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. We conduct a global inverse analysis of 2010–2018 GOSATobservations to better understand the factors controllingatmospheric methane and its accelerating increase over the 2010–2018period. The inversion optimizes anthropogenic methane emissions and their2010–2018 trends on a 4∘×5∘grid, monthly regional wetland emissions, and annual hemisphericconcentrations of tropospheric OH (the main sink of methane). We use ananalytical solution to the Bayesian optimization problem that providesclosed-form estimates of error covariances and information content for thesolution. We verify our inversion results with independent methaneobservations from the TCCON and NOAA networks. Our inversion successfullyreproduces the interannual variability of the methane growth rate inferredfrom NOAA background sites. We find that prior estimates of fuel-relatedemissions reported by individual countries to the United Nations are toohigh for China (coal) and Russia (oil and gas) and too low for Venezuela(oil and gas) and the US (oil and gas). We show large 2010–2018 increases inanthropogenic methane emissions over South Asia, tropical Africa, andBrazil, coincident with rapidly growing livestock populations in theseregions. We do not find a significant trend in anthropogenic emissions overregions with high rates of production or use of fossil methane, including the US,Russia, and Europe. Our results indicate that the peak methane growth ratesin 2014–2015 are driven by low OH concentrations (2014) and high fireemissions (2015), while strong emissions from tropical (Amazon and tropicalAfrica) and boreal (Eurasia) wetlands combined with increasing anthropogenicemissions drive high growth rates in 2016–2018. Our best estimate is thatOH did not contribute significantly to the 2010–2018 methane trend otherthan the 2014 spike, though error correlation with global anthropogenicemissions limits confidence in this result.

Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations