aerocm.climate_models.gwpstar_climate_model

This module contains the GWP* climate model implementation.

GWPStarClimateModel

GWPStarClimateModel(start_year, end_year, specie_name, specie_inventory, specie_settings, model_settings)

Bases: ClimateModel

Class for the GWP* climate model implementation.

Notes

Reference: Lynch et al. (2020), https://doi.org/10.1088/1748-9326/ab6d7e

Source code in aerocm/utils/classes.py

def __init__(
        self,
        start_year: int,
        end_year: int,
        specie_name: str,
        specie_inventory: list | np.ndarray,
        specie_settings: dict,
        model_settings: dict,
):
    """Initialize the climate model with the provided settings.

    Parameters
    ----------
    start_year : int
        Start year of the simulation.
    end_year : int
        End year of the simulation.
    specie_name : str
        Name of the species.
    specie_inventory : list or np.ndarray
        Emission profile for the species.
    specie_settings : dict
        Dictionary containing species settings.
    model_settings : dict
        Dictionary containing model settings.
    """

    # --- Validate parameters ---
    self.validate_model_settings(model_settings)
    self.validate_specie_settings(specie_name, specie_settings)
    self.validate_inventory(start_year, end_year, specie_inventory)

    # --- Store parameters ---
    self.start_year = start_year
    self.end_year = end_year
    self.specie_name = specie_name
    self.specie_inventory = specie_inventory
    self.specie_settings = specie_settings
    self.model_settings = model_settings

run

run(return_df=False)

Run the GWP* climate model with the assigned input data.

Parameters:

Name	Type	Description	Default
return_df	bool	If True, returns the results as a pandas DataFrame, by default False.	False

Returns:

Name	Type	Description
output_data	dict	Dictionary containing the results of the climate model.

Source code in aerocm/climate_models/gwpstar_climate_model.py

def run(self, return_df: bool = False) -> dict | pd.DataFrame:
    """Run the GWP* 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 climate model.
    """

    # --- Extract model settings ---
    tcre = self.model_settings["tcre"]

    # --- 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)

    # --- 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 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:
        radiative_forcing = sensitivity_rf * specie_inventory
        effective_radiative_forcing = radiative_forcing * ratio_erf_rf

        gwpstar_variation_duration = np.nan
        gwpstar_s_coefficient = np.nan

        if (
                specie_name == "Contrails"
                or specie_name == "NOx - ST O3 increase"
                or specie_name == "Soot"
                or specie_name == "Sulfur"
                or specie_name == "H2O"
        ):
            gwpstar_variation_duration = 6
            gwpstar_s_coefficient = 0.0

        elif specie_name == "NOx - CH4 decrease and induced":
            gwpstar_variation_duration = 20
            gwpstar_s_coefficient = 0.25

        equivalent_emissions = (
                gwpstar_equivalent_emissions_function(
                    start_year,
                    end_year,
                    emissions_erf=effective_radiative_forcing,
                    gwpstar_variation_duration=gwpstar_variation_duration,
                    gwpstar_s_coefficient=gwpstar_s_coefficient,
                )
                / 10 ** 12
        )  # Conversion from kgCO2-we to GtCO2-we

    cumulative_equivalent_emissions = np.zeros(len(specie_inventory))
    cumulative_equivalent_emissions[0] = equivalent_emissions[0]
    for k in range(1, len(cumulative_equivalent_emissions)):
        cumulative_equivalent_emissions[k] = (
                cumulative_equivalent_emissions[k - 1] + equivalent_emissions[k]
        )
    temperature = tcre * cumulative_equivalent_emissions * efficacy_erf

    # --- 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

gwpstar_equivalent_emissions_function

gwpstar_equivalent_emissions_function(start_year, end_year, emissions_erf, gwpstar_variation_duration, gwpstar_s_coefficient)

GWP* equivalent emissions function.

Parameters:

Name	Type	Description	Default
start_year	int	Start year of the simulation.	required
end_year	int	End year of the simulation.	required
emissions_erf	ndarray	Emissions effective radiative forcing profile.	required
gwpstar_variation_duration	int	GWP* variation duration.	required
gwpstar_s_coefficient	float	GWP* s coefficient.	required

Returns:

Name	Type	Description
emissions_equivalent_emissions	ndarray	Emissions equivalent emissions profile.

Notes

Reference: Smith et al. (2021), https://doi.org/10.1038/s41612-021-00169-8

Source code in aerocm/climate_models/gwpstar_climate_model.py

def gwpstar_equivalent_emissions_function(
    start_year: int,
    end_year: int,
    emissions_erf: np.ndarray,
    gwpstar_variation_duration: int,
    gwpstar_s_coefficient: float,
):
    """GWP* equivalent emissions function.

    Parameters
    ----------
    start_year : int
        Start year of the simulation.
    end_year : int
        End year of the simulation.
    emissions_erf : np.ndarray
        Emissions effective radiative forcing profile.
    gwpstar_variation_duration : int
        GWP* variation duration.
    gwpstar_s_coefficient : float
        GWP* s coefficient.

    Returns
    -------
    emissions_equivalent_emissions : np.ndarray
        Emissions equivalent emissions profile.

    Notes
    -----
    Reference: Smith et al. (2021), https://doi.org/10.1038/s41612-021-00169-8
    """

    # Global
    climate_time_horizon = 100
    (
        agwp_rf_co2,
        agwp_erf_co2,
        aegwp_rf_co2,
        aegwp_erf_co2,
        agtp_co2,
        iagtp_co2,
        atr_co2,
    ) = co2_ipcc_pulse_absolute_metrics(climate_time_horizon)
    co2_agwp_h = agwp_rf_co2

    # g coefficient for GWP*
    if gwpstar_s_coefficient == 0:
        g_coefficient = 1
    else:
        g_coefficient = (
            1 - np.exp(-gwpstar_s_coefficient / (1 - gwpstar_s_coefficient))
        ) / gwpstar_s_coefficient

    # Main
    emissions_erf_variation = np.zeros(end_year - start_year + 1)
    for k in range(start_year, end_year + 1):
        if k - start_year >= gwpstar_variation_duration:
            emissions_erf_variation[k - start_year] = (
                emissions_erf[k - start_year]
                - emissions_erf[k - gwpstar_variation_duration - start_year]
            ) / gwpstar_variation_duration
        else:
            emissions_erf_variation[k - start_year] = (
                emissions_erf[k - start_year] / gwpstar_variation_duration
            )
    emissions_equivalent_emissions = np.zeros(end_year - start_year + 1)
    for k in range(start_year, end_year + 1):
        emissions_equivalent_emissions[k - start_year] = (
            g_coefficient
            * (1 - gwpstar_s_coefficient)
            * climate_time_horizon
            / co2_agwp_h
            * emissions_erf_variation[k - start_year]
        ) + g_coefficient * gwpstar_s_coefficient / co2_agwp_h * emissions_erf[
            k - start_year
        ]

    return emissions_equivalent_emissions