Why an improved version
The Global Warming Potential (GWP) is a commonly used, simple model to "normalize" the warming impact of different climate pollutants to $CO_2$ equivalents. This approach works well for long-lived climate pollutants (LLCPs) but fails for short-lived climate pollutants (SLCPs). The improved version IGWP accounts much better for impacts of SLCPs.
Scientific background
This project:
is based on the findings in this paper: Cain, M., Lynch, J., Allen, M.R., Fuglestedt, D.J. & Macey, A.H. (2019). Improved calculation of warming- equivalent emissions for short-lived climate pollutants. npj Climate and Atmospheric Science. 2(29). Retrieved from https://www.nature.com/articles/s41612-019-0086-4
inspired by: https://gitlab.ouce.ox.ac.uk/OMP_climate_pollutants/co2-warming-equivalence/
and uses the simple emissions-based impulse response and carbon cycle model FaIR: https://github.com/OMS-NetZero/FAIR
The maths
$$IGWP = GWP_H * (r * \frac{\Delta E_{SLCP}}{\Delta t} * H + s * E_{SLCP})$$
with:
- $IGWP$ - Improved Global Warming Potential
- $GWP_H$ - Global Warming Potential for period $H$ (e.g. $GWP_{100}$ for 100 years)
- $H$ time-horizon (commonly 100 years)
- $r$ - flow term faction, found to be 0.75 with linear regression
- $s$ - stock term fraction, found to be 0.25 with linear regression, condition: $r + s = 1$
- $\Delta E_{SLCP}$ - change of rate of short-lived climate pollutant
- ${\Delta t}$ - time difference for $\Delta E_{SLCP}$, typical value: 20 years
- $E_{SLCP}$ emission short-lived climate pollutant for investigated year
pip install igwpfrom igwp.core import get_emission_data_paths, make_gwps_improved
from igwp.plotting import plot_all
rcp_scenarios = get_emission_data_paths()
plot_all(rcp_scenarios, make_df=make_gwps_improved)
This plot reproduces the Fig.1 in the paper descriobung IGWP (although with this name, https://www.nature.com/articles/s41612-019-0086-4).
from igwp.core import make_gwps_improved
paths = get_emission_data_paths()
df26 = make_gwps_improved(file_name=paths['RCP 2.6'])
df26.loc[2000:2020]
import pandas as pd
additional_emssions = pd.Series([0.1, 0.5, 0.2], index=[2005, 2006, 2007])
additional_emssions.name = 'CH_4 [Gt/yr]'
additional_emssions.index.name = 'Year'
additional_emssions
df26 = make_gwps_improved(file_name=paths['RCP 2.6'], additional_emssions=additional_emssions)
df26.loc[2000:2010]
Notice the different impacts of this $CH_4$ "flush" on these GWP models.