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273 | def run(self, return_df: bool = False) -> dict | pd.DataFrame:
"""Run the IPCC climate model with the assigned input data.
Parameters
----------
return_df : bool, optional
If True, returns the results as a pandas DataFrame, by default False.
Returns
-------
output_data : dict
Dictionary containing the results of the LWE climate model.
"""
# --- Extract species settings ---
sensitivity_rf = self.specie_settings.get("sensitivity_rf", None)
ratio_erf_rf = self.specie_settings["ratio_erf_rf"]
efficacy_erf = self.specie_settings.get("efficacy_erf", 1.0)
ch4_change_per_species = self.specie_settings.get(
"ch4_change_per_species", 0.0
) # only for NOx/H2 leakage - CH4 and induced
# --- Extract simulation settings ---
start_year = self.start_year
end_year = self.end_year
specie_name = self.specie_name
specie_inventory = self.specie_inventory
years = list(range(start_year, end_year + 1))
# --- Run the IPCC climate model ---
if specie_name == "CO2":
equivalent_emissions = (
specie_inventory / 10**12
) # Conversion from kgCO2 to GtCO2
co2_molar_mass = 44.01 * 1e-3 # [kg/mol]
air_molar_mass = 28.97e-3 # [kg/mol]
atmosphere_total_mass = 5.1352e18 # [kg]
radiative_efficiency = (
1.33e-5 # radiative efficiency [W/m^2/ppb] with AR6 value
)
A_co2_unit = (
radiative_efficiency
* 1e9
* air_molar_mass
/ (co2_molar_mass * atmosphere_total_mass)
) # RF per unit mass increase in atmospheric abundance of CO2 [W/m^2/kg]
A_co2 = A_co2_unit * specie_inventory
a = [0.2173, 0.2240, 0.2824, 0.2763]
tau = [0, 394.4, 36.54, 4.304]
radiative_forcing_from_year = np.zeros(
(len(specie_inventory), len(specie_inventory))
)
# Radiative forcing induced in year j by the species emitted in year i
for i in range(0, len(specie_inventory)):
for j in range(0, len(specie_inventory)):
if i <= j:
radiative_forcing_from_year[i, j] = A_co2[i] * a[0]
for k in [1, 2, 3]:
radiative_forcing_from_year[i, j] += (
A_co2[i] * a[k] * np.exp(-(j - i) / tau[k])
)
radiative_forcing = np.zeros(len(specie_inventory))
for k in range(0, len(specie_inventory)):
radiative_forcing[k] = np.sum(radiative_forcing_from_year[:, k])
effective_radiative_forcing = radiative_forcing * ratio_erf_rf
else:
if (
specie_name == "NOx - CH4 and induced"
or specie_name == "H2 leakage - CH4 and induced"
):
min_year = min(start_year, 1939)
max_year = max(end_year, 2051)
tau_reference_year = [min_year, 1940, 1980, 1994, 2004, 2050, max_year]
tau_reference_values = [11, 11, 10.1, 10, 9.85, 10.25, 10.25]
tau_function = interp1d(
tau_reference_year, tau_reference_values, kind="linear"
)
years = list(range(start_year, end_year + 1))
tau = tau_function(years)
ch4_molar_mass = 16.04e-3 # [kg/mol]
air_molar_mass = 28.97e-3 # [kg/mol]
atmosphere_total_mass = 5.1352e18 # [kg]
radiative_efficiency = 3.454545e-4 # radiative efficiency [W/m^2/ppb] with AR6 value (5.7e-4) without indirect effects
A_CH4_unit = (
radiative_efficiency
* 1e9
* air_molar_mass
/ (ch4_molar_mass * atmosphere_total_mass)
) # RF per unit mass increase in atmospheric abundance of CH4 [W/m^2/kg]
A_CH4 = A_CH4_unit * ch4_change_per_species * specie_inventory
f1 = 0.5 # Indirect effect on ozone
f2 = 0.15 # Indirect effect on stratospheric water
radiative_forcing_from_year = np.zeros(
(len(specie_inventory), len(specie_inventory))
)
# Radiative forcing induced in year j by the species emitted in year i
for i in range(0, len(specie_inventory)):
for j in range(0, len(specie_inventory)):
if i <= j:
radiative_forcing_from_year[i, j] = (
(1 + f1 + f2) * A_CH4[i] * np.exp(-(j - i) / tau[j])
)
radiative_forcing = np.zeros(len(specie_inventory))
for k in range(0, len(specie_inventory)):
radiative_forcing[k] = np.sum(radiative_forcing_from_year[:, k])
effective_radiative_forcing = radiative_forcing * ratio_erf_rf
else:
radiative_forcing = sensitivity_rf * specie_inventory
effective_radiative_forcing = radiative_forcing * ratio_erf_rf
## Temperature
temperature = np.zeros(len(effective_radiative_forcing))
c = [0.631, 0.429]
d = [8.4, 409.5]
if specie_name == "CO2":
for k in range(0, len(temperature)):
for ki in range(0, k + 1):
term = 0
for j in [0, 1]:
term += a[0] * c[j] * (1 - np.exp((ki - k) / d[j]))
for i in [1, 2, 3]:
term += (
a[i]
* tau[i]
* c[j]
/ (tau[i] - d[j])
* (np.exp((ki - k) / tau[i]) - np.exp((ki - k) / d[j]))
)
temperature[k] += A_co2[ki] * term
elif (
specie_name == "NOx - CH4 and induced"
or specie_name == "H2 leakage - CH4 and induced"
):
for k in range(0, len(temperature)):
for ki in range(0, k + 1):
term = 0
for j in [0, 1]:
term += (
tau[k]
* c[j]
/ (tau[k] - d[j])
* (np.exp((ki - k) / tau[k]) - np.exp((ki - k) / d[j]))
)
temperature[k] += efficacy_erf * (1 + f1 + f2) * A_CH4[ki] * term
else:
tau = 1
for k in range(0, len(temperature)):
for ki in range(0, k + 1):
term = 0
for j in [0, 1]:
term += (
tau
* c[j]
/ (tau - d[j])
* (np.exp((ki - k) / tau) - np.exp((ki - k) / d[j]))
)
temperature[k] += (
efficacy_erf * effective_radiative_forcing[ki] * term
)
# --- Prepare output ---
output_data = {
"radiative_forcing": radiative_forcing,
"effective_radiative_forcing": effective_radiative_forcing,
"temperature": temperature,
}
if return_df:
output_data = pd.DataFrame(output_data, index=years)
output_data.index.name = "Year"
return output_data
|