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

pip install 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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	$CH_4$ GWP	$CH_4$ GWP*	$CH_4$ IGWP
Years
2000	8.405793	-0.896435	1.429122
2001	8.495458	-0.924463	1.430517
2002	8.584206	-0.957068	1.428251
2003	8.672059	-0.994154	1.422399
2004	8.759072	-1.035440	1.413188
2005	8.845276	-1.080772	1.400740
2006	8.961719	-0.974904	1.509252
2007	9.077956	-0.870071	1.616936
2008	9.193784	-0.767278	1.722987
2009	9.309613	-0.664485	1.829040
2010	9.425441	-0.561693	1.935091
2011	9.201847	-1.113669	1.465210
2012	8.978253	-1.665646	0.995329
2013	8.754659	-2.217622	0.525448
2014	8.531065	-2.769599	0.055567
2015	8.307471	-3.321576	-0.414314
2016	8.083877	-3.873554	-0.884196
2017	7.860283	-4.425530	-1.354077
2018	7.636689	-4.977507	-1.823958
2019	7.413095	-5.529483	-2.293839
2020	7.189501	-6.081460	-2.763720

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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	$CH_4$ GWP	$CH_4$ GWP*	$CH_4$ IGWP
Years
2000	8.405793	-0.896435	1.429122
2001	8.495458	-0.924463	1.430517
2002	8.584206	-0.957068	1.428251
2003	8.672059	-0.994154	1.422399
2004	8.759072	-1.035440	1.413188
2005	11.645276	12.919228	12.600740
2006	22.961719	69.025096	57.509252
2007	14.677956	27.129929	24.016936
2008	9.193784	-0.767278	1.722987
2009	9.309613	-0.664485	1.829040
2010	9.425441	-0.561693	1.935091

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