We use 2009–2011 space-borne methane observations from the Greenhouse Gases Observing SATellite (GOSAT) to estimate global and North American methane emissions with 4° × 5° and up to 50 km × 50 km spatial resolution, respectively. GEOS-Chem and GOSAT data are first evaluated with atmospheric methane observations from surface and tower networks (NOAA/ESRL, TCCON) and aircraft (NOAA/ESRL, HIPPO), using the GEOS-Chem chemical transport model as a platform to facilitate comparison of GOSAT with in situ data. This identifies a high-latitude bias between the GOSAT data and GEOS-Chem that we correct via quadratic regression. Our global adjoint-based inversion yields a total methane source of 539 Tg a⁻¹ with some important regional corrections to the EDGARv4.2 inventory used as a prior. Results serve as dynamic boundary conditions for an analytical inversion of North American methane emissions using radial basis functions to achieve high resolution of large sources and provide error characterization. We infer a US anthropogenic methane source of 40.2–42.7 Tg a⁻¹, as compared to 24.9–27.0 Tg a⁻¹ in the EDGAR and EPA bottom-up inventories, and 30.0–44.5 Tg a⁻¹ in recent inverse studies. Our estimate is supported by independent surface and aircraft data and by previous inverse studies for California. We find that the emissions are highest in the southern–central US, the Central Valley of California, and Florida wetlands; large isolated point sources such as the US Four Corners also contribute. Using prior information on source locations, we attribute 29–44 % of US anthropogenic methane emissions to livestock, 22–31 % to oil/gas, 20 % to landfills/wastewater, and 11–15 % to coal. Wetlands contribute an additional 9.0–10.1 Tg a⁻¹.

In a 3.5-year long study, the long-term performance of a mobile, solar absorption Bruker EM27/SUN spectrometer, used for greenhouse gas observations, is checked with respect to a co-located reference Bruker IFS 125HR spectrometer, which is part of the Total Carbon Column Observing Network (TCCON). We find that the EM27/SUN is stable on timescales of several years; the drift per year between the EM27/SUN and the official TCCON product is 0.02 ppmv for XCO₂ and 0.9 ppbv for XCH₄, which is within the 1σ precision of the comparison, 0.6 ppmv for XCO₂ and 4.3 ppbv for XCH₄. The bias between the two data sets is 3.9 ppmv for XCO₂ and 13.0 ppbv for XCH₄. In order to avoid sensitivity-dependent artifacts, the EM27/SUN is also compared to a truncated IFS 125HR data set derived from full-resolution TCCON interferograms. The drift is 0.02 ppmv for XCO₂ and 0.2 ppbv for XCH₄ per year, with 1σ precisions of 0.4 ppmv for XCO₂ and 1.4 ppbv for XCH₄, respectively. The bias between the two data sets is 0.6 ppmv for XCO₂ and 0.5 ppbv for XCH₄. With the presented long-term stability, the EM27/SUN qualifies as an useful supplement to the existing TCCON network in remote areas. To achieve consistent performance, such an extension requires careful testing of any spectrometers involved by application of common quality assurance measures. One major aim of the COllaborative Carbon Column Observing Network (COCCON) infrastructure is to provide these services to all EM27/SUN operators. In the framework of COCCON development, the performance of an ensemble of 30 EM27/SUN spectrometers was tested and found to be very uniform, enhanced by the centralized inspection performed at the Karlsruhe Institute of Technology prior to deployment. Taking into account measured instrumental line shape parameters for each spectrometer, the resulting average bias across the ensemble with respect to the reference EM27/SUN used in the long-term study in XCO₂ is 0.20 ppmv, while it is 0.8 ppbv for XCH₄. The average standard deviation of the ensemble is 0.13 ppmv for XCO₂ and 0.6 ppbv for XCH₄. In addition to the robust metric based on absolute differences, we calculate the standard deviation among the empirical calibration factors. The resulting 2σ uncertainty is 0.6 ppmv for XCO₂ and 2.2 ppbv for XCH₄. As indicated by the executed long-term study on one device presented here, the remaining empirical calibration factor deduced for each individual instrument can be assumed constant over time. Therefore the application of these empirical factors is expected to further improve the EM27/SUN network conformity beyond the scatter among the empirical calibration factors reported above.

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.

At the beginning of 2009 new space-borne observations of dry-air column-averaged mole fractions of atmospheric methane (XCH₄) became available from the Thermal And Near infrared Sensor for carbon Observations–Fourier Transform Spectrometer (TANSO-FTS) instrument on board the Greenhouse Gases Observing SATellite (GOSAT). Until April 2012 concurrent {methane (CH₄) retrievals} were provided by the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) instrument on board the ENVironmental SATellite (ENVISAT). The GOSAT and SCIAMACHY XCH₄ retrievals can be compared during the period of overlap. We estimate monthly average CH₄ emissions between January 2010 and December 2011, using the TM5-4DVAR inverse modelling system. In addition to satellite data, high-accuracy measurements from the Cooperative Air Sampling Network of the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA ESRL) are used, providing strong constraints on the remote surface atmosphere. We discuss five inversion scenarios that make use of different GOSAT and SCIAMACHY XCH₄ retrieval products, including two sets of GOSAT proxy retrievals processed independently by the Netherlands Institute for Space Research (SRON)/Karlsruhe Institute of Technology (KIT), and the University of Leicester (UL), and the RemoTeC "Full-Physics" (FP) XCH₄ retrievals available from SRON/KIT. The GOSAT-based inversions show significant reductions in the root mean square (rms) difference between retrieved and modelled XCH₄, and require much smaller bias corrections compared to the inversion using SCIAMACHY retrievals, reflecting the higher precision and relative accuracy of the GOSAT XCH₄. Despite the large differences between the GOSAT and SCIAMACHY retrievals, 2-year average emission maps show overall good agreement among all satellite-based inversions, with consistent flux adjustment patterns, particularly across equatorial Africa and North America. Over North America, the satellite inversions result in a significant redistribution of CH₄ emissions from North-East to South-Central United States. This result is consistent with recent independent studies suggesting a systematic underestimation of CH₄ emissions from North American fossil fuel sources in bottom-up inventories, likely related to natural gas production facilities. Furthermore, all four satellite inversions yield lower CH₄ fluxes across the Congo basin compared to the NOAA-only scenario, but higher emissions across tropical East Africa. The GOSAT and SCIAMACHY inversions show similar performance when validated against independent shipboard and aircraft observations, and XCH₄ retrievals available from the Total Carbon Column Observing Network (TCCON).

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).

The global atmospheric methane growth rates reported by NOAA for 2020 and 2021 are the largest since systematic measurements began in 1983. To explore the underlying reasons for these anomalous growth rates, we use newly available methane data from the Japanese Greenhouse gases Observing SATellite (GOSAT) to estimate methane surface emissions. Relative to baseline values in 2019, we find that a significant global increase in methane emissions of 27.0 ± 11.3 and 20.8 ± 11.4 Tg is needed to reproduce observed atmospheric methane in 2020 and 2021, respectively, assuming fixed climatological values for OH. We see the largest annual increases in methane emissions during 2020 over Eastern Africa (14 ± 3 Tg), tropical Asia (3 ± 4 Tg), tropical South America (5 ± 4 Tg), and temperate Eurasia (3 ± 3 Tg), and the largest reductions are observed over China (−6 ± 3 Tg) and India (−2 ± 3 Tg). We find comparable emission changes in 2021, relative to 2019, except for tropical and temperate South America where emissions increased by 9 ± 4 and 4 ± 3 Tg, respectively, and for temperate North America where emissions increased by 5 ± 2 Tg. The elevated contributions we saw in 2020 over the western half of Africa (−5 ± 3 Tg) are substantially reduced in 2021, compared to our 2019 baseline. We find statistically significant positive correlations between anomalies of tropical methane emissions and groundwater, consistent with recent studies that have highlighted a growing role for microbial sources over the tropics. Emission reductions over India and China are expected in 2020 due to the Covid-19 lockdown but continued in 2021, which we do not currently understand. To investigate the role of reduced OH concentrations during the Covid-19 lockdown in 2020 on the elevated atmospheric methane growth in 2020–2021, we extended our inversion state vector to include monthly scaling factors for OH concentrations over six latitude bands. During 2020, we find that tropospheric OH is reduced by 1.4 ± 1.7 % relative to the corresponding 2019 baseline value. The corresponding revised global growth of a posteriori methane emissions in 2020 decreased by 34 % to 17.9 ± 13.2 Tg, relative to the a posteriori value that we inferred using fixed climatological OH values, consistent with sensitivity tests using the OH climatology inversion using reduced values for OH. The counter statement is that 66 % of the global increase in atmospheric methane during 2020 was due to increased emissions, particularly from tropical regions. Regional flux differences between the joint methane–OH inversion and the OH climatology inversion in 2020 are typically much smaller than 10 %. We find that OH is reduced by a much smaller amount during 2021 than in 2020, representing about 10 % of the growth of atmospheric methane in that year. Therefore, we conclude that most of the observed increase in atmospheric methane during 2020 and 2021 is due to increased emissions, with a significant contribution from reduced levels of OH.