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. We use 2010–2015 observations of atmospheric methane columns fromthe GOSAT satellite instrument in a global inverse analysis to improveestimates of methane emissions and their trends over the period, as well asthe global concentration of tropospheric OH (the hydroxyl radical, methane'smain sink) and its trend. Our inversion solves the Bayesian optimizationproblem analytically including closed-form characterization of errors. Thisallows us to (1) quantify the information content from the inversion towardsoptimizing methane emissions and its trends, (2) diagnose error correlationsbetween constraints on emissions and OH concentrations, and (3) generate alarge ensemble of solutions testing different assumptions in the inversion.We show how the analytical approach can be used, even when prior errorstandard deviation distributions are lognormal. Inversion results show largeoverestimates of Chinese coal emissions and Middle East oil and gas emissions inthe EDGAR v4.3.2 inventory but little error in the United States where we use a newgridded version of the EPA national greenhouse gas inventory as priorestimate. Oil and gas emissions in the EDGAR v4.3.2 inventory show largedifferences with national totals reported to the United Nations FrameworkConvention on Climate Change (UNFCCC), and our inversion is generally moreconsistent with the UNFCCC data. The observed 2010–2015 growth inatmospheric methane is attributed mostly to an increase in emissions fromIndia, China, and areas with large tropical wetlands. The contribution fromOH trends is small in comparison. We find that the inversion provides strongindependent constraints on global methane emissions (546 Tg a −1 ) andglobal mean OH concentrations (atmospheric methane lifetime against oxidationby tropospheric OH of 10.8±0.4  years), indicating that satelliteobservations of atmospheric methane could provide a proxy for OHconcentrations in the future.

atmospheric chemistry and physics

Global distribution of methane emissions, emission trends, and OH concentrations and trends inferred from an inversion of GOSAT satellite data for 2010–2015