dynamic_characterization.ipcc_ar6.radiative_forcing

Module Contents

Functions

IRF_co2(→ callable)	Impulse Resonse Function (IRF) of CO2
characterize_co2(...)	Calculate the cumulative or marginal radiative forcing (CRF) from CO2 for each year in a given period.
characterize_co2_uptake(...)	The same as characterize_co2, but with a negative sign for uptake of CO2.
characterize_co(...)	Calculate the cumulative or marginal radiative forcing (CRF) from CO for each year in a given period.
characterize_ch4(...)	Calculate the cumulative or marginal radiative forcing (CRF) from CH4 for each year in a given period.
characterize_n2o(...)	Calculate the cumulative or marginal radiative forcing (CRF) from N2O for each year in a given period.
create_generic_characterization_function(...)	Creates a characterization function for a GHG based on a decay series, by calling the nested method characterize_generic().

dynamic_characterization.ipcc_ar6.radiative_forcing.IRF_co2(year) → callable

Impulse Resonse Function (IRF) of CO2

Parameters:

year (int) – The year after emission for which the IRF is calculated.

Returns:

The IRF value for the given year.

Return type:

float

dynamic_characterization.ipcc_ar6.radiative_forcing.characterize_co2(series, period: int | None = 100, cumulative: bool | None = False) → dynamic_characterization.classes.CharacterizedRow

Calculate the cumulative or marginal radiative forcing (CRF) from CO2 for each year in a given period.

Based on characterize_co2 from bw_temporalis, but updated numerical values from IPCC AR6 Ch7 & SM.

If cumulative is True, the cumulative CRF is calculated. If cumulative is False, the marginal CRF is calculated. Takes a single row of the TimeSeries Pandas DataFrame (corresponding to a set of (date/amount/flow/activity). For each year in the given period, the CRF is calculated. Units are watts/square meter/kilogram of CO2.

Returns:

• A CharacterizedRow object (namedtuple) with the following fields

• - date (datetime64[s])

• - amount (float)

• - flow (str)

• - activity (str)

See also

Joos2013

Relevant scientific publication on CRF: https://doi.org/10.5194/acp-13-2793-2013

Schivley2015

Relevant scientific publication on the numerical calculation of CRF: https://doi.org/10.1021/acs.est.5b01118

Forster2023

Updated numerical values from IPCC AR6 Chapter 7 (Table 7.15): https://doi.org/10.1017/9781009157896.009

dynamic_characterization.ipcc_ar6.radiative_forcing.characterize_co2_uptake(series, period: int | None = 100, cumulative: bool | None = False) → dynamic_characterization.classes.CharacterizedRow

The same as characterize_co2, but with a negative sign for uptake of CO2.

Based on characterize_co2 from bw_temporalis, but updated numerical values from IPCC AR6 Ch7 & SM.

Calculate the negative cumulative or marginal radiative forcing (CRF) from CO2-uptake for each year in a given period.

If cumulative is True, the cumulative CRF is calculated. If cumulative is False, the marginal CRF is calculated. Takes a single row of the TimeSeries Pandas DataFrame (corresponding to a set of (date/amount/flow/activity). For each year in the given period, the CRF is calculated. Units are watts/square meter/kilogram of CO2.

Returns:

• A CharacterizedRow object (namedtuple) with the following fields

• - date (datetime64[s])

• - amount (float)

• - flow (str)

• - activity (str)

See also

Joos2013

Relevant scientific publication on CRF: https://doi.org/10.5194/acp-13-2793-2013

Schivley2015

Relevant scientific publication on the numerical calculation of CRF: https://doi.org/10.1021/acs.est.5b01118

Forster2023

Updated numerical values from IPCC AR6 Chapter 7 (Table 7.15): https://doi.org/10.1017/9781009157896.009

dynamic_characterization.ipcc_ar6.radiative_forcing.characterize_co(series, period: int | None = 100, cumulative: bool | None = False) → dynamic_characterization.classes.CharacterizedRow

Calculate the cumulative or marginal radiative forcing (CRF) from CO for each year in a given period.

This is exactly the same function as for CO2, it’s just scaled by the ratio of molar masses of CO and CO2. This is because CO is very short-lived (lifetime ~2 months) and we assume that it completely reacts to CO2 within the first year.

Based on characterize_co2 from bw_temporalis, but updated numerical values from IPCC AR6 Ch7 & SM.

Calculate the cumulative or marginal radiative forcing (CRF) from CO2 for each year in a given period.

If cumulative is True, the cumulative CRF is calculated. If cumulative is False, the marginal CRF is calculated. Takes a single row of the TimeSeries Pandas DataFrame (corresponding to a set of (date/amount/flow/activity). For each year in the given period, the CRF is calculated. Units are watts/square meter/kilogram of CO2.

Returns:

• A CharacterizedRow object (namedtuple) with the following fields

• - date (datetime64[s])

• - amount (float)

• - flow (str)

• - activity (str)

See also

Joos2013

Relevant scientific publication on CRF: https://doi.org/10.5194/acp-13-2793-2013

Schivley2015

Relevant scientific publication on the numerical calculation of CRF: https://doi.org/10.1021/acs.est.5b01118

Forster2023

Updated numerical values from IPCC AR6 Chapter 7 (Table 7.15): https://doi.org/10.1017/9781009157896.009

dynamic_characterization.ipcc_ar6.radiative_forcing.characterize_ch4(series, period: int = 100, cumulative=False) → dynamic_characterization.classes.CharacterizedRow

Calculate the cumulative or marginal radiative forcing (CRF) from CH4 for each year in a given period.

Based on characterize_methane from bw_temporalis, but updated numerical values from IPCC AR6 Ch7 & SM.

This DOES include indirect effects of CH4 on ozone and water vapor, but DOES NOT include the decay to CO2. For more info on that, see the deprecated version of bw_temporalis.

If cumulative is True, the cumulative CRF is calculated. If cumulative is False, the marginal CRF is calculated. Takes a single row of the TimeSeries Pandas DataFrame (corresponding to a set of (date/amount/flow/activity). For earch year in the given period, the CRF is calculated. Units are watts/square meter/kilogram of CH4.

Parameters:

• series (array-like) – A single row of the TimeSeries dataframe.

• period (int, optional) – Time period for calculation (number of years), by default 100

• cumulative (bool, optional) – Should the RF amounts be summed over time?

Returns:

• A CharacterizedRow object (namedtuple) with the following fields

• - date (datetime64[s])

• - amount (float)

• - flow (str)

• - activity (str)

See also

Joos2013

Relevant scientific publication on CRF: https://doi.org/10.5194/acp-13-2793-2013

Schivley2015

Relevant scientific publication on the numerical calculation of CRF: https://doi.org/10.1021/acs.est.5b01118

Forster2023

Updated numerical values from IPCC AR6 Chapter 7 (Table 7.15): https://doi.org/10.1017/9781009157896.009

dynamic_characterization.ipcc_ar6.radiative_forcing.characterize_n2o(series, period: int = 100, cumulative=False) → dynamic_characterization.classes.CharacterizedRow

Calculate the cumulative or marginal radiative forcing (CRF) from N2O for each year in a given period.

Based on characterize_methane from bw_temporalis, but updated numerical values from IPCC AR6 Ch7 & SM.

If cumulative is True, the cumulative CRF is calculated. If cumulative is False, the marginal CRF is calculated. Takes a single row of the TimeSeries Pandas DataFrame (corresponding to a set of (date/amount/flow/activity). For earch year in the given period, the CRF is calculated. Units are watts/square meter/kilogram of N2O.

Parameters:

• series (array-like) – A single row of the TimeSeries dataframe.

• period (int, optional) – Time period for calculation (number of years), by default 100

• cumulative (bool, optional) – Should the RF amounts be summed over time?

Returns:

• A CharacterizedRow object (namedtuple) with the following fields

• - date (datetime64[s])

• - amount (float)

• - flow (str)

• - activity (str)

See also

Joos2013

Relevant scientific publication on CRF: https://doi.org/10.5194/acp-13-2793-2013

Schivley2015

Relevant scientific publication on the numerical calculation of CRF: https://doi.org/10.1021/acs.est.5b01118

Forster2023

Updated numerical values from IPCC AR6 Chapter 7 (Table 7.15): https://doi.org/10.1017/9781009157896.009

dynamic_characterization.ipcc_ar6.radiative_forcing.create_generic_characterization_function(decay_series) → dynamic_characterization.classes.CharacterizedRow

Creates a characterization function for a GHG based on a decay series, by calling the nested method characterize_generic().

Parameters:

decay_series (np.ndarray) – A decay series for a specific GHG. This is retrieved from ../data/decay_multipliers.pkl

Return type:

A function called characterize_generic, which in turn returns a TimeSeries dataframe that contains the forcing of the emission of the row over the given period based on the decay series of that biosphere flow.