This study presents top-down estimates of coal-related methane emissions in Southeast Asia derived from a multi-model ensemble within the Methane Inversion Inter-Comparison for Asia (MICA) project. Using seven atmospheric inversion systems, we applied a standardized protocol featuring consistent prior emission inventories and a comprehensive suite of atmospheric constraints. These observations integrate GOSAT satellite retrievals, the NOAA ObsPack CH₄ dataset, and additional in situ measurements from Asian monitoring sites. Monthly sectoral emission fluxes from both in-situ-based and GOSAT-based inversion simulations were aggregated to characterize regional and national-level contributions and trends.Total regional coal-related methane emissions are estimated at 7.90 Tg yr⁻¹ (range: 5.12–9.21 Tg yr⁻¹; median, min-max) for 2019-2021, with Indonesia identified as the dominant source, contributing 7.10 Tg yr⁻¹ (4.50–8.29 Tg yr⁻¹). Indonesia accounts for approximately 90 % of coal production in the region and remains a major global exporter, followed by Vietnam as the second-largest producer and consumer. In Indonesia, coal-related emissions exhibit a statistically significant increasing trend based on the Mann–Kendall trend test (p < 0.05), with mean posterior emissions rising nearly fourfold from 2.02 to 8.47 Tg yr⁻¹ between 2010 and 2021. Notably, Indonesia’s most recent National Greenhouse Gas Inventory (NGHGI) reports energy-sector (including coal) methane emissions of 0.784 Tg yr⁻¹ for 2019 , nearly an order of magnitude lower than our estimates. Emissions from Vietnam are estimated at 0.66 Tg yr⁻¹ (0.47–0.74 Tg yr⁻¹) for 2019-2021; while no significant trend was detected over the full study period, a statistically significant increase was observed during 2017–2021.The rapid growth of coal-related methane emissions poses a critical challenge to Southeast Asia’s climate targets and decarbonization pathways. Our fingdings reveal a substantial discrepancy between top-down estimates and national inventories, identifying a vital opportunity for high-impact mitigation. Prioritizing the recovery of coal mine methane (CMM) is therefore essential; it transforms a significant environmental liability into a valuable energy resource while simultaneously enhancing operational safety. Given the nearly fourfold increase in emissions detected since 2010, aggressive mitigation of the coal sector is imperative if regional climate commitments are to be achieved.

Wetlands are the largest natural source of atmospheric methane (CH4), yet comprehensive global budgets are typically delayed by years, preventing a timely understanding of CH4 sources, sinks, and trends. To reduce this delay, we present a model emulator-driven framework and accompanying workflow that enable timely, continuous emission updates using a machine-learning emulator to reconstruct spatially explicit monthly emission fields at 1 degrees & times; 1 degrees resolution. We apply this framework to a global dataset of natural vegetated wetland CH4 emissions to extend the most recent Global Methane Budget (GMB; Saunois et al., 2025) record that covers the 2000-2020 emissions through 2025. In the test data (similar to 30 % of the total dataset), the emulator achieved a global R-2 of 0.65 +/- 0.003 (mean +/- 95 % CI, hereafter) and an RMSE of 5.49 +/- 0.12 & times; 10(-3) Tg CH4 yr(-1). The emulator is trained on 35 GMB model estimates, including 22 process-based models and 13 atmospheric inversions, paired with 10 ensemble realizations of 11 gridded climate predictor variables from atmospheric reanalyses. Our results show that the global mean predicted wetland CH4 emissions for 2021-2025 (157.8 +/- 2.4 Tg CH4 yr(-1)) are not significantly higher (similar to 0.05 Tg CH4 yr(-1)) than the 2000-2020 baseline. However, this stability masks a significant hemispheric redistribution of emissions. We detect an increase in Northern Hemisphere (NH) emissions in 2021-2025, with mid- and high-latitudes increasing by 0.76 +/- 0.07 and 0.35 +/- 0.03 Tg CH4 yr(-1), respectively, while the tropics and Southern Hemisphere (SH) extratropics show offsetting negative trends (-0.95 +/- 0.19 and -0.11 +/- 0.02 Tg CH4 yr(-1), respectively). The predicted emissions are able to capture the low emissions in 2023 in South America linked to El Ni & ntilde;o-related drought, as reported by recent studies (Ciais et al., 2026; Quinn et al., 2025). Furthermore, we identify a distinct seasonal amplification of global emission trends that peaks in late boreal summer. This new modeled dataset and operational framework bridge the gap between the latest updated budgets and low-latency monitoring, providing a scalable capacity to frequently update global emission estimates and critical early warnings of regional wetland feedback loops. The data are publicly available at https://doi.org/10.5281/zenodo.18870108 (Li et al., 2026).

Inverse modelling is employed to reconcile greenhouse gas (GHG) emission inventories, based on bottom-up methods, with the observed atmospheric GHG concentrations. The Community Inversion Framework (CIF) was created to unify inverse-model developments and simplify the generation of inversions. It makes atmospheric transport models and inversion algorithms easily interchangeable and facilitates the comparison of inversion results obtained using such diverse components.After several years of development and the coupling of CIF with a wide range of transport models used by the inversion community, we present the first intercomparison study conducted with CIF. This exercise focuses on Europe and aims to refine CO₂ natural emissions for the year 2019, following a strict protocol. It involves five transport models (CHIMERE, ICON-ART, LMDz, STILT, and WRF-CHEM) and two inversion algorithms (variational and ensemble-based). Two additional transport models, TM5 and FLEXPART, will be incorporated in the near future.The results show a good agreement, both across transport models, and inversion algorithms. It paves the way towards using CIF as an operational tool for intercomparison studies. It also highlights its strong potential to support the systematic derivation of GHG budgets with multiple transport models, enable a proper and easy quantification of the modelling uncertainty, and improve the robustness of emission estimates, for any relevant atmospheric species, at any scale.

Constraining methane (CH4) emissions at high spatial and temporal resolution is critical for accurate European greenhouse gas budgets and mitigation policy. We use the Community Inversion Framework to estimate monthly CH4 fluxes across Europe (2017–2022) at 0.2°×0.2°, coupling FLEXPART and assimilating observations from 46 in situ stations, including Integrated Carbon Observation System (ICOS) and non-ICOS sites. Prior emissions combine anthropogenic inventories, biomass burning estimates, wetland models, and climatological natural sources. The inversion substantially improves agreement with atmospheric observations (r2=0.87, RMSE=24.4 ppb, mean bias=-2.1 ppb), performing best at northern European stations. For the European Union countries, together with the UK, Norway, and Switzerland, we estimate total methane emissions of 23.3±2.3 Tg CH4 yr−1, which is 6.6 % higher than the prior. For these countries, we found an average anthropogenic emissions of 17.6 Tg CH4 yr−1, with a decreasing trend of 0.3 Tg yr−1. This estimate exceeds the prior by 11 %, EDGARv8 by 4 %, and UNFCCC NGHGI (2023) by 3 %, while remaining consistent with recent studies. Country-level differences are notable, with higher emissions estimated for the Netherlands and Germany, and lower for Romania and Italy. Sectoral changes mainly reflect agricultural increases, alongside reductions in northern wetlands and southern geological sources. Sensitivity tests highlight the influence of spatial spread of emissions distribution assumptions and the importance of dense observational networks for refining regional CH4 budgets.

The isotopic composition of atmospheric methane (delta 13C-CH4) provides critical constraints for attributing methane emissions to specific sources. In this study, we present updated global maps of delta 13C-CH4 source signatures across five major methane-emitting sectors (fossil fuels and geological, agriculture and waste, biomass and biofuel burning, wetlands, and other natural sources) for the period 1998-2022. These maps integrate recent spatially explicit datasets and literature-derived observations, and include explicit quantification of both intrinsic (within-sector) and aggregation-related uncertainties. Building upon previous global compilations, our dataset extends the temporal coverage to 2022, harmonizes sectoral definitions with the Global Methane Budget framework, and provides a consistent and traceable quantification of uncertainties suitable for atmospheric inversions. We assess the influence of these updated source signatures on the modeled atmospheric delta 13C-CH4 using forward simulations within the Community Inversion Framework (CIF) coupled to the LMDz transport model. A comprehensive sensitivity analysis quantifies the impacts of key drivers of uncertainty, including emission flux datasets, OH sinks, kinetic isotope effects, and isotopic source signatures. We show that uncertainties in methane oxidation chemistry and source signatures, particularly from agriculture and waste, dominate the variability in the modeled delta 13C-CH4 signal, while the impact of flux aggregation choices is comparatively minor. The updated isotopic dataset is provided on a global 1 degrees & times;1 degrees grid, supporting future atmospheric inversions and improved methane budget assessments at global and regional scales. Practical guidelines for configuring isotopic inversions, including recommended uncertainty specifications and key parameters to optimize, are also provided, offering a framework for next-generation delta 13C-CH4 inversion studies. The final version of the gridded delta 13C-CH4 source signature dataset is available under CC BY 4.0 (, 10.57780/ESA-6D202E9).

Abstract. Wetlands are the largest natural source of atmospheric methane (CH4), yet comprehensive global budgets are typically delayed by several years, preventing a timely understanding of CH4 sources, sinks, and their trends. To reduce this delay, we present a model emulator-driven framework and accompanying workflow that enable timely, continuous emission updates and applying the framework to a global dataset of natural vegetated wetland CH4 emissions to extend the most recent Global Methane Budget (GMB; Saunois et al., 2025) record through 2025 at monthly 1°x1° resolution. We developed a machine-learning emulator to reconstruct spatially explicit monthly emission fields (global R2 =0.65 ± 0.003 (mean ± 95 % CI, hereafter) and RMSE=5.49 ± 0.12 ×10-3 Tg CH4/year in test data which is ~30 % of the total data). The emulator is trained on 35 GMB model estimates (22 process-based model estimates and 13 atmospheric inversion estimates) paired with 10 ensemble realizations of 11 gridded climate predictor variables from atmospheric reanalyses. While the global mean predicted wetland CH4 emissions for 2021–2025 (157.83 ± 2.38 Tg CH4/year) are only marginally higher (~0.05 Tg CH4/year) than the 2000–2020 baseline, this stability masks a significant hemispheric redistribution of emissions. We detect a surge in Northern Hemisphere emissions in 2021–2025, with mid- and high-latitudes increasing by 0.76 ± 0.07 (z-score: 2.21) and 0.35 ± 0.03 Tg/year (z-score:1.01), respectively, while the tropics and Southern Hemisphere extratropics show offsetting negative trends (-0.95 ± 0.19 and -0.11 ± 0.02 Tg/year with z-scores of -2.81 and -0.34, respectively). The predicted emissions capture the low emissions in 2023 in South America linked to El Niño-related drought, as reported by recent studies (Ciais et al., 2026; Quinn et al., 2025). Post-2020 growth rates of emission anomalies are a magnitude higher than that in 2000–2025, suggesting an intensification of emission variability. Furthermore, we identify a distinct seasonal amplification of global emission growth peaking in late boreal summer. This new dataset and operational framework bridge the gap between latest updated budgets and low-latency monitoring, providing a scalable capacity to frequently update global emission estimates and critical early warnings of regional wetland feedback loops. The data are publicly available at https://doi.org/10.5281/zenodo.18870108 (Li et al., 2026).

Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. CH4 is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2), and both emissions and atmospheric concentrations of CH4 have continued to increase since 2007 after a temporary pause. The relative importance of CH4 emissions compared to those of CO2 for temperature change is related to its shorter atmospheric lifetime, stronger radiative effect, and acceleration in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in quantifying the factors responsible for the observed atmospheric growth rate arise from diverse, geographically overlapping CH4 sources and from the uncertain magnitude and temporal change in the destruction of CH4 by short-lived and highly variable hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to improve, synthesise, and update the global CH4 budget regularly and to stimulate new research on the methane cycle. Following Saunois et al. (2016, 2020), we present here the third version of the living review paper dedicated to the decadal CH4 budget, integrating results of top-down CH4 emission estimates (based on in situ and Greenhouse Gases Observing SATellite (GOSAT) atmospheric observations and an ensemble of atmospheric inverse-model results) and bottom-up estimates (based on process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). We present a budget for the most recent 2010–2019 calendar decade (the latest period for which full data sets are available), for the previous decade of 2000–2009 and for the year 2020. The revision of the bottom-up budget in this 2025 edition benefits from important progress in estimating inland freshwater emissions, with better counting of emissions from lakes and ponds, reservoirs, and streams and rivers. This budget also reduces double counting across freshwater and wetland emissions and, for the first time, includes an estimate of the potential double counting that may exist (average of 23 Tg CH4 yr−1). Bottom-up approaches show that the combined wetland and inland freshwater emissions average 248 [159–369] Tg CH4 yr−1 for the 2010–2019 decade. Natural fluxes are perturbed by human activities through climate, eutrophication, and land use. In this budget, we also estimate, for the first time, this anthropogenic component contributing to wetland and inland freshwater emissions. Newly available gridded products also allowed us to derive an almost complete latitudinal and regional budget based on bottom-up approaches. For the 2010–2019 decade, global CH4 emissions are estimated by atmospheric inversions (top-down) to be 575 Tg CH4 yr−1 (range 553–586, corresponding to the minimum and maximum estimates of the model ensemble). Of this amount, 369 Tg CH4 yr−1 or ∼ 65 % is attributed to direct anthropogenic sources in the fossil, agriculture, and waste and anthropogenic biomass burning (range 350–391 Tg CH4 yr−1 or 63 %–68 %). For the 2000–2009 period, the atmospheric inversions give a slightly lower total emission than for 2010–2019, by 32 Tg CH4 yr−1 (range 9–40). The 2020 emission rate is the highest of the period and reaches 608 Tg CH4 yr−1 (range 581–627), which is 12 % higher than the average emissions in the 2000s. Since 2012, global direct anthropogenic CH4 emission trends have been tracking scenarios that assume no or minimal climate mitigation policies proposed by the Intergovernmental Panel on Climate Change (shared socio-economic pathways SSP5 and SSP3). Bottom-up methods suggest 16 % (94 Tg CH4 yr−1) larger global emissions (669 Tg CH4 yr−1, range 512–849) than top-down inversion methods for the 2010–2019 period. The discrepancy between the bottom-up and the top-down budgets has been greatly reduced compared to the previous differences (167 and 156 Tg CH4 yr−1 in Saunois et al. (2016, 2020) respectively), and for the first time uncertainties in bottom-up and top-down budgets overlap. Although differences have been reduced between inversions and bottom-up, the most important source of uncertainty in the global CH4 budget is still attributable to natural emissions, especially those from wetlands and inland freshwaters. The tropospheric loss of methane, as the main contributor to methane lifetime, has been estimated at 563 [510–663] Tg CH4 yr−1 based on chemistry–climate models. These values are slightly larger than for 2000–2009 due to the impact of the rise in atmospheric methane and remaining large uncertainty (∼ 25 %). The total sink of CH4 is estimated at 633 [507–796] Tg CH4 yr−1 by the bottom-up approaches and at 554 [550–567] Tg CH4 yr−1 by top-down approaches. However, most of the top-down models use the same OH distribution, which introduces less uncertainty to the global budget than is likely justified. For 2010–2019, agriculture and waste contributed an estimated 228 [213–242] Tg CH4 yr−1 in the top-down budget and 211 [195–231] Tg CH4 yr−1 in the bottom-up budget. Fossil fuel emissions contributed 115 [100–124] Tg CH4 yr−1 in the top-down budget and 120 [117–125] Tg CH4 yr−1 in the bottom-up budget. Biomass and biofuel burning contributed 27 [26–27] Tg CH4 yr−1 in the top-down budget and 28 [21–39] Tg CH4 yr−1 in the bottom-up budget. We identify five major priorities for improving the CH4 budget: (i) producing a global, high-resolution map of water-saturated soils and inundated areas emitting CH4 based on a robust classification of different types of emitting ecosystems; (ii) further development of process-based models for inland-water emissions; (iii) intensification of CH4 observations at local (e.g. FLUXNET-CH4 measurements, urban-scale monitoring, satellite imagery with pointing capabilities) to regional scales (surface networks and global remote sensing measurements from satellites) to constrain both bottom-up models and atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) integration of 3D variational inversion systems using isotopic and/or co-emitted species such as ethane as well as information in the bottom-up inventories on anthropogenic super-emitters detected by remote sensing (mainly oil and gas sector but also coal, agriculture, and landfills) to improve source partitioning. The data presented here can be downloaded from https://doi.org/10.18160/GKQ9-2RHT (Martinez et al., 2024).

We present the CH4 and N2O budgets for anthropogenic and natural sources and sinks of Australasia (Australia and New Zealand) from 2010 to 2019 using bottom-up and top-down methods, in line with the RECCAP-2 initiative, with extensions to 2022. We show that the bottom-up CH4 budget for Australasia (2010-2019) was a net source of 14.1 +/- 5.5 Tg CH4 yr(-1), with Australia and New Zealand contributing 84% and 16%, respectively. Anthropogenic sources contributed 55% of all CH4 emissions, the rest coming from natural sources, primarily wetlands. The bottom-up N2O budget was a net source of 0.5 +/- 0.3 Tg N2O yr(-1), with Australia contributing the majority (92%), mainly from natural sources (82%). Australasia top-down CH4 (10.4 +/- 0.5 Tg CH4 yr(-1)) and N2O budgets (0.8 +/- 0.5 Tg N2O yr(-1)) differ in magnitude from the bottom-up budgets but remain consistent within their uncertainties. Similar consistency is observed for Australia, while New Zealand shows significant discrepancies, particularly for N2O, where the bottom-up estimate is 71% higher than the top-down estimate. In terms of trends, bottom-up natural wetland CH4 emissions increased in both countries between 2010 and 2019. CH4 emissions from enteric fermentation slightly declined in Australia but increased in New Zealand. Soil N2O emissions from nitrogen additions increased in both countries, with a significant rise in New Zealand driving the overall positive trend in anthropogenic emissions. These findings highlight critical sectors with large mitigation potential and the significance of monitoring natural sources for possible biogeochemical-climate feedback.

Abstract. Satellite observations from the Sentinel-5P TROPOMI instrument, combined with inverse modeling, provide a valuable resource for quantifying regional methane (CH4) emissions. This study compares the emissions estimated from variational inversions in 2019 over Europe (0.5° resolution) assimilating three TROPOMI products of dry-column methane mole fractions (XCH4). The SRON (v2.4, operational product), BLENDED (v1.0), and WFMD (v1.8) products are retrieved from distinct algorithms. They differ in coverage, error characterization, and XCH4 spatial distribution. Results indicate that the largest contributions to XCH4 differences may be attributed to aerosol scattering and sensitivity to albedo. The derived 2019 European CH4 emission budgets show a relative increase of +2 % for SRON, and a decrease of -1 %, -33 % and -9 %, respectively, for BLENDED, WFMD and surface-based inversions. Seasonal emissions are highly correlated across the inversions. Spatial emission patterns and optimized boundary conditions are similar for the non-independent SRON and BLENDED but differ substantially from WFMD. Evaluation with independent surface stations shows error reduction for about half of the sites, with BLENDED performing best. However, no product is systematically closer to the emissions estimated when assimilating surface observations. Observing System Simulation Experiments (OSSEs) are used to disentangle the drivers of differences between the posterior emissions. They reveal that observation density and errors, but also averaging kernels and prior profiles play a key role in the inversion's capacity to constrain the emissions. Using consistent error definition and quality filters increases the consistency of the OSSEs, paving the way for more consistent emission estimates.

Abstract. Top-down methane CH4 flux estimates involve large uncertainties stemming from three main sources: (1) the coverage of the observing system, (2) systematic and random errors in the observation data and the priors and (3) errors in the atmospheric transport model. Quantifying these uncertainties is challenging, and methodological studies suggest they can be substantial. While global-scale uncertainties in total CH4 emissions are relatively small (±5%), they increase significantly at regional scales exceeding ±20% for high latitudes. Differences in satellite and in situ measurement uncertainties, as well as variations in data density, further influence the precision of CH4 flux estimates. Sectoral disaggregation of uncertainties improves error attribution, leading to more reliable regional flux assessments and trend detection. Its benefits are amplified in high-emission regions due to larger absolute uncertainties and more complex source mixtures.

We present a new version of the offline transport model from the LMDz atmospheric general circulation model. It paves the globe with hexagons and 12 pentagons of similar surface areas rather than with regular longitude‐latitude rectangles. It is available in a complete, nonlinear version and in a linearized configuration for use in variational atmospheric inversions. It runs on graphics processing units like the previous version. The previous advection approach and physical parameterizations have been kept while the code has been restructured for better numerical efficiency. The change of mesh was made necessary by the evolution of the parent LMDz model, but the technical and scientific evaluation of the new version with a roughly constant number of cells shows some interesting advantages. This evaluation is based on an 11‐year simulation of sulfur hexafluoride and on a 10‐year atmospheric inversion assimilating column‐averaged dry air mole fractions of carbon dioxide (CO 2 ) retrieved from measurements of NASA's second Orbiting Carbon Observatory. As it is used in variational inversion and at 90‐km resolution with 79 layers in the vertical, we find that the new offline model is twice as fast. Further, it shows improved interhemispheric transport, some small improvements in terms of inferred (posterior) atmospheric concentrations and small differences in terms of inferred surface fluxes compared to the previous version. These assets allowed it to be commissioned in its 90‐km configuration for the operational CO 2 inversions of the European Copernicus Atmosphere Monitoring Service.

There are great expectations about the detection and the quantification of NOx emissions using NO2 tropospheric columns from satellite observations and inverse systems. This study assesses the potential of the OMI-QA4ECV and TROPOMI satellite observations to improve the knowledge of European NOx emissions at the regional scale and to inform about the spatio-temporal variability of NOx anthropogenic emissions in 2019 compared to 2005, at the resolution of 0.5° over Europe. We first characterize the level of consistency between retrievals from OMI-QA4ECV and from the more recent reprocessing of the TROPOMI data, called TROPOMI-RPRO-v02.04, and the implications of the possible inconsistencies for inversions. Furthermore, starting from European emission estimates from the TNO-GHGco-v3 inventory for the year 2005, regional inversions using the Community Inversion Framework coupled to the CHIMERE chemistry-transport model and assimilating satellite NO2 tropospheric columns from OMI and TROPOMI have been performed to estimate the European annual and seasonal budgets for the year 2019. Both the OMI and TROPOMI inversions show decreases in European NOx anthropogenic emission budgets in 2019 compared to 2005. Nevertheless, the magnitude of the reductions of the NOx anthropogenic emissions is different with OMI and TROPOMI data, with decreases in EU-27 + UK between 2005 and 2019 of 16 % and 45 %, respectively. A TROPOMI inversion giving more weight to the satellite data becomes consistent with the independent TNO-GHGco-v3 inventory for the year 2019, with annual budgets for EU-27 + UK showing absolute relative difference of only 4 %. These TROPOMI inversions are therefore in agreement with the magnitude of the decline in NOx emissions declared by countries, when aggregated at the European scale. However, our results – with OMI and TROPOMI data leading to different magnitudes of corrections on NOx anthropogenic emissions – suggest that more observational constraints would be required to sharpen the European emission estimates.

Methane (CH4) is the second most important greenhouse gas, contributing to approximately 30% of the additional greenhouse effect since 1750. Its varied sources and relatively short lifetime in the atmosphere (~9 years) offer interesting mitigation opportunities. To develop practical strategies for mitigating climate change, precise quantification of methane fluxes and a better understanding of its spatial distribution and biogeochemical cycling are imperative. The observations currently used to infer methane sources and sinks face limitations affecting calculation accuracy. Surface stations measuring CH4 are sparse and notably absent in major emitting regions. In contrast, satellite-derived data, while providing broader coverage, present systematic errors and estimate atmospheric composition with an accuracy range of 1-10%. Additionally, passive satellite shortwave infrared (SWIR) measurements exhibit higher sensitivity near surface emission sources but are less effective in high latitude regions. Conversely, passive satellite thermal infrared (TIR) measurements have a higher sensitivity between the free troposphere and the stratosphere.Current worksare currently being developed to integrate TIR and SWIR to obtain consolidated CH4 information on the vertical atmospheric profile. This studyaims on improving methane flux estimates using the top-down approach, which integrates observations, flux priors, and an atmospheric chemical transport model utilizing Bayesian methodology. This will be perfomed on the inversion system developed at the LSCE (Community Inversion Framework – CIF) using the global transport model LMDz. We analyze the information provided by different observing systems (TIR, SWIR and surface network) at the global scale and for a period between June 2018 and June 2020. In a first step, the sensitivity of the fluxes to the observations is estimated. In a second step, Observing System Simulation Experiments are performed to evaluate the performance of the different observations system to retrieve the target fluxes. Considering both steps, observing systems are chosen to provide the best information in terms of sensitivity and spatial representation (vertical and horizontal).

In this study, we provide an update on the methodology and data used by Deng et al. (2022) to compare the national greenhouse gas inventories (NGHGIs) and atmospheric inversion model ensembles contributed by international research teams coordinated by the Global Carbon Project. The comparison framework uses transparent processing of the net ecosystem exchange fluxes of carbon dioxide (CO2) from inversions to provide estimates of terrestrial carbon stock changes over managed land that can be used to evaluate NGHGIs. For methane (CH4), and nitrous oxide (N2O), we separate anthropogenic emissions from natural sources based directly on the inversion results to make them compatible with NGHGIs. Our global harmonized NGHGI database was updated with inventory data until February 2023 by compiling data from periodical United Nations Framework Convention on Climate Change (UNFCCC) inventories by Annex I countries and sporadic and less detailed emissions reports by non-Annex I countries given by national communications and biennial update reports. For the inversion data, we used an ensemble of 22 global inversions produced for the most recent assessments of the global budgets of CO2, CH4, and N2O coordinated by the Global Carbon Project with ancillary data. The CO2 inversion ensemble in this study goes through 2021, building on our previous report from 1990 to 2019, and includes three new satellite inversions compared to the previous study and an improved managed-land mask. As a result, although significant differences exist between the CO2 inversion estimates, both satellite and in situ inversions over managed lands indicate that Russia and Canada had a larger land carbon sink in recent years than reported in their NGHGIs, while the NGHGIs reported a significant upward trend of carbon sink in Russia but a downward trend in Canada. For CH4 and N2O, the results of the new inversion ensembles are extended to 2020. Rapid increases in anthropogenic CH4 emissions were observed in developing countries, with varying levels of agreement between NGHGIs and inversion results, while developed countries showed a slowly declining or stable trend in emissions. Much denser sampling of atmospheric CO2 and CH4 concentrations by different satellites, coordinated into a global constellation, is expected in the coming years. The methodology proposed here to compare inversion results with NGHGIs can be applied regularly for monitoring the effectiveness of mitigation policy and progress by countries to meet the objectives of their pledges. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.13887128 (Deng et al., 2024).

Estimating methane emissions using atmospheric inversion methods that assimilate only methane observations presents challenges in accurately attributing methane sources and sinks to specific emission sectors. We explore the potential of co-assimilating observations of co-emitted species alongside methane observations to address this challenge and provide improved sectoral distribution of methane emissions. Ethane is a promising candidate for this purpose. It is primarily co-emitted with methane in the fossil fuel emission sector, particularly through fugitive emissions from natural gas extraction, and is also co-emitted in the biomass and biofuel burning emission sectors, with negligible emissions in other sectors. To assess the potential of co-assimilating ethane observations with methane observations, we use a global chemistry-transport model, LMDZ-SACS, with the Community Inversion Framework (CIF) to perform response functions analysis on methane and ethane emissions over a multi-year period. We utilize the response functions results to meaningfully construct full source-receptor relationship matrices at available observation site, as well as, error covariance matrices for a control vector that includes both species emissions and initial conditions, and perform a large set of analytical inversions that assimilate in-situ and flask observations of both methane and ethane, as well as methane-only observations. This methodology can not only provide improved estimates of the sectoral distribution of methane sources and sinks, but also extends the scope of the analysis to include ethane emissions.

Emissions from fossil fuel exploitation are a leading contributor to global anthropogenic methane emissions, but are highly uncertain. The lack of reliable estimates hinders monitoring of the progress on pledges towards methane reductions. Here we analyze methane emissions from exploitation of coal, oil and gas for major producing nations across a suite of bottom-up inventories and global inversions. Larger disagreement in emissions exists for the oil/gas sector across the inventories compared to coal, arising mostly from disparate data sources for emission factors. Moreover, emissions reported to the United Nations Framework Convention on Climate Change are lower than other bottom-up and inversion estimates, with many countries lacking reporting in the past decades. Finally, comparison with previous global inversions, revealed a strong influence of the prior inventory on the inferred sub-sectoral emissions magnitude. This study highlights the need to improve consensus on the methodological inputs among the bottom-up inventories in order to obtain more consistent inverse modelling results at the sub-sectoral level.