(isd:ied, jsd:jed)); o2_csurf=0.0 + allocate(o2_sc_no(isd:ied, jsd:jed)); o2_sc_no=0.0 + allocate(htotal_field(isd:ied,jsd:jed,nk),co3_ion_field(isd:ied,jsd:jed,nk)) + htotal_field=0.0 ; co3_ion_field=0.0 + + !nnz: Since the generic_COBALT_update_from_source() subroutine is called by this time + ! the following if block is not really necessary (since this calculation is already done in source). + ! It is only neccessary if source routine is commented out for debugging. + !Note: In order for this to work we should NOT zero out the coupler values for generic tracers + ! This zeroing is done for non-generic TOPAZ by calling zero_ocean_sfc. + ! Since the coupler values here are non-cumulative there is no need to zero them out anyway. + + if (cobalt%init .OR. cobalt%force_update_fluxes) then + !Get necessary fields + call g_tracer_get_pointer(tracer_list,'dic' ,'field', dic_field) + call g_tracer_get_pointer(tracer_list,'po4' ,'field', po4_field) + call g_tracer_get_pointer(tracer_list,'sio4' ,'field', sio4_field) + call g_tracer_get_pointer(tracer_list,'alk' ,'field', alk_field) + call g_tracer_get_pointer(tracer_list,'nh4' ,'field', nh4_field) + + call g_tracer_get_values(tracer_list,'htotal' ,'field', htotal_field,isd,jsd,ntau=1) + call g_tracer_get_values(tracer_list,'co3_ion','field',co3_ion_field,isd,jsd,ntau=1) + + do j = jsc, jec ; do i = isc, iec !{ + cobalt%htotallo(i,j) = cobalt%htotal_scale_lo * htotal_field(i,j,1) + cobalt%htotalhi(i,j) = cobalt%htotal_scale_hi * htotal_field(i,j,1) + enddo; enddo ; !} i, j + + if(present(dzt)) then + ! 2017/08/11 jgj is cobalt type defined/passed here ? + ! do j = jsc, jec ; do i = isc, iec !{ + ! cobalt%zt(i,j,1) = dzt(i,j,1) + ! enddo; enddo ; !} i, j + elseif (trim(co2_calc) == 'mocsy') then + call mpp_error(FATAL,"mocsy method of co2_calc needs dzt to be passed to the FMS_ocmip2_co2calc subroutine.") + endif + + call FMS_ocmip2_co2calc(CO2_dope_vec,grid_tmask(:,:,1), & + SST(:,:), SSS(:,:), & + dic_field(:,:,1,tau), & + po4_field(:,:,1,tau), & + sio4_field(:,:,1,tau), & + alk_field(:,:,1,tau), & + cobalt%htotallo, cobalt%htotalhi, & + !InOut + htotal_field(:,:,1), & + !Optional In + co2_calc=trim(co2_calc), & + !! jgj 2017/08/11 + !!zt=cobalt%zt(:,:,1), & + zt=dzt(:,:,1), & + !OUT + co2star=co2_csurf(:,:), alpha=co2_alpha(:,:), & + pCO2surf=cobalt%pco2_csurf(:,:), & + co3_ion=co3_ion_field(:,:,1), & + omega_arag=cobalt%omegaa(:,:,1), & + omega_calc=cobalt%omegac(:,:,1)) + + !Set fields !nnz: if These are pointers do I need to do this? + call g_tracer_set_values(tracer_list,'htotal' ,'field',htotal_field ,isd,jsd,ntau=1) + call g_tracer_set_values(tracer_list,'co3_ion','field',co3_ion_field,isd,jsd,ntau=1) + + call g_tracer_set_values(tracer_list,'dic','alpha',co2_alpha ,isd,jsd) + call g_tracer_set_values(tracer_list,'dic','csurf',co2_csurf ,isd,jsd) + + + if (do_14c) then !< mol/m3/atm + nh3_alpha(i,j) = nh3_alpha(i,j)/saltout_correction(101325./(1e-3*rdgas*wtmair*(SST(i,j)+273.15)*nh3_alpha(i,j)),vb_nh3,SSS(i,j)) * 1./cobalt%Rho_0 !mol/kg/atm + if (phos_nh3_override) then + nh3_csurf(i,j) = nh4_field(i,j,1,tau)/(1.+10**(pka_nh3-(max(min(11.,phos_nh3_exchange(i,j)),3.)))) + else + nh3_csurf(i,j) = nh4_field(i,j,1,tau)/(1.+10**(pka_nh3+log10(min(max(1e-11,htotal_field(i,j,1)),1e-3)))) + end if + cobalt%pnh3_csurf(i,j) = cobalt%nh3_csurf(i,j)/nh3_alpha(i,j)*1.e6 !in uatm + enddo;enddo + + call g_tracer_set_values(tracer_list,'nh4','alpha',nh3_alpha ,isd,jsd) + call g_tracer_set_values(tracer_list,'nh4','csurf',nh3_csurf ,isd,jsd) + + end if + !!nnz: If source is called uncomment the following + cobalt%init = .false. !nnz: This is necessary since the above two calls appear in source subroutine too. + endif + + call g_tracer_get_values(tracer_list,'dic','alpha', co2_alpha ,isd,jsd) + call g_tracer_get_values(tracer_list,'dic','csurf', co2_csurf ,isd,jsd) + + call g_tracer_get_values(tracer_list,'o2','alpha', o2_alpha ,isd,jsd) + call g_tracer_get_values(tracer_list,'o2','csurf', o2_csurf ,isd,jsd) + + + do j=jsc,jec ; do i=isc,iec + !This calculation needs an input of SST and SSS + sal = SSS(i,j) ; ST = SST(i,j) + + !nnz: + !Note: In the following calculations in order to get results for co2 and o2 + ! identical with cobalt code in MOM cobalt%Rho_0 must be replaced with rho(i,j,1,tau) + ! This is achieved by uncommenting the following if desired. + !! cobalt%Rho_0 = rho(i,j,1,tau) + ! But since %Rho_0 plays the role of a unit conversion factor in this module + ! it may be safer to keep it as a constant (1035.0) rather than the actual variable + ! surface density rho(i,j,1,tau) + + !--------------------------------------------------------------------- + ! CO2 + !--------------------------------------------------------------------- + + !--------------------------------------------------------------------- + ! Compute the Schmidt number of CO2 in seawater using the + ! formulation presented by Wanninkhof (1992, J. Geophys. Res., 97, + ! 7373-7382). + ! 2018/01/17 jgj update Schmidt number for CO2 to use + ! Wanninkhof, Limnol. Oceanogr: Methods, 12, 2014, 351-362 + !--------------------------------------------------------------------- + if (trim(as_param_cobalt) == 'W92') then + co2_sc_no(i,j) = cobalt%a1_co2 + ST*(cobalt%a2_co2 + ST*(cobalt%a3_co2 + ST*cobalt%a4_co2)) * & + grid_tmask(i,j,1) + else if ((trim(as_param_cobalt) == 'W14') .or. (trim(as_param_cobalt) == 'gfdl_cmip6')) then + co2_sc_no(i,j) = cobalt%a1_co2 + ST*(cobalt%a2_co2 + ST*(cobalt%a3_co2 + & + ST*(cobalt%a4_co2 + ST*cobalt%a5_co2))) * grid_tmask(i,j,1) + endif +! sc_no_term = sqrt(660.0 / (sc_co2 + epsln)) +! +! co2_alpha(i,j) = co2_alpha(i,j)* sc_no_term * cobalt%Rho_0 !nnz: MOM has rho(i,j,1,tau) +! co2_csurf(i,j) = co2_csurf(i,j)* sc_no_term * cobalt%Rho_0 !nnz: MOM has rho(i,j,1,tau) +! +! in 'ocmip2_new' atmos_ocean_fluxes.F90 coupler formulation, the schmidt number is carried in explicitly +! + co2_alpha(i,j) = co2_alpha(i,j) * cobalt%Rho_0 !nnz: MOM has rho(i,j,1,tau) + co2_csurf(i,j) = co2_csurf(i,j) * cobalt%Rho_0 !nnz: MOM has rho(i,j,1,tau) + + !--------------------------------------------------------------------- + ! O2 + !--------------------------------------------------------------------- + ! Compute the oxygen saturation concentration at 1 atm total + ! pressure in mol/kg given the temperature (t, in deg C) and + ! the salinity (s, in permil) + ! + ! From Garcia and Gordon (1992), Limnology and Oceonography. + ! The formula used is from page 1310, eq (8). + ! + ! *** Note: the "a3*ts^2" term (in the paper) is incorrect. *** + ! *** It shouldn't be there. *** + ! + ! o2_saturation is defined between T(freezing) <= T <= 40 deg C and + ! 0 permil <= S <= 42 permil + ! 2015/05/15 jgj ESM2.6 has values of 60+ in Red Sea - impose + ! bounds on salinity to keep it in 0-42 range + ! + ! check value: T = 10 deg C, S = 35 permil, + ! o2_saturation = 0.282015 mol m-3 + !--------------------------------------------------------------------- + ! + ! jgj 2015/05/14 impose temperature and salinity bounds for o2sat + sal = min(42.0,max(0.0,sal)) + tt = 298.15 - min(40.0,max(0.0,ST)) + tk = 273.15 + min(40.0,max(0.0,ST)) + ts = log(tt / tk) + ts2 = ts * ts + ts3 = ts2 * ts + ts4 = ts3 * ts + ts5 = ts4 * ts + + !The atmospheric code needs solubilities in units of mol/m3/atm + o2_saturation = (1000.0/22391.6) * grid_tmask(i,j,1) * & !convert from ml/l to mol m-3 + exp( cobalt%a_0 + cobalt%a_1*ts + cobalt%a_2*ts2 + cobalt%a_3*ts3 + cobalt%a_4*ts4 + cobalt%a_5*ts5 + & + (cobalt%b_0 + cobalt%b_1*ts + cobalt%b_2*ts2 + cobalt%b_3*ts3 + cobalt%c_0*sal)*sal) + + !--------------------------------------------------------------------- + ! Compute the Schmidt number of O2 in seawater using the + ! formulation proposed by Keeling et al. (1998, Global Biogeochem. + ! Cycles, 12, 141-163). + ! 2018/01/17 jgj update Schmidt number for O2 to use + ! Wanninkhof, Limnol. Oceanogr: Methods, 12, 2014, 351-362 + !--------------------------------------------------------------------- + ! + ! In 'ocmip2_generic' atmos_ocean_fluxes.F90 coupler formulation, + ! the schmidt number is carried in explicitly + ! + if (trim(as_param_cobalt) == 'W92') then + o2_sc_no(i,j) = cobalt%a1_o2 + ST * (cobalt%a2_o2 + ST * (cobalt%a3_o2 + ST * cobalt%a4_o2 )) * & + grid_tmask(i,j,1) + else if ((trim(as_param_cobalt) == 'W14') .or. (trim(as_param_cobalt) == 'gfdl_cmip6')) then + o2_sc_no(i,j) = cobalt%a1_o2 + ST*(cobalt%a2_o2 + ST*(cobalt%a3_o2 + & + ST*(cobalt%a4_o2 + ST*cobalt%a5_o2))) * grid_tmask(i,j,1) + endif + ! + ! renormalize the alpha value for atm o2 + ! data table override for o2_flux_pcair_atm is now set to 0.21 + ! + o2_alpha(i,j) = (o2_saturation / 0.21) + o2_csurf(i,j) = o2_field(i,j,1,tau) * cobalt%Rho_0 !nnz: MOM has rho(i,j,1,tau) + + enddo; enddo + + ! + ! Set %csurf, %alpha and %sc_no for these tracers. This will mark them + ! for sending fluxes to coupler + ! + call g_tracer_set_values(tracer_list,'dic','alpha',co2_alpha,isd,jsd) + call g_tracer_set_values(tracer_list,'dic','csurf',co2_csurf,isd,jsd) + call g_tracer_set_values(tracer_list,'dic','sc_no',co2_sc_no,isd,jsd) + + call g_tracer_set_values(tracer_list,'o2', 'alpha',o2_alpha, isd,jsd) + call g_tracer_set_values(tracer_list,'o2', 'csurf',o2_csurf, isd,jsd) + call g_tracer_set_values(tracer_list,'o2', 'sc_no',o2_sc_no, isd,jsd) + + if (do_nh3_atm_ocean_exchange) then + call g_tracer_get_values(tracer_list,'nh4','alpha', nh3_alpha ,isd,jsd) + call g_tracer_get_values(tracer_list,'nh4','csurf', nh3_csurf ,isd,jsd) + + do j=jsc,jec ; do i=isc,iec + !nh3 + !f1p + nh3_sc_no(i,j) = schmidt_w(sst(i,j),sss(i,j),vb_nh3)*grid_tmask(i,j,1) + nh3_csurf(i,j) = nh3_csurf(i,j)*cobalt%Rho_0 + nh3_alpha(i,j) = nh3_alpha(i,j)*cobalt%Rho_0 + end do;end do + + call g_tracer_set_values(tracer_list,'nh4', 'alpha',nh3_alpha, isd,jsd) + call g_tracer_set_values(tracer_list,'nh4', 'csurf',nh3_csurf, isd,jsd) + call g_tracer_set_values(tracer_list,'nh4', 'sc_no',nh3_sc_no, isd,jsd) + + end if + + if (do_14c) then !<> + + deallocate(co2_alpha,co2_csurf,& + co2_sc_no,o2_alpha, & + c14o2_alpha,c14o2_csurf, & + o2_csurf,o2_sc_no, nh3_alpha,nh3_csurf,& + nh3_sc_no,phos_nh3_exchange) + + end subroutine generic_COBALT_set_boundary_values + + + ! + ! + ! End the module. + ! + ! + ! Deallocate all work arrays + ! + ! + ! + + + subroutine generic_COBALT_end + character(len=fm_string_len), parameter :: sub_name = 'generic_COBALT_end' + call user_deallocate_arrays + end subroutine generic_COBALT_end + + ! + ! This is an internal sub, not a public interface. + ! Allocate all the work arrays to be used in this module. + ! + subroutine user_allocate_arrays + integer :: isc,iec,jsc,jec,isd,ied,jsd,jed,nk,ntau,n + + call g_tracer_get_common(isc,iec,jsc,jec,isd,ied,jsd,jed,nk,ntau) + + !Allocate all the private arrays. + + !Used in ocmip2_co2calc + CO2_dope_vec%isc = isc ; CO2_dope_vec%iec = iec + CO2_dope_vec%jsc = jsc ; CO2_dope_vec%jec = jec + CO2_dope_vec%isd = isd ; CO2_dope_vec%ied = ied + CO2_dope_vec%jsd = jsd ; CO2_dope_vec%jed = jed + + allocate(cobalt%htotallo(isd:ied,jsd:jed)) + allocate(cobalt%htotalhi(isd:ied,jsd:jed)) + + ! + ! allocate and initialize array elements of all phytoplankton groups + ! CAS: add fluxes for additional explicit phytoplankton loss terms + + do n = 1, NUM_PHYTO + allocate(phyto(n)%def_fe(isd:ied,jsd:jed,nk)) ; phyto(n)%def_fe = 0.0 + allocate(phyto(n)%def_p(isd:ied,jsd:jed,nk)) ; phyto(n)%def_p = 0.0 + allocate(phyto(n)%f_fe(isd:ied,jsd:jed,nk)) ; phyto(n)%f_fe = 0.0 + allocate(phyto(n)%f_n(isd:ied,jsd:jed,nk)) ; phyto(n)%f_n = 0.0 + allocate(phyto(n)%felim(isd:ied,jsd:jed,nk)) ; phyto(n)%felim = 0.0 + allocate(phyto(n)%irrlim(isd:ied,jsd:jed,nk)) ; phyto(n)%irrlim = 0.0 + allocate(phyto(n)%jzloss_fe(isd:ied,jsd:jed,nk)) ; phyto(n)%jzloss_fe = 0.0 + allocate(phyto(n)%jzloss_n(isd:ied,jsd:jed,nk)) ; phyto(n)%jzloss_n = 0.0 + allocate(phyto(n)%jzloss_p(isd:ied,jsd:jed,nk)) ; phyto(n)%jzloss_p = 0.0 + allocate(phyto(n)%jzloss_sio2(isd:ied,jsd:jed,nk)) ; phyto(n)%jzloss_sio2 = 0.0 + allocate(phyto(n)%jaggloss_fe(isd:ied,jsd:jed,nk)) ; phyto(n)%jaggloss_fe = 0.0 + allocate(phyto(n)%jaggloss_n(isd:ied,jsd:jed,nk)) ; phyto(n)%jaggloss_n = 0.0 + allocate(phyto(n)%jaggloss_p(isd:ied,jsd:jed,nk)) ; phyto(n)%jaggloss_p = 0.0 + allocate(phyto(n)%jaggloss_sio2(isd:ied,jsd:jed,nk)); phyto(n)%jaggloss_sio2 = 0.0 + allocate(phyto(n)%jvirloss_fe(isd:ied,jsd:jed,nk)) ; phyto(n)%jvirloss_fe = 0.0 + allocate(phyto(n)%jvirloss_n(isd:ied,jsd:jed,nk)) ; phyto(n)%jvirloss_n = 0.0 + allocate(phyto(n)%jvirloss_p(isd:ied,jsd:jed,nk)) ; phyto(n)%jvirloss_p = 0.0 + allocate(phyto(n)%jvirloss_sio2(isd:ied,jsd:jed,nk)); phyto(n)%jvirloss_sio2 = 0.0 + allocate(phyto(n)%jexuloss_fe(isd:ied,jsd:jed,nk)) ; phyto(n)%jexuloss_fe = 0.0 + allocate(phyto(n)%jexuloss_n(isd:ied,jsd:jed,nk)) ; phyto(n)%jexuloss_n = 0.0 + allocate(phyto(n)%jexuloss_p(isd:ied,jsd:jed,nk)) ; phyto(n)%jexuloss_p = 0.0 + allocate(phyto(n)%jhploss_fe(isd:ied,jsd:jed,nk)) ; phyto(n)%jhploss_fe = 0.0 + allocate(phyto(n)%jhploss_n(isd:ied,jsd:jed,nk)) ; phyto(n)%jhploss_n = 0.0 + allocate(phyto(n)%jhploss_p(isd:ied,jsd:jed,nk)) ; phyto(n)%jhploss_p = 0.0 + allocate(phyto(n)%jhploss_sio2(isd:ied,jsd:jed,nk)) ; phyto(n)%jhploss_sio2 = 0.0 + allocate(phyto(n)%juptake_fe(isd:ied,jsd:jed,nk)) ; phyto(n)%juptake_fe = 0.0 + allocate(phyto(n)%juptake_nh4(isd:ied,jsd:jed,nk)) ; phyto(n)%juptake_nh4 = 0.0 + allocate(phyto(n)%juptake_no3(isd:ied,jsd:jed,nk)) ; phyto(n)%juptake_no3 = 0.0 + allocate(phyto(n)%juptake_po4(isd:ied,jsd:jed,nk)) ; phyto(n)%juptake_po4 = 0.0 + allocate(phyto(n)%jprod_n(isd:ied,jsd:jed,nk)) ; phyto(n)%jprod_n = 0.0 + allocate(phyto(n)%liebig_lim(isd:ied,jsd:jed,nk)) ; phyto(n)%liebig_lim = 0.0 + allocate(phyto(n)%mu(isd:ied,jsd:jed,nk)) ; phyto(n)%mu = 0.0 + allocate(phyto(n)%po4lim(isd:ied,jsd:jed,nk)) ; phyto(n)%po4lim = 0.0 + allocate(phyto(n)%q_fe_2_n(isd:ied,jsd:jed,nk)) ; phyto(n)%q_fe_2_n = 0.0 + allocate(phyto(n)%q_p_2_n(isd:ied,jsd:jed,nk)) ; phyto(n)%q_p_2_n = 0.0 + allocate(phyto(n)%q_si_2_n(isd:ied,jsd:jed,nk)) ; phyto(n)%q_si_2_n = 0.0 + allocate(phyto(n)%theta(isd:ied,jsd:jed,nk)) ; phyto(n)%theta = 0.0 + allocate(phyto(n)%f_mu_mem(isd:ied,jsd:jed,nk)) ; phyto(n)%f_mu_mem = 0.0 + allocate(phyto(n)%mu_mix(isd:ied,jsd:jed,nk)) ; phyto(n)%mu_mix = 0.0 + allocate(phyto(n)%agg_lim(isd:ied,jsd:jed,nk)) ; phyto(n)%agg_lim = 0.0 + allocate(phyto(n)%nh4lim(isd:ied,jsd:jed,nk)) ; phyto(n)%nh4lim = 0.0 + allocate(phyto(n)%no3lim(isd:ied,jsd:jed,nk)) ; phyto(n)%no3lim = 0.0 + enddo + ! + ! allocate and initialize array elements of only one phytoplankton group + ! + allocate(phyto(DIAZO)%juptake_n2(isd:ied,jsd:jed,nk)) ; phyto(DIAZO)%juptake_n2 = 0.0 + allocate(phyto(DIAZO)%o2lim(isd:ied,jsd:jed,nk)) ; phyto(DIAZO)%o2lim = 0.0 + allocate(phyto(LARGE)%juptake_sio4(isd:ied,jsd:jed,nk)) ; phyto(LARGE)%juptake_sio4 = 0.0 + allocate(phyto(LARGE)%silim(isd:ied,jsd:jed,nk)) ; phyto(LARGE)%silim = 0.0 + ! + ! allocate and initialize arrays for bacteria + ! + allocate(bact(1)%f_n(isd:ied,jsd:jed,nk)) ; bact(1)%f_n = 0.0 + allocate(bact(1)%jzloss_n(isd:ied,jsd:jed,nk)) ; bact(1)%jzloss_n = 0.0 + allocate(bact(1)%jzloss_p(isd:ied,jsd:jed,nk)) ; bact(1)%jzloss_p = 0.0 + allocate(bact(1)%jvirloss_n(isd:ied,jsd:jed,nk)) ; bact(1)%jvirloss_n = 0.0 + allocate(bact(1)%jvirloss_p(isd:ied,jsd:jed,nk)) ; bact(1)%jvirloss_p = 0.0 + allocate(bact(1)%jhploss_n(isd:ied,jsd:jed,nk)) ; bact(1)%jhploss_n = 0.0 + allocate(bact(1)%jhploss_p(isd:ied,jsd:jed,nk)) ; bact(1)%jhploss_p = 0.0 + allocate(bact(1)%juptake_ldon(isd:ied,jsd:jed,nk)) ; bact(1)%juptake_ldon = 0.0 + allocate(bact(1)%juptake_ldop(isd:ied,jsd:jed,nk)) ; bact(1)%juptake_ldop = 0.0 + allocate(bact(1)%jprod_nh4(isd:ied,jsd:jed,nk)) ; bact(1)%jprod_nh4 = 0.0 + allocate(bact(1)%jprod_po4(isd:ied,jsd:jed,nk)) ; bact(1)%jprod_po4 = 0.0 + allocate(bact(1)%jprod_n(isd:ied,jsd:jed,nk)) ; bact(1)%jprod_n = 0.0 + allocate(bact(1)%o2lim(isd:ied,jsd:jed,nk)) ; bact(1)%o2lim = 0.0 + allocate(bact(1)%ldonlim(isd:ied,jsd:jed,nk)) ; bact(1)%ldonlim = 0.0 + allocate(bact(1)%temp_lim(isd:ied,jsd:jed,nk)) ; bact(1)%temp_lim = 0.0 + ! + ! CAS: allocate and initialize array elements for all zooplankton groups + ! + do n = 1, NUM_ZOO + allocate(zoo(n)%f_n(isd:ied,jsd:jed,nk)) ; zoo(n)%f_n = 0.0 + allocate(zoo(n)%jzloss_n(isd:ied,jsd:jed,nk)) ; zoo(n)%jzloss_n = 0.0 + allocate(zoo(n)%jzloss_p(isd:ied,jsd:jed,nk)) ; zoo(n)%jzloss_p = 0.0 + allocate(zoo(n)%jhploss_n(isd:ied,jsd:jed,nk)) ; zoo(n)%jhploss_n = 0.0 + allocate(zoo(n)%jhploss_p(isd:ied,jsd:jed,nk)) ; zoo(n)%jhploss_p = 0.0 + allocate(zoo(n)%jingest_n(isd:ied,jsd:jed,nk)) ; zoo(n)%jingest_n = 0.0 + allocate(zoo(n)%jingest_p(isd:ied,jsd:jed,nk)) ; zoo(n)%jingest_p = 0.0 + allocate(zoo(n)%jingest_sio2(isd:ied,jsd:jed,nk)) ; zoo(n)%jingest_sio2 = 0.0 + allocate(zoo(n)%jingest_fe(isd:ied,jsd:jed,nk)) ; zoo(n)%jingest_fe = 0.0 + allocate(zoo(n)%jprod_fed(isd:ied,jsd:jed,nk)) ; zoo(n)%jprod_fed = 0.0 + allocate(zoo(n)%jprod_fedet(isd:ied,jsd:jed,nk)) ; zoo(n)%jprod_fedet = 0.0 + allocate(zoo(n)%jprod_ndet(isd:ied,jsd:jed,nk)) ; zoo(n)%jprod_ndet = 0.0 + allocate(zoo(n)%jprod_pdet(isd:ied,jsd:jed,nk)) ; zoo(n)%jprod_pdet = 0.0 + allocate(zoo(n)%jprod_ldon(isd:ied,jsd:jed,nk)) ; zoo(n)%jprod_ldon = 0.0 + allocate(zoo(n)%jprod_ldop(isd:ied,jsd:jed,nk)) ; zoo(n)%jprod_ldop = 0.0 + allocate(zoo(n)%jprod_srdon(isd:ied,jsd:jed,nk)) ; zoo(n)%jprod_srdon = 0.0 + allocate(zoo(n)%jprod_srdop(isd:ied,jsd:jed,nk)) ; zoo(n)%jprod_srdop = 0.0 + allocate(zoo(n)%jprod_sldon(isd:ied,jsd:jed,nk)) ; zoo(n)%jprod_sldon = 0.0 + allocate(zoo(n)%jprod_sldop(isd:ied,jsd:jed,nk)) ; zoo(n)%jprod_sldop = 0.0 + allocate(zoo(n)%jprod_sidet(isd:ied,jsd:jed,nk)) ; zoo(n)%jprod_sidet = 0.0 + allocate(zoo(n)%jprod_sio4(isd:ied,jsd:jed,nk)) ; zoo(n)%jprod_sio4 = 0.0 + allocate(zoo(n)%jprod_po4(isd:ied,jsd:jed,nk)) ; zoo(n)%jprod_po4 = 0.0 + allocate(zoo(n)%jprod_nh4(isd:ied,jsd:jed,nk)) ; zoo(n)%jprod_nh4 = 0.0 + allocate(zoo(n)%jprod_n(isd:ied,jsd:jed,nk)) ; zoo(n)%jprod_n = 0.0 + allocate(zoo(n)%o2lim(isd:ied,jsd:jed,nk)) ; zoo(n)%o2lim = 0.0 + allocate(zoo(n)%temp_lim(isd:ied,jsd:jed,nk)) ; zoo(n)%temp_lim = 0.0 + enddo + + ! higher predator ingestion + allocate(cobalt%hp_jingest_n(isd:ied,jsd:jed,nk)) ; cobalt%hp_jingest_n = 0.0 + allocate(cobalt%hp_jingest_p(isd:ied,jsd:jed,nk)) ; cobalt%hp_jingest_p = 0.0 + allocate(cobalt%hp_jingest_sio2(isd:ied,jsd:jed,nk)) ; cobalt%hp_jingest_sio2 = 0.0 + allocate(cobalt%hp_jingest_fe(isd:ied,jsd:jed,nk)) ; cobalt%hp_jingest_fe = 0.0 + + allocate(cobalt%f_alk(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_alk=0.0 + allocate(cobalt%f_cadet_arag(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_cadet_arag=0.0 + allocate(cobalt%f_cadet_calc(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_cadet_calc=0.0 + allocate(cobalt%f_dic(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_dic=0.0 + allocate(cobalt%f_fed(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_fed=0.0 + allocate(cobalt%f_fedet(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_fedet=0.0 + allocate(cobalt%f_ldon(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_ldon=0.0 + allocate(cobalt%f_ldop(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_ldop=0.0 + allocate(cobalt%f_lith(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_lith=0.0 + allocate(cobalt%f_lithdet(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_lithdet=0.0 + allocate(cobalt%f_ndet(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_ndet=0.0 + allocate(cobalt%f_nh4(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_nh4=0.0 + allocate(cobalt%f_no3(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_no3=0.0 + allocate(cobalt%f_o2(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_o2=0.0 + allocate(cobalt%f_pdet(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_pdet=0.0 + allocate(cobalt%f_po4(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_po4=0.0 + allocate(cobalt%f_srdon(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_srdon=0.0 + allocate(cobalt%f_srdop(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_srdop=0.0 + allocate(cobalt%f_sldon(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_sldon=0.0 + allocate(cobalt%f_sldop(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_sldop=0.0 + allocate(cobalt%f_sidet(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_sidet=0.0 + allocate(cobalt%f_silg(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_silg=0.0 + allocate(cobalt%f_sio4(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_sio4=0.0 + allocate(cobalt%co3_sol_arag(isd:ied, jsd:jed, 1:nk)) ; cobalt%co3_sol_arag=0.0 + allocate(cobalt%co3_sol_calc(isd:ied, jsd:jed, 1:nk)) ; cobalt%co3_sol_calc=0.0 + allocate(cobalt%f_chl(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_chl=0.0 + if (do_nh3_diag) allocate(cobalt%f_nh3(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_nh3=0.0 + allocate(cobalt%f_co3_ion(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_co3_ion=0.0 + allocate(cobalt%f_htotal(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_htotal=0.0 + allocate(cobalt%f_irr_mem(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_irr_mem=0.0 + allocate(cobalt%f_cased(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_cased=0.0 + allocate(cobalt%f_cadet_arag_btf(isd:ied, jsd:jed, 1:nk)); cobalt%f_cadet_arag_btf=0.0 + allocate(cobalt%f_cadet_calc_btf(isd:ied, jsd:jed, 1:nk)); cobalt%f_cadet_calc_btf=0.0 + allocate(cobalt%f_fedet_btf(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_fedet_btf=0.0 + allocate(cobalt%f_lithdet_btf(isd:ied, jsd:jed, 1:nk)); cobalt%f_lithdet_btf=0.0 + allocate(cobalt%f_ndet_btf(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_ndet_btf=0.0 + allocate(cobalt%f_pdet_btf(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_pdet_btf=0.0 + allocate(cobalt%f_sidet_btf(isd:ied, jsd:jed, 1:nk)) ; cobalt%f_sidet_btf=0.0 + + allocate(cobalt%jnbact(isd:ied, jsd:jed, 1:nk)) ; cobalt%jnbact=0.0 + allocate(cobalt%jndi(isd:ied, jsd:jed, 1:nk)) ; cobalt%jndi=0.0 + allocate(cobalt%jnsm(isd:ied, jsd:jed, 1:nk)) ; cobalt%jnsm=0.0 + allocate(cobalt%jnlg(isd:ied, jsd:jed, 1:nk)) ; cobalt%jnlg=0.0 + allocate(cobalt%jnsmz(isd:ied, jsd:jed, 1:nk)) ; cobalt%jnsmz=0.0 + allocate(cobalt%jnmdz(isd:ied, jsd:jed, 1:nk)) ; cobalt%jnmdz=0.0 + allocate(cobalt%jnlgz(isd:ied, jsd:jed, 1:nk)) ; cobalt%jnlgz=0.0 + allocate(cobalt%jalk(isd:ied, jsd:jed, 1:nk)) ; cobalt%jalk=0.0 + allocate(cobalt%jalk_plus_btm(isd:ied, jsd:jed, 1:nk)); cobalt%jalk_plus_btm=0.0 + allocate(cobalt%jcadet_arag(isd:ied, jsd:jed, 1:nk)) ; cobalt%jcadet_arag=0.0 + allocate(cobalt%jcadet_calc(isd:ied, jsd:jed, 1:nk)) ; cobalt%jcadet_calc=0.0 + allocate(cobalt%jdic(isd:ied, jsd:jed, 1:nk)) ; cobalt%jdic=0.0 + allocate(cobalt%jdic_plus_btm(isd:ied, jsd:jed, 1:nk)); cobalt%jdic_plus_btm=0.0 + allocate(cobalt%jdin_plus_btm(isd:ied, jsd:jed, 1:nk)); cobalt%jdin_plus_btm=0.0 + allocate(cobalt%jfed(isd:ied, jsd:jed, 1:nk)) ; cobalt%jfed=0.0 + allocate(cobalt%jfed_plus_btm(isd:ied, jsd:jed, 1:nk)); cobalt%jfed_plus_btm=0.0 + allocate(cobalt%jfedi(isd:ied, jsd:jed, 1:nk)) ; cobalt%jfedi=0.0 + allocate(cobalt%jfelg(isd:ied, jsd:jed, 1:nk)) ; cobalt%jfelg=0.0 + allocate(cobalt%jfesm(isd:ied, jsd:jed, 1:nk)) ; cobalt%jfesm=0.0 + allocate(cobalt%jfedet(isd:ied, jsd:jed, 1:nk)) ; cobalt%jfedet=0.0 + allocate(cobalt%jldon(isd:ied, jsd:jed, 1:nk)) ; cobalt%jldon=0.0 + allocate(cobalt%jldop(isd:ied, jsd:jed, 1:nk)) ; cobalt%jldop=0.0 + allocate(cobalt%jlith(isd:ied, jsd:jed, 1:nk)) ; cobalt%jlith=0.0 + allocate(cobalt%jlithdet(isd:ied, jsd:jed, 1:nk)) ; cobalt%jlithdet=0.0 + allocate(cobalt%jndet(isd:ied, jsd:jed, 1:nk)) ; cobalt%jndet=0.0 + allocate(cobalt%jnh4(isd:ied, jsd:jed, 1:nk)) ; cobalt%jnh4=0.0 + allocate(cobalt%jnh4_plus_btm(isd:ied, jsd:jed, 1:nk)); cobalt%jnh4_plus_btm=0.0 + allocate(cobalt%jno3(isd:ied, jsd:jed, 1:nk)) ; cobalt%jno3=0.0 + allocate(cobalt%jno3_plus_btm(isd:ied, jsd:jed, 1:nk)); cobalt%jno3_plus_btm=0.0 + allocate(cobalt%jo2(isd:ied, jsd:jed, 1:nk)) ; cobalt%jo2=0.0 + allocate(cobalt%jo2_plus_btm(isd:ied, jsd:jed, 1:nk)) ; cobalt%jo2_plus_btm=0.0 + allocate(cobalt%jpdet(isd:ied, jsd:jed, 1:nk)) ; cobalt%jpdet=0.0 + allocate(cobalt%jpo4(isd:ied, jsd:jed, 1:nk)) ; cobalt%jpo4=0.0 + allocate(cobalt%jpo4_plus_btm(isd:ied, jsd:jed, 1:nk)); cobalt%jpo4_plus_btm=0.0 + allocate(cobalt%jsrdon(isd:ied, jsd:jed, 1:nk)) ; cobalt%jsrdon=0.0 + allocate(cobalt%jsrdop(isd:ied, jsd:jed, 1:nk)) ; cobalt%jsrdop=0.0 + allocate(cobalt%jsldon(isd:ied, jsd:jed, 1:nk)) ; cobalt%jsldon=0.0 + allocate(cobalt%jsldop(isd:ied, jsd:jed, 1:nk)) ; cobalt%jsldop=0.0 + allocate(cobalt%jsidet(isd:ied, jsd:jed, 1:nk)) ; cobalt%jsidet=0.0 + allocate(cobalt%jsilg(isd:ied, jsd:jed, 1:nk)) ; cobalt%jsilg=0.0 + allocate(cobalt%jsio4(isd:ied, jsd:jed, 1:nk)) ; cobalt%jsio4=0.0 + allocate(cobalt%jsio4_plus_btm(isd:ied, jsd:jed, 1:nk)); cobalt%jsio4_plus_btm=0.0 + allocate(cobalt%jprod_fed(isd:ied, jsd:jed, 1:nk)) ; cobalt%jprod_fed=0.0 + allocate(cobalt%jprod_fedet(isd:ied, jsd:jed, 1:nk)) ; cobalt%jprod_fedet=0.0 + allocate(cobalt%jprod_ndet(isd:ied, jsd:jed, 1:nk)) ; cobalt%jprod_ndet=0.0 + allocate(cobalt%jprod_pdet(isd:ied, jsd:jed, 1:nk)) ; cobalt%jprod_pdet=0.0 + allocate(cobalt%jprod_ldon(isd:ied, jsd:jed, 1:nk)) ; cobalt%jprod_ldon=0.0 + allocate(cobalt%jprod_ldop(isd:ied, jsd:jed, 1:nk)) ; cobalt%jprod_ldop=0.0 + allocate(cobalt%jprod_sldon(isd:ied, jsd:jed, 1:nk)) ; cobalt%jprod_sldon=0.0 + allocate(cobalt%jprod_sldop(isd:ied, jsd:jed, 1:nk)) ; cobalt%jprod_sldop=0.0 + allocate(cobalt%jprod_srdon(isd:ied, jsd:jed, 1:nk)) ; cobalt%jprod_srdon=0.0 + allocate(cobalt%jprod_srdop(isd:ied, jsd:jed, 1:nk)) ; cobalt%jprod_srdop=0.0 + allocate(cobalt%jprod_sidet(isd:ied, jsd:jed, 1:nk)) ; cobalt%jprod_sidet=0.0 + allocate(cobalt%jprod_sio4(isd:ied, jsd:jed, 1:nk)) ; cobalt%jprod_sio4=0.0 + allocate(cobalt%jprod_lithdet(isd:ied, jsd:jed, 1:nk)); cobalt%jprod_lithdet=0.0 + allocate(cobalt%jprod_cadet_arag(isd:ied, jsd:jed, 1:nk)); cobalt%jprod_cadet_arag=0.0 + allocate(cobalt%jprod_cadet_calc(isd:ied, jsd:jed, 1:nk)); cobalt%jprod_cadet_calc=0.0 + allocate(cobalt%jprod_nh4(isd:ied, jsd:jed, 1:nk)) ; cobalt%jprod_nh4=0.0 + allocate(cobalt%jprod_nh4_plus_btm(isd:ied, jsd:jed, 1:nk)) ; cobalt%jprod_nh4_plus_btm=0.0 + allocate(cobalt%jprod_po4(isd:ied, jsd:jed, 1:nk)) ; cobalt%jprod_po4=0.0 + allocate(cobalt%det_jzloss_n(isd:ied, jsd:jed, 1:nk)) ; cobalt%det_jzloss_n=0.0 + allocate(cobalt%det_jzloss_p(isd:ied, jsd:jed, 1:nk)) ; cobalt%det_jzloss_p=0.0 + allocate(cobalt%det_jzloss_fe(isd:ied, jsd:jed, 1:nk)); cobalt%det_jzloss_fe=0.0 + allocate(cobalt%det_jzloss_si(isd:ied, jsd:jed, 1:nk)); cobalt%det_jzloss_si=0.0 + allocate(cobalt%det_jhploss_n(isd:ied, jsd:jed, 1:nk)); cobalt%det_jhploss_n=0.0 + allocate(cobalt%det_jhploss_p(isd:ied, jsd:jed, 1:nk)); cobalt%det_jhploss_p=0.0 + allocate(cobalt%det_jhploss_fe(isd:ied, jsd:jed, 1:nk)); cobalt%det_jhploss_fe=0.0 + allocate(cobalt%det_jhploss_si(isd:ied, jsd:jed, 1:nk)); cobalt%det_jhploss_si=0.0 + allocate(cobalt%jdiss_cadet_arag(isd:ied, jsd:jed, 1:nk)); cobalt%jdiss_cadet_arag=0.0 + allocate(cobalt%jdiss_cadet_arag_plus_btm(isd:ied, jsd:jed, 1:nk)); cobalt%jdiss_cadet_arag_plus_btm=0.0 + allocate(cobalt%jdiss_cadet_calc(isd:ied, jsd:jed, 1:nk)); cobalt%jdiss_cadet_calc=0.0 + allocate(cobalt%jdiss_cadet_calc_plus_btm(isd:ied, jsd:jed, 1:nk)); cobalt%jdiss_cadet_calc_plus_btm=0.0 + allocate(cobalt%jdiss_sidet(isd:ied, jsd:jed, 1:nk)) ; cobalt%jdiss_sidet=0.0 + allocate(cobalt%jremin_ndet(isd:ied, jsd:jed, 1:nk)) ; cobalt%jremin_ndet=0.0 + allocate(cobalt%jremin_pdet(isd:ied, jsd:jed, 1:nk)) ; cobalt%jremin_pdet=0.0 + allocate(cobalt%jremin_fedet(isd:ied, jsd:jed, 1:nk)) ; cobalt%jremin_fedet=0.0 + allocate(cobalt%jfe_ads(isd:ied, jsd:jed, 1:nk)) ; cobalt%jfe_ads=0.0 + allocate(cobalt%jfe_coast(isd:ied, jsd:jed, 1:nk)) ; cobalt%jfe_coast=0.0 + allocate(cobalt%kfe_eq_lig(isd:ied, jsd:jed, 1:nk)) ; cobalt%kfe_eq_lig=0.0 + allocate(cobalt%feprime(isd:ied, jsd:jed, 1:nk)) ; cobalt%feprime=0.0 + allocate(cobalt%ligand(isd:ied, jsd:jed, 1:nk)) ; cobalt%ligand=0.0 + allocate(cobalt%fe_sol(isd:ied, jsd:jed, 1:nk)) ; cobalt%fe_sol=0.0 + allocate(cobalt%expkT(isd:ied, jsd:jed, 1:nk)) ; cobalt%expkT=0.0 + allocate(cobalt%expkreminT(isd:ied, jsd:jed, 1:nk)) ; cobalt%expkreminT=0.0 + allocate(cobalt%hp_o2lim(isd:ied, jsd:jed, 1:nk)) ; cobalt%hp_o2lim=0.0 + allocate(cobalt%hp_temp_lim(isd:ied, jsd:jed, 1:nk)) ; cobalt%hp_temp_lim=0.0 + allocate(cobalt%irr_inst(isd:ied, jsd:jed, 1:nk)) ; cobalt%irr_inst=0.0 + allocate(cobalt%irr_mix(isd:ied, jsd:jed, 1:nk)) ; cobalt%irr_mix=0.0 + allocate(cobalt%jno3denit_wc(isd:ied, jsd:jed, 1:nk)) ; cobalt%jno3denit_wc=0.0 + allocate(cobalt%jo2resp_wc(isd:ied, jsd:jed, 1:nk)) ; cobalt%jo2resp_wc=0.0 + allocate(cobalt%jnitrif(isd:ied, jsd:jed, 1:nk)) ; cobalt%jnitrif=0.0 + allocate(cobalt%omega_arag(isd:ied, jsd:jed, 1:nk)) ; cobalt%omega_arag=0.0 + allocate(cobalt%omega_calc(isd:ied, jsd:jed, 1:nk)) ; cobalt%omega_calc=0.0 + allocate(cobalt%omegaa(isd:ied, jsd:jed, 1:nk)) ; cobalt%omegaa=0.0 + allocate(cobalt%omegac(isd:ied, jsd:jed, 1:nk)) ; cobalt%omegac=0.0 + allocate(cobalt%tot_layer_int_c(isd:ied, jsd:jed,1:nk)) ; cobalt%tot_layer_int_c=0.0 + allocate(cobalt%tot_layer_int_fe(isd:ied, jsd:jed,1:nk)) ; cobalt%tot_layer_int_fe=0.0 + allocate(cobalt%tot_layer_int_n(isd:ied, jsd:jed, 1:nk)) ; cobalt%tot_layer_int_n=0.0 + allocate(cobalt%tot_layer_int_p(isd:ied, jsd:jed, 1:nk)) ; cobalt%tot_layer_int_p=0.0 + allocate(cobalt%tot_layer_int_si(isd:ied, jsd:jed, 1:nk)); cobalt%tot_layer_int_si=0.0 + allocate(cobalt%tot_layer_int_o2(isd:ied, jsd:jed, 1:nk)); cobalt%tot_layer_int_o2=0.0 + allocate(cobalt%tot_layer_int_alk(isd:ied, jsd:jed, 1:nk)); cobalt%tot_layer_int_alk=0.0 + allocate(cobalt%total_filter_feeding(isd:ied,jsd:jed,1:nk)); cobalt%total_filter_feeding=0.0 + allocate(cobalt%nlg_diatoms(isd:ied, jsd:jed, 1:nk)); cobalt%nlg_diatoms=0.0 + allocate(cobalt%q_si_2_n_lg_diatoms(isd:ied, jsd:jed, 1:nk)); cobalt%q_si_2_n_lg_diatoms=0.0 + allocate(cobalt%zt(isd:ied, jsd:jed, 1:nk)) ; cobalt%zt=0.0 + allocate(cobalt%zm(isd:ied, jsd:jed, 1:nk)) ; cobalt%zm=0.0 + allocate(cobalt%b_alk(isd:ied, jsd:jed)) ; cobalt%b_alk=0.0 + allocate(cobalt%b_dic(isd:ied, jsd:jed)) ; cobalt%b_dic=0.0 + allocate(cobalt%b_fed(isd:ied, jsd:jed)) ; cobalt%b_fed=0.0 + allocate(cobalt%b_nh4(isd:ied, jsd:jed)) ; cobalt%b_nh4=0.0 + allocate(cobalt%b_no3(isd:ied, jsd:jed)) ; cobalt%b_no3=0.0 + allocate(cobalt%b_o2(isd:ied, jsd:jed)) ; cobalt%b_o2=0.0 + allocate(cobalt%b_po4(isd:ied, jsd:jed)) ; cobalt%b_po4=0.0 + allocate(cobalt%b_sio4(isd:ied, jsd:jed)) ; cobalt%b_sio4=0.0 + allocate(cobalt%pco2_csurf(isd:ied, jsd:jed)) ; cobalt%pco2_csurf=0.0 + allocate(cobalt%co2_csurf(isd:ied, jsd:jed)) ; cobalt%co2_csurf=0.0 + allocate(cobalt%co2_alpha(isd:ied, jsd:jed)) ; cobalt%co2_alpha=0.0 + allocate(cobalt%nh3_csurf(isd:ied, jsd:jed)) ; cobalt%nh3_csurf=0.0 + allocate(cobalt%nh3_alpha(isd:ied, jsd:jed)) ; cobalt%nh3_alpha=0.0 + allocate(cobalt%pnh3_csurf(isd:ied, jsd:jed)) ; cobalt%pnh3_csurf=0.0 + allocate(cobalt%fcadet_arag_btm(isd:ied, jsd:jed)) ; cobalt%fcadet_arag_btm=0.0 + allocate(cobalt%fcadet_calc_btm(isd:ied, jsd:jed)) ; cobalt%fcadet_calc_btm=0.0 + allocate(cobalt%ffedet_btm(isd:ied, jsd:jed)) ; cobalt%ffedet_btm=0.0 + allocate(cobalt%flithdet_btm(isd:ied, jsd:jed)) ; cobalt%flithdet_btm=0.0 + allocate(cobalt%fpdet_btm(isd:ied, jsd:jed)) ; cobalt%fpdet_btm=0.0 + allocate(cobalt%fndet_btm(isd:ied, jsd:jed)) ; cobalt%fndet_btm=0.0 + allocate(cobalt%fsidet_btm(isd:ied, jsd:jed)) ; cobalt%fsidet_btm=0.0 + allocate(cobalt%fcased_burial(isd:ied, jsd:jed)) ; cobalt%fcased_burial=0.0 + allocate(cobalt%fcased_redis(isd:ied, jsd:jed)) ; cobalt%fcased_redis=0.0 + allocate(cobalt%fcased_redis_surfresp(isd:ied, jsd:jed)) ; cobalt%fcased_redis_surfresp=0.0 + allocate(cobalt%cased_redis_coef(isd:ied, jsd:jed)) ; cobalt%cased_redis_coef=0.0 + allocate(cobalt%cased_redis_delz(isd:ied, jsd:jed)) ; cobalt%cased_redis_delz=0.0 + allocate(cobalt%ffe_sed(isd:ied, jsd:jed)) ; cobalt%ffe_sed=0.0 + allocate(cobalt%ffe_geotherm(isd:ied, jsd:jed)) ; cobalt%ffe_geotherm=0.0 + allocate(cobalt%ffe_iceberg(isd:ied, jsd:jed)) ; cobalt%ffe_iceberg=0.0 + allocate(cobalt%fnfeso4red_sed(isd:ied, jsd:jed)) ; cobalt%fnfeso4red_sed=0.0 + allocate(cobalt%fno3denit_sed(isd:ied, jsd:jed)) ; cobalt%fno3denit_sed=0.0 + allocate(cobalt%fnoxic_sed(isd:ied, jsd:jed)) ; cobalt%fnoxic_sed=0.0 + allocate(cobalt%frac_burial(isd:ied, jsd:jed)) ; cobalt%frac_burial=0.0 + allocate(cobalt%fndet_burial(isd:ied, jsd:jed)) ; cobalt%fndet_burial=0.0 + allocate(cobalt%fpdet_burial(isd:ied, jsd:jed)) ; cobalt%fpdet_burial=0.0 +!============================================================================================================== +! JGJ 2016/08/08 CMIP6 OcnBgchem + allocate(cobalt%dissoc(isd:ied, jsd:jed, 1:nk)) ; cobalt%dissoc=0.0 + allocate(cobalt%o2sat(isd:ied, jsd:jed, 1:nk)) ; cobalt%o2sat=0.0 + allocate(cobalt%remoc(isd:ied, jsd:jed, 1:nk)) ; cobalt%remoc=0.0 + allocate(cobalt%tot_layer_int_doc(isd:ied, jsd:jed, 1:nk)); cobalt%tot_layer_int_doc=0.0 + allocate(cobalt%tot_layer_int_poc(isd:ied, jsd:jed, 1:nk)); cobalt%tot_layer_int_poc=0.0 + allocate(cobalt%tot_layer_int_dic(isd:ied, jsd:jed, 1:nk)); cobalt%tot_layer_int_dic=0.0 + allocate(cobalt%f_alk_int_100(isd:ied, jsd:jed)) ; cobalt%f_alk_int_100=0.0 + allocate(cobalt%f_dic_int_100(isd:ied, jsd:jed)) ; cobalt%f_dic_int_100=0.0 + allocate(cobalt%f_din_int_100(isd:ied, jsd:jed)) ; cobalt%f_din_int_100=0.0 + allocate(cobalt%f_fed_int_100(isd:ied, jsd:jed)) ; cobalt%f_fed_int_100=0.0 + allocate(cobalt%f_po4_int_100(isd:ied, jsd:jed)) ; cobalt%f_po4_int_100=0.0 + allocate(cobalt%f_sio4_int_100(isd:ied, jsd:jed)) ; cobalt%f_sio4_int_100=0.0 + allocate(cobalt%jalk_100(isd:ied, jsd:jed)) ; cobalt%jalk_100=0.0 + allocate(cobalt%jdic_100(isd:ied, jsd:jed)) ; cobalt%jdic_100=0.0 + allocate(cobalt%jdin_100(isd:ied, jsd:jed)) ; cobalt%jdin_100=0.0 + allocate(cobalt%jfed_100(isd:ied, jsd:jed)) ; cobalt%jfed_100=0.0 + allocate(cobalt%jpo4_100(isd:ied, jsd:jed)) ; cobalt%jpo4_100=0.0 + allocate(cobalt%jsio4_100(isd:ied, jsd:jed)) ; cobalt%jsio4_100=0.0 + allocate(cobalt%jprod_ptot_100(isd:ied, jsd:jed)) ; cobalt%jprod_ptot_100=0.0 + allocate(cobalt%wc_vert_int_c(isd:ied, jsd:jed)) ; cobalt%wc_vert_int_c=0.0 + allocate(cobalt%wc_vert_int_dic(isd:ied, jsd:jed)) ; cobalt%wc_vert_int_dic=0.0 + allocate(cobalt%wc_vert_int_doc(isd:ied, jsd:jed)) ; cobalt%wc_vert_int_doc=0.0 + allocate(cobalt%wc_vert_int_poc(isd:ied, jsd:jed)) ; cobalt%wc_vert_int_poc=0.0 + allocate(cobalt%wc_vert_int_n(isd:ied, jsd:jed)) ; cobalt%wc_vert_int_n=0.0 + allocate(cobalt%wc_vert_int_p(isd:ied, jsd:jed)) ; cobalt%wc_vert_int_p=0.0 + allocate(cobalt%wc_vert_int_fe(isd:ied, jsd:jed)) ; cobalt%wc_vert_int_fe=0.0 + allocate(cobalt%wc_vert_int_si(isd:ied, jsd:jed)) ; cobalt%wc_vert_int_si=0.0 + allocate(cobalt%wc_vert_int_o2(isd:ied, jsd:jed)) ; cobalt%wc_vert_int_o2=0.0 + allocate(cobalt%wc_vert_int_alk(isd:ied, jsd:jed)) ; cobalt%wc_vert_int_alk=0.0 + allocate(cobalt%wc_vert_int_jdiss_sidet(isd:ied, jsd:jed)) ; cobalt%wc_vert_int_jdiss_sidet=0.0 + allocate(cobalt%wc_vert_int_jdiss_cadet(isd:ied, jsd:jed)) ; cobalt%wc_vert_int_jdiss_cadet=0.0 + allocate(cobalt%wc_vert_int_jo2resp(isd:ied, jsd:jed)) ; cobalt%wc_vert_int_jo2resp=0.0 + allocate(cobalt%wc_vert_int_jprod_cadet(isd:ied, jsd:jed)) ; cobalt%wc_vert_int_jprod_cadet=0.0 + allocate(cobalt%wc_vert_int_jno3denit(isd:ied, jsd:jed)) ; cobalt%wc_vert_int_jno3denit=0.0 + allocate(cobalt%wc_vert_int_jnitrif(isd:ied, jsd:jed)) ; cobalt%wc_vert_int_jnitrif=0.0 + allocate(cobalt%wc_vert_int_juptake_nh4(isd:ied, jsd:jed)) ; cobalt%wc_vert_int_juptake_nh4=0.0 + allocate(cobalt%wc_vert_int_jprod_nh4(isd:ied, jsd:jed)) ; cobalt%wc_vert_int_jprod_nh4=0.0 + allocate(cobalt%wc_vert_int_juptake_no3(isd:ied, jsd:jed)) ; cobalt%wc_vert_int_juptake_no3=0.0 + allocate(cobalt%wc_vert_int_nfix(isd:ied, jsd:jed)) ; cobalt%wc_vert_int_nfix=0.0 +!============================================================================================================== + ! + ! allocate 100m integrated quantities + ! + do n = 1, NUM_PHYTO + allocate(phyto(n)%jprod_n_100(isd:ied,jsd:jed)) ; phyto(n)%jprod_n_100 = 0.0 + allocate(phyto(n)%jprod_n_new_100(isd:ied,jsd:jed)) ; phyto(n)%jprod_n_new_100 = 0.0 + allocate(phyto(n)%jzloss_n_100(isd:ied,jsd:jed)) ; phyto(n)%jzloss_n_100 = 0.0 + allocate(phyto(n)%jexuloss_n_100(isd:ied,jsd:jed)) ; phyto(n)%jexuloss_n_100 = 0.0 + allocate(phyto(n)%f_n_100(isd:ied,jsd:jed)) ; phyto(n)%f_n_100 = 0.0 + allocate(phyto(n)%juptake_fe_100(isd:ied,jsd:jed)) ; phyto(n)%juptake_fe_100 = 0.0 + allocate(phyto(n)%juptake_po4_100(isd:ied,jsd:jed)) ; phyto(n)%juptake_po4_100 = 0.0 + ! Biomass-weighted limitation terms (for cmip) + allocate(phyto(n)%nlim_bw_100(isd:ied,jsd:jed)) ; phyto(n)%nlim_bw_100 = 0.0 + allocate(phyto(n)%plim_bw_100(isd:ied,jsd:jed)) ; phyto(n)%plim_bw_100 = 0.0 + allocate(phyto(n)%irrlim_bw_100(isd:ied,jsd:jed)) ; phyto(n)%irrlim_bw_100 = 0.0 + allocate(phyto(n)%def_fe_bw_100(isd:ied,jsd:jed)) ; phyto(n)%def_fe_bw_100 = 0.0 + enddo + allocate(phyto(DIAZO)%jprod_n_n2_100(isd:ied,jsd:jed)); phyto(DIAZO)%jprod_n_n2_100 = 0.0 + allocate(phyto(SMALL)%jvirloss_n_100(isd:ied,jsd:jed)) ; phyto(SMALL)%jvirloss_n_100 = 0.0 + allocate(phyto(SMALL)%jaggloss_n_100(isd:ied,jsd:jed)) ; phyto(SMALL)%jaggloss_n_100 = 0.0 + allocate(phyto(LARGE)%jaggloss_n_100(isd:ied,jsd:jed)) ; phyto(LARGE)%jaggloss_n_100 = 0.0 + allocate(cobalt%jprod_allphytos_100(isd:ied,jsd:jed)) ; cobalt%jprod_allphytos_100 = 0.0 + allocate(cobalt%jprod_diat_100(isd:ied,jsd:jed)) ; cobalt%jprod_diat_100 = 0.0 + allocate(phyto(LARGE)%juptake_sio4_100(isd:ied,jsd:jed)) ; phyto(LARGE)%juptake_sio4_100 = 0.0 + + do n = 1, NUM_ZOO + allocate(zoo(n)%jprod_n_100(isd:ied,jsd:jed)) ; zoo(n)%jprod_n_100 = 0.0 + allocate(zoo(n)%jingest_n_100(isd:ied,jsd:jed)) ; zoo(n)%jingest_n_100 = 0.0 + allocate(zoo(n)%jremin_n_100(isd:ied,jsd:jed)) ; zoo(n)%jremin_n_100 = 0.0 + allocate(zoo(n)%f_n_100(isd:ied,jsd:jed)) ; zoo(n)%f_n_100 = 0.0 + enddo + + do n = 1,2 + allocate(zoo(n)%jzloss_n_100(isd:ied,jsd:jed)) ; zoo(n)%jzloss_n_100 = 0.0 + allocate(zoo(n)%jprod_don_100(isd:ied,jsd:jed)) ; zoo(n)%jprod_don_100 = 0.0 + enddo + + do n = 2,3 + allocate(zoo(n)%jhploss_n_100(isd:ied,jsd:jed)) ; zoo(n)%jhploss_n_100 = 0.0 + allocate(zoo(n)%jprod_ndet_100(isd:ied,jsd:jed)) ; zoo(n)%jprod_ndet_100 = 0.0 + enddo + + allocate(cobalt%hp_jingest_n_100(isd:ied,jsd:jed)) ; cobalt%hp_jingest_n_100 = 0.0 + allocate(cobalt%hp_jremin_n_100(isd:ied,jsd:jed)) ; cobalt%hp_jremin_n_100 = 0.0 + allocate(cobalt%hp_jprod_ndet_100(isd:ied,jsd:jed)) ; cobalt%hp_jprod_ndet_100 = 0.0 + + allocate(bact(1)%jprod_n_100(isd:ied,jsd:jed)) ; bact(1)%jprod_n_100 = 0.0 + allocate(bact(1)%jzloss_n_100(isd:ied,jsd:jed)) ; bact(1)%jzloss_n_100 = 0.0 + allocate(bact(1)%jvirloss_n_100(isd:ied,jsd:jed)); bact(1)%jvirloss_n_100 = 0.0 + allocate(bact(1)%jremin_n_100(isd:ied,jsd:jed)) ; bact(1)%jremin_n_100 = 0.0 + allocate(bact(1)%juptake_ldon_100(isd:ied,jsd:jed)) ; bact(1)%juptake_ldon_100 = 0.0 + allocate(bact(1)%f_n_100(isd:ied,jsd:jed)) ; bact(1)%f_n_100 = 0.0 + + allocate(cobalt%jprod_lithdet_100(isd:ied,jsd:jed)) ; cobalt%jprod_lithdet_100 = 0.0 + allocate(cobalt%jprod_sidet_100(isd:ied,jsd:jed)) ; cobalt%jprod_sidet_100 = 0.0 + allocate(cobalt%jprod_cadet_calc_100(isd:ied,jsd:jed)) ; cobalt%jprod_cadet_calc_100 = 0.0 + allocate(cobalt%jprod_cadet_arag_100(isd:ied,jsd:jed)) ; cobalt%jprod_cadet_arag_100 = 0.0 + allocate(cobalt%jremin_ndet_100(isd:ied,jsd:jed)) ; cobalt%jremin_ndet_100 = 0.0 + allocate(cobalt%jprod_mesozoo_200(isd:ied,jsd:jed)) ; cobalt%jprod_mesozoo_200 = 0.0 + + allocate(cobalt%f_ndet_100(isd:ied,jsd:jed)) ; cobalt%f_ndet_100 = 0.0 + allocate(cobalt%f_don_100(isd:ied,jsd:jed)) ; cobalt%f_don_100 = 0.0 + allocate(cobalt%f_silg_100(isd:ied,jsd:jed)) ; cobalt%f_silg_100 = 0.0 + allocate(cobalt%f_mesozoo_200(isd:ied,jsd:jed)) ; cobalt%f_mesozoo_200 = 0.0 + + allocate(cobalt%fndet_100(isd:ied,jsd:jed)) ; cobalt%fndet_100 = 0.0 + allocate(cobalt%fpdet_100(isd:ied,jsd:jed)) ; cobalt%fpdet_100 = 0.0 + allocate(cobalt%fsidet_100(isd:ied,jsd:jed)) ; cobalt%fsidet_100 = 0.0 + allocate(cobalt%flithdet_100(isd:ied,jsd:jed)) ; cobalt%flithdet_100 = 0.0 + allocate(cobalt%fcadet_calc_100(isd:ied,jsd:jed)) ; cobalt%fcadet_calc_100 = 0.0 + allocate(cobalt%fcadet_arag_100(isd:ied,jsd:jed)) ; cobalt%fcadet_arag_100 = 0.0 + allocate(cobalt%ffedet_100(isd:ied,jsd:jed)) ; cobalt%ffedet_100 = 0.0 + + allocate(cobalt%btm_temp(isd:ied,jsd:jed)) ; cobalt%btm_temp = 0.0 + allocate(cobalt%btm_o2(isd:ied,jsd:jed)) ; cobalt%btm_o2 = 0.0 + allocate(cobalt%btm_htotal(isd:ied,jsd:jed)) ; cobalt%btm_htotal = 0.0 + allocate(cobalt%btm_co3_ion(isd:ied,jsd:jed)) ; cobalt%btm_co3_ion = 0.0 + allocate(cobalt%btm_co3_sol_arag(isd:ied,jsd:jed)) ; cobalt%btm_co3_sol_arag = 0.0 + allocate(cobalt%btm_co3_sol_calc(isd:ied,jsd:jed)) ; cobalt%btm_co3_sol_calc = 0.0 + allocate(cobalt%cased_2d(isd:ied,jsd:jed)) ; cobalt%cased_2d = 0.0 + + allocate(cobalt%o2min(isd:ied, jsd:jed)) ; cobalt%o2min=0.0 + allocate(cobalt%z_o2min(isd:ied, jsd:jed)) ; cobalt%z_o2min=0.0 + allocate(cobalt%z_sat_arag(isd:ied, jsd:jed)) ; cobalt%z_sat_arag=0.0 + allocate(cobalt%z_sat_calc(isd:ied, jsd:jed)) ; cobalt%z_sat_calc=0.0 + allocate(cobalt%mask_z_sat_arag(isd:ied, jsd:jed)) ; cobalt%mask_z_sat_arag = .FALSE. + allocate(cobalt%mask_z_sat_calc(isd:ied, jsd:jed)) ; cobalt%mask_z_sat_calc = .FALSE. + if (do_14c) then !<> + allocate(cobalt%runoff_flux_alk(isd:ied, jsd:jed)); cobalt%runoff_flux_alk=0.0 + allocate(cobalt%runoff_flux_dic(isd:ied, jsd:jed)); cobalt%runoff_flux_dic=0.0 + allocate(cobalt%runoff_flux_di14c(isd:ied, jsd:jed)); cobalt%runoff_flux_di14c=0.0 + allocate(cobalt%runoff_flux_lith(isd:ied, jsd:jed)); cobalt%runoff_flux_lith=0.0 + allocate(cobalt%runoff_flux_fed(isd:ied, jsd:jed)); cobalt%runoff_flux_fed=0.0 + allocate(cobalt%runoff_flux_no3(isd:ied, jsd:jed)); cobalt%runoff_flux_no3=0.0 + allocate(cobalt%runoff_flux_ldon(isd:ied, jsd:jed)); cobalt%runoff_flux_ldon=0.0 + allocate(cobalt%runoff_flux_sldon(isd:ied, jsd:jed)); cobalt%runoff_flux_sldon=0.0 + allocate(cobalt%runoff_flux_srdon(isd:ied, jsd:jed)); cobalt%runoff_flux_srdon=0.0 + allocate(cobalt%runoff_flux_ndet(isd:ied, jsd:jed)); cobalt%runoff_flux_ndet=0.0 + allocate(cobalt%runoff_flux_po4(isd:ied, jsd:jed)); cobalt%runoff_flux_po4=0.0 + allocate(cobalt%runoff_flux_ldop(isd:ied, jsd:jed)); cobalt%runoff_flux_ldop=0.0 + allocate(cobalt%runoff_flux_sldop(isd:ied, jsd:jed)); cobalt%runoff_flux_sldop=0.0 + allocate(cobalt%runoff_flux_srdop(isd:ied, jsd:jed)); cobalt%runoff_flux_srdop=0.0 + allocate(cobalt%dry_fed(isd:ied, jsd:jed)); cobalt%dry_fed=0.0 + allocate(cobalt%wet_fed(isd:ied, jsd:jed)); cobalt%wet_fed=0.0 + allocate(cobalt%dry_lith(isd:ied, jsd:jed)); cobalt%dry_lith=0.0 + allocate(cobalt%wet_lith(isd:ied, jsd:jed)); cobalt%wet_lith=0.0 + allocate(cobalt%dry_no3(isd:ied, jsd:jed)); cobalt%dry_no3=0.0 + allocate(cobalt%wet_no3(isd:ied, jsd:jed)); cobalt%wet_no3=0.0 + allocate(cobalt%dry_nh4(isd:ied, jsd:jed)); cobalt%dry_nh4=0.0 + allocate(cobalt%wet_nh4(isd:ied, jsd:jed)); cobalt%wet_nh4=0.0 + allocate(cobalt%dry_po4(isd:ied, jsd:jed)); cobalt%dry_po4=0.0 + allocate(cobalt%wet_po4(isd:ied, jsd:jed)); cobalt%wet_po4=0.0 + allocate(cobalt%stf_gas_dic(isd:ied, jsd:jed)); cobalt%stf_gas_dic=0.0 + allocate(cobalt%stf_gas_o2(isd:ied, jsd:jed)); cobalt%stf_gas_o2=0.0 + allocate(cobalt%deltap_dic(isd:ied, jsd:jed)); cobalt%deltap_dic=0.0 + allocate(cobalt%deltap_o2(isd:ied, jsd:jed)); cobalt%deltap_o2=0.0 + + + end subroutine user_allocate_arrays + + ! + ! This is an internal sub, not a public interface. + ! Deallocate all the work arrays allocated by user_allocate_arrays. + ! + subroutine user_deallocate_arrays + integer n + + deallocate(cobalt%htotalhi,cobalt%htotallo) + + do n = 1, NUM_PHYTO + deallocate(phyto(n)%def_fe) + deallocate(phyto(n)%def_p) + deallocate(phyto(n)%f_fe) + deallocate(phyto(n)%f_n) + deallocate(phyto(n)%felim) + deallocate(phyto(n)%irrlim) + deallocate(phyto(n)%jzloss_fe) + deallocate(phyto(n)%jzloss_n) + deallocate(phyto(n)%jzloss_p) + deallocate(phyto(n)%jzloss_sio2) + deallocate(phyto(n)%jaggloss_n) + deallocate(phyto(n)%jaggloss_p) + deallocate(phyto(n)%jaggloss_fe) + deallocate(phyto(n)%jaggloss_sio2) + deallocate(phyto(n)%jvirloss_n) + deallocate(phyto(n)%jvirloss_p) + deallocate(phyto(n)%jvirloss_fe) + deallocate(phyto(n)%jvirloss_sio2) + deallocate(phyto(n)%jexuloss_n) + deallocate(phyto(n)%jexuloss_p) + deallocate(phyto(n)%jexuloss_fe) + deallocate(phyto(n)%jhploss_fe) + deallocate(phyto(n)%jhploss_n) + deallocate(phyto(n)%jhploss_p) + deallocate(phyto(n)%juptake_fe) + deallocate(phyto(n)%juptake_nh4) + deallocate(phyto(n)%juptake_no3) + deallocate(phyto(n)%juptake_po4) + deallocate(phyto(n)%jprod_n) + deallocate(phyto(n)%liebig_lim) + deallocate(phyto(n)%mu) + deallocate(phyto(n)%po4lim) + deallocate(phyto(n)%q_fe_2_n) + deallocate(phyto(n)%q_p_2_n) + deallocate(phyto(n)%q_si_2_n) + deallocate(phyto(n)%theta) + deallocate(phyto(n)%f_mu_mem) + deallocate(phyto(n)%mu_mix) + deallocate(phyto(n)%agg_lim) + deallocate(phyto(n)%juptake_fe_100) + deallocate(phyto(n)%juptake_po4_100) + deallocate(phyto(n)%nh4lim) + deallocate(phyto(n)%no3lim) + deallocate(phyto(n)%nlim_bw_100) + deallocate(phyto(n)%plim_bw_100) + deallocate(phyto(n)%irrlim_bw_100) + deallocate(phyto(n)%def_fe_bw_100) + enddo + deallocate(phyto(DIAZO)%juptake_n2) + deallocate(phyto(DIAZO)%o2lim) + deallocate(phyto(LARGE)%juptake_sio4) + deallocate(phyto(LARGE)%juptake_sio4_100) + deallocate(phyto(LARGE)%silim) + + ! bacteria + deallocate(bact(1)%f_n) + deallocate(bact(1)%jzloss_n) + deallocate(bact(1)%jzloss_p) + deallocate(bact(1)%jvirloss_n) + deallocate(bact(1)%jvirloss_p) + deallocate(bact(1)%jhploss_n) + deallocate(bact(1)%jhploss_p) + deallocate(bact(1)%juptake_ldon) + deallocate(bact(1)%juptake_ldop) + deallocate(bact(1)%jprod_nh4) + deallocate(bact(1)%jprod_po4) + deallocate(bact(1)%jprod_n) + deallocate(bact(1)%o2lim) + deallocate(bact(1)%ldonlim) + deallocate(bact(1)%temp_lim) + + ! zooplankton + do n = 1, NUM_ZOO + deallocate(zoo(n)%f_n) + deallocate(zoo(n)%jzloss_n) + deallocate(zoo(n)%jzloss_p) + deallocate(zoo(n)%jhploss_n) + deallocate(zoo(n)%jhploss_p) + deallocate(zoo(n)%jingest_n) + deallocate(zoo(n)%jingest_p) + deallocate(zoo(n)%jingest_sio2) + deallocate(zoo(n)%jingest_fe) + deallocate(zoo(n)%jprod_ndet) + deallocate(zoo(n)%jprod_pdet) + deallocate(zoo(n)%jprod_ldon) + deallocate(zoo(n)%jprod_ldop) + deallocate(zoo(n)%jprod_srdon) + deallocate(zoo(n)%jprod_srdop) + deallocate(zoo(n)%jprod_sldon) + deallocate(zoo(n)%jprod_sldop) + deallocate(zoo(n)%jprod_fedet) + deallocate(zoo(n)%jprod_fed) + deallocate(zoo(n)%jprod_sidet) + deallocate(zoo(n)%jprod_sio4) + deallocate(zoo(n)%jprod_po4) + deallocate(zoo(n)%jprod_nh4) + deallocate(zoo(n)%jprod_n) + deallocate(zoo(n)%o2lim) + deallocate(zoo(n)%temp_lim) + enddo + + deallocate(cobalt%f_alk) + deallocate(cobalt%f_cadet_arag) + deallocate(cobalt%f_cadet_calc) + deallocate(cobalt%f_dic) + deallocate(cobalt%f_fed) + deallocate(cobalt%f_fedet) + deallocate(cobalt%f_ldon) + deallocate(cobalt%f_ldop) + deallocate(cobalt%f_lith) + deallocate(cobalt%f_lithdet) + deallocate(cobalt%f_ndet) + deallocate(cobalt%f_nh4) + deallocate(cobalt%f_no3) + deallocate(cobalt%f_o2) + deallocate(cobalt%f_pdet) + deallocate(cobalt%f_po4) + deallocate(cobalt%f_srdon) + deallocate(cobalt%f_srdop) + deallocate(cobalt%f_sldon) + deallocate(cobalt%f_sldop) + deallocate(cobalt%f_sidet) + deallocate(cobalt%f_silg) + deallocate(cobalt%f_sio4) + deallocate(cobalt%co3_sol_arag) + deallocate(cobalt%co3_sol_calc) + deallocate(cobalt%f_chl) + if (allocated(cobalt%f_nh3)) deallocate(cobalt%f_nh3) + deallocate(cobalt%f_co3_ion) + deallocate(cobalt%f_htotal) + deallocate(cobalt%f_irr_mem) + deallocate(cobalt%f_cased) + deallocate(cobalt%f_cadet_arag_btf) + deallocate(cobalt%f_cadet_calc_btf) + deallocate(cobalt%f_fedet_btf) + deallocate(cobalt%f_lithdet_btf) + deallocate(cobalt%f_ndet_btf) + deallocate(cobalt%f_pdet_btf) + deallocate(cobalt%f_sidet_btf) + deallocate(cobalt%jnbact) + deallocate(cobalt%jndi) + deallocate(cobalt%jnsm) + deallocate(cobalt%jnlg) + deallocate(cobalt%jnsmz) + deallocate(cobalt%jnmdz) + deallocate(cobalt%jnlgz) + deallocate(cobalt%jalk) + deallocate(cobalt%jalk_plus_btm) + deallocate(cobalt%jcadet_arag) + deallocate(cobalt%jcadet_calc) + deallocate(cobalt%jdic) + deallocate(cobalt%jdic_plus_btm) + deallocate(cobalt%jdin_plus_btm) + deallocate(cobalt%jfed) + deallocate(cobalt%jfed_plus_btm) + deallocate(cobalt%jfedi) + deallocate(cobalt%jfelg) + deallocate(cobalt%jfesm) + deallocate(cobalt%jfedet) + deallocate(cobalt%jldon) + deallocate(cobalt%jldop) + deallocate(cobalt%jlith) + deallocate(cobalt%jlithdet) + deallocate(cobalt%jndet) + deallocate(cobalt%jnh4) + deallocate(cobalt%jnh4_plus_btm) + deallocate(cobalt%jno3) + deallocate(cobalt%jno3_plus_btm) + deallocate(cobalt%jo2) + deallocate(cobalt%jo2_plus_btm) + deallocate(cobalt%jpdet) + deallocate(cobalt%jpo4) + deallocate(cobalt%jpo4_plus_btm) + deallocate(cobalt%jsrdon) + deallocate(cobalt%jsrdop) + deallocate(cobalt%jsldon) + deallocate(cobalt%jsldop) + deallocate(cobalt%jsidet) + deallocate(cobalt%jsilg) + deallocate(cobalt%jsio4) + deallocate(cobalt%jsio4_plus_btm) + deallocate(cobalt%jprod_ndet) + deallocate(cobalt%jprod_pdet) + deallocate(cobalt%jprod_ldon) + deallocate(cobalt%jprod_ldop) + deallocate(cobalt%jprod_sldop) + deallocate(cobalt%jprod_sldon) + deallocate(cobalt%jprod_srdon) + deallocate(cobalt%jprod_srdop) + deallocate(cobalt%jprod_fed) + deallocate(cobalt%jprod_fedet) + deallocate(cobalt%jprod_sidet) + deallocate(cobalt%jprod_sio4) + deallocate(cobalt%jprod_lithdet) + deallocate(cobalt%jprod_cadet_arag) + deallocate(cobalt%jprod_cadet_calc) + deallocate(cobalt%jprod_nh4) + deallocate(cobalt%jprod_nh4_plus_btm) + deallocate(cobalt%jprod_po4) + deallocate(cobalt%det_jzloss_n) + deallocate(cobalt%det_jzloss_p) + deallocate(cobalt%det_jzloss_fe) + deallocate(cobalt%det_jzloss_si) + deallocate(cobalt%det_jhploss_n) + deallocate(cobalt%det_jhploss_p) + deallocate(cobalt%det_jhploss_fe) + deallocate(cobalt%det_jhploss_si) + deallocate(cobalt%jdiss_cadet_arag) + deallocate(cobalt%jdiss_cadet_arag_plus_btm) + deallocate(cobalt%jdiss_cadet_calc) + deallocate(cobalt%jdiss_cadet_calc_plus_btm) + deallocate(cobalt%jdiss_sidet) + deallocate(cobalt%jremin_ndet) + deallocate(cobalt%jremin_pdet) + deallocate(cobalt%jremin_fedet) + deallocate(cobalt%jfe_ads) + deallocate(cobalt%jfe_coast) + deallocate(cobalt%kfe_eq_lig) + deallocate(cobalt%feprime) + deallocate(cobalt%ligand) + deallocate(cobalt%fe_sol) + deallocate(cobalt%expkT) + deallocate(cobalt%expkreminT) + deallocate(cobalt%hp_o2lim) + deallocate(cobalt%hp_temp_lim) + deallocate(cobalt%hp_jingest_n) + deallocate(cobalt%hp_jingest_p) + deallocate(cobalt%hp_jingest_sio2) + deallocate(cobalt%hp_jingest_fe) + deallocate(cobalt%irr_inst) + deallocate(cobalt%irr_mix) + deallocate(cobalt%jno3denit_wc) + deallocate(cobalt%jo2resp_wc) + deallocate(cobalt%jnitrif) + deallocate(cobalt%omega_arag) + deallocate(cobalt%omega_calc) + deallocate(cobalt%omegaa) + deallocate(cobalt%omegac) + deallocate(cobalt%tot_layer_int_c) + deallocate(cobalt%tot_layer_int_fe) + deallocate(cobalt%tot_layer_int_n) + deallocate(cobalt%tot_layer_int_p) + deallocate(cobalt%tot_layer_int_si) + deallocate(cobalt%tot_layer_int_alk) + deallocate(cobalt%tot_layer_int_o2) + deallocate(cobalt%total_filter_feeding) + deallocate(cobalt%nlg_diatoms) + deallocate(cobalt%q_si_2_n_lg_diatoms) + deallocate(cobalt%zt) + deallocate(cobalt%zm) +!============================================================================================================== +! JGJ 2016/08/08 CMIP6 OcnBgchem + deallocate(cobalt%dissoc) + deallocate(cobalt%o2sat) + deallocate(cobalt%remoc) + deallocate(cobalt%tot_layer_int_doc) + deallocate(cobalt%tot_layer_int_poc) + deallocate(cobalt%tot_layer_int_dic) + +!============================================================================================================== + + deallocate(cobalt%b_alk) + deallocate(cobalt%b_dic) + deallocate(cobalt%b_fed) + deallocate(cobalt%b_nh4) + deallocate(cobalt%b_no3) + deallocate(cobalt%b_o2) + deallocate(cobalt%b_po4) + deallocate(cobalt%b_sio4) + deallocate(cobalt%pco2_csurf) + deallocate(cobalt%co2_csurf) + deallocate(cobalt%co2_alpha) + deallocate(cobalt%nh3_csurf) + deallocate(cobalt%pnh3_csurf) + deallocate(cobalt%nh3_alpha) + deallocate(cobalt%fcadet_arag_btm) + deallocate(cobalt%fcadet_calc_btm) + deallocate(cobalt%ffedet_btm) + deallocate(cobalt%flithdet_btm) + deallocate(cobalt%fpdet_btm) + deallocate(cobalt%fndet_btm) + deallocate(cobalt%fsidet_btm) + deallocate(cobalt%fcased_burial) + deallocate(cobalt%fcased_redis) + deallocate(cobalt%fcased_redis_surfresp) + deallocate(cobalt%cased_redis_coef) + deallocate(cobalt%cased_redis_delz) + deallocate(cobalt%ffe_sed) + deallocate(cobalt%ffe_geotherm) + deallocate(cobalt%ffe_iceberg) + deallocate(cobalt%fnfeso4red_sed) + deallocate(cobalt%fno3denit_sed) + deallocate(cobalt%fnoxic_sed) + deallocate(cobalt%frac_burial) + deallocate(cobalt%fndet_burial) + deallocate(cobalt%fpdet_burial) + deallocate(cobalt%jprod_allphytos_100) + deallocate(cobalt%jprod_diat_100) + deallocate(cobalt%hp_jingest_n_100) + deallocate(cobalt%hp_jremin_n_100) + deallocate(cobalt%hp_jprod_ndet_100) + deallocate(cobalt%jprod_lithdet_100) + deallocate(cobalt%jprod_sidet_100) + deallocate(cobalt%jprod_cadet_arag_100) + deallocate(cobalt%jprod_cadet_calc_100) + deallocate(cobalt%jprod_mesozoo_200) + deallocate(cobalt%jremin_ndet_100) + deallocate(cobalt%f_ndet_100) + deallocate(cobalt%f_don_100) + deallocate(cobalt%f_silg_100) + deallocate(cobalt%f_mesozoo_200) + deallocate(cobalt%fndet_100) + deallocate(cobalt%fpdet_100) + deallocate(cobalt%fsidet_100) + deallocate(cobalt%fcadet_calc_100) + deallocate(cobalt%fcadet_arag_100) + deallocate(cobalt%ffedet_100) + deallocate(cobalt%flithdet_100) + deallocate(cobalt%btm_temp) + deallocate(cobalt%btm_o2) + deallocate(cobalt%btm_htotal) + deallocate(cobalt%btm_co3_ion) + deallocate(cobalt%btm_co3_sol_arag) + deallocate(cobalt%btm_co3_sol_calc) + deallocate(cobalt%cased_2d) + deallocate(cobalt%o2min) + deallocate(cobalt%z_o2min) + deallocate(cobalt%z_sat_arag) + deallocate(cobalt%z_sat_calc) + deallocate(cobalt%mask_z_sat_arag) + deallocate(cobalt%mask_z_sat_calc) +!============================================================================================================== +! JGJ 2016/08/08 CMIP6 OcnBgchem + deallocate(cobalt%f_alk_int_100) + deallocate(cobalt%f_dic_int_100) + deallocate(cobalt%f_din_int_100) + deallocate(cobalt%f_fed_int_100) + deallocate(cobalt%f_po4_int_100) + deallocate(cobalt%f_sio4_int_100) + deallocate(cobalt%jalk_100) + deallocate(cobalt%jdic_100) + deallocate(cobalt%jdin_100) + deallocate(cobalt%jfed_100) + deallocate(cobalt%jpo4_100) + deallocate(cobalt%jsio4_100) + deallocate(cobalt%jprod_ptot_100) + deallocate(cobalt%wc_vert_int_c) + deallocate(cobalt%wc_vert_int_dic) + deallocate(cobalt%wc_vert_int_doc) + deallocate(cobalt%wc_vert_int_poc) + deallocate(cobalt%wc_vert_int_n) + deallocate(cobalt%wc_vert_int_p) + deallocate(cobalt%wc_vert_int_fe) + deallocate(cobalt%wc_vert_int_si) + deallocate(cobalt%wc_vert_int_o2) + deallocate(cobalt%wc_vert_int_alk) + deallocate(cobalt%wc_vert_int_jdiss_sidet) + deallocate(cobalt%wc_vert_int_jdiss_cadet) + deallocate(cobalt%wc_vert_int_jo2resp) + deallocate(cobalt%wc_vert_int_jprod_cadet) + deallocate(cobalt%wc_vert_int_jno3denit) + deallocate(cobalt%wc_vert_int_jnitrif) + deallocate(cobalt%wc_vert_int_juptake_nh4) + deallocate(cobalt%wc_vert_int_jprod_nh4) + deallocate(cobalt%wc_vert_int_juptake_no3) + deallocate(cobalt%wc_vert_int_nfix) +!============================================================================================================== + + do n = 1, NUM_PHYTO + deallocate(phyto(n)%jprod_n_100) + deallocate(phyto(n)%jprod_n_new_100) + deallocate(phyto(n)%jzloss_n_100) + deallocate(phyto(n)%jexuloss_n_100) + deallocate(phyto(n)%f_n_100) + enddo + deallocate(phyto(DIAZO)%jprod_n_n2_100) + deallocate(phyto(SMALL)%jvirloss_n_100) + deallocate(phyto(SMALL)%jaggloss_n_100) + deallocate(phyto(LARGE)%jaggloss_n_100) + + do n = 1, NUM_ZOO + deallocate(zoo(n)%jprod_n_100) + deallocate(zoo(n)%jingest_n_100) + deallocate(zoo(n)%jremin_n_100) + deallocate(zoo(n)%f_n_100) + enddo + + do n = 1,2 + deallocate(zoo(n)%jzloss_n_100) + deallocate(zoo(n)%jprod_don_100) + enddo + + do n = 2,3 + deallocate(zoo(n)%jhploss_n_100) + deallocate(zoo(n)%jprod_ndet_100) + enddo + + deallocate(bact(1)%jprod_n_100) + deallocate(bact(1)%jzloss_n_100) + deallocate(bact(1)%jvirloss_n_100) + deallocate(bact(1)%jremin_n_100) + deallocate(bact(1)%juptake_ldon_100) + deallocate(bact(1)%f_n_100) + + if (do_14c) then !<> + deallocate(cobalt%runoff_flux_alk) + deallocate(cobalt%runoff_flux_dic) + deallocate(cobalt%runoff_flux_di14c) + deallocate(cobalt%runoff_flux_lith) + deallocate(cobalt%runoff_flux_fed) + deallocate(cobalt%runoff_flux_no3) + deallocate(cobalt%runoff_flux_ldon) + deallocate(cobalt%runoff_flux_sldon) + deallocate(cobalt%runoff_flux_srdon) + deallocate(cobalt%runoff_flux_ndet) + deallocate(cobalt%runoff_flux_po4) + deallocate(cobalt%runoff_flux_ldop) + deallocate(cobalt%runoff_flux_sldop) + deallocate(cobalt%runoff_flux_srdop) + deallocate(cobalt%dry_fed) + deallocate(cobalt%wet_fed) + deallocate(cobalt%dry_lith) + deallocate(cobalt%wet_lith) + deallocate(cobalt%dry_no3) + deallocate(cobalt%wet_no3) + deallocate(cobalt%dry_nh4) + deallocate(cobalt%wet_nh4) + deallocate(cobalt%dry_po4) + deallocate(cobalt%wet_po4) + deallocate(cobalt%stf_gas_dic) + deallocate(cobalt%stf_gas_o2) + deallocate(cobalt%deltap_dic) + deallocate(cobalt%deltap_o2) + + end subroutine user_deallocate_arrays + + +!f1p + function calc_pka_nh3(tc,salt) result(pka) + !temperature, salinity + real, intent(in) :: tc,salt + real :: pka,tk +!Bell 2007 +! pka = 10.0423-0.0315536*tc+0.003071*salt + +!Clegg 1995 + real, parameter :: a1=0.0500616 + real, parameter :: a2=-9.412696 + real, parameter :: a3=-2.029559e-7 + real, parameter :: a4=-0.0142372 + real, parameter :: a5=1.46041e-5 + real, parameter :: a6=3.730005 + real, parameter :: a7=7.14045e-5 + real, parameter :: a8=-0.0229021 + real, parameter :: a9=-5.521278e-7 + real, parameter :: a10=1.95413e-4 + + tk=tc+273.15; + pka = 9.244605-2729.33*(1/298.15-1./tk) & + + (a1+a2/tk+a3*tk**2.)*salt**0.5 & + + (a4+a5*tk+a6/tk)*salt & + + (a7+a8/tk)*salt**2. & + + (a9+a10/tk)*salt**3.; + + end function calc_pka_nh3 + +!salting out correction for solubility (Johnson 2010, Ocean Science) + function saltout_correction(kh,vb,salt) result(C) + real, intent(in) :: Kh,vb,salt + real*8 :: T,log_kh,theta2 + real :: theta,C + log_kh = log(kh) + theta = (7.3353282561828962e-04 + (3.3961477466551352e-05*log_kh) + (-2.4088830102075734e-06*(log_kh)**2) + (1.5711393120941302e-07*(log_kh)**3))*log(vb) + C = 10**(theta*salt) + end function saltout_correction + + !schmidt number in water + function schmidt_w(t,s,vb,rho) result(sc) + !schmidt number of the gas in the water + real, intent(in) :: t,s,vb + real, intent(in), optional :: rho + real :: sc + + sc=2.*v_sw(t,s,rho)/(d_hm(t,s,vb)+d_wc(t,s,vb)) + end function schmidt_w + + function v_sw(t,s,rho) result(v) + real, intent(in) :: t,s + real, intent(in), optional :: rho + real :: n,p,v + n=n_sw(t,s)*1e-3 + if (present(rho)) then + p=rho + else + p=p_sw(t,s) + end if + v = 1e4*n/p + end function v_sw + + function p_sw(t,s) result(p) + !density of sea water + !millero and poisson (1981) + real, intent(in) :: t,s + real :: p, a, b, c + a = 0.824493-(4.0899e-3*t)+(7.6438e-5*(t**2))-(8.2467e-7*(t**3))+(5.3875e-9*(t**4)) + b = -5.72466e-3+(1.0277e-4*t)-(1.6546e-6*(t**2)) + c = 4.8314e-4 + ! density of pure water + p = 999.842594+(6.793952e-2*t)-(9.09529e-3*(t**2))+(1.001685e-4*(t**3))-(1.120083e-6*(t**4))+(6.536332e-9*(t**5)) + !salinity correction + p = (p+(a*s)+(b*(s**(1.5)))+(c*s)) + end function p_sw + + function d_wc(t,s,vb) result(d) + real, intent(in) :: t,s,vb + real :: d + real, parameter :: phi = 2.6 + !wilkie and chang 1955 + d = ((t+273.15)*7.4e-8*(phi*18.01)**0.5)/((n_sw(t,s))*(vb**0.6)) + end function d_wc + + function d_hm(t,s,vb) result(d) + real, intent(in) :: t,s,vb + real :: d, epsilonstar + ! hayduk 1982 + epsilonstar = (9.58/vb)-1.12 + d=1.25e-8*(vb**(-0.19)-0.292)*((t+273.15)**(1.52))*((n_sw(t,s))**epsilonstar) + end function d_hm + + function n_sw(t,s) result(n) + !dynamic viscosity + !laliberte 2007 + real :: n + real, intent(in) :: t,s !temperature (c) and salinity + !salt in the order nacl,kcl,cacl2,mgcl2,mgso4 + real, parameter :: mass_fraction(5) = (/ 0.798,0.022,0.033,0.047,0.1 /) + real, parameter :: v1(5) = (/ 16.22 , 6.4883, 32.028, 24.032, 72.269/) + real, parameter :: v2(5) = (/ 1.3229 , 1.3175, 0.78792, 2.2694, 2.2238/) + real, parameter :: v3(5) = (/ 1.4849 , -0.7785, -1.1495, 3.7108, 6.6037/) + real, parameter :: v4(5) = (/ 0.0074691 , 0.09272, 0.0026995, 0.021853, 0.0079004/) + real, parameter :: v5(5) = (/ 30.78 , -1.3, 780860., -1.1236, 3340.1/) + real, parameter :: v6(5) = (/ 2.0583 , 2.0811, 5.8442,0.14474, 6.1304/) + + real :: n_0,ln_n_m,w_i_ln_n_i_tot,ni,w_i_tot,w_i + integer :: i + w_i_tot=0 + w_i_ln_n_i_tot=0 + do i=1,5 + w_i = mass_fraction(i)*s/1000 + w_i_tot = w_i+w_i_tot + enddo + do i=1,5 + w_i = mass_fraction(i)*s/1000 + ni = (exp(((v1(i)*w_i_tot**v2(i))+v3(i))/((v4(i)*t) + 1)))/((v5(i)*(w_i_tot**v6(i)))+1) + w_i_ln_n_i_tot = w_i_ln_n_i_tot + (w_i*log(ni)) + enddo + n_0 = (t+246)/(137.37+(5.2842*t)+(0.05594*(t**2))) + ln_n_m = (1-w_i_tot)*log(n_0)+w_i_ln_n_i_tot + n = exp(ln_n_m) + end function n_sw + + + +end module generic_COBALT diff --git a/globe.py b/globe.py index 0c2f5e4..7869a29 100644 --- a/globe.py +++ b/globe.py @@ -1,10 +1,15 @@ import os # import sys import numpy as np -from setup.initialize import import_model +import yaml +from setup.initialize import import_bgc_model, import_physical_model from functions.bgc_rate_eqns import bgc_rate_eqns -import matplotlib.pyplot as plt - +from functions.calculate_averages import average +from pom.calculations import density_profile, kinetic_energy_profile, temperature_and_salinity_profiles, zonal_velocity_profile, meridional_velocity_profile +from pom.forcing import forcing_manager +from pom.initialize import initialize_pom +from pom.coupling import pom_bgc_1d +from pom.check_phys import dens, u, ub, v, vb, t, tb, s, sb, q2, q2b, q2l, q2lb, km, kh, kq # ---------------------------------------------------------------------------------------------------- # Import and initialize model # ---------------------------------------------------------------------------------------------------- @@ -19,138 +24,172 @@ # sys.exit() # check_file = os.path.exists(file) # first_check = False -# file = 'bfm17.yaml' -file = 'bfm17-matlab.yaml' + +# Import physical model +file = 'physical.yaml' file_path = os.getcwd() + '/' + file -base_element, parameters, reactions, tracers = import_model(file_path) +physical = import_physical_model(file_path) + +file = 'tests/bfm17/bfm17-1d-2.yaml' +file_path = os.getcwd() + '/' + file +base_element, reactions, tracers = import_bgc_model(file_path, physical) + +# ---------------------------------------------------------------------------------------------------- +# Initialize POM1D (if necessary) +# ---------------------------------------------------------------------------------------------------- +if physical["environment"]["forcing"] == "pom1d": + with open(os.getcwd() + '/pom1d.yaml', 'r') as f: + pom1d = yaml.full_load(f) + physical, forcing = initialize_pom(pom1d, physical) + physical = density_profile(physical) + # pom1d["general"]["coriolis"] = 2. * pom1d["general"]["earth_angular_velocity"] * np.sin(physical["environment"]["latitude"] * 2. * np.pi / 360.) + pom1d["general"]["coriolis"] = 2. * pom1d["general"]["earth_angular_velocity"] * np.sin(physical["environment"]["latitude"] * 2. * (3.14159265359) / 360.) + -j=0 -irrad = np.zeros(parameters["simulation"]["iters"]) -light_lim = np.zeros(parameters["simulation"]["iters"]) -nut_lim = np.zeros(parameters["simulation"]["iters"]) # ---------------------------------------------------------------------------------------------------- # Begin simulation # ---------------------------------------------------------------------------------------------------- -for iter in range(0,parameters["simulation"]["iters"]-1): - # light_lim[iter], irrad[iter], nut_lim[iter] = bgc_rate_eqns(iter, base_element, parameters, tracers) - bgc_rate_eqns(iter, base_element, parameters, tracers) - # bgc_rate_eqns(iter) +# for iter in range(0,physical["simulation"]["iters"]-1): +# bgc_rate_eqns(iter, base_element, parameters, tracers) + +for iter in range(0,physical["simulation"]["iters"]-1): + + # Turbulence closure + physical["kinetic_energy"]["kef"][:] = physical["kinetic_energy"]["keb"][:] + physical["kinetic_energy"]["kelf"][:] = physical["kinetic_energy"]["kelb"][:] + physical = kinetic_energy_profile(physical, pom1d) + + # Define forcings + physical, forcing = forcing_manager(iter, physical, forcing, pom1d) + + # Temperature and salinity computation + if pom1d["general"]["idiagn"] == 0: + # Prognostic mode + # Temperature and salinity fully computed by model + physical["temperature"]["surf"] = physical["temperature"]["tf"][0] + physical["salinity"]["surf"] = physical["salinity"]["sf"][0] + if pom1d["relaxation_times"]["trt"] != 0: + for j in range(0, physical["water_column"]["num_layers"]): + if (-physical["vertical_grid"]["dzz"][j] * physical["water_column"]["column_depth"]) >= physical["water_column"]["upper_depth"]: + physical["temperature"]["adv"][j] = (physical["temperature"]["ti"][j] - physical["temperature"]["t"][j]) / (pom1d["relaxation_times"]["trt"] * physical["simulation"]["sec_per_day"]) + + if pom1d["relaxation_times"]["srt"] != 0: + for j in range(0, physical["water_column"]["num_layers"]): + if (-physical["vertical_grid"]["dzz"][j] * physical["water_column"]["column_depth"]) >= physical["water_column"]["upper_depth"]: + physical["salinity"]["adv"][j] = (physical["salinity"]["si"][j] - physical["salinity"]["s"][j]) / (pom1d["relaxation_times"]["srt"] * physical["simulation"]["sec_per_day"]) + + # Calculate surface salinity flux + physical["salinity"]["surf_flux"] = -(physical["salinity"]["surf"] - physical["salinity"]["s"][0]) * pom1d["relaxation_times"]["srt"] / physical["simulation"]["sec_per_day"] + + # Calculate temperature + physical["temperature"]["tf"][:] = physical["temperature"]["tb"][:] + (physical["temperature"]["adv"][:] * physical["simulation"]["dt2"]) + physical["temperature"] = temperature_and_salinity_profiles(physical, pom1d, physical["temperature"], 'Temperature') + + # Calculate salinity + physical["salinity"]["sf"][:] = physical["salinity"]["sb"][:] + (physical["salinity"]["adv"][:] * physical["simulation"]["dt2"]) + physical["salinity"] = temperature_and_salinity_profiles(physical, pom1d, physical["salinity"], 'Salinity') + + # Mix the timestep (Asselin filter) + physical["temperature"]["t"][:] = physical["temperature"]["t"][:] + 0.5 * pom1d["general"]["smoth"] * (physical["temperature"]["tf"] + physical["temperature"]["tb"][:] - 2. * physical["temperature"]["t"]) + physical["salinity"]["s"][:] = physical["salinity"]["s"][:] + 0.5 * pom1d["general"]["smoth"] * (physical["salinity"]["sf"][:] + physical["salinity"]["sb"][:] - 2. * physical["salinity"]["s"][:]) + + # Velocity computation + physical["velocity"]["uf"][:] = physical["velocity"]["ub"][:] + physical["simulation"]["dt2"] * pom1d["general"]["coriolis"] * physical["velocity"]["v"][:] + physical = zonal_velocity_profile(physical, pom1d) + + physical["velocity"]["vf"][:] = physical["velocity"]["vb"][:] - physical["simulation"]["dt2"] * pom1d["general"]["coriolis"] * physical["velocity"]["u"][:] + physical = meridional_velocity_profile(physical, pom1d) + + # Mix the timestep (Asselin filter) + physical["kinetic_energy"]["ke"][:] = physical["kinetic_energy"]["ke"][:] + 0.5 * pom1d["general"]["smoth"] * (physical["kinetic_energy"]["kef"][:] + physical["kinetic_energy"]["keb"][:] - 2. * physical["kinetic_energy"]["ke"][:]) + physical["kinetic_energy"]["kel"][:] = physical["kinetic_energy"]["kel"][:] + 0.5 * pom1d["general"]["smoth"] * (physical["kinetic_energy"]["kelf"][:] + physical["kinetic_energy"]["kelb"][:] - 2. * physical["kinetic_energy"]["kel"][:]) + + physical["velocity"]["u"][:] = physical["velocity"]["u"][:] + 0.5 * pom1d["general"]["smoth"] * (physical["velocity"]["uf"][:] + physical["velocity"]["ub"][:] - 2. * physical["velocity"]["u"][:]) + physical["velocity"]["v"][:] = physical["velocity"]["v"][:] + 0.5 * pom1d["general"]["smoth"] * (physical["velocity"]["vf"][:] + physical["velocity"]["vb"][:] - 2. * physical["velocity"]["v"][:]) + + # Restore the time sequence + physical["kinetic_energy"]["keb"][:] = physical["kinetic_energy"]["ke"][:] + physical["kinetic_energy"]["ke"][:] = physical["kinetic_energy"]["kef"][:] + physical["kinetic_energy"]["kelb"][:] = physical["kinetic_energy"]["kel"][:] + physical["kinetic_energy"]["kel"][:] = physical["kinetic_energy"]["kelf"][:] + + physical["velocity"]["ub"][:] = physical["velocity"]["u"][:] + physical["velocity"]["u"][:] = physical["velocity"]["uf"][:] + physical["velocity"]["vb"][:] = physical["velocity"]["v"][:] + physical["velocity"]["v"][:] = physical["velocity"]["vf"][:] + + physical["temperature"]["tb"][:] = physical["temperature"]["t"][:] + physical["temperature"]["t"][:] = physical["temperature"]["tf"][:] + physical["salinity"]["sb"][:] = physical["salinity"]["s"][:] + physical["salinity"]["s"][:] = physical["salinity"]["sf"][:] + + # Update density + physical = density_profile(physical) + + if iter < 10: + delta_q2 = physical["kinetic_energy"]["ke"][:150] - q2[iter] + delta_q2b = physical["kinetic_energy"]["keb"][:150] - q2b[iter] + delta_q2l = physical["kinetic_energy"]["kel"][:150] - q2l[iter] + delta_q2lb = physical["kinetic_energy"]["kelb"][:150] - q2lb[iter] + + delta_t = physical["temperature"]["t"][:150] - t[iter] + delta_tb = physical["temperature"]["tb"][:150] - tb[iter] + delta_s = physical["salinity"]["s"][:150] - s[iter] + delta_sb = physical["salinity"]["sb"][:150] - sb[iter] + + delta_u = physical["velocity"]["u"][:150] - u[iter] + delta_ub = physical["velocity"]["ub"][:150] - ub[iter] + delta_v = physical["velocity"]["v"][:150] - v[iter] + delta_vb = physical["velocity"]["vb"][:150] - vb[iter] + + delta_rho = physical["density"][:150] - dens[iter] + + delta_km = physical["diffusion"]["momentum"][:] - km[iter] + delta_kh = physical["diffusion"]["tracers"][:] - kh[iter] + delta_kq = physical["diffusion"]["kinetic_energy"][:] - kq[iter] + + x = 1 + + pom_bgc_1d(iter, base_element, physical, pom1d, tracers) + +# ---------------------------------------------------------------------------------------------------- +# Write outputs to npz file +# ---------------------------------------------------------------------------------------------------- concentration = [] -tracer_indices = {} -index = 0 # counting number to keep track of tracer index in concentration matrix -for t in tracers: - num_constituents = len(tracers[t].composition) - tracer_indices[t] = list(np.arange(index, index+num_constituents, 1)) +npp_exists = False # initialize writing of npp +tracer_indices = {} # used to keep track of tracer/consitient index in concentration matriz +index = 0 # counting number for tracer indices +for trac in tracers: + num_constituents = len(tracers[trac].composition) + tracer_indices[trac] = list(np.arange(index, index+num_constituents, 1)) # identify tracer constituents with their own index for i in range(num_constituents): - concentration.append(tracers[t].conc[i,...]) + concentration.append(tracers[trac].conc[i,...]) # add concentration to matrix - index += 1 + index += 1 # update index -concentration = np.array(concentration,dtype=float) + # Update npp + if tracers[trac].type == "phytoplankton": + if not npp_exists: # first phytoplankton group + npp = tracers[trac].npp + npp_exists = True # npp now exists, update to True to append with npp from later phytoplankton groups + else: # subsequent phytoplankton groups + npp += tracers[trac].npp -np.savez('bfm17-0522-fdm.npz',concentration=concentration,time=parameters["simulation"]["time"]) -np.savez('tracer_indices_bfm17-0522.npz',**tracer_indices) +concentration = np.array(concentration,dtype=float) # convert concentration from list to array -print('Simulation complete.') +conc_daily, conc_monthly = average(concentration,physical,'concentration') +np.savez('concentration.npz',daily=conc_daily,monthly=conc_monthly) +if npp_exists: + npp_daily, npp_monthly = average(npp,physical,'npp') + np.savez('npp.npz',daily=npp_daily,monthly=npp_monthly) + + +# np.savez('bfm17-1d-2-6.npz',concentration=concentration,npp=npp,time=physical["simulation"]["time"]) +np.savez('tracer_indices_bfm17-1d-2-6.npz',**tracer_indices) -# np.savez('phyto-0424.npz',gpp=tracers['phyto1'].gpp, rsp=tracers['phyto1'].rsp, exu=tracers['phyto1'].exu, -# lys=tracers['phyto1'].lys, npp=tracers['phyto1'].npp, psn=tracers['phyto1'].psn, -# uptn=tracers['phyto1'].uptn, uptp=tracers['phyto1'].uptp) - -# np.savez('light_globe.npz',irrad=irrad, light_lim=light_lim) -# np.savez('nut_lim_globe.npz',nut_lim=nut_lim) -x=1 - -# # Convert time array from seconds to months -# sec_mon = 60 * 60 * 24 * 30 -# time = parameters["simulation"]["time"]/sec_mon - -# # Ticks and labels for plots -# xticks = [0,2,4,6,8,10,12] -# xlabel = ['J','M','M','J','S','N','J'] - -# # Create plots - -# # Folder path -# folder = os.getcwd() + '/tests/2npzd-0404' - -# # Nutrients -# fig, axs = plt.subplots(1,2,figsize=(12,5)) - -# axs[0].plot(time,concentration[0]) -# axs[0].set_title("Nitrate") -# axs[0].set_xlabel("Time [months]") -# axs[0].set_xticks(xticks,xlabel) -# axs[0].set_ylabel("mmol N $\mathregular{m^{-3}}$") - -# axs[1].plot(time,concentration[1]) -# axs[1].set_title("Phosphate") -# axs[1].set_xlabel("Time [months]") -# axs[1].set_xticks(xticks,xlabel) -# axs[1].set_ylabel("mmol P $\mathregular{m^{-3}}$") - -# fig.suptitle("Nutrients") -# fig.tight_layout() -# nut = os.path.join(folder,"nutrients.jpg") -# plt.savefig(nut) - -# # Phytoplankton -# fig, axs = plt.subplots(1,2,figsize=(12,5)) - -# axs[0].plot(time,concentration[2]) -# axs[0].set_title("Nitrate") -# axs[0].set_xlabel("Time [months]") -# axs[0].set_xticks(xticks,xlabel) -# axs[0].set_ylabel("mmol N $\mathregular{m^{-3}}$") - -# axs[1].plot(time,concentration[3]) -# axs[1].set_title("Phosphate") -# axs[1].set_xlabel("Time [months]") -# axs[1].set_xticks(xticks,xlabel) -# axs[1].set_ylabel("mmol P $\mathregular{m^{-3}}$") - -# fig.suptitle("Phytoplankton") -# fig.tight_layout() -# phyto = os.path.join(folder,"phytoplankton.jpg") -# plt.savefig(phyto) - -# # Zooplankton -# fig, axs = plt.subplots(1,2,figsize=(12,5)) - -# axs[0].plot(time,concentration[4]) -# axs[0].set_title("Nitrate") -# axs[0].set_xlabel("Time [months]") -# axs[0].set_xticks(xticks,xlabel) -# axs[0].set_ylabel("mmol N $\mathregular{m^{-3}}$") - -# axs[1].plot(time,concentration[5]) -# axs[1].set_title("Phosphate") -# axs[1].set_xlabel("Time [months]") -# axs[1].set_xticks(xticks,xlabel) -# axs[1].set_ylabel("mmol P $\mathregular{m^{-3}}$") - -# fig.suptitle("Zooplankton") -# fig.tight_layout() -# zoo = os.path.join(folder,"zooplankton.jpg") -# plt.savefig(zoo) - -# # Detritus -# fig, axs = plt.subplots(1,2,figsize=(12,5)) - -# axs[0].plot(time,concentration[6]) -# axs[0].set_title("Nitrate") -# axs[0].set_xlabel("Time [months]") -# axs[0].set_xticks(xticks,xlabel) -# axs[0].set_ylabel("mmol N $\mathregular{m^{-3}}$") - -# axs[1].plot(time,concentration[7]) -# axs[1].set_title("Phosphate") -# axs[1].set_xlabel("Time [months]") -# axs[1].set_xticks(xticks,xlabel) -# axs[1].set_ylabel("mmol P $\mathregular{m^{-3}}$") - -# fig.suptitle("Detritus") -# fig.tight_layout() -# pom = os.path.join(folder,"detritus.jpg") -# plt.savefig(pom) + +# ---------------------------------------------------------------------------------------------------- +# Simulation complete +# ---------------------------------------------------------------------------------------------------- +print('Simulation complete.') diff --git a/inputs/BFM17_BERM_INIT/init_prof_Am_150m_bermuda_killworth.da b/inputs/BFM17_BERM_INIT/init_prof_Am_150m_bermuda_killworth.da new file mode 100644 index 0000000..5f8c377 Binary files /dev/null and b/inputs/BFM17_BERM_INIT/init_prof_Am_150m_bermuda_killworth.da differ diff --git a/inputs/BFM17_BERM_INIT/init_prof_DOC_150m_bermuda_killworth.da b/inputs/BFM17_BERM_INIT/init_prof_DOC_150m_bermuda_killworth.da new file mode 100644 index 0000000..3446ab0 Binary files /dev/null and b/inputs/BFM17_BERM_INIT/init_prof_DOC_150m_bermuda_killworth.da differ diff --git a/inputs/BFM17_BERM_INIT/init_prof_N_150m_bermuda_killworth.da b/inputs/BFM17_BERM_INIT/init_prof_N_150m_bermuda_killworth.da new file mode 100644 index 0000000..0add074 Binary files /dev/null and b/inputs/BFM17_BERM_INIT/init_prof_N_150m_bermuda_killworth.da differ diff --git a/inputs/BFM17_BERM_INIT/init_prof_Oxy_150m_bermuda_killworth.da b/inputs/BFM17_BERM_INIT/init_prof_Oxy_150m_bermuda_killworth.da new file mode 100644 index 0000000..d9c3f16 Binary files /dev/null and b/inputs/BFM17_BERM_INIT/init_prof_Oxy_150m_bermuda_killworth.da differ diff --git a/inputs/BFM17_BERM_INIT/init_prof_POC_150m_bermuda_killworth.da b/inputs/BFM17_BERM_INIT/init_prof_POC_150m_bermuda_killworth.da new file mode 100644 index 0000000..f5769fd Binary files /dev/null and b/inputs/BFM17_BERM_INIT/init_prof_POC_150m_bermuda_killworth.da differ diff --git a/inputs/BFM17_BERM_INIT/init_prof_P_150m_bermuda_killworth.da b/inputs/BFM17_BERM_INIT/init_prof_P_150m_bermuda_killworth.da new file mode 100644 index 0000000..541029a Binary files /dev/null and b/inputs/BFM17_BERM_INIT/init_prof_P_150m_bermuda_killworth.da differ diff --git a/inputs/BFM17_BERM_INIT/init_prof_Pc_150m_bermuda_killworth.da b/inputs/BFM17_BERM_INIT/init_prof_Pc_150m_bermuda_killworth.da new file mode 100644 index 0000000..754bb01 Binary files /dev/null and b/inputs/BFM17_BERM_INIT/init_prof_Pc_150m_bermuda_killworth.da differ diff --git a/inputs/BFM17_BERM_INIT/init_prof_S_150m_bermuda_killworth.da b/inputs/BFM17_BERM_INIT/init_prof_S_150m_bermuda_killworth.da new file mode 100644 index 0000000..09f4347 Binary files /dev/null and b/inputs/BFM17_BERM_INIT/init_prof_S_150m_bermuda_killworth.da differ diff --git a/inputs/BFM17_BERM_INIT/init_prof_T_150m_bermuda_killworth.da b/inputs/BFM17_BERM_INIT/init_prof_T_150m_bermuda_killworth.da new file mode 100644 index 0000000..1bcda7e Binary files /dev/null and b/inputs/BFM17_BERM_INIT/init_prof_T_150m_bermuda_killworth.da differ diff --git a/inputs/BFM17_BERM_INIT/init_prof_Zc_150m_bermuda_killworth.da b/inputs/BFM17_BERM_INIT/init_prof_Zc_150m_bermuda_killworth.da new file mode 100644 index 0000000..7726db6 --- /dev/null +++ b/inputs/BFM17_BERM_INIT/init_prof_Zc_150m_bermuda_killworth.da @@ -0,0 +1,9 @@ +&ݰ^""@=ӂP"@"YD}"@ͣũ"@`"@lr"@8(#@Da5P#@nLw#@%`#@_s3q#@b#@o} $@+$@oL$@|l$@򈄒$@J$@}$@Mak$@vT$@t%@γhK%@Ѩz %@!Q"%@=>j%@E%@bB%@R>gE%@%@EMZ +&@BW&@c%@k%@Y!%@|ebW%@q}Gü%@, 7%@3%@Z;%@Jp%@dnf[%@kYqZ E%@˺-%@t%@[L :$@< +$@[$@G?Tc̪$@M #f$@o$@mP$@[0$@h$@R#@';#@|f#@|wW#@5H&\#@#35#@ #@۸"@~"@B"@UGhCb"@%X4"@1]("@-&N!@8 ע!@Ɍ4o!@p9!@ւ !@_SC @( @6l< @nQ=<@rMAP@@;L9 +/@U@V@쵵T@߷ڕ@ g@Γ@J3@Duk@B=$@Q%@b +ڿ9@% w@hXbV@́g|@}@~y@P @w@7@a@~ee@PL@AePL@Q@{1@!Ɓo@h@1@c$@dR?@t@P[|@䫒\Z@=)z@0s]@(I@lVA@x2@KC @1QiT @(0DQ,^ @ \"4 @?n +@|? +@ \ @O^@Sg7xx,@ԣ+H}@ \ No newline at end of file diff --git a/inputs/Data_ML/Chla_1yr_climatology.mat b/inputs/Data_ML/Chla_1yr_climatology.mat new file mode 100644 index 0000000..2040e0d Binary files /dev/null and b/inputs/Data_ML/Chla_1yr_climatology.mat differ diff --git a/inputs/Data_ML/NPP_1yr_climatology.mat b/inputs/Data_ML/NPP_1yr_climatology.mat new file mode 100644 index 0000000..e1b81cd Binary files /dev/null and b/inputs/Data_ML/NPP_1yr_climatology.mat differ diff --git a/inputs/Data_ML/Nitrate_1yr_climatology.mat b/inputs/Data_ML/Nitrate_1yr_climatology.mat new file mode 100644 index 0000000..155b831 Binary files /dev/null and b/inputs/Data_ML/Nitrate_1yr_climatology.mat differ diff --git a/inputs/Data_ML/Oxy_1yr_climatology.mat b/inputs/Data_ML/Oxy_1yr_climatology.mat new file mode 100644 index 0000000..20d3370 Binary files /dev/null and b/inputs/Data_ML/Oxy_1yr_climatology.mat differ diff --git a/inputs/Data_ML/POC_1yr_climatology.mat b/inputs/Data_ML/POC_1yr_climatology.mat new file mode 100644 index 0000000..66e4cff Binary files /dev/null and b/inputs/Data_ML/POC_1yr_climatology.mat differ diff --git a/inputs/Data_ML/PON_1yr_climatology.mat b/inputs/Data_ML/PON_1yr_climatology.mat new file mode 100644 index 0000000..444fc9e Binary files /dev/null and b/inputs/Data_ML/PON_1yr_climatology.mat differ diff --git a/inputs/Data_ML/POP_1yr_climatology.mat b/inputs/Data_ML/POP_1yr_climatology.mat new file mode 100644 index 0000000..c67713a Binary files 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b/inputs/POM_BFM17/monthly_clima_ISM_150m_bermuda_killworth.da new file mode 100644 index 0000000..1c4e9c1 Binary files /dev/null and b/inputs/POM_BFM17/monthly_clima_ISM_150m_bermuda_killworth.da differ diff --git a/inputs/POM_BFM17/monthly_clima_salt_150m_bermuda_killworth2.da b/inputs/POM_BFM17/monthly_clima_salt_150m_bermuda_killworth2.da new file mode 100644 index 0000000..4a7b20f Binary files /dev/null and b/inputs/POM_BFM17/monthly_clima_salt_150m_bermuda_killworth2.da differ diff --git a/inputs/POM_BFM17/monthly_clima_temp_150m_bermuda_killworth2.da b/inputs/POM_BFM17/monthly_clima_temp_150m_bermuda_killworth2.da new file mode 100644 index 0000000..8ed674e Binary files /dev/null and b/inputs/POM_BFM17/monthly_clima_temp_150m_bermuda_killworth2.da differ diff --git a/inputs/POM_BFM17/monthly_clima_w_150m_bermuda_ekman.da b/inputs/POM_BFM17/monthly_clima_w_150m_bermuda_ekman.da new file mode 100644 index 0000000..2323c93 Binary files /dev/null and b/inputs/POM_BFM17/monthly_clima_w_150m_bermuda_ekman.da differ diff --git a/inputs/POM_BFM17/monthly_surf_qs_bermuda_killworth2.da b/inputs/POM_BFM17/monthly_surf_qs_bermuda_killworth2.da new file mode 100644 index 0000000..cb93478 --- /dev/null +++ b/inputs/POM_BFM17/monthly_surf_qs_bermuda_killworth2.da @@ -0,0 +1 @@ +Qb@T.na@ 4`b@<؇]d@wF.q@aɈt@A"t@mzs@UdOm@Cךp@gȾk@tۺg@Qb@ \ No newline at end of file diff --git a/inputs/POM_BFM17/monthly_surf_rad_bermuda_killworth2.da b/inputs/POM_BFM17/monthly_surf_rad_bermuda_killworth2.da new file mode 100644 index 0000000..7966781 --- /dev/null +++ b/inputs/POM_BFM17/monthly_surf_rad_bermuda_killworth2.da @@ -0,0 +1 @@ +Qb@Qb@Qb@T.na@T.na@T.na@ 4`b@ 4`b@ 4`b@<؇]d@<؇]d@<؇]d@wF.q@wF.q@wF.q@aɈt@aɈt@aɈt@A"t@A"t@A"t@mzs@mzs@mzs@UdOm@UdOm@UdOm@Cךp@Cךp@Cךp@gȾk@gȾk@gȾk@tۺg@tۺg@tۺg@Qb@Qb@Qb@ \ No newline at end of file diff --git a/inputs/POM_BFM17/monthly_surf_salt_bermuda_150m_killworth2.da b/inputs/POM_BFM17/monthly_surf_salt_bermuda_150m_killworth2.da new file mode 100644 index 0000000..7f88af7 --- /dev/null +++ b/inputs/POM_BFM17/monthly_surf_salt_bermuda_150m_killworth2.da @@ -0,0 +1 @@ +}`:BB@e@]B@$VB@*^HhB@iSMB@{e|B@cFhCB@'cQ]B@wB@ŚMB@x7B@վe7B@}`:BB@ \ No newline at end of file diff --git a/inputs/POM_BFM17/monthly_surf_wind_stress_bermuda_killworth2.da b/inputs/POM_BFM17/monthly_surf_wind_stress_bermuda_killworth2.da new file mode 100644 index 0000000..c129047 --- /dev/null +++ b/inputs/POM_BFM17/monthly_surf_wind_stress_bermuda_killworth2.da @@ -0,0 +1,2 @@ +7ɔ?um?]&yNҙ?Ivy? t9?1w^(?X塐h?iͱ*ΰ?$샨 add to list of parameters + params.append({ + "name": new_key, + "lower": value["lower"], + "initial": value["initial"], + "upper": value["upper"] + }) + elif isinstance(value, dict): + # Contains additional + params.extend(extract_parameters(value, new_key)) + + return params + + +def generate_dakota_input(yaml_file, output_file="dakota.in"): + + with open(yaml_file, "r") as f: + config = yaml.safe_load(f) + + dakota_config = config.get("dakota", {}) + params_config = config.get("parameters", {}) + + param_list = extract_parameters(params_config) + + text = "" + text += build_environment(dakota_config.get("environment", {})) + text += build_method(dakota_config.get("method", {})) + text += build_model(dakota_config.get("model", {})) + text += build_variables(param_list, dakota_config.get("variables", {})) + text += build_interface(dakota_config.get("interface", {})) + text += build_response(dakota_config.get("responses", {})) + + with open(output_file, "w") as f: + f.write(text) + + return param_list + + +# METHOD_MAP = { +# "sampling": 1, +# "moga": 2, +# "ngsa2": 3, +# "nlpql_sqp": 4, +# "bayes_calibration": 5 +# } +# with open("dakota.yaml", "r") as f: +# config = yaml.safe_load(f) + +# param_list = extract_parameters(config["parameters"]) + +# num_params = len(param_list) +# print("Number of continuous variables:", num_params) + +# dakota_vars_block = build_variables(param_list) + +# with open("dakota.in", "w") as f: +# f.write(dakota_vars_block) + +generate_dakota_input("dakota.yaml") +x=1 \ No newline at end of file diff --git a/output_file.txt b/output_file.txt new file mode 100644 index 0000000..108c8b6 --- /dev/null +++ b/output_file.txt @@ -0,0 +1,513 @@ +month_counter = 1 +month_counter = 1 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 1 +month_counter = 1 +month_counter = 1 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 +month_counter = 2 +month_counter = 3 +month_counter = 4 +month_counter = 5 +month_counter = 6 +month_counter = 7 +month_counter = 8 +month_counter = 9 +month_counter = 10 +month_counter = 11 +month_counter = 12 +month_counter = 1 diff --git a/physical.yaml b/physical.yaml new file mode 100644 index 0000000..e763a36 --- /dev/null +++ b/physical.yaml @@ -0,0 +1,31 @@ +simulation: + dt: 360. # time step [s] + days: 1825 # days in simulation [d] + configuration: 1d # Options: 0d or 1d + +water_column: + column_depth: 150. # depth [m] + upper_depth: 5. # depth where lateral advection starts [m] + num_layers: 151 # number of layers in water column + surf_log: 2 # surface logarithmic layers distribution + bot_log: 150 # bottom logarithmic layers distribution + +environment: + forcing: pom1d # Options: pom1d or seasonal + # pom1d: forces system using climatological data with one-dimensional POM1D ocean circulation + # seasonal: forces system using sinusoidal forcing between summer and winter extremes + base_temp: 10.0 + PAR_frac: 0.4 + light_attenuation_water: 0.0435 # [1/m] + latitude: 45.0 + seasonal_cycling: + summer_mld: 10.0 + winter_mld: 40.0 + summer_salt: 36.5 + winter_salt: 37. + summer_sun: 300.0 + winter_sun: 20.0 + summer_temp: 30. + winter_temp: 10. + summer_wind: 2. + winter_wind: 6. \ No newline at end of file diff --git a/pom/calculations.py b/pom/calculations.py new file mode 100644 index 0000000..579a2cf --- /dev/null +++ b/pom/calculations.py @@ -0,0 +1,482 @@ +import numpy as np +from pom.check_diffusion import a, c, vh, vhp + +# def density_profile(physical): + +# gravity = 9.806 +# vertical_density_profile = np.zeros(physical["water_column"]["num_layers"]) + +# pressure = -gravity * 1.025 * physical["vertical_grid"]["dzz"][:-1] * physical["water_column"]["column_depth"] * 0.01 + +# cr = 1449.1 + (0.0821*pressure) + (4.55*physical["temperature"]["tb"][:-1]) - (0.045*np.power(physical["temperature"]["tb"][:-1],2)) + (1.34*(physical["salinity"]["sb"][:-1] - 35.0)) +# cr = pressure/np.power(cr,2) + +# density = 999.842594 + (6.793952e-02*physical["temperature"]["tb"][:-1]) - (9.095290e-03*np.power(physical["temperature"]["tb"][:-1],2)) \ +# + (1.001685e-04*np.power(physical["temperature"]["tb"][:-1],3)) - (1.120083e-06*np.power(physical["temperature"]["tb"][:-1],4)) + (6.536332e-09*np.power(physical["temperature"]["tb"][:-1],5)) \ +# + (0.824493 - (4.0899e-03*physical["temperature"]["tb"][:-1]) + (7.6438e-05*np.power(physical["temperature"]["tb"][:-1],2)) +# - (8.2467e-07*np.power(physical["temperature"]["tb"][:-1],3)) + (5.3875e-09*np.power(physical["temperature"]["tb"][:-1],4))) * physical["salinity"]["sb"][:-1] \ +# + (-5.72466e-03 + 1.0227e-04*physical["temperature"]["tb"][:-1] - (1.6546e-06*np.power(physical["temperature"]["tb"][:-1],2))) * (np.power(np.abs(physical["salinity"]["sb"][:-1]),1.5)) \ +# + (4.8314e-04*np.power(physical["salinity"]["sb"][:-1],2)) + 1.0e05*cr*(1.0 - (2*cr)) + +# vertical_density_profile[:-1] = (density - 1000.) * 1.e-03 +# vertical_density_profile[-1] = vertical_density_profile[-2] + +# physical["density"] = vertical_density_profile + +# return physical + + +def density_profile(physical): + + gravity = 9.806 + vertical_density_profile = np.zeros(physical["water_column"]["num_layers"]) + pressure = -gravity * 1.025 * physical["vertical_grid"]["dzz"][:-1] * physical["water_column"]["column_depth"] * 0.01 + + # density = 999.842594 + (6.793952E-02 * physical["temperature"]["tb"][:-1]) - (9.095290E-03 * np.power(physical["temperature"]["tb"][:-1],2)) \ + # + (1.001685E-04 * np.power(physical["temperature"]["tb"][:-1],3)) - (1.120083E-06 * np.power(physical["temperature"]["tb"][:-1],4)) \ + # + (6.536332E-09 * np.power(physical["temperature"]["tb"][:-1],5)) + # density = density + (0.824493 - (4.0899E-03 * physical["temperature"]["tb"][:-1]) + (7.6438E-05 * np.power(physical["temperature"]["tb"][:-1],2)) \ + # - (8.2467E-07 * np.power(physical["temperature"]["tb"][:-1],3)) + (5.3875E-09 * np.power(physical["temperature"]["tb"][:-1],4))) * physical["salinity"]["sb"][:-1] \ + # + (-5.72466E-03 + (1.0227E-04 * physical["temperature"]["tb"][:-1]) - (1.6546E-06 * np.power(physical["temperature"]["tb"][:-1],2))) * (np.power(np.abs(physical["salinity"]["sb"][:-1]),1.5)) \ + # + 4.8314E-4*np.power(physical["salinity"]["sb"][:-1],2) + + density = 999.842594 + (6.793952E-02 * physical["temperature"]["t"][:-1]) - (9.095290E-03 * np.power(physical["temperature"]["t"][:-1],2)) \ + + (1.001685E-04 * np.power(physical["temperature"]["t"][:-1],3)) - (1.120083E-06 * np.power(physical["temperature"]["t"][:-1],4)) \ + + (6.536332E-09 * np.power(physical["temperature"]["t"][:-1],5)) + density = density + (0.824493 - (4.0899E-03 * physical["temperature"]["t"][:-1]) + (7.6438E-05 * np.power(physical["temperature"]["t"][:-1],2)) \ + - (8.2467E-07 * np.power(physical["temperature"]["t"][:-1],3)) + (5.3875E-09 * np.power(physical["temperature"]["t"][:-1],4))) * physical["salinity"]["s"][:-1] \ + + (-5.72466E-03 + (1.0227E-04 * physical["temperature"]["t"][:-1]) - (1.6546E-06 * np.power(physical["temperature"]["t"][:-1],2))) * (np.power(np.abs(physical["salinity"]["s"][:-1]),1.5)) \ + + 4.8314E-4*np.power(physical["salinity"]["s"][:-1],2) + + vertical_density_profile[:-1] = (density - 1000.) * 1.E-03 + vertical_density_profile[-1] = vertical_density_profile[-2] + + physical["density"] = vertical_density_profile + + return physical + + +def kinetic_energy_profile(physical, pom1d): + + # INITIALIZE VARIABLES + A = np.zeros(physical["water_column"]["num_layers"]) + C = np.zeros(physical["water_column"]["num_layers"]) + VH = np.zeros(physical["water_column"]["num_layers"]) + VHP = np.zeros(physical["water_column"]["num_layers"]) + + KN = np.zeros(physical["water_column"]["num_layers"]) + GH = np.zeros(physical["water_column"]["num_layers"]) + SH = np.zeros(physical["water_column"]["num_layers"]) + SM = np.zeros(physical["water_column"]["num_layers"]) + + DTEF = np.zeros(physical["water_column"]["num_layers"]) + BPROD = np.zeros(physical["water_column"]["num_layers"]) + PROD = np.zeros(physical["water_column"]["num_layers"]) + SPROD = np.zeros(physical["water_column"]["num_layers"]) + pressure = 0. + + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + # LOCAL ARRAYS + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + BOYGR = np.zeros(physical["water_column"]["num_layers"]); CC = np.zeros(physical["water_column"]["num_layers"]) + TEMP1 = np.zeros(physical["water_column"]["num_layers"]); TEMP2 = np.zeros(physical["water_column"]["num_layers"]); TEMP3 = np.zeros(physical["water_column"]["num_layers"]) + + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + # DATA STATEMENTS + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + A1 = 0.92 + B1 = 16.6 + A2 = 0.74 + B2 = 10.1 + C1 = 0.08 + E1 = 1.8 + E2 = 1.33 + E3 = 1.0 + von_karman_constant = 0.40 # KAPPA + SQ = 0.2 + CIWC = 1.0 + gravity = 9.806 + SMALL = 1.E-08 + + A[1:-1] = -physical["simulation"]["dt2"] * (physical["diffusion"]["kinetic_energy"][2:] + physical["diffusion"]["kinetic_energy"][1:-1] + 2 * pom1d["background_diffusion"]["umol"]) * \ + 0.5 / (physical["vertical_grid"]["dzz"][:-2] * physical["vertical_grid"]["dz"][1:-1] * physical["water_column"]["column_depth"] * physical["water_column"]["column_depth"]) + C[1:-1] = -physical["simulation"]["dt2"] * (physical["diffusion"]["kinetic_energy"][:-2] + physical["diffusion"]["kinetic_energy"][1:-1] + 2 * pom1d["background_diffusion"]["umol"]) * \ + 0.5 / (physical["vertical_grid"]["dzz"][:-2] * physical["vertical_grid"]["dz"][:-2] * physical["water_column"]["column_depth"] * physical["water_column"]["column_depth"]) + + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + # THE FOLLOWING SECTION SOLVES FOR THE EQUATION + # DT2*(KQ*Q2')' - Q2*(2.*DT2*DTEF+1.) = -Q2B + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + + CONST1 = 16.6 ** 0.6666667 * CIWC + + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + # BOUNDARY CONDITIONS + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + VH[0] = 0.0 + VHP[0] = np.sqrt(physical["stresses"]["wsu"] ** 2 + physical["stresses"]["wsv"] ** 2) * CONST1 + physical["kinetic_energy"]["kef"][physical["water_column"]["num_layers"] - 1] = 0.5 * np.sqrt((physical["stresses"]["bsu"] + physical["stresses"]["bsu"]) ** 2 + + (physical["stresses"]["bsv"] + physical["stresses"]["bsv"]) ** 2) * CONST1 + + physical["kinetic_energy"]["keb"][1:-1] = np.abs(physical["kinetic_energy"]["keb"][1:-1]) + physical["kinetic_energy"]["kelb"][1:-1] = np.abs(physical["kinetic_energy"]["kelb"][1:-1]) + BOYGR[1:-1] = gravity * (physical["density"][:-2] - physical["density"][1:-1]) / (physical["vertical_grid"]["dzz"][:-2] * physical["water_column"]["column_depth"]) + DTEF[1:-1] = physical["kinetic_energy"]["keb"][1:-1] * np.sqrt(physical["kinetic_energy"]["keb"][1:-1]) / (B1 * physical["kinetic_energy"]["kelb"][1:-1] + SMALL) + SPROD[1:-1] = .25 * physical["diffusion"]["momentum"][1:-1] * \ + ((physical["velocity"]["u"][1:-1] + physical["velocity"]["u"][1:-1] - physical["velocity"]["u"][0:-2] - physical["velocity"]["u"][0:-2]) ** 2 + + (physical["velocity"]["v"][1:-1] + physical["velocity"]["v"][1:-1] - physical["velocity"]["v"][0:-2] - physical["velocity"]["v"][0:-2]) ** 2) / \ + (physical["vertical_grid"]["dzz"][0:-2] * physical["water_column"]["column_depth"]) ** 2 * CIWC ** 2 + BPROD[1:-1] = physical["diffusion"]["tracers"][1:-1] * BOYGR[1:-1] + PROD[1:-1] = SPROD[1:-1] + BPROD[1:-1] + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + # SWEEP DOWNWARD + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + for i in range(1, physical["water_column"]["num_layers"] - 1): + VHP[i] = 1. / (A[i] + C[i] * (1. - VH[i - 1]) - (2. * physical["simulation"]["dt2"] * DTEF[i] + 1.)) + VH[i] = A[i] * VHP[i] + VHP[i] = (-2. * physical["simulation"]["dt2"] * PROD[i] + C[i] * VHP[i - 1] - physical["kinetic_energy"]["keb"][i]) * VHP[i] + + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + # SWEEP UPWARD + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + for i in range(2, physical["water_column"]["num_layers"]+1): # 104 + k = physical["water_column"]["num_layers"] - i + physical["kinetic_energy"]["kef"][k] = VH[k] * physical["kinetic_energy"]["kef"][k + 1] + VHP[k] + + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + # THE FOLLOWING SEECTION SOLVES FOR TEH EQUATION + # DT2(KQ*Q2L')' - Q2L*(DT2*DTEF+1.) = -Q2LB + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + # BOUNDARY CONDITIONS + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + + VH[0] = 0. + VHP[0] = 0. + physical["kinetic_energy"]["kelf"][physical["water_column"]["num_layers"] - 1] = 0. + + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + # SWEEP DOWNWARD + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + + for i in range(1, physical["water_column"]["num_layers"] - 1): + DTEF[i] = DTEF[i] * (1. + E2 * ((1. / np.abs(physical["vertical_grid"]["z"][i] - physical["vertical_grid"]["z"][0]) + + 1. / np.abs(physical["vertical_grid"]["z"][i] - physical["vertical_grid"]["z"][physical["water_column"]["num_layers"]-1])) + * physical["vertical_grid"]["l"][i] / (physical["water_column"]["column_depth"] * von_karman_constant)) ** 2) + VHP[i] = 1. / (A[i] + C[i] * (1. - VH[i - 1]) - (physical["simulation"]["dt2"] * DTEF[i] + 1.)) + VH[i] = A[i] * VHP[i] + VHP[i] = (physical["simulation"]["dt2"] * (- (SPROD[i] + E3 * BPROD[i]) * physical["vertical_grid"]["l"][i] * E1) + + C[i] * VHP[i - 1] - physical["kinetic_energy"]["kelb"][i]) * VHP[i] + + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + # SWEEP UPWARD + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + + for i in range(2, physical["water_column"]["num_layers"]+1): + k = physical["water_column"]["num_layers"] - i + physical["kinetic_energy"]["kelf"][k] = VH[k] * physical["kinetic_energy"]["kelf"][k + 1] + VHP[k] + + for i in range(1, physical["water_column"]["num_layers"] - 1): + if physical["kinetic_energy"]["kef"][i] > SMALL or physical["kinetic_energy"]["kelf"][i] > SMALL: + continue + else: + physical["kinetic_energy"]["kef"][i] = SMALL + physical["kinetic_energy"]["kelf"][i] = SMALL + + physical["kinetic_energy"]["kef"][:-1] = np.abs(physical["kinetic_energy"]["kef"][:-1]) + physical["kinetic_energy"]["kelf"][:-1] = np.abs(physical["kinetic_energy"]["kelf"][:-1]) + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + # THE FOLLOWING SECTION SOLVES FOR KM AND KH + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + + COEF1 = A2 * (1. - 6. * A1 / B1) + COEF2 = 3. * A2 * B2 + 18. * A1 * A2 + COEF3 = A1 * (1. - 3. * C1 - 6. * A1 / B1) + COEF4 = 18. * A1 * A1 + 9. * A1 * A2 + COEF5 = 9. * A1 * A2 + + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + # NOTE THAT SM AND SH LIMIT TO INFINITY WHEN GH APPROACHES 0.0288 + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + physical["vertical_grid"]["l"][0] = 0. + physical["vertical_grid"]["l"][physical["water_column"]["num_layers"] - 1] = 0. + GH[0] = 0. + GH[physical["water_column"]["num_layers"] - 1] = 0. + + physical["vertical_grid"]["l"][1:-1] = physical["kinetic_energy"]["kelf"][1:-1] / physical["kinetic_energy"]["kef"][1:-1] + GH[1:-1] = np.power(physical["vertical_grid"]["l"][1:-1],2) / physical["kinetic_energy"]["kef"][1:-1] * BOYGR[1:-1] + for i in range(0, physical["water_column"]["num_layers"]): + GH[i] = min(GH[i], .028) + SH = COEF1 / (1. - COEF2 * GH) + SM = COEF3 + SH * COEF4 * GH + SM = SM / (1. - COEF5 * GH) + + KN = physical["vertical_grid"]["l"] * np.sqrt(np.abs(physical["kinetic_energy"]["ke"])) + physical["diffusion"]["kinetic_energy"] = 0.5 * (KN * 0.41 * SM + physical["diffusion"]["kinetic_energy"]) + physical["diffusion"]["momentum"] = 0.5 * (KN * SM + physical["diffusion"]["momentum"]) + physical["diffusion"]["tracers"] = 0.5 * (KN * SH + physical["diffusion"]["tracers"]) + + return physical + + +def meridional_velocity_profile(physical, pom1d): + """ + Description: Calculates meridional (V) velocity profile + Solves for the equation: dti2 * (KM * V')' - V = -VB + + :return: data array for meridional velocity profile + """ + physical["stresses"]["wsv"] + A = np.zeros(physical["water_column"]["num_layers"]) + C = np.zeros(physical["water_column"]["num_layers"]) + VH = np.zeros(physical["water_column"]["num_layers"]) + VHP = np.zeros(physical["water_column"]["num_layers"]) + + A[:-2] = -physical["simulation"]["dt2"] * (physical["diffusion"]["momentum"][1:-1] + pom1d["background_diffusion"]["umol"]) / \ + (physical["vertical_grid"]["dz"][:-2] * physical["vertical_grid"]["dzz"][:-2] * physical["water_column"]["column_depth"] * physical["water_column"]["column_depth"]) + C[1:-1] = -physical["simulation"]["dt2"] * (physical["diffusion"]["momentum"][1:-1] + pom1d["background_diffusion"]["umol"]) / \ + (physical["vertical_grid"]["dz"][1:-1] * physical["vertical_grid"]["dzz"][:-2] * physical["water_column"]["column_depth"] * physical["water_column"]["column_depth"]) + + VH[0] = A[0] / (A[0] - 1.) + VHP[0] = (-physical["simulation"]["dt2"] * physical["stresses"]["wsv"] / (-physical["vertical_grid"]["dz"][0] * physical["water_column"]["column_depth"]) - physical["velocity"]["vf"][0]) / (A[0] - 1.) + + # 98 CONTINUE + + for i in range(1, physical["water_column"]["num_layers"] - 2): + VHP[i] = 1. / (A[i] + C[i] * (1. - VH[i - 1]) - 1.) + VH[i] = A[i] * VHP[i] + VHP[i] = (C[i] * VHP[i - 1] - physical["velocity"]["vf"][i]) * VHP[i] + + CBC = 0.0 + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + # TO RESTORE BOTTOM B.L. DELETE NEXT LINE + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + + physical["velocity"]["vf"][physical["water_column"]["num_layers"] - 2] = (C[physical["water_column"]["num_layers"] - 1] * VHP[physical["water_column"]["num_layers"] - 3] - physical["velocity"]["vf"][physical["water_column"]["num_layers"] - 2]) / ( + CBC * physical["simulation"]["dt2"] / (-physical["vertical_grid"]["dz"][physical["water_column"]["num_layers"] - 2] * physical["water_column"]["column_depth"]) - 1. - (VH[physical["water_column"]["num_layers"] - 3] - 1.) * C[physical["water_column"]["num_layers"] - 2]) + + for i in range(1, physical["water_column"]["num_layers"] - 1): + k = physical["water_column"]["num_layers"] - 1 - i + physical["velocity"]["vf"][k - 1] = VH[k - 1] * physical["velocity"]["vf"][k] + VHP[k - 1] + + physical["stresses"]["bsv"] = -CBC * physical["velocity"]["vf"][physical["water_column"]["num_layers"] - 2] # 92 + + return physical + + +def mixed_layer_depth(physical): + small = 1.e-06 + + for i in range(0,physical["water_column"]["num_layers"]-1): + + if physical["temperature"]["t"][0] > physical["temperature"]["t"][i]+0.2: + break + + physical["mld"][i] = physical["vertical_grid"]["zz"][i] - (physical["temperature"]["t"][i] + 0.2 - physical["temperature"]["t"][0]) * \ + (physical["vertical_grid"]["zz"][i] - physical["vertical_grid"]["zz"][i+1]) / \ + (physical["temperature"]["t"][i] - physical["temperature"]["t"][i+1] + small) + + return physical + + +def temperature_and_salinity_profiles(physical, pom1d, property, case): + """ + Description: Solves for the conservative (temperature & salinity) and non-conservative (BFM state variables) scalars + Handles the surface and bottom boundary conditions + NOTE: Conservative scalars are only calculated when the system is run in prognostic mode + + :return: data arrays for the 'property' (temperature, salinity, or BFM state variable) + """ + # Read property data + if case == 'Temperature': + forward = property["tf"] + surface_value = property["surf"] + surface_flux = property["surf_flux"] + bottom_flux = property["bot_flux"] + elif case == 'Salinity': + forward = property["sf"] + surface_value = property["surf"] + surface_flux = property["surf_flux"] + bottom_flux = property["bot_flux"] + elif case == 'BGC': + forward = property["bf"] + surface_value = property["surf"] + surface_flux = property["surf_flux"] + bottom_flux = property["bot_flux"] + + # FLAG FOR BOUNDARY CONDITION DEFINITION + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + # NBC=1: SURF. B.C. IS WFSURF+SWRAD. NO RADIATIVE PENETRATION. + # NBC=2; SURF. B.C. IS WFSURF. SWRAD PENETRATES WATER COLUMN. + # NBC=3; SURF. B.C. IS TSURF. NO SWRAD RADIATIVE PENETRATION. + # NBC=4; SURF. B.C. IS TSURF. SWRAD PENETRATES WATER COLUMN. + # + # NOTE THAT WTSURF (=WFSURF) AND SWRAD ARE NEGATIVE VALUES WHEN FLUX IS "IN" THE WATER COLUMN + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + + # FLAG FOR JERLOV WATER TYPE CHOICE + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + # JERLOV WATER TYPE CHOICE IS RELEVANT ONLY WHEN NBC = 2 OR NBC = 4. + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + if case == 'Temperature': + nbc = pom1d["flags"]["nbct"] + umol = pom1d["background_diffusion"]["umolt"] + elif case == 'Salinity': + nbc = pom1d["flags"]["nbcs"] + umol = pom1d["background_diffusion"]["umols"] + elif case == 'BGC': + nbc = pom1d["flags"]["nbcbgc"] + umol = pom1d["background_diffusion"]["umolbgc"] + + A = np.zeros(physical["water_column"]["num_layers"]) + C = np.zeros(physical["water_column"]["num_layers"]) + VH = np.zeros(physical["water_column"]["num_layers"]) + VHP = np.zeros(physical["water_column"]["num_layers"]) + + # SW PROFILE + vertical_radiation_profile = np.zeros(physical["water_column"]["num_layers"]) + + # IRRADIANCE PARAMETERS AFTER PAULSON & SIMPSON JPO 1977, 952-956 + RP = [0.58, 0.62, 0.67, 0.77, 0.78] + AD1 = [0.35, 0.60, 1.00, 1.50, 1.40] + AD2 = [23.00, 20.00, 17.00, 14.00, 7.90] + + # JERLOV WATER TYPES + # NTP = 1 2 3 4 5 + # JERLOV TYPE = I IA IB II III + + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + # START COMPUTATION OF VERTICAL PROFILE + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + A[:-2] = -physical["simulation"]["dt2"] * (physical["diffusion"]["tracers"][1:-1] + umol) / (physical["vertical_grid"]["dz"][:-2] * physical["vertical_grid"]["dzz"][:-2] * physical["water_column"]["column_depth"] * physical["water_column"]["column_depth"]) + C[1:-1] = -physical["simulation"]["dt2"] * (physical["diffusion"]["tracers"][1:-1] + umol) / (physical["vertical_grid"]["dz"][1:-1] * physical["vertical_grid"]["dzz"][:-2] * physical["water_column"]["column_depth"] * physical["water_column"]["column_depth"]) + + vertical_radiation_profile[:] = 0. + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + # SURFACE BOUNDARY CONDITION + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + + # *** PENETRATIVE RADIATION CALCULATION. AT THE BOTTOM ANY UNATTENUATED IS DEPOSITED IN THE BOTTOM LAYER. + + if nbc == 1: + VH[0] = A[0] / (A[0] - 1.) + if case == 'BGC': # Exclude shortwave radiation in calculations + VHP[0] = -physical["simulation"]["dt2"] * (surface_flux + 0.) / (-physical["vertical_grid"]["dz"][0] * physical["water_column"]["column_depth"]) - forward[0] + else: + VHP[0] = -physical["simulation"]["dt2"] * (surface_flux + physical["swrad"]) / (-physical["vertical_grid"]["dz"][0] * physical["water_column"]["column_depth"]) - forward[0] + VHP[0] = VHP[0] / (A[0] - 1.) + + elif nbc == 2: + if case == 'BGC': # Exclude shortwave radiation in calculations + vertical_radiation_profile[:] = 0. * (RP[pom1d["flags"]["ntp"]] * np.exp(physical["vertical_grid"]["z"][:] * physical["water_column"]["column_depth"] / AD1[pom1d["flags"]["ntp"]]) + (1. - RP[pom1d["flags"]["ntp"]] * np.exp(physical["vertical_grid"]["z"][:] * physical["water_column"]["column_depth"] / AD2[pom1d["flags"]["ntp"]]))) # *** + else: + vertical_radiation_profile[:] = physical["swrad"] * (RP[pom1d["flags"]["ntp"]] * np.exp(physical["vertical_grid"]["z"][:] * physical["water_column"]["column_depth"] / AD1[pom1d["flags"]["ntp"]]) + (1. - RP[pom1d["flags"]["ntp"]] * np.exp(physical["vertical_grid"]["z"][:] * physical["water_column"]["column_depth"] / AD2[pom1d["flags"]["ntp"]]))) # *** + vertical_radiation_profile[physical["water_column"]["num_layers"] - 1] = 0. + + VH[0] = A[0] / (A[0] - 1.) + VHP[0] = physical["simulation"]["dt2"] * (surface_flux + vertical_radiation_profile[0] - vertical_radiation_profile[1]) / (physical["vertical_grid"]["dz"][0] * physical["water_column"]["column_depth"]) - forward[0] + VHP[0] = VHP[0] / (A[0] - 1.) + + elif nbc == 3: + VH[0] = 0. + VHP[0] = surface_value + + elif nbc == 4: + if case == 'BGC': # Exclude shortwave radiation in calculations + vertical_radiation_profile[:] = 0. * (RP[pom1d["flags"]["ntp"]] * np.exp(physical["vertical_grid"]["z"][:] * physical["water_column"]["column_depth"] / AD1[pom1d["flags"]["ntp"]]) + (1. - RP[pom1d["flags"]["ntp"]] * np.exp(physical["vertical_grid"]["z"][:] * physical["water_column"]["column_depth"] / AD2[pom1d["flags"]["ntp"]]))) # *** + else: + vertical_radiation_profile[:] = physical["swrad"] * (RP[pom1d["flags"]["ntp"]] * np.exp(physical["vertical_grid"]["z"][:] * physical["water_column"]["column_depth"] / AD1[pom1d["flags"]["ntp"]]) + (1. - RP[pom1d["flags"]["ntp"]] * np.exp(physical["vertical_grid"]["z"][:] * physical["water_column"]["column_depth"] / AD2[pom1d["flags"]["ntp"]]))) # *** + vertical_radiation_profile[physical["water_column"]["num_layers"] - 1] = 0. + + VH[0] = 0. + VHP[0] = surface_value + + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + # THE FOLLOWING SECTION SOLVES THE EQUATION + # DT2*(KH*FF')' -FF = -FB + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + + for i in range(1, physical["water_column"]["num_layers"] - 2): + VHP[i] = 1 / (A[i] + C[i] * (1 - VH[i - 1]) - 1) + VH[i] = A[i] * VHP[i] + VHP[i] = (C[i] * VHP[i - 1] - forward[i] + physical["simulation"]["dt2"] * (vertical_radiation_profile[i] - vertical_radiation_profile[i + 1]) / (physical["water_column"]["column_depth"] * physical["vertical_grid"]["dz"][i])) * VHP[i] + + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + # APPLY A NON ADIABATIC BOTTOM BOUNDARY CONDITION + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + + forward[physical["water_column"]["num_layers"] - 2] = (C[physical["water_column"]["num_layers"] - 2] * VHP[physical["water_column"]["num_layers"] - 3] - forward[physical["water_column"]["num_layers"] - 2] \ + + (bottom_flux * physical["simulation"]["dt2"] / (physical["vertical_grid"]["dz"][physical["water_column"]["num_layers"] - 2] * physical["water_column"]["column_depth"]))) \ + / (C[physical["water_column"]["num_layers"] - 2] * (1 - VH[physical["water_column"]["num_layers"] - 3]) - 1) + + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + # APPLY A NON ADIABATIC BOTTOM BOUNDARY CONDITION + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + + for i in range(3, physical["water_column"]["num_layers"]+1): + k = (physical["water_column"]["num_layers"]) - i + forward[k] = VH[k] * forward[k+1] + VHP[k] + + + # Update property data + if case == 'Temperature': + property["tf"] = forward + property["surf"] = surface_value + property["surf_flux"] = surface_flux + property["bot_flux"] = bottom_flux + elif case == 'Salinity': + property["sf"] = forward + property["surf"] = surface_value + property["surf_flux"] = surface_flux + property["bot_flux"] = bottom_flux + elif case == 'BGC': + property["bf"] = forward + property["surf"] = surface_value + property["surf_flux"] = surface_flux + property["bot_flux"] = bottom_flux + + + return property + + +def zonal_velocity_profile(physical, pom1d): + """ + Description: Calculates zonal (U) velocity profile + Solves for the equation: dti2 * (KM * U')' - U = -UB + + :return: data array for zonal velocity profile + """ + A = np.zeros(physical["water_column"]["num_layers"]) + C = np.zeros(physical["water_column"]["num_layers"]) + VH = np.zeros(physical["water_column"]["num_layers"]) + VHP = np.zeros(physical["water_column"]["num_layers"]) + + A[:-2] = -physical["simulation"]["dt2"] * (physical["diffusion"]["momentum"][1:-1] + pom1d["background_diffusion"]["umol"]) / \ + (physical["vertical_grid"]["dz"][:-2] * physical["vertical_grid"]["dzz"][:-2] * physical["water_column"]["column_depth"] * physical["water_column"]["column_depth"]) + C[1:-1] = -physical["simulation"]["dt2"] * (physical["diffusion"]["momentum"][1:-1] + pom1d["background_diffusion"]["umol"]) / \ + (physical["vertical_grid"]["dz"][1:-1] * physical["vertical_grid"]["dzz"][:-2] * physical["water_column"]["column_depth"] * physical["water_column"]["column_depth"]) + + + VH[0] = A[0] / (A[0] - 1.) + VHP[0] = (-physical["simulation"]["dt2"] * physical["stresses"]["wsu"] / (-physical["vertical_grid"]["dz"][0] * physical["water_column"]["column_depth"]) - physical["velocity"]["uf"][0]) / (A[0] - 1.) + + for i in range(1, physical["water_column"]["num_layers"] - 2): + VHP[i] = 1. / (A[i] + C[i] * (1. - VH[i - 1]) - 1.) + VH[i] = A[i] * VHP[i] + VHP[i] = (C[i] * VHP[i - 1] - physical["velocity"]["uf"][i]) * VHP[i] + + VH[0] = A[0] / (A[0] - 1.) + VHP[0] = (-physical["simulation"]["dt2"] * physical["stresses"]["wsu"] / (-physical["vertical_grid"]["dz"][0] * physical["water_column"]["column_depth"]) - physical["velocity"]["uf"][0]) / (A[0] - 1.) + + CBC = 0.0 + # -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= + + physical["velocity"]["uf"][physical["water_column"]["num_layers"] - 2] = (C[physical["water_column"]["num_layers"] - 2] * VHP[physical["water_column"]["num_layers"] - 3] - physical["velocity"]["uf"][physical["water_column"]["num_layers"] - 2]) / ( + CBC * physical["simulation"]["dt2"] / (-physical["vertical_grid"]["dz"][physical["water_column"]["num_layers"] - 2] * physical["water_column"]["column_depth"]) - 1. - (VH[physical["water_column"]["num_layers"] - 3] - 1.) * C[physical["water_column"]["num_layers"] - 2]) + for i in range(1, physical["water_column"]["num_layers"] - 1): + k = physical["water_column"]["num_layers"] - 1 - i + physical["velocity"]["uf"][k - 1] = VH[k - 1] * physical["velocity"]["uf"][k] + VHP[k - 1] + physical["stresses"]["bsu"] = -CBC * physical["velocity"]["uf"][physical["water_column"]["num_layers"] - 2] # 92 + + return physical diff --git a/pom/check_conc.py b/pom/check_conc.py new file mode 100644 index 0000000..25cb90a --- /dev/null +++ b/pom/check_conc.py @@ -0,0 +1,135 @@ +o2 = [ +[215.31078739938329 , 215.45031144062267 , 215.58762270092402 , 215.72271315660115 , 215.85557427641379 , 215.98619705664984 , 216.11457190955284 , 216.24068873283787 , 216.36453721872260 , 216.48610675396259 , 216.60538668568756 , 216.72236661155137 , 216.83703621987510 , 216.94938546608185 , 217.05940467482708 , 217.16708441582594 , 217.27241543369624 , 217.37538875119350 , 217.47599576283159 , 217.57422821616828 , 217.67007832051672 , 217.76353869367557 , 217.85460245670382 , 217.94326319148325 , 218.02951496430666 , 218.11335229937558 , 218.19477021879422 , 218.27376423182460 , 218.35033040971555 , 218.42446533321245 , 218.49616618782281 , 218.56543076948802 , 218.63225749796322 , 218.69664532985234 , 218.75859384589100 , 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-1.4993630434399255E-008, -1.4839144793605644E-008, -1.4685395448853473E-008, -1.4532368173776062E-008, -1.4380049156103142E-008, -1.4228424531103887E-008, -1.4077480146620971E-008, -1.3927201548594063E-008, -1.3777574027852143E-008, -1.3628582713962425E-008, -1.3480212779254084E-008, -1.3332449526189687E-008, -1.3185278530274241E-008, -1.3038685795338228E-008, -1.2892657940062918E-008, -1.2747182395296573E-008, -1.2602247583964889E-008, -1.2457842948366186E-008, -1.2313958727495813E-008, -1.2170586084564149E-008, -1.2027716850676277E-008, -1.1885343012873576E-008, -1.1743456983102136E-008, -1.1602051925308360E-008, -1.1461123946174624E-008, -1.1320703872784690E-008, -1.1181332133739347E-008, -1.1051156990535194E-008]] diff --git a/pom/compare_values.py b/pom/compare_values.py new file mode 100644 index 0000000..6382b2f --- /dev/null +++ b/pom/compare_values.py @@ -0,0 +1,67 @@ +from pom.check_rates import o2rates, no3rates, nh4rates, po4rates, phytocrates, phytonrates, phytoprates, phytolrates, zoocrates, zoonrates, zooprates, domcrates, domnrates, domprates, pomcrates, pomnrates, pomprates +from pom.check_conc import o2, no3, nh4, po4, phyto1, zoo1, dom1, pom1 + +def compare_values(iter, tracers): + + if iter < 3: + d_dt_diff = { + "o2": tracers["o2"].d_dt[0,:] - o2rates[iter], + "no3": tracers["no3"].d_dt[0,:] - no3rates[iter], + "nh4": tracers["nh4"].d_dt[0,:] - nh4rates[iter], + "po4": tracers["po4"].d_dt[0,:] - po4rates[iter], + "phyc": tracers["phyto1"].d_dt[0,:] - phytocrates[iter], + "phyn": tracers["phyto1"].d_dt[1,:] - phytonrates[iter], + "phyp": tracers["phyto1"].d_dt[2,:] - phytoprates[iter], + "phyl": tracers["phyto1"].d_dt[3,:] - phytolrates[iter], + "zooc": tracers["zoo1"].d_dt[0,:] - zoocrates[iter], + "zoon": tracers["zoo1"].d_dt[1,:] - zoonrates[iter], + "zoop": tracers["zoo1"].d_dt[2,:] - zooprates[iter], + "domc": tracers["dom1"].d_dt[0,:] - domcrates[iter], + "domn": tracers["dom1"].d_dt[1,:] - domnrates[iter], + "domp": tracers["dom1"].d_dt[2,:] - domprates[iter], + "pomc": tracers["pom1"].d_dt[0,:] - pomcrates[iter], + "pomn": tracers["pom1"].d_dt[1,:] - pomnrates[iter], + "pomp": tracers["pom1"].d_dt[2,:] - pomprates[iter] + } + else: + d_dt_diff = { + "o2": None, + "no3": None, + "nh4": None, + "po4": None, + "phyc": None, + "phyn": None, + "phyp": None, + "phyl": None, + "zooc": None, + "zoon": None, + "zoop": None, + "domc": None, + "domn": None, + "domp": None, + "pomc": None, + "pomn": None, + "pomp": None + } + + conc_diff = { + "o2": tracers["o2"].conc[0,:,iter] - o2[iter], + "no3": tracers["no3"].conc[0,:,iter] - no3[iter], + "nh4": tracers["nh4"].conc[0,:,iter] - nh4[iter], + "po4": tracers["po4"].conc[0,:,iter] - po4[iter], + "phyc": tracers["phyto1"].conc[0,:,iter] - phyto1[0][iter], + "phyn": tracers["phyto1"].conc[1,:,iter] - phyto1[1][iter], + "phyp": tracers["phyto1"].conc[2,:,iter] - phyto1[2][iter], + "phyl": tracers["phyto1"].conc[3,:,iter] - phyto1[3][iter], + "zooc": tracers["zoo1"].conc[0,:,iter] - zoo1[0][iter], + "zoon": tracers["zoo1"].conc[1,:,iter] - zoo1[1][iter], + "zoop": tracers["zoo1"].conc[2,:,iter] - zoo1[2][iter], + "domc": tracers["dom1"].conc[0,:,iter] - dom1[0][iter], + "domn": tracers["dom1"].conc[1,:,iter] - dom1[1][iter], + "domp": tracers["dom1"].conc[2,:,iter] - dom1[2][iter], + "pomc": tracers["pom1"].conc[0,:,iter] - pom1[0][iter], + "pomn": tracers["pom1"].conc[1,:,iter] - pom1[1][iter], + "pomp": tracers["pom1"].conc[2,:,iter] - pom1[2][iter], + } + + return d_dt_diff, conc_diff \ No newline at end of file diff --git a/pom/coupling.py b/pom/coupling.py new file mode 100644 index 0000000..3d62a38 --- /dev/null +++ b/pom/coupling.py @@ -0,0 +1,627 @@ +import numpy as np +from pom.calculations import temperature_and_salinity_profiles +from functions.bgc_rate_eqns import bgc_rate_eqns +import os +# from pom.check_rates import o2rates, no3rates, nh4rates, po4rates, phytocrates, phytonrates, phytoprates, phytolrates, zoocrates, zoonrates, zooprates, domcrates, domnrates, domprates, pomcrates, pomnrates, pomprates +# from pom.check_conc import o2, no3, nh4, po4, phyto1, zoo1, dom1, pom1 +from pom.compare_values import compare_values + +phytoc0 = [0.13600693026060057, +0.13788003350628575, +0.13783314465202093, +0.14077789534980695, +0.1406810805896549, +0.14682565519044186, +0.1469538277508386, +0.15355533110108344, +0.15380860337581495, +0.15782714350506166, +0.15823479872248547, +0.1603634138729713, +0.16088357936184067, +0.16229641730321456, +0.16290433707771607, +0.1640265756317987, +0.1646512411912123, +0.1656892933940167, +0.16631613267471046, +0.1672801529436927, +0.16791557987374542, +0.16881654457000914, +0.16945076008004398, +0.17030422937999282, +0.17093152355074928, +0.1717461382009736, +0.17236375721963207, +0.1731440730732346, +0.1737537447907273, +0.17450355689311903, +0.17510471623443227, +0.1758270206516259, +0.17642106478320435, +0.17711808642920107, +0.177705748745344, +0.1783798852086758, +0.17896198303280988, +0.17961509006864942, +0.1801919679459737, +0.18082550828448518, +0.18139692518735268, +0.182012595189088, +0.18257749847798416, +0.18317799559609163, +0.18373411837356493, +0.18432338606561416, +0.18486732106459608, +0.1854495871781066, +0.1859787882754807, +0.1865556972379106, +0.18707352725578905, +0.1876417647664717, +0.1881523512368167, +0.1887092230186375, +0.18921355314393296, +0.1897590418370027, +0.19025716311759905, +0.190791741597451, +0.19128346616931985, +0.19180778594882614, +0.19229288917344098, +0.19280768371838275, +0.19328581852583587, +0.19379192274072263, +0.194262651965612, +0.1947609137088863, +0.19522384904008236, +0.1957149463426617, +0.19616996978725082, +0.19665421630946184, +0.1971016047149702, +0.19757892343012395, +0.19801920670128392, +0.19848934083368955, +0.19892303144177167, +0.19938579787071883, +0.1998132372438923, +0.2002686325704546, +0.20068998025642543, +0.20113816460355233, +0.20155344655522792, +0.2019946876590887, +0.2024038543466883, +0.20283846568374028, +0.20324145270409028, +0.20366973081010561, +0.2040665180679751, +0.20448868860114627, +0.20487933962380078, +0.20529553244377985, +0.20568019346555458, +0.20609045976182916, +0.20646932114801636, +0.2068736799088542, +0.20724692789170784, +0.20764541145421228, +0.20801319731004164, +0.20840587461636734, +0.20876830855703515, +0.2091552847541635] + +dphytoc0_dt = [2.4755744070392944e-06 , +2.2818396418391657e-06 , +2.2830341677615567e-06 , +2.2686918872596887e-06 , +2.2631694970350074e-06 , +2.3088564741477703e-06 , +2.3005284577016885e-06 , +2.363951069053597e-06 , +2.353414212408722e-06 , +2.3787004279543424e-06 , +2.3696736578622503e-06 , +2.3648220235825352e-06 , +2.356605711214298e-06 , +2.341537671875432e-06 , +2.334906171846532e-06 , +2.3156927041796382e-06 , +2.3073953662189327e-06 , +2.290994489265538e-06 , +2.281223684531372e-06 , +2.264658357630849e-06 , +2.2558781161608068e-06 , +2.2389947710257535e-06 , +2.2296407128710994e-06 , +2.212967684752981e-06 , +2.2030348032225503e-06 , +2.1868218986392617e-06 , +2.17655538456412e-06 , +2.160814793884954e-06 , +2.1501336671641675e-06 , +2.1348928277316772e-06 , +2.1239528780967814e-06 , +2.1091385052853723e-06 , +2.0979410461269117e-06 , +2.0835443499021336e-06 , +2.0721490724615306e-06 , +2.0581576345582797e-06 , +2.046618290433258e-06 , +2.0330190124558263e-06 , +2.02137422716915e-06 , +2.008139435767859e-06 , +1.9964271632435622e-06 , +1.9835094894301315e-06 , +1.9717970978552274e-06 , +1.9591176290273324e-06 , +1.9475207547016606e-06 , +1.934966935424715e-06 , +1.923634666677869e-06 , +1.9110932665015686e-06 , +1.9001260139055388e-06 , +1.8875769911782143e-06 , +1.8768559057773658e-06 , +1.8644408980059532e-06 , +1.8538214454643014e-06 , +1.8416445345551042e-06 , +1.8310944160824061e-06 , +1.8191601048320136e-06 , +1.8086824106099311e-06 , +1.7969771336869415e-06 , +1.7865851763037274e-06 , +1.775090849882436e-06 , +1.7648014729909736e-06 , +1.7534994389117582e-06 , +1.7433346005846775e-06 , +1.7322045376119722e-06 , +1.7221883409255328e-06 , +1.7112102560978293e-06 , +1.701362676678202e-06 , +1.6905217165558564e-06 , +1.6808506665341032e-06 , +1.6701423414158121e-06 , +1.6606402108366028e-06 , +1.6500706040408564e-06 , +1.6407201770962077e-06 , +1.6302992212878232e-06 , +1.6210836202099393e-06 , +1.610817583308048e-06 , +1.6017259202439877e-06 , +1.5916148079521084e-06 , +1.5826428104608803e-06 , +1.5726816051006693e-06 , +1.5638300785355485e-06 , +1.554011039992666e-06 , +1.545283838110929e-06 , +1.5355987342470363e-06 , +1.5270005711428142e-06 , +1.5174426501061089e-06 , +1.508977003867226e-06 , +1.4995424169148317e-06 , +1.4912101731463094e-06 , +1.4818982941662773e-06 , +1.4736977139891325e-06 , +1.4645101050009422e-06 , +1.4564379194967568e-06 , +1.4473765001009257e-06 , +1.4394292115735164e-06 , +1.4304946653826841e-06 , +1.4226692864403713e-06 , +1.4138603737862277e-06 , +1.406154494834026e-06 , +1.3974682583182601e-06 ] + +bfm_rates = 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, +4.937871377266315e-07 , +-5.52469082986163e-07 , +2.2631694970350074e-06 , +8.49986147904011e-09 , +2.0306996086612133e-11 , +6.255414927657017e-09 , +-2.3379029858387204e-06 , +-2.9458289500257597e-08 , +-1.8381034661908385e-09 , +-1.0615598833472438e-05 , +-1.3714987465242234e-07 , +-8.552311076545056e-09 , +-2.6411124454273308e-05 , +-3.3287391485520697e-07 , +-2.077571834457708e-08 , +], +[ +-0.0003752665818835991 , +3.1639713468201996e-08 , +-2.383108130799395e-09 , +5.02857139213085e-07 , +-5.523669498893376e-07 , +2.3088564741477703e-06 , +6.844237006471142e-09 , +1.5376006624380294e-11 , +6.230174264538123e-09 , +-2.4166912927924013e-06 , +-3.045111170292951e-08 , +-1.9000523845737113e-09 , +-1.0470164510265753e-05 , +-1.361531124900626e-07 , +-8.489953065341617e-09 , +-2.703309113931567e-05 , +-3.407140438957646e-07 , +-2.1265084024911053e-08 , +], +[ +-0.0003769640383178212 , +3.164107950483612e-08 , +-2.394453321353933e-09 , +5.028369572434554e-07 , +-5.523275669843113e-07 , +2.3005284577016885e-06 , +6.900295703560546e-09 , +1.554153531681241e-11 , +6.183281681097236e-09 , +-2.4142880538089856e-06 , +-3.04208314574219e-08 , +-1.898162993159106e-09 , +-1.0463120994259812e-05 , +-1.361991325931837e-07 , +-8.49283284923432e-09 , +-2.7033791869320973e-05 , +-3.407228355750564e-07 , +-2.1265625197759508e-08 , +], +[ +-0.00039426791592601115 , +3.213689957938693e-08 , +-1.7606956921209657e-09 , +5.120687661724612e-07 , +-5.522340643856784e-07 , +2.363951069053597e-06 , +4.8837900132310866e-09 , +9.471377168283964e-12 , +6.226572716407266e-09 , +-2.5051757339126082e-06 , +-3.1566122718635774e-08 , +-1.9696254900908206e-09 , +-1.029489827543615e-05 , +-1.347943431002449e-07 , +-8.404983452434143e-09 , +-2.7677025659191022e-05 , +-3.488313946746907e-07 , +-2.177176201403025e-08 , +], +[ +-0.00039591372259588305 , +3.214705416004671e-08 , +-1.763100246848316e-09 , +5.121958669953381e-07 , +-5.521836338409e-07 , +2.353414212408722e-06 , +4.9240671663099915e-09 , +9.61111827018977e-12 , +6.162951586769304e-09 , +-2.503034405463463e-06 , +-3.153914414752849e-08 , +-1.967942112046762e-09 , +-1.0283461427933966e-05 , +-1.3484226059473362e-07 , +-8.407981292188216e-09 , +-2.7688454572758568e-05 , +-3.489754291725377e-07 , +-2.178074187408192e-08 , +], +[ +-0.00040481056943918913 , +3.2392535722272814e-08 , +-1.3660691006337756e-09 , +5.167762870228568e-07 , +-5.520622557958231e-07 , +2.3787004279543424e-06 , +3.826258649588383e-09 , +6.203050145371472e-12 , +6.115953300262629e-09 , +-2.5525230300539776e-06 , +-3.216276784759683e-08 , +-2.0068542247854025e-09 , +-1.0165753807648383e-05 , +-1.339664911460235e-07 , +-8.353230248409845e-09 , +-2.801619563359032e-05 , +-3.53107217578191e-07 , +-2.2038654299222934e-08 , +]] + + +globe_rates = np.zeros((100,18)) + +def pom_bgc_1d(iter, base_element, physical, pom1d, tracers): + + physical = bgc_physical(physical, pom1d) + bgc_rate_eqns(iter, base_element, physical, pom1d, tracers) + # print('i= ',iter) + # # print(tracers["phyto1"].conc[0,0,iter] - phytoc0[iter]) + # # print(tracers["phyto1"].d_dt[0,0] - dphytoc0_dt[iter]) + + # globe_rates[iter,0] = tracers["o2"].d_dt[0,0] + # globe_rates[iter,1] = tracers["po4"].d_dt[0,0] + # globe_rates[iter,2] = tracers["no3"].d_dt[0,0] + # globe_rates[iter,3] = tracers["nh4"].d_dt[0,0] + # globe_rates[iter,4] = tracers["hs"].d_dt[0,0] + # globe_rates[iter,5] = tracers["phyto1"].d_dt[0,0] + # globe_rates[iter,6] = tracers["phyto1"].d_dt[1,0] + # globe_rates[iter,7] = tracers["phyto1"].d_dt[2,0] + # globe_rates[iter,8] = tracers["phyto1"].d_dt[3,0] + # globe_rates[iter,9] = tracers["zoo1"].d_dt[0,0] + # globe_rates[iter,10] = tracers["zoo1"].d_dt[1,0] + # globe_rates[iter,11] = tracers["zoo1"].d_dt[2,0] + # globe_rates[iter,12] = tracers["dom1"].d_dt[0,0] + # globe_rates[iter,13] = tracers["dom1"].d_dt[1,0] + # globe_rates[iter,14] = tracers["dom1"].d_dt[2,0] + # globe_rates[iter,15] = tracers["pom1"].d_dt[0,0] + # globe_rates[iter,16] = tracers["pom1"].d_dt[1,0] + # globe_rates[iter,17] = tracers["pom1"].d_dt[2,0] + + # print(globe_rates[iter,:] - bfm_rates[iter]) + o2_surf = tracers["o2"].conc[0,0,iter] + no3_surf = tracers["no3"].conc[0,0,iter] + nh4_surf= tracers["nh4"].conc[0,0,iter] + po4_surf = tracers["po4"].conc[0,0,iter] + phyc_surf = tracers["phyto1"].conc[0,0,iter] + phyn_surf = tracers["phyto1"].conc[1,0,iter] + phyp_surf = tracers["phyto1"].conc[2,0,iter] + phyl_surf = tracers["phyto1"].conc[3,0,iter] + zc_surf = tracers["zoo1"].conc[0,0,iter] + zn_surf = tracers["zoo1"].conc[1,0,iter] + zp_surf = tracers["zoo1"].conc[2,0,iter] + dc_surf = tracers["dom1"].conc[0,0,iter] + dn_surf = tracers["dom1"].conc[1,0,iter] + dp_surf = tracers["dom1"].conc[2,0,iter] + pc_surf = tracers["pom1"].conc[0,0,iter] + pn_surf = tracers["pom1"].conc[1,0,iter] + pp_surf = tracers["pom1"].conc[2,0,iter] + o2_bot = tracers["o2"].conc[0,-1,iter] + no3_bot = tracers["no3"].conc[0,-1,iter] + nh4_bot = tracers["nh4"].conc[0,-1,iter] + po4_bot = tracers["po4"].conc[0,-1,iter] + phyc_bot = tracers["phyto1"].conc[0,-1,iter] + phyn_bot = tracers["phyto1"].conc[1,-1,iter] + phyp_bot = tracers["phyto1"].conc[2,-1,iter] + phyl_bot = tracers["phyto1"].conc[3,-1,iter] + zc_bot = tracers["zoo1"].conc[0,-1,iter] + zn_bot = tracers["zoo1"].conc[1,-1,iter] + zp_bot = tracers["zoo1"].conc[2,-1,iter] + dc_bot = tracers["dom1"].conc[0,-1,iter] + dn_bot = tracers["dom1"].conc[1,-1,iter] + dp_bot = tracers["dom1"].conc[2,-1,iter] + pc_bot = tracers["pom1"].conc[0,-1,iter] + pn_bot = tracers["pom1"].conc[1,-1,iter] + pp_bot = tracers["pom1"].conc[2,-1,iter] + + if iter < 5: + d_dt_diff, conc_diff = compare_values(iter, tracers) + # if iter == 9: + # x=1 + vertical_diffusivity(iter, physical, pom1d, tracers) + + +def bgc_physical(physical, pom1d): + + # Initialize dictionary + physical["bgc_phys_vars"] = {} + + # 1D arrays for bgc calculations + physical["bgc_phys_vars"]["temperature"] = physical["temperature"]["tb"][:-1] + physical["bgc_phys_vars"]["salinity"] = physical["salinity"]["sb"][:-1] + physical["bgc_phys_vars"]["density"] = (physical["density"][:-1] * 1.E+03) + 1.E+03 + physical["bgc_phys_vars"]["ism"] = physical["ism"] + physical["bgc_phys_vars"]["z"] = physical["vertical_grid"]["dz"][:-1] * physical["water_column"]["column_depth"] + physical["bgc_phys_vars"]["dz"] = physical["vertical_grid"]["dz"][:-1] + physical["bgc_phys_vars"]["surface_PAR"] = -physical["swrad"] * pom1d["general"]["water_specific_heat_times_density"] + physical["bgc_phys_vars"]["weddy"] = physical["weddy"] + physical["bgc_phys_vars"]["wgen"] = physical["wgen"] + + wind = np.sqrt(physical["stresses"]["wsu"]**2 + physical["stresses"]["wsv"]**2) * 1.E+03 + physical["bgc_phys_vars"]["wind"] = np.sqrt(wind/(1.25 * 0.0014)) + + return physical + + +def vertical_advection(physical, bgc_state_var, sinking_velocity): + """" + Description: Handles the sinking of BFM state variablles. Sinking is treated as downward vertical advection + computed with upstream finite differences. + NOTE: Downward velocities are negative + """ + # sinking velocity input from vdiff_SOS + bgc_state_var["b"][-1] = bgc_state_var["b"][-2] + bgc_state_var["bb"][-1] = bgc_state_var["bb"][-2] + + bgc_state_var["bf"][0] = physical["vertical_grid"]["dzr"][0] * bgc_state_var["b"][0] * sinking_velocity[1] + for i in range(1,physical["water_column"]["num_layers"]-1): + bgc_state_var["bf"][i] = physical["vertical_grid"]["dzr"][i] * (bgc_state_var["b"][i] * sinking_velocity[i + 1] - bgc_state_var["b"][i - 1] * sinking_velocity[i]) + + return bgc_state_var + + +def vertical_diffusivity(iter, physical, pom1d, tracers): + """ + Description: Calculates the vertical diffusivity of BFM biochemical components and + integrats BFM state variables with Source Splitting (SoS) method + """ + + # The input general cir. vertical vel. is suppose to be in m/s + W_ON = 1.0 + + # The input eddy vertical vel. is provided in m/d + Weddy_ON = 0.1/physical["simulation"]["sec_per_day"] # to m/s + + # Relaxation velocities + trelax_o2 = pom1d["relaxation_velocities"]["nrt_o2"] / physical["simulation"]["sec_per_day"] + trelax_po4 = pom1d["relaxation_velocities"]["nrt_po4"] / physical["simulation"]["sec_per_day"] + trelax_no3 = pom1d["relaxation_velocities"]["nrt_no3"] / physical["simulation"]["sec_per_day"] + trelax_nh4 = pom1d["relaxation_velocities"]["nrt_nh4"] + + # Loop over bgc state variables + for key in tracers: + if key == "phyto1": + x=1 + for const in tracers[key].composition: + # Get tracer for time stepping + index = tracers[key].composition.index(const) + + # Zeroing of previous tracer + bgc_state_var = { + "b": np.zeros(physical["water_column"]["num_layers"]), # Current + "bf": np.zeros(physical["water_column"]["num_layers"]), # Forward + "bb": np.zeros(physical["water_column"]["num_layers"]), # Backward + "surf": 0., # Surface value + "surf_flux": 0., # Surface flux + "bot_flux": 0. # Bottom flux + } + + # Load BFM state variable + if iter == 0: # Initialize backward time level on first iteration + bgc_state_var["b"][:-1] = tracers[key].conc[index,:,iter] + bgc_state_var["bb"][:-1] = tracers[key].conc[index,:,iter] + else: # Current and backward time levels previously calculated + bgc_state_var["b"][:-1] = tracers[key].conc[index,:,iter] + bgc_state_var["bb"][:-1] = tracers[key].conc[index,:,iter-1] + + bgc_state_var["b"][-1] = bgc_state_var["b"][-2] + bgc_state_var["bb"][-1] = bgc_state_var["bb"][-2] + + # Calculate tracer sinking velocity + sinking_velocity = W_ON*physical["bgc_phys_vars"]["wgen"] + Weddy_ON*physical["bgc_phys_vars"]["weddy"] + + if key == 'o2': + bgc_state_var["surf_flux"] = -(tracers[key].surf_flux[0,0] / physical["simulation"]["sec_per_day"]) + bgc_state_var["bot_flux"] = (tracers[key].conc[index,-1,iter] - physical["nutrients"]["o2b"]) * trelax_o2 + elif key == 'no3': + bgc_state_var["surf_flux"] = 0. + bgc_state_var["bot_flux"] = (tracers[key].conc[index,-1,iter] - physical["nutrients"]["no3b"]) * trelax_no3 + elif key == 'nh4': + bgc_state_var["surf_flux"] = 0. + bgc_state_var["bot_flux"] = physical["nutrients"]["ponb_grad"] * trelax_nh4 + elif key == 'po4': + bgc_state_var["surf_flux"] = 0. + bgc_state_var["bot_flux"] = (tracers[key].conc[index,-1,iter] - physical["nutrients"]["po4b"]) * trelax_po4 + # elif key == 'co2': + # bgc_state_var["surf_flux"] = 0. + # bgc_state_var["bot_flux"] = (tracers[key].conc[index,-1,iter] - physical["nutrients"]["no3b"]) * trelax_no3 + # elif key == 'sio4': + # bgc_state_var["surf_flux"] = 0. + # bgc_state_var["bot_flux"] = (tracers[key].conc[index,-1,iter] - physical["nutrients"]["no3b"]) * trelax_no3 + + if hasattr(tracers[key],"sinking_velocity"): + # Include additional sinking velocity for sinking tracers + sinking_velocity[:-1] -= tracers[key].sinking_velocity / physical["simulation"]["sec_per_day"] + + if tracers[key].type == "phytoplankton": + # Final sink value for phytoplankton + sinking_velocity[-1] = sinking_velocity[-2] + + if tracers[key].type == "detritus": + # Final sink value for particulate detritus + if tracers[key].form == "particulate": sinking_velocity[-1] = sinking_velocity[-2] + + # Sinking: upstream vertical advection + bgc_state_var = vertical_advection(physical, bgc_state_var, sinking_velocity) + + # Source splitting (SoS) leapfrog integration + for i in range(0,physical["water_column"]["num_layers"]-1): + bgc_state_var["bf"][i] = bgc_state_var["bb"][i] + physical["simulation"]["dt2"]*((bgc_state_var["bf"][i]/physical["water_column"]["column_depth"]) + tracers[key].d_dt[index,i]) + + # Compute vertical diffusion and terminate integration (implicit leapfrogging) + bgc_state_var = temperature_and_salinity_profiles(physical, pom1d, bgc_state_var, 'BGC') + + if key == 'o2': + x=1 + + # Clipping (if needed) + for i in range(0,physical["water_column"]["num_layers"]-1): + bgc_state_var["bf"][i] = max(1.E-20,bgc_state_var["bf"][i]) + + # Mix the time step and restore time sequence + tracers[key].conc[index,:,iter] = bgc_state_var["b"][:-1] + 0.5 * pom1d["general"]["smoth"] * (bgc_state_var["bf"][:-1] + bgc_state_var["bb"][:-1] - 2.*bgc_state_var["b"][:-1]) + tracers[key].conc[index,:,iter+1] = bgc_state_var["bf"][:-1] + + return + diff --git a/pom/data_classes.py b/pom/data_classes.py new file mode 100644 index 0000000..1898e5a --- /dev/null +++ b/pom/data_classes.py @@ -0,0 +1,112 @@ +import numpy as np + +class Diffusion: + def __init__(self,vertical_layers): + self.tracers = np.zeros(vertical_layers) + self.momentum = np.zeros(vertical_layers) + self.kinetic_energy = np.zeros(vertical_layers) + +class ForcingManagerCounters: + def __init__(self): + self.day_counter = 0 + self.day_interpolator = 0 + self.ratio_day = 0 + self.month_counter = 0 + self.month_interpolator = 0 + self.ratio_month = 0 + self.timesteps_per_day = 0 + self.timesteps_per_month = 0 + +class KineticEnergyAndVelocity: + def __init__(self,initial_value,vertical_layers): + self.current = initial_value * np.ones(vertical_layers) + self.forward = initial_value * np.ones(vertical_layers) + self.backward = initial_value * np.ones(vertical_layers) + +class MonthlyForcing: + def __init__(self,vertical_layers): + self.sclim = np.zeros(vertical_layers) + self.tclim = np.zeros(vertical_layers) + self.wclim = np.zeros(vertical_layers) + self.weddy1 = np.zeros(vertical_layers) + self.weddy2 = np.zeros(vertical_layers) + self.ism = np.zeros(vertical_layers-1) + self.wsu = 0 + self.wsv = 0 + self.swrad = 0 + self.wtsurf = 0 + self.qcorr = 0 + self.NO3_s = 0 + self.NH4_s = 0 + self.PO4_s = 0 + self.SIO4_s = 0 + self.O2_b = 0 + self.NO3_b = 0 + self.PO4_b = 0 + self.PON_b = 0 + +class Nutrients: + def __init__(self): + self.NO3surf = 0 + self.NH4surf = 0 + self.PO4surf = 0 + self.SIO4surf = 0 + self.O2bott = 0 + self.NO3bott = 0 + self.PO4bott = 0 + self.PONbott_grad = 0 + +class Stresses: + def __init__(self): + self.zonal = 0 + self.meridional = 0 + +class TemperatureAndSalinity: + def __init__(self,vertical_layers): + self.current = np.zeros(vertical_layers) + self.forward = np.zeros(vertical_layers) + self.backward = np.zeros(vertical_layers) + self.interpolated = np.zeros(vertical_layers) + self.surface_value = 0 + self.surface_flux = 0 + self.bottom_flux = 0 + self.lateral_advection = np.zeros(vertical_layers) + +class VerticalGrid: + def __init__(self,vertical_layers): + self.length_scale = np.ones(vertical_layers) + self.vertical_coordinates = np.zeros(vertical_layers) + self.vertical_coordinates_staggered = np.zeros(vertical_layers) + self.vertical_spacing = np.zeros(vertical_layers) + self.vertical_spacing_staggered = np.zeros(vertical_layers) + self.vertical_spacing_reciprocal = np.zeros(vertical_layers) + + def coordinate_system(self,surface_layers_with_log_distribution,bottom_layers_with_log_distribution,vertical_layers): + surface_logspace_layers = surface_layers_with_log_distribution - 2. + bottom_logspace_layers = vertical_layers - bottom_layers_with_log_distribution - 1. + + BB = (bottom_layers_with_log_distribution - surface_layers_with_log_distribution) + 4. + CC = surface_layers_with_log_distribution - 2. + initial_spacing = 2. / BB / np.exp(.693147 * (surface_layers_with_log_distribution - 2)) + + self.vertical_coordinates_staggered[0] = -0.5 * initial_spacing + + for i in range(1,int(surface_layers_with_log_distribution)-1): + self.vertical_coordinates[i-1] = -initial_spacing * 2**(i-2) + self.vertical_coordinates_staggered[i-1] = -initial_spacing * 2**(i-1.5) + + for i in range(int(surface_layers_with_log_distribution)-1,vertical_layers+1): + self.vertical_coordinates[i-1] = -(i - surface_logspace_layers) / (bottom_layers_with_log_distribution + - surface_layers_with_log_distribution + 4.) + self.vertical_coordinates_staggered[i-1] = -(i - surface_logspace_layers + 0.5) / (bottom_layers_with_log_distribution + - surface_layers_with_log_distribution + 4.) + + self.vertical_spacing[:-1] = self.vertical_coordinates[:-1] - self.vertical_coordinates[1:] + self.vertical_spacing_staggered[:-1] = self.vertical_coordinates_staggered[:-1] - self.vertical_coordinates_staggered[1:] + + self.vertical_spacing[-1] = 1.e-06 # small value to avoid division by zero for vertical_spacing_reciprocal + self.vertical_spacing_reciprocal = 1. / self.vertical_spacing # take reciprocal + self.vertical_spacing[-1] = 0. # correct value + + self.length_scale[0] = 0. + self.length_scale[-1] = 0. \ No newline at end of file diff --git a/pom/forcing.py b/pom/forcing.py new file mode 100644 index 0000000..f5b78e6 --- /dev/null +++ b/pom/forcing.py @@ -0,0 +1,347 @@ +import numpy as np +import os +from pom.initialize import read_pom_inputs + + +def forcing_manager(iter, physical, forcing, pom1d): + """ + Description: Handles the forcing data reading and interpolation. + Accomodates perpetual year monthly forcing time series. + Data currently handled are time series of monthly averaged data. + + :return: forcing data + """ + # INITIALISATION AND FIRST FORCING READING + if iter == 0: + + # Month and day counters + forcing["counters"]["day_counter"] = 1 + forcing["counters"]["month_counter"] = 0 + + # Timesteps to cover one day/month + forcing["counters"]["timesteps_per_day"] = physical["simulation"]["sec_per_day"] / physical["simulation"]["dt"] + forcing["counters"]["timesteps_per_month"] = 30 * forcing["counters"]["timesteps_per_day"] + + # Month and day interpolators + forcing["counters"]["day_interpolator"] = -1 + forcing["counters"]["month_interpolator"] = (forcing["counters"]["timesteps_per_month"] / 2.) - 1 # Climatological forcing data centered at day 15 of each month + + # Reading for the first month + forcing["month1"] = write_forcing_data(forcing["counters"]["month_counter"], physical, pom1d) + + # Update the day counter + forcing["counters"]["day_counter"] = forcing["counters"]["day_counter"] + 1 + + # Update the month counter + forcing["counters"]["month_counter"] = forcing["counters"]["month_counter"] + 1 + + # Reading for the second month + forcing["month2"] = write_forcing_data(forcing["counters"]["month_counter"], physical, pom1d) + + # Update interpolation counters + forcing["counters"]["day_interpolator"] = forcing["counters"]["day_interpolator"] + 1 + forcing["counters"]["day_ratio"] = forcing["counters"]["day_interpolator"] / forcing["counters"]["timesteps_per_day"] + + if forcing["counters"]["day_ratio"] == 1: + x=1 + + forcing["counters"]["month_interpolator"] = forcing["counters"]["month_interpolator"] + 1 + forcing["counters"]["month_ratio"] = forcing["counters"]["month_interpolator"] / forcing["counters"]["timesteps_per_month"] + + # Interpolate wind stress + physical["stresses"]["wsu"] = forcing["month1"]["wsu"] + forcing["counters"]["month_ratio"] * (forcing["month2"]["wsu"] - forcing["month1"]["wsu"]) + physical["stresses"]["wsv"] = forcing["month1"]["wsv"] + forcing["counters"]["month_ratio"] * (forcing["month2"]["wsv"] - forcing["month1"]["wsv"]) + + # Interpolate heat flux + if pom1d["general"]["idiagn"] == 0: + physical["temperature"]["surf_flux"] = forcing["month1"]["wtsurf"] + forcing["counters"]["month_ratio"] * (forcing["month2"]["wtsurf"] - forcing["month1"]["wtsurf"]) + physical["swrad"] = forcing["month1"]["swrad"] + forcing["counters"]["month_ratio"] * (forcing["month2"]["swrad"] - forcing["month1"]["swrad"]) + else: + physical["temperature"]["surf_flux"] = 0 # not needed for diagnostic mode (idiagn = 1), see 4.6.5 in manual + physical["swrad"] = forcing["month1"]["swrad"] + forcing["counters"]["month_ratio"] * (forcing["month2"]["swrad"] - forcing["month1"]["swrad"]) + + # Interpolate temperature and salinity profiles + physical["temperature"]["ti"] = forcing["month1"]["tclim"] + forcing["counters"]["month_ratio"] * (forcing["month2"]["tclim"] - forcing["month1"]["tclim"]) + physical["salinity"]["si"] = forcing["month1"]["sclim"] + forcing["counters"]["month_ratio"] * (forcing["month2"]["sclim"] - forcing["month1"]["sclim"]) + physical["wgen"] = forcing["month1"]["wclim"] + forcing["counters"]["month_ratio"] * (forcing["month2"]["wclim"] - forcing["month1"]["wclim"]) + + if forcing["counters"]["month_ratio"] <= 0.5: + physical["weddy"] = forcing["month1"]["weddy1"] + else: + physical["weddy"] = forcing["month1"]["weddy2"] + + if pom1d["general"]["idiagn"] == 0: + physical["temperature"]["surf"] = physical["temperature"]["ti"][0] + physical["salinity"]["surf"] = physical["salinity"]["si"][0] + elif pom1d["general"]["idiagn"] == 1: + physical["temperature"]["tf"] = physical["temperature"]["ti"] + physical["salinity"]["sf"] = physical["salinity"]["si"] + + # Interpolate suspended inorganic matter + physical["ism"] = forcing["month1"]["ism"] + forcing["counters"]["month_ratio"] * (forcing["month2"]["ism"] - forcing["month1"]["ism"]) + + # Interpolate surface and bottom nutrients + physical["nutrients"]["no3s"] = forcing["month1"]["no3s"] + forcing["counters"]["month_ratio"] * (forcing["month2"]["no3s"] - forcing["month1"]["no3s"]) + physical["nutrients"]["nh4s"] = forcing["month1"]["nh4s"] + forcing["counters"]["month_ratio"] * (forcing["month2"]["nh4s"] - forcing["month1"]["nh4s"]) + physical["nutrients"]["po4s"] = forcing["month1"]["po4s"] + forcing["counters"]["month_ratio"] * (forcing["month2"]["po4s"] - forcing["month1"]["po4s"]) + physical["nutrients"]["sio4s"] = forcing["month1"]["sio4s"] + forcing["counters"]["month_ratio"] * (forcing["month2"]["sio4s"] - forcing["month1"]["sio4s"]) + + physical["nutrients"]["o2b"] = forcing["month1"]["o2b"] + forcing["counters"]["month_ratio"] * (forcing["month2"]["o2b"] - forcing["month1"]["o2b"]) + physical["nutrients"]["no3b"] = forcing["month1"]["no3b"] + forcing["counters"]["month_ratio"] * (forcing["month2"]["no3b"] - forcing["month1"]["no3b"]) + physical["nutrients"]["po4b"] = forcing["month1"]["po4b"] + forcing["counters"]["month_ratio"] * (forcing["month2"]["po4b"] - forcing["month1"]["po4b"]) + physical["nutrients"]["ponb_grad"] = forcing["month1"]["ponb"] + forcing["counters"]["month_ratio"] * (forcing["month2"]["ponb"] - forcing["month1"]["ponb"]) + + if forcing["counters"]["month_interpolator"] == forcing["counters"]["timesteps_per_month"]: + + # Update the month counter + if os.path.exists('output_file.txt'): + output_file = open('output_file.txt','a') + output_file.write('month_counter = ') + output_file.write(str(forcing["counters"]["month_counter"])) + output_file.write('\n') + output_file.close() + else: + output_file = open('output_file.txt','w') + output_file.write('month_counter = ') + output_file.write(str(forcing["counters"]["month_counter"])) + output_file.write('\n') + output_file.close() + + print('month_counter = ',forcing["counters"]["month_counter"]) + forcing["counters"]["month_counter"] = forcing["counters"]["month_counter"] + 1 + + # Reset the interpolator + forcing["counters"]["month_interpolator"] = 0 + + # Shift the monthly data + forcing["month1"] = forcing["month2"] + + # If 12 months have passed, restart the reading sequence + if forcing["counters"]["month_counter"] > 12: + forcing["counters"]["month_counter"] = 0 + forcing["month1"] = write_forcing_data(forcing["counters"]["month_counter"], physical, pom1d) + + forcing["counters"]["month_counter"] = forcing["counters"]["month_counter"] + 1 + + # Read the following month + forcing["month2"] = write_forcing_data(forcing["counters"]["month_counter"], physical, pom1d) + + return physical, forcing + + + +def write_forcing_data(month_counter, physical, pom1d): + """ + Definition: Writes forcing data for each month. + """ + forcing_data = read_pom_inputs(physical, pom1d) + month_data = {} + + # Climatology + month_data["sclim"] = forcing_data["sclim"][:,month_counter] + month_data["tclim"] = forcing_data["tclim"][:,month_counter] + month_data["wclim"] = forcing_data["wclim"][:,month_counter] + + # Intermittant eddy w velocities + month_data["weddy1"] = forcing_data["weddy1"][:,month_counter] + month_data["weddy2"] = forcing_data["weddy2"][:,month_counter] + + # Inorganic suspended matter + month_data["ism"] = forcing_data["ism"][:-1,month_counter] + + month_data["wsu"] = -0.001 * forcing_data["wsu"][month_counter] # N/m2-->m2/s2 + month_data["wsv"] = -0.001 * forcing_data["wsv"][month_counter] # N/m2-->m2/s2 + + # Heat Flux + month_data["swrad"] = -forcing_data["swrad"][month_counter] / pom1d["general"]["water_specific_heat_times_density"] # W/m2-->deg.C*m/s + month_data["wtsurf"] = -forcing_data["wtsurf"][month_counter] / pom1d["general"]["water_specific_heat_times_density"] # W/m2-->deg.C*m/s + month_data["qcorr"] = forcing_data["qcorr"][month_counter] + + # Surface nutrients + month_data["no3s"] = forcing_data["no3s"][month_counter] + month_data["nh4s"] = forcing_data["nh4s"][month_counter] + month_data["po4s"] = forcing_data["po4s"][month_counter] + month_data["sio4s"] = forcing_data["sio4s"][month_counter] + + # Bottom nutrients + month_data["o2b"] = forcing_data["o2b"][month_counter] + month_data["no3b"] = forcing_data["no3b"][month_counter] + month_data["po4b"] = forcing_data["po4b"][month_counter] + month_data["ponb"] = forcing_data["ponb"][month_counter] + + return month_data + +# def forcing_manager(time_loop_counter,counters,month1_data,month2_data,pom_bfm_parameters): +# """ +# Description: Handles the forcing data reading and interpolation. +# Accomodates perpetual year monthly forcing time series. +# Data currently handled are time series of monthly averaged data. + +# :return: forcing data +# """ +# # INITIALISATION AND FIRST FORCING READING +# if time_loop_counter == 0: + +# # Month and day counters +# counters.day_counter = 1 +# counters.month_counter = 0 + +# # Timesteps to cover one day/month +# counters.timesteps_per_day = pom_bfm_parameters.sec_per_day / pom_bfm_parameters.dti +# forcing["counters"]["timesteps_per_month"] = 30 * counters.timesteps_per_day + +# # Month and day interpolators +# counters.day_interpolator = -1 +# counters.month_interpolator = (counters.timesteps_per_month / 2.) - 1 # Climatological forcing data centered at day 15 of each month + +# # Reading for the first month +# month1_data = write_forcing_data(counters.month_counter) + +# # Update the day counter +# counters.day_counter = counters.day_counter + 1 + +# # Update the month counter +# counters.month_counter = counters.month_counter + 1 + +# # Reading for the second month +# month2_data = write_forcing_data(counters.month_counter) + +# # Update interpolation counters +# counters.day_interpolator = counters.day_interpolator + 1 +# counters.ratio_day = counters.day_interpolator / counters.timesteps_per_day + +# counters.month_interpolator = counters.month_interpolator + 1 +# counters.ratio_month = counters.month_interpolator / counters.timesteps_per_month + +# # Interpolate wind stress +# wind_stress = Stresses() +# wind_stress.zonal = month1_data.wsu + counters.ratio_month * (month2_data.wsu - month1_data.wsu) +# wind_stress.meridional = month1_data.wsv + counters.ratio_month * (month2_data.wsv - month1_data.wsv) + +# # Interpolate heat flux +# if pom_bfm_parameters.idiagn == 0: +# wtsurf = month1_data.wtsurf + counters.ratio_month * (month2_data.wtsurf - month1_data.wtsurf) +# swrad = month1_data.swrad + counters.ratio_month * (month2_data.swrad - month1_data.swrad) +# else: +# wtsurf = 0 # not needed for diagnostic mode (idiagn = 1), see 4.6.5 in manual +# swrad = month1_data.swrad + counters.ratio_month * (month2_data.swrad - month1_data.swrad) + + + +# # Interpolate temperature and salinity profiles +# tstar = month1_data.tclim + counters.ratio_month * (month2_data.tclim - month1_data.tclim) +# sstar = month1_data.sclim + counters.ratio_month * (month2_data.sclim - month1_data.sclim) +# wgen = month1_data.wclim + counters.ratio_month * (month2_data.wclim - month1_data.wclim) + +# if counters.ratio_month <= 0.5: +# weddy = month1_data.weddy1 +# else: +# weddy = month1_data.weddy2 + +# if pom_bfm_parameters.idiagn == 0: +# tsurf = tstar[0] +# ssurf = sstar[0] +# elif pom_bfm_parameters.idiagn == 1: +# tf = tstar +# sf = sstar + +# # Interpolate suspended inorganic matter +# ism = month1_data.ism + counters.ratio_month * (month2_data.ism - month1_data.ism) + +# # Interpolate surface and bottom nutrients +# nutrients = Nutrients() + +# nutrients.NO3surf = month1_data.NO3_s + counters.ratio_month * (month2_data.NO3_s - month1_data.NO3_s) +# nutrients.NH4surf = month1_data.NH4_s + counters.ratio_month * (month2_data.NH4_s - month1_data.NH4_s) +# nutrients.PO4surf = month1_data.PO4_s + counters.ratio_month * (month2_data.PO4_s - month1_data.PO4_s) +# nutrients.SIO4surf = month1_data.SIO4_s + counters.ratio_month * (month2_data.SIO4_s - month1_data.SIO4_s) + +# nutrients.O2bott = month1_data.O2_b + counters.ratio_month * (month2_data.O2_b - month1_data.O2_b) +# nutrients.NO3bott = month1_data.NO3_b + counters.ratio_month * (month2_data.NO3_b - month1_data.NO3_b) +# nutrients.PO4bott = month1_data.PO4_b + counters.ratio_month * (month2_data.PO4_b - month1_data.PO4_b) +# nutrients.PONbott_grad = month1_data.PON_b + counters.ratio_month * (month2_data.PON_b - month1_data.PON_b) + +# if counters.month_interpolator == counters.timesteps_per_month: + +# # Update the month counter +# if os.path.exists('output_file.txt'): +# output_file = open('output_file.txt','a') +# output_file.write('month_counter = ') +# output_file.write(str(counters.month_counter)) +# output_file.write('\n') +# output_file.close() +# else: +# output_file = open('output_file.txt','w') +# output_file.write('month_counter = ') +# output_file.write(str(counters.month_counter)) +# output_file.write('\n') +# output_file.close() + +# print('month_counter = ',counters.month_counter) +# counters.month_counter = counters.month_counter + 1 + +# # Reset the interpolator +# counters.month_interpolator = 0 + +# # Shift the monthly data +# month1_data = month2_data + +# # If 12 months have passed, restart the reading sequence +# if counters.month_counter > 12: +# counters.month_counter = 0 +# month1_data = write_forcing_data(counters.month_counter) + +# counters.month_counter = counters.month_counter + 1 + +# # Read the following month +# month2_data = write_forcing_data(counters.month_counter) + +# return tf, tstar, sf, sstar, swrad, wtsurf, wind_stress, wgen, weddy, month1_data, month2_data, counters, nutrients, ism + + + +# def write_forcing_data(month_counter): +# """ +# Definition: Writes forcing data for each month. +# """ +# pom_bfm_parameters = PomBfm() +# month_data = MonthlyForcing(pom_bfm_parameters.vertical_layers) + +# wind_speed_zonal, wind_speed_meridional, surface_salinity, solar_radiation, inorganic_suspended_matter, \ +# salinity_climatology, temperature_climatology, w_velocity_climatology, w_eddy_velocity_1, \ +# w_eddy_velocity_2, salinity, temperature, shortwave_radiation, surface_heat_flux, kinetic_energy_loss, \ +# NO3_surf, NH4_surf, PO4_surf, SIO4_surf, O2_bott, NO3_bott, PO4_bott, PON_bott = read_pom_input(pom_bfm_parameters.vertical_layers) + +# # Climatology +# month_data.sclim = salinity_climatology[:,month_counter] +# month_data.tclim = temperature_climatology[:,month_counter] +# month_data.wclim = w_velocity_climatology[:,month_counter] + +# # Intermittant eddy w velocities +# month_data.weddy1 = w_eddy_velocity_1[:,month_counter] +# month_data.weddy2 = w_eddy_velocity_2[:,month_counter] + +# # Inorganic suspended matter +# month_data.ism = inorganic_suspended_matter[:-1,month_counter] + +# month_data.wsu = -0.001 * wind_speed_zonal[month_counter] # N/m2-->m2/s2 +# month_data.wsv = -0.001 * wind_speed_meridional[month_counter] # N/m2-->m2/s2 + +# # Heat Flux +# month_data.swrad = -shortwave_radiation[month_counter] / pom_bfm_parameters.water_specific_heat_times_density # W/m2-->deg.C*m/s +# month_data.wtsurf = -surface_heat_flux[month_counter] / pom_bfm_parameters.water_specific_heat_times_density # W/m2-->deg.C*m/s +# month_data.qcorr = kinetic_energy_loss[month_counter] + +# # Surface nutrients +# month_data.NO3_s = NO3_surf[month_counter] +# month_data.NH4_s = NH4_surf[month_counter] +# month_data.PO4_s = PO4_surf[month_counter] +# month_data.SIO4_s = SIO4_surf[month_counter] + +# # Bottom nutrients +# month_data.O2_b = O2_bott[month_counter] +# month_data.NO3_b = NO3_bott[month_counter] +# month_data.PO4_b = PO4_bott[month_counter] +# month_data.PON_b = PON_bott[month_counter] + +# return month_data \ No newline at end of file diff --git a/pom/initialize.py b/pom/initialize.py new file mode 100644 index 0000000..6544941 --- /dev/null +++ b/pom/initialize.py @@ -0,0 +1,281 @@ +import numpy as np +import os + +def initialize_pom(pom1d, physical): + + # Initialize dictionaries + physical["diffusion"] = { + "tracers": np.zeros(physical["water_column"]["num_layers"]), + "momentum": np.zeros(physical["water_column"]["num_layers"]), + "kinetic_energy": np.zeros(physical["water_column"]["num_layers"]) + } + physical["kinetic_energy"] = { + # Kinetic energy + "ke": 1.E-07 * np.ones(physical["water_column"]["num_layers"]), + "keb": 1.E-07 * np.ones(physical["water_column"]["num_layers"]), + "kef": 1.E-07 * np.ones(physical["water_column"]["num_layers"]), + # Kinetic energy * length scale + "kel": 1.E-07 * np.ones(physical["water_column"]["num_layers"]), + "kelb": 1.E-07 * np.ones(physical["water_column"]["num_layers"]), + "kelf": 1.E-07 * np.ones(physical["water_column"]["num_layers"]) + } + physical["velocity"] = { + # Zonal (U) + "u": np.zeros(physical["water_column"]["num_layers"]), + "ub": np.zeros(physical["water_column"]["num_layers"]), + "uf": np.zeros(physical["water_column"]["num_layers"]), + # Meridional (V) + "v": np.zeros(physical["water_column"]["num_layers"]), + "vb": np.zeros(physical["water_column"]["num_layers"]), + "vf": np.zeros(physical["water_column"]["num_layers"]) + } + physical["temperature"] = { + "t": np.zeros(physical["water_column"]["num_layers"]), # Current + "tb": np.zeros(physical["water_column"]["num_layers"]), # Backward + "tf": np.zeros(physical["water_column"]["num_layers"]), # Forward + "ti": np.zeros(physical["water_column"]["num_layers"]), # Interpolated (prognostic mode) + "adv": np.zeros(physical["water_column"]["num_layers"]), # Lateral advection + "surf": 0., # Surface value + "surf_flux": 0., # Surface flux + "bot_flux": 0., # Bottom flux + } + physical["salinity"] = { + "s": np.zeros(physical["water_column"]["num_layers"]), # Current + "sb": np.zeros(physical["water_column"]["num_layers"]), # Backward + "sf": np.zeros(physical["water_column"]["num_layers"]), # Forward + "si": np.zeros(physical["water_column"]["num_layers"]), # Interpolated (prognostic mode) + "adv": np.zeros(physical["water_column"]["num_layers"]), # Lateral advection + "surf": 0., # Surface value + "surf_flux": 0., # Surface flux + "bot_flux": 0., # Bottom flux + } + physical["nutrients"] = { + # Surface + "no3s": 0., # Nitrate + "nh4s": 0., # Ammonium + "po4s": 0., # Phosphate + "sio4s": 0., # Silicate + # Bottom + "o2b": 0., # Oxygen + "no3b": 0., # Nitrate + "po4b": 0., # Phosphate + "ponb_grad": 0. # PON gradient + } + physical["stresses"] = { + # Wind + "wsu": 0., # Zonal + "wsv": 0., # Meridional + # Bottom boundary layer + "bsu": 0., # Zonal + "bsv": 0. # Meridional + } + physical["ism"] = np.zeros(physical["water_column"]["num_layers"]) # Inorganic suspended matter + physical["swrad"] = 0. # Shortwave radiation + physical["wgen"] = np.zeros(physical["water_column"]["num_layers"]) + physical["weddy"] = np.zeros(physical["water_column"]["num_layers"]) + physical["mld"] = np.zeros(physical["water_column"]["num_layers"]) # Mixed layer depth + + # Read initial conditions + # Temperature + physical["temperature"]["t"] = np.fromfile(os.getcwd() + pom1d["input_files"]["temperature_IC"]) + physical["temperature"]["tb"] = np.fromfile(os.getcwd() + pom1d["input_files"]["temperature_IC"]) + + # Salinity + physical["salinity"]["s"] = np.fromfile(os.getcwd() + pom1d["input_files"]["salinity_IC"]) + physical["salinity"]["sb"] = np.fromfile(os.getcwd() + pom1d["input_files"]["salinity_IC"]) + + # ---------------------------------------------------------------------------------------------------- + # Initialize forcing data + # ---------------------------------------------------------------------------------------------------- + forcing = { + "counters": { + "day_counter": 0, + "day_interpolator": 0, + "day_ratio": 0, + "month_counter": 0, + "month_interpolator": 0, + "month_ratio": 0, + "timesteps_per_day": 0, + "timesteps_per_month": 0 + }, + "month1": { + "sclim": np.zeros(physical["water_column"]["num_layers"]), # Salinity climatology + "tclim": np.zeros(physical["water_column"]["num_layers"]), # Temperature climatology + "wclim": np.zeros(physical["water_column"]["num_layers"]), # W velocity climatology + "weddy1": np.zeros(physical["water_column"]["num_layers"]), # Intermittant eddy w velocity 1 + "weddy2": np.zeros(physical["water_column"]["num_layers"]), # Intermittant eddy w velocity 2 + "ism": np.zeros(physical["water_column"]["num_layers"]), # Inorganic suspended matter + "wsu": 0, # Zonal (U) velocity + "wsv": 0, # Meridional (V) velocity + "swrad": 0, # Shortwave radiation + "wtsurf": 0, # Surface heat flux + "qcorr": 0, # Kinetic energy loss + "no3s": 0, # Surface no3 + "nh4s": 0, # Surface nh4 + "po4s": 0, # Surface po4 + "sio4s": 0, # Surface sio4 + "o2b": 0, # Bottom o2 + "no3b": 0, # Bottom no3 + "po4b": 0, # Bottom po4 + "ponb": 0 # Bottom pon + }, + "month2": { + "sclim": np.zeros(physical["water_column"]["num_layers"]), # Salinity climatology + "tclim": np.zeros(physical["water_column"]["num_layers"]), # Temperature climatology + "wclim": np.zeros(physical["water_column"]["num_layers"]), # W velocity climatology + "weddy1": np.zeros(physical["water_column"]["num_layers"]), # Intermittant eddy w velocity 1 + "weddy2": np.zeros(physical["water_column"]["num_layers"]), # Intermittant eddy w velocity 2 + "ism": np.zeros(physical["water_column"]["num_layers"]), # Inorganic suspended matter + "wsu": 0, # Zonal (U) velocity + "wsv": 0, # Meridional (V) velocity + "swrad": 0, # Shortwave radiation + "wtsurf": 0, # Surface heat flux + "qcorr": 0, # Kinetic energy loss + "no3s": 0, # Surface no3 + "nh4s": 0, # Surface nh4 + "po4s": 0, # Surface po4 + "sio4s": 0, # Surface sio4 + "o2b": 0, # Bottom o2 + "no3b": 0, # Bottom no3 + "po4b": 0, # Bottom po4 + "ponb": 0 # Bottom pon + } + } + + + return physical, forcing + + +def read_pom_inputs(physical, pom1d): + """ + Description: Opens forcing files reading the paths specified in the pom_input namelist. + + :return: data arrays for wind stress, surface salinity, solar radiation, inorganic + suspended matter, salinity and temperature vertical profiles, general circulation + for w velocity, intermediate eddy velocities, salinity and temperature initial + conditions, heat flux loss, and surface and bottom nutrients + """ + + # Length of input arrays + array_length = 13 # months (D-J-F-M-A-M-J-J-A-S-O-N-D) + + # Wind speed + wind_speed_data = np.fromfile(os.getcwd() + pom1d["input_files"]["wind_stress"]) + wsu = np.zeros(array_length) + wsv = np.zeros(array_length) + for i in range(0,array_length): + wsu[i] = wind_speed_data[2*i + 0] + wsv[i] = wind_speed_data[2*i + 1] + + # SOlar radiation, shortwave radiation, and heat flux + solar_radiation = np.fromfile(os.getcwd() + pom1d["input_files"]["shortwave_solar_radiation"]) + heat_flux_loss_data = np.fromfile(os.getcwd() + pom1d["input_files"]["heat_flux_loss"]) + swrad = np.zeros(array_length) + wtsurf = np.zeros(array_length) + qcorr = np.zeros(array_length) + for i in range(0,array_length): + swrad[i] = heat_flux_loss_data[3*i + 0] + wtsurf[i] = heat_flux_loss_data[3*i + 1] + qcorr[i] = heat_flux_loss_data[3*i + 2] + + # Inorganic suspended matter + inorganic_suspended_matter_data = np.fromfile(os.getcwd() + pom1d["input_files"]["inorganic_suspended_matter"]) + ism = np.zeros((physical["water_column"]["num_layers"],array_length)) + for i in range(0,array_length): + for x in range(0, physical["water_column"]["num_layers"]): + ism[x,i] = inorganic_suspended_matter_data[physical["water_column"]["num_layers"] * i + x] + + # Surface salinity + surface_salinity = np.fromfile(os.getcwd() + pom1d["input_files"]["surface_salinity"]) + + # Salinity climatology (diagnostic mode) + salinity_vertical_profile_data = np.fromfile(os.getcwd() + pom1d["input_files"]["salinity"]) + sclim = np.zeros((physical["water_column"]["num_layers"],array_length)) + for i in range(0,array_length): + for x in range(0, physical["water_column"]["num_layers"]): + sclim[x,i] = salinity_vertical_profile_data[physical["water_column"]["num_layers"] * i + x] + + # Salinity IC + salinity = np.fromfile(os.getcwd() + pom1d["input_files"]["salinity_IC"]) + + # Temperature climatology (diagnostic mode) + temperature_vertical_profile_data = np.fromfile(os.getcwd() + pom1d["input_files"]["temperature"]) + tclim = np.zeros((physical["water_column"]["num_layers"],array_length)) + for i in range(0,array_length): + for x in range(0, physical["water_column"]["num_layers"]): + tclim[x,i] = temperature_vertical_profile_data[physical["water_column"]["num_layers"] * i + x] + + # Temperature IC + temperature = np.fromfile(os.getcwd() + pom1d["input_files"]["temperature_IC"]) + + # General circulation w velocity climatology + general_circulation_w_velocity_data = np.fromfile(os.getcwd() + pom1d["input_files"]["w_velocity"]) + wclim = np.zeros((physical["water_column"]["num_layers"],array_length)) + for i in range(0,array_length): + for x in range(0, physical["water_column"]["num_layers"]): + wclim[x,i] = general_circulation_w_velocity_data[physical["water_column"]["num_layers"] * i + x] + + # Intermittant eddy w velocity 1 + intermediate_eddy_w_velocity_1_data = np.fromfile(os.getcwd() + pom1d["input_files"]["eddy_w_velocity_1"]) + weddy1 = np.zeros((physical["water_column"]["num_layers"],array_length)) + for i in range(0,array_length): + for x in range(0, physical["water_column"]["num_layers"]): + weddy1[x,i] = intermediate_eddy_w_velocity_1_data[physical["water_column"]["num_layers"] * i + x] + + + # Intermittant eddy w velocity 2 + intermediate_eddy_w_velocity_2_data = np.fromfile(os.getcwd() + pom1d["input_files"]["eddy_w_velocity_2"]) + weddy2 = np.zeros((physical["water_column"]["num_layers"],array_length)) + for i in range(0,array_length): + for x in range(0, physical["water_column"]["num_layers"]): + weddy2[x,i] = intermediate_eddy_w_velocity_2_data[physical["water_column"]["num_layers"] * i + x] + + # Surface nutrients + surface_nutrients_data = np.fromfile(os.getcwd() + pom1d["input_files"]["surface_nutrients"]) + no3s = np.zeros(array_length) + nh4s = np.zeros(array_length) + po4s = np.zeros(array_length) + sio4s = np.zeros(array_length) + for i in range(0,array_length): + no3s[i] = surface_nutrients_data[4*i + 0] + nh4s[i] = surface_nutrients_data[4*i + 1] + po4s[i] = surface_nutrients_data[4*i + 2] + sio4s[i] = surface_nutrients_data[4*i + 3] + + # Bottom nutrients + bottom_nutrients_data = np.fromfile(os.getcwd() + pom1d["input_files"]["bottom_nutrients"]) + o2b = np.zeros(array_length) + no3b = np.zeros(array_length) + po4b = np.zeros(array_length) + ponb = np.zeros(array_length) + for i in range(0,array_length): + o2b[i] = bottom_nutrients_data[4*i + 0] + no3b[i] = bottom_nutrients_data[4*i + 1] + po4b[i] = bottom_nutrients_data[4*i + 2] + ponb[i] = bottom_nutrients_data[4*i + 3] + + forcing_data = { + "sclim": sclim, # Salinity climatology + "tclim": tclim, # Temperature climatology + "wclim": wclim, # W velocity climatology + "weddy1": weddy1, # Intermittant eddy w velocity 1 + "weddy2": weddy2, # Intermittant eddy w velocity 1 + "ism": ism, # Inorganic suspended matter + "wsu": wsu, # Zonal (U) velocity + "wsv": wsv, # Meridional (V) velocity + "swrad": swrad, # Shortwave radiation + "wtsurf": wtsurf, # Surface heat flux + "qcorr": qcorr, # Kinetic energy loss + "no3s": no3s, # Surface no3 + "nh4s": nh4s, # Surface nh4 + "po4s": po4s, # Surface po4 + "sio4s": sio4s, # Surface sio4 + "o2b": o2b, # Bottom o2 + "no3b": no3b, # Bottom no3 + "po4b": po4b, # Bottom po4 + "ponb": ponb # Bottom pon + } + + return forcing_data + + diff --git a/pom/parameters.py b/pom/parameters.py new file mode 100644 index 0000000..99de924 --- /dev/null +++ b/pom/parameters.py @@ -0,0 +1,76 @@ +from math import ceil +import numpy as np +import os +current_path = os.getcwd() + +class PomBfm: + def __init__(self): + self.pom_only = False # True if just pom, False if pom_bfm + + # Simulation + self.dti = 360.0 # timestep [s] + self.dti2 = 2 * self.dti # twice the timestep [s] + self.days = 730 # length of simulation [days] + self.months = ceil(self.days/30) # total months in simulation (for data averaging) + self.savef = 1 # output averaging and saving frequency [days] + self.sec_per_day = 86400 + self.dayi = 1./self.sec_per_day + self.iterations_needed = int(self.days * self.sec_per_day / self.dti) + + # Water Column + self.h = 150.0 # depth [m] + self.upperh = 5.0 # depth where lateral advection starts [m] + self.vertical_layers = 151 # number of layers in water column + self.num_boxes = self.vertical_layers - 1 + self.kl1 = 2 # surface logarithmic layers distribution + self.kl2 = 150 # bottom logarithmic layers distribution + + # General + self.water_specific_heat_times_density = 4.187e06 + self.earth_angular_velocity = 7.29e-05 + self.alat = 45.0 # latitude [degrees] + self.idiagn = 1 # switch between prognostic (idiagn = 0) and diagnostic (idiagn = 1) mode + self.smoth = 0.1 # parameter for hasselin filter + self.ihotst = 0 # switch for cold start (ihotst = 0) and hot start, ie reading restart (ihotst = 1) + self.coriolis = 2. * self.earth_angular_velocity * np.sin(self.alat * 2. * np.pi / 360.) + + # Relaxation Velocities [m/d] + self.nrt_o2o = 0.06 # oxygen + self.nrt_n1p = 0.06 # phosphate + self.nrt_n3n = 0.06 # nitrate + self.nrt_n4n = 0.05 # ammonium + + # Flags + self.nbct = 2 # temperature boundary conditions + self.nbcs = 1 # salinity boundary conditions + self.nbcbfm = 1 # bfm boundary conditions + self.ntp = 2 # jerlov water type ( 1 = I, 2 = IA, 3 = IB, 4 = II, 5 = III) + + # Background Diffusions [m2/s] + self.umol = 1.e-06 # general + self.umolt = 1.e-07 # temperature + self.umols = 1.3e-07 # salalinity + self.umolbfm = 1.e-04 # bfm + + # Relaxation Times [s] + self.trt = 0 # lateral temperature advection + self.srt = 1 # lateral salinity advection + self.ssrt = 5.68 # surface salinity flux + + +class PomInputFiles: + def __init__(self): + self.wind_stress = current_path + '/inputs/POM_BFM17/monthly_surf_wind_stress_bermuda_killworth2.da' + self.surface_salinity = current_path + '/inputs/POM_BFM17/monthly_surf_salt_bermuda_150m_killworth2.da' + self.shortwave_solar_radiation = current_path + '/inputs/POM_BFM17/monthly_surf_qs_bermuda_killworth2.da' + self.inorganic_suspended_matter = current_path + '/inputs/POM_BFM17/monthly_clima_ISM_150m_bermuda_killworth.da' + self.salinity_vertical_profile = current_path + '/inputs/POM_BFM17/monthly_clima_salt_150m_bermuda_killworth2.da' + self.temperature_vertical_profile = current_path + '/inputs/POM_BFM17/monthly_clima_temp_150m_bermuda_killworth2.da' + self.general_circulation_w_velocity = current_path + '/inputs/POM_BFM17/monthly_clima_w_150m_bermuda_ekman.da' + self.intermediate_eddy_w_velocity_1 = current_path + '/inputs/POM_BFM17/bimonthly_random_eddy_w_150m_bermuda_norm1.da' + self.intermediate_eddy_w_velocity_2 = current_path + '/inputs/POM_BFM17/bimonthly_random_eddy_w_150m_bermuda_norm2.da' + self.salinity_initial_conditions = current_path + '/inputs/POM_BFM17/init_prof_S_150m_bermuda_killworth2.da' + self.temperature_initial_conditions = current_path + '/inputs/POM_BFM17/init_prof_T_150m_bermuda_killworth2.da' + self.heat_flux_loss = current_path + '/inputs/POM_BFM17/monthly_surf_rad_bermuda_killworth2.da' + self.surface_nutrients = current_path + '/inputs/POM_BFM17/NutrientsARPAOGS.da' + self.bottom_nutrients = current_path + '/inputs/POM_BFM17/monthly_bott_nut_bermuda_150m_killworth.da' \ No newline at end of file diff --git a/pom1d.yaml b/pom1d.yaml new file mode 100644 index 0000000..b337177 --- /dev/null +++ b/pom1d.yaml @@ -0,0 +1,45 @@ +general: + water_specific_heat_times_density: 4.187E+06 + earth_angular_velocity: 7.29E-05 + idiagn: 1 # switch between prognostic (idiagn = 0) and diagnostic (idiagn = 1) mode + smoth: 0.1 # parameter for hasselin filter + ihotst: 0 # switch for cold start (ihotst = 0) and hot start, ie reading restart (ihotst = 1) + +flags: + nbct: 2 # temperature boundary conditions + nbcs: 1 # salinity boundary conditions + nbcbgc: 1 # bgc boundary conditions + ntp: 2 # jerlov water type ( 1 = I, 2 = IA, 3 = IB, 4 = II, 5 = III) + +relaxation_times: + trt: 0 # lateral temperature advection + srt: 1 # lateral salinity advection + ssrt: 5.68 # surface salinity flux + +relaxation_velocities: + nrt_o2: 0.06 # oxygen + nrt_po4: 0.06 # phosphate + nrt_no3: 0.06 # nitrate + nrt_nh4: 0.05 # ammonium + +background_diffusion: + umol: 1.e-06 # general + umolt: 1.e-07 # temperature + umols: 1.3e-07 # salalinity + umolbgc: 1.e-04 # bgc + +input_files: + wind_stress: '/inputs/POM_BFM17/monthly_surf_wind_stress_bermuda_killworth2.da' + shortwave_solar_radiation: '/inputs/POM_BFM17/monthly_surf_qs_bermuda_killworth2.da' + inorganic_suspended_matter: '/inputs/POM_BFM17/monthly_clima_ISM_150m_bermuda_killworth.da' + surface_salinity: '/inputs/POM_BFM17/monthly_surf_salt_bermuda_150m_killworth2.da' + salinity: '/inputs/POM_BFM17/monthly_clima_salt_150m_bermuda_killworth2.da' + salinity_IC: '/inputs/POM_BFM17/init_prof_S_150m_bermuda_killworth2.da' + temperature: '/inputs/POM_BFM17/monthly_clima_temp_150m_bermuda_killworth2.da' + temperature_IC: '/inputs/POM_BFM17/init_prof_T_150m_bermuda_killworth2.da' + w_velocity: '/inputs/POM_BFM17/monthly_clima_w_150m_bermuda_ekman.da' + eddy_w_velocity_1: '/inputs/POM_BFM17/bimonthly_random_eddy_w_150m_bermuda_norm1.da' + eddy_w_velocity_2: '/inputs/POM_BFM17/bimonthly_random_eddy_w_150m_bermuda_norm2.da' + heat_flux_loss: '/inputs/POM_BFM17/monthly_surf_rad_bermuda_killworth2.da' + surface_nutrients: '/inputs/POM_BFM17/NutrientsARPAOGS.da' + bottom_nutrients: '/inputs/POM_BFM17/monthly_bott_nut_bermuda_150m_killworth.da' diff --git a/reactions.json b/reactions.json deleted file mode 100644 index cb950a8..0000000 --- a/reactions.json +++ /dev/null @@ -1,929 +0,0 @@ -{ - "inorganic_reactions":[ - { - "type": "remineralization", - "consumed": "dom1", - "produced": "DIC", - "parameters": { - - } - }, - { - "type": "remineralization", - "consumed": "pom1", - "produced": "DIC", - "parameters": { - - } - }, - { - "type": "air-sea flux", - "consumed": "DIC", - "produced": "DIC", - "parameters": { - - } - }, - - { - "type": "denitrtification", - "consumed": ["NO3","S"], - "produced": "N2", - "parameters": { - - } - }, - - { - "type": "remineralization", - "consumed": "dom1", - "produced": "NH4", - "parameters": { - - } - }, - { - "type": "remineralization", - "consumed": "pom1", - "produced": "NH4", - "parameters": { - - } - }, - - { - "type": "nitrification", - "consumed": ["NH4","O2"], - "produced": "NO3", - "parameters": { - - } - }, - - { - "type": "air-sea flux", - "consumed": "O2", - "produced": "O2", - "parameters": { - - } - }, - { - "type": "remineralization", - "consumed": ["dom1","O2"], - "produced": "", - "parameters": { - - } - }, - { - "type": "remineralization", - "consumed": ["pom1","O2"], - "produced": "", - "parameters": { - - } - }, - - { - "type": "remineralization", - "consumed": "dom1", - "produced": "PO4", - "parameters": { - - } - }, - { - "type": "remineralization", - "consumed": "pom1", - "produced": "PO4", - "parameters": { - - } - }, - - { - "type": "reoxidation", - "consumed": ["O2","S"], - "produced": "", - "parameters": { - - } - }, - - { - "type": "remineralization", - "consumed": "pom1", - "produced": "SiO2", - "parameters": { - - } - } - ], - "bac1_reactions":[ - { - "type": "lysis", - "consumed": "bac1", - "produced": "dom1", - "parameters": { - "dens_mort_rate": 0.0, - "spec_mort_rate": 0.1, - "dom_excr_frac_c": 0.6, - "dom_excr_frac_n": 0.72, - "dom_excr_frac_p": 0.832 - } - }, - { - "type": "uptake", - "consumed": "dom1", - "produced": "bac1", - "parameters": { - - } - }, - { - "type": "release", - "consumed": "bac1", - "produced": "dom2", - "parameters": { - - } - }, - { - "type": "uptake", - "consumed": "dom2", - "produced": "bac1", - "parameters": { - - } - }, - { - "type": "release", - "consumed": "bac1", - "produced": "dom3", - "parameters": { - - } - }, - { - "type": "uptake", - "consumed": "dom3", - "produced": "bac1", - "parameters": { - - } - }, - { - "type": "lysis", - "consumed": "bac1", - "produced": "pom1", - "parameters": { - "dens_mort_rate": 0.0, - "spec_mort_rate": 0.1, - "dom_excr_frac_c": 0.6, - "dom_excr_frac_n": 0.72, - "dom_excr_frac_p": 0.832 - } - }, - { - "type": "uptake", - "consumed": "pom1", - "produced": "bac1", - "parameters": { - - } - }, - { - "type": "respiration", - "consumed": ["bac1","O2"], - "produced": ["DIC","HS"], - "parameters": { - "spec_resp_rate": 0.01, - "anoxic_resp_frac": 0.2, - "activity_resp_frac": 0.6 - } - }, - { - "type": "release", - "consumed": "bac1", - "produced": "NH4", - "parameters": { - - } - }, - { - "type": "uptake", - "consumed": "NH4", - "produced": "bac1", - "parameters": { - - } - }, - { - "type": "release", - "consumed": "bac1", - "produced": "PO4", - "parameters": { - - } - }, - { - "type": "uptake", - "consumed": "PO4", - "produced": "bac1", - "parameters": { - - } - } - ], - "phyto1_reactions":[ - { - "type": "lysis", - "consumed": "phyto1", - "produced": "dom1", - "parameters": { - - } - }, - { - "type": "uptake", - "consumed": "dom1", - "produced": "phyto1", - "parameters": { - - } - }, - { - "type": "exudation", - "consumed": "phyto1", - "produced": "dom2", - "parameters": { - - } - }, - { - "type": "lysis", - "consumed": "phyto1", - "produced": "pom1", - "parameters": { - - } - }, - { - "type": "chlorophyll synthesis", - "consumed": "phyto1", - "produced": "phyto1", - "parameters": { - - } - }, - { - "type": "primary production", - "consumed": "DIC", - "produced": ["phyto1","O2"], - "parameters": { - - } - }, - { - "type": "respiration", - "consumed": ["phyto1","O2"], - "produced": "DIC", - "parameters": { - - } - }, - { - "type": "uptake", - "consumed": "Fe", - "produced": "phyto1", - "parameters": { - - } - }, - { - "type": "uptake", - "consumed": "NH4", - "produced": "phyto1", - "parameters": { - - } - }, - { - "type": "uptake", - "consumed": "NO3", - "produced": "phyto1", - "parameters": { - - } - }, - { - "type": "uptake", - "consumed": "PO4", - "produced": "phyto1", - "parameters": { - - } - }, - { - "type": "uptake", - "consumed": "SiO2", - "produced": "phyto1", - "parameters": { - - } - } - ], - "phyto2_reactions":[ - { - "type": "lysis", - "consumed": "phyto2", - "produced": "dom1", - "parameters": { - - } - }, - { - "type": "uptake", - "consumed": "dom1", - "produced": "phyto2", - "parameters": { - - } - }, - { - "type": "exudation", - "consumed": "phyto2", - "produced": "dom2", - "parameters": { - - } - }, - { - "type": "lysis", - "consumed": "phyto2", - "produced": "pom1", - "parameters": { - - } - }, - { - "type": "chlorophyll synthesis", - "consumed": "phyto2", - "produced": "phyto2", - "parameters": { - - } - }, - { - "type": "primary production", - "consumed": "DIC", - "produced": ["phyto2","O2"], - "parameters": { - - } - }, - { - "type": "respiration", - "consumed": ["phyto2","O2"], - "produced": "DIC", - "parameters": { - - } - }, - { - "type": "uptake", - "consumed": "Fe", - "produced": "phyto2", - "parameters": { - - } - }, - { - "type": "uptake", - "consumed": "NH4", - "produced": "phyto2", - "parameters": { - - } - }, - { - "type": "uptake", - "consumed": "NO3", - "produced": "phyto2", - "parameters": { - - } - }, - { - "type": "uptake", - "consumed": "PO4", - "produced": "phyto2", - "parameters": { - - } - } - ], - "phyto3_reactions":[ - { - "type": "lysis", - "consumed": "phyto3", - "produced": "dom1", - "parameters": { - - } - }, - { - "type": "uptake", - "consumed": "dom1", - "produced": "phyto3", - "parameters": { - - } - }, - { - "type": "exudation", - "consumed": "phyto3", - "produced": "dom2", - "parameters": { - - } - }, - { - "type": "lysis", - "consumed": "phyto3", - "produced": "pom1", - "parameters": { - - } - 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at end of file diff --git a/reduction.yaml b/reduction.yaml new file mode 100644 index 0000000..cd44d64 --- /dev/null +++ b/reduction.yaml @@ -0,0 +1,25 @@ +reduction: + title: average phytoplankton concentration + + target: # target tracer constituents for reduction scheme + phyto1: [n] + phyto2: [n] + phyto3: [n] + + safe: # tracers to be retained in reduction, regardless of interaction coefficient + no3: [n] + nh4: [n] + po4: [p] + sio4: [si] + fe: [fe] + + mode: average # average, peak, or time_of_peak + # "average": average concentration over specified time period + # "peak": peak concentration during specified time period + # "time_of_peak": time of peak concentration during specified time period + + period: # time period (in days) used in mode + start: 730 + end: 820 + + diff --git a/reduction/error_fcns.py b/reduction/error_fcns.py new file mode 100644 index 0000000..1b199d8 --- /dev/null +++ b/reduction/error_fcns.py @@ -0,0 +1,1666 @@ +import numpy +import copy +from scipy.integrate import solve_ivp + + +def calc_error_lo_1(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in peak chlorophyll concentration during a spring bloom + This is for the sum of p1l + p2l + p3l + p4l + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Get sum of chl-a concentrations (p1l + p2l + p3l + p4l) + chl_sum_conc_full = solution_full_model.y[13] + solution_full_model.y[18] + solution_full_model.y[22] + solution_full_model.y[26] + chl_sum_conc_reduced = solution_reduced_model.y[13] + solution_reduced_model.y[18] + solution_reduced_model.y[22] + solution_reduced_model.y[26] + + # Set time range for searching for max value + # January to March in year 2 + t_min = 86400*365*2 + t_max = t_min + 86400*90 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + spring_bloom_full = numpy.zeros((num_boxes,(index_t_max_full-index_t_min_full))) + spring_bloom_reduced = numpy.zeros((num_boxes,(index_t_max_reduced-index_t_min_reduced))) + + p1l = 13 # P1l index + p2l = 18 # P2l index + p3l = 22 # P3l index + p4l = 26 # P4l index + for i in range(0,num_boxes): + for j_full in range(index_t_min_full,index_t_max_full): + spring_bloom_full[i,(j_full-index_t_min_full)] = solution_full_model.y[p1l+(i*50),j_full] + solution_full_model.y[p2l+(i*50),j_full] \ + + solution_full_model.y[p3l+(i*50),j_full] + solution_full_model.y[p4l+(i*50),j_full] + for j_reduced in range(index_t_min_reduced,index_t_max_reduced): + spring_bloom_reduced[i,(j_reduced-index_t_min_reduced)] = solution_reduced_model.y[p1l+(i*50),j_reduced] + solution_reduced_model.y[p2l+(i*50),j_reduced] \ + + solution_reduced_model.y[p3l+(i*50),j_reduced] + solution_reduced_model.y[p4l+(i*50),j_reduced] + + # Find peak chl-a concentration in spring bloom + spring_bloom_peak_full = numpy.max(spring_bloom_full) + spring_bloom_peak_reduced = numpy.max(spring_bloom_reduced) + + # Compute the error + error = numpy.abs(100*numpy.abs(spring_bloom_peak_full - spring_bloom_peak_reduced)/spring_bloom_peak_full) + + # If peak is zero in reduced model, set error to zero + if spring_bloom_peak_reduced == 0.0: + error = 100 + + return error, solution_reduced_model + + +def calc_error_lo_2(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in peak chlorophyll concentration during a spring bloom + This is for the sum of p2l + p3l + p4l + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Get sum of chl-a concentrations (p1l + p2l + p3l + p4l) + chl_sum_conc_full = solution_full_model.y[18] + solution_full_model.y[22] + solution_full_model.y[26] + chl_sum_conc_reduced = solution_reduced_model.y[18] + solution_reduced_model.y[22] + solution_reduced_model.y[26] + + # Set time range for searching for max value + # January to March in year 2 + t_min = 86400*365*2 + t_max = t_min + 86400*90 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + # slice list of chl concentrations + spring_bloom_full = chl_sum_conc_full[index_t_min_full:index_t_max_full] + spring_bloom_reduced = chl_sum_conc_reduced[index_t_min_reduced:index_t_max_reduced] + + # Find peak chl-a concentration in spring bloom + spring_bloom_peak_full = max(spring_bloom_full) + spring_bloom_peak_reduced = max(spring_bloom_reduced) + + # Compute the error + error = numpy.abs(100*numpy.abs(spring_bloom_peak_full - spring_bloom_peak_reduced)/spring_bloom_peak_full) + + # If peak is zero in reduced model, set error to zero + if spring_bloom_peak_reduced == 0.0: + error = 100 + + return error, solution_reduced_model + + +def calc_error_lo_3(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in peak chlorophyll concentration during a spring bloom + This is for the sum of p3l and p4l + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Get sum of chl-a concentrations (p1l + p2l + p3l + p4l) + chl_sum_conc_full = solution_full_model.y[22] + solution_full_model.y[26] + chl_sum_conc_reduced = solution_reduced_model.y[22] + solution_reduced_model.y[26] + + # Set time range for searching for max value + # January to March in year 2 + t_min = 86400*365*2 + t_max = t_min + 86400*90 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + # slice list of chl concentrations + spring_bloom_full = chl_sum_conc_full[index_t_min_full:index_t_max_full] + spring_bloom_reduced = chl_sum_conc_reduced[index_t_min_reduced:index_t_max_reduced] + + # Find peak chl-a concentration in spring bloom + spring_bloom_peak_full = max(spring_bloom_full) + spring_bloom_peak_reduced = max(spring_bloom_reduced) + + # Compute the error + error = numpy.abs(100*numpy.abs(spring_bloom_peak_full - spring_bloom_peak_reduced)/spring_bloom_peak_full) + + # If peak is zero in reduced model, set error to zero + if spring_bloom_peak_reduced == 0.0: + error = 100 + + return error, solution_reduced_model + + +def calc_error_lo_4(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in average chlorophyll concentration during a spring bloom + This is for the sum of p1l + p2l + p3l + p4l + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Get sum of chl-a concentrations (p1l + p2l + p3l + p4l) + chl_sum_conc_full = solution_full_model.y[13] + solution_full_model.y[18] + solution_full_model.y[22] + solution_full_model.y[26] + chl_sum_conc_reduced = solution_reduced_model.y[13] + solution_reduced_model.y[18] + solution_reduced_model.y[22] + solution_reduced_model.y[26] + + # Set time range for searching for max value + # January to March in year 2 + t_min = 86400*365*2 + t_max = t_min + 86400*90 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + # slice list of chl concentrations + spring_bloom_full = chl_sum_conc_full[index_t_min_full:index_t_max_full] + spring_bloom_reduced = chl_sum_conc_reduced[index_t_min_reduced:index_t_max_reduced] + + # Find average chl-a concentration in spring bloom + spring_bloom_avg_full = numpy.mean(spring_bloom_full) + spring_bloom_avg_reduced = numpy.mean(spring_bloom_reduced) + + # Compute the error + error = numpy.abs(100*numpy.abs(spring_bloom_avg_full - spring_bloom_avg_reduced)/spring_bloom_avg_full) + + # If peak is zero in reduced model, set error to zero + if spring_bloom_avg_reduced == 0.0: + error = 100 + + return error, solution_reduced_model + + +def calc_error_lo_5(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in average chlorophyll concentration during a spring bloom + This is for the sum of p2l + p3l + p4l + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Get sum of chl-a concentrations (p1l + p2l + p3l + p4l) + chl_sum_conc_full = solution_full_model.y[18] + solution_full_model.y[22] + solution_full_model.y[26] + chl_sum_conc_reduced = solution_reduced_model.y[18] + solution_reduced_model.y[22] + solution_reduced_model.y[26] + + # Set time range for searching for max value + # January to March in year 2 + t_min = 86400*365*2 + t_max = t_min + 86400*90 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + # slice list of chl concentrations + spring_bloom_full = chl_sum_conc_full[index_t_min_full:index_t_max_full] + spring_bloom_reduced = chl_sum_conc_reduced[index_t_min_reduced:index_t_max_reduced] + + # Find average chl-a concentration in spring bloom + spring_bloom_avg_full = numpy.mean(spring_bloom_full) + spring_bloom_avg_reduced = numpy.mean(spring_bloom_reduced) + + # Compute the error + error = numpy.abs(100*numpy.abs(spring_bloom_avg_full - spring_bloom_avg_reduced)/spring_bloom_avg_full) + + # If average is zero in reduced model, set error to zero + if spring_bloom_avg_reduced == 0.0: + error = 100 + + return error, solution_reduced_model + + +def calc_error_lo_6(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in average chlorophyll concentration during a spring bloom + This is for the sum of p3l and p4l + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Get sum of chl-a concentrations (p1l + p2l + p3l + p4l) + chl_sum_conc_full = solution_full_model.y[22] + solution_full_model.y[26] + chl_sum_conc_reduced = solution_reduced_model.y[22] + solution_reduced_model.y[26] + + # Set time range for searching for max value + # January to March in year 2 + t_min = 86400*365*2 + t_max = t_min + 86400*90 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + # slice list of chl concentrations + spring_bloom_full = chl_sum_conc_full[index_t_min_full:index_t_max_full] + spring_bloom_reduced = chl_sum_conc_reduced[index_t_min_reduced:index_t_max_reduced] + + # Find average chl-a concentration in spring bloom + spring_bloom_avg_full = numpy.mean(spring_bloom_full) + spring_bloom_avg_reduced = numpy.mean(spring_bloom_reduced) + + # Compute the error + error = numpy.abs(100*numpy.abs(spring_bloom_avg_full - spring_bloom_avg_reduced)/spring_bloom_avg_full) + + # If average is zero in reduced model, set error to zero + if spring_bloom_avg_reduced == 0.0: + error = 100 + + return error, solution_reduced_model + + +def calc_error_lo_7(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in average phytoplankton carbon concentration during a spring bloom + This is for the sum of p1c + p2c + p3c + p4c + This is for the spring bloom (Jan - March) in the 2nd year of a 10 year simulation + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Set time range for searching for max value + # January to March in year 2 + t_min = 86400*365*2 + t_max = t_min + 86400*90 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + spring_bloom_full = numpy.zeros((num_boxes,(index_t_max_full-index_t_min_full))) + spring_bloom_reduced = numpy.zeros((num_boxes,(index_t_max_reduced-index_t_min_reduced))) + + p1c = 10 # P1c index + p2c = 15 # P2c index + p3c = 19 # P3c index + p4c = 23 # P4c index + for i in range(0,num_boxes): + for j_full in range(index_t_min_full,index_t_max_full): + spring_bloom_full[i,(j_full-index_t_min_full)] = solution_full_model.y[p1c+(i*50),j_full] + solution_full_model.y[p2c+(i*50),j_full] \ + + solution_full_model.y[p3c+(i*50),j_full] + solution_full_model.y[p4c+(i*50),j_full] + for j_reduced in range(index_t_min_reduced,index_t_max_reduced): + spring_bloom_reduced[i,(j_reduced-index_t_min_reduced)] = solution_reduced_model.y[p1c+(i*50),j_reduced] + solution_reduced_model.y[p2c+(i*50),j_reduced] \ + + solution_reduced_model.y[p3c+(i*50),j_reduced] + solution_reduced_model.y[p4c+(i*50),j_reduced] + + # Find average phytoplankton carbon concentration in spring bloom + spring_bloom_avg_full = numpy.mean(spring_bloom_full) + spring_bloom_avg_reduced = numpy.mean(spring_bloom_reduced) + + # Compute the error + error = numpy.abs(100*numpy.abs(spring_bloom_avg_full - spring_bloom_avg_reduced)/spring_bloom_avg_full) + + # If average is zero in reduced model, set error to zero + if spring_bloom_avg_reduced == 0.0: + error = 100 + + return error, solution_reduced_model + + +def calc_error_lo_8(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in peak phytoplankton carbon concentration during a spring bloom + This is for the sum of p1c + p2c + p3c + p4c + This is for the spring bloom (Jan - March) in the 2nd year of a 10 year simulation + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Set time range for searching for max value + # January to March in year 8 + t_min = 86400*365*2 + t_max = t_min + 86400*90 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + # Slice list of phytoplankton carbon concentrations + spring_bloom_full = numpy.zeros((num_boxes,(index_t_max_full-index_t_min_full))) + spring_bloom_reduced = numpy.zeros((num_boxes,(index_t_max_reduced-index_t_min_reduced))) + + p1c = 10 # P1c index + p2c = 15 # P2c index + p3c = 19 # P3c index + p4c = 23 # P4c index + for i in range(0,num_boxes): + for j_full in range(index_t_min_full,index_t_max_full): + spring_bloom_full[i,(j_full-index_t_min_full)] = solution_full_model.y[p1c+(i*50),j_full] + solution_full_model.y[p2c+(i*50),j_full] \ + + solution_full_model.y[p3c+(i*50),j_full] + solution_full_model.y[p4c+(i*50),j_full] + for j_reduced in range(index_t_min_reduced,index_t_max_reduced): + spring_bloom_reduced[i,(j_reduced-index_t_min_reduced)] = solution_reduced_model.y[p1c+(i*50),j_reduced] + solution_reduced_model.y[p2c+(i*50),j_reduced] \ + + solution_reduced_model.y[p3c+(i*50),j_reduced] + solution_reduced_model.y[p4c+(i*50),j_reduced] + + # Find peak phytoplankton carbon concentration in spring bloom + spring_bloom_peak_full = numpy.max(spring_bloom_full) + spring_bloom_peak_reduced = numpy.max(spring_bloom_reduced) + + # Compute the error + error = numpy.abs(100*numpy.abs(spring_bloom_peak_full - spring_bloom_peak_reduced)/spring_bloom_peak_full) + + # If peak is zero in reduced model, set error to zero + if spring_bloom_peak_reduced == 0.0: + error = 100 + + return error, solution_reduced_model + + +def calc_error_lo_9(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in time of peak phytoplankton carbon concentration during a spring bloom + This is for the sum of p1c + p2c + p3c + p4c + This is for the spring bloom (Jan - March) in the 8th year of a 10 year simulation + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Get sum of phytoplankton carbon concentrations (p1l + p2l + p3l + p4l) + pc_sum_conc_full = solution_full_model.y[10] + solution_full_model.y[15] + solution_full_model.y[19] + solution_full_model.y[23] + pc_sum_conc_reduced = solution_reduced_model.y[10] + solution_reduced_model.y[15] + solution_reduced_model.y[19] + solution_reduced_model.y[23] + + # Set time range for searching for max value + # January to March in year 2 + t_min = 86400*365*2 + t_max = t_min + 86400*90 + + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + spring_bloom_full = numpy.zeros((num_boxes,(index_t_max_full-index_t_min_full))) + spring_bloom_reduced = numpy.zeros((num_boxes,(index_t_max_reduced-index_t_min_reduced))) + + p1c = 10 # P1c index + p2c = 15 # P2c index + p3c = 19 # P3c index + p4c = 23 # P4c index + for i in range(0,num_boxes): + for j_full in range(index_t_min_full,index_t_max_full): + spring_bloom_full[i,(j_full-index_t_min_full)] = solution_full_model.y[p1c+(i*50),j_full] + solution_full_model.y[p2c+(i*50),j_full] \ + + solution_full_model.y[p3c+(i*50),j_full] + solution_full_model.y[p4c+(i*50),j_full] + for j_reduced in range(index_t_min_reduced,index_t_max_reduced): + spring_bloom_reduced[i,(j_reduced-index_t_min_reduced)] = solution_reduced_model.y[p1c+(i*50),j_reduced] + solution_reduced_model.y[p2c+(i*50),j_reduced] \ + + solution_reduced_model.y[p3c+(i*50),j_reduced] + solution_reduced_model.y[p4c+(i*50),j_reduced] + + # Slice list of phytoplankton carbon concentrations and time data + time_full = solution_full_model.t[index_t_min_full:index_t_max_full] + time_reduced = solution_reduced_model.t[index_t_min_reduced:index_t_max_reduced] + + # Find the index for the peak phytoplankton carbon concentration + pc_peak_index_full = numpy.argmax(spring_bloom_full,axis=1) + pc_peak_index_reduced = numpy.argmax(spring_bloom_reduced,axis=1) + + # Find the time of the peak phytoplankton carbon + pc_peak_time_full = time_full[pc_peak_index_full] + pc_peak_time_reduced = time_reduced[pc_peak_index_reduced] + + pc_peak_full = numpy.zeros(num_boxes) + pc_peak_reduced = numpy.zeros(num_boxes) + for i in range(0,num_boxes): + pc_peak_full[i] = spring_bloom_full[i,pc_peak_index_full[i]] + pc_peak_reduced[i] = spring_bloom_reduced[i,pc_peak_index_reduced[i]] + + err1 = numpy.zeros(num_boxes) + err2 = numpy.zeros(num_boxes) + for i in range(0,num_boxes): + err1[i] = numpy.abs(100*numpy.abs(pc_peak_time_full[i] - pc_peak_time_reduced[i])/pc_peak_time_full[i]) + err2[i] = numpy.abs(100*numpy.abs(pc_peak_full[i] - pc_peak_reduced[i])/pc_peak_full[i]) + if pc_peak_time_reduced[i] == 0.0: + err1[i] = 100 + if pc_peak_reduced[i] == 0.0: + err2[i] =100 + + error_time_of_peak = numpy.max(err1) + error_peak = numpy.max(err2) + + if error_peak > 80: + error = error_peak + else: + error = error_time_of_peak + + return error, solution_reduced_model + + +def calc_error_dic_1(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in average dissolved inorganic carbon (DIC) concentration + during the month of january in the second year of the ten year simulation + DIC is 'O3c' and its index is 48 + """ + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Set time range for searching for max value + t_min = 86400*365*2 + t_max = t_min + 86400*31 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + dic_full = numpy.zeros((num_boxes,(index_t_max_full-index_t_min_full))) + dic_reduced = numpy.zeros((num_boxes,(index_t_max_reduced-index_t_min_reduced))) + + index = 48 # O3c index + for i in range(0,num_boxes): + for j_full in range(index_t_min_full,index_t_max_full): + dic_full[i,(j_full-index_t_min_full)] = solution_full_model.y[index+(i*50),j_full] + for j_reduced in range(index_t_min_reduced,index_t_max_reduced): + dic_reduced[i,(j_reduced-index_t_min_reduced)] = solution_reduced_model.y[index+(i*50),j_reduced] + + # Find average DIC concentration + dic_avg_full = numpy.mean(dic_full) + dic_avg_reduced = numpy.mean(dic_reduced) + + # Compute the error + error = numpy.abs(100*numpy.abs(dic_avg_full - dic_avg_reduced)/dic_avg_full) + if dic_avg_reduced == 0.0: + error = 100 + + return error, solution_reduced_model + + +def calc_error_dic_2(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in annual average dissolved inorganic carbon (DIC) concentration + during the second year of the ten year simulation + DIC is 'O3c' and its index is 48 + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Set time range for searching for max value + t_min = 86400*365*2 + t_max = 86400*365*3 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + dic_full = numpy.zeros((num_boxes,(index_t_max_full-index_t_min_full))) + dic_reduced = numpy.zeros((num_boxes,(index_t_max_reduced-index_t_min_reduced))) + + index = 48 # O3c index + for i in range(0,num_boxes): + for j_full in range(index_t_min_full,index_t_max_full): + dic_full[i,(j_full-index_t_min_full)] = solution_full_model.y[index+(i*50),j_full] + for j_reduced in range(index_t_min_reduced,index_t_max_reduced): + dic_reduced[i,(j_reduced-index_t_min_reduced)] = solution_reduced_model.y[index+(i*50),j_reduced] + + # Find peak chl-a concentration + dic_avg_full = numpy.mean(dic_full) + dic_avg_reduced = numpy.mean(dic_reduced) + + # Compute the error + error = numpy.abs(100*numpy.abs(dic_avg_full - dic_avg_reduced)/dic_avg_full) + + # If average is zero in reduced model, set error to zero + if dic_avg_reduced == 0.0: + error = 100 + + return error, solution_reduced_model + + +def calc_error_dic_3(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in annual average dissolved inorganic carbon (DIC) concentration + during fifth year of the ten year simulation + DIC is 'O3c' and its index is 48 + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Set time range for searching for max value + t_min = 86400*365*5 + t_max = 86400*365*6 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + dic_full = numpy.zeros((num_boxes,(index_t_max_full-index_t_min_full))) + dic_reduced = numpy.zeros((num_boxes,(index_t_max_reduced-index_t_min_reduced))) + + index = 48 # O3c index + for i in range(0,num_boxes): + for j_full in range(index_t_min_full,index_t_max_full): + dic_full[i,(j_full-index_t_min_full)] = solution_full_model.y[index+(i*50),j_full] + for j_reduced in range(index_t_min_reduced,index_t_max_reduced): + dic_reduced[i,(j_reduced-index_t_min_reduced)] = solution_reduced_model.y[index+(i*50),j_reduced] + + # Find average DIC concentration + dic_avg_full = numpy.mean(dic_full) + dic_avg_reduced = numpy.mean(dic_reduced) + + # Compute the error + error = numpy.abs(100*numpy.abs(dic_avg_full - dic_avg_reduced)/dic_avg_full) + + # If average is zero in reduced model, set error to zero + if dic_avg_reduced == 0.0: + error = 100 + + return error, solution_reduced_model + + +def calc_error_dic_4(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in peak dissolved inorganic carbon (DIC) concentration + during the second year of the ten year simulation + DIC is 'O3c' and its index is 48 + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Set time range for searching for max value + t_min = 86400*365*2 + t_max = 86400*365*3 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + dic_full = numpy.zeros((num_boxes,(index_t_max_full-index_t_min_full))) + dic_reduced = numpy.zeros((num_boxes,(index_t_max_reduced-index_t_min_reduced))) + + index = 48 # O3c index + for i in range(0,num_boxes): + for j_full in range(index_t_min_full,index_t_max_full): + dic_full[i,(j_full-index_t_min_full)] = solution_full_model.y[index+(i*50),j_full] + for j_reduced in range(index_t_min_reduced,index_t_max_reduced): + dic_reduced[i,(j_reduced-index_t_min_reduced)] = solution_reduced_model.y[index+(i*50),j_reduced] + + # Find peak DIC concentration + # if statement in case reduction automation removes all spcies + if len(species_removed) < 50: + dic_peak_full = numpy.max(dic_full) + dic_peak_reduced = numpy.max(dic_reduced) + + # Compute the error + error = numpy.abs(100*numpy.abs(dic_peak_full - dic_peak_reduced)/dic_peak_full) + + # if all species are removed then error = nan + else: + error = numpy.nan + + return error, solution_reduced_model + + +def calc_error_pon_1(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in the peak particulate organic nitrogen (PON) concentration during the second year of the ten year simulation. + PON is the sum of all Phytoplankton nitrogen constituents and detrital nitrogen ('P1n' + 'P2n' + 'P3n' + 'P4n' + 'R6n'). Their indices are 11, 16, 20, 24, and 45. + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Set time range for searching for max value + t_min = 86400*365*2 + t_max = 86400*365*3 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + pon_full = numpy.zeros((num_boxes,(index_t_max_full-index_t_min_full))) + pon_reduced = numpy.zeros((num_boxes,(index_t_max_reduced-index_t_min_reduced))) + index_p1n = 11 + index_p2n = 16 + index_p3n = 20 + index_p4n = 24 + index_r6n = 45 # R6n index + for i in range(0,num_boxes): + for j_full in range(index_t_min_full,index_t_max_full): + pon_full[i,(j_full-index_t_min_full)] = solution_full_model.y[index_p1n+(i*50),j_full] + solution_full_model.y[index_p2n+(i*50),j_full] + solution_full_model.y[index_p3n+(i*50),j_full] + solution_full_model.y[index_p4n+(i*50),j_full] + solution_full_model.y[index_r6n+(i*50),j_full] + + for j_reduced in range(index_t_min_reduced,index_t_max_reduced): + pon_reduced[i,(j_reduced-index_t_min_reduced)] = solution_reduced_model.y[index_p1n+(i*50),j_reduced] + solution_reduced_model.y[index_p2n+(i*50),j_reduced] + solution_reduced_model.y[index_p3n+(i*50),j_reduced] + solution_reduced_model.y[index_p4n+(i*50),j_reduced] + solution_reduced_model.y[index_r6n+(i*50),j_reduced] + + # Find peak r6n concentration + pon_peak_full = numpy.max(pon_full) + pon_peak_reduced = numpy.max(pon_reduced) + + # Compute the error + error = numpy.abs(100*numpy.abs(pon_peak_full - pon_peak_reduced)/pon_peak_full) + + # If peak is zero in reduced model, set error to zero + if pon_peak_reduced == 0.0: + error = 100 + + return error, solution_reduced_model + + +def calc_error_pon_2(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in the average particulate organic phosphate concentration + during the second year of the ten year simulation. + DIC is 'R6n' and its index is 45 + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Set time range for searching for max value + t_min = 86400*365*2 + t_max = 86400*365*3 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + pon_full = numpy.zeros((num_boxes,(index_t_max_full-index_t_min_full))) + pon_reduced = numpy.zeros((num_boxes,(index_t_max_reduced-index_t_min_reduced))) + index_p1n = 11 + index_p2n = 16 + index_p3n = 20 + index_p4n = 24 + index_r6n = 45 # R6n index + for i in range(0,num_boxes): + for j_full in range(index_t_min_full,index_t_max_full): + pon_full[i,(j_full-index_t_min_full)] = solution_full_model.y[index_p1n+(i*50),j_full] + solution_full_model.y[index_p2n+(i*50),j_full] + solution_full_model.y[index_p3n+(i*50),j_full] + solution_full_model.y[index_p4n+(i*50),j_full] + solution_full_model.y[index_r6n+(i*50),j_full] + + for j_reduced in range(index_t_min_reduced,index_t_max_reduced): + pon_reduced[i,(j_reduced-index_t_min_reduced)] = solution_reduced_model.y[index_p1n+(i*50),j_reduced] + solution_reduced_model.y[index_p2n+(i*50),j_reduced] + solution_reduced_model.y[index_p3n+(i*50),j_reduced] + solution_reduced_model.y[index_p4n+(i*50),j_reduced] + solution_reduced_model.y[index_r6n+(i*50),j_reduced] + + + # Find avergae pon concentration + pon_avg_full = numpy.mean(pon_full) + pon_avg_reduced = numpy.mean(pon_reduced) + + # Compute the error + error = numpy.abs(100*numpy.abs(pon_avg_full - pon_avg_reduced)/pon_avg_full) + + + # If average is zero in reduced model, set error to zero + if pon_avg_reduced == 0.0: + error = 100 + + return error, solution_reduced_model + + +def calc_error_pon_3(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in time of peak particulate organic nitrogen (PON) concentration + during the second year of the ten year simulation + PON is 'R6n' and its index is 45 + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Set time range for searching for max value in full model + t_min = 86400*365*2 + t_max = 86400*365*3 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + pon_full = numpy.zeros((num_boxes,(index_t_max_full-index_t_min_full))) + pon_reduced = numpy.zeros((num_boxes,(index_t_max_reduced-index_t_min_reduced))) + index_p1n = 11 + index_p2n = 16 + index_p3n = 20 + index_p4n = 24 + index_r6n = 45 # R6n index + for i in range(0,num_boxes): + for j_full in range(index_t_min_full,index_t_max_full): + pon_full[i,(j_full-index_t_min_full)] = solution_full_model.y[index_p1n+(i*50),j_full] + solution_full_model.y[index_p2n+(i*50),j_full] + solution_full_model.y[index_p3n+(i*50),j_full] + solution_full_model.y[index_p4n+(i*50),j_full] + solution_full_model.y[index_r6n+(i*50),j_full] + + for j_reduced in range(index_t_min_reduced,index_t_max_reduced): + pon_reduced[i,(j_reduced-index_t_min_reduced)] = solution_reduced_model.y[index_p1n+(i*50),j_reduced] + solution_reduced_model.y[index_p2n+(i*50),j_reduced] + solution_reduced_model.y[index_p3n+(i*50),j_reduced] + solution_reduced_model.y[index_p4n+(i*50),j_reduced] + solution_reduced_model.y[index_r6n+(i*50),j_reduced] + + # slice list of time + time_full = solution_full_model.t[index_t_min_full:index_t_max_full] + time_reduced = solution_reduced_model.t[index_t_min_reduced:index_t_max_reduced] + + # Find the index for the peak pon concentration + pon_peak_index_full = numpy.argmax(pon_full,axis=1) + pon_peak_index_reduced = numpy.argmax(pon_reduced,axis=1) + + # Find the time of the peak pon + pon_peak_time_full = time_full[pon_peak_index_full] + pon_peak_time_reduced = time_reduced[pon_peak_index_reduced] + + err = numpy.zeros(num_boxes) + for i in range(0,num_boxes): + err[i] = numpy.abs(100*numpy.abs(pon_peak_time_full[i] - pon_peak_time_reduced[i])/pon_peak_time_full[i]) + + if pon_peak_time_reduced[i] == 0.0: + err[i] = 100 + + error = numpy.max(err) + return error, solution_reduced_model + + +def calc_error_oxy_1(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in average oxygen concentration + during the month of january in the second year of the ten year simulation + DIC is 'O2o and its index is 0 + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Set time range for searching for max value + t_min = 86400*365*2 + t_max = t_min + 86400*31 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + o2o_full = numpy.zeros((num_boxes,(index_t_max_full-index_t_min_full))) + o2o_reduced = numpy.zeros((num_boxes,(index_t_max_reduced-index_t_min_reduced))) + + index = 0 + for i in range(0,num_boxes): + for j_full in range(index_t_min_full,index_t_max_full): + o2o_full[i,(j_full-index_t_min_full)] = solution_full_model.y[index+(i*50),j_full] + for j_reduced in range(index_t_min_reduced,index_t_max_reduced): + o2o_reduced[i,(j_reduced-index_t_min_reduced)] = solution_reduced_model.y[index+(i*50),j_reduced] + + # Find average o2o concentration + o2o_avg_full = numpy.mean(o2o_full) + o2o_avg_reduced = numpy.mean(o2o_reduced) + + # Compute the error + error = numpy.abs(100*numpy.abs(o2o_avg_full - o2o_avg_reduced)/o2o_avg_full) + + # If avg is zero in reduced model, set error to zero + if o2o_avg_reduced == 0.0: + error = 100 + + return error, solution_reduced_model + + +def calc_error_oxy_2(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in the average oxygen concentration + during the second year of the ten year simulation. + DIC is 'O2o' and its index is 0 + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Set time range for searching for max value + t_min = 86400*365*2 + t_max = 86400*365*3 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + o2o_full = numpy.zeros((num_boxes,(index_t_max_full-index_t_min_full))) + o2o_reduced = numpy.zeros((num_boxes,(index_t_max_reduced-index_t_min_reduced))) + + index = 0 + for i in range(0,num_boxes): + for j_full in range(index_t_min_full,index_t_max_full): + o2o_full[i,(j_full-index_t_min_full)] = solution_full_model.y[index+(i*50),j_full] + for j_reduced in range(index_t_min_reduced,index_t_max_reduced): + o2o_reduced[i,(j_reduced-index_t_min_reduced)] = solution_reduced_model.y[index+(i*50),j_reduced] + + # Find avergae o2o concentration + o2o_avg_full = numpy.mean(o2o_full) + o2o_avg_reduced = numpy.mean(o2o_reduced) + + # Compute the error + error = numpy.abs(100*numpy.abs(o2o_avg_full - o2o_avg_reduced)/o2o_avg_full) + + # If average is zero in reduced model, set error to zero + if o2o_avg_reduced == 0.0: + error = 100 + + return error, solution_reduced_model + + +def calc_error_oxy_3(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in the peak oxygen concentration + during the second year of the ten year simulation. + DIC is 'O2o' and its index is 0 + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Set time range for searching for max value + t_min = 86400*365*2 + t_max = 86400*365*3 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + o2o_full = numpy.zeros((num_boxes,(index_t_max_full-index_t_min_full))) + o2o_reduced = numpy.zeros((num_boxes,(index_t_max_reduced-index_t_min_reduced))) + + index = 0 + for i in range(0,num_boxes): + for j_full in range(index_t_min_full,index_t_max_full): + o2o_full[i,(j_full-index_t_min_full)] = solution_full_model.y[index+(i*50),j_full] + for j_reduced in range(index_t_min_reduced,index_t_max_reduced): + o2o_reduced[i,(j_reduced-index_t_min_reduced)] = solution_reduced_model.y[index+(i*50),j_reduced] + + + # Find peak o2o concentration + o2o_peak_full = numpy.max(o2o_full) + o2o_peak_reduced = numpy.max(o2o_reduced) + + # Compute the error + error = numpy.abs(100*numpy.abs(o2o_peak_full - o2o_peak_reduced)/o2o_peak_full) + + # If peak is zero in reduced model, set error to zero + if o2o_peak_reduced == 0.0: + error = 100 + + return error, solution_reduced_model + + +def calc_error_oxy_4(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in time of peak oxygen concentration + during the second year of the ten year simulation + oxygen is 'O2o' and its index is 0 + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Set time range for searching for max value + t_min = 86400*365*2 + t_max = 86400*365*3 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + o2o_full = numpy.zeros((num_boxes,(index_t_max_full-index_t_min_full))) + o2o_reduced = numpy.zeros((num_boxes,(index_t_max_reduced-index_t_min_reduced))) + + index = 0 + for i in range(0,num_boxes): + for j_full in range(index_t_min_full,index_t_max_full): + o2o_full[i,(j_full-index_t_min_full)] = solution_full_model.y[index+(i*50),j_full] + for j_reduced in range(index_t_min_reduced,index_t_max_reduced): + o2o_reduced[i,(j_reduced-index_t_min_reduced)] = solution_reduced_model.y[index+(i*50),j_reduced] + + # slice list of time + time_full = solution_full_model.t[index_t_min_full:index_t_max_full] + time_reduced = solution_reduced_model.t[index_t_min_reduced:index_t_max_reduced] + + # Find the index for the peak o2o concentration + o2o_peak_index_full = numpy.argmax(o2o_full,axis=1) + o2o_peak_index_reduced = numpy.argmax(o2o_reduced,axis=1) + + # Find the time of the peak o2o + o2o_peak_time_full = time_full[o2o_peak_index_full] + o2o_peak_time_reduced = time_reduced[o2o_peak_index_reduced] + + err = numpy.zeros(num_boxes) + for i in range(0,num_boxes): + err[i] = numpy.abs(100*numpy.abs(o2o_peak_time_full[i] - o2o_peak_time_reduced[i])/o2o_peak_time_full[i]) + + if o2o_peak_time_reduced[i] == 0.0: + err[i] = 100 + + error = numpy.max(err) + # # Compute the error + # error = numpy.abs(100*numpy.abs(o2o_peak_time_full - o2o_peak_time_reduced)/o2o_peak_time_full) + + # # If time of peak is zero in reduced model, set error to zero + # if o2o_peak_time_reduced == 0.0: + # error = 100 + + return error, solution_reduced_model + + +def calc_error_in_1(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in average phoshate concentration in the second year of the simulation + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Set time range for searching for max value + t_min = 86400*365*2 + t_max = 86400*365*3 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + n1p_full = numpy.zeros((num_boxes,(index_t_max_full-index_t_min_full))) + n1p_reduced = numpy.zeros((num_boxes,(index_t_max_reduced-index_t_min_reduced))) + + index = 1 # N1p index + for i in range(0,num_boxes): + for j_full in range(index_t_min_full,index_t_max_full): + n1p_full[i,(j_full-index_t_min_full)] = solution_full_model.y[index+(i*50),j_full] + for j_reduced in range(index_t_min_reduced,index_t_max_reduced): + n1p_reduced[i,(j_reduced-index_t_min_reduced)] = solution_reduced_model.y[index+(i*50),j_reduced] + + # Find average chl-a concentration in spring bloom + n1p_avg_full = numpy.mean(n1p_full) + n1p_avg_reduced = numpy.mean(n1p_reduced) + + # Compute the error + error = numpy.abs(100*numpy.abs(n1p_avg_full - n1p_avg_reduced)/n1p_avg_full) + + # If peak is zero in reduced model, set error to zero + if n1p_avg_reduced == 0.0: + error = 100 + + return error, solution_reduced_model + + +def calc_error_in_2(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in peak phoshate concentration in the second year of the simulation + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Set time range for searching for max value + t_min = 86400*365*2 + t_max = 86400*365*3 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + n1p_full = numpy.zeros((num_boxes,(index_t_max_full-index_t_min_full))) + n1p_reduced = numpy.zeros((num_boxes,(index_t_max_reduced-index_t_min_reduced))) + + index = 1 # N1p index + for i in range(0,num_boxes): + for j_full in range(index_t_min_full,index_t_max_full): + n1p_full[i,(j_full-index_t_min_full)] = solution_full_model.y[index+(i*50),j_full] + for j_reduced in range(index_t_min_reduced,index_t_max_reduced): + n1p_reduced[i,(j_reduced-index_t_min_reduced)] = solution_reduced_model.y[index+(i*50),j_reduced] + + # Find peak o2o concentration + n1p_peak_full = numpy.max(n1p_full) + n1p_peak_reduced = numpy.max(n1p_reduced) + + # Compute the error + error = numpy.abs(100*numpy.abs(n1p_peak_full - n1p_peak_reduced)/n1p_peak_full) + + # If peak is zero in reduced model, set error to zero + if n1p_peak_reduced == 0.0: + error = 100 + + return error, solution_reduced_model + + +def calc_error_in_3(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in average nitrate concentration in the second year of the simulation + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + n3n_reduced = copy.copy(c0) + num_boxes = n3n_reduced.shape[0] + multiplier = numpy.ones(n3n_reduced.shape[1]) + for index in species_removed.values(): + n3n_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, n3n_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + n3n_reduced = n3n_reduced.reshape((num_boxes,50)) + + # Set time range for searching for max value + t_min = 86400*365*2 + t_max = 86400*365*3 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + + n3n_full = numpy.zeros((num_boxes,(index_t_max_full-index_t_min_full))) + n3n_reduced = numpy.zeros((num_boxes,(index_t_max_reduced-index_t_min_reduced))) + + index = 2 # N3n index + for i in range(0,num_boxes): + for j_full in range(index_t_min_full,index_t_max_full): + n3n_full[i,(j_full-index_t_min_full)] = solution_full_model.y[index+(i*50),j_full] + for j_reduced in range(index_t_min_reduced,index_t_max_reduced): + n3n_reduced[i,(j_reduced-index_t_min_reduced)] = solution_reduced_model.y[index+(i*50),j_reduced] + + # Find average n3n concentration in spring bloom + n3n_avg_full = numpy.mean(n3n_full) + n3n_avg_reduced = numpy.mean(n3n_reduced) + + # Compute the error + error = numpy.abs(100*numpy.abs(n3n_avg_full - n3n_avg_reduced)/n3n_avg_full) + + # If peak is zero in reduced model, set error to zero + if n3n_avg_reduced == 0.0: + error = 100 + + return error, solution_reduced_model + + +def calc_error_in_4(species_removed, solution_full_model, t_span, c0, bfm_phys_vars): + """ calculates error in peak nitrate concentration in the second year of the simulation + """ + + # Integrate reduced model + # Rempove indicated species in concentration list and create a multiplier for rate eqns to "remove species" + conc_reduced = copy.copy(c0) + num_boxes = conc_reduced.shape[0] + multiplier = numpy.ones(conc_reduced.shape[1]) + for index in species_removed.values(): + conc_reduced[:,index] = 0.0 + multiplier[index] = 0.0 + solution_reduced_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, multiplier, True, num_boxes, True), t_span, conc_reduced.ravel(), method='RK23') + + # Reshape concentration matrix + conc_reduced = conc_reduced.reshape((num_boxes,50)) + + # Set time range for searching for max value + t_min = 86400*365*2 + t_max = 86400*365*3 + + # Find index associated for t_min and t_max for slicing the list + for index,time in enumerate(solution_full_model.t): + if time >= t_min: + index_t_min_full = index + break + for index,time in enumerate(solution_full_model.t): + if time > t_max: + index_t_max_full = index + break + + for index,time in enumerate(solution_reduced_model.t): + if time >= t_min: + index_t_min_reduced = index + break + for index,time in enumerate(solution_reduced_model.t): + if time > t_max: + index_t_max_reduced = index + break + + n3n_full = numpy.zeros((num_boxes,(index_t_max_full-index_t_min_full))) + n3n_reduced = numpy.zeros((num_boxes,(index_t_max_reduced-index_t_min_reduced))) + + index = 2 # N3n index + for i in range(0,num_boxes): + for j_full in range(index_t_min_full,index_t_max_full): + n3n_full[i,(j_full-index_t_min_full)] = solution_full_model.y[index+(i*50),j_full] + for j_reduced in range(index_t_min_reduced,index_t_max_reduced): + n3n_reduced[i,(j_reduced-index_t_min_reduced)] = solution_reduced_model.y[index+(i*50),j_reduced] + + # Find peak n3n concentration + n3n_peak_full = numpy.max(n3n_full) + n3n_peak_reduced = numpy.max(n3n_reduced) + + # Compute the error + error = numpy.abs(100*numpy.abs(n3n_peak_full - n3n_peak_reduced)/n3n_peak_full) + + # If peak is zero in reduced model, set error to zero + if n3n_peak_reduced == 0.0: + error = 100 + + return error, solution_reduced_model diff --git a/reduction/modified_DRGEP.py b/reduction/modified_DRGEP.py new file mode 100644 index 0000000..3880916 --- /dev/null +++ b/reduction/modified_DRGEP.py @@ -0,0 +1,311 @@ +import copy +import logging +import numpy +import reduction.error_fcns as error_fcns +from bfm.bfm50.bfm50_rate_eqns import reduced_bfm50_rate_eqns +from bfm.data_classes import BfmPhysicalVariables +from reduction.pyMARS_DRGEP_functions import get_importance_coeffs +from scipy.integrate import solve_ivp + + +# Names of species in the system +species_names = ['O2o', 'N1p', 'N3n', 'N4n', 'O4n', 'N5s', 'N6r', 'B1c', 'B1n', 'B1p', + 'P1c', 'P1n', 'P1p', 'P1l', 'P1s', 'P2c', 'P2n', 'P2p', 'P2l', + 'P3c', 'P3n', 'P3p', 'P3l', 'P4c', 'P4n', 'P4p', 'P4l', + 'Z3c', 'Z3n', 'Z3p', 'Z4c', 'Z4n', 'Z4p', 'Z5c', 'Z5n', 'Z5p', + 'Z6c', 'Z6n', 'Z6p', 'R1c', 'R1n', 'R1p', 'R2c', 'R3c', 'R6c', + 'R6n', 'R6p', 'R6s', 'O3c', 'O3h'] + + +def calc_modified_DRGEP_dic(rate_eqn_fcn, conc, t, bfm_phys_vars): + """ Calculates the percent difference between the new rate eqn and the original. + The new rate is based on turning one species 'off' """ + + c_original = copy.copy(conc) + c_new = copy.copy(conc) + number_of_species = conc.shape[1] + num_boxes = conc.shape[0] + mult = numpy.ones(number_of_species) + # calculate original rate values + dc_dt_og = rate_eqn_fcn(t, c_new, bfm_phys_vars, mult, False, num_boxes, True) + + percent_error_matrix = numpy.zeros([number_of_species, number_of_species, num_boxes]) + + for j in range(number_of_species): + c_new[:,j] = 0.0 + + dc_dt_new = rate_eqn_fcn(t, c_new, bfm_phys_vars, mult, False, num_boxes, True) + new = dc_dt_new + og = dc_dt_og + percent_error = calc_percent_error(new, og, number_of_species, num_boxes) + percent_error_matrix[:,j,:] = percent_error + c_new[:,j] = c_original[:,j] + + + percent_error = numpy.amax(percent_error_matrix,axis=2) + + # Find the maximum value along each row (used for normalization) + row_max = numpy.amax(percent_error, axis=1) + + # Normalize percent_error_matrix by row max which is new_dic_matrix + dic_matrix = numpy.zeros([number_of_species, number_of_species]) + for i in range(number_of_species): + if row_max[i] == 0: + dic_matrix[i,:] = 0.0 + else: + dic_matrix[i,:] = percent_error[i,:]/row_max[i] + + # Set diagonals to zero to avoid self_directing graph edges + numpy.fill_diagonal(dic_matrix, 0.0) + + return dic_matrix + + +def calc_percent_error(new_matrix,old_matrix,num_species,num_boxes): + """ Calculates the percent error between two matricies. + This is used for the calculating the New Method's direct interaction coeffs + """ + percent_error = numpy.zeros_like(new_matrix) + if new_matrix.shape == old_matrix.shape: + for i in range(0,num_species): + for k in range(0,num_boxes): + if new_matrix[i,k] != old_matrix[i,k]: + percent_error[i,k] = 100*(abs(new_matrix[i,k] - old_matrix[i,k])/abs(old_matrix[i,k])) + if numpy.isnan(percent_error[i,k]): + percent_error[i,k] = 0 + + + return percent_error + + +def setup_reduction(reduction, tracers, tracer_indices): + + # Collect indices of target tracers + targets = reduction["targets"] + target_indices = [] + for tracer in targets: + + pass + + + +def modified_DRGEP(conc,bfm_phys_vars): + """ + Description: Apply Modififed DRGEP reduction strategy to BFM. + Target and safe species for each error function listed below. + -------------------------------------------------------------------------------------------------------------- + Error Function | Target Species | Safe Species + -------------------------------------------------------------------------------------------------------------- + calc_error_lo_1 | ['P1l', 'P2l', 'P3l', 'P4l'] | ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + calc_error_lo_2 | ['P2l', 'P3l', 'P4l'] | ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + calc_error_lo_3 | ['P3l', 'P4l'] | ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + calc_error_lo_4 | ['P1l', 'P2l', 'P3l', 'P4l'] | ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + calc_error_lo_5 | ['P2l', 'P3l', 'P4l'] | ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + calc_error_lo_6 | ['P3l', 'P4l'] | ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + calc_error_lo_7 | ['P1c', 'P2c', 'P3c', 'P4c'] | ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + calc_error_lo_8 | ['P1c', 'P2c', 'P3c', 'P4c'] | ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + calc_error_lo_9 | ['P1c', 'P2c', 'P3c', 'P4c'] | ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + -------------------------------------------------------------------------------------------------------------- + calc_error_dic_1 | ['O3c'] | ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + calc_error_dic_2 | ['O3c'] | ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + calc_error_dic_3 | ['O3c'] | ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + calc_error_dic_4 | ['O3c'] | ['N3n', 'N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + -------------------------------------------------------------------------------------------------------------- + calc_error_pon_1 | ['P1n', 'P2n', 'P3n', 'P4n', 'R6n'] | ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + calc_error_pon_2 | ['P1n', 'P2n', 'P3n', 'P4n', 'R6n'] | ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + calc_error_pon_3 | ['P1n', 'P2n', 'P3n', 'P4n', 'R6n'] | ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + -------------------------------------------------------------------------------------------------------------- + calc_error_oxy_1 | ['O2o'] | ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + calc_error_oxy_2 | ['O2o'] | ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + calc_error_oxy_3 | ['O2o'] | [] + calc_error_oxy_4 | ['O2o'] | ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + -------------------------------------------------------------------------------------------------------------- + calc_error_in_1 | ['N1p'] | ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + calc_error_in_2 | ['N1p'] | ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + calc_error_in_3 | ['N3n'] | ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + calc_error_in_4 | ['N3n'] | ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + -------------------------------------------------------------------------------------------------------------- + """ + + # Information for reduction + # User needs to update these values + scenario = 'lo_1' + species_targets = ['P1l', 'P2l', 'P3l', 'P4l'] + species_safe = ['N4n', 'P1l', 'P2l', 'P3l', 'P4l'] + error_limit = 5 + error_fcn = error_fcns.calc_error_lo_1 + rate_eqn_fcn = reduced_bfm50_rate_eqns + + # Time span for integration + t_span = [0,86400*365*7] + + # Time at which the DIC values are obtained + t = 86400*365 + + # Log input data to file + for handler in logging.root.handlers[:]: + logging.root.removeHandler(handler) + logging.basicConfig(filename='output.log', level=logging.INFO) + logging.info(100 * '-') + logging.info('Scenario: {}'.format(scenario)) + logging.info('Error function: {}'.format(error_fcn)) + logging.info('Error limit: {}'.format(error_limit)) + logging.info('Target species: {}'.format(species_targets)) + logging.info('Retained species: {}'.format(species_safe)) + + # Get direct interaction coefficients + dic_matrix = calc_modified_DRGEP_dic(rate_eqn_fcn, conc, t, bfm_phys_vars) + + # Get overall interaction coefficients + overall_interaction_coeffs = get_importance_coeffs(species_names, species_targets, [dic_matrix]) + + # Make dictionary of target species and their index values + target_species = {} + for index, species in enumerate(species_names): + if species in species_targets: + target_species[species] = index + + # Slice concentration matrix + spacing = 25 + num_boxes = int(numpy.floor(conc.shape[0]/spacing)+1) + conc_sliced = numpy.zeros((num_boxes,conc.shape[1])) + phys_vars_sliced = BfmPhysicalVariables(num_boxes,bfm_phys_vars.pH) + for i in range(0,num_boxes-1): + slice = spacing*i + conc_sliced[i,:] = conc[slice,:] + phys_vars_sliced.depth[i] = bfm_phys_vars.depth[slice] + conc_sliced[-1] = conc[-1,:] + phys_vars_sliced.depth[-1] = bfm_phys_vars.depth[-1] + + # Run new method + reduction_data = run_modified_DRGEP(overall_interaction_coeffs, error_limit, error_fcn, species_names, species_targets, species_safe, rate_eqn_fcn, t_span, conc_sliced, phys_vars_sliced) + + return reduction_data, error_limit + + +def reduce_modified_DRGEP(error_fcn, last_error, species_names, species_safe, threshold, overall_interaction_coeffs, last_num_species, solution_full_model, solution_reduced_model, t_span, c0, bfm_phys_vars): + """ calculates the number of species and error for a given threshold value + """ + + # Find species to remove using cutoff threshold + species_removed = {} + for species in overall_interaction_coeffs: + if overall_interaction_coeffs[species] < threshold and species not in species_safe: + species_removed[species] = numpy.nan + # Assign index to dictionary values for the species_removed + for index, species in enumerate(species_names): + if species in species_removed: + species_removed[species] = index + + # Count how many species are remaining + num_species = len(species_names) - len(species_removed) + + if num_species == last_num_species: + error = last_error + solution_reduced_model = solution_reduced_model + else: + # For the reduced model, call function to get error + error, solution_reduced_model = error_fcn(species_removed, solution_full_model, t_span, c0, bfm_phys_vars) + + return error, num_species, species_removed, solution_reduced_model + + +def run_modified_DRGEP(overall_interaction_coeffs, error_limit, error_fcn, species_names, species_targets, species_safe, rate_eqn_fcn, t_span, c0, bfm_phys_vars): + + """ Iterates through different threshold values to find a reduced model that meets error criteria + """ + + assert species_targets, 'Need to specify at least one target species.' + + # begin reduction iterations + logging.info('Beginning reduction loop') + logging.info(45 * '-') + logging.info('Threshold | Number of species | Max error (%)') + + # make lists to store data + threshold_data = [] + num_species_data = [] + error_data = [] + species_removed_data = [] + solution_reduced_models = [] + output_data = {} + + # start with detailed (starting) model + num_species = c0.shape[1] + num_boxes = c0.shape[0] + mult = numpy.ones(num_species) + solution_full_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, mult, True, num_boxes, True), t_span, c0.ravel(), method='RK23')#,args=(bfm_phys_vars,multiplier,True,num_boxes,True)) + + first = True + error_current = 0.0 + threshold = 3e-2 + threshold_increment = 1e-5 + threshold_multiplier = 2 + while error_current <= error_limit: + if first: solution_reduced_model = solution_full_model + error_current, num_species, species_removed, solution_reduced_model = reduce_modified_DRGEP(error_fcn, error_current, species_names, species_safe, threshold, overall_interaction_coeffs, num_species, solution_full_model, solution_reduced_model, t_span, c0, bfm_phys_vars) + + # reduce threshold if past error limit on first iteration + if first and error_current > error_limit: + error_current = 0.0 + threshold /= 10 + threshold_increment /= 10 + if threshold <= 1e-10: + raise SystemExit( + 'Threshold value dropped below 1e-10 without producing viable reduced model' + ) + logging.info('Threshold value too high, reducing by factor of 10') + continue + + logging.info(f'{threshold:^9.2e} | {num_species:^17} | {error_current:^.5f}') + + # store data + threshold_data.append(threshold) + num_species_data.append(num_species) + error_data.append(error_current) + species_removed_data.append(species_removed) + solution_reduced_models.append(solution_reduced_model) + + threshold += threshold_increment + # threshold *= threshold_multiplier + first = False + + # Stop reduction process if num species reaches one + if num_species <= 1: + break + + # Stop interating if threshold exceeds value + if threshold >= 3e-2: + # if threshold >= 1: # maximum threshold for oxy_3 error function + break + + if error_current > error_limit: + threshold -= (2 * threshold_increment) + error_current, num_species, species_removed, solution_reduced_model = reduce_modified_DRGEP(error_fcn, error_current, species_names, species_safe, threshold, overall_interaction_coeffs, num_species, solution_full_model, solution_reduced_model, t_span, c0, bfm_phys_vars) + + if error_data[-1] > error_limit: + model = -2 + else: + model = -1 + + # Make dictionary of species remaining in reduced model + remaining_species = {} + for index, species in enumerate(species_names): + if species not in species_removed_data[model]: + remaining_species[species] = index + + logging.info(45 * '-') + logging.info('New method reduction complete.') + logging.info('Remaining species: {}'.format(remaining_species)) + logging.info('Removed species: {}'.format(species_removed_data[model])) + logging.info(100 * '-') + + # Store all output data to a dictionary + output_data['threshold_data'] = threshold_data + output_data['num_species_data'] = num_species_data + output_data['error_data'] = error_data + output_data['species_removed_data'] = species_removed_data + output_data['solution_full_model'] = solution_full_model + output_data['solution_reduced_models'] = solution_reduced_models + + return output_data diff --git a/reduction/other_functions.py b/reduction/other_functions.py new file mode 100644 index 0000000..1121060 --- /dev/null +++ b/reduction/other_functions.py @@ -0,0 +1,51 @@ +import numpy +from scipy.integrate import solve_ivp +from bfm.bfm50.bfm50_rate_eqns import reduced_bfm50_rate_eqns +import time +import datetime + +def solution_time(conc, bfm_phys_vars, mult,): + """ calculates the computation time for uncoupled bfm integration (RK23) + """ + + integration_time = numpy.zeros(5) + # Time span for integration + t_span = [0, 86400*365*10] + + for i in range(0,5): + + # Record integration start time + start = time.process_time() + + # Integrate model + solution_full_model = solve_ivp(lambda time, conc: reduced_bfm50_rate_eqns(time, conc, bfm_phys_vars, mult, True, 150, True), t_span, conc.ravel(), method='RK23') + + + # Record integration end time + end = time.process_time() + + # Time of integration + integration_time[i] = end - start + print(integration_time[i]) + + mean = numpy.mean(integration_time) + avg_integration_time = str(datetime.timedelta(seconds=mean)) + + return avg_integration_time + + +def extract_dict(dictionary): + + data = list(dictionary.values()) + values = numpy.array(data) + + return values + + +def fill_dict(dictionary,array,species_names): + + for i, sp in enumerate(species_names): + dictionary[sp] = array[i] + + return dictionary + diff --git a/reduction/pyMARS_DRGEP_functions.py b/reduction/pyMARS_DRGEP_functions.py new file mode 100644 index 0000000..fe5a517 --- /dev/null +++ b/reduction/pyMARS_DRGEP_functions.py @@ -0,0 +1,204 @@ +"""Module containing Directed Relation Graph with Error Propagation (DRGEP) method + NOTE: These functions are copied directly from the pyMARS code: + https://github.com/Niemeyer-Research-Group/pyMARS +""" +import networkx +from heapq import heappush, heappop +from itertools import count + +def mod_dijkstra(G, source, get_weight, pred=None, paths=None, + cutoff=None, target=None + ): + """Modified implementation of Dijkstra's algorithm for DRGEP method. + + Multiples values along graph pathways instead of adding and returns a dictionary + with nodes as keys and values containing the greatest path to that node. + Each edge weight must be <= 1 so that the further they are from the source, + the less important they are. + + Parameters + ---------- + G : networkx.Graph + Graph to be considered + source : str or int + Starting node for path + get_weight: function + Function for getting edge weight + pred: list, optional + List of predecessors of a node + paths: dict, optional + Path from the source to a target node. + target : str or int, optional + Ending node for path + cutoff : int or float, optional + Depth to stop the search. Only paths of length <= cutoff are returned. + + Returns + ------- + distance, path : dict + Returns a tuple of two dictionaries keyed by node. + The first dictionary stores distance from the source. + The second stores the path from the source to that node. + pred, distance : dict + Returns two dictionaries representing a list of predecessors + of a node and the distance to each node. + distance : dict + Dictionary of greatest lengths keyed by target. + + """ + G_succ = G.succ if G.is_directed() else G.adj #Adjaceny list + push = heappush + pop = heappop + dist = {} # dictionary of final distances + seen = {source: 0} + c = count() + fringe = [] # use heapq with (distance,label) tuples + push(fringe, (0, next(c), source)) + while fringe: + cont = 0 + (d, _, v) = pop(fringe) + if v in dist and d < dist[v]: + continue # already searched this node. + if v == source: + d = 1 + dist[v] = d + if v == target: + break + for u, e in G_succ[v].items(): #For all adjancent edges, get weights and multiply them by current path taken. + cost = get_weight(v, u, e) + if cost is None: + continue + vu_dist = dist[v] * get_weight(v, u, e) + if cutoff is not None: + if vu_dist < cutoff: + continue + #if v in dist: + #if vu_dist > dist[v]: + #raise ValueError('Contradictory paths found:', + #'weights greater than one?') + elif u not in seen or vu_dist > seen[u]: #If this path to u is greater than any other path we've seen, push it to the heap to be added to dist. + seen[u] = vu_dist + push(fringe, (vu_dist, next(c), u)) + if paths is not None: + paths[u] = paths[v] + [u] + if pred is not None: + pred[u] = [v] + elif vu_dist == seen[u]: + if pred is not None: + pred[u].append(v) + + if paths is not None: + return (dist, paths) + if pred is not None: + return (pred, dist) + return dist + + +def ss_dijkstra_path_length_modified(G, source, cutoff=None, weight='weight'): + """Compute the greatest path length via multiplication between source and all other + reachable nodes for a weighted graph with all weights <= 1. + + Parameters + ---------- + G : networkx.Graph + Graph to be considered + source : node label + Starting node for path + weight: str, optional (default='weight') + Edge data key corresponding to the edge weight. + cutoff : int or float, optional + Depth to stop the search. Only paths of length <= cutoff are returned. + + Returns + ------- + length : dictionary + Dictionary of shortest lengths keyed by target. + + Examples + -------- + >>> G=networkx.path_graph(5) + >>> length=networkx.ss_dijkstra_path_length_modified(G,0) + >>> length[4] + 1 + >>> print(length) + {0: 0, 1: 1, 2: 1, 3: 1, 4: 1} + + Notes + ----- + Edge weight attributes must be numerical and <= 1. + Distances are calculated as products of weighted edges traversed. + Don't use a cutoff. + + See Also + -------- + single_source_dijkstra() + + """ + if G.is_multigraph(): + get_weight = lambda u, v, data: min( + eattr.get(weight, 1) for eattr in data.values()) + else: + get_weight = lambda u, v, data: data.get(weight, 1) + + return mod_dijkstra(G, source, get_weight, cutoff=cutoff) + + +def graph_search_drgep(graph, target_species): + """Searches graph to generate a dictionary of the greatest paths to all species from one of the targets. + + Parameters + ---------- + graph : networkx.DiGraph + Graph representing model + target_species : list of str + List of target species to search from + + Returns + ------- + overall_coefficients : dict + Overall interaction coefficients; maximum over all paths from all targets to each species + + """ + overall_coefficients = {} + for target in target_species: + coefficients = ss_dijkstra_path_length_modified(graph, target) + # ensure target has importance of 1.0 + coefficients[target] = 1.0 + + for sp in coefficients: + overall_coefficients[sp] = max(overall_coefficients.get(sp, 0.0), coefficients[sp]) + + return overall_coefficients + + +def get_importance_coeffs(species_names, target_species, matrices): + """Calculate importance coefficients for all species + + Parameters + ---------- + species_names : list of str + Species names + target_species : list of str + List of target species + matrices : list of numpy.ndarray + List of adjacency matrices + + Returns + ------- + importance_coefficients : dict + Maximum coefficients over all sampled states + + """ + importance_coefficients = {sp:0.0 for sp in species_names} + name_mapping = {i: sp for i, sp in enumerate(species_names)} + for matrix in matrices: + graph = networkx.DiGraph(matrix) + networkx.relabel_nodes(graph, name_mapping, copy=False) + coefficients = graph_search_drgep(graph, target_species) + + importance_coefficients = { + sp:max(coefficients.get(sp, 0.0), importance_coefficients[sp]) + for sp in importance_coefficients + } + + return importance_coefficients diff --git a/setup/bacteria.py b/setup/bacteria.py index 9078814..443f9dd 100644 --- a/setup/bacteria.py +++ b/setup/bacteria.py @@ -2,23 +2,27 @@ import sys import numpy as np from functions.seasonal_cycling import * -from functions.other_functions import concentration_ratio, tracer_elements - +from functions.other_functions import concentration_ratio, monod, nutrient_limitation, tracer_elements +from fractions import Fraction class Bacteria(): """ """ - def __init__(self, abbrev, iters, reactions, **tracer): + + def __init__(self, abbrev, base_element, iters, num_layers, reactions, **tracer): self.abbrev = abbrev self.name = tracer["long_name"] self.type = tracer["type"] - # Nutrient limitation - self.nutrients = [] - self.nutrient_half_sat = [] - self.nutrient_limitation_type = tracer["parameters"]["nutrient_limitation"] - self.nutrient_limitation_factor = 0. + # Nutrient limitation + self.nutrient_limitation = tracer["parameters"]["nutrient_limitation"] + self.nutrient_limitation_factor = {} + self.nutrient_colimitation = 0. + + # Temperature regulation + self.temperature_regulation = tracer["parameters"]["temperature_regulation"] + self.temp_regulation_factor = 1. # Composition and concentration arrays self.composition = [] @@ -27,17 +31,51 @@ def __init__(self, abbrev, iters, reactions, **tracer): sys.exit("Bacteria: Element required for " + self.name + ". Check documentation adn edit input file.") else: for key in tracer["composition"]: - available_elements = ['c','n','p'] + available_elements = ['c','n','p','fe'] if key in available_elements: + # Add constituent to composition/concentration self.composition.append(key) - conc.append(tracer["composition"][key]) + + # Set initial conditions + if isinstance(tracer["composition"][key], str): # Read initial conditions from file + conc.append( np.fromfile(os.getcwd() + tracer["composition"][key]) ) + elif isinstance(tracer["composition"][key], (int,float)): # Create array of initial conditions + if num_layers == 1: # 0d configuration + conc.append(tracer["composition"][key]) + else: # 1d configuration + conc.append(tracer["composition"][key] * np.ones(num_layers)) + elif isinstance(tracer["composition"][key], (list,np.ndarray)): + conc.append(np.array(tracer["composition"][key])) + elif isinstance(tracer["composition"][key], dict): # Create array of initial conditions based off ratio to base element + index = self.composition.index(base_element) + if num_layers == 1: # 0d configuration + conc.append(tracer["composition"][key][base_element] * conc[index] ) + else: # 1d configuration + conc.append(tracer["composition"][key][base_element] * conc[index] * np.ones(num_layers)) + else: sys.exit("Bacteria: Element '" + key + "' not recognized. Check documentation and edit input file.") - hold = np.zeros((len(conc),iters)) - hold[...,0] = conc - self.conc = np.array(hold) - self.d_dt = np.zeros_like(conc) - self.conc_ratio = np.zeros_like(conc) + + # hold = np.zeros((len(conc),iters),dtype=np.ndarray) + # hold[...,0] = conc + # self.conc = hold + # self.d_dt = np.zeros_like(conc) + # self.conc_ratio = np.zeros_like(conc) + + if num_layers > 1: # Model as "boxes" between layers (num_layers-1) + self.conc = np.zeros((len(self.composition),num_layers-1,iters),dtype=float) + for const in range(0,len(self.composition)): + self.conc[const,:,0] = conc[const][:-1] + else: # Model as single box + self.conc = np.zeros((len(self.composition),iters),dtype=float) + for const in range(0,len(self.composition)): + self.conc[const,:,0] = conc[const] + self.d_dt = np.zeros_like(self.conc[...,0],dtype=float) + self.conc_ratio = np.ones_like(self.conc[...,0],dtype=float) + + + # Production + self.upt = {} # Uptake # Add relevant reactions self.reactions = [] @@ -54,59 +92,288 @@ def __init__(self, abbrev, iters, reactions, **tracer): self.reactions = [item for item in self.reactions if item["type"] == "uptake"] + [item for item in self.reactions if item["type"] != "uptake"] - # def __init__(self, abbrev, composition, iters, long_name, parameters, reactions, type): - # self.name = long_name - # self.type = type - - # # Nutrient limitation - # self.nutrients = [] - # self.nutrient_half_sat = [] - # self.nutrient_limitation_type = parameters["nutrient_limitation"] - # self.nutrient_limitation_factor = 0. - - # # Composition and concentration arrays - # self.composition = [] - # conc = [] - # if len(composition) < 1: - # sys.exit("Bacteria: Element required for " + long_name + ". Check documentation adn edit input file.") - # else: - # for key in composition: - # available_elements = ['c','n','p'] - # if key in available_elements: - # self.composition.append(key) - # conc.append(composition[key]) - # else: - # sys.exit("Bacteria: Element '" + key + "' not recognized. Check documentation and edit input file.") - # hold = np.zeros((len(conc),iters)) - # hold[...,0] = conc - # self.conc = np.array(hold) - # self.d_dt = np.zeros_like(conc) - # self.conc_ratio = np.zeros_like(conc) - - # # Add relevant reactions - # self.reactions = [] - # for reac in reactions: - # # Add reaction to dictionary - # if "consumed" in reac and reac["consumed"] != None: consumed = reac["consumed"] - # else: consumed = {"empty": "empty"} - # if "produced" in reac and reac["produced"] != None: produced = reac["produced"] - # else: produced = {"empty": "empty"} - # if ( abbrev in consumed.keys() ) or ( abbrev in produced.keys() ): - # self.reactions.append(reac) + def bac(self, iter, base_element, physical, tracers): - # # Reorder reactions (uptake needs to appear first) - # self.reactions = [item for item in self.reactions if item["type"] == "uptake"] + [item for item in self.reactions if item["type"] != "uptake"] + # Calculate oxygen limitation factor (if necessary) + if "oxygen_inhibition" in self.__dict__: + # Optional minimum concentration for aerobic/anerobic operations + if "min_o2" in self.oxygen_inhibition: o2 = np.maximum(tracers["o2"].conc[...,iter] , self.oxygen_inhibition["min_o2"] * np.ones_like(tracers["o2"].conc[...,iter])) + else: o2 = tracers["o2"].conc[...,iter] + self.oxy_limitation_factor = monod(o2, self.oxygen_inhibition["half_sat"], self.oxygen_inhibition["exponent"]) + + pass + + + def add_nutrient(self, nutrient): + """ + Add nutrients to phytoplankton and append dictionary of uptake rates + """ + self.upt[nutrient] = np.zeros_like(self.conc[0,...],dtype=float) + self.nutrient_limitation_factor[nutrient] = np.zeros_like(self.conc[0,...],dtype=float) + + + def calculate_nutrient_limitation(self, base_element, iter, tracers): + """ + Definition:: Calculates nutrient limitation factor as either a minimum, product, or sum of all nutrients which limit phytoplankton growth + """ + + fN = [] + for key in self.nutrient_limitation: + if key != "colimitation": + # Get nutrient chemical constituent + element = tracers[key].composition[0] + + # Get index of nutrient chemcical constituent in phytoplankton composition dictionary + element_index = self.composition.index(element) + + # Get index of nutrient chemcical constituent in cell quota dictionary + quota_index = self.cell_quota.index(element) + + if self.nutrient_limitation[key]["type"] == "internal": + # Calculate nutrient limitation factor + func = ( self.conc_ratio[element_index] - self.cell_quota["min"][quota_index]) / ( self.cell_quota["opt"][quota_index] - self.cell_quota["min"][quota_index] ) + + # Ensures nonzero value + func = np.maximum(1.E-20*np.ones_like(func), func) + + # Update dictionary + self.nutrient_limitation_factor[key] = func + + # Append fN for colimitation calculation + if key in self.nutrient_limitation["colimitation"]["nutrients"]: + fN.append(func) + + elif self.nutrient_limitation[key]["type"] == "external": + # Determine if Hill exponent exists for monod function + if "exponent" in self.nutrient_limitation[key]: + exponent = self.nutrient_limitation[key]["exponent"] + else: # Default to 1. (no scaling) + exponent = 1. + + # Calculate nutrient limitation factor + func = monod(tracers[key].conc[...,iter], self.nutrient_limitation[key]["half_sat"], exponent) + + # Ensures nonzero value + func = np.maximum(1.E-20*np.ones_like(func), func) + + # Update dictionary + self.nutrient_limitation_factor[key] = func + # Append fN for colimitation calculation + if key in self.nutrient_limitation["colimitation"]["nutrients"]: + fN.append(func) + + else: + sys.exit("Nutrient limitation type not recognized. Check documentation and edit input file.") + + if "colimitation" in self.nutrient_limitation.keys(): # Multiple nutrients available + fN = np.array(fN) + if self.nutrient_limitation["colimitation"] == "minimum": + self.nutrient_colimitation = np.min(fN, axis=0) + elif self.nutrient_limitation["colimitation"] == "product": + self.nutrient_colimitation = np.prod(fN, axis=0) + elif self.nutrient_limitation["colimitation"] == "sum": + self.nutrient_colimitation == np.sum(fN, axis=0) + else: + sys.exit("Nutrient colimitation not recognized. Check documentation and edit input file.") + else: # Only one nutrient available + self.nutrient_colimitation = fN - def add_nutrient(self, nutrient,half_sat): - self.nutrients.append(nutrient) - self.nutrient_half_sat.append(half_sat) - def mortality(): - pass + def lysis(self, iter, base_element, parameters, c, p, ec, ep, ic, ip, tracers): - def uptake(): - pass + # Extract dict + c = c[0] + p = p[0] + ec = ec[c] + ep = ep[p] + ic = ic[c] + ip = ip[p] - def bac(): - pass + # Locate index of base element + index = self.composition.index(base_element) + bac = np.array(tracers[self.abbrev].conc[index][iter]) + + lysis = parameters["lysis_rate"] * self.temp_regulation_factor * ( bac**2 ) + + # Convert lysis rate (if necessary) + if "convert_lysis" in parameters: + if parameters["convert_lysis"] == "cell_quota": + quota_index = self.nutrient_limitation["nutrients"].index(c) + lysis *= self.nutrient_limitation_factor[quota_index] + else: + if isinstance(parameters["convert_lysis"],(int,float)) and not isinstance(parameters["convert_lysis"],bool): + lysis *= parameters["convert_lysis"] + elif isinstance(parameters["convert_lysis"],str): + lysis *= float(Fraction(parameters["convert_lysis"])) + + + # Update d_dt + tracers[c].d_dt -= lysis + + # Apply partition to organic matter group (if necessary) + if "partition" in parameters: tracers[p].d_dt += lysis * parameters["partition"] + else: tracers[p].d_dt += lysis + + + def mortality(self, iter, parameters, c, p, ec, ep, ic, ip, tracers): + """ + Definition:: Calculates the non-grazing mortality of planktoninc species + """ + + # Extract dict + c = c[0] + p = p[0] + ec = ec[c] + ep = ep[p] + ic = ic[c] + ip = ip[p] + tc = np.array(tracers[c].conc[ic][iter]) + + # Calculate mortality rate + mortality = ( parameters["mortality_rate"][0] * tc ) + ( parameters["mortality_rate"][1] * (tc**2) ) + + # Oxygen limitation + if "oxygen_limited" in parameters and parameters["oxygen_limited"]: + oxy_limitation_factor = np.minimum(1., nutrient_limitation(tracers["o2"].conc[...,iter], parameters["half_sat_oxygen"])) + mortality += (1. - oxy_limitation_factor) * parameters["mortality_rate_oxy"] * tc + + # # Temperature regulation + # if self.temp_limited: + # mortality = mortality #* self.temp_regulation_factor + + # Calculate concentration ratios + # concentration_ratio(iter, ic, tracers[c]) + # concentration_ratio(iter, ip, tracers[p]) + + # Update d_dt + if parameters != None and "partition" in parameters: # Mortality can be partitioned between dissolved and particulate detrital pools + tracers[c].d_dt -= ec * tracers[c].conc_ratio * mortality * parameters["partition"] + tracers[p].d_dt += ep * tracers[c].conc_ratio * mortality * parameters["partition"] + + else: + tracers[c].d_dt -= ec * tracers[c].conc_ratio * mortality + tracers[p].d_dt += ep * tracers[c].conc_ratio * mortality + + + def uptake(self, iter, base_element, parameters, c, p, ec, ep, ic, tracers): + + # Extract dict + ec = ec[c] + ep = ep[p] + ic = ic[c] + ip = ip[p] + + # Identify the chemical constituent of the nutrient(s) + element = ec[c[0]] + + # Get concentration of constituent in phytoplankton if present + if element in self.composition: element_index = self.composition.index(element) + + if parameters["strategy"] == "coupled": + coupled_uptake = parameters["coupled_uptake"] + linked_nutrients = coupled_uptake["link"] + + # Extract uptake rates of linked nutrients + uptake_rates = [] + for nut in linked_nutrients: + uptake_rates.append(self.uptake_rates[nut]) + + # Calculate total linked uptake rate if multiple linked nutrients are used + if len(linked_nutrients) > 1: # use numpy "maximum" to ensure minimum uptake of 0. + if coupled_uptake["method"] == "max": linked_uptake = np.maximum(np.maximum(uptake_rates), np.zeros_like(self.uptake_rates[nut])) + elif coupled_uptake["method"] == "min": linked_uptake = np.maximum(np.minimum(uptake_rates), np.zeros_like(self.uptake_rates[nut])) + elif coupled_uptake["method"] == "product": linked_uptake = np.maximum(np.prod(uptake_rates), np.zeros_like(self.uptake_rates[nut])) + elif coupled_uptake["method"] == "sum": linked_uptake = np.maximum(np.sum(uptake_rates), np.zeros_like(self.uptake_rates[nut])) + + if isinstance(parameters["convert_uptake"],(int,float)) and not isinstance(parameters["convert_uptake"],bool): + uptake = self.nutrient_limitation_factor[element] * linked_uptake * coupled_uptake["convert_uptake"] + elif isinstance(parameters["convert_uptake"],str): + uptake = self.nutrient_limitation_factor[element] * linked_uptake * float(Fraction(parameters["convert_uptake"])) + + # Update d_dt + tracers[c].d_dt -= np.array(ec) * np.maximum(uptake, np.zeros_like(uptake)) + if element in self.composition: self.d_dt[element_index] += np.maximum(uptake, np.zeros_like(uptake)) + + elif parameters["strategy"] == "independent": + # Get concentration of element in bacterioplankton + if element in self.composition: + index = self.composition.index(element) + else: # If element doesn't isn't directly resolved, use base element with conversion factor + index = self.composition.index(base_element) + + bac = np.array(self.conc[index][iter]) + + # Calculate maximum uptake rate + max_uptake = (parameters["max_growth_rate"] + parameters["basal_metabolic_rate"]) / parameters["max_growth_efficiency"] + + # Calculate actual uptake + uptake = max_uptake * monod(tracers[c].conc[ic][iter], self.nutrient_limitation_factor["dom1"], 1.) * bac + + # Multiply by conversion (if necessary) + if "convert_uptake" in parameters: + if isinstance(parameters["convert_uptake"],(int,float)) and not isinstance(parameters["convert_uptake"],bool): + uptake *= coupled_uptake["convert_uptake"] + elif isinstance(parameters["convert_uptake"],str): + uptake *= float(Fraction(parameters["convert_uptake"])) + + if self.temperature_regulation["temp_limited"]: + uptake *= self.temp_regulation_factor + + # Update d_dt + tracers[c].d_dt -= uptake + if element in self.composition: self.d_dt[index] += uptake + + + + def respiration(self, iter, base_element, parameters, c, p, tracers): + + # Locate index of base element + index = self.composition.index(base_element) + + # Get concentration of base element + bac = tracers[self.abbrev].conc[index[iter]] + + # Calculate respiration rate + respiration = self.temp_regulation_rator * parameters["respiration_rate"] * bac + + # Aeorbic --> O2 repired + if tracers["o2"].conc[...,iter] > self.oxygen_inhibition["min_o2"]: + # tracers["o2"].d_dt -= respiration * parameters["convert_o2"] + if "o2" in c: + if isinstance(parameters["convert_o2"],(int,float)) and not isinstance(parameters["convert_o2"],bool): + tracers["o2"].d_dt -= respiration * parameters["convert_o2"] + elif isinstance(parameters["convert_o2"],str): + tracers["o2"].d_dt -= respiration * float(Fraction(parameters["convert_o2"])) + + if "co2" in p: + if base_element == "c": tracers["co2"].d_dt += respiration + else: + if isinstance(parameters["convert_co2"],(int,float)) and not isinstance(parameters["convert_co2"],bool): + tracers["co2"].d_dt += respiration * parameters["convert_co2"] + elif isinstance(parameters["convert_co2"],str): + tracers["co2"].d_dt += respiration * float(Fraction(parameters["convert_co2"])) + + # Update d_dt + self.d_dt[index] -= respiration + tracers[p].d_dt += respiration + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/setup/detritus.py b/setup/detritus.py index aa11741..81e21eb 100644 --- a/setup/detritus.py +++ b/setup/detritus.py @@ -3,22 +3,30 @@ import numpy as np from functions.seasonal_cycling import * from functions.other_functions import concentration_ratio, tracer_elements - +from fractions import Fraction class Detritus(): """ """ - def __init__(self, abbrev, iters, reactions, **tracer): + + def __init__(self, abbrev, base_element, iters, num_layers, reactions, **tracer): self.abbrev = abbrev self.name = tracer["long_name"] self.type = tracer["type"] + self.form = tracer["form"] # Light limitation if "light_attenuation" in tracer["parameters"]: self.light_attenuation = tracer["parameters"]["light_attenuation"] else: self.light_attenuation = 0. + + # Sedimentation + if "sedimentation" in tracer["parameters"]: + if tracer["parameters"]["sedimentation"]["sinking"] == True: + self.sinking_velocity = np.ones(num_layers-1) * tracer["parameters"]["sedimentation"]["sinking_rate"] + self.sinking_velocity[-1] = tracer["parameters"]["sedimentation"]["burial_velocity"] # Composition and concentration arrays self.composition = [] @@ -29,16 +37,47 @@ def __init__(self, abbrev, iters, reactions, **tracer): for key in tracer["composition"]: available_elements = ['c','n','p','chl','fe','si','caco3'] if key in available_elements: + # Add constituent to composition/concentration self.composition.append(key) - conc.append(tracer["composition"][key]) + + # Set initial conditions + if isinstance(tracer["composition"][key], str): # Read initial conditions from file + conc.append( np.fromfile(os.getcwd() + tracer["composition"][key]) ) + elif isinstance(tracer["composition"][key], (int,float)): # Create array of initial conditions + if num_layers == 1: # 0d configuration + conc.append(tracer["composition"][key]) + else: # 1d configuration + conc.append(tracer["composition"][key] * np.ones(num_layers)) + elif isinstance(tracer["composition"][key], (list,np.ndarray)): + conc.append(np.array(tracer["composition"][key])) + elif isinstance(tracer["composition"][key], dict): # Create array of initial conditions based off ratio to base element + index = self.composition.index(base_element) + if num_layers == 1: # 0d configuration + conc.append(tracer["composition"][key][base_element] * conc[index] ) + else: # 1d configuration + conc.append(tracer["composition"][key][base_element] * conc[index] * np.ones(num_layers)) + else: sys.exit("Detritus: Element '" + key + "' not recognized. Check documentation and edit input file.") - hold = np.zeros((len(conc),iters)) - hold[...,0] = conc - self.conc = np.array(hold) - self.d_dt = np.zeros_like(conc) - self.conc_ratio = np.zeros_like(conc) - self.conc_ratio = np.ones_like(self.conc[...,0]) + + # hold = np.zeros((len(conc),iters),dtype=np.ndarray) + # hold[...,0] = conc + # self.conc = hold + # self.d_dt = np.zeros_like(conc) + # self.conc_ratio = np.zeros_like(conc) + # self.conc_ratio = np.ones_like(self.conc[...,0]) + + if num_layers > 1: # Model as "boxes" between layers (num_layers-1) + self.conc = np.zeros((len(self.composition),num_layers-1,iters),dtype=float) + for const in range(0,len(self.composition)): + self.conc[const,:,0] = conc[const][:-1] + else: # Model as single box + self.conc = np.zeros((len(self.composition),iters),dtype=float) + for const in range(0,len(self.composition)): + self.conc[const,:,0] = conc[const] + self.d_dt = np.zeros_like(self.conc[...,0],dtype=float) + self.conc_ratio = np.ones_like(self.conc[...,0],dtype=float) + # Add relevant reactions self.reactions = [] @@ -52,17 +91,17 @@ def __init__(self, abbrev, iters, reactions, **tracer): self.reactions.append(reac) - def detritus(self, iter, base_element, tracers): - check_conc = self.conc[:,iter] + def detritus(self, iter, base_element, physical, tracers): + # check_conc = self.conc[:,iter] # Calculate bgc rates for reac in self.reactions: c, p, ec, ep, ic, ip = tracer_elements(base_element, reac, tracers) if reac["type"] == "remineralization": self.remineralization(iter, reac["parameters"], c, p, ec, ep, ic, ip, tracers) - if iter % 50 == 0: - x=1 - x=1 + # if iter % 50 == 0: + # x=1 + # x=1 def remineralization(self, iter, parameters, c, p, ec, ep, ic, ip, tracers): @@ -80,24 +119,32 @@ def remineralization(self, iter, parameters, c, p, ec, ep, ic, ip, tracers): # Get concentration of remineralized nutrient in organic matter pool index = list(ec).index(1.) - tc = np.array(tracers[consumed].conc[index][iter]) + tc = np.array(tracers[consumed].conc[index,:,iter]) if p[0] == None: pass else: p = p[0] ep = ep[p] ip = ip[p] - tp = np.array(tracers[p].conc[ip][iter]) + tp = np.array(tracers[p].conc[ip,:,iter]) remineralization = (parameters["remineralization_rate"]) * tc - tracers[consumed].d_dt -= ec * remineralization + # tracers[consumed].d_dt -= ec * remineralization + for i in range(len(ec)): + tracers[consumed].d_dt[i] -= ec[i] * remineralization + if "o2" in c: - tracers["o2"].d_dt -= remineralization / parameters["mw_carbon"] + if isinstance(parameters["convert_o2"],(int,float)) and not isinstance(parameters["convert_o2"],bool): + tracers["o2"].d_dt -= remineralization * parameters["convert_o2"] + elif isinstance(parameters["convert_o2"],str): + tracers["o2"].d_dt -= remineralization * float(Fraction(parameters["convert_o2"])) if p[0] == None: pass else: - tracers[p].d_dt += np.array(ep) * remineralization + # tracers[p].d_dt += np.array(ep) * remineralization + for j in range(len(ep)): + tracers[p].d_dt[j] += ep[j] * remineralization \ No newline at end of file diff --git a/setup/initialize.py b/setup/initialize.py index 8c863d3..cdada15 100644 --- a/setup/initialize.py +++ b/setup/initialize.py @@ -1,4 +1,5 @@ import numpy as np +import os import sys import yaml from setup.bacteria import Bacteria @@ -7,84 +8,147 @@ from setup.phytoplankton import Phytoplankton from setup.zooplankton import Zooplankton -# from setup.setup_solveivp import Bacteria, Detritus, Inorganic, Phytoplankton, Zooplankton +def coordinate_system(configuration, parameters): -def coordinate_system(parameters): + # Create empty dictionary for vertical grid parameters + vertical_grid = {} - parameters["dz"] = parameters["column_depth"] / parameters["num_layers"] - parameters["z"] = np.linspace(parameters["dz"]/2, parameters["column_depth"] - parameters["dz"]/2, parameters["num_layers"]) + # Create coordinate system + if configuration == "0d": + vertical_grid["dz"] = parameters["column_depth"] / parameters["num_layers"] + vertical_grid["z"] = np.linspace(parameters["dz"]/2, parameters["column_depth"] - parameters["dz"]/2, parameters["num_layers"]) - return parameters + elif configuration == "1d": + # Initialize vertical coordinate system arrays + l = np.ones(parameters["num_layers"]) # length scale + z = np.zeros(parameters["num_layers"]) # vertical coordinates + zz = np.zeros(parameters["num_layers"]) # staggered vertical coordinates + dz = np.zeros(parameters["num_layers"]) # vertical spacing + dzz = np.zeros(parameters["num_layers"]) # staggered vertical spacing + dzr = np.zeros(parameters["num_layers"]) # reciprocal of vertical spacing -def import_model(file_path): + # Calculate initial spacing + surface_logspace_layers = parameters["surf_log"] - 2. + bottom_logspace_layers = parameters["num_layers"] - parameters["bot_log"] - 1. + layers = (parameters["bot_log"] - parameters["surf_log"]) + 4. + + initial_spacing = 2. / layers / np.exp(0.693147 * (surface_logspace_layers)) + + dzz[0] = -0.5 * initial_spacing + + # Set vertical coordinates + for i in range(1, int(parameters["surf_log"])-1): + z[i-1] = -initial_spacing * 2**(i-2) + zz[i-1] = -initial_spacing * 2**(i-1.5) + + for i in range(int(parameters["surf_log"])-1, parameters["num_layers"]+1): + z[i-1] = -(i - surface_logspace_layers) / layers + zz[i-1] = -(i - surface_logspace_layers + 0.5) / layers + + # Set vertical spacing + dz[:-1] = z[:-1] - z[1:] + dzz[:-1] = zz[:-1] - zz[1:] + + dz[-1] = 1.E-06 # Small value to avoid division by zero for dzr + dzr = 1. / dz # Take reciprocal + dz[-1] = 0. # Correct dz value + + # Set length scale + l[0] = 0. + l[-1] = 0. + + vertical_grid["l"] = l + vertical_grid["z"] = z + vertical_grid["zz"] = zz + vertical_grid["dz"] = dz + vertical_grid["dzz"] = dzz + vertical_grid["dzr"] = dzr + + return vertical_grid + + +def import_bgc_model(file_path, physical): + + # Open file containing bgc data with open(file_path, 'r') as f: model_info = yaml.full_load(f) base_element = model_info["base_element"] model = model_info["tracers"] - parameters = model_info["parameters"] reactions = model_info["reactions"] - # ---------------------------------------------------------------------------------------------------- - # Check base element - # ---------------------------------------------------------------------------------------------------- - if base_element not in ['c','n','p']: - sys.exit("'" + base_element + "' not accepted as base element. Check documentation and edit input file.") - - # ---------------------------------------------------------------------------------------------------- - # Update coordinate system - # ---------------------------------------------------------------------------------------------------- - parameters["water_column"] = coordinate_system(parameters["water_column"]) - - # ---------------------------------------------------------------------------------------------------- - # Setup time array - # ---------------------------------------------------------------------------------------------------- - # Calculate number of iterations needed in simulation - iters = parameters["simulation"]["num_days"] * 86400 / parameters["simulation"]["timestep"] - iters = int(np.ceil(iters)) - - # Create time array (needed for calculating incident angle for PAR) - parameters["simulation"]["time"] = np.linspace(0,iters * parameters["simulation"]["timestep"], iters + 1) # +1 for zero-indexing - parameters["simulation"]["iters"] = iters + 1 # ---------------------------------------------------------------------------------------------------- # Read model tracers # ---------------------------------------------------------------------------------------------------- tracers = {} for key in model: if model[key]["type"] == "bacteria": - # tracers[key] = Bacteria(key, model[key]["composition"], parameters["simulation"]["iters"], model[key]["long_name"], model[key]["parameters"], reactions, model[key]["type"]) - tracers[key] = Bacteria(key, parameters["simulation"]["iters"], reactions, **model[key]) + tracers[key] = Bacteria(key, base_element, physical["simulation"]["iters"], physical["water_column"]["num_layers"], reactions, **model[key]) elif model[key]["type"] == "detritus": - # tracers[key] = Detritus(key, model[key]["composition"], parameters["simulation"]["iters"], model[key]["long_name"], reactions, model[key]["type"]) - tracers[key] = Detritus(key, parameters["simulation"]["iters"], reactions, **model[key]) + tracers[key] = Detritus(key, base_element, physical["simulation"]["iters"], physical["water_column"]["num_layers"], reactions, **model[key]) elif model[key]["type"] == "inorganic": - # tracers[key] = Inorganic(key, model[key]["composition"], parameters["simulation"]["iters"], model[key]["long_name"], reactions, model[key]["type"]) - tracers[key] = Inorganic(key, parameters["simulation"]["iters"], reactions, **model[key]) + tracers[key] = Inorganic(key, physical["simulation"]["iters"], physical["water_column"]["num_layers"], reactions, **model[key]) elif model[key]["type"] == "phytoplankton": - # tracers[key] = Phytoplankton(key, model[key]["composition"], parameters["simulation"]["iters"], model[key]["long_name"], model[key]["parameters"], reactions, model[key]["type"]) - tracers[key] = Phytoplankton(key, parameters["simulation"]["iters"], reactions, **model[key]) + tracers[key] = Phytoplankton(key, base_element, physical["simulation"]["iters"], physical["water_column"]["num_layers"], reactions, **model[key]) elif model[key]["type"] == "zooplankton": - # tracers[key] = Zooplankton(key, model[key]["composition"], parameters["simulation"]["iters"], model[key]["long_name"], model[key]["parameters"], reactions, model[key]["type"]) - tracers[key] = Zooplankton(key, parameters["simulation"]["iters"], reactions, **model[key]) + tracers[key] = Zooplankton(key, base_element, physical["simulation"]["iters"], physical["water_column"]["num_layers"], reactions, **model[key]) else: sys.exit("Warning: Functional group '" + model[key]["type"] + "' not accepted. Please review documentation and make necessary changes.") + + # tracers = {} + # for key in model: + # if model[key]["type"] == "bacteria": + # tracers[key] = Bacteria(key, physical["simulation"]["iters"], reactions, **model[key]) + # elif model[key]["type"] == "detritus": + # tracers[key] = Detritus(key, physical["simulation"]["iters"], reactions, **model[key]) + # elif model[key]["type"] == "inorganic": + # tracers[key] = Inorganic(key, physical["simulation"]["iters"], reactions, **model[key]) + # elif model[key]["type"] == "phytoplankton": + # tracers[key] = Phytoplankton(key, physical["simulation"]["iters"], reactions, **model[key]) + # elif model[key]["type"] == "zooplankton": + # tracers[key] = Zooplankton(key, physical["simulation"]["iters"], reactions, **model[key]) + # else: + # sys.exit("Warning: Functional group '" + model[key]["type"] + "' not accepted. Please review documentation and make necessary changes.") # ---------------------------------------------------------------------------------------------------- # Add necessary components to Phytoplankton and Zooplankton groups # ---------------------------------------------------------------------------------------------------- for key in reactions: if key["type"] == "grazing": # Add prey to zooplankton (used in rate calculations to determine sum of grazing rates) - # produced = list(key["produced"].keys())[0] - # consumed = list(key["consumed"].keys())[0] tracers[list(key["produced"].keys())[0]].add_prey(list(key["consumed"].keys())[0]) if key["type"] == "uptake": # Add nutrient to bacteria and phytoplankton (used in rate calculations for nutrient limitation) - # produced = list(key["produced"].keys())[0] - # consumed = list(key["consumed"].keys())[0] # Add half saturation constant if used in calculations - if "half_sat_nutrient" in key["parameters"]: - tracers[list(key["produced"].keys())[0]].add_nutrient(list(key["consumed"].keys())[0],key["parameters"]["half_sat_nutrient"]) - else: - tracers[list(key["produced"].keys())[0]].add_nutrient(list(key["consumed"].keys())[0],0.) + tracers[list(key["produced"].keys())[0]].add_nutrient(list(key["consumed"].keys())[0]) + + return base_element, reactions, tracers + + +def import_physical_model(file_path): + + # Open file containing physical data + with open(file_path, 'r') as f: physical = yaml.full_load(f) + + # ---------------------------------------------------------------------------------------------------- + # Initialize coordinate system + # ---------------------------------------------------------------------------------------------------- + physical["vertical_grid"] = coordinate_system(physical["simulation"]["configuration"],physical["water_column"]) + + # ---------------------------------------------------------------------------------------------------- + # Setup time array + # ---------------------------------------------------------------------------------------------------- + # Calculate number of iterations needed in simulation + physical["simulation"]["sec_per_day"] = 86400 + iters = physical["simulation"]["days"] * physical["simulation"]["sec_per_day"] / physical["simulation"]["dt"] + iters = int(np.ceil(iters)) + + # Create time array (needed for calculating incident angle for PAR) + physical["simulation"]["time"] = np.linspace(0,iters * physical["simulation"]["dt"], iters + 1) # +1 for zero-indexing + physical["simulation"]["iters"] = iters + 1 - return base_element, parameters, reactions, tracers + # Other variables for future calculations + physical["simulation"]["dt2"] = 2. * physical["simulation"]["dt"] # twice the timestep + physical["simulation"]["day_per_sec"] = 1. / 86400 + physical["simulation"]["months"] = int(np.ceil(physical["simulation"]["days"]/30)) + + return physical diff --git a/setup/inorganic.py b/setup/inorganic.py index 71abdf0..1bfa844 100644 --- a/setup/inorganic.py +++ b/setup/inorganic.py @@ -2,27 +2,28 @@ import sys import numpy as np from functions.seasonal_cycling import * -from functions.other_functions import concentration_ratio, nutrient_limitation, temperature_regulation, tracer_elements - +from functions.other_functions import concentration_ratio, nutrient_limitation, temperature_dependence, tracer_elements +from fractions import Fraction class Inorganic(): """ """ - def __init__(self, abbrev, iters, reactions, **tracer): + + def __init__(self, abbrev, iters, num_layers, reactions, **tracer): self.abbrev = abbrev self.name = tracer["long_name"] self.type = tracer["type"] # Temperature regulation - self.temp_limited = tracer["parameters"]["temp_limited"] - if self.temp_limited: - self.q10 = tracer["parameters"]["q10"] - self.temp_regulation_factor = 1. + self.temperature_regulation = tracer["parameters"]["temperature_regulation"] + self.temperature_regulation["temperature_regulation_factor"] = 1. - # Oxygen regulation + # Air-sea flux + if "surface_flux" in tracer["parameters"]: + if tracer["parameters"]["surface_flux"] == True: + self.surf_flux = 0. - # Concentration array self.composition = [] conc = [] @@ -31,14 +32,38 @@ def __init__(self, abbrev, iters, reactions, **tracer): else: pass for key in tracer["composition"]: + # Add constituent to composition/concentration self.composition.append(key) - conc.append(tracer["composition"][key]) - hold = np.zeros((len(conc),iters)) - hold[...,0] = conc - self.conc = np.array(hold) - self.d_dt = np.zeros_like(conc) - self.conc_ratio = np.ones_like(self.conc[...,0]) + # Set initial conditions + if isinstance(tracer["composition"][key], str): # Read initial conditions from file + conc.append( np.fromfile(os.getcwd() + tracer["composition"][key]) ) + elif isinstance(tracer["composition"][key], (int,float)): # Create array of initial conditions + if num_layers == 1: # 0d configuration + conc.append(tracer["composition"][key]) + else: # 1d configuration + conc.append(tracer["composition"][key] * np.ones(num_layers)) + elif isinstance(tracer["composition"][key], (list,np.ndarray)): + conc.append(np.array(tracer["composition"][key])) + + + # hold = np.zeros((len(conc),iters),dtype=np.ndarray) + # hold[...,0] = conc + # self.conc = np.array(hold) + # self.d_dt = np.zeros_like(conc) + # self.conc_ratio = np.ones_like(self.conc[...,0]) + + if num_layers > 1: # Model as "boxes" between layers (num_layers-1) + self.conc = np.zeros((len(self.composition),num_layers-1,iters),dtype=float) + for const in range(0,len(self.composition)): + self.conc[const,:,0] = conc[const][:-1] + else: # Model as single box + self.conc = np.zeros((len(self.composition),iters),dtype=float) + for const in range(0,len(self.composition)): + self.conc[const,:,0] = conc[const] + self.d_dt = np.zeros_like(self.conc[...,0],dtype=float) + self.conc_ratio = np.ones_like(self.conc[...,0],dtype=float) + # Add relevant reactions self.reactions = [] for reac in reactions: @@ -49,13 +74,56 @@ def __init__(self, abbrev, iters, reactions, **tracer): else: produced = {"empty": "empty"} if ( abbrev in consumed.keys() ) or ( abbrev in produced.keys() ): self.reactions.append(reac) + + + + # def __init__(self, abbrev, iters, reactions, **tracer): + # self.abbrev = abbrev + # self.name = tracer["long_name"] + # self.type = tracer["type"] + + # # Temperature regulation + # self.temperature_regulation = tracer["parameters"]["temperature_regulation"] + # self.temperature_regulation["temperature_regulation_factor"] = 1. + + # # Oxygen inhibition + + + # # Concentration array + # self.composition = [] + # conc = [] + # if len(tracer["composition"]) > 1: sys.exit("Inorganic: Only one element accepted per inorganic nutrient. Check documentation adn edit input file.") + # elif len(tracer["composition"]) < 1: sys.exit("Inorganic: Element required for " + self.name + ". Check documentation adn edit input file.") + # else: pass + + # for key in tracer["composition"]: + # self.composition.append(key) + # conc.append(tracer["composition"][key]) + # hold = np.zeros((len(conc),iters)) + # hold[...,0] = conc + # self.conc = np.array(hold) + # self.d_dt = np.zeros_like(conc) + # self.conc_ratio = np.ones_like(self.conc[...,0]) + + # # Add relevant reactions + # self.reactions = [] + # for reac in reactions: + # # Add reaction to dictionary + # if "consumed" in reac and reac["consumed"] != None: consumed = reac["consumed"] + # else: consumed = {"empty": "empty"} + # if "produced" in reac and reac["produced"] != None: produced = reac["produced"] + # else: produced = {"empty": "empty"} + # if ( abbrev in consumed.keys() ) or ( abbrev in produced.keys() ): + # self.reactions.append(reac) - def inorg(self, iter, base_element, base_temp, coordinates, dz, mixed_layer_depth, surface_PAR, temperature, salinity, wind, tracers): - check_conc = self.conc[:,iter] + def inorg(self, iter, base_element, physical, tracers): + # check_conc = self.conc[:,iter] - if self.temp_limited: - self.temp_regulation_factor = temperature_regulation(base_temp, temperature, self.q10) # calculate temperature regulation factor for nitrification + # if self.temp_limited: + if self.temperature_regulation["temp_limited"]: + # self.temperature_regulation["temperature_regulation_factor"] = temperature_dependence(base_temp, temperature, self) # calculate temperature regulation factor for nitrification + self.temperature_regulation["temperature_regulation_factor"] = temperature_dependence(physical["bgc_phys_vars"]["temperature"], self) # calculate temperature regulation factor for nitrification for reac in self.reactions: c, p, ec, ep, ic, ip = tracer_elements(base_element, reac, tracers) @@ -63,31 +131,38 @@ def inorg(self, iter, base_element, base_temp, coordinates, dz, mixed_layer_dept if "o2" in tracers: oxy_limitation_factor = nutrient_limitation(tracers["o2"].conc[...,iter],reac["parameters"]["half_sat_oxygen"]) else: oxy_limitation_factor = 1. self.nitrification(iter, reac["parameters"], oxy_limitation_factor, tracers) - if reac["type"] == "reaeration": self.reaeration(iter, reac["parameters"], dz, temperature, salinity, wind) + # if reac["type"] == "reaeration": self.reaeration(iter, reac["parameters"], physical["bgc_phys_vars"]["dz"], physical["bgc_phys_vars"]["temperature"], physical["bgc_phys_vars"]["salinity"], physical["bgc_phys_vars"]["wind"]) + if reac["type"] == "reaeration": self.reaeration(iter, reac["parameters"], physical["bgc_phys_vars"]["z"], physical["bgc_phys_vars"]["temperature"], physical["bgc_phys_vars"]["salinity"], physical["bgc_phys_vars"]["wind"]) if reac["type"] == "reoxidation" and self.abbrev == "hs": if "o2" in tracers: oxy_limitation_factor = nutrient_limitation(tracers["o2"].conc[...,iter],reac["parameters"]["half_sat_oxygen"]) else: oxy_limitation_factor = 1. self.reoxidation(iter, reac["parameters"], oxy_limitation_factor, tracers) - if iter % 50 == 0: - x=1 - if self.abbrev == 'nh4': - x=1 - if self.abbrev == 'o2': - x=1 + # if iter % 50 == 0: + # x=1 + # if self.abbrev == 'nh4': + # x=1 + # if self.abbrev == 'o2': + # o2=self.conc[0][iter] + # x=1 x=1 def nitrification(self, iter, parameters, oxy_limitation_factor, tracers): """ Definition:: Calculates nitrification rate """ - nitrification = self.temp_regulation_factor * oxy_limitation_factor * parameters["nitrification_rate"] * tracers["nh4"].conc[...,iter] + nitrification = self.temperature_regulation["temperature_regulation_factor"] * oxy_limitation_factor * parameters["nitrification_rate"] * tracers["nh4"].conc[...,iter] nitrification = np.maximum(np.zeros_like(nitrification),nitrification) tracers["nh4"].d_dt -= nitrification - if "o2" in tracers: tracers["o2"].d_dt -= parameters["nitrification_stoic_coeff"] * nitrification # '2. *' for stoichiometry, NH4(+) + 2O2 --> NO3(-) + H2O + 2H(+) tracers["no3"].d_dt += nitrification + if "o2" in tracers: + if isinstance(parameters["convert_o2"],(int,float)) and not isinstance(parameters["convert_o2"],bool): + tracers["o2"].d_dt -= nitrification * parameters["convert_o2"] + elif isinstance(parameters["convert_o2"],str): + tracers["o2"].d_dt -= nitrification * float(Fraction(parameters["convert_o2"])) + def reaeration(self, iter, parameters, dz, temperature, salinity, wind): @@ -95,8 +170,10 @@ def reaeration(self, iter, parameters, dz, temperature, salinity, wind): abt = (temperature + 273.15) / 100. # Calculate theoretical oxygen saturatino for temp + salt and conver into proper units of [mmol O2 / m3] - # oxy_sat = np.exp(-173.4292 + (249.6339/abt) + (143.3483*np.log(abt))-(21.8492*abt) + salinity*(-0.033096 + 0.014259*abt - 0.0017*(abt**2)))/(24.4665E-3) - oxy_sat = np.exp(-173.4292 + (249.6339/abt) + (143.3483*np.log(abt))-(21.8492*abt) + salinity*(-0.033096 + 0.014259*abt - 0.0017*(abt**2)))*44.661 + oxy_sat = np.exp(-173.4292 + (249.6339/abt) + (143.3483*np.log(abt))-(21.8492*abt) + salinity*(-0.033096 + 0.014259*abt - 0.0017*(abt**2)))/(24.4665E-3) + + # Use this one with BFM17 0D + # oxy_sat = np.exp(-173.4292 + (249.6339/abt) + (143.3483*np.log(abt))-(21.8492*abt) + salinity*(-0.033096 + 0.014259*abt - 0.0017*(abt**2)))*44.661 # Calculate Schmidt number, ratio between the kinematic viscosity and the molecular diffusivity of CO2 schmidt_number = parameters["k1"] - ( parameters["k2"]*temperature ) + ( parameters["k3"]*(temperature**2) ) - ( parameters["k4"]*(temperature**3) ) @@ -112,10 +189,14 @@ def reaeration(self, iter, parameters, dz, temperature, salinity, wind): wind_dependency = wind_dependency * 0.01 * 24 # Calculate flux of o2 - d_o2 = wind_dependency * (oxy_sat - self.conc[...,iter]) / dz + d_o2 = wind_dependency * (oxy_sat - self.conc[...,iter])# / dz + surf_o2 = np.zeros_like(dz) + surf_o2[0] = d_o2[0,0] / dz[0] + # Update d_dt - self.d_dt += d_o2 + self.d_dt += np.array(surf_o2, dtype=float) + self.surf_flux = np.array(d_o2) def reoxidation(self, iter, parameters, oxy_limitation_factor, tracers): @@ -126,4 +207,11 @@ def reoxidation(self, iter, parameters, oxy_limitation_factor, tracers): # Update d_dt tracers["hs"].d_dt -= reoxidation - tracers["o2"].d_dt -= reoxidation / parameters["reoxidation_stoic_coeff"] \ No newline at end of file + if isinstance(parameters["convert_o2"],(int,float)) and not isinstance(parameters["convert_o2"],bool): + tracers["o2"].d_dt -= reoxidation * parameters["convert_o2"] + elif isinstance(parameters["convert_o2"],str): + tracers["o2"].d_dt -= reoxidation * float(Fraction(parameters["convert_o2"])) + + + def caco3_saturation(): + pass \ No newline at end of file diff --git a/setup/phytoplankton.py b/setup/phytoplankton.py index c9a7d56..8ab4555 100644 --- a/setup/phytoplankton.py +++ b/setup/phytoplankton.py @@ -2,31 +2,26 @@ import sys import numpy as np from functions.seasonal_cycling import * -from functions.other_functions import concentration_ratio, irradiance, light_attenuation, light_limitation, max_growth_rate, nutrient_limitation, temperature_regulation, tracer_elements, switch - +from functions.other_functions import concentration_ratio, irradiance, light_attenuation, light_limitation, max_growth_rate, nutrient_limitation, monod, temperature_dependence, tracer_elements, switch +from fractions import Fraction +from pom.check_phy import nitr_lim, phos_lim, mult_lim, photo class Phytoplankton(): """ """ - def __init__(self, abbrev, iters, reactions, **tracer): + def __init__(self, abbrev, base_element, iters, num_layers, reactions, **tracer): self.abbrev = abbrev self.name = tracer["long_name"] self.type = tracer["type"] - # Nutrient limitation - self.nutrients = [] - self.nutrient_half_sat = [] - self.nutrient_limitation_type = tracer["parameters"]["nutrient_limitation"] - self.nutrient_limitation_factor = 0. self.nutrient_limitation = tracer["parameters"]["nutrient_limitation"] - # for key in tracer["parameters"]["nutrient_limitation"]: - # self.nutrient_limitation[key] = tracer["parameters"]["nutrient_limitation"][key] - + self.nutrient_limitation_factor = {} + self.nutrient_colimitation = 0. + # Intracellular nutrinet quotas - if "cell_quota" in tracer["parameters"]: - self.cell_quota = tracer["parameters"]["cell_quota"] + self.cell_quota = tracer["parameters"]["cell_quota"] # Light limitation if "light_attenuation" in tracer["parameters"]: @@ -35,44 +30,81 @@ def __init__(self, abbrev, iters, reactions, **tracer): self.light_attenuation = 0. # Temperature regulation - self.temp_limited = tracer["parameters"]["temp_limited"] - if self.temp_limited: - self.q10 = tracer["parameters"]["q10"] + self.temperature_regulation = tracer["parameters"]["temperature_regulation"] self.temp_regulation_factor = 1. + # Oxygen inhibition + if "oxygen_inhibition" in tracer["parameters"]: + self.oxygen_inhibition = tracer["parameters"]["oxygen_inhibition"] + self.oxy_limitation_factor = 1. + + # Sedimentation + if "sedimentation" in tracer["parameters"]: + if tracer["parameters"]["sedimentation"]["sinking"] == True: + self.sinking_velocity = np.ones(num_layers-1) * tracer["parameters"]["sedimentation"]["sinking_rate"] + self.sinking_velocity[-1] = tracer["parameters"]["sedimentation"]["burial_velocity"] + # Composition and concentration arrays self.composition = [] conc = [] if len(tracer["composition"]) < 1: - sys.exit("Phytoplankton: Element required for " + self.name + ". Check documentation adn edit input file.") + sys.exit("Phytoplankton: Element required for " + self.name + ". Check documentation and edit input file.") else: for key in tracer["composition"]: available_elements = ['c','n','p','chl','fe','si','caco3'] if key in available_elements: + # Add constituent to composition/concentration self.composition.append(key) - conc.append(tracer["composition"][key]) + + # Set initial conditions + if isinstance(tracer["composition"][key], str): # Read initial conditions from file + conc.append( np.fromfile(os.getcwd() + tracer["composition"][key]) ) + elif isinstance(tracer["composition"][key], (int,float)): # Create array of initial conditions + if num_layers == 1: # 0d configuration + conc.append(tracer["composition"][key]) + else: # 1d configuration + conc.append(tracer["composition"][key] * np.ones(num_layers)) + elif isinstance(tracer["composition"][key], (list,np.ndarray)): + conc.append(np.array(tracer["composition"][key])) + elif isinstance(tracer["composition"][key], dict): # Create array of initial conditions based off ratio to base element + index = self.composition.index(base_element) + if num_layers == 1: # 0d configuration + conc.append(tracer["composition"][key][base_element] * conc[index] ) + else: # 1d configuration + conc.append(tracer["composition"][key][base_element] * conc[index] * np.ones(num_layers)) + else: sys.exit("Phytoplankton: Element '" + key + "' not recognized. Check documentation and edit input file.") + + # hold = np.zeros((len(conc),iters),dtype=np.ndarray) + # hold[...,0] = conc + # self.conc = hold + # self.d_dt = np.zeros_like(conc) + # self.conc_ratio = np.copy(self.cell_quota["opt"]) + + if num_layers > 1: # Model as "boxes" between layers (num_layers-1) + self.conc = np.zeros((len(self.composition),num_layers-1,iters),dtype=float) + for const in range(0,len(self.composition)): + self.conc[const,:,0] = conc[const][:-1] + else: # Model as single box + self.conc = np.zeros((len(self.composition),iters),dtype=float) + for const in range(0,len(self.composition)): + self.conc[const,:,0] = conc[const] + self.d_dt = np.zeros_like(self.conc[...,0],dtype=float) + self.conc_ratio = np.ones_like(self.conc[...,0],dtype=float) - hold = np.zeros((len(conc),iters),dtype=np.ndarray) - for i in range(0,len(conc)): - hold[i,0] = np.array(conc[i]) - self.conc = np.array(hold) - self.d_dt = np.zeros_like(conc) - # self.conc_ratio = np.zeros_like(conc) - self.conc_ratio = np.copy(self.cell_quota["opt"]) # Production - # self.exu = np.ones_like(self.conc[0,...],dtype=float) # Exudation (Initialzied to 1 for use in respiration) self.exu = np.zeros_like(self.conc[0,...],dtype=float) # Exudation (Initialzied to 1 for use in respiration) self.gpp = np.zeros_like(self.conc[0,...],dtype=float) # Gross Primary Production (Initialized to 1 for use in exudation and respiration) self.lys = np.zeros_like(self.conc[0,...],dtype=float) # Lysis (carbon) self.npp = np.zeros_like(self.conc[0,...],dtype=float) # Net Primary Production self.psn = np.zeros_like(self.conc[0,...],dtype=float) # Photosynthesis self.rsp = np.zeros_like(self.conc[0,...],dtype=float) # Respiration + self.upt = {} # Uptake - self.uptn = np.zeros_like(self.conc[0,...],dtype=float) # Nitrogen uptake - self.uptp = np.zeros_like(self.conc[0,...],dtype=float) # Phosophorus uptake + # self.uptn = np.zeros_like(self.conc[0,...],dtype=float) # Nitrogen uptake + # self.uptp = np.zeros_like(self.conc[0,...],dtype=float) # Phosophorus uptake # Add relevant reactions self.reactions = [] @@ -108,216 +140,290 @@ def __init__(self, abbrev, iters, reactions, **tracer): self.calc_respiration = True break - def phyto(self, iter, base_element, base_temp, light_attenuation_water, coordinates, dz, mixed_layer_depth, surface_PAR, temperature, tracers): - - # Reset production variables - # self.exu = np.zeros_like((self.conc[0,...,0])) # Exudation (Initialzied to 1 for use in respiration) - # self.gpp = np.zeros_like(self.conc[0,...],dtype=float) # Gross Primary Production - # self.lys = np.zeros_like(self.conc[0,...,0]) # Lysis (carbon) - # self.npp = np.zeros_like(self.conc[0,...,0]) # Net Primary Production - # self.psn = np.zeros_like(self.conc[0,...,0]) # Photosynthesis - # self.rsp = np.zeros_like(self.conc[0,...,0]) # Respiration + # def phyto(self, iter, base_element, base_temp, light_attenuation_water, coordinates, dz, mixed_layer_depth, surface_PAR, temperature, tracers): + def phyto(self, iter, base_element, physical, tracers): + # Calculate nutrient limitation - self.calculate_nutrient_limitation(base_element, iter, tracers) + self.calculate_nutrient_limitation(iter, tracers) + + if iter < 10: + dif_nitr_lim = self.nutrient_limitation_factor["no3"] - nitr_lim[iter] + dif_phos_lim = self.nutrient_limitation_factor["po4"] - phos_lim[iter] + dif_mult_lim = self.nutrient_colimitation - mult_lim[iter] - # Calculate temp regulation factor - if self.temp_limited: - self.temp_regulation_factor = temperature_regulation(base_temp, temperature, self.q10) + # Calculate temp regulation factor (if necessary) + if self.temperature_regulation["temp_limited"]: + self.temp_regulation_factor = temperature_dependence(physical["bgc_phys_vars"]["temperature"], self) - check_conc = self.conc[:,iter] + # check_conc = self.conc[:,iter] # Calculate light extiction coefficient - k_PAR = light_attenuation(self.abbrev, iter, base_element, light_attenuation_water, tracers) + k_PAR = light_attenuation(self.abbrev, iter, base_element, physical["environment"]["light_attenuation_water"], tracers) - if iter % 50 == 0: - x=1 - x=1 # Calculate rates required for net primary production for reac in self.reactions: c, p, ec, ep, ic, ip = tracer_elements(base_element, reac, tracers) - if reac["type"] == "exudation": self.exudation(iter, reac["parameters"], c, p, tracers) - if reac["type"] == "gross_primary_production": self.gross_primary_production(iter, reac["parameters"], c, p, tracers) - if reac["type"] == "lysis": self.lysis(iter, reac["parameters"], c, p, ec, ep, ic, ip, tracers) - if reac["type"] == "photosynthesis": fI, irr = self.photosynthesis(iter, reac["parameters"], base_element, coordinates, dz, k_PAR, temperature, surface_PAR, tracers) - if reac["type"] == "respiration": activity_respiration, basal_respiration = self.respiration(iter, reac["parameters"], c, p, tracers) + if reac["type"] == "exudation": self.exudation(iter, base_element, reac["parameters"], c, p, ec, ep, ic, ip, tracers) + if reac["type"] == "gross_primary_production": self.gross_primary_production(iter, base_element, reac["parameters"], c, p, tracers) + if reac["type"] == "lysis": self.lysis(iter, base_element, reac["parameters"], c, p, ec, ep, ic, ip, tracers) + # if reac["type"] == "photosynthesis": fI, irr = self.photosynthesis(iter, reac["parameters"], physical["bgc_phys_vars"]["z"], physical["bgc_phys_vars"]["dz"], k_PAR, physical["bgc_phys_vars"]["temperature"], physical["bgc_phys_vars"]["surface_PAR"]) + if reac["type"] == "photosynthesis": fI, irr = self.photosynthesis(iter, reac["parameters"], physical["bgc_phys_vars"]["z"], physical["bgc_phys_vars"]["z"], k_PAR, physical["bgc_phys_vars"]["temperature"], physical["bgc_phys_vars"]["surface_PAR"]) + if reac["type"] == "respiration": activity_respiration, basal_respiration = self.respiration(iter, base_element, reac["parameters"], c, p, ec, ep, ic, ip, tracers) + x=1 # Calculate net primary production - self.net_primary_production(iter, tracers) + self.net_primary_production(iter, base_element, tracers) # Calculate remaining rates for reac in self.reactions: c, p, ec, ep, ic, ip = tracer_elements(base_element, reac, tracers) if reac["type"] == "chlorophyll_synthesis": - if self.calc_respiration: self.chlorophyll_synthesis(iter, reac["parameters"], activity_respiration, basal_respiration, coordinates, irr, k_PAR, surface_PAR, tracers) - else: self.chlorophyll_synthesis(iter, reac["parameters"], 0., 0., coordinates, irr, k_PAR, surface_PAR, tracers) - if reac["type"] == "uptake": self.uptake(iter, reac["parameters"], c, p, ec, ep, ic, tracers) - - - # for reac in self.reactions: - # c, p, ec, ep, ic, ip = tracer_elements(base_element, reac, tracers) - # if reac["type"] == "chlorophyll_synthesis": self.chlorophyll_synthesis(iter, reac["parameters"], coordinates, surface_PAR) - # if reac["type"] == "exudation": self.exudation(iter, reac["parameters"], c, p, tracers) - # if reac["type"] == "gross_primary_production": irrad = self.gross_primary_production(iter, reac["parameters"], coordinates, dz, mixed_layer_depth, surface_PAR, temperature, c, p, tracers) - # if reac["type"] == "mortality": self.mortality(iter, reac["parameters"], c, p, ec, ep, ic, ip, tracers) - # if reac["type"] == "respiration": self.respiration(iter, reac["parameters"], c, p, tracers) - # if reac["type"] == "uptake": fI = self.uptake(iter, reac["parameters"], coordinates, dz, mixed_layer_depth, surface_PAR, temperature, c, p, ec, ep, ic, ip, tracers) - - # Calculate net primary production - # self.npp[iter] = np.maximum(np.zeros_like(self.gpp[iter]), (self.gpp[iter] - self.exu[iter] - self.lys[iter] - self.rsp[iter])) - # if iter == 240: - # avg_gpp = np.average(self.gpp[0:iter]) - # avg_exu = np.average(self.exu[0:iter]) - # x=1 - - # return fI, irrad, self.nutrient_limitation_factor + if self.calc_respiration: self.chlorophyll_synthesis(iter, base_element, reac["parameters"], activity_respiration, basal_respiration, physical["bgc_phys_vars"]["z"], irr, k_PAR, physical["bgc_phys_vars"]["surface_PAR"], tracers) + else: self.chlorophyll_synthesis(iter, base_element, reac["parameters"], 0., 0., physical["bgc_phys_vars"]["z"], irr, k_PAR, physical["bgc_phys_vars"]["surface_PAR"], tracers) + if reac["type"] == "uptake": self.uptake(iter, base_element, reac["parameters"], c, p, ec, ep, ic, tracers) + x=1 + x=1 - def add_nutrient(self, nutrient, half_sat): - "Nitrate-Phosphate (N-P) co-limitation" - if nutrient == "no3" or nutrient == "po4": - self.nutrients.append(nutrient) - self.nutrient_half_sat.append(half_sat) + def add_nutrient(self, nutrient): + """ + Add nutrients to phytoplankton and append dictionary of uptake rates + """ + self.upt[nutrient] = np.zeros_like(self.conc[0,...],dtype=float) + self.nutrient_limitation_factor[nutrient] = np.zeros_like(self.conc[0,...],dtype=float) - def calculate_nutrient_limitation(self, base_element, iter, tracers): + def calculate_nutrient_limitation(self, iter, tracers): """ Definition:: Calculates nutrient limitation factor as either a minimum, product, or sum of all nutrients which limit phytoplankton growth """ - # nutrient_conc = np.zeros(len(self.nutrients),dtype=np.ndarray) - # for key in tracers: - # if key in self.nutrients: - # i = self.nutrients.index(key) - # nutrient_conc[i] = np.array(tracers[key].conc[...,iter]) - # lim = nutrient_limitation(nutrient_conc, self.nutrient_half_sat) - - # if len(lim) > 0: - # if self.nutrient_limitation_type == "minimum": - # self.nutrient_limitation_factor = np.min(lim, axis=0) - # elif self.nutrient_limitation_type == "product": - # self.nutrient_limitation_factor = np.prod(lim, axis=0) - # elif self.nutrient_limitation_type == "sum": - # self.nutrient_limitation_factor == np.sum(lim, axis=0) - - - fN = np.zeros(len(self.nutrient_limitation["nutrients"]),dtype=np.ndarray) - - if self.nutrient_limitation["type"] == "internal": - # Get index of base element - index = self.composition.index(base_element) - - # Calculate concentration ratios - # concentration_ratio(iter, index, self) - - for nut in self.nutrient_limitation["nutrients"]: - i = self.nutrient_limitation["nutrients"].index(nut) - # element = str(tracers[nut].composition[0]) - element = tracers[nut].composition[0] - element_index = self.composition.index(element) - func = ( self.conc_ratio[element_index] - self.nutrient_limitation["min_quota"][i]) / ( self.nutrient_limitation["opt_quota"][i] - self.nutrient_limitation["min_quota"][i] ) - func = np.maximum(1.E-20*np.ones_like(func), func) - fN[i] = np.minimum(np.ones_like(func), func) + fN = [] + for key in self.nutrient_limitation: + if key != "colimitation" and key != "include": + # Get nutrient chemical constituent + element = tracers[key].composition[0] - elif self.nutrient_limitation["type"] == "external": - for nut in self.nutrient_limitation["nutrients"]: - i = self.nutrient_limitation["nutrients"].index(nut) - # element = str(tracers[nut].composition[0]) - element = tracers[nut].composition[0] + # Get index of nutrient chemcical constituent in phytoplankton composition dictionary element_index = self.composition.index(element) - func = tracers[nut].conc[...,iter] / ( self.nutrient_half_sat[i] + tracers[nut].conc[...,iter] ) - fN[i] = np.maximum(1.E-20*np.ones_like(func), func) - else: - sys.exit("Nutrient limitation type not recognized. Check documentation and edit input file.") - + # Get index of nutrient chemcical constituent in cell quota dictionary + # quota_index = self.cell_quota.index(element) + quota_index = self.cell_quota["constituents"].index(element) + + if self.nutrient_limitation[key]["type"] == "internal": + # Calculate nutrient limitation factor + func = ( self.conc_ratio[element_index] - self.cell_quota["min"][quota_index]) / ( self.cell_quota["opt"][quota_index] - self.cell_quota["min"][quota_index] ) + + # Ensures nonzero value + # func = np.maximum(1.E-20*np.ones_like(func), func) + func = np.minimum(np.ones_like(func),np.maximum(1.E-20*np.ones_like(func), func)) # maximum value of 1. + + # Update dictionary + self.nutrient_limitation_factor[key] = np.minimum(np.ones_like(func), func) + + # Append fN for colimitation calculation + # if key in self.nutrient_limitation["colimitation"]["nutrients"]: + if key in self.nutrient_limitation["include"]: + fN.append(func) + + elif self.nutrient_limitation[key]["type"] == "external": + # Determine if Hill exponent exists for monod function + if "exponent" in self.nutrient_limitation[key]: + exponent = self.nutrient_limitation[key]["exponent"] + else: # Default to 1. (no scaling) + exponent = 1. + + # Calculate nutrient limitation factor + func = monod(tracers[key].conc[...,iter], self.nutrient_limitation[key]["half_sat"], exponent) + + # Ensures nonzero value + # func = np.maximum(1.E-20*np.ones_like(func), func) + func = np.minimum(np.ones_like(func),np.maximum(1.E-20*np.ones_like(func), func)) # maximum value of 1. + + # Update dictionary + self.nutrient_limitation_factor[key] = np.minimum(np.ones_like(func), func) + + # Append fN for colimitation calculation + # if key in self.nutrient_limitation["colimitation"]["nutrients"]: + if "colimitation" in self.nutrient_limitation and key in self.nutrient_limitation["include"]: + fN.append(func) + + else: + sys.exit("Nutrient limitation type not recognized. Check documentation and edit input file.") + if "colimitation" in self.nutrient_limitation.keys(): # Multiple nutrients available + fN = np.array(fN) if self.nutrient_limitation["colimitation"] == "minimum": - self.nutrient_limitation_factor = np.min(fN, axis=0) + self.nutrient_colimitation = np.min(fN, axis=0) elif self.nutrient_limitation["colimitation"] == "product": - self.nutrient_limitation_factor = np.prod(fN, axis=0) + self.nutrient_colimitation = np.prod(fN, axis=0) elif self.nutrient_limitation["colimitation"] == "sum": - self.nutrient_limitation_factor == np.sum(fN, axis=0) + self.nutrient_colimitation == np.sum(fN, axis=0) else: sys.exit("Nutrient colimitation not recognized. Check documentation and edit input file.") else: # Only one nutrient available - self.nutrient_limitation_factor = fN + self.nutrient_colimitation = fN + + + def aggregation(self, iter, base_element, parameters, c, ec, ic, tracers): + """ + Definition:: Calculates the aggregation loss of phytoplankton during high biomass, bloom conditions + Parameterized as a quadratic (density dependent) loss term + """ + # Extract dict + c = c[0] + p = p[0] + ec = ec[c] + ep = ep[p] + ic = ic[c] + ip = ip[p] + + # Get concentration of base element + index = self.composition.index(base_element) + phyto = np.array(tracers[self.abbrev].conc[index][iter]) + + # Calculate growth ratio + growth_ratio = np.minimum(np.ones_like(self.psn[iter]), self.psn[iter]/ ( self.temp_regulation_factor * parameters["aggregation_frac"] * parameters["max_photo_rate"])) + + # Calculate aggregation limit + agg_limit = (1. - growth_ratio)**2 + + # Calculate aggregation loss + agg_loss = agg_limit * parameters["aggegation_loss"] * (phyto**2) + # Convert aggregation rate (if necessary) + if "convert_aggregation" in parameters: + if parameters["convert_aggregation"] == "cell_quota": + quota_index = self.nutrient_limitation["nutrients"].index(c) + agg_loss *= self.nutrient_limitation_factor[quota_index] + else: + if isinstance(parameters["convert_aggregation"],(int,float)) and not isinstance(parameters["convert_aggregation"],bool): + agg_loss *= parameters["convert_aggregation"] + elif isinstance(parameters["convert_aggregation"],str): + agg_loss *= float(Fraction(parameters["convert_aggregation"])) + + # Calculate concentration ratio + concentration_ratio(iter, index, self) + + # Update d_dt + self.d_dt -= ec * tracers[c].conc_ratio * agg_loss + + # Apply partition to organic matter group (if necessary) + if "partition" in parameters: tracers[p].d_dt += ep * agg_loss * parameters["partition"] + else: tracers[p].d_dt += ep * agg_loss - def chlorophyll_synthesis(self, iter, parameters, activity_respiration, basal_respiration, coordinates, irr, k_PAR, surface_PAR, tracers): + + def chlorophyll_synthesis(self, iter, base_element, parameters, activity_respiration, basal_respiration, coordinates, irr, k_PAR, surface_PAR, tracers): # Needs: Chlorophyll quota (theta_chl in python bfm), Initial slope of PI curve (alpha_chl), Optimal value Epar/EK (p_EpEk_or), # Maximal productivity (rp0), Chl:C relaxation rate (p_tochl_relt) - # Get carbon concentration - carbon_index = self.composition.index("c") - # phyto_carbon = self.conc[carbon_index][iter] - phyto_carbon = np.array(tracers[self.abbrev].conc[carbon_index][iter]) + # # Get carbon concentration + # carbon_index = self.composition.index("c") + # phyto_carbon = np.array(tracers[self.abbrev].conc[carbon_index][iter]) + + # Get concentration of base element + index = self.composition.index(base_element) + phyto = np.array(tracers[self.abbrev].conc[index,:,iter]) # Get chlorophyll concentration chl_index = self.composition.index("chl") - # phyto_chl = self.conc[chl_index][iter] - phyto_chl = np.array(tracers[self.abbrev].conc[chl_index][iter]) + phyto_chl = np.array(tracers[self.abbrev].conc[chl_index,:,iter]) # Calculate irradiance - # k_PAR = light_attenuation(parameters, phyto_carbon) - # irrad = irradiance(parameters["eps_PAR"], surface_PAR, coordinates, k_PAR) - - # x = (self.psn[iter] - self.exu[iter] - activity_respiration) * phyto_carbon - # y = parameters["initial_PI_slope"] * ( phyto_chl + 1.E-20) * irr - rho_chl = parameters["chl_quota"] * np.minimum(np.ones_like(self.psn[iter]), (self.psn[iter] - self.exu[iter] - activity_respiration) * phyto_carbon / ( parameters["initial_PI_slope"] * ( phyto_chl + 1.E-20) * irr )) - chl_opt = parameters["optimal_Epar_Ek"] * parameters["max_photo_rate"] * phyto_carbon / ( parameters["initial_PI_slope"] * irr + 1.E-20 ) + rho_chl = parameters["chl_quota"] * np.minimum(np.ones_like(self.psn[:,iter]), (self.psn[:,iter] - self.exu[:,iter] - activity_respiration) * phyto / ( parameters["initial_PI_slope"] * ( phyto_chl + 1.E-20) * irr )) + chl_opt = parameters["optimal_Epar_Ek"] * parameters["max_photo_rate"] * phyto / ( parameters["initial_PI_slope"] * irr + 1.E-20 ) - chlorophyll_synthesis = rho_chl * ( self.psn[iter] - self.exu[iter] - activity_respiration ) * phyto_carbon \ - - ( self.lys[iter] + basal_respiration ) * phyto_chl - np.maximum(np.zeros_like(phyto_chl), phyto_chl - chl_opt) * parameters["chl_relax_rate"] + chlorophyll_synthesis = rho_chl * ( self.psn[:,iter] - self.exu[:,iter] - activity_respiration ) * phyto \ + - ( self.lys[:,iter] + basal_respiration ) * phyto_chl - np.maximum(np.zeros_like(phyto_chl), phyto_chl - chl_opt) * parameters["chl_relax_rate"] # Update d_dt tracers[self.abbrev].d_dt[chl_index] += chlorophyll_synthesis # self.d_dt[chl_index] += chlorophyll_synthesis - - def exudation(self, iter, parameters, c, p, tracers): - - # Locate index of carbon constituent - carbon_index_phyto = self.composition.index("c") - carbon_index_om = tracers[p[0]].composition.index("c") - # Calculate exudation - activity = self.psn[iter] * parameters["excreted_fraction"] - nutrient_stress = self.psn[iter] * ( 1. - parameters["excreted_fraction"] ) * ( 1. - self.nutrient_limitation_factor ) - exudation = (activity + nutrient_stress) * np.array(tracers[self.abbrev].conc[carbon_index_phyto][iter]) + def exudation(self, iter, base_element, parameters, c, p, ec, ep, ic, ip, tracers): + + # Extract dict + c = c[0] + p = p[0] + ec = ec[c] + ep = ep[p] + ic = ic[c] + ip = ip[p] + + if parameters["method"] == "constant": + exudation = parameters["excreted_fraction"] * tracers[c].conc[ic,:,iter] + + elif parameters["method"] == "photosynthesis": + # Calculate activity and nutrient stress components + activity = self.psn[:,iter] * parameters["excreted_fraction"] + # nutrient_stress = self.psn[iter] * ( 1. - parameters["excreted_fraction"] ) * ( 1. - self.nutrient_limitation_factor ) + nutrient_stress = self.psn[:,iter] * ( 1. - parameters["excreted_fraction"] ) * ( 1. - self.nutrient_colimitation ) + + # Calculate exudation rate + exudation = (activity + nutrient_stress) * np.array(tracers[self.abbrev].conc[ic,:,iter]) + + # Update exudation variable + # if ec == base_element: + if tracers[c].composition[ic] == base_element: + self.exu[:,iter] = activity + nutrient_stress + + elif parameters["method"] == "uptake": + # Calculate total uptake rate for element + uptake = np.zeros_like(tracers[p].conc[ip,:,iter]) + for nut in self.upt: + if tracers[nut].composition[0] == ep: + uptake += self.upt[nut,:,iter] + + # Calculate exudation rate + exudation = parameters["excreted_fraction"] * np.maximum(np.zeros_like(uptake), uptake) - # Update exudation variable - self.exu[iter] = activity + nutrient_stress + # Update exudation variable + if ec == base_element: + self.exu[:,iter] = exudation # Update d_dt - tracers[c[0]].d_dt[carbon_index_phyto] -= exudation - tracers[p[0]].d_dt[carbon_index_om] += exudation + # tracers[c[0]].d_dt[ic] -= exudation + # tracers[p[0]].d_dt[ip] += exudation + tracers[c].d_dt[ic] -= exudation + tracers[p].d_dt[ip] += exudation - def gross_primary_production(self, iter, parameters, c, p, tracers): + def gross_primary_production(self, iter, base_element, parameters, c, p, tracers): - # Locate index of carbon constituent - carbon_index = self.composition.index("c") + # Locate index of base element + index = self.composition.index(base_element) - # Get carbon concentration - # phyto_carbon = self.conc[carbon_index][iter] - phyto_carbon = np.array(tracers[self.abbrev].conc[carbon_index][iter]) + # Get base element concentration + phyto = np.array(tracers[self.abbrev].conc[index,:,iter]) # Calculate gross primary production - gross_primary_production = self.psn[iter] * phyto_carbon + gross_primary_production = self.psn[:,iter] * phyto # Update gross primary production variable - self.gpp[iter] = gross_primary_production + self.gpp[:,iter] = gross_primary_production # Update d_dt - self.d_dt[carbon_index] += gross_primary_production - if "o2" in p: tracers["o2"].d_dt += gross_primary_production / parameters["mw_carbon"] - if "co2"in c: tracers["co2"].d_dt -= gross_primary_production + self.d_dt[index,:] += gross_primary_production + if "o2" in p: + if isinstance(parameters["convert_o2"],(int,float)) and not isinstance(parameters["convert_o2"],bool): + tracers["o2"].d_dt += gross_primary_production * parameters["convert_o2"] + elif isinstance(parameters["convert_o2"],str): + tracers["o2"].d_dt += gross_primary_production * float(Fraction(parameters["convert_o2"])) + if "co2"in c: + if base_element == "c": tracers["co2"].d_dt -= gross_primary_production + else: + if isinstance(parameters["convert_co2"],(int,float)) and not isinstance(parameters["convert_co2"],bool): + tracers["co2"].d_dt -= gross_primary_production * parameters["convert_co2"] + elif isinstance(parameters["convert_co2"],str): + tracers["co2"].d_dt -= gross_primary_production * float(Fraction(parameters["convert_o2"])) + + + def lysis(self, iter, base_element, parameters, c, p, ec, ep, ic, ip, tracers): - - def lysis(self, iter, parameters, c, p, ec, ep, ic, ip, tracers): - # Needs: Nutrient limitation, Half sat for stress lysis (h_pnp from python bfm), Activity respiration fraction (d_P0 from python bfm), # Extra lysis rate (p_seo from python bfm), Half sat for extra lysis (p_sheo from python bfm) @@ -329,129 +435,133 @@ def lysis(self, iter, parameters, c, p, ec, ep, ic, ip, tracers): ic = ic[c] ip = ip[p] - # Locate index of carbon constituent - carbon_index = self.composition.index("c") - phyto_carbon = np.array(tracers[self.abbrev].conc[carbon_index][iter]) + # Locate index of base element + index = self.composition.index(base_element) + phyto = np.array(tracers[self.abbrev].conc[index,:,iter]) - # Calculate carbon ratios - carbon_index = self.composition.index('c') - if 'n' in self.composition: - nitrogen_index = self.composition.index('n') - # nit_carb = self.conc[nitrogen_index] / self.conc[carbon_index] - nit_carb = np.array(tracers[self.abbrev].conc[nitrogen_index][iter] / tracers[self.abbrev].conc[carbon_index][iter]) - else: - nit_carb = np.ones_like(phyto_carbon) - - if 'p' in self.composition: - phosphorus_index = self.composition.index('p') - # phos_carb = self.conc[phosphorus_index] / self.conc[carbon_index] - phos_carb = np.array(tracers[self.abbrev].conc[phosphorus_index][iter] / tracers[self.abbrev].conc[carbon_index][iter]) - else: - phos_carb = np.ones_like(phyto_carbon) - - # Extract nutrient quotas - if "no3" in self.nutrients: - quota_index = self.nutrient_limitation["nutrients"].index("no3") - min_nitrogen_quota = self.nutrient_limitation["min_quota"][quota_index] - else: - min_nitrogen_quota = 0. + if parameters["method"] == "cell_quota": + # Calculate element ratios + if 'n' in self.composition: + nitrogen_index = self.composition.index('n') + nit_base = np.array(tracers[self.abbrev].conc[nitrogen_index,:,iter] / tracers[self.abbrev].conc[index,:,iter]) + else: + nit_base = np.ones_like(phyto) - if "po4" in self.nutrients: - quota_index = self.nutrient_limitation["nutrients"].index("po4") - min_phosphorus_quota = self.nutrient_limitation["min_quota"][quota_index] - else: - min_phosphorus_quota = 0. - - # Calculate fraction of lysis released to dissolved pool - min_quota = np.minimum(min_nitrogen_quota/(nit_carb + 1.E-20), min_phosphorus_quota/(phos_carb + 1.E-20)) - apportioning_factor = np.minimum(np.ones_like(min_quota), min_quota) + if 'p' in self.composition: + phosphorus_index = self.composition.index('p') + phos_base = np.array(tracers[self.abbrev].conc[phosphorus_index,:,iter] / tracers[self.abbrev].conc[index,:,iter]) + else: + phos_base = np.ones_like(phyto) + + # Extract nutrient quotas + # if "no3" in self.nutrients: + # quota_index = self.nutrient_limitation["nutrients"].index("no3") + # min_nitrogen_quota = self.nutrient_limitation["min_quota"][quota_index] + if "no3" in self.nutrient_limitation: + quota_index = self.cell_quota["constituents"].index('n') + min_nitrogen_quota = self.cell_quota["min"][quota_index] + else: + min_nitrogen_quota = 0. + + # if "po4" in self.nutrients: + # quota_index = self.nutrient_limitation["nutrients"].index("po4") + # min_phosphorus_quota = self.nutrient_limitation["min_quota"][quota_index] + if "po4" in self.nutrient_limitation: + quota_index = self.cell_quota["constituents"].index('p') + min_phosphorus_quota = self.cell_quota["min"][quota_index] + else: + min_phosphorus_quota = 0. + + # Calculate fraction of lysis released to dissolved pool + min_quota = np.minimum(min_nitrogen_quota/(nit_base + 1.E-20), min_phosphorus_quota/(phos_base + 1.E-20)) + apportioning_factor = np.minimum(np.ones_like(min_quota), min_quota) + + # Calculate nutrient stress lysis + # nutrient_stress_lysis = ( parameters["max_stress_lysis_rate"] * parameters["half_sat_stress_lysis"] ) / ( self.nutrient_limitation_factor + parameters["half_sat_stress_lysis"] ) \ + nutrient_stress_lysis = ( parameters["max_stress_lysis_rate"] * parameters["half_sat_stress_lysis"] ) / ( self.nutrient_colimitation + parameters["half_sat_stress_lysis"] ) \ + + ( parameters["extra_lysis_rate"] * phyto ) / ( phyto + parameters["half_sat_extra_lysis"] + 1.E-20 ) + + # Apportion lysis between organic matter pools based on type == particulate or dissolved + # if parameters["om_type"] == "dissolved": lysis = ( 1 - apportioning_factor ) * nutrient_stress_lysis * phyto + # elif parameters["om_type"] == "particulate": lysis = apportioning_factor * nutrient_stress_lysis * phyto - # Calculate nutrient stress lysis - nutrient_stress_lysis = ( parameters["max_stress_lysis_rate"] * parameters["half_sat_stress_lysis"] ) / ( self.nutrient_limitation_factor + parameters["half_sat_stress_lysis"] ) \ - + ( parameters["extra_lysis_rate"] * phyto_carbon ) / ( phyto_carbon + parameters["half_sat_extra_lysis"] + 1.E-20 ) - - # Apportion lysis between organic matter pools based on type == particulate or dissolved - if parameters["om_type"] == "dissolved": lysis = ( 1 - apportioning_factor ) * nutrient_stress_lysis * phyto_carbon - elif parameters["om_type"] == "particulate": lysis = apportioning_factor * nutrient_stress_lysis * phyto_carbon + if tracers[p].form == "dissolved": lysis = ( 1 - apportioning_factor ) * nutrient_stress_lysis * phyto + elif tracers[p].form == "particulate": lysis = apportioning_factor * nutrient_stress_lysis * phyto - # Calculate concentration ratio - # concentration_ratio(iter, ic, tracers[c]) - # concentration_ratio(iter, ip, tracers[p]) + # Update lysis variable + self.lys[:,iter] = nutrient_stress_lysis - # Update lysis variable - self.lys[iter] = nutrient_stress_lysis + # Match phyto and organic matter concentration ratios + # ratios = np.zeros(len(tracers[p].conc_ratio)) + ratios = np.zeros_like(tracers[p].conc_ratio) + for const in self.composition: + if const in tracers[p].composition: + index_phyto = self.composition.index(const) + index_om = tracers[p].composition.index(const) + ratios[index_om] = tracers[c].conc_ratio[index_phyto] - # Match phyto and organic matter concentration ratios - ratios = np.zeros(len(tracers[p].conc_ratio)) - for const in self.composition: - if const in tracers[p].composition: - index_phyto = self.composition.index(const) - index_om = tracers[p].composition.index(const) - ratios[index_om] = tracers[c].conc_ratio[index_phyto] + # Update d_dt + # tracers[c].d_dt -= ec * tracers[c].conc_ratio * lysis + # tracers[p].d_dt += ep * ratios * lysis + for i in range(len(ec)): + tracers[c].d_dt[i] -= ec[i] * tracers[c].conc_ratio[i] * lysis + for j in range(len(ep)): + tracers[p].d_dt[j] += ep[j] * ratios[j] * lysis + # tracers[c].d_dt -= ((tracers[c].conc_ratio).T * ec).T * lysis + # tracers[p].d_dt += (ratios.T * ep).T * lysis + + elif parameters["method"] == "constant": + lysis = parameters["lysis_rate"] * self.temp_regulation_factor * ( phyto**2 ) + + # Convert lysis rate (if necessary) + if "convert_lysis" in parameters: + if parameters["convert_lysis"] == "cell_quota": + quota_index = self.nutrient_limitation["nutrients"].index(c) + lysis *= self.nutrient_limitation_factor[quota_index] + else: + if isinstance(parameters["convert_lysis"],(int,float)) and not isinstance(parameters["convert_lysis"],bool): + lysis *= parameters["convert_lysis"] + elif isinstance(parameters["convert_lysis"],str): + lysis *= float(Fraction(parameters["convert_lysis"])) + + # Update d_dt + tracers[c].d_dt -= lysis - # Update d_dt - tracers[c].d_dt -= ec * tracers[c].conc_ratio * lysis - tracers[p].d_dt += ep * ratios * lysis - # tracers[c].d_dt -= ec * tracers[c].conc_ratio * lysis - # # tracers[p].d_dt += ep * tracers[p].conc_ratio * lysis - # tracers[p].d_dt += ep * tracers[c].conc_ratio * lysis + # Apply partition to organic matter group (if necessary) + if "partition" in parameters: tracers[p].d_dt += lysis * parameters["partition"] + else: tracers[p].d_dt += lysis - def net_primary_production(self, iter, tracers): + def net_primary_production(self, iter, base_element, tracers): - # Locate index of carbon constituent - carbon_index = self.composition.index("c") + # Locate index of base element + index = self.composition.index(base_element) - # Get carbon concentration - # phyto_carbon = self.conc[carbon_index][iter] - phyto_carbon = np.array(tracers[self.abbrev].conc[carbon_index][iter]) + # Get base element concentration + phyto = np.array(tracers[self.abbrev].conc[index,:,iter]) # Calculate losses - # specific_losses = activity_excretion + activity_respiration + basal_respiration + nutrient_stress_excretion + nutrient_stress_lysis - specific_losses = self.exu[iter] + self.rsp[iter] + self.lys[iter] + specific_losses = self.exu[:,iter] + self.rsp[:,iter] + self.lys[:,iter] # Calculate net primary production - self.npp[iter] = np.maximum( np.zeros_like(phyto_carbon), ( self.psn[iter] - specific_losses ) * phyto_carbon ) + self.npp[:,iter] = np.maximum( np.zeros_like(phyto), ( self.psn[:,iter] - specific_losses ) * phyto ) - def photosynthesis(self, iter, parameters, base_element, coordinates, dz, k_PAR, temperature, surface_PAR, tracers): - - # Needs: Nutrient limitation, Light limitation, Temperature regulation factor, Maximal productivity (Vm) at 10C (rp0 from python bfm, p_sum from fortran bfm) - - # Get concentration of base element in phytoplankton - base_index = self.composition.index(base_element) - base_conc = np.array(tracers[self.abbrev].conc[base_index][iter]) - - # Get carbon concentration - if "c" in self.composition: - carbon_index = self.composition.index("c") - phyto_carbon = self.conc[carbon_index][iter] - else: - phyto_carbon = np.ones_like(base_conc) + def photosynthesis(self, iter, parameters, coordinates, dz, k_PAR, temperature, surface_PAR): - # Locate index of chlorophyll constituent if present - if "chl" in self.composition: - chl_index = self.composition.index("chl") - phyto_chl = self.conc[chl_index][iter] - pl_pc = phyto_chl / phyto_carbon # Chl:C ratio (used in light limitation) - else: - phyto_chl = base_conc - pl_pc = 1. - - # Calculate maximal productivity - if parameters["max_photo_rate"] == "variable": + # Maximal productivity + if parameters["max_photo_rate"] == "eppley": Vm = max_growth_rate(parameters, temperature) else: Vm = parameters["max_photo_rate"] - if iter == 360: - x=1 - - # Calculate light limitation - if parameters["light_limitation"] in ["monod", "platt", "smith"]: - # k_PAR = light_attenuation(parameters, phyto_chl) + # Light limitation + if parameters["light_limitation"] in ["geider", "monod", "platt", "smith"]: + # Calculate irradiance at surface irrad = irradiance(parameters["eps_PAR"], surface_PAR, coordinates, k_PAR) - exp, irr, fI = light_limitation(parameters, dz, irrad, k_PAR, pl_pc, Vm) + + # Calculate light limitation + exp, irr, fI = light_limitation(self, iter, parameters, dz, irrad, k_PAR, Vm) else: fI = parameters["light_limitation"] irr = 1.E-20 @@ -459,499 +569,382 @@ def photosynthesis(self, iter, parameters, base_element, coordinates, dz, k_PAR, # Update photosynthesis variable # self.psn[iter] = self.nutrient_limitation_factor * self.temp_regulation_factor * Vm * fI + # + # + # !!! ADD IN SILICATE LIMITATION OPTION FROM BFM !!! + # + # fpplim = 1. # this will change once silicate is added - self.psn[iter] = fpplim * self.temp_regulation_factor * Vm * fI + self.psn[:,iter] = fpplim * self.temp_regulation_factor * Vm * fI + + diff_psn = self.psn[0,iter] - photo[iter] + if abs(diff_psn) > 1E-18: + x=1 return fI, irr - def respiration(self, iter, parameters, c, p, tracers): + def respiration(self, iter, base_element, parameters, c, p, ec, ep, ic, ip, tracers): """ Definition:: Calculates phytoplankton respiration """ # Needs: Activity and Basal respiration rates (gammaP in python bfm), Activity and Nutrient stress excretion + # Extract dict + if p[0] is not None: + if len(p) > 1: # if "co2" also included as produced element + for index in len(p): + if p[index] == "co2": pass + else: p = p[index] + else: p = p[0] + ep = ep[p] + ip = ip[p] - # Locate index of carbon constituent - carbon_index = self.composition.index("c") + # Locate index of base element + index = self.composition.index(base_element) - # Get carbon concentration - # phyto_carbon = self.conc[carbon_index][iter] - phyto_carbon = np.array(tracers[self.abbrev].conc[carbon_index][iter]) + # Get base_element concentration + phyto = np.array(tracers[self.abbrev].conc[index,:,iter]) # Respiration - activity_respiration = parameters["activity_respiration_frac"] * ( self.psn[iter] - self.exu[iter] ) + activity_respiration = parameters["activity_respiration_frac"] * ( self.psn[:,iter] - self.exu[:,iter] ) basal_respiration = self.temp_regulation_factor * parameters["basal_respiration_rate"] total_respiration = activity_respiration + basal_respiration - respiration = total_respiration * phyto_carbon + respiration = total_respiration * phyto # Update d_dt - tracers[self.abbrev].d_dt[carbon_index] -= respiration - if "o2" in c: tracers["o2"].d_dt -= respiration / parameters["mw_carbon"] - if "co2" in p: tracers["co2"].d_dt += respiration + tracers[self.abbrev].d_dt[index] -= respiration + if p[0] is not None: tracers[p].d_dt[ip] += respiration + if "o2" in c: + if isinstance(parameters["convert_o2"],(int,float)) and not isinstance(parameters["convert_o2"],bool): + tracers["o2"].d_dt -= respiration * parameters["convert_o2"] + elif isinstance(parameters["convert_o2"],str): + tracers["o2"].d_dt -= respiration * float(Fraction(parameters["convert_o2"])) + + if "co2" in p: + if base_element == "c": tracers["co2"].d_dt += respiration + else: + if isinstance(parameters["convert_co2"],(int,float)) and not isinstance(parameters["convert_co2"],bool): + tracers["co2"].d_dt += respiration * parameters["convert_co2"] + elif isinstance(parameters["convert_co2"],str): + tracers["co2"].d_dt += respiration * float(Fraction(parameters["convert_co2"])) # Update respiration variable - self.rsp[iter] = total_respiration + self.rsp[:,iter] = total_respiration return activity_respiration, basal_respiration - - def uptake(self, iter, parameters, c, p, ec, ep, ic, tracers): - """ - Can consume single nutrient (Nitrate/Phosphate) or multiple nutrients (Nitrate AND Ammonium if both available) - Presence of Ammonium affects uptake of Nitrate - """ - - # Locate carbon index and concentration (if present) - if "c" in self.composition: - carbon_index = self.composition.index("c") - # phyto_carbon = self.conc[carbon_index][iter] - phyto_carbon = np.array(tracers[p[0]].conc[carbon_index][iter]) - else: - phyto_carbon = 1. - - # # Get concentration of nutrient in phytoplankton - # p = p[0] - # ep = ep[p] - # nutrient_index = list(ep).index(1.) - # phyto_nutrient = np.array(tracers[p].conc[nutrient_index][iter]) - - # Nutrient uptake can go to dissolved nutrient pool if uptake rate is negative - # Get concentration of nutrient in phytoplankton - # Get location of dissolved nutrient pool (if necessary) - if len(p) > 1: - phy = list(p).index(self.abbrev) - ephy = ep[self.abbrev] - phyto_nutrient_index = list(ephy).index(1.) - phyto_nutrient = np.array(tracers[self.abbrev].conc[phyto_nutrient_index][iter]) - - if phy == 0: i = 1 - else: i = 0 - om = p[i] - eom = ep[om] - om_nutrient_index = list(eom).index(1.) - om_nutrient = np.array(tracers[om].conc[om_nutrient_index][iter]) - else: - phy = p[0] - ephy = ep[phy] - phyto_nutrient_index = list(ephy).index(1.) - phyto_nutrient = np.array(tracers[phy].conc[phyto_nutrient_index][iter]) + def uptake(self, iter, base_element, parameters, c, p, ec, ep, ic, tracers): + # Extract dict - if len(c) > 1: - # Get concentrations - # phyto_nitrogen = self.conc[nit_index][iter] - no3 = np.array(tracers["no3"].conc[0][iter]) - nh4 = np.array(tracers["nh4"].conc[0][iter]) - - # Calculate preference for Ammonium uptake - nh4_preference = parameters["half_sat_nh4_preference"] / ( parameters["half_sat_nh4_preference"] + nh4 + 1.E-20) - - # Calculate maximum nitrogen uptake - max_uptake_no3 = parameters["specific_affinity"] * no3 * phyto_carbon * nh4_preference - max_uptake_nh4 = parameters["specific_affinity"] * nh4 * phyto_carbon - max_uptake_DIN = max_uptake_no3 + max_uptake_nh4 - - # Extract nutrient quota - quota_index = self.nutrient_limitation["nutrients"].index("no3") - nutrient_quota = self.nutrient_limitation["opt_quota"][quota_index] - - # Intracellular missing amount of N - missing_nit = parameters["max_photo_rate"] * self.temp_regulation_factor * ( parameters["luxury_storage"] * nutrient_quota * phyto_carbon - phyto_nutrient ) - - # N uptake based on net assimilation of C - assim_uptake = parameters["luxury_storage"] * nutrient_quota * self.npp[iter] - - # Actual uptake of nitrogen - actual_uptake = np.minimum(max_uptake_DIN, missing_nit + assim_uptake) - - upt_switch = switch(actual_uptake) - - no3_uptake = upt_switch * actual_uptake * max_uptake_no3 / (max_uptake_DIN + 1.E-20) - nh4_uptake = upt_switch * actual_uptake * max_uptake_nh4 / (max_uptake_DIN + 1.E-20) - - # phyto_uptake = -actual_uptake * (1. - upt_switch) - uptake_to_phyto = no3_uptake + nh4_uptake - uptake_to_om = -actual_uptake * (1. - upt_switch) - - # Update d_dt - tracers["no3"].d_dt -= no3_uptake - tracers["nh4"].d_dt -= nh4_uptake - tracers[self.abbrev].d_dt[phyto_nutrient_index] += uptake_to_phyto - if len(p) > 1: - tracers[self.abbrev].d_dt -= ephy * uptake_to_om - tracers[om].d_dt += eom * uptake_to_om - - self.uptn[iter] = uptake_to_phyto - - else: - # Get concentration of nutrient - c = c[0] - ec = ec[c] - ic = ic[c] - nutrient = np.array(tracers[c].conc[ic][iter]) + # ec = ec[c] + # ep = ep[p] + # ic = ic[c] + # ip = ip[p] - # Calculate maximum nutrient uptake - max_uptake = parameters["specific_affinity"] * nutrient * phyto_carbon + # Identify the chemical constituent of the nutrient(s) + # element = ec[c[0]] + element = tracers[c[0]].composition[0] - # Extract nutrient quota - quota_index = self.nutrient_limitation["nutrients"].index(c) - nutrient_quota = self.nutrient_limitation["opt_quota"][quota_index] + # Get concentration of constituent in phytoplankton if present + if element in self.composition: element_index = self.composition.index(element) - # Intracellular missing amount of nutrient - missing = parameters["max_photo_rate"] * self.temp_regulation_factor * ( parameters["luxury_storage"] * nutrient_quota * phyto_carbon - phyto_nutrient ) + if parameters["strategy"] == "coupled": + coupled_uptake = parameters["coupled_uptake"] + linked_nutrients = coupled_uptake["link"] - # Nutrient uptake based on net assimilation of C - assim_uptake = parameters["luxury_storage"] * nutrient_quota * self.npp[iter] + # Extract uptake rates of linked nutrients + uptake_rates = [] + for nut in linked_nutrients: + uptake_rates.append(self.uptake_rates[nut]) - # Actual uptake of nutrient - actual_uptake = np.minimum(max_uptake, missing + assim_uptake) + # Calculate total linked uptake rate if multiple linked nutrients are used + if len(linked_nutrients) > 1: # use numpy "maximum" to ensure minimum uptake of 0. + if coupled_uptake["method"] == "max": linked_uptake = np.maximum(np.maximum(uptake_rates), np.zeros_like(self.uptake_rates[nut])) + elif coupled_uptake["method"] == "min": linked_uptake = np.maximum(np.minimum(uptake_rates), np.zeros_like(self.uptake_rates[nut])) + elif coupled_uptake["method"] == "product": linked_uptake = np.maximum(np.prod(uptake_rates), np.zeros_like(self.uptake_rates[nut])) + elif coupled_uptake["method"] == "sum": linked_uptake = np.maximum(np.sum(uptake_rates), np.zeros_like(self.uptake_rates[nut])) - upt_switch = switch(actual_uptake) + if isinstance(parameters["convert_uptake"],(int,float)) and not isinstance(parameters["convert_uptake"],bool): + uptake = self.nutrient_limitation_factor[element] * linked_uptake * coupled_uptake["convert_uptake"] + elif isinstance(parameters["convert_uptake"],str): + uptake = self.nutrient_limitation_factor[element] * linked_uptake * float(Fraction(parameters["convert_uptake"])) - uptake_to_phyto = upt_switch * actual_uptake - uptake_to_om = -actual_uptake * (1. - upt_switch) + # Update d_dt - # tracers[c].d_dt -= np.array(ec) * uptake - # tracers[self.abbrev].d_dt += ep * phyto_uptake - tracers[c].d_dt -= np.array(ec) * uptake_to_phyto - tracers[self.abbrev].d_dt += ephy * uptake_to_phyto - if len(p) > 1: - tracers[self.abbrev].d_dt -= ephy * uptake_to_om - tracers[om].d_dt += eom * uptake_to_om - - if c == 'po4': self.uptp[iter] = uptake_to_phyto - - - # def uptake(self, iter, parameters, c, p, ec, ep, ic, tracers, surface_PAR): - - # # light limitation (platt) - # fI = parameters["Vm"] * ( 1. - np.exp(-parameters["alpha"] * surface_PAR / ( parameters["Vm"] + 1.E-20 )) ) - - # if len(c) > 1: - # no3 = np.array(tracers["no3"].conc[0][iter]) - # nh4 = np.array(tracers["nh4"].conc[0][iter]) - - # f_nh4 = nutrient_limitation(nh4, parameters["kNH4"]) - # f_no3 = nutrient_limitation(no3, parameters["kNO3"]) - - # uptake_nh4 = parameters["Vm"] * fI * self.temp_regulation_factor * f_nh4 - # uptake_no3 = parameters["Vm"] * fI * self.temp_regulation_factor * (1. - f_nh4) * f_no3 - - # tracers["no3"].d_dt -= uptake_no3 - # tracers["nh4"].d_dt -= uptake_nh4 - # tracers[p].d_dt += uptake_no3 + uptake_nh4 - - # else: - # nut = np.array(tracers[c[0]]).conc[0][iter] - - # f_nut = nutrient_limitation(nut, parameters["kN"]) - - # uptake = parameters["Vm"] * fI * self.temp_regulation_factor * f_nut - - # tracers[c[0]].d_dt -= uptake - # tracers[p[0]].d_dt += ep[p[0]] * uptake - - - # def photosynthesis(self) - - - - - - # def chlorophyll_synthesis(self, iter, parameters, coordinates, surface_PAR): - # """ - # Definition:: Calculates chlorophyll synthesis based on gains and losses of phytoplankton carbon - # """ - # # Locate indexes of carbon and chlorophyll constituents - # carbon_index = self.composition.index("c") - # chl_index = self.composition.index("chl") - - # # Get carbon and chlorophyll concentrations - # phyto_c = self.conc[carbon_index][iter] - # phyto_chl = self.conc[chl_index][iter] - - # # Calculate chlorophyll-carbon ratio - # pl_pc = phyto_chl / phyto_c - - # # Calculate irradiance - # k_PAR = light_attenuation(parameters, phyto_c) - # irrad = irradiance(parameters["eps_PAR"], surface_PAR, coordinates, k_PAR) - - # # Calculate chlorophyll regulation (rho_chl) - # factor = ( 1 - parameters["activity_respiration_frac"] ) / ( parameters["initial_PI_slope"] * irrad * phyto_chl + 1E-20 ) # 1E-20 to prevent divide by zero error - # rho_chl = parameters["max_chl_c"] * np.minimum(np.ones_like(phyto_chl), np.maximum(np.zeros_like(phyto_chl),factor * (self.gpp[iter] - self.exu[iter]))) - # # rho_chl = parameters["max_chl_c"] * np.minimum(np.ones_like(phyto_chl), factor * (self.gpp[iter] - self.exu[iter])) - - # # Calculate chlorophyll synthesis - # synthesis = rho_chl * ( 1 - parameters["activity_respiration_frac"] ) * ( self.gpp[iter] - self.exu[iter] ) - pl_pc * ( self.lys[iter] + self.rsp[iter] ) - # # synthesis = np.maximum(np.zeros_like(phyto_chl),synthesis) # synthesis >= 0 - - # # Update d_dt - # self.d_dt[chl_index] += synthesis - - - - # def exudation(self, iter, parameters, c, p, tracers): - # """ - # Definition:: Calculates the amount of unassimilated carbon released by phytoplankton into dissolved carbon pool - # """ - # # Locate index of carbon constituent - # carbon_index_phyto = self.composition.index("c") - # carbon_index_om = tracers[p[0]].composition.index("c") - - # # Exudation - # # exudation = ( parameters["excreted_fraction"] + ( 1 - parameters["excreted_fraction"] ) * ( 1 - self.nutrient_limitation_factor ) ) * self.gpp[iter] - # exudation = ( 1 - parameters["excreted_fraction"] ) * ( 1 - self.nutrient_limitation_factor ) * self.gpp[iter] - - # # Update d_dt - # tracers[c[0]].d_dt[carbon_index_phyto] -= exudation - # tracers[p[0]].d_dt[carbon_index_om] += exudation - - # # Update exu variable - # self.exu[iter] = exudation - - - # def gross_primary_production(self, iter, parameters, coordinates, dz, mixed_layer_depth, surface_PAR, temperature, c, p, tracers): - # """ - # Definition:: Calculate gross primary production - # """ - - # # Get carbon concentration - # carbon_index = self.composition.index("c") - # phytoc = self.conc[carbon_index][iter] - - # # Locate index of chlorophyll constituent if present - # if "chl" in self.composition: - # chl_index = self.composition.index("chl") - # phytol = self.conc[chl_index][iter] - # pl_pc = phytol / phytoc # Chl:C ratio (used in light limitation) - # else: - # pl_pc = 1. - - # # Calculate growth rate - # if parameters["max_photo_rate"] == "variable": - # Vm = max_growth_rate(parameters, temperature) - # else: - # Vm = parameters["max_photo_rate"] - - # # Calculate light limitation - # # if parameters["light_limitation"] == "variable": - # if parameters["light_limitation"] in ["monod", "platt", "smith"]: - # # k_PAR = light_attenuation(parameters, phytoc) - # k_PAR = light_attenuation(parameters, phytol) - # irrad = irradiance(parameters["eps_PAR"], surface_PAR, coordinates, k_PAR) - # # fI = light_limitation(parameters, dz, irrad, k_PAR, mixed_layer_depth, surface_PAR, Vm) - # fI = light_limitation(parameters, dz, irrad, k_PAR, pl_pc, Vm) - # # fI = light_limitation(parameters, coordinates, irrad, k_PAR, pl_pc, Vm) - # else: - # fI = parameters["light_limitation"] - - # if fI == 1: - # x=1 - # # Calculate gross primary production - # gpp = Vm * self.temp_regulation_factor * fI * phytoc - - # # Update d_dt - # self.d_dt[carbon_index] += gpp - # if "o2" in p: tracers["o2"].d_dt += gpp / parameters["mw_carbon"] - # if "co2"in c: tracers["co2"].d_dt -= gpp - - # # Update gpp variable - # self.gpp[iter] = gpp - - # return irrad - - - # def lysis(self, iter, parameters, c, p, ec, ep, ic, ip, tracers): - # """ - # Definition:: Calculate lysis rate of phytoplankton to organic matter pools - # """ - # # Extract dict - # c = c[0] - # p = p[0] - # ec = ec[c] - # ep = ep[p] - # ic = ic[c] - # ip = ip[p] - # tc = np.array(tracers[c].conc[ic][iter]) - # tp = np.array(tracers[p].conc[ip][iter]) - - # # Calculate carbon ratios - # carbon_index = self.composition.index('c') - # if 'n' in self.composition: - # nitrogen_index = self.composition.index('n') - # # nit_carb = self.conc[nitrogen_index] / self.conc[carbon_index] - # nit_carb = np.array(tracers[self.abbrev].conc[nitrogen_index][iter] / tracers[self.abbrev].conc[carbon_index][iter]) - # else: - # nit_carb = np.ones_like(tp) - - # if 'p' in self.composition: - # phosphorus_index = self.composition.index('p') - # # phos_carb = self.conc[phosphorus_index] / self.conc[carbon_index] - # phos_carb = np.array(tracers[self.abbrev].conc[phosphorus_index][iter] / tracers[self.abbrev].conc[carbon_index][iter]) - # else: - # phos_carb = np.ones_like(tp) - - # # Calculate fraction of lysis released to dissolved pool - # min_quota = np.minimum(parameters["min_nitrogen_quota"]/(nit_carb + 1.E-20), parameters["min_phosphorus_quota"]/(phos_carb + 1.E-20)) - # apportioning_factor = np.minimum(np.ones_like(min_quota), min_quota) - - # # Calculate nutrient stress limitation - # lim = nutrient_limitation(parameters["nutrient_stress_threshold"], self.nutrient_limitation_factor) - - # # Calculate lysis rate - # if parameters["type"] == "dissolved": lysis = ( 1 - apportioning_factor ) * ( lim * parameters["max_lysis_rate"] ) - # elif parameters["type"] == "particulate": lysis = apportioning_factor * ( lim * parameters["max_lysis_rate"] ) - - # # Calculate concentration ratios - # concentration_ratio(iter, ic, tracers[c]) - # concentration_ratio(iter, ip, tracers[p]) - - # # Update d_dt - # tracers[c].d_dt -= ec * tracers[c].conc_ratio * lysis - # tracers[p].d_dt += ep * tracers[p].conc_ratio * lysis - - # # Update lys variable - # self.lys += tracers[c].conc_ratio[carbon_index] * lysis - - - # def mortality(self, iter, parameters, c, p, ec, ep, ic, ip, tracers): - # """ - # Definition:: Calculates the non-grazing mortality of planktoninc species - # Lysis is parameterized as a quadratic mortality rate (the carbon constituent of lysis is added to the 'lys' variable for the calculation of net primary production) - # """ - # # Extract dict - # c = c[0] - # p = p[0] - # ec = ec[c] - # ep = ep[p] - # ic = ic[c] - # ip = ip[p] - # tc = np.array(tracers[c].conc[ic][iter]) - - # # Calculate mortality rate - # mortality = ( parameters["mortality_rate"][0] * tc ) + ( parameters["mortality_rate"][1] * (tc**2) ) - - # # Temperature regulation - # if self.temp_limited: - # mortality = mortality * self.temp_regulation_factor - - # # Oxygen limitation - # if "oxygen_limited" in parameters and parameters["oxygen_limited"]: - # fO = nutrient_limitation(tc,parameters["half_sat_oxygen"]) - # mortality = mortality * (1 - fO) - - # # Calculate concentration ratios - # concentration_ratio(iter, ic, tracers[c]) - # concentration_ratio(iter, ip, tracers[p]) - - # # Update d_dt - # if "partition" in parameters: # Mortality can be partitioned between dissolved and particulate detrital pools - # tracers[c].d_dt -= ec * tracers[c].conc_ratio * mortality * parameters["partition"] - # tracers[p].d_dt += ep * tracers[p].conc_ratio * mortality * parameters["partition"] - - # else: - # tracers[c].d_dt -= ec * tracers[c].conc_ratio * mortality - # tracers[p].d_dt += ep * tracers[p].conc_ratio * mortality - - # # Update 'lys' variable if phytoplankton contains a carbon constituent - # if "c" in self.composition: - # carbon_index = self.composition.index("c") - # self.lys[iter] += parameters["mortality_rate"][1] * (self.conc[carbon_index][iter]**2) - - - # def respiration(self, iter, parameters, c, p, tracers): - # """ - # Definition:: Calculates phytoplankton respiration - # """ - # # Locate index of carbon constituent - # carbon_index = self.composition.index("c") - - # # Get carbon concentration - # phyto = self.conc[carbon_index][iter] - - # # Respiration - # respiration = parameters["respiration_rate"] * self.temp_regulation_factor * phyto + parameters["activity_respiration_frac"] * (self.gpp[iter] - self.exu[iter]) - - # # Update d_dt - # self.d_dt[carbon_index] -= respiration - # if "o2" in c: tracers["o2"].d_dt -= respiration / parameters["mw_carbon"] - # if "co2" in p: tracers["co2"].d_dt += respiration - - # # Update rsp variable - # self.rsp[iter] = respiration - + tracers[c].d_dt -= np.array(ec) * np.maximum(uptake, np.zeros_like(uptake)) + if element in self.composition: self.d_dt[element_index] += np.maximum(uptake, np.zeros_like(uptake)) + + elif parameters["strategy"] == "independent": + + if len(c) > 1: # Used if no3 and nh4 are consumed together rather than individually + if len(p) > 1: # If uptake can be source of organic matter + phy = list(p).index(self.abbrev) + ephy = ep[self.abbrev] + phyto_nutrient_index = list(ephy).index(1.) + phyto_nutrient = np.array(tracers[self.abbrev].conc[phyto_nutrient_index,:,iter]) + + if phy == 0: i = 1 + else: i = 0 + om = p[i] + eom = ep[om] + om_nutrient_index = list(eom).index(1.) + om_nutrient = np.array(tracers[om].conc[om_nutrient_index,:,iter]) + else: + phy = p[0] + ephy = ep[phy] + phyto_nutrient_index = list(ephy).index(1.) + phyto_nutrient = np.array(tracers[phy].conc[phyto_nutrient_index,:,iter]) + + # Get concentration of element in phytoplankton + if parameters["form"] == "affinity": # use specific affinity + index = self.composition.index(base_element) + else: # use nutrient constituent + index = self.composition.index("n") + # if "n" in self.composition: + # index = self.composition.index("n") + # else: # If element doesn't isn't directly resolved, use base element with conversion factor + # index = self.composition.index(base_element) + phyto = np.array(self.conc[index,:,iter]) + + # Get nutrient concentrations + no3 = np.array(tracers["no3"].conc[0,:,iter]) + nh4 = np.array(tracers["nh4"].conc[0,:,iter]) + + # Calculate preference for Ammonium uptake + nh4_preference = parameters["half_sat_nh4_preference"] / ( parameters["half_sat_nh4_preference"] + nh4 + 1.E-20) + + # Calculate maximum nitrogen uptake + max_uptake_no3 = parameters["specific_affinity"] * no3 * phyto * nh4_preference + max_uptake_nh4 = parameters["specific_affinity"] * nh4 * phyto + max_uptake_DIN = max_uptake_no3 + max_uptake_nh4 + + # Extract nutrient quota + # quota_index = self.nutrient_limitation["nutrients"].index("no3") + # nutrient_quota = self.nutrient_limitation["opt_quota"][quota_index] + quota_index = self.cell_quota["constituents"].index('n') + nutrient_quota = self.cell_quota["opt"][quota_index] + + # Intracellular missing amount of N + missing_nit = parameters["max_photo_rate"] * self.temp_regulation_factor * ( parameters["luxury_storage"] * nutrient_quota * phyto - phyto_nutrient ) + + # N uptake based on net assimilation of C + assim_uptake = parameters["luxury_storage"] * nutrient_quota * self.npp[:,iter] + + # Actual uptake of nitrogen + actual_uptake = np.minimum(max_uptake_DIN, missing_nit + assim_uptake) + + upt_switch = switch(actual_uptake) + + no3_uptake = upt_switch * actual_uptake * max_uptake_no3 / (max_uptake_DIN + 1.E-20) + nh4_uptake = upt_switch * actual_uptake * max_uptake_nh4 / (max_uptake_DIN + 1.E-20) + + # phyto_uptake = -actual_uptake * (1. - upt_switch) + uptake_to_phyto = no3_uptake + nh4_uptake + uptake_to_om = -actual_uptake * (1. - upt_switch) + + # Calculate n2 uptake for nitrogen fixers (if necessary) + if "n2" in c: + n2_uptake = ( 1. - self.nutrient_limitation_factor["no3"] - self.nutrient_limitation_factor["nh4"] ) * self.psn[iter] * phyto_nutrient + uptake_to_phyto += np.maximum(n2_uptake, np.zeros_like(n2_uptake)) + + # Update d_dt + tracers["no3"].d_dt -= no3_uptake + tracers["nh4"].d_dt -= nh4_uptake + tracers[self.abbrev].d_dt[phyto_nutrient_index] += uptake_to_phyto + if len(p) > 1: + for i in range(len(ephy)): + tracers[self.abbrev].d_dt[i] -= ephy[i] * uptake_to_om + for j in range(len(eom)): + tracers[om].d_dt[j] += eom[j] * uptake_to_om + + # self.uptn[iter] = uptake_to_phyto - # def uptake(self, iter, parameters, coordinates, dz, mixed_layer_depth, surface_PAR, temperature, c, p, ec, ep, ic, ip, tracers): - - # # Extract dict - # c = c[0] - # p = p[0] - # ec = ec[c] - # ep = ep[p] - # ic = ic[c] - # ip = ip[p] - # tc = np.array(tracers[c].conc[ic][iter]) - - # # Get concentration of nutrient in phytoplankton - # nutrient_index = list(ep).index(1.) - # tp = np.array(tracers[p].conc[nutrient_index][iter]) - - # # Get carbon concentration - # if "c" in self.composition: - # carbon_index = self.composition.index("c") - # phytoc = self.conc[carbon_index][iter] - # else: - # phytoc = np.ones_like(tp) - - # # Locate index of chlorophyll constituent if present - # if "chl" in self.composition: - # chl_index = self.composition.index("chl") - # phyto = self.conc[chl_index][iter] - # pl_pc = phyto / phytoc # Chl:C ratio (used in light limitation) - # else: - # phyto = tp - # pl_pc = 1. - - # # Calculate growth rate - # if parameters["max_photo_rate"] == "variable": - # Vm = max_growth_rate(parameters, temperature) - # else: - # Vm = parameters["max_photo_rate"] - - # # Calculate light limitation - # # if parameters["light_limitation"] == "variable": - # if parameters["light_limitation"] in ["monod", "platt", "smith"]: - # # k_PAR = light_attenuation(parameters, phytoc) - # k_PAR = light_attenuation(parameters, phyto) - # irrad = irradiance(parameters["eps_PAR"], surface_PAR, coordinates, k_PAR) - # # fI = light_limitation(parameters, dz, irrad, k_PAR, mixed_layer_depth, surface_PAR, Vm) - # fI = light_limitation(parameters, dz, irrad, k_PAR, pl_pc, Vm) - # else: - # fI = parameters["light_limitation"] - - # # Calculate nutrient limitation - # if parameters["nutrient_limitation"] == "variable": - # fN = nutrient_limitation(tc, parameters["half_sat_nutrient"]) - # else: - # fN = parameters["nutrient_limitation"] + else: + c = c[0] + if c == "no3": + # Get concentration of element in phytoplankton + if "n" in self.composition: + index = self.composition.index("n") + else: # If element doesn't isn't directly resolved, use base element with conversion factor + index = self.composition.index(base_element) + + phyto = np.array(self.conc[index][iter]) + + # Determine uptake strategy + if parameters["basis"] == "constant": + uptake = parameters["constant"] * self.nutrient_limitation_factor["no3"] * phyto + elif parameters["basis"] == "growth": + uptake = self.psn[iter] * self.nutrient_limitation_factor["no3"] * phyto + elif parameters["basis"] == "nutrient": + pass + + # Multiply by Monod function of nutrient limitation (if necessary) + if "nh4" in tracers and self.nutrient_limitation["no3"]["nh4_inhibited"]: # no3_lim / (no3_lim + nh4_lim) + uptake *= monod(self.nutrient_limitation_factor["no3"], self.nutrient_limitation_factor["nh4"], 1.) + + # Update d_dt + tracers[c].d_dt -= np.maximum(uptake, np.zeros_like(uptake)) + if "n" in self.composition: self.d_dt[index] += np.maximum(uptake, np.zeros_like(uptake)) + + elif c == "nh4": + # Get concentration of element in phytoplankton + if "n" in self.composition: + index = self.composition.index("n") + else: # If element doesn't isn't directly resolved, use base element with conversion factor + index = self.composition.index(base_element) + + phyto = np.array(self.conc[index][iter]) + + # Determine uptake strategy + if parameters["basis"] == "constant": + uptake = parameters["constant"] * self.nutrient_limitation_factor["nh4"] * phyto + elif parameters["basis"] == "growth": + uptake = self.psn[iter] * self.nutrient_limitation_factor["nh4"] * phyto + elif parameters["basis"] == "nutrient": + pass + + # Multiply by Monod function of nutrient limitation (if necessary) + if self.nutrient_limitation["no3"]["nh4_inhibited"]: # nh4_lim / (nh4_lim + no3_lim) + uptake *= monod(self.nutrient_limitation_factor["nh4"], self.nutrient_limitation_factor["no3"], 1.) + + # Update d_dt + tracers[c].d_dt -= np.maximum(uptake, np.zeros_like(uptake)) + if "n" in self.composition: self.d_dt[index] += np.maximum(uptake, np.zeros_like(uptake)) + + elif c == "po4": + # Get concentration of element in phytoplankton + # if "p" in self.composition: + # index = self.composition.index("p") + # else: # If element doesn't isn't directly resolved, use base element with conversion factor + # index = self.composition.index(base_element) + + if parameters["form"] == "affinity": # use specific affinity + index = self.composition.index(base_element) + else: # use nutrient constituent + index = self.composition.index("p") + phyto = np.array(self.conc[index,:,iter]) + + # Determine uptake strategy + if parameters["basis"] == "constant": + uptake = parameters["constant"] * self.nutrient_limitation["po4"] * phyto + elif parameters["basis"] == "growth": + pass + elif parameters["basis"] == "nutrient": + if len(p) > 1: # If uptake can be source of organic matter + phy = list(p).index(self.abbrev) + ephy = ep[self.abbrev] + phyto_nutrient_index = list(ephy).index(1.) + phyto_nutrient = np.array(tracers[self.abbrev].conc[phyto_nutrient_index,:,iter]) + + if phy == 0: i = 1 + else: i = 0 + om = p[i] + eom = ep[om] + om_nutrient_index = list(eom).index(1.) + om_nutrient = np.array(tracers[om].conc[om_nutrient_index,:,iter]) + else: + phy = p[0] + ephy = ep[phy] + phyto_nutrient_index = list(ephy).index(1.) + phyto_nutrient = np.array(tracers[phy].conc[phyto_nutrient_index,:,iter]) + + # Get concentration of nutrient + # c = c[0] + ec = ec[c] + ic = ic[c] + nutrient = np.array(tracers[c].conc[ic,:,iter]) + + # Calculate maximum nutrient uptake + max_uptake = parameters["specific_affinity"] * nutrient * phyto + + # Extract nutrient quota + # quota_index = self.nutrient_limitation["nutrients"].index(c) + # nutrient_quota = self.nutrient_limitation["opt_quota"][quota_index] + quota_index = self.cell_quota["constituents"].index('p') + nutrient_quota = self.cell_quota["opt"][quota_index] + + # Intracellular missing amount of nutrient + missing = parameters["max_photo_rate"] * self.temp_regulation_factor * ( parameters["luxury_storage"] * nutrient_quota * phyto - phyto_nutrient ) + + # Nutrient uptake based on net assimilation of C + assim_uptake = parameters["luxury_storage"] * nutrient_quota * self.npp[:,iter] + + # Actual uptake of nutrient + actual_uptake = np.minimum(max_uptake, missing + assim_uptake) + # actual_uptake = np.maximum(np.zeros_like(max_uptake), np.minimum(max_uptake,missing + assim_uptake)) + + upt_switch = switch(actual_uptake) + + uptake_to_phyto = upt_switch * actual_uptake + uptake_to_om = -actual_uptake * (1. - upt_switch) + + # Update d_dt + # tracers[c].d_dt -= np.array(ec) * uptake + # tracers[self.abbrev].d_dt += ep * phyto_uptake + tracers[c].d_dt -= np.array(ec) * uptake_to_phyto + # tracers[self.abbrev].d_dt += ephy * uptake_to_phyto + if len(p) > 1: + # tracers[self.abbrev].d_dt -= ephy * uptake_to_om + # tracers[om].d_dt += eom * uptake_to_om + for i in range(len(ephy)): + # tracers[self.abbrev].d_dt[i] += ephy[i] * uptake_to_phyto + tracers[self.abbrev].d_dt[i] += ephy[i] * np.minimum(max_uptake, missing + assim_uptake) + # tracers[self.abbrev].d_dt[i] += ephy[i] * np.maximum(np.zeros_like(max_uptake), np.minimum(max_uptake,missing + assim_uptake)) + for j in range(len(eom)): + tracers[om].d_dt[j] += eom[j] * uptake_to_om + else: + for i in range(len(ephy)): + tracers[self.abbrev].d_dt[i] += ephy[i] * uptake_to_phyto + + + # if c == 'po4': self.uptp[iter] = uptake_to_phyto + + elif c == "fe": + # Get concentration of element in phytoplankton + if "fe" in self.composition: + index = self.composition.index("fe") + else: # If element doesn't isn't directly resolved, use base element with conversion factor + index = self.composition.index(base_element) + + phyto = np.array(self.conc[index][iter]) + + # Determine uptake strategy + if parameters["basis"] == "constant": + uptake = parameters["constant"] * self.temp_regulation_factor * self.nutrient_limitation["fe"] * phyto + elif parameters["basis"] == "growth": + pass + elif parameters["basis"] == "nutrient": + pass + + # Mulitply by conversion factor (if needed) + if "convert_uptake" in parameters: + if isinstance(parameters["convert_uptake"],(int,float)) and not isinstance(parameters["convert_uptake"],bool): + uptake *= coupled_uptake["convert_uptake"] + elif isinstance(parameters["convert_uptake"],str): + uptake *= float(Fraction(parameters["convert_uptake"])) + + # Uptake is zero if Fe:Base ratio meets or exceeds maximum ratio + if "fe" in self.composition: # Only need to calculate if iron is directly resolved + concentration_ratio(iter, index, self.conc) + if self.conc_ratio[index] >= self.cell_quota["max"]: uptake = np.zeros_like(uptake) + + # Update d_dt + tracers[c].d_dt -= uptake + if "fe" in self.composition: self.d_dt[index] += uptake + + elif c == "sio4": + pass - # # Calculate nutrient uptake - # uptake = Vm * self.temp_regulation_factor * fN * fI * tp - # # Calculate concentration ratio - # # concentration_ratio(iter, ic, tracers[c]) - # # concentration_ratio(iter, ip, tracers[p]) - - # # Update d_dt - # tracers[c].d_dt -= np.array(ec) * uptake - # tracers[p].d_dt += ep * uptake - # # tracers[c].d_dt -= ec * tracers[c].conc_ratio * uptake - # # tracers[p].d_dt += ep * tracers[p].conc_ratio * uptake - - # return fI - +assim_uptake_po4_fortran = [1.6814743045847532E-004, 1.6931080142746542E-004, 1.7034718892522456E-004, 1.7124839238892624E-004, 1.7200641914569083E-004, 1.7261361994317368E-004, 1.7306281544991105E-004, 1.7334742814187104E-004, 1.7346160323831496E-004, 1.7340031546787010E-004, 1.7315946010880919E-004, 1.7273593534617560E-004, 1.7212769082820340E-004, 1.7133377211496077E-004, 1.7035434040819778E-004, 1.6919067063121392E-004, 1.6784513011071069E-004, 1.6632114248375522E-004, 1.6462313935402135E-004, 1.6275650204654449E-004, 1.6072749261816771E-004, 1.5854317708766078E-004, 1.5621134427765224E-004, 1.5374041409832085E-004, 1.5113933855591651E-004, 1.4841750169954302E-004, 1.4558462443464392E-004, 1.4265067271827456E-004, 1.3962576863043333E-004, 1.3652010582862866E-004, 1.3334386988191963E-004, 1.3010716451778638E-004, 1.2681994397299381E-004, 1.2349195209338499E-004, 1.2013266794871410E-004, 1.1675125841887996E-004, 1.1335653731508610E-004, 1.0995693126504217E-004, 1.0656045181663018E-004, 1.0317467307159795E-004, 9.9806713758458234E-005, 9.6463225389579004E-005, 9.3150386448991637E-005, 8.9873903090643864E-005, 8.6639008396373639E-005, 8.3450467622888839E-005, 8.0312587670042159E-005, 7.7229229436542887E-005, 7.4203822420670947E-005, 7.1239381050164309E-005, 6.8338522726765148E-005, 6.5503486835033779E-005, 6.2736154754105639E-005, 6.0038070212806188E-005, 5.7410460073713753E-005, 5.4854254981585908E-005, 5.2370110003010785E-005, 4.9958424784680614E-005, 4.7619363282449247E-005, 4.5352872445592850E-005, 4.3158699298633454E-005, 4.1036407654583617E-005, 3.8985393924133195E-005, 3.7004904147754821E-005, 3.5094049396605343E-005, 3.3251820012956318E-005, 3.1477099567931615E-005, 2.9768678658235373E-005, 2.8125268217343930E-005, 2.6545512156783862E-005, 2.5027998953392441E-005, 2.3571271936914568E-005, 2.2173838038410357E-005, 2.0834175805129546E-005, 1.9550742750530925E-005, 1.8321982088222062E-005, 1.7146328852375955E-005, 1.6022215500419999E-005, 1.4948076960008753E-005, 1.3922355033718204E-005, 1.2943501438227491E-005, 1.2009981926658952E-005, 1.1120280712209818E-005, 1.0272904163103515E-005, 9.4663837372109869E-006, 8.6992783507400361E-006, 7.9701765038140244E-006, 7.2776985410142118E-006, 6.6204992006639646E-006, 5.9972703866830856E-006, 5.4067439563747782E-006, 4.8476949803307107E-006, 4.3189435741996670E-006, 3.8193575591245758E-006, 3.3478549361397293E-006, 2.9034055154335121E-006, 2.4850307436573477E-006, 2.0918005771205208E-006, 1.7228260615431317E-006, 1.3772473094822884E-006, 1.0520394109618397E-006, 7.4841771380025374E-007, 4.6804596173191955E-007, 2.0961681546458945E-007, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000] +max_uptake_po4_fortran = [2.3322032227480109E-006, 2.2159108475233858E-006, 2.0998711028945595E-006, 1.9842753398030213E-006, 1.8693113766360872E-006, 1.7551634056100194E-006, 1.6420118394842449E-006, 1.5300333072479151E-006, 1.4194006239194659E-006, 1.3102827304881900E-006, 1.2028446317255370E-006, 1.0972473799608120E-006, 9.9364776330898771E-007, 8.9219762015618414E-007, 7.9304387361634361E-007, 6.9632883884215754E-007, 6.0219048337636842E-007, 5.1076244145566155E-007, 4.2217397946064903E-007, 3.3654996770127697E-007, 2.5401084762582901E-007, 1.7467246886914725E-007, 9.8645668974234488E-008, 2.6036578892845950E-008, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 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3.6935801907979173E-006, 3.8328792119465435E-006, 3.9714204567534590E-006, 4.1089993985000854E-006, 4.2454265102210280E-006, 4.3805354554980369E-006, 4.5141929016444797E-006, 4.6463082619130499E-006, 4.7768394966921376E-006, 4.9057870009249371E-006, 5.0331612569504544E-006, 5.1589012589541241E-006, 5.2724123375982313E-006, 5.3541463433315518E-006, 5.4319452973488174E-006, 5.5075421877648860E-006, 5.5823389327309226E-006, 5.6573264921061468E-006, 5.7331130970281323E-006, 5.8099970472481038E-006, 5.8880481629593514E-006, 5.9671797792140002E-006, 6.0472013522239294E-006, 6.1278581098764976E-006, 6.2088597847395375E-006, 6.2899004396107329E-006, 6.3706714335944688E-006, 6.4508692431604923E-006, 6.5301999362917668E-006, 6.6083812134502764E-006, 6.6851428103308001E-006, 6.7602267634381726E-006, 6.8333876646758279E-006, 6.9043918490528632E-006, 6.9730165925652158E-006, 7.0390495369602778E-006, 7.1022875296419982E-006, 7.1625361494518770E-006, 7.2196094152230139E-006, 7.2733296219871357E-006, 7.3235272517727152E-006, 7.3700411753602513E-006, 7.4127189379442331E-006, 7.4514162396539726E-006, 7.4859967502779660E-006, 7.5163315155465281E-006, 7.5422980716673646E-006, 7.5637799817002112E-006, 7.5806667531754469E-006, 7.5928538873846757E-006, 7.6002426334996788E-006, 7.6027391109787930E-006, 7.6002539777471323E-006, 7.5927031315267865E-006, 7.5800077173674855E-006, 7.5620942335531342E-006, 7.5388940064738222E-006, 7.5103432356458035E-006, 7.4763832308054822E-006, 7.4369606331940958E-006, 7.3920276158567506E-006, 7.3415420426939044E-006] +missing_phos_fortran = [-4.9957272742735606E-008, -5.0454978393331247E-008, -5.0941101536620295E-008, -5.1415617226168751E-008, -5.1878501590894929E-008, -5.2329731387397093E-008, -5.2769281594467596E-008, -5.3197126492745426E-008, -5.3613240345620858E-008, -5.4017597573092607E-008, -5.4410172662684859E-008, -5.4790942620494878E-008, -5.5159884497243434E-008, -5.5516977482923595E-008, -5.5862203192919519E-008, -5.6195544896863892E-008, 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-4.7148580909864540E-008, -4.6596826999513232E-008, -4.6030361751826386E-008, -4.5448613935129467E-008, -4.4851129231991955E-008, -4.4237637964938566E-008, -4.3608080016147110E-008, -4.2962498875351804E-008, -4.2214816050624246E-008, -4.1379919087224370E-008, -4.0552448942760530E-008, -3.9737029625135600E-008, -3.8937233709256498E-008, -3.8155645456921419E-008, -3.7393629390350867E-008, -3.6651566977113799E-008, -3.5929138791260594E-008, -3.5225573545795923E-008, -3.4539838715617690E-008, -3.3870778513554224E-008, -3.3217206846149662E-008, -3.2577966263296735E-008, -3.1951962423069169E-008, -3.1338182331029288E-008, -3.0735701320091658E-008, -3.0143682200266415E-008, -2.9561368906288448E-008, -2.8988078927561544E-008, -2.8423201503786064E-008, -2.7866187192945254E-008, -2.7316541616888558E-008, -2.6773821391643654E-008, -2.6237631238303883E-008, -2.5707620705229486E-008, -2.5183479677628861E-008, -2.4664934402888467E-008, -2.4151743650008856E-008, -2.3643695546599161E-008, -2.3140604482013675E-008, -2.2642308555846334E-008, -2.2148666941739484E-008, -2.1659557756607734E-008, -2.1174875938648599E-008, -2.0694531556254494E-008, -2.0218448013633852E-008, -1.9746560094171522E-008, -1.9278811395998950E-008, -1.8815152030744231E-008, -1.8355537792469954E-008, -1.7899931133984144E-008, -1.7448300828629705E-008, -1.7000622371276229E-008, -1.6556876819779322E-008, -1.6117050326588691E-008, -1.5681133986064885E-008, -1.5249123649609786E-008, -1.4821019731072175E-008, -1.4396826961287538E-008] +actual_uptake_po4_fortran = [2.3322032227480109E-006, 2.2159108475233858E-006, 2.0998711028945595E-006, 1.9842753398030213E-006, 1.8693113766360872E-006, 1.7551634056100194E-006, 1.6420118394842449E-006, 1.5300333072479151E-006, 1.4194006239194659E-006, 1.3102827304881900E-006, 1.2028446317255370E-006, 1.0972473799608120E-006, 9.9364776330898771E-007, 8.9219762015618414E-007, 7.9304387361634361E-007, 6.9632883884215754E-007, 6.0219048337636842E-007, 5.1076244145566155E-007, 4.2217397946064903E-007, 3.3654996770127697E-007, 2.5401084762582901E-007, 1.7467246886914725E-007, 9.8645668974234488E-008, 2.6036578892845950E-008, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 0.0000000000000000, 2.5010916228694582E-008, 1.0060151774625405E-007, 1.8012627835063843E-007, 2.6350673229612082E-007, 3.5065963302137560E-007, 4.4149695547807778E-007, 5.3592580033760004E-007, 6.3384839444999480E-007, 7.3516211394711313E-007, 8.3975950362100850E-007, 9.4752830529255471E-007, 1.0583515079658796E-006, 1.1721074286052046E-006, 1.2886698320185539E-006, 1.4079080881666861E-006, 1.5296872801970861E-006, 1.6538682266194152E-006, 1.7803070497872489E-006, 1.9088542237249201E-006, 2.0393541288224435E-006, 2.1716448100857106E-006, 2.3055581376139173E-006, 2.4409212041729019E-006, 2.5775565360750501E-006, 2.7152820982625041E-006, 2.8539115018167596E-006, 2.9932540151126812E-006, 3.1331135258763517E-006, 3.2732874696606639E-006, 3.4135668252029530E-006, 3.5537369395992472E-006, 3.6935801907979173E-006, 3.8328792119465435E-006, 3.9714204567534590E-006, 4.1089993985000854E-006, 4.2454265102210280E-006, 3.7727607321250627E-006, 3.3018245743879028E-006, 2.8579569014983826E-006, 2.4401796144253557E-006, 2.0475629391555822E-006, 1.6792179815269846E-006, 1.3342848106069366E-006, 1.0098245949112155E-006, 7.0703779471302943E-007, 4.2749351278915900E-007, 1.6987978583945385E-007, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000, -0.0000000000000000] +missing_assim_sum = np.zeros(len(assim_uptake_po4_fortran)) +for i in range(0,len(assim_uptake_po4_fortran)): + missing_assim_sum[i] = missing_phos_fortran[i] + assim_uptake_po4_fortran[i] \ No newline at end of file diff --git a/setup/zooplankton.py b/setup/zooplankton.py index 5a423a1..0420679 100644 --- a/setup/zooplankton.py +++ b/setup/zooplankton.py @@ -2,13 +2,13 @@ import os import sys import numpy as np -from functions.other_functions import concentration_ratio, nutrient_limitation, temperature_regulation, tracer_elements - +from functions.other_functions import concentration_ratio, nutrient_limitation, temperature_dependence, tracer_elements +from fractions import Fraction class Zooplankton(): """ """ - def __init__(self, abbrev, iters, reactions, **tracer): + def __init__(self, abbrev, base_element, iters, num_layers, reactions, **tracer): self.abbrev = abbrev self.name = tracer["long_name"] self.type = tracer["type"] @@ -18,13 +18,11 @@ def __init__(self, abbrev, iters, reactions, **tracer): self.cell_quota = tracer["parameters"]["cell_quota"] # Temperature regulation - self.temp_limited = tracer["parameters"]["temp_limited"] - if self.temp_limited: - self.q10 = tracer["parameters"]["q10"] + self.temperature_regulation = tracer["parameters"]["temperature_regulation"] self.temp_regulation_factor = 1. - # Oxygen limitation - self.oxygen_limited = tracer["parameters"]["oxygen_limited"] + # Oxygen inhibition + self.oxygen_inhibition = tracer["parameters"]["oxygen_inhibition"] self.oxy_limitation_factor = 1. # Grazing parameters @@ -43,19 +41,46 @@ def __init__(self, abbrev, iters, reactions, **tracer): for key in tracer["composition"]: available_elements = ['c','n','p','fe'] if key in available_elements: + # Add constituent to composition/concentration self.composition.append(key) - # conc.append(np.array(tracer["composition"][key])) - conc.append(tracer["composition"][key]) + + # Set initial conditions + if isinstance(tracer["composition"][key], str): # Read initial conditions from file + conc.append( np.fromfile(os.getcwd() + tracer["composition"][key]) ) + elif isinstance(tracer["composition"][key], (int,float)): # Create array of initial conditions + if num_layers == 1: # 0d configuration + conc.append(tracer["composition"][key]) + else: # 1d configuration + conc.append(tracer["composition"][key] * np.ones(num_layers)) + elif isinstance(tracer["composition"][key], (list,np.ndarray)): + conc.append(np.array(tracer["composition"][key])) + elif isinstance(tracer["composition"][key], dict): # Create array of initial conditions based off ratio to base element + index = self.composition.index(base_element) + if num_layers == 1: # 0d configuration + conc.append(tracer["composition"][key][base_element] * conc[index] ) + else: # 1d configuration + conc.append(tracer["composition"][key][base_element] * conc[index] * np.ones(num_layers)) + else: sys.exit("Zooplankton: Element '" + key + "' not recognized. Check documentation and edit input file.") - hold = np.zeros((len(conc),iters),dtype=np.ndarray) - for i in range(0,len(conc)): - hold[i,0] = np.array(conc[i]) + + # hold = np.zeros((len(conc),iters),dtype=np.ndarray) # hold[...,0] = conc - self.conc = np.array(hold) - self.d_dt = np.zeros_like(conc) - # self.conc_ratio = np.zeros_like(conc) - self.conc_ratio = np.copy(self.cell_quota["opt"]) + # self.conc = hold + # self.d_dt = np.zeros_like(conc) + # self.conc_ratio = np.copy(self.cell_quota["opt"]) + + if num_layers > 1: # Model as "boxes" between layers (num_layers-1) + self.conc = np.zeros((len(self.composition),num_layers-1,iters),dtype=float) + for const in range(0,len(self.composition)): + self.conc[const,:,0] = conc[const][:-1] + else: # Model as single box + self.conc = np.zeros((len(self.composition),iters),dtype=float) + for const in range(0,len(self.composition)): + self.conc[const,:,0] = conc[const] + self.d_dt = np.zeros_like(self.conc[...,0],dtype=float) + self.conc_ratio = np.ones_like(self.conc[...,0],dtype=float) + # Add relevant reactions self.reactions = [] @@ -86,16 +111,18 @@ def __init__(self, abbrev, iters, reactions, **tracer): self.reactions = [item for item in self.reactions if item["type"] == "grazing"] + [item for item in self.reactions if item["type"] != "grazing"] - def zoo(self, iter, base_element, base_temp, temperature, tracers): + def zoo(self, iter, base_element, physical, tracers): - check_conc = self.conc[:,iter] + # check_conc = self.conc[:,iter] # Zero out grazing rates self.grazing_rates = {prey: 0. for prey in self.grazing_rates} + self.graze_sum = np.zeros_like(self.conc_ratio, dtype=float) - # Calculate temp regulation factor - if self.temp_limited: - self.temp_regulation_factor = temperature_regulation(base_temp, temperature, self.q10) + # Calculate temp regulation factor (if necessary) + if self.temperature_regulation["temp_limited"]: + # self.temp_regulation_factor = temperature_dependence(base_temp, temperature, self) + self.temp_regulation_factor = temperature_dependence(physical["bgc_phys_vars"]["temperature"], self) # Calculate bgc rates for reac in self.reactions: @@ -111,13 +138,13 @@ def zoo(self, iter, base_element, base_temp, temperature, tracers): basl_respiration = 0. if self.calc_grazing: pass else: all_grazing = np.zeros_like(self.conc_ratio) - self.excretion(iter, reac["parameters"], c, p, ec, ep, ic, ip, tracers, all_grazing, activity_respiration, basl_respiration) + self.excretion(iter, base_element, reac["parameters"], c, p, ec, ep, ic, ip, tracers, all_grazing, activity_respiration, basl_respiration) if reac["type"] == "mortality": self.mortality(iter, reac["parameters"], c, p, ec, ep, ic, ip, tracers) - if reac["type"] == "respiration": activity_respiration, basl_respiration = self.respiration(iter, reac["parameters"], c, p, tracers) + if reac["type"] == "respiration": activity_respiration, basl_respiration = self.respiration(iter, base_element, reac["parameters"], c, p, tracers) - if iter % 50 == 0: - x=1 - x=1 + # if iter % 50 == 0: + # x=1 + # x=1 # return rsp @@ -161,7 +188,7 @@ def egestion(self, iter, parameters, c, p, ec, ep, ic, ip, tracers): tracers[p].d_dt += ep * tracers[p].conc_ratio * egestion - def excretion(self, iter, parameters, c, p, ec, ep, ic, ip, tracers, all_grazing, activity_respiration, basal_respiration): + def excretion(self, iter, base_element, parameters, c, p, ec, ep, ic, ip, tracers, all_grazing, activity_respiration, basal_respiration): """ Definition:: Calculates excretion of zooplankton to nutrient or detrital pool. Excretion can be represented either as a constant rate or as a fraction of zooplankton grazing on phytoplankton. @@ -175,64 +202,79 @@ def excretion(self, iter, parameters, c, p, ec, ep, ic, ip, tracers, all_grazing ep = ep[p] ic = ic[c] ip = ip[p] - nutrient_index = list(ec).index(1.0) - tc = np.array(tracers[c].conc[nutrient_index][iter]) - tp = np.array(tracers[p].conc[ip][iter]) - - if "c" in self.composition: - carbon_index = self.composition.index("c") + nutrient_index = list(ec).index(1.0) # Index of excreted nutrient + tc = np.array(tracers[c].conc[nutrient_index,:,iter]) + index = self.composition.index(base_element) # Index of base element if parameters["function"] == "constant": excretion = parameters["excretion_rate"] * tc # Calculate excretion of excess nutrient (above optimal nutrient quota) if necessary if tracers[p].type == "inorganic": - if "c" in self.composition: - carbon_index = self.composition.index("c") - carbon_ratio = tc / np.array(tracers[c].conc[carbon_index][iter]) + element_ratio = tc / np.array(tracers[c].conc[index][iter]) + excretion = excretion * np.maximum(0,element_ratio - parameters["optimal_nutrient_quota"]) - excretion = excretion * np.maximum(0,carbon_ratio - parameters["optimal_nutrient_quota"]) - elif parameters["function"] == "grazing": # Sum grazing rates for all chemical constituents - graze_sum = np.zeros(len(self.composition)) - for const in self.composition: - const_index = self.composition.index(const) - graze_sum[const_index] = np.sum(all_grazing[const_index]) + # graze_sum = np.zeros(len(self.composition)) + # for const in self.composition: + # const_index = self.composition.index(const) + # # graze_sum[const_index] = np.sum(all_grazing[const_index]) + # graze_sum[const_index] = np.sum(self.grazing_rates[const_index]) - # # Sum grazing rates for base element - # graze_sum = sum([rate[ip] # * tracers[prey].conc[ic][iter] - # for prey, rate in self.grazing_rates.items()]) - - # excretion = self.assimilation_efficiency * ( 1 - self.ingestion_efficiency ) * graze_sum if tracers[p].type == "detritus": - # excretion = self.ingestion_efficiency * ( 1 - self.assimilation_efficiency ) * graze_sum - excretion = self.ingestion_efficiency * graze_sum - if "c" in self.composition: # Scale excretion of carbon by assimilation efficiency - excretion[carbon_index] = excretion[carbon_index] * ( 1. - self.assimilation_efficiency ) + # excretion = self.ingestion_efficiency * graze_sum + excretion = self.ingestion_efficiency * self.graze_sum + excretion[index] = excretion[index] * ( 1. - self.assimilation_efficiency ) elif tracers[p].type == "inorganic": - excreted_carbon = np.maximum(np.zeros_like(graze_sum[nutrient_index]), graze_sum[carbon_index] * (1. - self.ingestion_efficiency) - activity_respiration) - excreted_nutrient = np.maximum(np.zeros_like(graze_sum[nutrient_index]), ( all_grazing[nutrient_index] * (1. - self.ingestion_efficiency) ) + ( basal_respiration * self.conc_ratio[nutrient_index] )) - # graze_nutrient = (parameters["optimal_nutrient_quota"] * graze_sum)/(graze_sum + 1.E-20) - parameters["optimal_nutrient_quota"] + # excreted_base = np.maximum(np.zeros_like(graze_sum[nutrient_index]), graze_sum[index] * (1. - self.ingestion_efficiency) - activity_respiration) + # excreted_nutrient = np.maximum(np.zeros_like(graze_sum[nutrient_index]), ( all_grazing[nutrient_index] * (1. - self.ingestion_efficiency) ) + ( basal_respiration * self.conc_ratio[nutrient_index] )) + excreted_base = np.maximum(np.zeros_like(self.graze_sum[nutrient_index]), self.graze_sum[index] * (1. - self.ingestion_efficiency) - activity_respiration) + # excreted_nutrient = np.maximum(np.zeros_like(self.graze_sum[nutrient_index]), ( all_grazing[nutrient_index] * (1. - self.ingestion_efficiency) ) + ( basal_respiration * self.conc_ratio[nutrient_index] )) + excreted_nutrient = np.maximum(np.zeros_like(self.graze_sum[nutrient_index]), ( self.graze_sum[nutrient_index] * (1. - self.ingestion_efficiency) ) + ( basal_respiration * self.conc_ratio[nutrient_index] )) - excretion = np.maximum(np.zeros_like(excreted_carbon), excreted_nutrient/(excreted_carbon + 1.E-20) - self.cell_quota["opt"][nutrient_index] ) * excreted_carbon + excretion = np.maximum(np.zeros_like(excreted_base), excreted_nutrient/(excreted_base + 1.E-20) - self.cell_quota["opt"][nutrient_index] ) * excreted_base + # excretion = np.maximum(np.zeros_like(excreted_base), excreted_nutrient/(excreted_base + 1.E-20) - self.conc_ratio[nutrient_index] ) * excreted_base # Update d_dt if tracers[p].type == "detritus": # Scale by concentration ratio for excretion to detrital pools + # Extract cell quotas from zooplankton + # ratios = np.zeros(len(self.conc_ratio)) + ratios = np.zeros_like(tracers[p].conc_ratio) + for const in self.composition: + if const in tracers[p].composition: + index_zoo = tracers[c].composition.index(const) + index_om = self.composition.index(const) + ratios[index_om] = tracers[c].conc_ratio[index_zoo] + if parameters != None and "partition" in parameters: - all_excretion = ec * excretion * parameters["partition"] - tracers[c].d_dt -= ec * excretion * parameters["partition"] - tracers[p].d_dt += ep * excretion * parameters["partition"] + # all_excretion = ec * excretion * parameters["partition"] + # tracers[c].d_dt -= ec * excretion * parameters["partition"] + # tracers[p].d_dt += ep * excretion * parameters["partition"] + + for i in range(len(ec)): + tracers[c].d_dt[i] -= ec[i] * excretion[i] * parameters["partition"][i] #* tracers[c].conc_ratio[i] + for j in range(len(ep)): + tracers[p].d_dt[j] += ep[j] * excretion[j] * parameters["partition"][j] #* ratios[j] + else: - tracers[c].d_dt -= ec * excretion - tracers[p].d_dt += ep * excretion + for i in range(len(ec)): + tracers[c].d_dt[i] -= ec[i] * excretion[i] * tracers[c].conc_ratio[i] + for j in range(len(ep)): + tracers[p].d_dt[j] += ep[j] * excretion[j] * ratios[j] + # tracers[c].d_dt -= ec * excretion + # tracers[p].d_dt += ep * excretion else: - tracers[c].d_dt -= ec * excretion - tracers[p].d_dt += np.array(ep) * excretion - + for i in range(len(ec)): + tracers[c].d_dt[i] -= ec[i] * excretion + for j in range(len(ep)): + tracers[p].d_dt[j] += ep[j] * excretion + # tracers[c].d_dt -= ec * excretion + # tracers[p].d_dt += np.array(ep) * excretion + def grazing(self, iter, parameters, c, p, ec, ep, ic, ip, tracers): """ Definition:: Calculates the zooplankton grazing rate on a particular species using user choice of the Ivlev Equation, @@ -248,8 +290,8 @@ def grazing(self, iter, parameters, c, p, ec, ep, ic, ip, tracers): ep = ep[p] ic = ic[c] ip = ip[p] - tc = np.array(tracers[c].conc[ic][iter]) - tp = np.array(tracers[p].conc[ip][iter]) + tc = np.array(tracers[c].conc[ic,:,iter]) + tp = np.array(tracers[p].conc[ip,:,iter]) # Extract grazing preference for prey pref = self.grazing_preferences[c] * tc @@ -259,7 +301,7 @@ def grazing(self, iter, parameters, c, p, ec, ep, ic, ip, tracers): # Calculate total food availability for prey, preference in self.grazing_preferences.items(): # Concentration of base element in prey - conc_prey = tracers[prey].conc[ic][iter] + conc_prey = tracers[prey].conc[ic,:,iter] # Capture efficiency for current prey in list of available eff_prey = conc_prey / ( conc_prey + parameters["feeding_threshold"] ) @@ -290,7 +332,19 @@ def grazing(self, iter, parameters, c, p, ec, ep, ic, ip, tracers): # Calculate total prey availability total_available = sum([avail for prey, avail in self.prey_availability.items()]) + # Calculate specific grazing rate for individual prey grazing = ( parameters["max_grazing_rate"] * self.prey_availability[c] ) / ( total_available + parameters["half_sat_grazing"] ) * tp + + # Calculate total uptake rate + uptake = ( parameters["max_grazing_rate"] * total_available ) / ( total_available + parameters["half_sat_grazing"] ) * tp + total_uptake = np.zeros_like(self.conc_ratio) + for prey in self.prey_availability: + for const in tracers[prey].composition: + if const in self.composition: + index_prey = tracers[prey].composition.index(const) + index_pred = self.composition.index(const) + total_uptake[index_pred] += (uptake / total_available) * tracers[prey].conc_ratio[index_prey] * self.prey_availability[prey] + # total_uptake[index_pred] += uptake * tracers[prey].conc_ratio[index_prey] * self.prey_availability[prey] elif parameters["function"] == "holling-3": # Sigmoidal # Calculate grazing preference @@ -302,41 +356,45 @@ def grazing(self, iter, parameters, c, p, ec, ep, ic, ip, tracers): # grazing = ( parameters["max_grazing_rate"] * pref ) / ( ( half_sat_grazing**2 ) + pref_sum ) * tp # Temperature regulation - if self.temp_limited: + if self.temperature_regulation["temp_limited"]: grazing = grazing * self.temp_regulation_factor - - rugc = self.temp_regulation_factor * parameters["max_grazing_rate"] * nutrient_limitation(self.prey_availability[c], parameters["half_sat_grazing"]) * tp - sut = rugc / self.prey_availability[c] - grazing = sut * self.prey_availability[c] + total_uptake = total_uptake * self.temp_regulation_factor - gn = grazing * tracers[c].conc_ratio[1] - gp = grazing * tracers[c].conc_ratio[2] - gl = grazing * tracers[c].conc_ratio[3] - - phyto_conc_ratios = tracers[c].conc_ratio # Extract cell quotas from prey - ratios = np.zeros(len(self.conc_ratio)) + ratios = np.zeros_like(tracers[p].conc_ratio) for const in self.composition: if const in tracers[c].composition: index_prey = tracers[c].composition.index(const) index_pred = self.composition.index(const) - # ratios[index_pred] = tracers[c].cell_quota["opt"][index_prey] ratios[index_pred] = tracers[c].conc_ratio[index_prey] - all_grazing = ep * ratios * grazing - grazing_on_prey = ec * tracers[c].conc_ratio * grazing - # grazing_on_phyto = ec * tracers[c].cell_quota["opt"] * grazing - # Update d_dt - tracers[c].d_dt -= grazing_on_prey - tracers[p].d_dt += all_grazing - + # grazing_on_prey = np.zeros_like(tracers[c].conc_ratio) + for i in range(len(ec)): + # grazing_on_prey[i] = ec[i] * tracers[c].conc_ratio[i] * grazing + # tracers[c].d_dt[i] -= grazing_on_prey[i] + tracers[c].d_dt[i] -= ec[i] * tracers[c].conc_ratio[i] * grazing + + all_grazing = np.zeros_like(tracers[p].conc_ratio) + for j in range(len(ep)): + all_grazing[j] = ep[j] * ratios[j] * grazing + tracers[p].d_dt[j] += all_grazing[j] + # Update grazing rate dictionary tracers[p].grazing_rates[c] = all_grazing - - return all_grazing - + + # Update graze_sum + for i in range(0,len(self.graze_sum)): + # self.graze_sum[i] = self.conc_ratio[i] * total_uptake + self.graze_sum[i] = total_uptake[i] + # self.graze_sum += all_grazing + + sut = total_uptake / total_available + x = tracers[c].conc_ratio * grazing + # y = self.conc_ratio * total_uptake + x = 1 + def mortality(self, iter, parameters, c, p, ec, ep, ic, ip, tracers): """ @@ -350,7 +408,7 @@ def mortality(self, iter, parameters, c, p, ec, ep, ic, ip, tracers): ep = ep[p] ic = ic[c] ip = ip[p] - tc = np.array(tracers[c].conc[ic][iter]) + tc = np.array(tracers[c].conc[ic,:,iter]) # Calculate mortality rate mortality = ( parameters["mortality_rate"][0] * tc ) + ( parameters["mortality_rate"][1] * (tc**2) ) @@ -358,7 +416,7 @@ def mortality(self, iter, parameters, c, p, ec, ep, ic, ip, tracers): # Oxygen limitation if "oxygen_limited" in parameters and parameters["oxygen_limited"]: oxy_limitation_factor = np.minimum(1., nutrient_limitation(tracers["o2"].conc[...,iter], parameters["half_sat_oxygen"])) - mortality += (1. - oxy_limitation_factor) * parameters["mortality_rate_oxy"] * tc + mortality += (1. - oxy_limitation_factor[0,:]) * parameters["mortality_rate_oxy"] * tc # # Temperature regulation # if self.temp_limited: @@ -368,49 +426,93 @@ def mortality(self, iter, parameters, c, p, ec, ep, ic, ip, tracers): # concentration_ratio(iter, ic, tracers[c]) # concentration_ratio(iter, ip, tracers[p]) + # Extract cell quotas from zooplankton + # ratios = np.zeros(len(self.conc_ratio)) + ratios = np.zeros_like(tracers[p].conc_ratio) + for const in self.composition: + if const in tracers[p].composition: + index_zoo = tracers[c].composition.index(const) + index_om = self.composition.index(const) + ratios[index_om] = tracers[c].conc_ratio[index_zoo] + # Update d_dt if parameters != None and "partition" in parameters: # Mortality can be partitioned between dissolved and particulate detrital pools # tracers[c].d_dt -= ec * tracers[c].cell_quota["opt"] * mortality * parameters["partition"] # tracers[p].d_dt += ep * tracers[c].cell_quota["opt"] * mortality * parameters["partition"] - all_mortality = ec * tracers[c].conc_ratio * mortality * parameters["partition"] - tracers[c].d_dt -= ec * tracers[c].conc_ratio * mortality * parameters["partition"] - tracers[p].d_dt += ep * tracers[c].conc_ratio * mortality * parameters["partition"] + # all_mortality = ec * tracers[c].conc_ratio * mortality * parameters["partition"] + # tracers[c].d_dt -= ec * tracers[c].conc_ratio * mortality * parameters["partition"] + # tracers[p].d_dt += ep * tracers[c].conc_ratio * mortality * parameters["partition"] + + # if parameters != None and "partition" in parameters: + # all_excretion = ec * excretion * parameters["partition"] + # tracers[c].d_dt -= ec * excretion * parameters["partition"] + # tracers[p].d_dt += ep * excretion * parameters["partition"] + + for i in range(len(ec)): + tracers[c].d_dt[i] -= ec[i] * mortality * parameters["partition"][i] * tracers[c].conc_ratio[i] + for j in range(len(ep)): + tracers[p].d_dt[j] += ep[j] * mortality * parameters["partition"][j] * ratios[j] + + # else: + # for i in range(len(ec)): + # tracers[c].d_dt[i] -= ec[i] * mortality + # for j in range(len(ep)): + # tracers[p].d_dt[j] += ep[j] * mortality + # tracers[c].d_dt -= ec * excretion + # tracers[p].d_dt += ep * excretion + else: # tracers[c].d_dt -= ec * tracers[c].cell_quota["opt"] * mortality # tracers[p].d_dt += ep * tracers[c].cell_quota["opt"] * mortality - tracers[c].d_dt -= ec * tracers[c].conc_ratio * mortality - tracers[p].d_dt += ep * tracers[c].conc_ratio * mortality + # tracers[c].d_dt -= ec * tracers[c].conc_ratio * mortality + # tracers[p].d_dt += ep * tracers[c].conc_ratio * mortality + for i in range(len(ec)): + tracers[c].d_dt[i] -= ec[i] * mortality * tracers[c].conc_ratio[i] + for j in range(len(ep)): + tracers[p].d_dt[j] += ep[j] * mortality * ratios[j] - def respiration(self, iter, parameters, c, p, tracers): + x=1 + + + def respiration(self, iter, base_element, parameters, c, p, tracers): """ Definition:: Calculates zooplankton respiration """ - # Locate index of carbon constituent - carbon_index = self.composition.index("c") - # Get carbon concentration - # zoo = self.conc[carbon_index][iter] - zoo = tracers[self.abbrev].conc[carbon_index][iter] + # Locate index of base element + index = self.composition.index(base_element) - # Sum grazing rates for carbon constituent - graze_sum = 0 - for prey, rate in self.grazing_rates.items(): - if "c" in tracers[prey].composition: graze_sum += rate[tracers[prey].composition.index("c")] + # Get concentration of base element + zoo = tracers[self.abbrev].conc[index,:,iter] - activity_respiration = (1 - self.assimilation_efficiency - self.ingestion_efficiency) * graze_sum - basl_respiration = self.temp_regulation_factor * parameters["respiration_rate"] * zoo - - # # Temperature regulation - # if self.temp_limited: respiration += parameters["respiration_rate"] * self.temp_regulation_factor * zoo - # else: respiration += parameters["respiration_rate"] * zoo - # basl_respiration = self.temp_regulation_factor * parameters["respiration_rate"] * zoo - # Update d_dt - total_respiration = activity_respiration + basl_respiration - self.d_dt[carbon_index] -= total_respiration - if "o2" in c: tracers["o2"].d_dt -= total_respiration / parameters["mw_carbon"] - if "co2" in p: tracers["co2"].d_dt += total_respiration + # Sum grazing rates for base element + # graze_sum = 0. + # for prey, rate in self.grazing_rates.items(): + # graze_sum += rate[tracers[prey].composition.index(base_element)] - return activity_respiration, basl_respiration \ No newline at end of file + # activity_respiration = (1 - self.assimilation_efficiency - self.ingestion_efficiency) * graze_sum + activity_respiration = (1 - self.assimilation_efficiency - self.ingestion_efficiency) * self.graze_sum[index] + basal_respiration = self.temp_regulation_factor * parameters["respiration_rate"] * zoo + + # Update d_dt + total_respiration = activity_respiration + basal_respiration + self.d_dt[index] -= total_respiration + if "o2" in c: + if isinstance(parameters["convert_o2"],(int,float)) and not isinstance(parameters["convert_o2"],bool): + tracers["o2"].d_dt -= total_respiration * parameters["convert_o2"] + elif isinstance(parameters["convert_o2"],str): + tracers["o2"].d_dt -= total_respiration * float(Fraction(parameters["convert_o2"])) + + if "co2" in p: + if base_element == "c": tracers["co2"].d_dt += total_respiration + else: + if isinstance(parameters["convert_co2"],(int,float)) and not isinstance(parameters["convert_co2"],bool): + tracers["co2"].d_dt += total_respiration * parameters["convert_co2"] + elif isinstance(parameters["convert_co2"],str): + tracers["co2"].d_dt += total_respiration * float(Fraction(parameters["convert_co2"])) + + return activity_respiration, basal_respiration + \ No newline at end of file diff --git a/test_np.jpg b/test_np.jpg deleted file mode 100644 index 022b11d..0000000 Binary files a/test_np.jpg and /dev/null differ diff --git a/test_np.py b/test_np.py deleted file mode 100644 index 9f87c7e..0000000 --- a/test_np.py +++ /dev/null @@ -1,40 +0,0 @@ -import os -import sys -import numpy as np -import matplotlib.pyplot as plt - -# SETUP_PATH = Path() -from setup.initialize import import_model -from functions.bgc_rate_eqns import bgc_rate_eqns - -# ---------------------------------------------------------------------------------------------------- -# GLOBE model simulation -# ---------------------------------------------------------------------------------------------------- -# Import and initialize model -file = 'test_np.yaml' -file_path = os.getcwd() + '/' + file -base_element, parameters, reactions, tracers = import_model(file_path) - -# Begin simulation -for iter in range(0,parameters["simulation"]["iters"]-1): - bgc_rate_eqns(iter, base_element, parameters, tracers) - -fig, ax = plt.subplots() -x = [0,2592000,5184000,7776000,10268000,12960000,15552000] -marks = ['J','F','M','A','M','J','J'] -ax.plot(parameters["simulation"]["time"],tracers["no3"].conc[0,:]) -ax.plot(parameters["simulation"]["time"],tracers["phyto1"].conc[0,:]) -plt.ylabel("mmol N / m^3") -plt.xlabel('Time [months]') -plt.xlim([0,15552000]) -plt.xticks(x,marks) - -# Shrink current axis by 10% -box = ax.get_position() -ax.set_position([box.x0+0.04, box.y0+0.02, box.width * 0.9, box.height]) - -# Put a legend to the right of the current axis -ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) -plt.legend(['N','P'],loc='center left', bbox_to_anchor=(1, 0.5)) - -plt.savefig("test_np.jpg") \ No newline at end of file diff --git a/test_np.yaml b/test_np.yaml deleted file mode 100644 index fd436b4..0000000 --- a/test_np.yaml +++ /dev/null @@ -1,61 +0,0 @@ -base_element: n # mmol N / m^3 - -tracers: - no3: - long_name: nitrate - type: inorganic - composition: - n: 0.003 # mmol N / m^3 - parameters: - temp_limited: False - phyto1: - long_name: diatoms - type: phytoplankton - composition: # element : initial concentration - n: 0.003 # mmol N / m^3 - parameters: - temp_limited: False - nutrient_limitation: - type: external - nutrients: ["no3"] - -parameters: - environment: - base_temp: 20.0 - summer_mld: 10.0 - winter_mld: 40.0 - summer_salt: 36.5 - winter_salt: 37.0 - summer_sun: 120.0 - winter_sun: 10.0 - summer_temp: 15.0 - winter_temp: 15.0 - summer_wind: 2.0 - winter_wind: 6.0 - PAR_frac: 0.4 - simulation: - latitude: 45.0 - num_days: 180 - timestep: 360.0 - water_column: - column_depth: 10.0 - num_layers: 1 - -reactions: - - type: mortality - consumed: - phyto1: [n] - produced: - no3: [n] - parameters: - mortality_rate: [0.05,0.0] - - type: uptake - consumed: - no3: [n] - produced: - phyto1: [n] - parameters: - nutrient_limitation: variable - half_sat_nutrient: 0.1 - light_limitation: 1.0 - max_photo_rate: 1.4 diff --git a/test_npz.jpg b/test_npz.jpg deleted file mode 100644 index 6cf6d2e..0000000 Binary files a/test_npz.jpg and /dev/null differ diff --git a/test_npz.py b/test_npz.py deleted file mode 100644 index c2ebbbd..0000000 --- a/test_npz.py +++ /dev/null @@ -1,41 +0,0 @@ -import os -import sys -import numpy as np -import matplotlib.pyplot as plt - -# SETUP_PATH = Path() -from setup.initialize import import_model -from functions.bgc_rate_eqns import bgc_rate_eqns - -# ---------------------------------------------------------------------------------------------------- -# GLOBE model simulation -# ---------------------------------------------------------------------------------------------------- -# Import and initialize model -file = 'test_npz.yaml' -file_path = os.getcwd() + '/' + file -base_element, parameters, reactions, tracers = import_model(file_path) - -# Begin simulation -for iter in range(0,parameters["simulation"]["iters"]-1): - bgc_rate_eqns(iter, base_element, parameters, tracers) - -fig, ax = plt.subplots() -x = [0,2592000,5184000,7776000,10268000,12960000,15552000] -marks = ['J','F','M','A','M','J','J'] -ax.plot(parameters["simulation"]["time"],tracers["no3"].conc[0,:]) -ax.plot(parameters["simulation"]["time"],tracers["phyto1"].conc[0,:]) -ax.plot(parameters["simulation"]["time"],tracers["zoo1"].conc[0,:]) -plt.ylabel("mmol N / m^3") -plt.xlabel('Time [months]') -plt.xlim([0,15552000]) -plt.xticks(x,marks) - -# Shrink current axis by 10% -box = ax.get_position() -ax.set_position([box.x0+0.04, box.y0+0.02, box.width * 0.9, box.height]) - -# Put a legend to the right of the current axis -ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) -plt.legend(['N','P','Z'],loc='center left', bbox_to_anchor=(1, 0.5)) - -plt.savefig("test_npz.jpg") \ No newline at end of file diff --git a/test_npz.yaml b/test_npz.yaml deleted file mode 100644 index 1644a2c..0000000 --- a/test_npz.yaml +++ /dev/null @@ -1,95 +0,0 @@ -base_element: n # mmol N / m^3 - -tracers: - no3: - long_name: nitrate - type: inorganic - composition: - n: 0.05 # mmol N / m^3 - parameters: - temp_limited: False - phyto1: - long_name: diatoms - type: phytoplankton - composition: # element : initial concentration - n: 0.5 # mmol N / m^3 - parameters: - temp_limited: False - nutrient_limitation: - type: external - nutrients: ["no3"] - zoo1: - long_name: mesozooplankton - type: zooplankton - composition: # element : initial concentration - n: 0.5 # mmol N / m^3 - parameters: - assimilation_efficiency: 0.4 - ingestion_efficiency: 1.0 - temp_limited: False - grazing_preferences: - phyto1: 1.0 - -parameters: - environment: - base_temp: 20.0 - summer_mld: 10.0 - winter_mld: 40.0 - summer_salt: 36.5 - winter_salt: 37.0 - summer_sun: 120.0 - winter_sun: 10.0 - summer_temp: 15.0 - winter_temp: 15.0 - summer_wind: 2.0 - winter_wind: 6.0 - PAR_frac: 0.4 - simulation: - latitude: 45.0 - num_days: 180 - timestep: 360.0 - water_column: - column_depth: 10.0 - num_layers: 1 - -reactions: - - type: grazing - consumed: - phyto1: [n] - produced: - zoo1: [n] - parameters: - function: holling-1 - max_grazing_rate: 1.4 - half_sat_grazing: 1.4 - # half_sat_grazing: 2.8 - - type: egestion - consumed: - zoo1: [n] - produced: - no3: [n] - parameters: - - type: mortality - consumed: - phyto1: [n] - produced: - no3: [n] - parameters: - mortality_rate: [0.05,0.0] - - type: mortality - consumed: - zoo1: [n] - produced: - no3: [n] - parameters: - mortality_rate: [0.12,0.0] - - type: uptake - consumed: - no3: [n] - produced: - phyto1: [n] - parameters: - nutrient_limitation: variable - half_sat_nutrient: 0.1 - light_limitation: 1.0 - max_photo_rate: 1.4 diff --git a/test_npzd.jpg b/test_npzd.jpg deleted file mode 100644 index 304670b..0000000 Binary files a/test_npzd.jpg and /dev/null differ diff --git a/test_npzd.py b/test_npzd.py deleted file mode 100644 index 0d53737..0000000 --- a/test_npzd.py +++ /dev/null @@ -1,115 +0,0 @@ -import os -import sys -import numpy as np -import matplotlib.pyplot as plt - -# SETUP_PATH = Path() -from setup.initialize import import_model -from functions.bgc_rate_eqns import bgc_rate_eqns - -# ---------------------------------------------------------------------------------------------------- -# GLOBE model simulation -# ---------------------------------------------------------------------------------------------------- -# Import and initialize model -file = 'tests/npzd/test_npzd.yaml' -file_path = os.getcwd() + '/' + file -base_element, parameters, reactions, tracers = import_model(file_path) - -# Begin simulation -for iter in range(0,parameters["simulation"]["iters"]-1): - bgc_rate_eqns(iter, base_element, parameters, tracers) - -# ---------------------------------------------------------------------------------------------------- -# Test case and parameters from Riley Brady -# ---------------------------------------------------------------------------------------------------- -# Here we set up the default parameters/coefficients. -DT = 1 # Time Step (in days) -NUM_STEPS = 365 # Number of time steps to be computed and plotted - -# Temperature-Dependent Growth Rate -a = 0.6 -b = 1.066 -c = 1 -T = 15 -Vm = a * b**(c*T) # Maximum growth rate (per day) - -# Other parameters -Kn = 1 # Half-saturation constant for nitrogen uptake (umolN per l) -Rm = 1 # Maximum grazing rate (per day) -g = 0.2 # Zooplankton death rate (per day) -lambda_Z = 0.2 # Grazing constant (umolN per l) -epsilon = 0.1 # Phyto death rate (per day) -f = 0.25 # Light intensity (assumed constant) - -# Detritus-related stuff. -alpha = 0.0 # Fraction of zoo. uptake that goes immediately to dissolved nutrients. -beta = 1.0 # Assimilation efficiency of zooplankton. -r = 0.15 # Respiration rate. -phi = 0.4 # Remineralization rate of detritus. - -# Set Initial Conditions (umol per L) -N_0 = 4 -P_0 = 2.5 -Z_0 = 1.5 -D_0 = 0 - -# Initialize Arrays -N = np.empty(NUM_STEPS, dtype="float") -P = np.empty(NUM_STEPS, dtype="float") -Z = np.empty(NUM_STEPS, dtype="float") -D = np.empty(NUM_STEPS, dtype="float") - -# Insert Initial Values -N[0] = N_0 -P[0] = P_0 -Z[0] = Z_0 -D[0] = D_0 - -# Here we use the Euler forward method to solve for t+1 and reference t. -for idx in np.arange(1, NUM_STEPS, 1): - t = idx - 1 - - # Common terms for simpler code - gamma_N = N[t] / (Kn + N[t]) - zoo_graze = Rm * (1 - np.exp(-lambda_Z * P[t])) * Z[t] - - # Equation calculations - N[idx] = DT * (-Vm*gamma_N*f*P[t] + alpha*zoo_graze + epsilon*P[t] + g*Z[t] + phi*D[t]) + N[t] - P[idx] = DT * (Vm*gamma_N*f*P[t] - zoo_graze - epsilon*P[t] - r*P[t]) + P[t] - Z[idx] = DT * (beta*zoo_graze - g*Z[t]) + Z[t] - D[idx] = DT * (r*P[t] + (1-alpha-beta)*zoo_graze - phi*D[t]) + D[t] - -x = np.arange(1, NUM_STEPS + 1, 1) - -# ---------------------------------------------------------------------------------------------------- -# Plot results -# ---------------------------------------------------------------------------------------------------- -fig, ax = plt.subplots() - -months = [0,30,60,90,120,150,180] -marks = ['J','F','M','A','M','J','J'] -ax.plot(x,tracers["no3"].conc[0,:-1]) -ax.plot(x,N,'--k') -ax.plot(x,tracers["phyto1"].conc[0,:-1]) -ax.plot(x,P,'--k') -ax.plot(x,tracers["zoo1"].conc[0,:-1]) -ax.plot(x,Z,'--k') -ax.plot(x,tracers["pom1"].conc[0,:-1]) -ax.plot(x,D,'--k') - -plt.ylabel("mmol N / m^3") -plt.xlabel("Time (days)") -plt.xlabel("Time [months]") -plt.xlim([0,180]) -plt.xticks(months,marks) -# plt.xlim([0,365]) - -# Shrink current axis by 10% -box = ax.get_position() -ax.set_position([box.x0, box.y0, box.width * 0.9, box.height]) - -# Put a legend to the right of the current axis -ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) -plt.legend(['N','','P','','Z','','D',''],loc='center left', bbox_to_anchor=(1, 0.5)) - -plt.savefig("test_npzd.jpg") diff --git a/tests/.DS_Store b/tests/.DS_Store new file mode 100644 index 0000000..fb8d2a1 Binary files /dev/null and b/tests/.DS_Store differ diff --git a/tests/bfm17/.DS_Store b/tests/bfm17/.DS_Store new file mode 100644 index 0000000..17a63c7 Binary files /dev/null and b/tests/bfm17/.DS_Store differ diff --git a/tests/bfm17/0522/dom.jpg b/tests/bfm17/0522/dom.jpg deleted file mode 100644 index ad14937..0000000 Binary files a/tests/bfm17/0522/dom.jpg and /dev/null differ diff --git a/tests/bfm17/0522/nutrients.jpg b/tests/bfm17/0522/nutrients.jpg deleted file mode 100644 index 337be32..0000000 Binary files a/tests/bfm17/0522/nutrients.jpg and /dev/null differ diff --git a/tests/bfm17/0522/phytoplankton.jpg b/tests/bfm17/0522/phytoplankton.jpg deleted file mode 100644 index 1549d20..0000000 Binary files a/tests/bfm17/0522/phytoplankton.jpg and /dev/null differ diff --git a/tests/bfm17/0522/pom.jpg b/tests/bfm17/0522/pom.jpg deleted file mode 100644 index 682fc78..0000000 Binary files a/tests/bfm17/0522/pom.jpg and /dev/null differ diff --git a/tests/bfm17/0522/zooplankton.jpg b/tests/bfm17/0522/zooplankton.jpg deleted file mode 100644 index 73aa3ca..0000000 Binary files a/tests/bfm17/0522/zooplankton.jpg and /dev/null differ diff --git a/tests/bfm17/__init__.py b/tests/bfm17/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/bfm17/bfm17-1d-2.yaml b/tests/bfm17/bfm17-1d-2.yaml new file mode 100644 index 0000000..c32e0f2 --- /dev/null +++ b/tests/bfm17/bfm17-1d-2.yaml @@ -0,0 +1,478 @@ +base_element: c + +tracers: + o2: + long_name: oxygen + type: inorganic + composition: + o: /inputs/BFM17_BERM_INIT/init_prof_Oxy_150m_bermuda_killworth.da + parameters: + temperature_regulation: + temp_limited: False + surface_flux: True + + no3: + long_name: nitrate + type: inorganic + composition: + n: /inputs/BFM17_BERM_INIT/init_prof_N_150m_bermuda_killworth.da + parameters: + temperature_regulation: + temp_limited: True + function: q10 + coefficient: 2. + base_temp: 20. + + nh4: + long_name: ammonium + type: inorganic + composition: + # n: /inputs/BFM17_BERM_INIT/init_prof_Am_150m_bermuda_killworth.da + n: 0. + parameters: + temperature_regulation: + temp_limited: True + function: q10 + coefficient: 2. + base_temp: 20. + + po4: + long_name: phosphate + type: inorganic + composition: + p: /inputs/BFM17_BERM_INIT/init_prof_P_150m_bermuda_killworth.da + parameters: + temperature_regulation: + temp_limited: False + + # hs: + # long_name: reduction equivalents + # type: inorganic + # composition: + # s: 1. # [mmol S / m^3] + # parameters: + # temperature_regulation: + # temp_limited: False + + phyto1: + long_name: diatoms + type: phytoplankton + composition: # element : initial concentration + c: /inputs/BFM17_BERM_INIT/init_prof_Pc_150m_bermuda_killworth.da + n: {c: 0.0126} + p: {c: 7.862E-04} + chl: {c: 0.02} + parameters: + light_attenuation: 0.03 # [m^2 / mg Chl] + max_photo_rate: 1.6 # [1/d] + cell_quota: + constituents: ["c","n","p","chl"] + max: [1., 0.025, 2.5E-03, 0.016] + min: [1., 6.87E-03, 4.29E-04, 0.016] + opt: [1., 1.26E-02, 7.86E-04, 0.016] + nutrient_limitation: + colimitation: minimum # mininimum, product, or sum of all limiting nutrients + include: ["no3","po4"] # nutrients included for calculation of colimitation factor + no3: + type: internal + function: droop + nh4_inihibited: True + nh4: + type: external + function: monod + half_sat: 1. + po4: + type: internal + function: droop + exudation: + excreted_fraction: 0.05 + growth: + light_attenuation: 0.03 + max_photo_rate: 1.6 + chl_quota: 0.016 # [mg Chl / mg C] + chl_relax_rate: 0. # [(mg C . m^2) / (mg Chl μE)] + initial_PI_slope: 1.52E-05 + eps_PAR: 0.4 + lysis: + extra_lysis_rate: 0. + half_sat_stress_lysis: 0.1 + half_sat_extra_lysis: 0.0 + max_stress_lysis_rate: 0.05 # [1/d] + respiration: + activity_respiration_frac: 0.05 # [-] + basal_respiration_rate: 0.05 # [1/d] + sedimentation: + sinking: False + sinking_rate: 0. + burial_velocity: 0. + temperature_regulation: + temp_limited: True + function: q10 + coefficient: 2. + base_temp: 20. + + zoo1: + long_name: mesozooplankton + type: zooplankton + composition: # element : initial concentration + c: /inputs/BFM17_BERM_INIT/init_prof_Zc_150m_bermuda_killworth.da + n: {c: 0.0126} + p: {c: 7.862E-04} + parameters: + assimilation_efficiency: 0.5 + ingestion_efficiency: 0.25 + cell_quota: + constituents: ["c","n","p"] + # opt: [1., 1.67E-02, 1.85E-03] + opt: [1., 1.258E-02, 7.862E-04] + grazing_preferences: + phyto1: 1.0 + zoo1: 1.0E-06 + temperature_regulation: + temp_limited: True + function: q10 + coefficient: 2.0 + base_temp: 20. + oxygen_inhibition: + oxygen_limited: True + function: monod + half_sat: 50. # [mmol O2 / m^3] + + dom1: + long_name: dissolved organic matter + type: detritus + form: dissolved + composition: + c: /inputs/BFM17_BERM_INIT/init_prof_DOC_150m_bermuda_killworth.da + # n: {c: 0.0126} + # p: {c: 7.862E-04} + n: {c: 6.3E-03} + p: {c: 3.931E-04} + parameters: + light_attenuation: 0.0 # [m^2 / mg C] + sedimentation: + sinking: False + sinking_rate: 0. # [m/d] + burial_velocity: 0. # [m/d] + + pom1: + long_name: particulate organic matter + type: detritus + form: particulate + composition: # element : initial concentration + c: /inputs/BFM17_BERM_INIT/init_prof_POC_150m_bermuda_killworth.da + n: {c: 0.0126} + p: {c: 7.862E-04} + parameters: + light_attenuation: 1.0E-04 # [m^2 / mg C] + sedimentation: + sinking: True + sinking_rate: 1. # [m/d] + burial_velocity: 1. # [m/d] +# ------------------------------------------------------------------------------------------------- +reactions: + - type: chlorophyll_synthesis + consumed: + produced: + phyto1: [chl] + parameters: + eps_PAR: 0.4 # [-] + initial_PI_slope: 1.52E-05 + chl_quota: 0.016 # [mg Chl / mg C] + chl_relax_rate: 0. # [(mg C . m^2) / (mg Chl μE)] + optimal_Epar_Ek: 0.0 + max_photo_rate: 1.6 # [1/d] + light_attenuation_water: 0.0435 # [1/m] + light_attenuation_phyto: 0.03 # [m^2 / mg Chl] + + # - type: egestion + # consumed: + # zoo1: [c,n,p] + # produced: + # dom1: [c,n,p] + # parameters: + # partition: [0.60,0.72,0.832] # c,n,p [-] + + # - type: egestion + # consumed: + # zoo1: [c,n,p] + # produced: + # pom1: [c,n,p] + # parameters: + # half_sat_oxygen: 0.5 # [mmol O2 / m^3] + # partition: [0.40,0.28,0.168] # c,n,p [-] + + - type: excretion + consumed: + zoo1: [n] + produced: + nh4: [n] + parameters: + function: grazing + optimal_nutrient_quota: 0.0167 # [mmol N / mg C] + + - type: excretion + consumed: + zoo1: [p] + produced: + po4: [p] + parameters: + function: grazing + optimal_nutrient_quota: 1.85E-03 # [mmol P / mg C] + + - type: excretion + consumed: + zoo1: [c,n,p] + produced: + dom1: [c,n,p] + parameters: + function: grazing + assimilation_efficiency: 0.25 + ingestion_efficiency: 0.5 + partition: [0.60,0.72,0.832] # c,n,p [-] + + - type: excretion + consumed: + zoo1: [c,n,p] + produced: + pom1: [c,n,p] + parameters: + function: grazing + assimilation_efficiency: 0.25 + ingestion_efficiency: 0.5 + partition: [0.40,0.28,0.168] # c,n,p [-] + + - type: exudation + consumed: + phyto1: [c] + produced: + dom1: [c] + parameters: + method: photosynthesis # options: uptake, photosynthesis + # "photosynthesis": exudation rate calculated using excreted fraction of photosynthesis + # "uptake": exudation rate calculated using constant excreted fraction of nutrient uptake + excreted_fraction: 0.05 # [-] + + - type: grazing + consumed: + phyto1: [c,n,p,chl] + produced: + zoo1: [c,n,p] + parameters: + function: holling-2 + ingestion_type: feeding_threshold + feeding_threshold: 50.0 # [mg C / m^3] + max_grazing_rate: 2.0 # [1/d] + half_sat_grazing: 200.0 # [mg C / m^3] + + - type: gross_primary_production + consumed: + produced: + phyto1: [c] + o2: [o] + parameters: + convert_o2: 1/12 + + - type: lysis + consumed: + phyto1: [c,n,p] + produced: + dom1: [c,n,p] + parameters: + method: cell_quota + extra_lysis_rate: 0.0 # [1/d] + half_sat_stress_lysis: 0.1 + half_sat_extra_lysis: 0.0 + max_stress_lysis_rate: 0.05 # [1/d] + om_type: dissolved + + - type: lysis + consumed: + phyto1: [c,n,p] + produced: + pom1: [c,n,p] + parameters: + method: cell_quota + extra_lysis_rate: 0.0 # [1/d] + half_sat_stress_lysis: 0.1 + half_sat_extra_lysis: 0.0 + max_stress_lysis_rate: 0.05 # [1/d] + om_type: particulate + + - type: mortality + consumed: + zoo1: [c,n,p] + produced: + dom1: [c,n,p] + parameters: + oxygen_limited: True + half_sat_oxygen: 0.5 # [mmol O2 / m^3] + mortality_rate: [1.E-06,0.0] # linear [1/d], quadratic [1/d^2] + mortality_rate_oxy: 0.25 # [1/d] + partition: [0.60,0.72,0.832] # c,n,p [-] + + - type: mortality + consumed: + zoo1: [c,n,p] + produced: + pom1: [c,n,p] + parameters: + oxygen_limited: True + half_sat_oxygen: 0.5 # [mmol O2 / m^3] + mortality_rate: [1.E-06,0.0] # linear [1/d], quadratic [1/d^2] + mortality_rate_oxy: 0.25 # [1/d] + partition: [0.40,0.28,0.168] # c,n,p [-] + + - type: nitrification + consumed: + nh4: [n] + o2: [o] + produced: + no3: [n] + parameters: + half_sat_oxygen: 10.0 # [mmol O2 / m^3] + nitrification_rate: 0.01 # [1/d] + convert_o2: 2.0 + + - type: photosynthesis + consumed: + phyto1: [c] + produced: + parameters: + eps_PAR: 0.4 + light_limitation: platt + light_location: integrated + light_attenuation_water: 0.0435 + light_attenuation_phyto: 0.03 + initial_PI_slope: 1.52E-05 # [(mg C . m^2) / (mg Chl μE)] + max_photo_rate: 1.6 # [1/d] + + - type: reaeration + consumed: + produced: + o2: [o] + parameters: + d: 0.31 + k1: 1953.4 + k2: 128.0 + k3: 3.9918 + k4: 0.050091 + schmidt: 660.0 + + - type: remineralization + consumed: + dom1: [c] + o2: [o] + produced: + parameters: + remineralization_rate: 0.05 # [1/d] + convert_o2: 1/12 + + - type: remineralization + consumed: + dom1: [n] + produced: + nh4: [n] + parameters: + remineralization_rate: 0.05 # [1/d] + + - type: remineralization + consumed: + dom1: [p] + produced: + po4: [p] + parameters: + remineralization_rate: 0.05 # [1/d] + + - type: remineralization + consumed: + pom1: [c] + o2: [o] + produced: + parameters: + remineralization_rate: 0.1 # [1/d] + convert_o2: 1/12 + + - type: remineralization + consumed: + pom1: [n] + produced: + nh4: [n] + parameters: + remineralization_rate: 0.1 # [1/d] + + - type: remineralization + consumed: + pom1: [p] + produced: + po4: [p] + parameters: + remineralization_rate: 0.1 # [1/d] + + # - type: reoxidation + # consumed: + # hs: [s] + # o2: [o] + # produced: + # parameters: + # half_sat_oxygen: 10.0 # [mmol O2 / m^3] + # reoxidation_rate: 0.05 # [1/d] + # convert_o2: 2. # [mmol O2 / mmol HS-] + + - type: respiration + consumed: + phyto1: [c] + o2: [o] + produced: + parameters: + activity_respiration_frac: 0.05 # [-] + basal_respiration_rate: 0.05 # [1/d] + convert_o2: 1/12 # [mmol O2 / unit base_element] + + - type: respiration + consumed: + zoo1: [c] + o2: [o] + produced: + parameters: + respiration_rate: 0.02 # [1/d] + convert_o2: 1/12 + + - type: uptake + consumed: + no3: [n] + nh4: [n] + produced: + phyto1: [n] + dom1: [n] + parameters: + basis: nutrient + strategy: independent + form: affinity # options: affinity or constituent + # affinity: uses specific affinity, based on cell quota w.r.t base element + # constituent: uses concentration of phytoplankton constituent of nutrient element + half_sat_nh4_preference: 1.5 + # half_sat_nh4_uptake: 1.5 # [mmol N-NH4 / m^3] + luxury_storage: 1. + max_photo_rate: 1.6 # [1/d] + specific_affinity: 0.025 # [m^3 / (mg C . d)] + + - type: uptake + consumed: + po4: [p] + produced: + phyto1: [p] + dom1: [p] + parameters: + basis: nutrient + strategy: independent + form: affinity # options: affinity or constituent + # affinity: uses specific affinity, based on cell quota w.r.t base element + # constituent: uses concentration of phytoplankton constituent of nutrient element + luxury_storage: 1. + max_photo_rate: 1.6 # [1/d] + specific_affinity: 0.0025 # [m^3 / (mg C . d)] + + + + \ No newline at end of file diff --git a/tests/bfm17/bfm17-1d.yaml b/tests/bfm17/bfm17-1d.yaml new file mode 100644 index 0000000..2d483c8 --- /dev/null +++ b/tests/bfm17/bfm17-1d.yaml @@ -0,0 +1,468 @@ +base_element: c + +tracers: + o2: + long_name: oxygen + type: inorganic + composition: + o: /inputs/BFM17_BERM_INIT/init_prof_Oxy_150m_bermuda_killworth.da + parameters: + temperature_regulation: + temp_limited: False + surface_flux: True + + no3: + long_name: nitrate + type: inorganic + composition: + n: /inputs/BFM17_BERM_INIT/init_prof_N_150m_bermuda_killworth.da + parameters: + temperature_regulation: + temp_limited: True + function: q10 + # coefficient: 2.367 + coefficient: 2. + base_temp: 20. + + nh4: + long_name: ammonium + type: inorganic + composition: + n: /inputs/BFM17_BERM_INIT/init_prof_Am_150m_bermuda_killworth.da + parameters: + temperature_regulation: + temp_limited: True + function: q10 + # coefficient: 2.367 + coefficient: 2. + base_temp: 20. + + po4: + long_name: phosphate + type: inorganic + composition: + p: /inputs/BFM17_BERM_INIT/init_prof_P_150m_bermuda_killworth.da + parameters: + temperature_regulation: + temp_limited: False + + hs: + long_name: reduction equivalents + type: inorganic + composition: + s: 1. # [mmol S / m^3] + parameters: + temperature_regulation: + temp_limited: False + + phyto1: + long_name: diatoms + type: phytoplankton + composition: # element : initial concentration + c: /inputs/BFM17_BERM_INIT/init_prof_Pc_150m_bermuda_killworth.da + n: {c: 0.0126} + p: {c: 7.862E-04} + chl: {c: 0.02} + parameters: + light_attenuation: 0.03 # [m^2 / mg Chl] + max_photo_rate: 1.6 # [1/d] + cell_quota: + constituents: ["c","n","p","chl"] + max: [1., 0.025, 2.5E-03, 0.016] + min: [1., 6.87E-03, 4.29E-04, 0.016] + opt: [1., 1.26E-02, 7.86E-04, 0.016] + nutrient_limitation: + colimitation: minimum # mininimum, product, or sum of all limiting nutrients + include: ["no3","po4"] # nutrients included for calculation of colimitation factor + no3: + type: internal + function: droop + nh4_inihibited: True + nh4: + type: external + function: monod + half_sat: 1. + po4: + type: internal + function: droop + sedimentation: + sinking: False + sinking_rate: 0. + burial_velocity: 0. + temperature_regulation: + temp_limited: True + function: q10 + coefficient: 2. + base_temp: 20. + + zoo1: + long_name: mesozooplankton + type: zooplankton + composition: # element : initial concentration + c: /inputs/BFM17_BERM_INIT/init_prof_Zc_150m_bermuda_killworth.da + n: {c: 0.0126} + p: {c: 7.862E-04} + parameters: + assimilation_efficiency: 0.5 + ingestion_efficiency: 0.25 + cell_quota: + constituents: ["c","n","p"] + # opt: [1., 1.67E-02, 1.85E-03] + opt: [1., 1.258E-02, 7.862E-04] + grazing_preferences: + phyto1: 1.0 + # zoo1: 1.0 + temperature_regulation: + temp_limited: True + function: q10 + coefficient: 2.0 + base_temp: 20. + oxygen_inhibition: + oxygen_limited: True + function: monod + half_sat: 50. # [mmol O2 / m^3] + + dom1: + long_name: dissolved organic matter + type: detritus + composition: + c: /inputs/BFM17_BERM_INIT/init_prof_DOC_150m_bermuda_killworth.da + n: {c: 0.0126} + p: {c: 7.862E-04} + parameters: + light_attenuation: 0.0 # [m^2 / mg C] + sedimentation: + sinking: False + sinking_rate: 0. # [m/d] + burial_velocity: 0. # [m/d] + + pom1: + long_name: particulate organic matter + type: detritus + composition: # element : initial concentration + c: /inputs/BFM17_BERM_INIT/init_prof_POC_150m_bermuda_killworth.da + n: {c: 0.0126} + p: {c: 7.862E-04} + parameters: + light_attenuation: 1.0E-04 # [m^2 / mg C] + sedimentation: + sinking: True + sinking_rate: 1. # [m/d] + burial_velocity: 1. # [m/d] +# ------------------------------------------------------------------------------------------------- +reactions: + - type: chlorophyll_synthesis + consumed: + produced: + phyto1: [chl] + parameters: + eps_PAR: 0.4 # [-] + initial_PI_slope: 1.52E-05 + chl_quota: 0.016 # [mg Chl / mg C] + chl_relax_rate: 0. # [(mg C . m^2) / (mg Chl μE)] + optimal_Epar_Ek: 0.0 + max_photo_rate: 1.6 # [1/d] + light_attenuation_water: 0.0435 # [1/m] + light_attenuation_phyto: 0.03 # [m^2 / mg Chl] + + # - type: egestion + # consumed: + # zoo1: [c,n,p] + # produced: + # dom1: [c,n,p] + # parameters: + # partition: [0.60,0.72,0.832] # c,n,p [-] + + # - type: egestion + # consumed: + # zoo1: [c,n,p] + # produced: + # pom1: [c,n,p] + # parameters: + # half_sat_oxygen: 0.5 # [mmol O2 / m^3] + # partition: [0.40,0.28,0.168] # c,n,p [-] + + - type: excretion + consumed: + zoo1: [n] + produced: + nh4: [n] + parameters: + function: grazing + optimal_nutrient_quota: 0.0167 # [mmol N / mg C] + + - type: excretion + consumed: + zoo1: [p] + produced: + po4: [p] + parameters: + function: grazing + optimal_nutrient_quota: 1.85E-03 # [mmol P / mg C] + + - type: excretion + consumed: + zoo1: [c,n,p] + produced: + dom1: [c,n,p] + parameters: + function: grazing + assimilation_efficiency: 0.25 + ingestion_efficiency: 0.5 + partition: [0.60,0.72,0.832] # c,n,p [-] + + - type: excretion + consumed: + zoo1: [c,n,p] + produced: + pom1: [c,n,p] + parameters: + function: grazing + assimilation_efficiency: 0.25 + ingestion_efficiency: 0.5 + partition: [0.40,0.28,0.168] # c,n,p [-] + + - type: exudation + consumed: + phyto1: [c] + produced: + dom1: [c] + parameters: + method: photosynthesis # options: uptake, photosynthesis + # "photosynthesis": exudation rate calculated using excreted fraction of photosynthesis + # "uptake": exudation rate calculated using constant excreted fraction of nutrient uptake + excreted_fraction: 0.05 # [-] + + - type: grazing + consumed: + phyto1: [c,n,p,chl] + produced: + zoo1: [c,n,p] + parameters: + function: holling-2 + ingestion_type: feeding_threshold + feeding_threshold: 50.0 # [mg C / m^3] + max_grazing_rate: 2.0 # [1/d] + half_sat_grazing: 200.0 # [mg C / m^3] + + - type: gross_primary_production + consumed: + produced: + phyto1: [c] + o2: [o] + parameters: + convert_o2: 1/12 + + - type: lysis + consumed: + phyto1: [c,n,p] + produced: + dom1: [c,n,p] + parameters: + method: cell_quota + extra_lysis_rate: 0.0 # [1/d] + half_sat_stress_lysis: 0.1 + half_sat_extra_lysis: 0.0 + max_stress_lysis_rate: 0.05 # [1/d] + om_type: dissolved + + - type: lysis + consumed: + phyto1: [c,n,p] + produced: + pom1: [c,n,p] + parameters: + method: cell_quota + extra_lysis_rate: 0.0 # [1/d] + half_sat_stress_lysis: 0.1 + half_sat_extra_lysis: 0.0 + max_stress_lysis_rate: 0.05 # [1/d] + om_type: particulate + + - type: mortality + consumed: + zoo1: [c,n,p] + produced: + dom1: [c,n,p] + parameters: + oxygen_limited: True + half_sat_oxygen: 0.5 # [mmol O2 / m^3] + # mortality_rate: [0.0,0.0] # linear [1/d], quadratic [1/d^2] + mortality_rate: [1.E-06,0.0] # linear [1/d], quadratic [1/d^2] + mortality_rate_oxy: 0.25 # [1/d] + partition: [0.60,0.72,0.832] # c,n,p [-] + + - type: mortality + consumed: + zoo1: [c,n,p] + produced: + pom1: [c,n,p] + parameters: + oxygen_limited: True + half_sat_oxygen: 0.5 # [mmol O2 / m^3] + # mortality_rate: [0.0,0.0] # linear [1/d], quadratic [1/d^2] + mortality_rate: [1.E-06,0.0] # linear [1/d], quadratic [1/d^2] + mortality_rate_oxy: 0.25 # [1/d] + partition: [0.40,0.28,0.168] # c,n,p [-] + + - type: nitrification + consumed: + nh4: [n] + o2: [o] + produced: + no3: [n] + parameters: + half_sat_oxygen: 10.0 # [mmol O2 / m^3] + nitrification_rate: 0.01 # [1/d] + convert_o2: 2.0 + + - type: photosynthesis + consumed: + phyto1: [c] + produced: + parameters: + eps_PAR: 0.4 + light_limitation: platt + light_location: integrated + light_attenuation_water: 0.0435 + light_attenuation_phyto: 0.03 + initial_PI_slope: 1.52E-05 # [(mg C . m^2) / (mg Chl μE)] + max_photo_rate: 1.6 # [1/d] + + - type: reaeration + consumed: + produced: + o2: [o] + parameters: + d: 0.31 + k1: 1953.4 + k2: 128.0 + k3: 3.9918 + k4: 0.050091 + schmidt: 660.0 + + - type: remineralization + consumed: + dom1: [c] + o2: [o] + produced: + parameters: + # remineralization_rate: 0.3 # [1/d] + remineralization_rate: 0.05 # [1/d] + convert_o2: 1/12 + + - type: remineralization + consumed: + dom1: [n] + produced: + nh4: [n] + parameters: + # remineralization_rate: 0.3 # [1/d] + remineralization_rate: 0.05 # [1/d] + + - type: remineralization + consumed: + dom1: [p] + produced: + po4: [p] + parameters: + # remineralization_rate: 0.25 # [1/d] + remineralization_rate: 0.05 # [1/d] + + - type: remineralization + consumed: + pom1: [c] + o2: [o] + produced: + parameters: + # remineralization_rate: 0.3 # [1/d] + remineralization_rate: 0.1 # [1/d] + convert_o2: 1/12 + + - type: remineralization + consumed: + pom1: [n] + produced: + nh4: [n] + parameters: + # remineralization_rate: 0.3 # [1/d] + remineralization_rate: 0.1 # [1/d] + + - type: remineralization + consumed: + pom1: [p] + produced: + po4: [p] + parameters: + # remineralization_rate: 0.3 # [1/d] + remineralization_rate: 0.1 # [1/d] + + - type: reoxidation + consumed: + hs: [s] + o2: [o] + produced: + parameters: + half_sat_oxygen: 10.0 # [mmol O2 / m^3] + reoxidation_rate: 0.05 # [1/d] + convert_o2: 2. # [mmol O2 / mmol HS-] + + - type: respiration + consumed: + phyto1: [c] + o2: [o] + produced: + parameters: + activity_respiration_frac: 0.05 # [-] + basal_respiration_rate: 0.05 # [1/d] + convert_o2: 1/12 # [mmol O2 / unit base_element] + + - type: respiration + consumed: + zoo1: [c] + o2: [o] + produced: + parameters: + respiration_rate: 0.02 # [1/d] + convert_o2: 1/12 + + - type: uptake + consumed: + no3: [n] + nh4: [n] + produced: + phyto1: [n] + dom1: [n] + parameters: + basis: nutrient + strategy: independent + form: affinity # options: affinity or constituent + # affinity: uses specific affinity, based on cell quota w.r.t base element + # constituent: uses concentration of phytoplankton constituent of nutrient element + half_sat_nh4_preference: 1.5 + # half_sat_nh4_preference: 1.0 + half_sat_nh4_uptake: 1.5 # [mmol N-NH4 / m^3] + luxury_storage: 1. + # luxury_storage: 1.5 + max_photo_rate: 1.6 # [1/d] + specific_affinity: 0.025 # [m^3 / (mg C . d)] + + - type: uptake + consumed: + po4: [p] + produced: + phyto1: [p] + dom1: [p] + parameters: + basis: nutrient + strategy: independent + form: affinity # options: affinity or constituent + # affinity: uses specific affinity, based on cell quota w.r.t base element + # constituent: uses concentration of phytoplankton constituent of nutrient element + luxury_storage: 1. + max_photo_rate: 1.6 # [1/d] + specific_affinity: 0.0025 # [m^3 / (mg C . d)] + + + + \ No newline at end of file diff --git a/tests/bfm17/bfm17.py b/tests/bfm17/bfm17.py deleted file mode 100644 index 12f95ae..0000000 --- a/tests/bfm17/bfm17.py +++ /dev/null @@ -1,256 +0,0 @@ -import numpy as np -from matplotlib import pyplot as plt -import os - -# Folder path -folder = os.getcwd() + '/tests/bfm17/0522' - -# Load solution -solution = np.load("bfm17-0522-fdm.npz", allow_pickle=True) -# conc = solution["conc"] # concentratrion matrix -conc = solution["concentration"] # concentratrion matrix -time = solution["time"] # time array - -# Load tracer indices -indices = np.load("tracer_indices_bfm17-0522.npz") -tracer_indices = {} -for file in indices.files: - tracer_indices[file] = list(indices[file]) - -o2 = tracer_indices["o2"][0] -no3 = tracer_indices["no3"][0] -nh4 = tracer_indices["nh4"][0] -po4 = tracer_indices["po4"][0] -pc = tracer_indices["phyto1"][0] -pn = tracer_indices["phyto1"][1] -pp = tracer_indices["phyto1"][2] -pl = tracer_indices["phyto1"][3] -zc = tracer_indices["zoo1"][0] -zn = tracer_indices["zoo1"][1] -zp = tracer_indices["zoo1"][2] -domc = tracer_indices["dom1"][0] -domn = tracer_indices["dom1"][1] -domp = tracer_indices["dom1"][2] -pomc = tracer_indices["pom1"][0] -pomn = tracer_indices["pom1"][1] -pomp = tracer_indices["pom1"][2] - -# Daily averages for concentrations -iters = np.linspace(0,len(time)-1,len(time)) -iters_per_day = 86400/360 # change this depending on timestep -conc_avg = np.zeros((conc.shape[0], int(np.floor((len(iters))/iters_per_day)))) - -day = 0 -for i in range(0,len(iters)): - conc_avg[:,day] += conc[:,i] - if i%int(iters_per_day) == 0 and i != 0: - conc_avg[:,day] = conc_avg[:,day]/iters_per_day - - day += 1 - if day == 730: - break - -# Convert time array from seconds to months -sec_mon = 60 * 60 * 24 * 30 -time = time/sec_mon -days = np.linspace(0,729,730) - -# Ticks and labels for plots -# xticks = [0,2,4,6,8,10,12,14,16,18,20,22,24] -xlabel = ['J','M','M','J','S','N','J','M','M','J','S','N','J'] - -xticks = [0,60,120,180,240,300,360,420,480,540,600,660,720] - -# Create plots -# Nutrients -fig, axs = plt.subplots(2,2,figsize=(12,12)) - -# axs[0,0].plot(time,conc[o2]) -axs[0,0].plot(days,conc_avg[o2]) -axs[0,0].set_title("Oxygen") -axs[0,0].set_xlabel("Time [months]") -axs[0,0].set_xticks(xticks,xlabel) -# axs[0,0].set_xlim([0,24]) -axs[0,0].set_xlim([0,730]) -axs[0,0].set_ylabel("mmol O $\mathregular{m^{-3}}$") - -# axs[0,1].plot(time,conc[no3]) -axs[0,1].plot(days,conc_avg[no3]) -axs[0,1].set_title("Nitrate") -axs[0,1].set_xlabel("Time [months]") -axs[0,1].set_xticks(xticks,xlabel) -# axs[0,1].set_xlim([0,24]) -axs[0,1].set_xlim([0,730]) -axs[0,1].set_ylabel("mmol N $\mathregular{m^{-3}}$") - -# axs[1,0].plot(time,conc[nh4]) -axs[1,0].plot(days,conc_avg[nh4]) -axs[1,0].set_title("Ammonium") -axs[1,0].set_xlabel("Time [months]") -axs[1,0].set_xticks(xticks,xlabel) -# axs[1,0].set_xlim([0,24]) -axs[1,0].set_xlim([0,730]) -axs[1,0].set_ylabel("mmol N $\mathregular{m^{-3}}$") - -# axs[1,1].plot(time,conc[po4]) -axs[1,1].plot(days,conc_avg[po4]) -axs[1,1].set_title("Phosphate") -axs[1,1].set_xlabel("Time [months]") -axs[1,1].set_xticks(xticks,xlabel) -# axs[1,1].set_xlim([0,24]) -axs[1,1].set_xlim([0,730]) -axs[1,1].set_ylabel("mmol P $\mathregular{m^{-3}}$") - -fig.suptitle("Nutrients") -fig.tight_layout() -nut = os.path.join(folder,"nutrients.jpg") -plt.savefig(nut) - -# Phytoplankton -fig, axs = plt.subplots(2,2,figsize=(12,12)) - -# axs[0,0].plot(time,conc[pc]) -axs[0,0].plot(days,conc_avg[pc]) -axs[0,0].set_title("Carbon") -axs[0,0].set_xlabel("Time [months]") -axs[0,0].set_xticks(xticks,xlabel) -# axs[0,0].set_xlim([0,24]) -axs[0,0].set_xlim([0,730]) -axs[0,0].set_ylabel("mg C $\mathregular{m^{-3}}$") - -# axs[0,1].plot(time,conc[pn]) -axs[0,1].plot(days,conc_avg[pn]) -axs[0,1].set_title("Nitrogen") -axs[0,1].set_xlabel("Time [months]") -axs[0,1].set_xticks(xticks,xlabel) -# axs[0,1].set_xlim([0,24]) -axs[0,1].set_xlim([0,730]) -axs[0,1].set_ylabel("mmol N $\mathregular{m^{-3}}$") - -# axs[1,0].plot(time,conc[pp]) -axs[1,0].plot(days,conc_avg[pp]) -axs[1,0].set_title("Phosphorus") -axs[1,0].set_xlabel("Time [months]") -axs[1,0].set_xticks(xticks,xlabel) -# axs[1,0].set_xlim([0,24]) -axs[1,0].set_xlim([0,730]) -axs[1,0].set_ylabel("mmol P $\mathregular{m^{-3}}$") - -# axs[1,1].plot(time,conc[pl]) -axs[1,1].plot(days,conc_avg[pl]) -axs[1,1].set_title("Chlorophyll-a") -axs[1,1].set_xlabel("Time [months]") -axs[1,1].set_xticks(xticks,xlabel) -# axs[1,1].set_xlim([0,24]) -axs[1,1].set_xlim([0,730]) -axs[1,1].set_ylabel("mg Chl-a $\mathregular{m^{-3}}$") - -fig.suptitle("Phytoplankton") -fig.tight_layout() -phyto = os.path.join(folder,"phytoplankton.jpg") -plt.savefig(phyto) - -# Zooplankton -fig, axs = plt.subplots(1,3,figsize=(15,5)) - -# axs[0].plot(time,conc[zc]) -axs[0].plot(days,conc_avg[zc]) -axs[0].set_title("Carbon") -axs[0].set_xlabel("Time [months]") -axs[0].set_xticks(xticks,xlabel) -# axs[0].set_xlim([0,24]) -axs[0].set_xlim([0,730]) -axs[0].set_ylabel("mg C $\mathregular{m^{-3}}$") - -# axs[0].plot(time,conc[zn]) -axs[1].plot(days,conc_avg[zn]) -axs[1].set_title("Nitrogen") -axs[1].set_xlabel("Time [months]") -axs[1].set_xticks(xticks,xlabel) -# axs[1].set_xlim([0,24]) -axs[1].set_xlim([0,730]) -axs[1].set_ylabel("mmol N $\mathregular{m^{-3}}$") - -# axs[2].plot(time,conc[zp]) -axs[2].plot(days,conc_avg[zp]) -axs[2].set_title("Phosphorus") -axs[2].set_xlabel("Time [months]") -axs[2].set_xticks(xticks,xlabel) -# axs[2].set_xlim([0,24]) -axs[2].set_xlim([0,730]) -axs[2].set_ylabel("mmol P $\mathregular{m^{-3}}$") - -fig.suptitle("Zooplankton") -fig.tight_layout() -zoo = os.path.join(folder,"zooplankton.jpg") -plt.savefig(zoo) - -# DOM -fig, axs = plt.subplots(1,3,figsize=(15,5)) - -# axs[0].plot(time,conc[domc]) -axs[0].plot(days,conc_avg[domc]) -axs[0].set_title("Carbon") -axs[0].set_xlabel("Time [months]") -axs[0].set_xticks(xticks,xlabel) -# axs[0].set_xlim([0,24]) -axs[0].set_xlim([0,730]) -axs[0].set_ylabel("mg C $\mathregular{m^{-3}}$") - -# axs[1].plot(time,conc[domn]) -axs[1].plot(days,conc_avg[domn]) -axs[1].set_title("Nitrogen") -axs[1].set_xlabel("Time [months]") -axs[1].set_xticks(xticks,xlabel) -# axs[1].set_xlim([0,24]) -axs[1].set_xlim([0,730]) -axs[1].set_ylabel("mmol N $\mathregular{m^{-3}}$") - -# axs[2].plot(time,conc[domp]) -axs[2].plot(days,conc_avg[domp]) -axs[2].set_title("Phosphorus") -axs[2].set_xlabel("Time [months]") -axs[2].set_xticks(xticks,xlabel) -# axs[2].set_xlim([0,24]) -axs[2].set_xlim([0,730]) -axs[2].set_ylabel("mmol P $\mathregular{m^{-3}}$") - -fig.suptitle("Dissolved Organic Matter") -fig.tight_layout() -dom = os.path.join(folder,"dom.jpg") -plt.savefig(dom) - -# DOM -fig, axs = plt.subplots(1,3,figsize=(15,5)) - -# axs[0].plot(time,conc[pomc]) -axs[0].plot(days,conc_avg[pomc]) -axs[0].set_title("Carbon") -axs[0].set_xlabel("Time [months]") -axs[0].set_xticks(xticks,xlabel) -# axs[0].set_xlim([0,24]) -axs[0].set_xlim([0,730]) -axs[0].set_ylabel("mg C $\mathregular{m^{-3}}$") - -# axs[1].plot(time,conc[pomn]) -axs[1].plot(days,conc_avg[pomn]) -axs[1].set_title("Nitrogen") -axs[1].set_xlabel("Time [months]") -axs[1].set_xticks(xticks,xlabel) -# axs[1].set_xlim([0,24]) -axs[1].set_xlim([0,730]) -axs[1].set_ylabel("mmol N $\mathregular{m^{-3}}$") - -# axs[2].plot(time,conc[pomp]) -axs[2].plot(days,conc_avg[pomp]) -axs[2].set_title("Phosphorus") -axs[2].set_xlabel("Time [months]") -axs[2].set_xticks(xticks,xlabel) -# axs[2].set_xlim([0,24]) -axs[2].set_xlim([0,730]) -axs[2].set_ylabel("mmol P $\mathregular{m^{-3}}$") - -fig.suptitle("Particulate Organic Matter") -fig.tight_layout() -pom = os.path.join(folder,"pom.jpg") -plt.savefig(pom) \ No newline at end of file diff --git a/bfm17.yaml b/tests/bfm17/bfm17.yaml similarity index 79% rename from bfm17.yaml rename to tests/bfm17/bfm17.yaml index 5ad9ace..69d03af 100644 --- a/bfm17.yaml +++ b/tests/bfm17/bfm17.yaml @@ -1,4 +1,4 @@ -base_element: c # [mg C / m^3] +base_element: c tracers: o2: @@ -7,7 +7,8 @@ tracers: composition: o: 230. # [mmol O / m^3] parameters: - temp_limited: False + temperature_regulation: + temp_limited: False no3: long_name: nitrate @@ -15,8 +16,11 @@ tracers: composition: n: 1. # [mmol N / m^3] parameters: - temp_limited: True - q10: 2.367 + temperature_regulation: + temp_limited: True + function: q10 + coefficient: 2.367 + base_temp: 10. nh4: long_name: ammonium @@ -24,8 +28,11 @@ tracers: composition: n: 0.06 # [mmol N / m^3] parameters: - temp_limited: True - q10: 2.367 + temperature_regulation: + temp_limited: True + function: q10 + coefficient: 2.367 + base_temp: 10. po4: long_name: phosphate @@ -33,7 +40,8 @@ tracers: composition: p: 0.06 # [mmol P / m^3] parameters: - temp_limited: False + temperature_regulation: + temp_limited: False hs: long_name: reduction equivalents @@ -41,7 +49,8 @@ tracers: composition: s: 1. # [mmol S / m^3] parameters: - temp_limited: False + temperature_regulation: + temp_limited: False phyto1: long_name: diatoms @@ -49,27 +58,35 @@ tracers: composition: # element : initial concentration c: 12. # [mg C / m^3] n: 0.1512 # [mmol N / m^3] - # p: 9.4344E-03 # [mmol P / m^3] - p: 9.432E-03 # [mmol P / m^3] + p: 9.432E-03 # [mmol P / m^3] chl: 0.192 # [mg Chl / m^3] parameters: - # nutrient_limitation: minimum # mininimum, product, or sum of all limiting nutrients - # nutrient_limitation_type: internal # internal (cell quota for nutrient w.r.t. base element) or external (availability of nutrient in system using half-saturation constant) - temp_limited: True - q10: 2.0 - nutrient_limitation: - type: internal # internal (cell quota for nutrient w.r.t. base element) or external (availability of nutrient in system using half-saturation constant) - colimitation: minimum # mininimum, product, or sum of all limiting nutrients - nutrients: ["no3", "po4"] - max_quota: [0.025, 2.5E-03] - min_quota: [6.873E-03, 4.29E-04] - opt_quota: [1.26E-02, 7.86E-04] + light_attenuation: 0.03 # [m^2 / mg Chl] + max_photo_rate: 1.6 # [1/d] cell_quota: constituents: ["c","n","p","chl"] max: [1., 0.025, 2.5E-03, 0.016] min: [1., 6.873E-03, 4.29E-04, 0.016] opt: [1., 1.26E-02, 7.86E-04, 0.016] - light_attenuation: 0.03 # [m^2 / mg Chl] + nutrient_limitation: + colimitation: minimum # mininimum, product, or sum of all limiting nutrients + include: ["no3","po4"] # nutrients included for calculation of colimitation factor + no3: + type: internal + function: droop + nh4_inihibited: True + nh4: + type: external + function: monod + half_sat: 1. + po4: + type: internal + function: droop + temperature_regulation: + temp_limited: True + function: q10 + coefficient: 2. + base_temp: 10. zoo1: long_name: mesozooplankton @@ -86,9 +103,15 @@ tracers: opt: [1., 1.67E-02, 1.85E-03] grazing_preferences: phyto1: 1.0 - temp_limited: True - q10: 2.0 - oxygen_limited: True + temperature_regulation: + temp_limited: True + function: q10 + coefficient: 2.0 + base_temp: 10. + oxygen_inhibition: + oxygen_limited: True + function: monod + half_sat: 50. # [mmol O2 / m^3] dom1: long_name: dissolved organic matter @@ -190,7 +213,6 @@ reactions: parameters: function: grazing optimal_nutrient_quota: 1.85E-03 # [mmol P / mg C] - # optimal_nutrient_quota: 0.000786 - type: excretion consumed: @@ -220,6 +242,9 @@ reactions: produced: dom1: [c] parameters: + method: photosynthesis # options: uptake, photosynthesis + # "photosynthesis": exudation rate calculated using excreted fraction of photosynthesis + # "uptake": exudation rate calculated using constant excreted fraction of nutrient uptake excreted_fraction: 0.05 # [-] - type: grazing @@ -230,7 +255,7 @@ reactions: parameters: function: holling-2 ingestion_type: feeding_threshold - feeding_threshold: 50.0 + feeding_threshold: 50.0 # [mg C / m^3] max_grazing_rate: 2.0 # [1/d] half_sat_grazing: 200.0 # [mg C / m^3] @@ -240,7 +265,7 @@ reactions: phyto1: [c] o2: [o] parameters: - mw_carbon: 12.0 + convert_o2: 1/12 - type: lysis consumed: @@ -248,6 +273,7 @@ reactions: produced: dom1: [c,n,p] parameters: + method: cell_quota extra_lysis_rate: 0.0 # [1/d] half_sat_stress_lysis: 0.1 half_sat_extra_lysis: 0.0 @@ -260,6 +286,7 @@ reactions: produced: pom1: [c,n,p] parameters: + method: cell_quota extra_lysis_rate: 0.0 # [1/d] half_sat_stress_lysis: 0.1 half_sat_extra_lysis: 0.0 @@ -299,7 +326,7 @@ reactions: parameters: half_sat_oxygen: 10.0 # [mmol O2 / m^3] nitrification_rate: 0.01 # [1/d] - nitrification_stoic_coeff: 2.0 + convert_o2: 2.0 - type: photosynthesis consumed: @@ -333,7 +360,7 @@ reactions: produced: parameters: remineralization_rate: 0.3 # [1/d] - mw_carbon: 12.0 + convert_o2: 1/12 - type: remineralization consumed: @@ -358,7 +385,7 @@ reactions: produced: parameters: remineralization_rate: 0.3 # [1/d] - mw_carbon: 12.0 + convert_o2: 1/12 - type: remineralization consumed: @@ -384,7 +411,7 @@ reactions: parameters: half_sat_oxygen: 10.0 # [mmol O2 / m^3] reoxidation_rate: 0.05 # [1/d] - reoxidation_stoic_coeff: 0.5 # [mmol HS- / mmol O2] + convert_o2: 2. # [mmol O2 / mmol HS-] - type: respiration consumed: @@ -394,8 +421,8 @@ reactions: parameters: activity_respiration_frac: 0.05 # [-] basal_respiration_rate: 0.05 # [1/d] - mw_carbon: 12.0 - + convert_o2: 1/12 # [mmol O2 / unit base_element] + - type: respiration consumed: zoo1: [c] @@ -403,7 +430,7 @@ reactions: produced: parameters: respiration_rate: 0.02 # [1/d] - mw_carbon: 12.0 + convert_o2: 1/12 - type: uptake consumed: @@ -413,6 +440,11 @@ reactions: phyto1: [n] dom1: [n] parameters: + basis: nutrient + strategy: independent + form: affinity # options: affinity or constituent + # affinity: uses specific affinity, based on cell quota w.r.t base element + # constituent: uses concentration of phytoplankton constituent of nutrient element half_sat_nh4_preference: 1.0 half_sat_nh4_uptake: 1.5 # [mmol N-NH4 / m^3] luxury_storage: 1.5 @@ -426,10 +458,15 @@ reactions: phyto1: [p] dom1: [p] parameters: + basis: nutrient + strategy: independent + form: affinity # options: affinity or constituent + # affinity: uses specific affinity, based on cell quota w.r.t base element + # constituent: uses concentration of phytoplankton constituent of nutrient element luxury_storage: 1.0 max_photo_rate: 1.6 # [1/d] specific_affinity: 0.0025 # [m^3 / (mg C . d)] - + \ No newline at end of file diff --git a/tests/bfm17/bfm17_new_structure.yaml b/tests/bfm17/bfm17_new_structure.yaml new file mode 100644 index 0000000..e3f7c32 --- /dev/null +++ b/tests/bfm17/bfm17_new_structure.yaml @@ -0,0 +1,429 @@ +base_element: c + +tracers: + o2: + long_name: oxygen + type: inorganic + composition: # element : initial concentration (from data file) + o: /inputs/BFM17_BERM_INIT/init_prof_Oxy_150m_bermuda_killworth.da + parameters: + temperature_regulation: + temp_limited: False + surface_flux: True + + reaeration: + d: 0.31 + k1: 1953.4 + k2: 128.0 + k3: 3.9918 + k4: 0.050091 + schmidt: 660.0 + + no3: + long_name: nitrate + type: inorganic + composition: # element : initial concentration (from data file) + n: /inputs/BFM17_BERM_INIT/init_prof_N_150m_bermuda_killworth.da + parameters: + temperature_regulation: + temp_limited: True + function: q10 # options: arrhenius, eppley, or q10 + # arrhenius: needs "activation_energy" and "coefficient" as additional parameters + # eppley: needs "coefficient" as additional parameter + # q10: needs "base_temp" and "coefficient" as additional parameters + coefficient: 2. + base_temp: 20. + + nh4: + long_name: ammonium + type: inorganic + composition: # element : initial concentration + n: 0. + parameters: + temperature_regulation: + temp_limited: True + function: q10 # options: arrhenius, eppley, or q10 + # arrhenius: needs "activation_energy" and "coefficient" as additional parameters + # eppley: needs "coefficient" as additional parameter + # q10: needs "base_temp" and "coefficient" as additional parameters + coefficient: 2. + base_temp: 20. + + nitrification: + half_sat_oxygen: 10.0 # [mmol O2 / m^3] + nitrification_rate: 0.01 # [1/d] + convert_o2: 2.0 + + po4: + long_name: phosphate + type: inorganic + composition: # element : initial concentration (from data file) + p: /inputs/BFM17_BERM_INIT/init_prof_P_150m_bermuda_killworth.da + parameters: + temperature_regulation: + temp_limited: False + + phyto1: + long_name: diatoms + type: phytoplankton + composition: # element : initial concentration (from data file) + c: /inputs/BFM17_BERM_INIT/init_prof_Pc_150m_bermuda_killworth.da + n: {c: 0.0126} # {base_element : initial conversion factor} + p: {c: 7.862E-04} # {base_element : initial conversion factor} + chl: {c: 0.02} # {base_element : initial conversion factor} + parameters: + cell_quota: + n: {max: 0.025, min: 6.87E-03, opt: 1.26E-02} + p: {max: 2.5E-03, min: 4.29E-04, opt: 7.86E-04} + chl: {max: 0.016, min: 0.016, opt: 0.016} + + nutrient_limitation: + colimitation: minimum # mininimum, product, or sum of all limiting nutrients + include: ["no3","po4"] # nutrients included for calculation of colimitation factor + no3: + type: internal + function: droop + nh4_inihibited: True + nh4: + type: external + function: monod + half_sat: 1. + po4: + type: internal + function: droop + + sedimentation: + sinking: False + sinking_rate: 0. + burial_velocity: 0. + + temperature_regulation: + temp_limited: True + function: q10 # options: arrhenius, eppley, or q10 + # arrhenius: needs "activation_energy" and "coefficient" as additional parameters + # eppley: needs "coefficient" as additional parameter + # q10: needs "base_temp" and "coefficient" as additional parameters + coefficient: 2. + base_temp: 20. + + exudation: + method: photosynthesis # options: uptake, photosynthesis + # "photosynthesis": exudation rate calculated using excreted fraction of photosynthesis + # "uptake": exudation rate calculated using constant excreted fraction of nutrient uptake + excreted_fraction: 0.05 # [-] + + growth: + light_limitation: platt # options: monod, geider, platt, or smith + light_location: integrated # options: top, middle, or integrated + # top: light calculated at top of the cell + # middle: light calculated in middle of cell + # integrated: light integrated through cell + light_attenuation: 0.03 + max_photo_rate: 1.6 + chl_quota: 0.016 # [mg Chl / mg C] + chl_relax_rate: 0. # [(mg C . m^2) / (mg Chl μE)] + initial_PI_slope: 1.52E-05 + eps_PAR: 0.4 + convert_o2: 1/12 # [mmol 02 / mg C] + + lysis: + method: cell_quota # options: constant or cell_quota + # constant: based on constant lysis rate + # cell_quota: based on elemental ratios within zooplankton cell + extra_lysis_rate: 0. + half_sat_stress_lysis: 0.1 + half_sat_extra_lysis: 0.0 + max_stress_lysis_rate: 0.05 # [1/d] + + respiration: + activity_respiration_frac: 0.05 # [-] + basal_respiration_rate: 0.05 # [1/d] + convert_o2: 1/12 # [mmol 02 / mg C] + + uptake: + n: + basis: nutrient # (if strategy is "independent") options: constant, growth, or nutrient + # constant: based on constant uptake rate + # growth: based on photosynthesis and nutrient limitation + # nutrient: based on nutrient availabilty + strategy: independent # options: independent or coupled + # independent: calculates uptake rate based on the nutrient + # coupled: uses conversion factor to calculate uptake rated based on uptake of a different nutrient + form: affinity # options: affinity or constituent + # affinity: uses specific affinity, based on cell quota w.r.t base element + # constituent: uses concentration of phytoplankton constituent of nutrient element + half_sat_nh4_preference: 1.5 + luxury_storage: 1. + max_photo_rate: 1.6 # [1/d] + specific_affinity: 0.025 # [m^3 / (mg C . d)] + p: + basis: nutrient # (if strategy is "independent") options: constant, growth, or nutrient + # constant: based on constant uptake rate + # growth: based on photosynthesis and nutrient limitation + # nutrient: based on nutrient availabilty + strategy: independent # options: independent or coupled + # independent: calculates uptake rate based on the nutrient + # coupled: uses conversion factor to calculate uptake rated based on uptake of a different nutrient + form: affinity # options: affinity or constituent + # affinity: uses specific affinity, based on cell quota w.r.t base element + # constituent: uses concentration of phytoplankton constituent of nutrient element + luxury_storage: 1. + max_photo_rate: 1.6 # [1/d] + specific_affinity: 0.0025 # [m^3 / (mg C . d)] + + zoo1: + long_name: mesozooplankton + type: zooplankton + composition: # element : initial concentration (from data file) + c: /inputs/BFM17_BERM_INIT/init_prof_Zc_150m_bermuda_killworth.da + n: {c: 0.0126} # {base_element : initial conversion factor} + p: {c: 7.862E-04} # {base_element : initial conversion factor} + parameters: + cell_quota: + n: {opt: 1.258E-02} + p: {opt: 7.862E-04} + + temperature_regulation: + temp_limited: True + function: q10 # options: arrhenius, eppley, or q10 + # arrhenius: needs "activation_energy" and "coefficient" as additional parameters + # eppley: needs "coefficient" as additional parameter + # q10: needs "base_temp" and "coefficient" as additional parameters + coefficient: 2.0 + base_temp: 20. + + oxygen_inhibition: + oxygen_limited: True + function: monod + half_sat: 50. # [mmol O2 / m^3] + + excretion: + assimilation_efficiency: 0.25 + ingestion_efficiency: 0.5 + function: # options: constant or grazing + # constant: based on constant excretion rate + # grazing: based on ingestion efficiency of total grazing + n: grazing + p: grazing + dom1: grazing + pom1: grazing + partition: + dom1: {c: 0.60, n: 0.72, p: 0.832} # c,n,p [-] + pom1: {c: 0.40, n: 0.28,p: 0.168} # c,n,p [-] + + grazing: + grazing_preferences: {phyto1: 1., zoo1: 1.E-06} + function: holling-2 + ingestion_type: feeding_threshold + feeding_threshold: 50.0 # [mg C / m^3] + max_grazing_rate: 2.0 # [1/d] + half_sat_grazing: 200.0 # [mg C / m^3] + + mortality: + oxygen_limited: True + half_sat_oxygen: 0.5 # [mmol O2 / m^3] + mortality_rate: + linear: 1.E-06 # linear [1/d] + quadratic: 0.0 # quadratic [1/d^2] + mortality_rate_oxy: 0.25 # [1/d] + partition: + dom1: {c: 0.60, n: 0.72, p: 0.832} # c,n,p [-] + pom1: {c: 0.40, n: 0.28,p: 0.168} # c,n,p [-] + + respiration: + respiration_rate: 0.02 # [1/d] + convert_o2: 1/12 # [mmol 02 / mg C] + + dom1: + long_name: dissolved organic matter + type: detritus + form: dissolved + composition: # element : initial concentration (from data file) + c: /inputs/BFM17_BERM_INIT/init_prof_DOC_150m_bermuda_killworth.da + n: {c: 6.3E-03} # {base_element : initial conversion factor} + p: {c: 3.931E-04} # {base_element : initial conversion factor} + parameters: + light_attenuation: 0.0 # [m^2 / mg C] + + remineralization: + remineralization_rate: 0.05 # [1/d] + convert_o2: 1/12 # [mmol 02 / mg C] + + sedimentation: + sinking: False + sinking_rate: 0. # [m/d] + burial_velocity: 0. # [m/d] + + pom1: + long_name: particulate organic matter + type: detritus + form: particulate + composition: # element : initial concentration (from data file) + c: /inputs/BFM17_BERM_INIT/init_prof_POC_150m_bermuda_killworth.da + n: {c: 0.0126} # {base_element : initial conversion factor} + p: {c: 7.862E-04} # {base_element : initial conversion factor} + parameters: + light_attenuation: 1.0E-04 # [m^2 / mg C] + + remineralization: + remineralization_rate: 0.1 # [1/d] + convert_o2: 1/12 # [mmol 02 / mg C] + + sedimentation: + sinking: True + sinking_rate: 1. # [m/d] + burial_velocity: 1. # [m/d] + +# ------------------------------------------------------------------------------------------------- +reactions: + - type: chlorophyll_synthesis + consumed: + produced: + phyto1: [chl] + + - type: excretion + consumed: + zoo1: [n] + produced: + nh4: [n] + + - type: excretion + consumed: + zoo1: [p] + produced: + po4: [p] + + - type: excretion + consumed: + zoo1: [c,n,p] + produced: + dom1: [c,n,p] + + - type: excretion + consumed: + zoo1: [c,n,p] + produced: + pom1: [c,n,p] + + - type: exudation + consumed: + phyto1: [c] + produced: + dom1: [c] + + - type: grazing + consumed: + phyto1: [c,n,p,chl] + produced: + zoo1: [c,n,p] + + - type: gross_primary_production + consumed: + produced: + phyto1: [c] + o2: [o] + + - type: lysis + consumed: + phyto1: [c,n,p] + produced: + dom1: [c,n,p] + + - type: lysis + consumed: + phyto1: [c,n,p] + produced: + pom1: [c,n,p] + + - type: mortality + consumed: + zoo1: [c,n,p] + produced: + dom1: [c,n,p] + + - type: mortality + consumed: + zoo1: [c,n,p] + produced: + pom1: [c,n,p] + + - type: nitrification + consumed: + nh4: [n] + o2: [o] + produced: + no3: [n] + + - type: photosynthesis + consumed: + phyto1: [c] + produced: + + - type: reaeration + consumed: + produced: + o2: [o] + + - type: remineralization + consumed: + dom1: [c] + o2: [o] + produced: + + - type: remineralization + consumed: + dom1: [n] + produced: + nh4: [n] + + - type: remineralization + consumed: + dom1: [p] + produced: + po4: [p] + + - type: remineralization + consumed: + pom1: [c] + o2: [o] + produced: + + - type: remineralization + consumed: + pom1: [n] + produced: + nh4: [n] + + - type: remineralization + consumed: + pom1: [p] + produced: + po4: [p] + + - type: respiration + consumed: + phyto1: [c] + o2: [o] + produced: + + - type: respiration + consumed: + zoo1: [c] + o2: [o] + produced: + + - type: uptake + consumed: + no3: [n] + nh4: [n] + produced: + phyto1: [n] + dom1: [n] + + - type: uptake + consumed: + po4: [p] + produced: + phyto1: [p] + dom1: [p] + \ No newline at end of file diff --git a/tests/bfm17/bfm_standalone.py b/tests/bfm17/bfm_standalone.py new file mode 100644 index 0000000..fcf0639 --- /dev/null +++ b/tests/bfm17/bfm_standalone.py @@ -0,0 +1,42 @@ +import netCDF4 as nc +import os +import numpy as np +from matplotlib import pyplot as plt + +path = os.getcwd() + '/tests/bfm17/data/BFM_standalone_pelagic.nc' +variables = nc.Dataset(path).variables + +o2 = np.asarray(variables['O2o'][:]) +po4 = np.asarray(variables['N1p'][:]) +no3 = np.asarray(variables['N3n'][:]) +nh4 = np.asarray(variables['N4n'][:]) + +pc = np.asarray(variables['P2c'][:]) +pn = np.asarray(variables['P2n'][:]) +pp = np.asarray(variables['P2p'][:]) +pl = np.asarray(variables['P2l'][:]) +exu = np.asarray(variables['exPPYc'][:]) +gpp = np.asarray(variables['ruPPYc'][:]) +uptn = np.asarray(variables['ruPPYn'][:]) +uptp = np.asarray(variables['ruPPYp'][:]) + +domc = np.asarray(variables['R1c'][:]) +domn = np.asarray(variables['R1n'][:]) +domp = np.asarray(variables['R1p'][:]) + +pomc = np.asarray(variables['R6c'][:]) +pomn = np.asarray(variables['R6n'][:]) +pomp = np.asarray(variables['R6p'][:]) + +resz = np.asarray(variables['resZOOc'][:]) +nspz = np.asarray(variables['ruZOOc'][:]) +zc = np.asarray(variables['Z5c'][:]) + +remzn = np.asarray(variables['remZOOn'][:]) +remzp = np.asarray(variables['remZOOp']) + +fI = np.asarray(variables['eiPPY_iiP2_'][:]) +ETW = np.asarray(variables['ETW'][:]) +EIR = np.asarray(variables['EIR'][:]) + +x = np.linspace(0,729,730) \ No newline at end of file diff --git a/tests/bfm17/data/.DS_Store b/tests/bfm17/data/.DS_Store new file mode 100644 index 0000000..c733e44 Binary files /dev/null and b/tests/bfm17/data/.DS_Store differ diff --git a/bfm17/BFM_standalone_pelagic-0515.nc b/tests/bfm17/data/BFM_standalone_pelagic.nc similarity index 100% rename from bfm17/BFM_standalone_pelagic-0515.nc rename to tests/bfm17/data/BFM_standalone_pelagic.nc diff --git a/bfm17-0522-fdm.npz b/tests/bfm17/data/bfm17.npz similarity index 72% rename from bfm17-0522-fdm.npz rename to tests/bfm17/data/bfm17.npz index 44dd3fc..0acdd4c 100644 Binary files a/bfm17-0522-fdm.npz and b/tests/bfm17/data/bfm17.npz differ diff --git a/tests/bfm17/data/npp.npz b/tests/bfm17/data/npp.npz new file mode 100644 index 0000000..9717f84 Binary files /dev/null and b/tests/bfm17/data/npp.npz differ diff --git a/tracer_indices_bfm17-0522.npz b/tests/bfm17/data/tracer_indices_bfm17.npz similarity index 100% rename from tracer_indices_bfm17-0522.npz rename to tests/bfm17/data/tracer_indices_bfm17.npz diff --git a/tests/bfm17/figures/nutrients.jpg b/tests/bfm17/figures/nutrients.jpg new file mode 100644 index 0000000..bb4c970 Binary files /dev/null and b/tests/bfm17/figures/nutrients.jpg differ diff --git a/tests/bfm17/figures/organic.jpg b/tests/bfm17/figures/organic.jpg new file mode 100644 index 0000000..759fb25 Binary files /dev/null and b/tests/bfm17/figures/organic.jpg differ diff --git a/tests/bfm17/plot_bfm.py b/tests/bfm17/plot_bfm.py new file mode 100644 index 0000000..4b601b4 --- /dev/null +++ b/tests/bfm17/plot_bfm.py @@ -0,0 +1,155 @@ +import netCDF4 as nc +import os +import numpy as np +from matplotlib import pyplot as plt + +folder = os.getcwd() + '/tests/bfm17' + +# Get Fortran Data -------------------------------------------------------------------- +path = os.getcwd() + '/tests/bfm17/data/BFM_standalone_pelagic.nc' +variables = nc.Dataset(path).variables + +o2_bfm17 = np.asarray(variables['O2o'][:730]) +po4_bfm17 = np.asarray(variables['N1p'][:730]) +no3_bfm17 = np.asarray(variables['N3n'][:730]) +nh4_bfm17 = np.asarray(variables['N4n'][:730]) + +pc_bfm17 = np.asarray(variables['P2c'][:730]) +domc_bfm17 = np.asarray(variables['R1c'][:730]) +pomc_bfm17 = np.asarray(variables['R6c'][:730]) +zc_bfm17 = np.asarray(variables['Z5c'][:730]) + +# Get GLOBE Data -------------------------------------------------------------------- +# Load solution +path = os.getcwd() + "/tests/bfm17/data/bfm17.npz" +solution = np.load(path, allow_pickle=True) +conc = solution["concentration"] # concentratrion matrix +time = solution["time"] # time array + +# Load tracer indices +path = os.getcwd() + "/tests/bfm17/data/tracer_indices_bfm17.npz" +indices = np.load(path) +tracer_indices = {} +for file in indices.files: + tracer_indices[file] = list(indices[file]) + +o2 = tracer_indices["o2"][0] +no3 = tracer_indices["no3"][0] +nh4 = tracer_indices["nh4"][0] +po4 = tracer_indices["po4"][0] +pc = tracer_indices["phyto1"][0] +pn = tracer_indices["phyto1"][1] +pp = tracer_indices["phyto1"][2] +pl = tracer_indices["phyto1"][3] +zc = tracer_indices["zoo1"][0] +zn = tracer_indices["zoo1"][1] +zp = tracer_indices["zoo1"][2] +domc = tracer_indices["dom1"][0] +domn = tracer_indices["dom1"][1] +domp = tracer_indices["dom1"][2] +pomc = tracer_indices["pom1"][0] +pomn = tracer_indices["pom1"][1] +pomp = tracer_indices["pom1"][2] + +# Daily averages for concentrations +iters = np.linspace(0,len(time)-1,len(time)) +iters_per_day = 86400/360 # change this depending on timestep +conc_avg = np.zeros((conc.shape[0], int(np.floor((len(iters))/iters_per_day)))) + +day = 0 +for i in range(0,len(iters)): + conc_avg[:,day] += conc[:,i] + if i%int(iters_per_day) == 0 and i != 0: + conc_avg[:,day] = conc_avg[:,day]/iters_per_day + + day += 1 + if day == 730: + break + +# Convert time array from seconds to months +sec_mon = 60 * 60 * 24 * 30 +time = time/sec_mon +days = np.linspace(0,729,730) + +xlabel = ['J','A','J','O','J','A','J','O','J'] +xticks = [0,90,180,270,360,450,540,630,720] + +# Create Plots -------------------------------------------------------------------- +# Nutrients +fig, axs = plt.subplots(2,2,figsize=(10,10),sharex=True) + +axs[0,0].plot(days,conc_avg[o2],label='GLOBE') +axs[0,0].plot(days,o2_bfm17,linestyle=(0, (5, 10)),color='black',label='BFM17') +axs[0,0].set_title("(a) Oxygen") +axs[0,0].set_xlim([0,730]) +axs[0,0].set_ylabel("mmol O $\mathregular{m^{-3}}$") + +axs[0,1].plot(days,conc_avg[no3],label='GLOBE') +axs[0,1].plot(days,no3_bfm17,linestyle=(0, (5, 10)),color='black',label='BFM17') +axs[0,1].set_title("(b) Nitrate") +axs[0,1].set_xlim([0,730]) +axs[0,1].set_ylabel("mmol N $\mathregular{m^{-3}}$") + +axs[1,0].plot(days,conc_avg[nh4],label='GLOBE') +axs[1,0].plot(days,nh4_bfm17,linestyle=(0, (5, 10)),color='black',label='BFM17') +axs[1,0].set_title("(c) Ammonium") +axs[1,0].set_xlabel("Time [months]") +axs[1,0].set_xticks(xticks,xlabel) +axs[1,0].set_xlim([0,730]) +axs[1,0].set_ylabel("mmol N $\mathregular{m^{-3}}$") + +axs[1,1].plot(days,conc_avg[po4],label='GLOBE') +axs[1,1].plot(days,po4_bfm17,linestyle=(0, (5, 10)),color='black',label='BFM17') +axs[1,1].set_title("(d) Phosphate") +axs[1,1].set_xlabel("Time [months]") +axs[1,1].set_xticks(xticks,xlabel) +axs[1,1].set_xlim([0,730]) +axs[1,1].set_ylabel("mmol P $\mathregular{m^{-3}}$") + +handles, labels = axs[0,0].get_legend_handles_labels() +fig.legend(handles,labels, loc='lower center', ncol=2) + +# fig.suptitle("Nutrients") +fig.tight_layout(h_pad=2.5,w_pad=2.5,rect=[0,0.025,1,1]) +nut = os.path.join(folder + "/figures","nutrients.jpg") +plt.savefig(nut) + + +# Organic +fig, axs = plt.subplots(2,2,figsize=(10,10), sharex=True) + +axs[0,0].plot(days,conc_avg[pc],label='GLOBE') +axs[0,0].plot(days,pc_bfm17,linestyle=(0, (5, 10)),color='black',label='BFM17') +axs[0,0].set_title("(a) Phytoplankton -- Carbon") +axs[0,0].set_xlim([0,730]) +axs[0,0].set_ylabel("mg C $\mathregular{m^{-3}}$") + +axs[0,1].plot(days,conc_avg[zc],label='GLOBE') +axs[0,1].plot(days,zc_bfm17,linestyle=(0, (5, 10)),color='black',label='BFM17') +axs[0,1].set_title("(b) Zooplankton -- Carbon") +axs[0,1].set_xlim([0,730]) +axs[0,1].set_ylabel("mg C $\mathregular{m^{-3}}$") + +axs[1,0].plot(days,conc_avg[domc],label='GLOBE') +axs[1,0].plot(days,domc_bfm17,linestyle=(0, (5, 10)),color='black',label='BFM17') +axs[1,0].set_title("(c) Dissolved Organic Carbon") +axs[1,0].set_xlabel("Time [months]") +axs[1,0].set_xticks(xticks,xlabel) +axs[1,0].set_xlim([0,730]) +axs[1,0].set_ylabel("mg C $\mathregular{m^{-3}}$") + +axs[1,1].plot(days,conc_avg[pomc],label='GLOBE') +axs[1,1].plot(days,pomc_bfm17,linestyle=(0, (5, 10)),color='black',label='BFM17') +axs[1,1].set_title("(d) Particulate Organic Carbon") +axs[1,1].set_xlabel("Time [months]") +axs[1,1].set_xticks(xticks,xlabel) +axs[1,1].set_xlim([0,730]) +axs[1,1].set_ylabel("mg C $\mathregular{m^{-3}}$") + +handles, labels = axs[0,0].get_legend_handles_labels() +fig.legend(handles,labels, loc='lower center', ncol=2) + +# fig.suptitle("Organic Carbon") +fig.tight_layout(h_pad=2.5,w_pad=2.5,rect=[0,0.025,1,1]) +nut = os.path.join(folder + "/figures","organic.jpg") +plt.savefig(nut) \ No newline at end of file diff --git a/tests/npzd/__init__.py b/tests/npzd/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/npzd/data/npzd.npz b/tests/npzd/data/npzd.npz new file mode 100644 index 0000000..bebb081 Binary files /dev/null and b/tests/npzd/data/npzd.npz differ diff --git a/tests/npzd/data/tracer_indices_npzd.npz b/tests/npzd/data/tracer_indices_npzd.npz new file mode 100644 index 0000000..866a3be Binary files /dev/null and b/tests/npzd/data/tracer_indices_npzd.npz differ diff --git a/tests/npzd/figures/npzd.jpg b/tests/npzd/figures/npzd.jpg new file mode 100644 index 0000000..72e4c16 Binary files /dev/null and b/tests/npzd/figures/npzd.jpg differ diff --git a/tests/npzd/figures/npzd.png b/tests/npzd/figures/npzd.png new file mode 100644 index 0000000..bf1d5e5 Binary files /dev/null and b/tests/npzd/figures/npzd.png differ diff --git a/tests/npzd/test_npzd.yaml b/tests/npzd/npzd.yaml similarity index 63% rename from tests/npzd/test_npzd.yaml rename to tests/npzd/npzd.yaml index 2fe3c2a..abc3c33 100644 --- a/tests/npzd/test_npzd.yaml +++ b/tests/npzd/npzd.yaml @@ -7,8 +7,10 @@ tracers: composition: n: 4.0 # mmol N / m^3 parameters: - temp_limited: False - q10: 2.0 + temperature_regulation: + temp_limited: False + function: q10 + coefficient: 2.0 phyto1: long_name: diatoms @@ -16,11 +18,18 @@ tracers: composition: # element : initial concentration n: 2.5 # mmol N / m^3 parameters: + cell_quota: + constituents: ["n"] + opt: [1.] nutrient_limitation: - type: external - nutrients: ["no3"] - temp_limited: False - q10: 2.0 + no3: + type: external + # function: monod + half_sat: 1. + temperature_regulation: + temp_limited: False + function: q10 + coefficient: 2.0 zoo1: long_name: mesozooplankton @@ -30,18 +39,31 @@ tracers: parameters: assimilation_efficiency: 1.0 ingestion_efficiency: 1.0 + cell_quota: + constituents: ["n"] + opt: [1.] grazing_preferences: phyto1: 1.0 - temp_limited: False - q10: 2.0 + temperature_regulation: + temp_limited: False + function: q10 + coefficient: 2.0 + oxygen_inhibition: + oxygen_limited: False pom1: long_name: particulate organic matter type: detritus composition: # element : initial concentration n: 0.0 # mmol N / m^3 + parameters: + light_attenuation: 0. +# ------------------------------------------------------------------------------------------------- parameters: + constants: + einstein_to_watts: 0.217 + environment: base_temp: 20.0 # degC summer_mld: 10.0 # m @@ -50,18 +72,23 @@ parameters: winter_salt: 37.0 # psu summer_sun: 120.0 # W / m^2 winter_sun: 10.0 # W / m^2 - summer_temp: 15.0 # degC - winter_temp: 15.0 # degC + summer_temp: 15.5 # degC + winter_temp: 15.5 # degC summer_wind: 10.0 # m/s winter_wind: 20.0 # m/s + light_attenuation_water: 0.0435 # [1/m] + latitude: 45.0 + simulation: latitude: 45.0 # deg num_days: 365 # d timestep: 86400.0 # s + water_column: column_depth: 10.0 # m num_layers: 1 +# ------------------------------------------------------------------------------------------------- reactions: - type: grazing consumed: @@ -72,6 +99,7 @@ reactions: function: ivlev max_grazing_rate: 1.0 ivlev: 0.2 + half_sat_grazing: 0. - type: egestion consumed: @@ -88,21 +116,14 @@ reactions: parameters: function: grazing - - type: mortality + - type: exudation consumed: phyto1: [n] produced: no3: [n] parameters: - mortality_rate: [0.1,0.] - - - type: mortality - consumed: - phyto1: [n] - produced: - pom1: [n] - parameters: - mortality_rate: [0.15,0.] + method: constant + excreted_fraction: 0.1 - type: mortality consumed: @@ -112,20 +133,27 @@ reactions: parameters: mortality_rate: [0.2,0.] - - type: uptake - consumed: - no3: [n] - produced: + - type: photosynthesis + consumed: phyto1: [n] + produced: parameters: - nutrient_limitation: variable - half_sat_nutrient: 1 light_limitation: 0.25 - max_photo_rate: variable + max_photo_rate: eppley + type: base_b a: 0.6 b: 1.066 c: 1.0 + - type: uptake + consumed: + no3: [n] + produced: + phyto1: [n] + parameters: + strategy: independent + basis: growth + - type: remineralization consumed: pom1: [n] @@ -133,3 +161,12 @@ reactions: no3: [n] parameters: remineralization_rate: 0.4 + + - type: respiration + consumed: + phyto1: [n] + produced: + pom1: [n] + parameters: + activity_respiration_frac: 0. + basal_respiration_rate: 0.15 \ No newline at end of file diff --git a/tests/npzd/plot_npzd.py b/tests/npzd/plot_npzd.py new file mode 100644 index 0000000..6ae369e --- /dev/null +++ b/tests/npzd/plot_npzd.py @@ -0,0 +1,158 @@ +import os +import numpy as np +import matplotlib.pyplot as plt + +folder = os.getcwd() + '/tests/npzd' + +# ---------------------------------------------------------------------------------------------------- +# GLOBE model simulation +# ---------------------------------------------------------------------------------------------------- +# Get GLOBE Data -------------------------------------------------------------------- +# Load solution +path = os.getcwd() + "/tests/npzd/data/npzd.npz" +solution = np.load(path, allow_pickle=True) +conc = solution["concentration"] # concentratrion matrix +time = solution["time"] # time array + +# Load tracer indices +path = os.getcwd() + "/tests/npzd/data/tracer_indices_npzd.npz" +indices = np.load(path, allow_pickle=True) +tracer_indices = {} +for file in indices.files: + tracer_indices[file] = list(indices[file]) + +no3 = conc[tracer_indices["no3"][0]] +phyto = conc[tracer_indices["phyto1"][0]] +zoo = conc[tracer_indices["zoo1"][0]] +pom = conc[tracer_indices["pom1"][0]] + +# ---------------------------------------------------------------------------------------------------- +# Test case and parameters from Riley Brady +# ---------------------------------------------------------------------------------------------------- +# Here we set up the default parameters/coefficients. +DT = 1 # Time Step (in days) +NUM_STEPS = 365 # Number of time steps to be computed and plotted + +# Temperature-Dependent Growth Rate +a = 0.6 +b = 1.066 +c = 1 +T = 15 +Vm = a * b**(c*T) # Maximum growth rate (per day) + +# Other parameters +Kn = 1 # Half-saturation constant for nitrogen uptake (umolN per l) +Rm = 1 # Maximum grazing rate (per day) +g = 0.2 # Zooplankton death rate (per day) +lambda_Z = 0.2 # Grazing constant (umolN per l) +epsilon = 0.1 # Phyto death rate (per day) +f = 0.25 # Light intensity (assumed constant) + +# Detritus-related stuff. +alpha = 0.0 # Fraction of zoo. uptake that goes immediately to dissolved nutrients. +beta = 1.0 # Assimilation efficiency of zooplankton. +r = 0.15 # Respiration rate. +phi = 0.4 # Remineralization rate of detritus. + +# Set Initial Conditions (umol per L) +N_0 = 4 +P_0 = 2.5 +Z_0 = 1.5 +D_0 = 0 + +# Initialize Arrays +N = np.empty(NUM_STEPS, dtype="float") +P = np.empty(NUM_STEPS, dtype="float") +Z = np.empty(NUM_STEPS, dtype="float") +D = np.empty(NUM_STEPS, dtype="float") + +# Insert Initial Values +N[0] = N_0 +P[0] = P_0 +Z[0] = Z_0 +D[0] = D_0 + +# Here we use the Euler forward method to solve for t+1 and reference t. +for idx in np.arange(1, NUM_STEPS, 1): + t = idx - 1 + + # Common terms for simpler code + gamma_N = N[t] / (Kn + N[t]) + zoo_graze = Rm * (1 - np.exp(-lambda_Z * P[t])) * Z[t] + + # Equation calculations + N[idx] = DT * (-Vm*gamma_N*f*P[t] + alpha*zoo_graze + epsilon*P[t] + g*Z[t] + phi*D[t]) + N[t] + P[idx] = DT * (Vm*gamma_N*f*P[t] - zoo_graze - epsilon*P[t] - r*P[t]) + P[t] + Z[idx] = DT * (beta*zoo_graze - g*Z[t]) + Z[t] + D[idx] = DT * (r*P[t] + (1-alpha-beta)*zoo_graze - phi*D[t]) + D[t] + + # bgc_rate_eqns(t, base_element, parameters, tracers) + + # dn = N[idx] - tracers["no3"].conc[0][idx] + # dp = P[idx] - tracers["phyto1"].conc[0][idx] + # dz = Z[idx] - tracers['zoo1'].conc[0][idx] + # dd = D[idx] - tracers['pom1'].conc[0][idx] + + # pause = 1 + + +x = np.arange(1, NUM_STEPS + 1, 1) + +# ---------------------------------------------------------------------------------------------------- +# Plot results +# ---------------------------------------------------------------------------------------------------- +# fig, ax = plt.subplots() + +months = [0,30,60,90,120,150,180] +marks = ['J','F','M','A','M','J','J'] +marks_blank = ['','','','','','',''] + +fig, axs = plt.subplots(2,2,figsize=(10,10), sharex=True) + +# axs[0,0].plot(x,tracers["no3"].conc[0,:-1],label='GLOBE') +axs[0,0].plot(x,no3[:-1],label='GLOBE') +axs[0,0].plot(x,N,linestyle=(0, (5, 10)),color='black',label='NPZD') +axs[0,0].set_title("(a) Nitrate") +axs[0,0].set_xticks(months,marks_blank) +axs[0,0].set_xlim([0,180]) +axs[0,0].set_ylabel("mmol N $\mathregular{m^{-3}}$") + +# axs[0,1].plot(x,tracers["pom1"].conc[0,:-1],label='GLOBE') +axs[0,1].plot(x,pom[:-1],label='GLOBE') +axs[0,1].plot(x,D,linestyle=(0, (5, 10)),color='black',label='NPZD') +axs[0,1].set_title("(b) Particulate Organic Nitrogen") +axs[0,1].set_xticks(months,marks_blank) +axs[0,1].set_xlim([0,180]) + +# axs[1,0].plot(x,tracers["phyto1"].conc[0,:-1],label='GLOBE') +axs[1,0].plot(x,phyto[:-1],label='GLOBE') +axs[1,0].plot(x,P,linestyle=(0, (5, 10)),color='black',label='NPZD') +axs[1,0].set_title("(c) Phytoplankton") +axs[1,0].set_xlabel("Time [months]") +axs[1,0].set_xticks(months,marks) +axs[1,0].set_xlim([0,180]) +axs[1,0].set_ylabel("mmol N $\mathregular{m^{-3}}$") + +# axs[1,1].plot(x,tracers["zoo1"].conc[0,:-1],label='GLOBE') +axs[1,1].plot(x,zoo[:-1],label='GLOBE') +axs[1,1].plot(x,Z,linestyle=(0, (5, 10)),color='black',label='NPZD') +axs[1,1].set_title("(d) Zooplankton") +axs[1,1].set_xlabel("Time [months]") +axs[1,1].set_xticks(months,marks) +axs[1,1].set_xlim([0,180]) + +handles, labels = axs[0,0].get_legend_handles_labels() +fig.legend(handles,labels, loc='lower center', ncol=2) + +fig.tight_layout(h_pad=2.5,w_pad=2.5,rect=[0,0.025,1,1]) + +npzd = os.path.join(folder + "/figures", "npzd.jpg") +plt.savefig(npzd) + +error = np.zeros((4,len(N))) +error[0,:] = (N - no3[:-1]) / (N + 1.E-20) +error[1,:] = (P - phyto[:-1]) / (P + 1.E-20) +error[2,:] = (Z - zoo[:-1]) / (Z + 1.E-20) +error[3,:] = (D - pom[:-1]) / (D + 1.E-20) + +x=1 \ No newline at end of file diff --git a/tracer_indices-0220.npz b/tracer_indices-0220.npz deleted file mode 100644 index 0976c85..0000000 Binary files a/tracer_indices-0220.npz and /dev/null differ diff --git a/tracer_indices-0404.npz b/tracer_indices-0404.npz deleted file mode 100644 index 0976c85..0000000 Binary files a/tracer_indices-0404.npz and 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