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Global 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. Plain Language Summary: Small particles in the air have important effects on Earth's climate and air quality. Representing the number and size of these particles in global models is challenging because their processes are complex. This factor has often limited global‐model horizontal resolution because fine global resolution models (e.g., 25 km × 25 km or smaller) generally ran too slowly but would be useful for representing details missed at traditional coarse resolution (e.g., 200 km × 250 km). We start with a detailed particle scheme that previously only ran at coarse global resolution because fine resolution would take too long. We present the initial use of this scheme in an updated model version, with a structure allowing a fast‐running, high‐memory model with fine resolution, by using hundreds to thousands of computer cores. In the updated structure, model speed increases with the number of cores used. We find that the total number of particles in the model is more with fine compared to coarse model resolution. These increases are most in Earth's remote regions and for particles which come from gas. Using fine model resolution globally when representing particles could change our understanding of how they impact Earth's climate and air quality. Key Points: We couple aerosol microphysics with GEOS‐Chem's High‐Performance configuration for fine (25 km × 25 km) global‐resolution capabilityGlobal‐mean aerosol number increases with model resolution, dominated by particles smaller than 10 nm in the over‐ocean free troposphereToward finer horizontal resolution, enhanced particle precursor loading in the free troposphere promotes particle formation and growth [ABSTRACT FROM AUTHOR]

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) Tga-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) Tga-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. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: The amount of methane, a powerful greenhouse gas, has been growing in Earth's atmosphere during the last decade, and scientists disagree about which methane sources and sinks are responsible for the growth. One clue into understanding methane's sources and sinks is their seasonality—their month‐to‐month cycles that happen every year. Measurements of atmospheric methane taken at the Earth's surface and using satellite instruments show a steep increase each summer in the Northern Hemisphere that is not replicated when methane is simulated in a global chemical transport model, indicating missing information about source and sink seasonalities. To investigate, we use that model to simulate 24 representations of methane's largest source, emissions from wetlands, and 22 representations of its largest sink, chemical loss by the hydroxyl radical (OH). We find that OH is unlikely to cause the summer increase and model bias, but the amount, spatial distribution, and seasonal cycles of global wetland emissions are the strongest drivers. We suggest that these characteristics are linked to the underlying mechanisms determining wetland area and methane production in wetland models. The results unveil the role of global wetlands in driving methane's seasonality and inform research to analyze methane's long‐term trends. Key Points: Northern Hemisphere atmospheric methane shows a summer increase not replicated by the GEOS‐Chem model with its default sources and sinksThe summer increase's timing and magnitude is determined by the magnitude, seasonality, and spatial distribution of NH wetland emissionsInversions of atmospheric methane observations should use a suitable wetland emission inventory and optimize hemispheric OH concentrations [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Venezuela has long been identified as an area with large methane emissions, especially in the Lake Maracaibo region with intensive oil exploitation, 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 at the national scale with the Integrated Methane Inversion (IMI) framework and at regional scale with theWeather Research and Forecasting (WRF) model. In the IMI analysis, we find 2019 Venezuelan emissions of 7.5 (5.7-9.3) Tg a-1, where about half of emissions can be informed by TROPOMI observations and emissions from oil exploitation are a factor ~1.6 higher than in bottom-up inventories. Using WRF, we find 2019 emissions of 1.2 (1.0 - 1.5) Tg a-1 from the Lake Maracaibo area, close to bottom-up estimates. Our WRF estimate is ~40% lower than the regional result from the IMI due to differences in the meteorology used by the two models. We only find a small, non-significant, trend in emissions between 2018 and 2020 around the lake, implying the area's methane emission intensity expressed against oil/gas production has doubled over the time period to ~20%. This value is much higher than what has previously been found for other oil/gas production regions and indicates there could be large emissions from abandoned infrastructure. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We quantify weekly methane emissions at 0.25 ∘ × 0.3125 ∘ (≈25 × 25 km 2) resolution from the Permian Basin, the largest oil production basin in the US, by inverse analysis of satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI) from May 2018 to October 2020. The mean oil and gas emission from the region (± standard deviation of weekly estimates) was 3.7 ± 0.9 Tg a -1 , higher than previous TROPOMI inversion estimates that may have used biased prior emissions or background assumptions. We find strong week-to-week variability in emissions superimposed on longer-term trends, and these are consistent with independent inferences of temporal emission variability from tower, aircraft, and multispectral satellite data. New well development and natural gas spot price were significant drivers of variability in emissions over our study period but the concurrent 50 % increase in oil and gas production was not. The methane intensity (methane emitted per unit of methane gas produced) averaged 4.6 % ± 1.3 % and steadily decreased from 5 %–6 % in 2018 to 3 %–4 % in 2020. While the decreasing trend suggests improvement in operator practices during the study period, methane emissions from the Permian Basin remained high, with methane intensity an order of magnitude above the industry target of <0.2 %. Our success in using TROPOMI satellite observations for weekly estimates of emissions from a major oil production basin shows promise for application to near-real-time monitoring in support of climate change mitigation efforts. [ABSTRACT FROM AUTHOR]

We quantify 2019 methane emissions in the contiguous U.S. (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 U.S. 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 livestock emissions of 10.4 (10.0–10.7) Tg a-1, oil and gas of 10.4 (10.1–10.7) Tg a-1, coal of 1.5 (1.2–1.9) Tg a-1, landfills of 6.9 (6.4–7.5) Tg a-1, wastewater of 0.6 (0.5–0.7), and 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 73 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 34 % larger than the 2022 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. [ABSTRACT FROM AUTHOR]

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 ∼25 km × 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 non-linear 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 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 postprocessing 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°x2.5° spatial resolution for 2019 using TROPOMI satellite observations. CHEEREIO achieves a 50-fold improvement in computational performance compared to the equivalent analytical inversion of TROPOMI observations. [ABSTRACT FROM AUTHOR]

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 ~25 km × 25 km resolution, using spatially allocated national 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 estimate (+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, 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 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 capture of associated gas, modern infrastructure, and 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 of total anthropogenic methane emissions in the region, making a significant contribution toward the Global Methane Pledge. [ABSTRACT FROM AUTHOR]

We describe a new generation of the high-performance GEOS-Chem (GCHP) global model of atmospheric composition developed as part of the GEOS-Chem version 13 series. GEOS-Chem is an open-source grid-independent model that can be used online within a meteorological simulation or offline using archived meteorological data. GCHP is an offline implementation of GEOS-Chem driven by NASA Goddard Earth Observing System (GEOS) meteorological data for massively parallel simulations. Version 13 offers major advances in GCHP for ease of use, computational performance, versatility, resolution, and accuracy. Specific improvements include (i) stretched-grid capability for higher resolution in user-selected regions, (ii) more accurate transport with new native cubed-sphere GEOS meteorological archives including air mass fluxes at hourly temporal resolution with spatial resolution up to C720 (∼ 12 km), (iii) easier build with a build system generator (CMake) and a package manager (Spack), (iv) software containers to enable immediate model download and configuration on local computing clusters, (v) better parallelization to enable simulation on thousands of cores, and (vi) multi-node cloud capability. The C720 data are now part of the operational GEOS forward processing (GEOS-FP) output stream, and a C180 (∼ 50 km) consistent archive for 1998–present is now being generated as part of a new GEOS-IT data stream. Both of these data streams are continuously being archived by the GEOS-Chem Support Team for access by GCHP users. Directly using horizontal air mass fluxes rather than inferring from wind data significantly reduces global mean error in calculated surface pressure and vertical advection. A technical performance demonstration at C720 illustrates an attribute of high resolution with population-weighted tropospheric NO 2 columns nearly twice those at a common resolution of 2 ∘ × 2.5 ∘. [ABSTRACT FROM AUTHOR]

We present a user-friendly, cloud-based facility for quantifying methane emissions with 0.25 ∘ × 0.3125 ∘ (≈ 25 km × 25 km) resolution by inverse analysis of satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI). The facility is built on an Integrated Methane Inversion optimal estimation workflow (IMI 1.0) and supported for use on the Amazon Web Services (AWS) cloud. It exploits the GEOS-Chem chemical transport model and TROPOMI data already resident on AWS, thus avoiding cumbersome big-data download. Users select a region and period of interest, and the IMI returns an analytical solution for the Bayesian optimal estimate of period-average emissions on the 0.25 ∘ × 0.3125 ∘ grid including error statistics, information content, and visualization code for inspection of results. The inversion uses an advanced research-grade algorithm fully documented in the literature. An out-of-the-box inversion with rectilinear grid and default prior emission estimates can be conducted with no significant learning curve. Users can also configure their inversions to infer emissions for irregular regions of interest, swap in their own prior emission inventories, and modify inversion parameters. Inversion ensembles can be generated at minimal additional cost once the Jacobian matrix for the analytical inversion has been constructed. A preview feature allows users to determine the TROPOMI information content for their region and time period of interest before actually performing the inversion. The IMI is heavily documented and is intended to be accessible by researchers and stakeholders with no expertise in inverse modelling or high-performance computing. We demonstrate the IMI's capabilities by applying it to estimate methane emissions from the US oil-producing Permian Basin in May 2018. [ABSTRACT FROM AUTHOR]

We quantify weekly methane emissions at 0.25°×0.3125° (≈25×25 km²) resolution from the Permian Basin, the largest oil production basin in the United States, by inverse analysis of satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI) from May 2018 to October 2020. The mean oil and gas emission from the region (± standard deviation of weekly estimates) was 3.7 ± 0.9 Tg a-1, higher than previous TROPOMI inversion estimates that may have used too-low prior emissions or biased background assumptions. We find strong week-to-week variability in emissions superimposed on longer-term trends, and these are consistent with independent inferences of temporal emission variability from tower, aircraft, and multispectral satellite data. New well development and local natural gas spot price were significant drivers of variability in emissions over our study period, but the concurrent 50 % increase in oil and gas production was not. The methane intensity (methane emitted per unit of methane gas produced) averaged 4.6 % ± 1.3 % and steadily decreased over the period from 5–6 % in 2018 to 3–4 % in 2020. While the decreasing trend suggests improvement in operator practices during the study period, methane emissions from the Permian Basin remained high, with methane intensity an order of magnitude above recent industry targets of <0.2 %. Our success in using TROPOMI satellite observations for weekly estimates of emissions from a major oil production basin shows promise for application to near-real-time monitoring in support of climate change mitigation efforts. [ABSTRACT FROM AUTHOR]

We use satellite methane observations from the Tropospheric Monitoring Instrument (TROPOMI), for May 2018 to February 2020, to quantify methane emissions from individual oil and natural gas (O/G) basins in the US and Canada using a high-resolution (∼25 km) atmospheric inverse analysis. Our satellite-derived emission estimates show good consistency with in situ field measurements (R=0.96) in 14 O/G basins distributed across the US and Canada. Aggregating our results to the national scale, we obtain O/G -related methane emission estimates of 12.6±2.1 Tg a -1 for the US and 2.2±0.6 Tg a -1 for Canada, 80 % and 40 %, respectively, higher than the national inventories reported to the United Nations. About 70 % of the discrepancy in the US Environmental Protection Agency (EPA) inventory can be attributed to five O/G basins, the Permian, Haynesville, Anadarko, Eagle Ford, and Barnett basins, which in total account for 40 % of US emissions. We show more generally that our TROPOMI inversion framework can quantify methane emissions exceeding 0.2–0.5 Tg a -1 from individual O/G basins, thus providing an effective tool for monitoring methane emissions from large O/G basins globally. [ABSTRACT FROM AUTHOR]

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 0.25∘×0.3125∘ 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. [ABSTRACT FROM AUTHOR]

Hydroelectric reservoirs can emit significant quantities of methane, particularly through degassing at turbine outlets. Improved understanding of processes affecting hydroelectric reservoir CH4 emissions is thus important as the world economy transitions to renewable forms of energy production. Here we develop and evaluate a new mechanistic model of CH4 emissions: ResME ([Res]ervoir [M]ethane [E]missions), which estimates carbon inputs and methanogenesis to predict CH4 release via ebullition and diffusion, plant emissions, and downstream emissions. ResME results demonstrate that the relative importance of allochthonous and autochthonous carbon input to methane emissions varies by latitude, with allochthonous carbon contributions typically being higher in tropical reservoirs. Results also demonstrate that total reservoir emissions are highly dependent on turbine intake depths, which are not typically reported. Potential maximum degassing emissions from existing hydroelectric reservoirs are estimated as 11 ± 4 Tg C/yr, if all reservoirs had deep turbine intakes and stratified for 5 months per year. In comparison, the estimated diffusive, ebullitive, and plant CH4 emissions are estimated to be 2.8 ± 0.2 Tg C/yr (where the true uncertainty is much higher than the model standard error). Future work should focus on improving estimates of reservoir carbon inputs and decomposition rates, as well as surveying turbine intake depths. Satellite measurements from missions such as TROPOMI may also help constrain hydropower methane emissions. Plain Language Summary: Methane is an important greenhouse gas that is naturally produced in lake and reservoir sediment, among other sources. Hydroelectric power reservoirs produce renewable energy, yet also emit methane at their surfaces, and from turbines and downstream reaches. To better understand drivers and pathways of methane emissions, we have developed a new mechanistic model for methane emissions as a function of carbon inputs, chemical decomposition, and physical processes. Results also show that downstream methane emissions have the potential to exceed surface emissions if turbines pull from stratified, anoxic waters. Large uncertainties remain in model inputs, and future work should focus on improved understanding of carbon loading to reservoirs, as well as decomposition rates and turbine intake depths. Key Points: Our mechanistic model (Reservoir Methane Emissions) illuminates the main drivers of hydropower methane emissionsEmissions from downstream degassing are comparable to surface emissions when turbines are parameterized with deep water intakesWe estimate global emissions from hydropower surfaces as 2.8 ± 0.2 Tg C/yr, plus 11 ± 4 Tg C/yr from downstream degassing [ABSTRACT FROM AUTHOR]

A major computational barrier in global modeling of atmospheric chemistry is the numerical integration of the coupled kinetic equations describing the chemical mechanism. Machine-learned (ML) solvers can offer order of magnitude speedup relative to conventional implicit solvers but past implementations have suffered from fast error growth and only run for short simulation times (<1 month). A successful ML solver for global models must avoid error growth over yearlong simulations and allow for reinitialization of the chemical trajectory by transport at every time step. Here, we explore the capability of a neural network solver equipped with an autoencoder to achieve stable full-year simulations of tropospheric oxidant chemistry in the global 3-D Goddard Earth Observing System (GEOS)-Chem model, replacing its standard mechanism (228 species) by the Super-Fast mechanism (12 species) to avoid the curse of dimensionality. We find that online training of the ML solver within GEOS-Chem is important for accuracy, whereas offline training from archived GEOS-Chem inputs/outputs produces large errors. After online training, we achieve stable 1-year simulations with five-fold speedup compared to the standard implicit Rosenbrock solver with global tropospheric normalized mean biases of -0.3% for ozone, 1% for hydrogen oxide radicals, and -5% for nitrogen oxides. The ML solver captures the diurnal and synoptic variability of surface ozone at polluted and clean sites. There are however large regional biases for ozone and NOx under remote conditions where chemical aging leads to error accumulation. These regional biases remain a major limitation for practical application, and ML emulation would be more difficult in a more complex mechanism. Plain Language Summary Global models of atmospheric chemistry are computationally expensive. A bottleneck is the chemical solver that integrates the large-dimensional coupled systems of kinetic equations describing the chemical mechanism. Machine learning (ML) could be transformative for reducing the cost of an atmospheric chemistry simulation by replacing the chemical solver with a faster emulator. However, past work found that ML chemical solvers experience rapid error growth and become unstable over time. Here, we present results achieving for the first time a stable full-year global simulation of atmospheric chemistry with 3 months seasonal ML solvers and with five-fold speedup in computational performance over the reference simulation. We show that online training of the ML solver synchronously with the atmospheric chemistry model simulation produces considerably more stable results than offline training from a static data set of simulation results. Although our work represents an important step for using ML solvers in global atmospheric chemistry models, more work is needed to extend it to large chemical mechanisms and to reduce errors during long-term chemical aging. [ABSTRACT FROM AUTHOR]

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 prior estimate the 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 0.25o×0.3125o resolution. The optimization is done analytically assuming lognormally distributed errors on prior emissions. Errors and information content on the optimal estimates are obtained directly from the analytical solution and also through a 36-member inversion ensemble. Our optimal estimate for total anthropogenic emissions in China is 65.0 (57.7-68.4) Tg a-1, where parentheses indicate uncertainty range. 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 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We quantify methane emissions and their 2010–2017 trends by sector in the contiguous United States (CONUS), Canada, and Mexico by inverse analysis of in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) atmospheric methane observations. The inversion uses as a prior estimate the national anthropogenic emission inventories for the three countries reported by the US Environmental Protection Agency (EPA), Environment and Climate Change Canada (ECCC), and the Instituto Nacional de Ecología y Cambio Climático (INECC) in Mexico to the United Nations Framework Convention on Climate Change (UNFCCC) and thus serves as an evaluation of these inventories in terms of their magnitudes and trends. Emissions are optimized with a Gaussian mixture model (GMM) at 0.5∘×0.625∘ resolution and for individual years. Optimization is done analytically using lognormal error forms. This yields closed-form statistics of error covariances and information content on the posterior (optimized) estimates, allows better representation of the high tail of the emission distribution, and enables construction of a large ensemble of inverse solutions using different observations and assumptions. We find that GOSAT and in situ observations are largely consistent and complementary in the optimization of methane emissions for North America. Mean 2010–2017 anthropogenic emissions from our base GOSAT + in situ inversion, with ranges from the inversion ensemble, are 36.9 (32.5–37.8) Tga-1 for CONUS, 5.3 (3.6–5.7) Tga-1 for Canada, and 6.0 (4.7–6.1) Tga-1 for Mexico. These are higher than the most recent reported national inventories of 26.0 Tga-1 for the US (EPA), 4.0 Tga-1 for Canada (ECCC), and 5.0 Tga-1 for Mexico (INECC). The correction in all three countries is largely driven by a factor of 2 underestimate in emissions from the oil sector with major contributions from the south-central US, western Canada, and southeastern Mexico. Total CONUS anthropogenic emissions in our inversion peak in 2014, in contrast to the EPA report of a steady decreasing trend over 2010–2017. This reflects offsetting effects of increasing emissions from the oil and landfill sectors, decreasing emissions from the gas sector, and flat emissions from the livestock and coal sectors. We find decreasing trends in Canadian and Mexican anthropogenic methane emissions over the 2010–2017 period, mainly driven by oil and gas emissions. Our best estimates of mean 2010–2017 wetland emissions are 8.4 (6.4–10.6) Tga-1 for CONUS, 9.9 (7.8–12.0) Tga-1 for Canada, and 0.6 (0.4–0.6) Tga-1 for Mexico. Wetland emissions in CONUS show an increasing trend of + 2.6 (+ 1.7 to + 3.8) %a-1 over 2010–2017 correlated with precipitation. [ABSTRACT FROM AUTHOR]

Methane emissions in Canada have both anthropogenic and natural sources. Anthropogenic emissions are estimated to be 4.1 Tg a -1 from 2010–2015 in the National Inventory Report submitted to the United Nation's Framework Convention on Climate Change (UNFCCC). Natural emissions, which are mostly due to boreal wetlands, are the largest methane source in Canada and highly uncertain, on the order of ∼ 20 Tg a -1 in biosphere process models. Aircraft studies over the last several years have provided "snapshot" emissions that conflict with inventory estimates. Here we use surface data from the Environment and Climate Change Canada (ECCC) in situ network and space-borne data from the Greenhouse Gases Observing Satellite (GOSAT) to determine 2010–2015 anthropogenic and natural methane emissions in Canada in a Bayesian inverse modelling framework. We use GEOS-Chem to simulate anthropogenic emissions comparable to the National Inventory and wetlands emissions using an ensemble of WetCHARTS v1.0 scenarios in addition to other minor natural sources. We conduct a comparative analysis of the monthly natural emissions and yearly anthropogenic emissions optimized by surface and satellite data independently. Mean 2010–2015 posterior emissions using ECCC surface data are 6.0 ± 0.4 Tg a -1 for total anthropogenic and 11.6 ± 1.2 Tg a -1 for total natural emissions. These results agree with our posterior emissions of 6.5 ± 0.7 Tg a -1 for total anthropogenic and 11.7 ± 1.2 Tg a -1 for total natural emissions using GOSAT data. The seasonal pattern of posterior natural emissions using either dataset shows slower to start emissions in the spring and a less intense peak in the summer compared to the mean of WetCHARTS scenarios. We combine ECCC and GOSAT data to characterize limitations towards sectoral and provincial-level inversions. We estimate energy + agriculture emissions to be 5.1 ± 1.0 Tg a -1 , which is 59 % higher than the national inventory. We attribute 39 % higher anthropogenic emissions to Western Canada than the prior. Natural emissions are lower across Canada. Inversion results are verified against independent aircraft data and surface data, which show better agreement with posterior emissions. This study shows a readjustment of the Canadian methane budget is necessary to better match atmospheric observations with lower natural emissions partially offset by higher anthropogenic emissions. [ABSTRACT FROM AUTHOR]

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° x 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 decrease by 83% in its latest UNFCCC report while Nigerian emissions increase sevenfold, 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 in GFEI v2 are lower than the EDGAR v6 and IEA inventories for all sectors though there is considerable variability in the comparison 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. Inversion results using GFEI as a prior estimate confirm the lower Russian emissions in the latest UNFCCC report but Nigerian 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 in GFEI 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. It 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper describes version 2.0 of the Global Change and Air Pollution (GCAP 2.0) model framework, a one-way offline coupling between version E2.1 of the NASA Goddard Institute for Space Studies (GISS) general circulation model (GCM) and the GEOS-Chem global 3-D chemical-transport model (CTM). Meteorology for driving GEOS-Chem has been archived from the E2.1 contributions to phase 6 of the Coupled Model Intercomparison Project (CMIP6) for the pre-industrial era and the recent past. In addition, meteorology is available for the near future and end of the century for seven future scenarios ranging from extreme mitigation to extreme warming. Emissions and boundary conditions have been prepared for input to GEOS-Chem that are consistent with the CMIP6 experimental design. The model meteorology, emissions, transport, and chemistry are evaluated in the recent past and found to be largely consistent with GEOS-Chem driven by the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) product and with observational constraints. [ABSTRACT FROM AUTHOR]

Emissions are a central component of atmospheric chemistry models. The Harmonized Emissions Component (HEMCO) is a software component for computing emissions from a user-selected ensemble of emission inventories and algorithms. It allows users to re-grid, combine, overwrite, subset, and scale emissions from different inventories through a configuration file and with no change to the model source code. The configuration file also maps emissions to model species with appropriate units. HEMCO can operate in offline stand-alone mode, but more importantly it provides an online facility for models to compute emissions at runtime. HEMCO complies with the Earth System Modeling Framework (ESMF) for portability across models. We present a new version here, HEMCO 3.0, that features an improved three-layer architecture to facilitate implementation into any atmospheric model and improved capability for calculating emissions at any model resolution including multiscale and unstructured grids. The three-layer architecture of HEMCO 3.0 includes (1) the Data Input Layer that reads the configuration file and accesses the HEMCO library of emission inventories and other environmental data, (2) the HEMCO Core that computes emissions on the user-selected HEMCO grid, and (3) the Model Interface Layer that re-grids (if needed) and serves the data to the atmospheric model and also serves model data to the HEMCO Core for computing emissions dependent on model state (such as from dust or vegetation). The HEMCO Core is common to the implementation in all models, while the Data Input Layer and the Model Interface Layer are adaptable to the model environment. Default versions of the Data Input Layer and Model Interface Layer enable straightforward implementation of HEMCO in any simple model architecture, and options are available to disable features such as re-gridding that may be done by independent couplers in more complex architectures. The HEMCO library of emission inventories and algorithms is continuously enriched through user contributions so that new inventories can be immediately shared across models. HEMCO can also serve as a general data broker for models to process input data not only for emissions but for any gridded environmental datasets. We describe existing implementations of HEMCO 3.0 in (1) the GEOS-Chem "Classic" chemical transport model with shared-memory infrastructure, (2) the high-performance GEOS-Chem (GCHP) model with distributed-memory architecture, (3) the NASA GEOS Earth System Model (GEOS ESM), (4) the Weather Research and Forecasting model with GEOS-Chem (WRF-GC), (5) the Community Earth System Model Version 2 (CESM2), and (6) the NOAA Global Ensemble Forecast System – Aerosols (GEFS-Aerosols), as well as the planned implementation in the NOAA Unified Forecast System (UFS). Implementation of HEMCO in CESM2 contributes to the Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA) by providing a common emissions infrastructure to support different simulations of atmospheric chemistry across scales. [ABSTRACT FROM AUTHOR]

We quantify methane emissions and their 2010-2017 trends by sector in the contiguous United States (CONUS), Canada, and Mexico by inverse analysis of in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) atmospheric methane observations. The inversion uses as prior estimate the national anthropogenic emission inventories for the three countries reported by the US Environmental Protection Agency (EPA), Environment and Climate Change Canada (ECCC), and the Instituto Nacional de Ecologia y Cambio Climatico (INECC) in Mexico to the United Nations Framework Convention on Climate Change (UNFCCC), and thus serves as an evaluation of these inventories in terms of their magnitudes and trends. Emissions are optimized with a Gaussian mixture model (GMM) at 0.5°Ã—0.625° resolution and for individual years. Optimization is done analytically using log-normal error forms. This yields closed-form statistics of error estimates and information content on the posterior (optimized) estimates, allows better representation of the high tail of the emission distribution, and enables construction of a large ensemble of inverse solutions using different observations and assumptions. We find that GOSAT and in situ observations are largely consistent and complementary in the optimization of methane emissions for North America. Mean 2010-2017 anthropogenic emissions from our base GOSAT + in situ inversion, with ranges from the inversion ensemble, are 36.9 (32.5-37.8) Tg a-1 for CONUS, 5.3 (3.6-5.7) Tg a-1 for Canada, and 6.0 (4.7-6.1) Tg a-1 for Mexico. These are higher than the most recent reported national inventories of 26.0 Tg a-1 for the US (EPA), 4.0 Tg a-1 for Canada (ECCC), and 5.0 Tg a-1 for Mexico (INECC). The correction in all three countries is largely driven by a factor of 2 underestimate in emissions from the oil sector with major contributions from the south-central US, western Canada, and southeast Mexico. Total CONUS anthropogenic emissions in our inversion peak in 2014, in contrast to the EPA report of a steady decreasing trend over 2010-2017. This reflects combined effects of increases in emissions from the oil and landfill sectors, decrease from the gas, and flat emissions from the livestock and coal sectors. We find decreasing trends in Canadian and Mexican anthropogenic methane emissions over the 2010-2017 period, mainly driven by oil and gas emissions. Our best estimates of mean 2010-2017 wetland emissions are 8.4 (6.4-10.6) Tg a-1 for CONUS, 9.9 (7.8-12.0) Tg a-1 for Canada, and 0.6 (0.4-0.6) Tg a-1 for Mexico. Wetland emissions in CONUS show an increasing trend of 2.6 (1.7-3.8) % a-1 over 2010-2017 correlated with precipitation. [ABSTRACT FROM AUTHOR]

Global high-resolution observations of atmospheric composition from satellites can greatly improve our understanding of surface emissions through inverse analyses. Variational inverse methods can optimize surface emissions at any resolution but do not readily quantify the error and information content of the posterior solution. The information content of satellite data may be much lower than its coverage would suggest because of failed retrievals, instrument noise, and error correlations that propagate through the inversion. Analytical solution of the inverse problem provides closed-form characterization of posterior error statistics and information content but requires the construction of the Jacobian matrix that relates emissions to atmospheric concentrations. Building the Jacobian matrix is computationally expensive at high resolution because it involves perturbing each emission element, typically individual grid cells, in the atmospheric transport model used as the forward model for the inversion. We propose and analyze two methods, reduced dimension and reduced rank, to construct the Jacobian matrix at greatly decreased computational cost while retaining information content. Both methods are two-step iterative procedures that begin from an initial native-resolution estimate of the Jacobian matrix constructed at no computational cost by assuming that atmospheric concentrations are most sensitive to local emissions. The reduced-dimension method uses this estimate to construct a Jacobian matrix on a multiscale grid that maintains a high resolution in areas with high information content and aggregates grid cells elsewhere. The reduced-rank method constructs the Jacobian matrix at native resolution by perturbing the leading patterns of information content given by the initial estimate. We demonstrate both methods in an analytical Bayesian inversion of Greenhouse Gases Observing Satellite (GOSAT) methane data with augmented information content over North America in July 2009. We show that both methods reproduce the results of the native-resolution inversion while achieving a factor of 4 improvement in computational performance. The reduced-dimension method produces an exact solution at a lower spatial resolution, while the reduced-rank method solves the inversion at native resolution in areas of high information content and defaults to the prior estimate elsewhere. [ABSTRACT FROM AUTHOR]

The nonlinear dependence of the dust saltation process on wind speed poses a challenge for models of varying resolutions. This challenge is of particular relevance for the next generation of chemical transport models with nimble capability for multiple resolutions. We develop and apply a method to harmonize dust emissions across simulations of different resolutions by generating offline grid-independent dust emissions driven by native high-resolution meteorological fields. We implement into the GEOS-Chem chemical transport model a high-resolution dust source function to generate updated offline dust emissions. These updated offline dust emissions based on high-resolution meteorological fields strengthen dust emissions over relatively weak dust source regions, such as in southern South America, southern Africa and the southwestern United States. Identification of an appropriate dust emission strength is facilitated by the resolution independence of offline emissions. We find that the performance of simulated aerosol optical depth (AOD) versus measurements from the AERONET network and satellite remote sensing improves significantly when using the updated offline dust emissions with the total global annual dust emission strength of 2000 Tgyr-1 rather than the standard online emissions in GEOS-Chem. The updated simulation also better represents in situ measurements from a global climatology. The offline high-resolution dust emissions are easily implemented in chemical transport models. The source code and global offline high-resolution dust emission inventory are publicly available. [ABSTRACT FROM AUTHOR]

We present the WRF-GC model v2.0, an online two-way coupling of the Weather Research and Forecasting (WRF) meteorological model (v3.9.1.1) and the GEOS-Chem model (v12.7.2). WRF-GC v2.0 is built on the modular framework of WRF-GC v1.0 and further includes aerosol–radiation interaction (ARI) and aerosol–cloud interaction (ACI) based on bulk aerosol mass and composition, as well as the capability to nest multiple domains for high-resolution simulations. WRF-GC v2.0 is the first implementation of the GEOS-Chem model in an open-source dynamic model with chemical feedbacks to meteorology. In WRF-GC, meteorological and chemical calculations are performed on the exact same 3-D grid system; grid-scale advection of meteorological variables and chemical species uses the same transport scheme and time steps to ensure mass conservation. Prescribed size distributions are applied to the aerosol types simulated by GEOS-Chem to diagnose aerosol optical properties and activated cloud droplet numbers; the results are passed to the WRF model for radiative and cloud microphysics calculations. WRF-GC is computationally efficient and scalable to massively parallel architectures. We use WRF-GC v2.0 to conduct sensitivity simulations with different combinations of ARI and ACI over China during January 2015 and July 2016. Our sensitivity simulations show that including ARI and ACI improves the model's performance in simulating regional meteorology and air quality. WRF-GC generally reproduces the magnitudes and spatial variability of observed aerosol and cloud properties and surface meteorological variables over East Asia during January 2015 and July 2016, although WRF-GC consistently shows a low bias against observed aerosol optical depths over China. WRF-GC simulations including both ARI and ACI reproduce the observed surface concentrations of PM 2.5 in January 2015 (normalized mean bias of - 9.3 %, spatial correlation r of 0.77) and afternoon ozone in July 2016 (normalized mean bias of 25.6 %, spatial correlation r of 0.56) over eastern China. WRF-GC v2.0 is open source and freely available from http://wrf.geos-chem.org (last access: 20 June 2021). [ABSTRACT FROM AUTHOR]

Emissions are a central component of atmospheric chemistry models. The Harmonized Emissions Component (HEMCO) is a software component for computing emissions from a user-selected ensemble of emission inventories and algorithms. While available in standalone mode, HEMCO also provides a general on-line facility for models to compute emissions at runtime. It allows users to re-grid, combine, overwrite, subset, and scale emissions from different inventories through a configuration file and with no change to the model source code. The configuration file also maps emissions to model species with appropriate units. HEMCO complies with the Earth System Modeling Framework (ESMF) for portability across models. We present here a new version HEMCO 3.0 that features an improved three-layer architecture to facilitate implementation into any atmospheric model, and improved capability for calculating emissions at any model resolution including multiscale and unstructured grids. The three-layer architecture of HEMCO 3.0 includes (1) a Data Input Layer that reads the configuration file and accesses the HEMCO library of emission inventories and other environmental data; (2) the HEMCO Core that computes emissions on the user-selected HEMCO grid; and (3) the Model Interface Layer that re-grids (if needed) and serves the data to the atmospheric model, and also serves model data to the HEMCO Core for computing emissions dependent on model state (such as from dust, vegetation, etc.). The HEMCO Core is common to the implementation in all models, while the Data Input Layer and the Model Interface Layer are adaptable to the model environment. Default versions of the Data Input Layer and Model Interface Layer enable straightforward implementation of HEMCO in any simple model architecture, and options are available to disable features such as re-gridding that may be done by independent couplers in more complex architectures. The HEMCO library of emission inventories and algorithms is continuously enriched through user contributions, so that new inventories can be immediately shared across models. HEMCO can also serve as a general data broker for models to process input data not only for emissions but for any gridded environmental datasets.We describe existing implementations of HEMCO 3.0 in (1) the GEOS-Chem 'Classic' chemical transport model with shared-memory infrastructure, (2) the high-performance GEOS-Chem (GCHP) model with distributed-memory architecture, (3) the NASA GEOS Earth System Model (GEOS ESM), (4) the Weather Research and Forecasting model with GEOS-Chem (WRF-GC), (5) the Community Earth System Model Version 2 (CESM2), and (6) the NOAA Global Ensemble Forecast System – Aerosols (GEFS-Aerosols), and the planned implementation in the NOAA Unified Forecast System (UFS). Implementation of HEMCO in the CESM2 model contributes to the Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA) by providing a common emissions infrastructure to support different simulations of atmospheric chemistry across scales. [ABSTRACT FROM AUTHOR]

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° x 2.5° horizontal resolution. We compare the results to an inversion using the sparser but more mature GOSAT satellite retrievals, as well as 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 optimization 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° x 2.5° scale of the inversion and large spatial variations of the number of observations make it less useful than GOSAT for quantifying emissions at that resolution. 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. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

We use 2010–2015 Greenhouse Gases Observing Satellite (GOSAT) observations of atmospheric methane columns over North America in a high-resolution inversion of methane emissions, including contributions from different sectors and their trends over the period. The inversion involves an analytical solution to the Bayesian optimization problem for a Gaussian mixture model (GMM) of the emission field with up to 0.5∘×0.625∘ resolution in concentrated source regions. The analytical solution provides a closed-form characterization of the information content from the inversion and facilitates the construction of a large ensemble of solutions exploring the effect of different uncertainties and assumptions in the inverse analysis. Prior estimates for the inversion include a gridded version of the Environmental Protection Agency (EPA) Inventory of US Greenhouse Gas Emissions and Sinks (GHGI) and the WetCHARTs model ensemble for wetlands. Our best estimate for mean 2010–2015 US anthropogenic emissions is 30.6 (range: 29.4–31.3) Tg a -1 , slightly higher than the gridded EPA inventory (28.7 (26.4–36.2) Tg a -1). The main discrepancy is for the oil and gas production sectors, where we find higher emissions than the GHGI by 35 % and 22 %, respectively. The most recent version of the EPA GHGI revises downward its estimate of emissions from oil production, and we find that these are lower than our estimate by a factor of 2. Our best estimate of US wetland emissions is 10.2 (5.6–11.1) Tg a -1 , on the low end of the prior WetCHARTs inventory uncertainty range (14.2 (3.3–32.4) Tg a -1), which calls for better understanding of these emissions. We find an increasing trend in US anthropogenic emissions over 2010–2015 of 0.4 % a -1 , lower than previous GOSAT-based estimates but opposite to the decrease reported by the EPA GHGI. Most of this increase appears driven by unconventional oil and gas production in the eastern US. We also find that oil and gas production emissions in Mexico are higher than in the nationally reported inventory, though there is evidence for a 2010–2015 decrease in emissions from offshore oil production. [ABSTRACT FROM AUTHOR]

Surface ozone is a severe air pollution problem in the North China Plain, which is home to 300 million people. Ozone concentrations are highest in summer, driven by fast photochemical production of hydrogen oxide radicals (HOx) that can overcome the radical titration caused by high emissions of nitrogen oxides (NOx) from fuel combustion. Ozone has been very low during winter haze (particulate) pollution episodes. However, the abrupt decrease of NOx emissions following the COVID-19 lockdown in January 2020 reveals a switch to fast ozone production during winter haze episodes with maximum daily 8-h average (MDA8) ozone concentrations of 60 to 70 parts per billion. We reproduce this switch with the GEOS-Chem model, where the fast production of ozone is driven by HOx radicals from photolysis of formaldehyde, overcoming radical titration from the decreased NOx emissions. Formaldehyde is produced by oxidation of reactive volatile organic compounds (VOCs), which have very high emissions in the North China Plain. This remarkable switch to an ozone-producing regime in January-February following the lockdown illustrates a more general tendency from 2013 to 2019 of increasing winter-spring ozone in the North China Plain and increasing association of high ozone with winter haze events, as pollution control efforts have targeted NOx emissions (30% decrease) while VOC emissions have remained constant. Decreasing VOC emissions would avoid further spreading of severe ozone pollution events into the winter-spring season. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We present the WRF-GC model v2.0, an online two-way coupling of the Weather Research and Forecasting (WRF) meteorological model (v3.9.1.1) and the GEOS-Chem chemical model (v12.7.2). WRF-GC v2.0 is built on the modular framework of WRF-GC v1.0 and further includes aerosol-radiation interactions (ARI) and aerosol-cloud interactions (ACI) based on bulk aerosol mass and composition, as well as the capability to nest multiple domains for high-resolution simulations. WRF- GC v2.0 is the first implementation of the GEOS-Chem model in an open-source dynamic model with chemical feedbacks to meteorology. We apply prescribed size distributions to the 10 aerosol types simulated by GEOS-Chem to diagnose aerosol optical properties and activated cloud droplet numbers; the results are passed to the WRF model for radiative and cloud microphysics calculations. We use WRF-GC v2.0 to conduct sensitivity simulations with different combinations of ARI and ACI over China during January 2015 and July 2016, with the goal of evaluating the simulated aerosol and cloud properties and the impacts of ARI and ACI on meteorology and air quality. WRF-GC reproduces the day-to-day variability of the aerosol optical depth (AOD) observed by the Aerosol Robotic Network (AERONET) project at four representative Chinese sites in January 2015, with temporal correlation coefficients of 0.56 to 0.85. The magnitudes and spatial distributions of the simulated liquid cloud effective radii, liquid cloud optical depths, surface downward shortwave radiation, and surface temperature over China for July 2016 are in good agreement with aircraft, satellite, and surface observations. WRF-GC simulations including both ARI and ACI reproduce the observed surface concentrations and spatial distributions of PM2.5 in January 2015 (normalized mean bias = -6.6 %, spatial correlation r = 0.74) and afternoon ozone in July 2016 (normalized mean bias = 19 %, spatial correlation r = 0.56) over Eastern China, respectively. Our sensitivity simulations show that including the ARI and ACI improved the model's performance in simulating ozone concentrations over China in July, 2016. WRF-GC v2.0 is open source and freely available from http://wrf.geos-chem.org. [ABSTRACT FROM AUTHOR]