"""Calculates CO2 response."""
import numpy as np
from openairclim.construct_conc import calc_inv_sums
from openairclim.construct_conc import interp_bg_conc
from openairclim.utils import tgco2_to_tgc
from openairclim.utils import kg_to_tg
# CONSTANTS
#
# alpha_j (list): [ppbv/Tg(C)] alpha_j coefficients of the impulse response function G_C
# for CO2 (e.g. from Table 1 of Sausen & Schumann (2000))
# with j being the eigenmodes of the impulse response
# https://doi.org/10.1023/A:1005579306109
ALPHA_ARR = [0.067, 0.1135, 0.152, 0.0970, 0.041]
# m_j (list): [1/yr] inverse of tau_j coefficients of the impulse response function G_C
# for CO2 (e.g. from Table I of Sausen & Schumann (2000))
M_ARR = [0.0, 1.0 / 313.8, 1.0 / 79.8, 1.0 / 18.8, 1.0 / 1.7]
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def get_co2_emissions(inv_dict):
"""Get total CO2 emissions in Tg for each inventory.
Args:
inv_dict (dict): Dictionary of emission inventory xarrays,
keys are inventory years
Returns:
dict: Dictionary with array of CO2 emissions in Tg
"""
# Sum up CO2 emissions in inventories
_inv_years, emis_co2 = calc_inv_sums("CO2", inv_dict)
# Convert kg to Tg
emis_co2_dict = {"CO2": kg_to_tg(emis_co2)}
return emis_co2_dict
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def greens_c(time):
"""Green's function / Impulse response for CO2 concentration
after (5) in Sausen & Schumann 2000
Args:
time (float): Time
Returns:
float: Impulse response for a certain point in time
"""
return np.sum(ALPHA_ARR * np.exp(np.multiply(M_ARR, -time)))
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def calc_co2_concentration(config: dict, emis_dict: dict) -> dict:
"""Calculates the CO2 concentration values in ppmv for emitted CO2 in Tg,
get method from config and execute corresponding subroutine
Args:
config (dict): Configuration dictionary from config
emis_dict (dict): Dictionary with arrays of emissions
for time range as defined in config, keys are species
Returns:
dict: Dictionary with array of CO2 concentration in ppmv
for time range as defined in config, key is species CO2
"""
method = config["responses"]["CO2"]["conc"]["method"]
if method == "Sausen&Schumann":
conc_co2_dict = calc_co2_ss(config, emis_dict)
return conc_co2_dict
else:
raise ValueError("CO2.conc.method in config file is invalid.")
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def calc_co2_ss(config, emis_dict):
"""Calculates the CO2 concentration values in ppmv for emitted CO2 in Tg
after Sausen&Schumann, 2000, formulas (4) and (5)
Args:
config (dict): Configuration dictionary from config
emis_dict (dict): Dictionary with arrays of emissions
for time range as defined in config, keys are species
Returns:
dict: Dictionary with array of CO2 concentration in ppmv
for time range as defined in config, key is species CO2
"""
time_config = config["time"]["range"]
time_range = np.arange(
time_config[0], time_config[1], time_config[2], dtype=int
)
delta_t = time_config[2]
# Convert Tg CO2 to Tg C
emis_co2_arr = tgco2_to_tgc(emis_dict["CO2"])
conc_co2_arr = np.zeros(len(time_range))
i = 0
for year in time_range:
j = 0
conc_co2 = 0
for year_dash in time_range[: (i + 1)]:
if emis_co2_arr[j] != 0.0: # optimize code
# (4) in Sausen & Schumann, 2000
conc_co2 = (
conc_co2
+ greens_c(year - year_dash) * emis_co2_arr[j] * delta_t
)
j = j + 1
conc_co2_arr[i] = conc_co2 / 1000.0 # convert ppbv -> ppmv
i = i + 1
return {"CO2": conc_co2_arr}
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def calc_co2_rf(config, conc_dict, conc_co2_bg_dict):
"""
Calculates the radiative forcing values for emitted CO2 concentrations,
after IPCC 2001, Ramaswamy, V. et al. in "Climate Change 2001:
The Scientific Basis. Contribution of Working Group I to the Third Assessment
Report of the Intergovernmental Panel of Climate Change"Table 6.2,
get method from config and execute corresponding subroutine
Args:
config (dict): Configuration dictionary from config
conc_dict (dict): Dictionary with array of concentrations
between the starting and ending years, key is species
conc_co2_bg_dict (dict): Dictionary of np.ndarray of background CO2 concentrations
between the starting and ending years, key is species
Raises:
ValueError: if CO2.rf.method not valid
Returns:
dict: Dictionary with np.ndarray of CO2 radiative forcing values
between the starting and ending years, key is species CO2
"""
method = config["responses"]["CO2"]["rf"]["method"]
if method == "IPCC_2001_1":
rf_dict = calc_co2_rf_ipcc_2001_1(conc_dict, conc_co2_bg_dict)
return rf_dict
elif method == "IPCC_2001_2":
rf_dict = calc_co2_rf_ipcc_2001_2(conc_dict, conc_co2_bg_dict)
return rf_dict
elif method == "IPCC_2001_3":
rf_dict = calc_co2_rf_ipcc_2001_3(conc_dict, conc_co2_bg_dict)
return rf_dict
elif method == "Etminan_2016":
conc_n2o_bg_dict = interp_bg_conc(config, "N2O")
rf_dict = calc_co2_rf_etminan_2016(
conc_dict, conc_co2_bg_dict, conc_n2o_bg_dict
)
return rf_dict
else:
raise ValueError("CO2.rf.method in config file is invalid.")
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def calc_co2_rf_ipcc_2001_1(conc_dict, conc_co2_bg_dict):
"""Calculates the radiative forcing values for emitted CO2 concentrations,
after IPCC 2001, Table 6.2, first row
Args:
conc_co2 (dict): Dictionary with array of concentrations
between the starting and ending years, keys is species
conc_co2_bg_dict (dict): Dictionary of np.ndarray of background CO2 concentrations
between the starting and ending years, key is species
Returns:
dict: Dictionary with array of CO2 radiative forcing values
between the starting and ending years, key is species CO2
"""
conc_co2_arr = conc_dict["CO2"]
conc_co2_bg = conc_co2_bg_dict["CO2"]
rf_co2_arr = 5.35 * np.log(1 + conc_co2_arr / conc_co2_bg)
return {"CO2": rf_co2_arr}
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def calc_co2_rf_ipcc_2001_2(conc_dict, conc_co2_bg_dict):
"""Calculates the radiative forcing values for emitted CO2 concentrations,
after IPCC 2001, Table 6.2, second row
Args:
conc_co2 (dict): Dictionary with array of concentrations
between the starting and ending years, keys is species
conc_co2_bg_dict (dict): Dictionary of np.ndarray of background CO2 concentrations
between the starting and ending years, key is species
Returns:
dict: Dictionary with array of CO2 radiative forcing values
between the starting and ending years, key is species CO2
"""
conc_co2_arr = conc_dict["CO2"]
conc_co2_bg = conc_co2_bg_dict["CO2"]
rf_co2_arr = 4.841 * np.log(1 + conc_co2_arr / conc_co2_bg) + 0.0906 * (
np.sqrt(conc_co2_arr + conc_co2_bg) - np.sqrt(conc_co2_bg)
)
return {"CO2": rf_co2_arr}
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def calc_co2_rf_ipcc_2001_3(conc_dict, conc_co2_bg_dict):
"""Calculates the radiative forcing values for emitted CO2 concentrations,
after IPCC 2001, Table 6.2, third row
Args:
conc_co2 (dict): Dictionary with array of concentrations
between the starting and ending years, keys is species
conc_co2_bg_dict (dict): Dictionary of np.ndarray of background CO2 concentrations
between the starting and ending years, key is species
Returns:
dict: Dictionary with array of CO2 radiative forcing values
between the starting and ending years, key is species CO2
"""
def g(conc):
return np.log(1.0 + 1.2 * conc + 0.005 * conc**2 + 1.4e-6 * conc**3)
conc_co2_arr = conc_dict["CO2"]
conc_co2_bg = conc_co2_bg_dict["CO2"]
rf_co2_arr = 3.35 * (g(conc_co2_arr + conc_co2_bg) - g(conc_co2_bg))
return {"CO2": rf_co2_arr}
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def calc_co2_rf_etminan_2016(
conc_dict: dict, conc_co2_bg_dict: dict, conc_n2o_bg_dict: dict
) -> dict:
"""Calculates the radiative forcing values for emitted CO2 concentrations after
Etminan, M., Myhre, G., Highwood, E. J., & Shine, K. P. (2016). Radiative forcing
of carbon dioxide, methane, and nitrous oxide: A significant revision of the
methane radiative forcing. Geophysical Research Letters, 43(24), 12-614.
https://doi.org/10.1002/2016GL071930
Args:
conc_dict (dict): Dictionary with array of concentrations
between the starting and ending years, keys is species
conc_co2_bg_dict (dict): Dictionary of np.ndarray of background CO2 concentrations
between the starting and ending years, key is species
conc_n2o_bg_dict (dict): Dictionary of np.ndarray of background N2O concentrations
between the starting and ending years, key is species
Returns:
dict: Dictionary with np.ndarray of CO2 radiative forcing values
between the starting and ending years, key is species CO2
"""
# TODO Check this method! Check units: ppmv vs. ppm ?
conc_co2_arr = conc_dict["CO2"]
conc_co2_bg_arr = conc_co2_bg_dict["CO2"]
conc_n2o_bg_arr = conc_n2o_bg_dict["N2O"]
a1 = -2.4e-7 # W/m²/ppm
b1 = 7.2e-4
c1 = -2.1e-4
c_0_arr = conc_co2_bg_arr
c_arr = conc_co2_bg_arr + conc_co2_arr
n_mean_arr = conc_n2o_bg_arr
rf_co2_arr = (
a1 * (c_arr - c_0_arr) ** 2
+ b1 * abs(c_arr - c_0_arr)
+ c1 * n_mean_arr
+ 5.36
) * np.log(c_arr / c_0_arr)
return {"CO2": rf_co2_arr}