Package: igwp
igwp - Improved Global Warming Potential (IGWP)A Global Warming Potential model with improved support for short-lived climate pollutions (SLCPs).Why an improved versionThe 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 backgroundThis 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 fromhttps://www.nature.com/articles/s41612-019-0086-4inspired 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/FAIRThe 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 yearInstallWithpip:pip install igwpWithconda:conda instal -c hydrocomputing igwpPlot all resultsThis plots shows the differences between GWP, GWP*, and IGWP.fromigwp.coreimportget_emission_data_paths,make_gwps_improvedfromigwp.plottingimportplot_allrcp_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 valuesThe results for the emissions based on GWP, GWP*, and IGWP for scenario RCP 2.6 for the years 2000 through 2020:fromigwp.coreimportmake_gwps_improvedpaths=get_emission_data_paths()df26=make_gwps_improved(file_name=paths['RCP 2.6'])df26.loc[2000:2020]<style scoped>
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}</style>$CH_4$ GWP$CH_4$ GWP*$CH_4$ IGWPYears20008.405793-0.8964351.42912220018.495458-0.9244631.43051720028.584206-0.9570681.42825120038.672059-0.9941541.42239920048.759072-1.0354401.41318820058.845276-1.0807721.40074020068.961719-0.9749041.50925220079.077956-0.8700711.61693620089.193784-0.7672781.72298720099.309613-0.6644851.82904020109.425441-0.5616931.93509120119.201847-1.1136691.46521020128.978253-1.6656460.99532920138.754659-2.2176220.52544820148.531065-2.7695990.05556720158.307471-3.321576-0.41431420168.083877-3.873554-0.88419620177.860283-4.425530-1.35407720187.636689-4.977507-1.82395820197.413095-5.529483-2.29383920207.189501-6.081460-2.763720Add emissions for some years and see effectsLet'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).importpandasaspdadditional_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_emssionsYear
2005    0.1
2006    0.5
2007    0.2
Name: CH_4 [Gt/yr], dtype: float64df26=make_gwps_improved(file_name=paths['RCP 2.6'],additional_emssions=additional_emssions)df26.loc[2000:2010]<style scoped>
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}</style>$CH_4$ GWP$CH_4$ GWP*$CH_4$ IGWPYears20008.405793-0.8964351.42912220018.495458-0.9244631.43051720028.584206-0.9570681.42825120038.672059-0.9941541.42239920048.759072-1.0354401.413188200511.64527612.91922812.600740200622.96171969.02509657.509252200714.67795627.12992924.01693620089.193784-0.7672781.72298720099.309613-0.6644851.82904020109.425441-0.5616931.935091Notice the different impacts of this $CH_4$ "flush" on these GWP models.