Urban areas are major sources of population-driven methane with high potential for mitigation, but emission quantification and sectoral attribution remain uncertain. Using satellite observations and a high-resolution (12 kilometers by 12 kilometers) atmospheric inversion framework, we find that emissions from 12 major US urban areas are 80% higher than the US Environmental Protection Agency Greenhouse Gas Inventory (EPA GHGI), with up to four times higher emissions in Houston but 32 to 37% lower emissions in Los Angeles and Cincinnati. Landfills are the principal cause of inventory underestimates, with city-level management practices driving large variations in per capita emissions. Examination of individual landfills with gas collection systems shows gas collection efficiencies averaging 38% (range: 5 to 90%), much lower than their reported average of 70% (range: 40 to 87%). An exception is Los Angeles, where we find landfill gas collection averaging 85%, suggesting large urban methane mitigation potential through improved landfill management.

We present a new 12 km nested resolution capability in the GEOS-Chem global model of atmospheric composition. This capability can be applied to simulations for any user-selected domain worldwide from March 2021 onward by accessing a new hourly cubed-sphere C720 (approximately 12 & times; 12 km(2) resolution) global wind archive from the NASA GEOS-FP meteorological data assimilation system. We regridded the archive to support rectilinear GEOS-Chem Classic nested grid simulations worldwide at 0.125 degrees & times; 0.15625 degrees resolution and denote this as the 12 km GEOS-Chem capability. We evaluate this 12 km configuration of GEOS-Chem by comparison with the standard 25 km (0.25 degrees & times; 0.3125 degrees resolution) nested configuration in simulations of transport tracers, oxidant-aerosol chemistry, and inversions of satellite data using the Integrated Methane Inversion (IMI). The 12 km simulation features stronger vertical transport (up to 20 % lower surface Rn-222 concentrations) because it better captures horizontal convergence both spatially and temporally. Aerosol lifetimes against deposition are shorter by a few percent. The 12 km oxidant-aerosol chemistry can better simulate urban observations of NO2, and shows stronger ozone urban titration together with slightly higher surface ozone background due to enhanced vertical transport. 12 km and 25 km inversions using the IMI show highly consistent results on the regional scale, but the 12 km inversion provides greater information and improved spatial detail to resolve emissions from different sectors.

Meeting climate policy goals to reduce methane emissions under the Paris Agreement and the Global Methane Pledge requires nations to set targets and quantify reductions. Individual countries report emissions by sector to the United Nations Framework Convention on Climate Change (UNFCCC) but there are large uncertainties. Here we optimize 2023 national emissions at up to 25 km grid resolution for 161 countries with a globally consistent open-source framework for inverse analysis of Tropospheric Monitoring Instrument (TROPOMI) satellite observations, using UNFCCC reports for prior estimates together with point source information from GHGSat and other satellites. We find global anthropogenic emissions to be 15% higher than UNFCCC reporting (32% for oil-gas), with national emissions more than 50% higher than reporting for a quarter of the countries. Oil-gas emission intensities vary by two orders of magnitude between countries. Sub-Saharan Africa has the highest livestock emission intensity of any region. Hydroelectric reservoirs, generally not included in UNFCCC reporting, contribute 6% of anthropogenic emissions globally. The framework allows updates for subsequent years, enabling monitoring of emission trends and support for improved reporting.

The hydroxyl radical (OH) is the main oxidant in the troposphere and controls the lifetime of many atmospheric pollutants, including methane. Global annual-mean tropospheric OH concentrations ([OH‾]) have been inferred since the late 1970s using the methyl chloroform (MCF) proxy. However, concentrations of MCF are now approaching the detection limit, and a replacement proxy is urgently needed. Previous inversions of GOSAT (Greenhouse Gases Observing Satellite) satellite measurements of methane in the shortwave infrared (SWIR) have shown success in quantifying [OH‾] independently of methane emissions, and observing system simulations have suggested that satellite measurements in the thermal infrared (TIR) may provide additional constraints on OH. Here we combine SWIR and TIR satellite observations from the GOSAT and AIRS instruments, respectively, in a 3-year (2013–2015) analytical Bayesian inversion optimizing both methane emissions and OH concentrations. We examine how much information can be obtained about the interannual, seasonal, and latitudinal features of the OH distribution. We use information from MCF data and the ACCMIP ensemble of global atmospheric chemistry models to construct a full prior error covariance matrix for OH concentrations for use in the inversion. This is essential to avoid an overfitting of the observations. Our results show that GOSAT alone is sufficient to quantify [OH‾] and its interannual variability independently of methane emissions and that AIRS adds little information. The ability to constrain the latitudinal variability of OH is limited by strong error correlations. There is no information on OH at midlatitudes, but there is some information on the NH/SH interhemispheric ratio, showing this ratio to be lower than currently simulated in models. There is also some information on the seasonal variation in OH concentrations, although it mainly confirms the variation simulated by the models.

We quantify weekly methane emissions and trends from oil and gas production in the US Permian Basin for 2019-2023, and in nearby basins for 2022-2023, by analytical inversion of Tropospheric Monitoring Instrument (TROPOMI) satellite observations with the Integrated Methane Inversion (IMI) at 25 km resolution. Permian oil and gas emissions averaged 4.0 ± 1.1 Tg a-1 over 2019-2023, with large seasonal variation but little interannual variability. Methane intensity fell from 5.2 to 3.2% as production surged. Intensity in the New Mexico Permian fell from 4.5 to 2.1%, approaching the state's 2026 target of <2%. Emissions were on average 50 ± 10% higher in winter than summer, which we corroborate with Permian Basin Tower Network measurements, Insight M aircraft data, and GHGSat satellite observations. This seasonality may be driven in part by higher winter emissions from liquid storage tanks due to decreased separator efficiency in cold conditions. Similar but weaker seasonality along with decreasing emissions and intensities is found in weekly inversions for the Anadarko, Barnett, Eagle Ford, and Haynesville basins in 2022-2023. Our work suggests that better weatherization of oil and gas facilities could significantly reduce methane emissions.

Satellite observations of atmospheric methane are a powerful resource for quantifying methane emissions over any region worldwide. The inverse methods needed to infer emissions from these observations require a high level of scientific and technical expertise as well as access to large computational and data processing resources. The Integrated Methane Inversion (IMI) is an open-access cloud computing tool designed for researchers and non-expert users to obtain total sector-resolved methane emissions worldwide at up to 0.25°×0.3125° (≈25×25km2) resolution by analytical inversion of TROPOMI satellite observations with closed-form error characterization. Here we describe IMI version 2.0 with vastly expanded capabilities relative to the original version. Major developments include (i) a new blended TROPOMI+GOSAT dataset for higher data quality, (ii) order-of-magnitude speed-up in Jacobian matrix construction, (iii) improved error characterization through use of super-observations, (iv) improved methods for initial and boundary conditions, (v) adaptive spatial resolution linked to observational information content, (vi) incorporation of point source observations in state vector construction, (vii) option to optimize tropospheric OH (main methane sink), (viii) global inversion capability, (ix) Kalman filter option for continuous monitoring of emissions, (x) updated default prior emission inventories, (xi) option for lognormal error probability density functions to characterize emissions, (xii) additional output visualization (sectoral emissions, temporal variability), and (xiii) containerization to facilitate download to local computing facilities and operation as part of the US GHG Center. A 2023 annual inversion with 28 d temporal resolution for the contiguous US (CONUS) is presented as a demonstration of IMI 2.0 capabilities.

The Global Fuel Exploitation Inventory (GFEI) is a global 0.1° × 0.1° resolution gridded inventory of methane emissions from oil, gas, and coal exploitation. Here, we present GFEI v3 with updated national emissions to 2020 using reports submitted to the United Nations Framework Convention on Climate Change (UNFCCC), leading to new global emissions of 23, 20, and 31 Tg a−1 for oil, gas, and coal, respectively. We also use new geospatial information from the Oil and Gas Infrastructure Mapping database (OGIM v1) for spatial distribution of global oil-gas methane emissions. We use coal mine locations from the Global Energy Monitor's Global Coal Mine Tracker (GCMT), combined with our own estimates for mine-level methane emissions, to distribute national emissions between coal mine locations. Our mine-level methane emission estimates use country specific emission factors for top producing countries supplemented with modeled emission factors based on coal mine depth and grade. We see the greatest change in the spatial distribution of emissions in GFEI v3 compared to v2 in China due to the use of GCMT for coal mine locations. Large point source plumes (super-emitters) observed by the NASA EMIT instrument are co-located with infrastructure identified in GFEI v3, but the magnitude of the measured emissions is poorly correlated with the gridded emissions in GFEI. This may reflect missing or misrepresented sources in GFEI v3 but also the sporadic nature of the super-emitter measurements used here. By aligning GFEI v3 with national UNFCCC reports and using state-of-the-science geospatial information, the inventory can be confronted with satellite observations of atmospheric methane through inverse modeling to evaluate and improve the UNFCCC reports. We plan to continue updating GFEI to align with reported national emissions and new geospatial information, including assessment of GFEI spatial accuracy through comparison to super-emitter detections. GFEI v3 emission grids by sector and subsector are available at https://doi.org/10.7910/DVN/HH4EUM (Scarpelli et al., 2024).

Abstract. The MethaneAIR aircraft remote sensing instrument observes methane dry air column mixing ratios (XCH4) over ~100×100 km2 scenes with sub-km resolution, from which methane emissions can be inferred by inverse analysis with an atmospheric transport model. It emulates the MethaneSAT satellite instrument launched in March 2024 to quantify emissions from oil/gas production regions. We show here how the single day MethaneAIR observations can be integrated with the global continuous but relatively coarse and sparse observations from the TROPOMI satellite instrument into a common Integrated Methane Inversion (IMI) platform for optimizing methane emissions. The IMI, originally designed for TROPOMI, is used here with 12×12 km2 spatial resolution and lognormal error probability density functions (PDFs) for prior estimates. Application to two scenes in oil/gas production basins of the western US shows remarkable consistency between independent MethaneAIR (single day) and TROPOMI (monthly) inversions including for emission hotspots, with some differences that may reflect temporal variability of emissions. The IMI is able to optimize emissions even when starting from a very poor prior estimate. Using TROPOMI inversion results as prior estimate improves the MethaneAIR inversions by correcting emissions upwind of the MethaneAIR observation scenes and by adding information to the original prior estimate.

We use 2021 TROPOMI and GOSAT satellite observations of atmospheric methane in an analytical inversion to quantify national methane emissions from South America at up to 25 km × 25 km resolution. From the inversion, we derive optimal posterior estimates of methane emissions, adjusting a combination of national anthropogenic emission inventories reported by individual countries to the United Nations Framework Convention on Climate Change (UNFCCC), the UNFCCC-based Global Fuel Exploitation Inventory (GFEIv2), and the Emissions Database for Global Atmospheric Research (EDGARv7) as prior estimates. We also evaluate two alternative wetland emission inventories (WetCHARTs and LPJ-wsl) as prior estimates. Our best posterior estimates for wetland emissions are consistent with previous inventories for the Amazon but lower for the Pantanal and higher for the Paraná. Our best posterior estimate of South American anthropogenic emissions is 48 (41–56) Tg a−1, where numbers in parentheses are the range from our inversion ensemble. This is 55 % higher than our prior estimate and is dominated by livestock (65 % of anthropogenic total). We find that TROPOMI and GOSAT observations can effectively optimize and separate national emissions by sector for 10 of the 13 countries and territories in the region, 7 of which account for 93 % of continental anthropogenic emissions: Brazil (19 (16–23) Tg a−1), Argentina (9.2 (7.9–11) Tg a−1), Venezuela (7.0 (5.5–9.9) Tg a−1), Colombia (5.0 (4.4–6.7) Tg a−1), Peru (2.4 (1.6–3.9) Tg a−1), Bolivia (0.96 (0.66–1.2) Tg a−1), and Paraguay (0.93 (0.88–1.0) Tg a−1). Our estimates align with the prior estimates for Brazil, Bolivia, and Paraguay but are significantly higher for other countries. Emissions in all countries are dominated by livestock (mainly enteric fermentation) except for oil–gas in Venezuela and landfills in Peru. Methane intensities from the oil–gas industry are high in Venezuela (33 %), Colombia (6.5 %), and Argentina (5.9 %). The livestock sector shows the largest difference between our top-down estimate and the UNFCCC prior estimates, and even countries using complex bottom-up methods report UNFCCC emissions significantly lower than our posterior estimate. These discrepancies could stem from underestimations in IPCC-recommended bottom-up calculations or uncertainties in the inversion from aggregation error and the prior spatial distribution of emissions.

Venezuela has long been identified as an area with large methane emissions and intensive oil exploitation, especially in the Lake Maracaibo region, but production has strongly decreased in recent years. The area is notoriously difficult to observe from space due to its complex topography and persistent cloud cover. We use the unprecedented coverage of the TROPOspheric Monitoring Instrument (TROPOMI) methane observations in analytical inversions with the Integrated Methane Inversion (IMI) framework at the national scale and at the local scale with the Weather Research and Forecasting model with chemistry (WRF-Chem). In the IMI analysis, we find Venezuelan emissions of 7.5 (5.7–9.3) Tg a−1 in 2019, where about half of emissions can be informed by TROPOMI observations, and emissions from oil exploitation are a factor of ∼ 1.6 higher than in bottom-up inventories. Using WRF, we find emissions of 1.2 (1.0–1.5) Tg a−1 from the Lake Maracaibo area in 2019, close to bottom-up estimates. Our WRF estimate is ∼ 40 % lower than the result over the same region from the IMI due to differences in the meteorology used by the two models. We find only a small, non-significant trend in emissions between 2018 and 2020 around the lake, implying the area's methane emission intensity expressed against oil and gas production has doubled over the time period, to ∼ 20 %. This value is much higher than what has previously been found for other oil and gas production regions and indicates that there could be large emissions from abandoned infrastructure.

We quantify 2019 annual mean methane emissions in the contiguous US (CONUS) at 0.25° × 0.3125° resolution by inverse analysis of atmospheric methane columns measured by the Tropospheric Monitoring Instrument (TROPOMI). A gridded version of the US Environmental Protection Agency (EPA) Greenhouse Gas Emissions Inventory (GHGI) serves as the basis for the prior estimate for the inversion. We optimize emissions and quantify observing system information content for an eight-member inversion ensemble through analytical minimization of a Bayesian cost function. We achieve high resolution with a reduced-rank characterization of the observing system that optimally preserves information content. Our optimal (posterior) estimate of anthropogenic emissions in CONUS is 30.9 (30.0–31.8) Tg a−1, where the values in parentheses give the spread of the ensemble. This is a 13 % increase from the 2023 GHGI estimate for CONUS in 2019. We find emissions for livestock of 10.4 (10.0–10.7) Tg a−1, for oil and gas of 10.4 (10.1–10.7) Tg a−1, for coal of 1.5 (1.2–1.9) Tg a−1, for landfills of 6.9 (6.4–7.5) Tg a−1, for wastewater of 0.6 (0.5–0.7), and for other anthropogenic sources of 1.1 (1.0–1.2) Tg a−1. The largest increase relative to the GHGI occurs for landfills (51 %), with smaller increases for oil and gas (12 %) and livestock (11 %). These three sectors are responsible for 89 % of posterior anthropogenic emissions in CONUS. The largest decrease (28 %) is for coal. We exploit the high resolution of our inversion to quantify emissions from 70 individual landfills, where we find emissions are on median 77 % larger than the values reported to the EPA's Greenhouse Gas Reporting Program (GHGRP), a key data source for the GHGI. We attribute this underestimate to overestimated recovery efficiencies at landfill gas facilities and to under-accounting of site-specific operational changes and leaks. We also quantify emissions for the 48 individual states in CONUS, which we compare to the GHGI's new state-level inventories and to independent state-produced inventories. Our posterior emissions are on average 27 % larger than the GHGI in the largest 10 methane-producing states, with the biggest upward adjustments in states with large oil and gas emissions, including Texas, New Mexico, Louisiana, and Oklahoma. We also calculate emissions for 95 geographically diverse urban areas in CONUS. Emissions for these urban areas total 6.0 (5.4–6.7) Tg a−1 and are on average 39 (27–52) % larger than a gridded version of the 2023 GHGI, which we attribute to underestimated landfill and gas distribution emissions.

Peroxyacetyl nitrate (PAN) is produced in the atmosphere by photochemical oxidation of non-methane volatile organic compounds in the presence of nitrogen oxides (NOx), and it can be transported over long distances at cold temperatures before decomposing thermally to release NOx in the remote troposphere. It is both a tracer and a precursor for transpacific ozone pollution transported from East Asia to North America. Here, we directly demonstrate this transport with PAN satellite observations from the infrared atmospheric sounding interferometer (IASI). We reprocess the IASI PAN retrievals by replacing the constant prior vertical profile with vertical shape factors from the GEOS-Chem model that capture the contrasting shapes observed from aircraft over South Korea (KORUS-AQ) and the North Pacific (ATom). The reprocessed IASI PAN observations show maximum transpacific transport of East Asian pollution in spring, with events over the Northeast Pacific offshore from the Western US associated in GEOS-Chem with elevated ozone in the lower free troposphere. However, these events increase surface ozone in the US by less than 1 ppbv because the East Asian pollution mainly remains offshore as it circulates the Pacific High.

AbstractGlobal modeling of aerosol‐particle number and size is important for understanding aerosol effects on Earth's climate and air quality. Fine‐resolution global models are desirable for representing nonlinear aerosol‐microphysical processes, their nonlinear interactions with dynamics and chemistry, and spatial heterogeneity. However, aerosol‐microphysical simulations are computationally demanding, which can limit the achievable global horizontal resolution. Here, we present the first coupling of the TwO‐Moment Aerosol Sectional (TOMAS) microphysics scheme with the High‐Performance configuration of the GEOS‐Chem model of atmospheric composition (GCHP), a coupling termed GCHP‐TOMAS. GCHP's architecture allows massively parallel GCHP‐TOMAS simulations including on the cloud, using hundreds of computing cores, faster runtimes, more memory, and finer global horizontal resolution (e.g., 25 km × 25 km, 7.8 × 105 model columns) versus the previous single‐node capability of GEOS‐Chem‐TOMAS (tens of cores, 200 km × 250 km, 1.3 × 104 model columns). GCHP‐TOMAS runtimes have near‐ideal scalability with computing‐core number. Simulated global‐mean number concentrations increase (dominated by free‐tropospheric over‐ocean sub‐10‐nm‐diameter particles) toward finer GCHP‐TOMAS horizontal resolution. Increasing the horizontal resolution from 200 km × 200–50 km × 50 km increases the global monthly mean free‐tropospheric total particle number by 18.5%, and over‐ocean sub‐10‐nm‐diameter particles by 39.8% at 4‐km altitude. With a cascade of contributing factors, free‐tropospheric particle‐precursor concentrations increase (32.6% at 4‐km altitude) with resolution, promoting new‐particle formation and growth that outweigh coagulation changes. These nonlinear effects have the potential to revise current understanding of processes controlling global aerosol number and aerosol impacts on Earth's climate and air quality.

Abstract Surface and satellite observations of atmospheric methane show smooth seasonal behavior in the Southern Hemisphere driven by loss from the hydroxyl (OH) radical. However, observations in the Northern Hemisphere show a sharp mid‐summer increase that is asymmetric with the Southern Hemisphere and not captured by the default configuration of the GEOS‐Chem chemical transport model. Using an ensemble of 22 OH model estimates and 24 wetland emission inventories in GEOS‐Chem, we show that the magnitude, latitudinal distribution, and seasonality of Northern Hemisphere wetland emissions are critical for reproducing the observed seasonality of methane in that hemisphere, with the interhemispheric OH ratio playing a lesser role. Reproducing the observed seasonality requires a wetland emission inventory with ∼80 Tg a−1 poleward of 10°N including significant emissions in South Asia, and an August peak in boreal emissions persisting into autumn. In our 24‐member wetland emission ensemble, only the LPJ‐wsl MERRA‐2 inventory has these attributes.

We use 2019 TROPOMI satellite observations of atmospheric methane in an analytical inversion to quantify methane emissions from the Middle East and North Africa at up to similar to 25 km x 25 km resolution, using spatially allocated national United Nations Framework Convention on Climate Change (UNFCCC) reports as prior estimates for the fuel sector. Our resulting best estimate of anthropogenic emissions for the region is 35% higher than the prior bottom-up inventories (+103% for gas, +53% for waste, +49% for livestock, -14% for oil) with large variability across countries. Oil and gas account for 38% of total anthropogenic emissions in the region. TROPOMI observations can effectively optimize and separate national emissions by sector for most of the 23 countries in the region, with 6 countries accounting for most of total anthropogenic emissions including Iran (5.3 (5.0-5.5) Tg a(-1); best estimate and uncertainty range), Turkmenistan (4.4 (2.8-5.1) Tg a(-1)), Saudi Arabia (4.3 (2.4-6.0) Tg a(-1)), Algeria (3.5 (2.4-4.4) Tg a(-1)), Egypt (3.4 (2.5-4.0) Tg a(-1)), and Turkey (3.0 (2.0-4.1) Tg a(-1)). Most oil-gas emissions are from the production (upstream) subsector, but Iran, Turkmenistan, and Saudi Arabia have large gas emissions from transmission and distribution subsectors. We identify a high number of annual oil-gas emission hotspots in Turkmenistan, Algeria, and Oman and offshore in the Persian Gulf. We show that oil-gas methane emissions for individual countries are not related to production, invalidating a basic premise in the construction of activity-based bottom-up inventories. Instead, local infrastructure and management practices appear to be key drivers of oil-gas emissions, emphasizing the need for including top-down information from atmospheric observations in the construction of oil-gas emission inventories. We examined the methane intensity, defined as the upstream oil-gas emission per unit of methane gas produced, as a measure of the potential for decreasing emissions from the oil-gas sector and using as reference the 0.2% target set by the industry. We find that the methane intensity in most countries is considerably higher than this target, reflecting leaky infrastructure combined with deliberate venting or incomplete flaring of gas. However, we also find that Kuwait, Saudi Arabia, and Qatar meet the industry target and thus show that the target is achievable through the capture of associated gas, modern infrastructure, and the concentration of operations. Decreasing methane intensities across the Middle East and North Africa to 0.2% would achieve a 90% decrease in oil-gas upstream emissions and a 26% decrease in total anthropogenic methane emissions in the region, making a significant contribution toward the Global Methane Pledge.

Nationally reported greenhouse gas inventories are a core component of the Paris Agreement transparency framework. Comparisons with emission estimates derived from atmospheric observations help identify improvements to reduce uncertainties and increase confidence in reported values. To facilitate comparisons over the contiguous United States, we present a 0.1°×0.1° gridded inventory of annual 2012-2018 anthropogenic methane emissions, allocated to 26 individual source categories, with scale-dependent error estimates. Our inventory is consistent with the U.S. Environmental Protection Agency (EPA) Inventory of U.S. Greenhouse Gas Emissions and Sinks (GHGI), submitted to the United Nations in 2020. Total emissions and patterns (spatial/temporal) reflect the activity and emission factor data underlying the GHGI, including many updates relative to a previous gridded GHGI that has been extensively compared with observations. These underlying data are not generally available in global gridded inventories, and comparison to EDGAR v6 shows large spatial differences, particularly for the oil and gas sectors. We also find strong regional variability across all sources in annual 2012-2018 spatial trends, highlighting the importance of understanding regional and facility-level activities. Our inventory represents the first time series of gridded GHGI methane emissions and enables robust comparisons of emissions and their trends with atmospheric observations.

Abstract. We present a versatile, powerful, and user-friendly chemical data assimilation toolkit for simultaneously optimizing emissions and concentrations of chemical species based on atmospheric observations from satellites or suborbital platforms. The CHemistry and Emissions REanalysis Interface with Observations (CHEEREIO) exploits the GEOS-Chem chemical transport model and a localized ensemble transform Kalman filter algorithm (LETKF) to determine the Bayesian optimal (posterior) emissions and/or concentrations of a set of species based on observations and prior information using an easy-to-modify configuration file with minimal changes to the GEOS-Chem or LETKF code base. The LETKF algorithm readily allows for nonlinear chemistry and produces flow-dependent posterior error covariances from the ensemble simulation spread. The object-oriented Python-based design of CHEEREIO allows users to easily add new observation operators such as for satellites. CHEEREIO takes advantage of the Harmonized Emissions Component (HEMCO) modular structure of input data management in GEOS-Chem to update emissions from the assimilation process independently from the GEOS-Chem code. It can seamlessly support GEOS-Chem version updates and is adaptable to other chemical transport models with similar modular input data structure. A post-processing suite combines ensemble output into consolidated NetCDF files and supports a wide variety of diagnostic data and visualizations. We demonstrate CHEEREIO's capabilities with an out-of-the-box application, assimilating global methane emissions and concentrations at weekly temporal resolution and 2∘ × 2.5∘ spatial resolution for 2019 using TROPOspheric Monitoring Instrument (TROPOMI) satellite observations. CHEEREIO achieves a 50-fold improvement in computational performance compared to the equivalent analytical inversion of TROPOMI observations.

The United States is the world's largest oil/gas methane emitter according to current national reports. Reducing these emissions is a top priority in the US government's climate action plan. Here, we use a 2010 to 2019 high-resolution inversion of surface and satellite observations of atmospheric methane to quantify emission trends for individual oil/gas production regions in North America and relate them to production and infrastructure. We estimate a mean US oil/gas methane emission of 14.8 (12.4 to 16.5) Tg a-1 for 2010 to 2019, 70% higher than reported by the US Environmental Protection Agency. While emissions in Canada and Mexico decreased over the period, US emissions increased from 2010 to 2014, decreased until 2017, and rose again afterward. Increases were driven by the largest production regions (Permian, Anadarko, Marcellus), while emissions in the smaller production regions generally decreased. Much of the year-to-year emission variability can be explained by oil/gas production rates, active well counts, and new wells drilled, with the 2014 to 2017 decrease driven by reduction in new wells and the 2017 to 2019 surge driven by upswing of production. We find a steady decrease in the oil/gas methane intensity (emission per unit methane gas production) for almost all major US production regions. The mean US methane intensity decreased from 3.7% in 2010 to 2.5% in 2019. If the methane intensity for the oil/gas supply chain continues to decrease at this pace, we may expect a 32% decrease in US oil/gas emissions by 2030 despite projected increases in production.