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main_metrics.py
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main_metrics.py
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# get_ipython().run_line_magic('matplotlib', 'inline')
# Import functions and updated RCP2.6 emissions (from IIASA RCP database)
import make_plots
import CDR_functions
# reload packages everytime that you let the code run (to update changes)
from importlib import reload
reload(CDR_functions)
reload(make_plots)
# import functions
from CDR_functions import *
from make_plots import *
# Defining the variables
DATES = np.arange(2000, 2101, step=1)
INDEX_2000 = 235 # index of year 2000 in RCPs scenarios
INDEX_2101 = 336 # index of year 2101 in RCPs scenarios
CH4GWP100 = 28 # IPCC AR5 GWP100 of methane
N2OGWP100 = 265 # IPCC AR5 GWP100 of N2O
CH4GWP20 = 84 # IPCC AR5 GWP20 of CH4
N2OGWP20 = 264 # IPCC AR5 GWP20 of N2O
CH4GTP100 = 4 # IPCC AR5 GWP100 of methane
N2OGTP100 = 234 # IPCC AR5 GWP100 of N2O
CH4GTP20 = 67 # IPCC AR5 GWP20 of CH4
N2OGTP20 = 277 # IPCC AR5 GWP20 of N2O
CH4GWP100_AR4 = 25 # GWP100 of methane used in FAO report (to calculate GHG intensity)
N2OGWP100_AR4 = 298 # GWP100 of N2O used in FAO report (to calculate GHG intensity)
YEAR_POLICY = 2020 # set year in which offsetting policy starts
N_RUNS = 1000 # number of ensemble members
# Using GWP100 as in IPCC AR4
SCENARIO1 = "SSP1-26"
SCENARIO2 = "const"
SCENARIO3 = "SSP3-70 (Baseline)"
saved_CM = False # switch to False to calculate CDR rates from scratch
N2Oanalysis = False # switch to True to perform same analysis for N2O
# ============== RUNNING CALCULATIONS =====================================================
# Make different agricultural methane + N2O emission pathways
E_CH4_agri_26, E_CH4_agri_const, E_CH4_agri_BAU, E_N2O_agri_26, E_N2O_agri_const, E_N2O_agri_BAU \
= make_agri_scenarios('CH4&N2O')
# Make different input emission pathways
E_RCP26, E_const, E_BAU = make_scenarios('CH4&N2O')
# N2O
E_RCP26_N2Oonly, E_const_N2Oonly, E_BAU_N2Oonly = make_scenarios('N2O')
# CH4
E_RCP26_CH4only, E_const_CH4only, E_BAU_CH4only = make_scenarios()
# Calculate tot CDR in RCP2.6 scenario - without offsetting scheme
E_CDR_RCP26_IMAGE = calc_CDR_RCP_2100()
if N2Oanalysis == True:
# run simulations of FaIR for RCP2.6, constant and BAU scenario, N2O
fair26_N2Oonly = run_fair(E_RCP26_N2Oonly, SCENARIO1, N_RUNS)
fairconst_N2Oonly = run_fair(E_const_N2Oonly, SCENARIO2, N_RUNS)
fairBAU_N2Oonly = run_fair(E_BAU_N2Oonly, SCENARIO3, N_RUNS)
print("Finished running FaIR simulations of input scenarios")
if saved_CM == False:
metricsN2O = [N2OGWP100, N2OGWP20]
CDR_N2O = ["CDR_GWP100_N2O_const", "CDR_GWP20_N2O_const", "CDR_GWP100_N2O_BAU", "CDR_GWP20_N2O_BAU"]
fair_N2O = ["fair_GWP100_N2O_const", "fair_GWP20_N2O_const", "fair_GWP100_N2O_BAU", "fair_GWP20_N2O_BAU"]
names_fair_N2O = ["GWP100", "GWP20"]
for i in np.arange(0, len(metricsN2O)):
N2OCM = metricsN2O[i]
# GWP100-based approach
# To offset N2O: calculate CDR rates and scenarios with the GWP100-based offsetting approach
CDR_N2O[i] = calc_CDR_GWP100(E_N2O_agri_const - E_N2O_agri_26,
GWP100=N2OCM * (44.01 / 28.01)) # first convert to MtN2O
CDR_N2O[i + 2] = calc_CDR_GWP100(E_N2O_agri_BAU - E_N2O_agri_26,
GWP100=N2OCM * (44.01 / 28.01)) # first convert to MtN2O
fair_N2O[i] = run_fair(E_const_N2Oonly, SCENARIO2, N_RUNS, CDR_N2O[i])
fair_N2O[i + 2] = run_fair(E_BAU_N2Oonly, SCENARIO3, N_RUNS, CDR_N2O[i + 2])
print("Finished CDR_GWPt ensemble simulation")
plot_fair_CDR(names_fair_N2O[i], fair26_N2Oonly, fairconst_N2Oonly, fairBAU_N2Oonly,
fair_N2O[i], fair_N2O[i + 2])
# GWP*-based approach
CDR_GWPstar_N2O_const = calc_CDR_GWPstar(E_N2O_agri_const - E_N2O_agri_26, N2OGWP100 * (44.01 / 28.01), 100)
CDR_GWPstar_N2O_BAU = calc_CDR_GWPstar(E_N2O_agri_BAU - E_N2O_agri_26, N2OGWP100 * (44.01 / 28.01), 100)
# Run fair for constant & BAU agricultural N2O emissions + GWP*-based CDR offsetting
fairconst_N2O_GWPstar = run_fair(E_const_N2Oonly, SCENARIO2, N_RUNS, CDR_GWPstar_N2O_const, index_end=2100 - 1765)
fairBAU_N2O_GWPstar = run_fair(E_BAU_N2Oonly, SCENARIO3, N_RUNS, CDR_GWPstar_N2O_BAU, index_end=2100 - 1765)
print("Finished CDR_GWP* ensemble simulation")
compare_metrics(fair26_N2Oonly,
fair_N2O[0],
fair_N2O[2],
fair_N2O[1],
fair_N2O[3],
fairconst_N2O_GWPstar,
fairBAU_N2O_GWPstar,
)
if N2Oanalysis == False:
# run simulations of FaIR for RCP2.6, constant and BAU scenario, CH4
fair26_CH4only = run_fair(E_RCP26_CH4only, SCENARIO1, N_RUNS)
fairconst_CH4only = run_fair(E_const_CH4only, SCENARIO2, N_RUNS)
fairBAU_CH4only = CDR_functions.run_fair(E_BAU_CH4only, SCENARIO3, N_RUNS)
# run simulations of FaIR for RCP2.6, constant and BAU scenario, CH4 - shorter version
fair26_CH4only_short = run_fair(E_RCP26_CH4only, SCENARIO1, N_RUNS, index_end=2100 - 1765)
fairconst_CH4only_short = run_fair(E_const_CH4only, SCENARIO2, N_RUNS, index_end=2100 - 1765)
fairBAU_CH4only_short = run_fair(E_BAU_CH4only, SCENARIO3, N_RUNS, index_end=2100 - 1765)
print("Finished running FaIR simulations of input scenarios")
if saved_CM == False:
metricsCH4 = [CH4GWP100, CH4GWP20]
metricsN2O = [N2OGWP100, N2OGWP20]
CDR_CH4 = ["CDR_GWP100_CH4_const", "CDR_GWP20_CH4_const", "CDR_GWP100_CH4_BAU", "CDR_GWP20_CH4_BAU"]
fair_CH4 = ["fair_GWP100_CH4_const", "fair_GWP20_CH4_const", "fair_GWP100_CH4_BAU", "fair_GWP20_CH4_BAU"]
for i in np.arange(0, len(metricsCH4)):
CH4CM = metricsCH4[i]
CDR_CH4[i] = calc_CDR_GWP100(E_CH4_agri_const - E_CH4_agri_26, CH4CM)
CDR_CH4[i + 2] = calc_CDR_GWP100(E_CH4_agri_BAU - E_CH4_agri_26, CH4CM)
fair_CH4[i] = run_fair(E_const_CH4only, SCENARIO2, N_RUNS, CDR_CH4[i])
fair_CH4[i + 2] = run_fair(E_BAU_CH4only, SCENARIO3, N_RUNS, CDR_CH4[i + 2])
print("Finished CDR_GWPt ensemble simulation")
# GWP*-based approach
CDR_GWPstar_CH4_const = calc_CDR_GWPstar(E_CH4_agri_const - E_CH4_agri_26, CH4GWP100, 100)
CDR_GWPstar_CH4_BAU = calc_CDR_GWPstar(E_CH4_agri_BAU - E_CH4_agri_26, CH4GWP100, 100)
# Run fair for constant & BAU agricultural N2O emissions + GWP*-based CDR offsetting
fairconst_CH4_GWPstar = run_fair(E_const_CH4only, SCENARIO2, N_RUNS, CDR_GWPstar_CH4_const, index_end=2100 - 1765)
fairBAU_CH4_GWPstar = run_fair(E_BAU_CH4only, SCENARIO3, N_RUNS, CDR_GWPstar_CH4_BAU, index_end=2100 - 1765)
print("Finished CDR_GWP* ensemble simulation")
# =============================== PRODUCE OUTPUTS ============================================================
# Start writing output
output_file = open("Outputs/offset_differentCM_output.txt", "w")
output_file.write(
"\n By 2100 in const (CH4): increase in ERF = "
+ str(np.sum(fairconst_CH4only.F_single - fair26_CH4only.F_single, axis=1)[335])
+ " & in dT = "
+ str(fairconst_CH4only.T_single[-1] - fair26_CH4only.T_single[-1])
+ "\n By 2100 in BAU (CH4): increase in ERF = "
+ str(np.sum(fairBAU_CH4only.F_single - fair26_CH4only.F_single, axis=1)[335])
+ " & in dT = "
+ str(fairBAU_CH4only.T_single[-1] - fair26_CH4only.T_single[-1])
)
names_CDR_CH4 = ["CDR_GWP100_CH4_const", "CDR_GWP20_CH4_const", "CDR_GWP100_CH4_BAU", "CDR_GWP20_CH4_BAU"]
names_fair_CH4 = ["GWP100", "GWP20"]
compare_metrics(fair26_CH4only,
fair_CH4[0],
fair_CH4[2],
fair_CH4[1],
fair_CH4[3],
fairconst_CH4_GWPstar,
fairBAU_CH4_GWPstar,
)
for i in np.arange(0, len(metricsCH4)):
# Check when maximal additional CDR rates
output_file.write(
"\n max " + names_CDR_CH4[i]
+ str(np.amin(CDR_CH4[i]))
+ " in year "
+ str(*np.where(CDR_CH4[i] == np.amin(CDR_CH4[i])))
+ "\n max : " + names_CDR_CH4[i+2]
+ str(np.amin(CDR_CH4[i+2]))
+ " in year "
+ str(*np.where(CDR_CH4[i+2] == np.amin(CDR_CH4[i+2])))
)
# Producing simulations of ERF and T anomalies under the two offsetting approaches
# Plot ERF & T anomalies for GWP100-based offsetting
#plot_fair_CDR(names_fair_tot[i], fair26, fairconst, fairBAU, fair_tot[i], fair_tot[i+2])
plot_fair_CDR(names_fair_CH4[i], fair26_CH4only, fairconst_CH4only, fairBAU_CH4only,
fair_CH4[i], fair_CH4[i+2])
#plot_fair_CDR(names_fair_N2O[i], fair26_N2Oonly, fairconst_N2Oonly, fairBAU_N2Oonly,
# fair_N2O[i], fair_N2O[i+2])
output_file.write(
"\n CH4 only:"
+ "\nConst: Mean divergence between "+ names_fair_CH4[i] + " & RCP2.6; ERF (CH4&N2O): "
+ str(np.mean(np.sum(fair26_CH4only.F_single[INDEX_2000:, :]
- fair_CH4[i].F_single[INDEX_2000:, :], axis=1)))
+ " & T anomaly: "
+ str(
np.mean(fair26_CH4only.T_single[INDEX_2000:] - fair_CH4[i].T_single[INDEX_2000:])
)
+ "\n BAU: Mean divergence between"+ names_fair_CH4[i] + " & RCP2.6; ERF: "
+ str(
np.mean(
np.sum(fair26_CH4only.F_single[INDEX_2000:, :]
- fair_CH4[i+2].F_single[INDEX_2000:, :], axis=1)))
+ " & T anomaly: "
+ str(np.mean(fair26_CH4only.T_single[INDEX_2000:] - fair_CH4[i+2].T_single[INDEX_2000:]))
)
output_file.write(
"\n CH4 only:"
+ "\n const: 2100 divergence between "+ names_fair_CH4[i] + " & RCP2.6; ERF: "
+ str(np.sum(fair26_CH4only.F_single[INDEX_2101 - 1, :]
- fair_CH4[i].F_single[INDEX_2101 - 1, :]))
+ " & T anomaly: "
+ str(fair26_CH4only.T_single[INDEX_2101 - 1] - fair_CH4[i].T_single[INDEX_2101 - 1])
+ "\n BAU: 2100 divergence between"+ names_fair_CH4[i] + " & RCP2.6; ERF: "
+ str(np.sum(fair26_CH4only.F_single[INDEX_2101 - 1, :]
- fair_CH4[i+2].F_single[INDEX_2101 - 1, :]))
+ " & T anomaly: "
+ str(fair26_CH4only.T_single[INDEX_2101 - 1] - fair_CH4[i+2].T_single[INDEX_2101 - 1])
)
# Check when maximal additional CDR rates
output_file.write(
"\n max CDR GWP* const:"
+ str(np.amin(CDR_GWPstar_CH4_const))
+ " in year "
+ str(*np.where(CDR_GWPstar_CH4_const == np.amin(CDR_GWPstar_CH4_const)))
+ "\n max CDR GWP* BAU: "
+ str(np.amin(CDR_GWPstar_CH4_BAU))
+ " in year "
+ str(*np.where(CDR_GWPstar_CH4_BAU == np.amin(CDR_GWPstar_CH4_BAU)))
)
output_file.write(
"\n CH4 only:"
+ "\n const: 2100 divergence between GWP* & RCP2.6; ERF: "
+ str(np.sum(fair26_CH4only_short.F_single[INDEX_2101 -2, :]
- fairconst_CH4_GWPstar.F_single[INDEX_2101 - 2, :]))
+ " & T anomaly: "
+ str(fair26_CH4only.T_single[INDEX_2101 - 2]
- fairconst_CH4_GWPstar.T_single[INDEX_2101 - 2])
+ "\n BAU: 2100 divergence between GWP* & RCP2.6; ERF: "
+ str(np.sum(fair26_CH4only.F_single[INDEX_2101 - 2, :]
- fairBAU_CH4_GWPstar.F_single[INDEX_2101 - 2, :]))
+ " & T anomaly: "
+ str(fair26_CH4only.T_single[INDEX_2101 - 2]
- fairBAU_CH4_GWPstar.T_single[INDEX_2101 - 2])
)
output_file.write(
"\n CH4 only:"
+ "\n const: max divergence between GWP* & RCP2.6; ERF: "
+ str(np.amax(np.sum(fair26_CH4only_short.F_single[INDEX_2000:, :]
- fairconst_CH4_GWPstar.F_single[INDEX_2000:, :], axis=1)))
+ " & T anomaly:"
+ str(np.amax(fair26_CH4only_short.T_single[INDEX_2000:]
- fairconst_CH4_GWPstar.T_single[INDEX_2000:]))
+ "\n BAU: max divergence between GWP* & RCP2.6; ERF: "
+ str(np.amax(np.sum(fair26_CH4only_short.F_single[INDEX_2000:, :]
- fairBAU_CH4_GWPstar.F_single[INDEX_2000:, :], axis=1)))
+ " & T anomaly:"
+ str(np.amax(fair26_CH4only_short.T_single[INDEX_2000:]
- fairBAU_CH4_GWPstar.T_single[INDEX_2000:]))
)
plot_fair_CDR("GWP*", fair26_CH4only_short, fairconst_CH4only_short, fairBAU_CH4only_short,
fairconst_CH4_GWPstar, fairBAU_CH4_GWPstar)
output_file.close()