igwp - Improved Global Warming Potential (IGWP)

A Global Warming Potential model with improved support for short-lived climate pollutions (SLCPs).

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

{% raw %} $$IGWP = GWP_H * (r * \frac{\Delta E_{SLCP}}{\Delta t} * H + s * E_{SLCP})$$ {% endraw %}

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

Install

With pip:

pip install igwp

With conda:

conda instal -c hydrocomputing igwp

Plot all results

This plots shows the differences between GWP, GWP*, and IGWP.

from 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).

Show some values

The results for the emissions based on GWP, GWP*, and IGWP for scenario RCP 2.6 for the years 2000 through 2020:

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]

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Add emissions for some years and see effects

Let's assume very strong increases of $CH_4$ emissions for the years 2005 through 2007 and see what impact ths has on the three different models. (Units of additional emssions are Gt $CH_4$ / yr).

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

Year
2005    0.1
2006    0.5
2007    0.2
Name: CH_4 [Gt/yr], dtype: float64

df26 = make_gwps_improved(file_name=paths['RCP 2.6'], additional_emssions=additional_emssions)
df26.loc[2000:2010]

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Notice the different impacts of this $CH_4$ "flush" on these GWP models.