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

Abstract

Abstract. We conduct a global inverse analysis of 2010–2018 GOSAT observations to better understand the factors controlling atmospheric methane and its accelerating increase over the 2010–2018 period. The inversion optimizes anthropogenic methane emissions and their 2010–2018 trends on a 4∘×5∘ grid, monthly regional wetland emissions, and annual hemispheric concentrations of tropospheric OH (the main sink of methane). We use an analytical solution to the Bayesian optimization problem that provides closed-form estimates of error covariances and information content for the solution. We verify our inversion results with independent methane observations from the TCCON and NOAA networks. Our inversion successfully reproduces the interannual variability of the methane growth rate inferred from NOAA background sites. We find that prior estimates of fuel-related emissions reported by individual countries to the United Nations are too high 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 in anthropogenic methane emissions over South Asia, tropical Africa, and Brazil, coincident with rapidly growing livestock populations in these regions. We do not find a significant trend in anthropogenic emissions over regions with high rates of production or use of fossil methane, including the US, Russia, and Europe. Our results indicate that the peak methane growth rates in 2014–2015 are driven by low OH concentrations (2014) and high fire emissions (2015), while strong emissions from tropical (Amazon and tropical Africa) and boreal (Eurasia) wetlands combined with increasing anthropogenic emissions drive high growth rates in 2016–2018. Our best estimate is that OH did not contribute significantly to the 2010–2018 methane trend other than the 2014 spike, though error correlation with global anthropogenic emissions limits confidence in this result.

Reply on RC2

“…in the south-central US, Venezuela, and the Middle East, consistent in magnitude with the previous inversion of 2010-2018 GOSAT data by Zhang et al [2021], who used the same prior estimate. These two inversions also have consistent magnitude of downward adjustments in the western US, Europe, Russia, and North China Plain.…”

“…This setup is the same as in Lu et al [2021] and Y. Zhang et al [2021] except for the higher horizontal resolution applied to non-wetland emissions. Together we have 4190 state vector elements, which requires a total of 4190 perturbed GEOS-Chem simulations and a base simulation to construct the full Jacobian matrix.…”

“…Prior error variances and covariances for monthly wetland emissions in the 14 subcontinental regions are calculated using the WetCHARTs model ensemble [Bloom et al, 2017] following Y. Zhang et al [2021].…”

“…The regularization parameter of 0.5 for GOSAT is larger than the values of 0.05 -0.1 in Maasakkers et al [2019], Y. Zhang et al [2021], and Lu et al [2021], which used a 4 o´5o resolution and several years of observations. The smaller value of for TROPOMI is due to its large number of collocated observations on the 2°´2.5° model grid.…”

“…TROPOMI would be far more valuable in a regional inversion at higher spatial resolution [Shen et al, 2021], although the regional biases discussed in Section 2 would still be a concern. Zhang et al [2021] previously reported an inversion of 2010-2018 GOSAT data using GEOS-Chem at 4 o´5o resolution. That inversion achieved 179 DOFS, compared to 238 DOFS in our inversion for just one year of GOSAT data at 2 o´2 .5 o resolution.…”