Abstract. Robust estimates of methane emissions are critical for understanding their impacts on atmospheric warming and air quality, and for assessing methane mitigation strategies. Gridded inventories, such as the U.S. Environmental Protection Agency's Greenhouse Gas Inventory (EPA GHGI), the Emissions Database for Global Atmospheric Research (EDGAR 2024), and the National Oceanic and Atmospheric Administration's Fossil Fuel Oil and Gas inventory (NOAA FOG), are constructed to evaluate large-scale emission patterns and support identifying emission mitigation priorities and prioritizing future measurements. However, substantial differences across inventories complicate such assessments. We benchmark EPA GHGI, EDGAR 2024, and NOAA FOG against flux estimates from an atmospheric inversion of Greenhouse Gases Observing Satellite (GOSAT) data from 2012 to 2020 over the Contiguous United States (CONUS). A key technical challenge is the heterogeneous sensitivity of satellite-derived fluxes, which depends on measurement uncertainty, coverage, and inversion model configuration. We account for this heterogeneity by applying an inversion operator to each inventory prior to comparison with the GOSAT-based estimates. The GOSAT estimates are most sensitive to oil and gas and livestock emissions; oil and gas emissions are consistent with NOAA FOG (14.1 Tg CH4 yr1 in 2015), but exceed EPA GHGI and EDGAR, particularly across Texas, Oklahoma, and Louisiana. GOSAT-based livestock emissions exceed EPA GHGI and EDGAR by 1–2 Tg CH4 yr1, with the largest differences in the Midwest and California. Despite these discrepancies, both activity and satellite based estimates show no observable trends from 2012 to 2020 in fossil and livestock emissions.
Worden et al. (Wed,) studied this question.