We evaluate the global atmospheric methane column retrievals from the new TROPOMI satellite instrument and apply them to a global inversion of methane sources for 2019 at 2^∘ × 2.5^∘ horizontal resolution. We compare the results to an inversion using the sparser but more mature GOSAT satellite retrievals and to a joint inversion using both TROPOMI and GOSAT. Validation of TROPOMI and GOSAT with TCCON ground-based measurements of methane columns, after correcting for retrieval differences in prior vertical profiles and averaging kernels using the GEOS-Chem chemical transport model, shows global biases of −2.7 ppbv for TROPOMI and −1.0 ppbv for GOSAT and regional biases of 6.7 ppbv for TROPOMI and 2.9 ppbv for GOSAT. Intercomparison of TROPOMI and GOSAT shows larger regional discrepancies exceeding 20 ppbv, mostly over regions with low surface albedo in the shortwave infrared where the TROPOMI retrieval may be biased. Our inversion uses an analytical solution to the Bayesian inference of methane sources, thus providing an explicit characterization of error statistics and information content together with the solution. TROPOMI has ∼ 100 times more observations than GOSAT, but error correlation on the 2^∘ × 2.5^∘ scale of the inversion and large spatial inhomogeneity in the number of observations make it less useful than GOSAT for quantifying emissions at that scale. Finer-scale regional inversions would take better advantage of the TROPOMI data density. The TROPOMI and GOSAT inversions show consistent downward adjustments of global oil–gas emissions relative to a prior estimate based on national inventory reports to the United Nations Framework Convention on Climate Change but consistent increases in the south-central US and in Venezuela. Global emissions from livestock (the largest anthropogenic source) are adjusted upward by TROPOMI and GOSAT relative to the EDGAR v4.3.2 prior estimate. We find large artifacts in the TROPOMI inversion over southeast China, where seasonal rice emissions are particularly high but in phase with extensive cloudiness and where coal emissions may be misallocated. Future advances in the TROPOMI retrieval together with finer-scale inversions and improved accounting of error correlations should enable improved exploitation of TROPOMI observations to quantify and attribute methane emissions on the global scale.

We quantify methane emissions in China and the contributions from different sectors by inverse analysis of 2019 TROPOMI satellite observations of atmospheric methane. The inversion uses as a prior estimate the latest 2014 national sector-resolved anthropogenic emission inventory reported by the Chinese government to the United Nations Framework Convention on Climate Change (UNFCCC) and thus serves as a direct evaluation of that inventory. Emissions are optimized with a Gaussian mixture model (GMM) at up to $\mathrm{0.25}{}^{\circ }\times \mathrm{0.3125}{}^{\circ }$ resolution. The optimization is done analytically assuming log-normally distributed errors on prior emissions. Errors and information content on the optimized estimates are obtained directly from the analytical solution and also through a 36-member inversion ensemble. Our best estimate for total anthropogenic emissions in China is 65.0 (57.7–68.4) Tg a⁻¹, where parentheses indicate the uncertainty range determined by the inversion ensemble. Contributions from individual sectors include 16.6 (15.6–17.6) Tg a⁻¹ for coal, 2.3 (1.8–2.5) for oil, 0.29 (0.23–0.32) for gas, 17.8 (15.1–21.0) for livestock, 9.3 (8.2–9.9) for waste, 11.9 (10.7–12.7) for rice paddies, and 6.7 (5.8–7.1) for other sources. Our estimate is 21% higher than the Chinese inventory reported to the UNFCCC (53.6 Tg a⁻¹), reflecting upward corrections to emissions from oil (+147 %), gas (+61 %), livestock (+37 %), waste (+41 %), and rice paddies (+34 %), but downward correction for coal (−15 %). It is also higher than previous inverse studies (43–62 Tg a⁻¹) that used the much sparser GOSAT satellite observations and were conducted at coarser resolution. We are in particular better able to separate coal and rice emissions. Our higher livestock emissions are attributed largely to northern China where GOSAT has little sensitivity. Our higher waste emissions reflect at least in part a rapid growth in wastewater treatment in China. Underestimate of oil emissions in the UNFCCC report appears to reflect unaccounted-for super-emitting facilities. Gas emissions in China are mostly from distribution, in part because of low emission factors from production and in part because 42 % of the gas is imported. Our estimate of emissions per unit of domestic gas production indicates a low life-cycle loss rate of 1.7 % (1.3 %–1.9 %), which would imply net climate benefits from the current “coal-to-gas” energy transition in China. However, this small loss rate is somewhat misleading considering China's high gas imports, including from Turkmenistan where emission per unit of gas production is very high.

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 $\mathrm{4}{}^{\circ }\times \mathrm{5}{}^{\circ }$ 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.

This study compares recent CO, NO_x, NMVOC, SO₂, BC, and OC anthropogenic emissions from several state‐of‐the‐art top‐down estimates to global and regional bottom‐up inventories and projections from five Shared Socioeconomic Pathways (SSPs) in several regions. Results show that top‐down emissions derived in several recent studies exhibit similar uncertainty as bottom‐up inventories in some regions for certain species and even less in the case of Chinese CO emissions. In general, the largest discrepancies are found outside of regions such as the United States, Europe, and Japan where the most accurate and detailed information on emissions is available. In some regions such as China, which has recently undergone dynamical economic growth and changes in air quality regulations, the top‐down estimates better capture recent emission trends than global bottom‐up inventories. These results show the potential of top‐down estimates to complement bottom‐up inventories and to aide in the development of emission scenarios, particularly in regions where global inventories lack the necessary up‐to‐date and accurate information regarding regional activity data and emission factors such as Africa and India. Areas of future work aimed at quantifying and reducing uncertainty are also highlighted. A regional comparison of recent CO and NO_x trends in the five SSPs indicate that SSP126, a strong pollution control scenario, best represents the trends from the top‐down and regional bottom‐up inventories in the United States, Europe, and China, while SSP460, a low‐pollution control scenario, lies closest to actual trends in West Africa. This analysis can be useful for air quality forecasting and near‐future pollution control/mitigation policy studies.

We use satellite (GOSAT) and in situ (GLOBALVIEWplus CH4 ObsPack) observations of atmospheric methane in a joint global inversion of methane sources, sinks, and trends for the 2010–2017 period. The inversion is done by analytical solution to the Bayesian optimization problem, yielding closed-form estimates of information content to assess the consistency and complementarity (or redundancy) of the satellite and in situ data sets. We find that GOSAT and in situ observations are to a large extent complementary, with GOSAT providing a stronger overall constraint on the global methane distributions, but in situ observations being more important for northern midlatitudes and for relaxing global error correlations between methane emissions and the main methane sink (oxidation by OH radicals). The in-situ-only and the GOSAT-only inversions alone achieve 113 and 212 respective independent pieces of information (DOFS) for quantifying mean 2010–2017 anthropogenic emissions on 1009 global model grid elements, and respective DOFS of 67 and 122 for 2010–2017 emission trends. The joint GOSAT+ in situ inversion achieves DOFS of 262 and 161 for mean emissions and trends, respectively. Thus, the in situ data increase the global information content from the GOSAT-only inversion by 20 %–30 %. The in-situ-only and GOSAT-only inversions show consistent corrections to regional methane emissions but are less consistent in optimizing the global methane budget. The joint inversion finds that oil and gas emissions in the US and Canada are underestimated relative to the values reported by these countries to the United Nations Framework Convention on Climate Change (UNFCCC) and used here as prior estimates, whereas coal emissions in China are overestimated. Wetland emissions in North America are much lower than in the mean WetCHARTs inventory used as a prior estimate. Oil and gas emissions in the US increase over the 2010–2017 period but decrease in Canada and Europe. The joint inversion yields a global methane emission of 551 Tg a⁻¹ averaged over 2010–2017 and a methane lifetime of 11.2 years against oxidation by tropospheric OH (86 % of the methane sink).

We present an updated version of the Global Fuel Exploitation Inventory (GFEI) for methane emissions and evaluate it with results from global inversions of atmospheric methane observations from satellite (GOSAT) and in situ platforms (GLOBALVIEWplus). GFEI allocates methane emissions from oil, gas, and coal sectors and subsectors to a 0.1^∘ × 0.1^∘ grid by using the national emissions reported by individual countries to the United Nations Framework Convention on Climate Change (UNFCCC) and mapping them to infrastructure locations. Our updated GFEI v2 gives annual emissions for 2010–2019 that incorporate the most recent UNFCCC national reports, new oil–gas well locations, and improved spatial distribution of emissions for Canada, Mexico, and China. Russia's oil–gas emissions in its latest UNFCCC report (4.1 Tg a⁻¹ for 2019) decrease by 83 % compared to its previous report while Nigeria's latest reported oil–gas emissions (3.1 Tg a⁻¹ for 2016) increase 7-fold compared to its previous report, reflecting changes in assumed emission factors. Global gas emissions in GFEI v2 show little net change from 2010 to 2019 while oil emissions decrease and coal emissions slightly increase. Global emissions from the oil, gas, and coal sectors in GFEI v2 (26, 22, and 33 Tg a⁻¹, respectively in 2019) are lower than the EDGAR v6 inventory (32, 44, and 37 Tg a⁻¹ in 2018) and lower than the IEA inventory for oil and gas (38 and 43 Tg a⁻¹ in 2019), though there is considerable variability between inventories for individual countries. GFEI v2 estimates higher emissions by country than the Climate TRACE inventory, with notable exceptions in Russia, the US, and the Middle East where TRACE is up to an order of magnitude higher than GFEI v2. Inversion results using GFEI as a prior estimate confirm the lower Russian emissions in the latest UNFCCC report but find that Nigeria's reported UNFCCC emissions are too high. Oil–gas emissions are generally underestimated by the national inventories for the highest emitting countries including the US, Venezuela, Uzbekistan, Canada, and Turkmenistan. Offshore emissions tend to be overestimated. Our updated GFEI v2 provides a platform for future evaluation of national emission inventories reported to the UNFCCC using the newer generation of satellite instruments such as TROPOMI with improved coverage and spatial resolution. This increased observational data density will be especially beneficial in regions where current inversion systems have limited sensitivity including Russia. Our work responds to recent aspirations of the Intergovernmental Panel on Climate Change (IPCC) to integrate top-down and bottom-up information into the construction of national emission inventories.