Data from modeling in support of the Global Methane Assessment, UN Environment, 2020
We focus our modeling on the response to 50% reductions in the anthropogenic increase in methane concentrations. A 50% value is chosen as that is large enough to give a clear signal over meteorological noise in the models and it is similar in magnitude to what methane technical controls could potentially achieve. Based on observations reported by NOAA's Earth System Research Laboratory, the 2015 concentration of methane was 1834 ppb, which is used in Run 3. Ice core data indicate that the 1750 value was about 722 pbb, hence the anthropogenic increase is 1112 ppb. A 50% reduction in that increase would therefore lower concentrations by 556 ppb, so that the atmospheric abundance would then be 1278 ppb, which is used in Run 4. The response is evaluated under present day (2015) conditions. Anthropogenic and biomass burning emissions are from the SSP3 dataset produced in support of CMIP6 and the IPCC AR6 cycle assessments. Though 2015 SSP emissions are projections taken from a model, for consistency with 2050 projections also used in the Global Methane Assessment, these were harmonized to 2010 historical emissions reported by the CEDS project (v2017-05-08). Five state-of-the-art climate models participated in the assessment: the CESM2(WACCM6) model developed at the National Center for Atmospheric Research in Boulder, CO, USA (Danabasoglu et al., 2020; Gettelman et al., 2020); the GFDL AM4.1/ESM4.1 (Dunne et al., 2019; Horowitz et al., 2020) model developed by the National Oceanographic and Atmospheric Administration in Princeton, NJ, USA; the GISS E2.1/E2.1-G model developed by the National Aeronautics and Space Agency in New York, NY, USA (Kelley et al., 2020; doi:10.22033/ESGF/CMIP6.1400); the MIROC-CHASER model developed by the Meteorological Research Institute in Tsukuba, Japan (Sudo et al., 2002; Watanabe et al., 2011; Sekiya et al., 2018); and the UKESM1 model developed jointly by the Met Office, Exeter, UK, and the UK academic community (Archibald et al., 2019; Sellar et al., 2019).
