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The methane chemical sink estimated by atmospheric chemistry models (bottom-up method) is significantly larger than estimates based on methyl-chloroform (MCF) inversions (top-down method). The difference is partly attributable to large uncertainties in hydroxyl radical (OH) concentrations simulated by the atmospheric chemistry models used to derive the bottom-up estimates. In this study, we propose a new approach based on OH precursor observations and a chemical box model. This approach improves the 3-dimensional distributions of tropospheric OH radicals obtained from atmospheric chemistry models and reconciles the bottom-up and top-down estimates of the methane sink due to chemical loss. By constraining the model simulated OH precursors with observations, the global tropospheric mean OH concentration ([OH]trop-M) is ~10×105 molec cm−3 (which is 2×105 molec cm−3 lower than the original model-simulated global [OH]trop-M) and agrees with that obtained by the top-down method based on MCF inversions. With the OH constrained by precursor observations, the methane chemical loss is 471–508 Tg yr−1 averaged from 2000 to 2009. The new adjusted estimate is more consistent with the top-down estimates in the recent global methane budget by the Global Carbon Project (GCP) (459–516 Tg yr−1) than the bottom-up estimates using the original model-simulated OH fields (577–612 Tg yr−1). The overestimation of global [OH]trop-M and methane chemical loss simulated by the atmospheric chemistry models is caused primarily by the models’ underestimation of carbon monoxide and total ozone column, and overestimation of nitrogen dioxide. Our results highlight that constraining the model simulated OH fields with available OH precursor observations can help improve the bottom-up estimated methane sink.

This study investigates atmospheric δ13CH4 trends, as produced by a global atmospheric transport model using CH4 inversions from CarbonTracker-Europe CH4 for 2000–2020, and compares them to observations. The CH4 inversions include the grouping of the emissions both by δ13CH4 isotopic signatures and process type to investigate the effect, and to estimate the CH4 magnitudes and model CH4 and δ13CH4 trends. In addition to inversion results, simulations of the global atmospheric transport model were performed with modified emissions. The estimated global CH4 trends for oil and gas were found to increase more than coal compared to the priors from 2000–2006 to 2007–2020. Estimated trends for coal emissions at 30∘ N–60∘ N are less than 50% of those from priors. Estimated global CH4 rice emissions trends are opposite to priors, with the largest contribution from the EQ to 60∘ N. The results of this study indicate that optimizing wetland emissions separately produces better agreement with the observed δ13CH4 trend than optimizing all biogenic emissions simultaneously. This study recommends optimizing separately biogenic emissions with similar isotopic signature to wetland emissions. In addition, this study suggests that fossil-based emissions were overestimated by 9% after 2012 and biogenic emissions are underestimated by 8% in the inversion using EDGAR v6.0 as priors.

Atmospheric CH4 mole fractions resumed their increase in 2007 after a plateau during the 1999–2006 period, indicating relative changes in the sources and sinks. Estimating sources by exploiting observations within an inverse modeling framework (top-down approaches) is a powerful approach. It is, nevertheless, challenging to efficiently differentiate co-located emission categories and sinks by using CH4 observations alone. As a result, top-down approaches are limited when it comes to fully understanding CH4 burden changes and attributing these changes to specific source variations. δ13C(CH4)source isotopic signatures of CH4 sources differ between emission categories (biogenic, thermogenic, and pyrogenic) and can therefore be used to address this limitation. Here, a new 3-D variational inverse modeling framework designed to assimilate δ13C(CH4) observations together with CH4 observations is presented. This system is capable of optimizing both the emissions and the associated source signatures of multiple emission categories at the pixel scale. To our knowledge, this represents the first attempt to carry out variational inversion assimilating δ13C(CH4) with a 3-D chemistry transport model (CTM) and to independently optimize isotopic source signatures of multiple emission categories. We present the technical implementation of joint CH4 and δ13C(CH4) constraints in a variational system and analyze how sensitive the system is to the setup controlling the optimization using the LMDz-SACS 3-D CTM. We find that assimilating δ13C(CH4) observations and allowing the system to adjust isotopic source signatures provide relatively large differences in global flux estimates for wetlands (−5.7 Tg CH4 yr−1), agriculture and waste (−6.4 Tg CH4 yr−1), fossil fuels (+8.6 Tg CH4 yr−1) and biofuels–biomass burning (+3.2 Tg CH4 yr−1) categories compared to the results inferred without assimilating δ13C(CH4) observations. More importantly, when assimilating both CH4 and δ13C(CH4) observations, but assuming that the source signatures are perfectly known, these differences increase by a factor of 3–4, strengthening the importance of having as accurate signature estimates as possible. Initial conditions, uncertainties in δ13C(CH4) observations, or the number of optimized categories have a much smaller impact (less than 2 Tg CH4 yr−1).

Regional land carbon budgets provide insights into the spatial distribution of the land uptake of atmospheric carbon dioxide and can be used to evaluate carbon cycle models and to define baselines for land-based additional mitigation efforts. The scientific community has been involved in providing observation-based estimates of regional carbon budgets either by downscaling atmospheric CO2 observations into surface fluxes with atmospheric inversions, by using inventories of carbon stock changes in terrestrial ecosystems, by upscaling local field observations such as flux towers with gridded climate and remote sensing fields, or by integrating data-driven or process-oriented terrestrial carbon cycle models. The first coordinated attempt to collect regional carbon budgets for nine regions covering the entire globe in the RECCAP-1 project has delivered estimates for the decade 2000–2009, but these budgets were not comparable between regions due to different definitions and component fluxes being reported or omitted. The recent recognition of lateral fluxes of carbon by human activities and rivers that connect CO2 uptake in one area with its release in another also requires better definitions and protocols to reach harmonized regional budgets that can be summed up to a globe scale and compared with the atmospheric CO2 growth rate and inversion results. In this study, using the international initiative RECCAP-2 coordinated by the Global Carbon Project, which aims to be an update to regional carbon budgets over the last 2 decades based on observations for 10 regions covering the globe with a better harmonization than the precursor project, we provide recommendations for using atmospheric inversion results to match bottom-up carbon accounting and models, and we define the different component fluxes of the net land atmosphere carbon exchange that should be reported by each research group in charge of each region. Special attention is given to lateral fluxes, inland water fluxes, and land use fluxes.

Assessing and understanding the causes of mismatches between inversion results and inventories is key to future greenhouse gas budget estimates and also a contribution to phase 2 of the REgional Carbon Cycle Assessment and Processes (RECCAP2) initiative. Here, we compare the latest greenhouse gas budgets from the updated National Greenhouse Gas Inventories (NGHGIs) submitted to the United Nations Framework Convention on Climate Change (UNFCCC) and from the updated atmospheric inversions based on both in-situ and satellite observations. We compile the latest global harmonized NGHGI database for all countries according to the updated national communications (NCs) and biennial update reports (BURs) by the end of the year 2022. With the updated global atmospheric inversion results of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) produced by the Global Carbon Project, we adjust the inversion results of all countries for comparison, considering lateral river transfer processes and trade in crop and timber products, as well as the role of carbon sequestration in unmanaged land not considered by NGHGI, and compare them with greenhouse gas budgets in national inventories. The results show that there is a good consistency in net land CO2 flux based on the global in-situ network and that using satellite CO2 retrievals over tropical countries like Congo, Brazil, Peru, South Africa, and so on. For CH4, while the uncertainties of inversion results have been reduced in the updated global inversion ensembles compared to the previous ones, it seems that oil and gas-extracting countries like Saudi Arabia and Qatar report lower emissions compared to those estimated by inversions. This study is a reapplication of the methodologies developed by our previous study, which are confirmed and could be applied regularly and show how inversion could be used systematically in the future to support the assessment and improve the inventory of the Paris Agreement.

The Arctic is a critical region in terms of global warming. Environmental changes are already progressing steadily in high northern latitudes, whereby, among other effects, a high potential for enhanced methane (CH4) emissions is induced. With CH4 being a potent greenhouse gas, additional emissions from Arctic regions may intensify global warming in the future through positive feedback. Various natural and anthropogenic sources are currently contributing to the Arctic's CH4 budget; however, the quantification of those emissions remains challenging. Assessing the amount of CH4 emissions in the Arctic and their contribution to the global budget still remains challenging. On the one hand, this is due to the difficulties in carrying out accurate measurements in such remote areas. Besides, large variations in the spatial distribution of methane sources and a poor understanding of the effects of ongoing changes in carbon decomposition, vegetation and hydrology also complicate the assessment. Therefore, the aim of this work is to reduce uncertainties in current bottom-up estimates of CH4 emissions as well as soil oxidation by implementing an inverse modelling approach in order to better quantify CH4 sources and sinks for the most recent years (2008 to 2019). More precisely, the objective is to detect occurring trends in the CH4 emissions and potential changes in seasonal emission patterns. The implementation of the inversion included footprint simulations obtained with the atmospheric transport model FLEXPART (FLEXible PARTicle dispersion model), various emission estimates from inventories and land surface models, and data on atmospheric CH4 concentrations from 41 surface observation sites in the Arctic nations. The results of the inversion showed that the majority of the CH4 sources currently present in high northern latitudes are poorly constrained by the existing observation network. Therefore, conclusions on trends and changes in the seasonal cycle could not be obtained for the corresponding CH4 sectors. Only CH4 fluxes from wetlands are adequately constrained, predominantly in North America. Within the period under study, wetland emissions show a slight negative trend in North America and a slight positive trend in East Eurasia. Overall, the estimated CH4 emissions are lower compared to the bottom-up estimates but higher than similar results from global inversions.

Knowledge of the spatial distribution of the fluxes of greenhouse gases (GHGs) and their temporal variability as well as flux attribution to natural and anthropogenic processes is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement and to inform its global stocktake. This study provides a consolidated synthesis of CH4 and N2O emissions using bottom-up (BU) and top-down (TD) approaches for the European Union and UK (EU27 + UK) and updates earlier syntheses (Petrescu et al., 2020, 2021). The work integrates updated emission inventory data, process-based model results, data-driven sector model results and inverse modeling estimates, and it extends the previous period of 1990–2017 to 2019. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported by parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. Uncertainties in NGHGIs, as reported to the UNFCCC by the EU and its member states, are also included in the synthesis. Variations in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, the activities included are a key source of bias between estimates, e.g., anthropogenic and natural fluxes, which in atmospheric inversions are sensitive to the prior geospatial distribution of emissions. For CH4 emissions, over the updated 2015–2019 period, which covers a sufficiently robust number of overlapping estimates, and most importantly the NGHGIs, the anthropogenic BU approaches are directly comparable, accounting for mean emissions of 20.5 Tg CH4 yr−1 (EDGARv6.0, last year 2018) and 18.4 Tg CH4 yr−1 (GAINS, last year 2015), close to the NGHGI estimates of 17.5±2.1 Tg CH4 yr−1. TD inversion estimates give higher emission estimates, as they also detect natural emissions. Over the same period, high-resolution regional TD inversions report a mean emission of 34 Tg CH4 yr−1. Coarser-resolution global-scale TD inversions result in emission estimates of 23 and 24 Tg CH4 yr−1 inferred from GOSAT and surface (SURF) network atmospheric measurements, respectively. The magnitude of natural peatland and mineral soil emissions from the JSBACH–HIMMELI model, natural rivers, lake and reservoir emissions, geological sources, and biomass burning together could account for the gap between NGHGI and inversions and account for 8 Tg CH4 yr−1. For N2O emissions, over the 2015–2019 period, both BU products (EDGARv6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9 Tg N2O yr−1, close to the NGHGI data (0.8±55 % Tg N2O yr−1). Over the same period, the mean of TD global and regional inversions was 1.4 Tg N2O yr−1 (excluding TOMCAT, which reported no data). The TD and BU comparison method defined in this study can be operationalized for future annual updates for the calculation of CH4 and N2O budgets at the national and EU27 + UK scales. Future comparability will be enhanced with further steps involving analysis at finer temporal resolutions and estimation of emissions over intra-annual timescales, which is of great importance for CH4 and N2O, and may help identify sector contributions to divergence between prior and posterior estimates at the annual and/or inter-annual scale. Even if currently comparison between CH4 and N2O inversion estimates and NGHGIs is highly uncertain because of the large spread in the inversion results, TD inversions inferred from atmospheric observations represent the most independent data against which inventory totals can be compared. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emission inventories for CH4, N2O and other GHGs. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.7553800 (Petrescu et al., 2023).

Atmospheric methane (CH4) concentrations have been rising since 2007 due to an imbalance between CH4 sources and sinks. The CH4 budget is generally estimated through top-down approaches using chemistry transport models (CTMs) and CH4 observations as constraints. The atmospheric isotopic CH4 composition, δ13C(CH4), can also provide additional constraints and helps to discriminate between emission categories. Nevertheless, to be able to use the information contained in these observations, the models must correctly account for processes influencing δ13C(CH4). The oxidation by chlorine (Cl) likely contributes less than 5 % to the total oxidation of atmospheric CH4. However, the large kinetic isotope effect of the Cl sink produces a large fractionation of 13C, compared with 12C in atmospheric CH4, and thus may strongly influence δ13C(CH4). When integrating the Cl sink in their setup to constrain the CH4 budget, which is not yet standard, atmospheric inversions prescribe different Cl fields, therefore leading to discrepancies between flux estimates. To quantify the influence of the Cl concentrations on CH4, δ13C(CH4), and CH4 budget estimates, we perform sensitivity simulations using four different Cl fields. We also test removing the tropospheric and the entire Cl sink. We find that the Cl fields tested here are responsible for between 0.3 % and 8.5 % of the total chemical CH4 sink in the troposphere and between 1.0 % and 1.6 % in the stratosphere. Prescribing these different Cl amounts in atmospheric inversions can lead to differences of up to 53.8 Tg CH4 yr−1 in global CH4 emissions and of up to 4.7 ‰ in the globally averaged isotopic signature of the CH4 source δ13C(CH4)source), although these differences are much smaller if only recent Cl fields are used. More specifically, each increase by 1000 molec.cm-3 in the mean tropospheric Cl concentration would result in an adjustment by +11.7 Tg CH4 yr−1, for global CH4 emissions, and −1.0 ‰, for the globally averaged δ13C(CH4)source. Our study also shows that the CH4 seasonal cycle amplitude is modified by less than 1 %–2 %, but the δ13C(CH4) seasonal cycle amplitude can be significantly modified by up to 10 %–20 %, depending on the latitude. In an atmospheric inversion performed with isotopic constraints, this influence can result in significant differences in the posterior source mixture. For example, the contribution from wetland emissions to the total emissions can be modified by about 0.8 % to adjust the globally averaged δ13C(CH4)source, corresponding to a 15 Tg CH4 yr−1 change. This adjustment is small compared to the current wetland source uncertainty, albeit far from negligible. Finally, tested Cl concentrations have a large influence on the simulated δ13C(CH4) vertical profiles above 30 km and a very small impact on the simulated CH4 vertical profiles. Overall, our model captures the observed CH4 and δ13C(CH4) vertical profiles well, especially in the troposphere, and it is difficult to prefer one Cl field over another based uniquely on the available observations of the vertical profiles.

Knowledge of the spatial distribution of the fluxes of greenhouse gases and their temporal variability as well as flux attribution to natural and anthropogenic processes is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement and to inform its Global Stocktake. This study provides a consolidated synthesis of CH4 and N2O emissions using bottom-up (BU) and top-down (TD) approaches for the European Union and UK (EU27+UK) and updates earlier syntheses (Petrescu et al., 2020, 2021). The work integrates updated emission inventory data, process-based model results, data-driven sector model results, inverse modelling estimates, and extends the previous period 1990–2017 to 2020. BU and TD products are compared with European National GHG Inventories (NGHGI) reported by Parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. The uncertainties of NGHGIs were evaluated using the standard deviation obtained by varying parameters of inventory calculations, reported by the EU Member States following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, the activities included are a key source of bias between estimates e.g. anthropogenic and natural fluxes, which, in atmospheric inversions are sensitive to the prior geospatial distribution of emissions. For CH4 emissions, over the updated 2015–2019 period, which covers a sufficiently robust number of overlapping estimates, and most importantly the NGHGIs, the anthropogenic BU approaches are directly comparable, accounting for mean emissions of 20.5 Tg CH4 yr−1 (EDGAR v5v6.0, last year 2018) and 18.4 Tg CH4 yr−1 (GAINS, 2015), close to the NGHGI estimates of 17.5 ± 2.1 Tg CH4 yr−1. TD inversions estimates give higher emission estimates, as they also detect natural emissions. Over the same period, high resolution regional TD inversions report a mean emission of 34 Tg CH4 yr−1. Coarser-resolution global-scale TD inversions result in emission estimates of 23 Tg CH4 yr−1 and 24 Tg CH4 yr−1 inferred from GOSAT and surface (SURF) network atmospheric measurements, respectively. The magnitude of natural peatland and mineral soils emissions from the JSBACH-HIMMELI model, natural rivers, lakes and reservoirs emissions, geological sources and biomass burning together could account for the gap between NGHGI and inversions and account for 8 Tg CH4 yr−1. For N2O emissions, over the 2015–2019 period, both BU products (EDGAR v5v6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9 Tg N2O yr−1, close to the NGHGI data (0.8 ± 55 % Tg N2O yr−1). Over the same period, the mean of TD global and regional inversions was 1.4 Tg N2O yr−1 (excluding TOMCAT which reported no data). The TD and BU comparison method defined in this study can be "operationalized" for future annual updates for the calculation of CH4 and N2O budgets at the national and EU27+UK scales. Future comparability will be enhanced with further steps involving analysis at finer temporal resolutions and estimation of emissions over intra-annual timescales, of great importance for CH4 and N2O, which may help identify sector contributions to divergence between prior and posterior estimates at the annual/inter-annual scale. Even if currently comparison between CH4 and N2O inversions estimates and NGHGIs is highly uncertain because of the large spread in the inversion results, TD inversions inferred from atmospheric observations represent the most independent data against which inventory totals can be compared. With anticipated improvements in atmospheric modelling and observations, as well as modelling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emissions inventories for CH4, N2O and other GHGs. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.6992472 (Petrescu et al., 2022).

A more accurate characterization of the sources and sinks of methane (CH4) and carbon dioxide (CO2) in the vulnerable Arctic environment is required to better predict climate change. A large-scale aircraft campaign took place in September 2020 focusing on Siberian coast. CH4 and CO2 were measured in situ during the campaign and form the core of the study. Measured ozone (O3) and carbon monoxide (CO) are used here as tracers. Compared to the reference (i.e., the seasonal value at Mauna Loa, Hawaii, US), median CH4 mixing ratios are fairly higher (1890-1969 ppb vs 1887 ppb) while CO2 mixing ratios from all flights are lower (408.09-411.50 ppm vs 411.52 ppm). We also report on 3 case studies. Our analysis suggests that during the campaign the European part of Russia’s Arctic and Western Siberia were subject to long-range transport of polluted air masses, while the East mainly was under the influence of local emission of greenhouse gases. The relative contributions of anthropogenic and natural sources of CH4 in Siberia are simulated using the Lagrangian model FLEXPART in order to identify dominant sources in the boundary layer and in the free troposphere. In western terrestrial flights, air masses composition is influenced by from wetlands and anthropogenic activities (waste management, the fossil fuel industry and to a lesser extent the agricultural sector), while in the East, emissions are dominated by freshwaters, wetlands, and the oceans, with an ambiguous contribution from likely anthropogenic sources related to fossil fuels. Our results generally highlight the importance of the contribution form freshwater and oceans emissions and, combined with the large uncertainties associated with them, suggest that the emission from these aquatic sources should receive more attention in Siberia.

The Arctic is particularly sensitive to global warming and the effects of the increasing temperatures can already be detected in this region by occurring events such as thawing permafrost and decreasing Arctic sea ice area. One of the possible consequences is the risk of enhanced regional greenhouse gas emissions such as methane (CH4) due to the exposure of large terrestrial carbon pools or subsea permafrost which have previously been shielded by ice and frozen soil. Various sources, both natural and anthropogenic, are presently emitting methane in the Arctic. Natural sources include wetlands and other freshwater biomes, as well as the ocean and biomass burning. Despite the relatively small population in this region, CH4 emissions due to human activities are also significant. The main anthropogenic sources are the extraction and distribution of fossil fuels in the Arctic nations and, to a lesser extent, livestock activities and waste management. However, assessing the amount of CH4 emissions in the Arctic and their contribution to the global budget still remains challenging due to the difficulties in carrying out accurate measurements in such remote areas. Besides, high variations in the spatial distribution of methane sources and a poor understanding of the effects of ongoing changes in carbon decomposition, vegetation and hydrology also complicate the assessment.Therefore, the aim of this work is to reduce uncertainties on methane emissions in high northern latitudes. In order to achieve that, an inverse modeling approach has been implemented by using observational data sets of CH4 concentrations obtained at 42 surface stations located in different Arctic regions for the period from 2008 to 2019, the atmospheric transport model FLEXPART, as well as available bottom-up estimates of methane emissions provided by process-based surface models and CH4 emission inventories. The results have been analysed with regards to seasonal and inter-annual fluctuations, spatial differences and trends over the period of study.

Characterizing methane sources in the Arctic remains challenging due to the remoteness, heterogeneity and variety of such emissions. In situ campaigns provide valuable datasets to reduce these uncertainties. Here we analyse data from the summer 2014 SWERUS-C3 campaign in the eastern Arctic Ocean, off the shore of Siberia and Alaska. Total concentrations of methane, as well as relative concentrations of 12CH4 and 13CH4, were measured continuously during this campaign for 35 d in July and August. Using a chemistry-transport model, we link observed concentrations and isotopic ratios to regional emissions and hemispheric transport structures. A simple inversion system helped constrain source signatures from wetlands in Siberia and Alaska, and oceanic sources, as well as the isotopic composition of lower-stratosphere air masses. The variation in the signature of lower-stratosphere air masses, due to strongly fractionating chemical reactions in the stratosphere, was suggested to explain a large share of the observed variability in isotopic ratios. These results point towards necessary efforts to better simulate large-scale transport and chemistry patterns to make relevant use of isotopic data in remote areas. It is also found that constant and homogeneous source signatures for each type of emission in a given region (mostly wetlands and oil and gas industry in our case at high latitudes) are not compatible with the strong synoptic isotopic signal observed in the Arctic. A regional gradient in source signatures is highlighted between Siberian and Alaskan wetlands, the latter having lighter signatures (more depleted in 13C). Finally, our results suggest that marine emissions of methane from Arctic continental-shelf sources are dominated by thermogenic-origin methane, with a secondary biogenic source as well.

Atmospheric methane (CH4) concentrations have been rising since 2007, resulting from an imbalance between CH4 sources and sinks. The CH4 budget is generally estimated through top-down approaches using CH4 observations as constraints. The atmospheric isotopic CH4 signal, δ13C(CH4), can also provide additional constraints and helps to discriminate between emission categories. The oxidation by chlorine (Cl) likely contributes less than 5 % to the total oxidation of atmospheric CH4. However, the Cl sink is highly fractionating, and thus strongly influences δ13C(CH4). As inversion studies do not prescribe the same Cl fields to constrain CH4 budget, it can lead to discrepancies between estimates. To quantify the influence of the Cl concentrations on CH4, δ13C(CH4) and CH4 budget estimates, we perform multiple sensitivity simulations using three Cl fields with concentrations that are realistic with regard to recent literature and one Cl field with concentrations that are very likely to be overestimated. We also test removing the tropospheric and the entire Cl sink in other sensitivity simulations. We find that the realistic Cl fields tested here are responsible for between 0.3 % and 1.8 % of the total chemical CH4 sink in the troposphere and between 1.0 % and 1.2 % in the stratosphere. Prescribing these different Cl amounts in surface-based inversions can lead to differences in global CH4 source adjustments of up to 12.3 TgCH4.yr−1. We also find that the globally-averaged isotopic signature of the CH4 sources inferred by a surface-based inversion assimilating δ13C(CH4) observations would decrease by 0.53 ‰ for each additional percent of contribution from the tropospheric Cl sink to the total sink. Finally, our study shows that CH4 seasonal cycle amplitude is modified by less than 1–2 % but δ13(CH4) seasonal cycle amplitude can be modified by up to 10–20 %, depending on the latitude.

International audience

In support of the Global Stocktake of the Paris Agreement on Climate change, this study presents a comprehensive framework to process the results of atmospheric inversions in order to make them suitable for evaluating UNFCCC national inventories of land-use carbon dioxide (CO2) emissions and removals, corresponding to the Land Use, Land Use Change and Forestry and waste sectors. We also deduced anthropogenic methane (CH4) emissions regrouped into fossil and agriculture and waste emissions, and anthropogenic nitrous oxide (N2O) emissions from inversions. To compare inversions with national reports, we compiled a new global harmonized database of national emissions and removals from periodical UNFCCC inventories by Annex I countries, and from sporadic and less detailed emissions reports by Non-Annex I countries, given by National Communications and Biennial Update Reports. The method to reconcile inversions with inventories is applied to selected large countries covering 78 % of the global land carbon uptake for CO2, as well as emissions and removals in the land use, land use change and forestry sector, and top-emitters of CH4 and N2O. Our method uses results from an ensemble of global inversions produced by the Global Carbon Project for the three greenhouse gases, with ancillary data. We examine the role of CO2 fluxes caused by lateral transfer processes from rivers and from trade in crop and wood products, and the role of carbon uptake in unmanaged lands, both not accounted for by the rules of inventories. Here we show that, despite a large spread across the inversions, the median of available inversion models points to a larger terrestrial carbon sink than inventories over temperate countries or groups of countries of the Northern Hemisphere like Russia, Canada and the European Union. For CH4, we find good consistency between the inversions assimilating only data from the global in-situ network and those using satellite CH4 retrievals, and a tendency for inversions to diagnose higher CH4 emissions estimates than reported by inventories. In particular, oil and gas extracting countries in Central Asia and the Persian Gulf region tend to systematically report lower emissions compared to those estimated by inversions. For N2O, inversions tend to produce higher anthropogenic emissions than inventories for tropical countries, even when attempting to consider only managed land emissions. In the inventories of many non-Annex I countries, this can be tentatively attributed to either a lack of reporting indirect N2O emissions from atmospheric deposition and from leaching to rivers, or to the existence of natural sources intertwined with managed lands, or to an under-estimation of N2O emission factors for direct agricultural soil emissions. The advantage of inversions is that they provide insights on seasonal and interannual greenhouse gas fluxes anomalies, e.g. during extreme events such as drought or abnormal fire episodes, whereas inventory methods are established to estimate trends and multi-annual changes. As a 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 inventory reports could be applied regularly for monitoring the effectiveness of mitigation policy and progress by countries to meet the objective of their pledges.

Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface fluxes of greenhouse gases (GHGs), pollutants and other trace gases. In the past decade, the research community has developed various inversion software, mainly using variational or ensemble Bayesian optimization methods, with various assumptions on uncertainty structures and prior information and with various atmospheric chemistry–transport models. Each of them can assimilate some or all of the available observation streams for its domain area of interest: flask samples, in situ measurements or satellite observations. Although referenced in peer-reviewed publications and usually accessible across the research community, most systems are not at the level of transparency, flexibility and accessibility needed to provide the scientific community and policy makers with a comprehensive and robust view of the uncertainties associated with the inverse estimation of GHG and reactive species fluxes. Furthermore, their development, usually carried out by individual research institutes, may in the future not keep pace with the increasing scientific needs and technical possibilities. We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is primarily a programming protocol to allow various inversion bricks to be exchanged among researchers. In practice, the ensemble of bricks makes a flexible, transparent and open-source Python-based tool to estimate the fluxes of various GHGs and reactive species both at the global and regional scales. It will allow for running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow for a comprehensive assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and we demonstrate how it operates in a simple academic case.

Atmospheric methane (CH4) concentrations have shown a puzzling resumption in growth since 2007 following a period of stabilization from 2000 to 2006. Multiple hypotheses have been proposed to explain the temporal variations in CH4 growth, and attribute the rise of atmospheric CH4 either to increases in emissions from fossil fuel activities, agriculture and natural wetlands, or to a decrease in the atmospheric chemical sink. Here, we use a comprehensive ensemble of CH4 source estimates and isotopic δ13C-CH4 source signature data to show that the resumption of CH4 growth is most likely due to increased anthropogenic emissions. Our emission scenarios that have the fewest biases with respect to isotopic composition suggest that the agriculture, landfill and waste sectors were responsible for 53 ± 13% of the renewed growth over the period 2007–2017 compared to 2000–2006; industrial fossil fuel sources explained an additional 34 ± 24%, and wetland sources contributed the least at 13 ± 9%. The hypothesis that a large increase in emissions from natural wetlands drove the decrease in atmospheric δ13C-CH4 values cannot be reconciled with current process-based wetland CH4 models. This finding suggests the need for increased wetland measurements to better understand the contemporary and future role of wetlands in the rise of atmospheric methane and climate feedback. Our findings highlight the predominant role of anthropogenic activities in driving the growth of atmospheric CH4 concentrations.

To track progress towards keeping warming well below 2 °C, as agreed upon in the Paris Agreement, comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions (GHG) is required. Here we provide a dataset on anthropogenic GHG emissions 1970–2019 with a broad country and sector coverage. We build the dataset from recent releases of the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases, and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with data on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three bookkeeping models. We provide an assessment of the uncertainties in each greenhouse gas at the 90 % confidence interval (5th–95th percentile) by combining statistical analysis and comparisons of global emissions inventories with an expert judgement informed by the relevant scientific literature. We identify important data gaps: CH4 and N2O emissions could be respectively 10–20 % higher than reported in EDGAR once all emissions are accounted. F-gas emissions estimates for individual species in EDGARv5 do not align well with atmospheric measurements and the F-gas total exceeds measured concentrations by about 30 %. However, EDGAR and official national emission reports under the UNFCCC do not comprehensively cover all relevant F-gas species. Excluded F-gas species such as chlorofluorocarbons (CFCs) or hydrochlorofluorocarbons (HCFCs) are larger than the sum of the reported species. GHG emissions in 2019 amounted to 59 ± 6.6 GtCO2eq: CO2 emissions from FFI were 38 ± 3.0 Gt, CO2 from LULUCF 6.6 ± 4.6 Gt, CH4 11 ± 3.3 GtCO2eq, N2O 2.4 ±1.5 GtCO2eq and F-gases 1.6 ± 0.49 GtCO2eq. Our analysis of global, anthropogenic GHG emission trends over the past five decades (1970–2019) highlights a pattern of varied, but sustained emissions growth. There is high confidence that global anthropogenic greenhouse gas emissions have increased every decade. Emission growth has been persistent across different (groups of) gases. While CO2 has accounted for almost 75 % of the emission growth since 1970 in terms of CO2eq as reported here, the combined F-gases have grown at a faster rate than other GHGs, albeit starting from low levels in 1970. Today, F-gases make a non-negligible contribution to global warming – even though CFCs and HCFCs, regulated under the Montreal Protocol and not included in our estimates, have contributed more. There is further high confidence that global anthropogenic GHG emission levels were higher in 2010-2019 than in any previous decade and GHG emission levels have grown across the most recent decade. While average annual greenhouse gas emissions growth slowed between 2010–2019 compared to 2000–2009, the absolute increase in average decadal GHG emissions from the 2000s to the 2010s has been the largest since the 1970s – and within all human history as suggested by available long-term data. We note considerably higher rates of change in GHG emissions between 2018 and 2019 than for the entire decade 2010–2019, which is numerically comparable with the period of high GHG emissions growth during the 2000s, but we place low confidence in this finding as the majority of the growth is driven by highly uncertain increases in CO2-LULUCF emissions as well as the use of preliminary data and extrapolation methodologies for these most recent years. While there is a growing number of countries today on a sustained emission reduction trajectory, our analysis further reveals that there are no global sectors that show sustained reductions in GHG emissions. We conclude by highlighting that tracking progress in climate policy requires substantial investments in independent GHG emission accounting and monitoring as well as the available national and international statistical infrastructures. The data associated with this article (Minx et al. 2021) can be found at https://doi.org/10.5281/zenodo.5053056.

Recent efforts have brought together bottom-up quantification approaches (inventories and process-based models) and top-down approaches using regional observations of methane atmospheric concentrations through inverse modelling to better estimate the northern high-latitude methane sources. Nevertheless, for both approaches, the relatively small number of available observations in northern high-latitude regions leaves gaps in our understanding of the drivers and distributions of the different types of regional methane sources. Observations of methane isotope ratios, performed with instruments that are becoming increasingly affordable and accurate, could bring new insights on the contributions of methane sources and sinks. Here, we present the source signal that could be observed from methane isotopic 13CH4 measurements if high-resolution observations were available and thus what requirements should be fulfilled in future instrument deployments in terms of accuracy in order to constrain different emission categories. This theoretical study uses the regional chemistry-transport model CHIMERE driven by different scenarios of isotopic signatures for each regional methane source mix. It is found that if the current network of methane monitoring sites were equipped with instruments measuring the isotopic signal continuously, only sites that are significantly influenced by emission sources could differentiate regional emissions with a reasonable level of confidence. For example, wetland emissions require daily accuracies lower than 0.2 ‰ for most of the sites. Detecting East Siberian Arctic Shelf (ESAS) emissions requires accuracies lower than 0.05 ‰ at coastal Russian sites (even lower for other sites). Freshwater emissions would be detectable with an uncertainty lower than 0.1 ‰ for most continental sites. Except Yakutsk, Siberian sites require stringent uncertainty (lower than 0.05 ‰) to detect anthropogenic emissions from oil and gas or coal production. Remote sites such as Zeppelin, Summit, or Alert require a daily uncertainty below 0.05 ‰ to detect any regional sources. These limits vary with the hypothesis on isotopic signatures assigned to the different sources.

Atmospheric CH4 mixing ratios resumed their increase in 2007 after a plateau during the period 1999–2006, suggesting varying sources and sinks as main drivers. Estimating sources by exploiting observations within an inverse modeling framework (top-down approaches) is a powerful approach. It is nevertheless challenging to efficiently differentiate co-located emission categories and sinks by using CH4 observations alone. As a result, top-down approaches are limited when it comes to fully understanding CH4 burden changes and attribute these changes to specific source variations. CH4 source isotopic signatures differ between emission categories (biogenic, thermogenic and pyrogenic), and can therefore be used to address this limitation. Here, a new 3-D variational inverse modeling framework designed to assimilate δ13C(CH4) observations together with CH4 observations is presented. This system is capable of optimizing both emissions and associated source signatures of multiple emission categories. We present the technical implementation of joint CH4 and δ13C(CH4) constraints in a variational system, and analyze how sensitive the system is to the setup controlling the optimization using the 3-D Chemistry-Transport Model LMDz-SACS. We find that assimilating δ13C(CH4) observations and allowing the system to adjust source isotopic signatures provide relatively large differences in global flux estimates for wetlands (5 Tg yr−1), microbial (6 Tg yr−1), fossil fuels (8 Tg yr−1) and biofuels-biomass burning (4 Tg yr−1) categories compared to the results inferred without assimilating δ13C(CH4) observations. More importantly, when assimilating both CH4 and δ13C(CH4) observations, but assuming source signatures are perfectly known increase these differences between the system with CH4 and the enhanced one with δ13C(CH4) by a factor 3 or 4, strengthening the importance of having as accurate as possible signatures. Initial conditions, uncertainties on δ13C(CH4) observations or the number of optimized categories have a much smaller impact (less than 2 Tg yr−1).

Atmospheric CH4 mixing ratios resumed their increase in 2007 after a plateau during the period 1999-2006, suggesting a change of mix between sources and/or varying sinks. Exploiting observations within an inverse modeling framework (top-down estimates) is a powerful approach that reconciles observed and simulated CH4 mixing ratios using prior knowledge of CH4 sources and sinks. It is nevertheless challenging to efficiently differentiate co-located emissions from different sectors categories with CH4 observations alone. As a result, understanding CH4 burden changes and attributing these changes to specific source variations are difficult. CH4 source isotopic signatures differ between emission categories (biogenic, thermogenic and pyrogenic), and can therefore be included to disentangle overlapping sources. However, assimilating 13CH4 observations using inversion methods is challenging, especially with a variational framework. Here, a new 3-D variational inverse modeling framework implemented within the Community Inversion Framework [Berchet et al., 2020] and designed to assimilate 13CH4 and CH3D observations along CH4 observations is presented. This system is capable of optimizing emissions and associated source signatures of multiple emission categories independently at the pixel scale. Multiple tracers are transported by the LMDz 3-D model in order to properly simulate the clumped isotopologues of CH4. We present very briefly the technical implementation of such multi-constraints in the variational system and show preliminary results of long-term inversions for the period 1998-2018.Berchet, A., Sollum, E., Thompson, R. L., Pison, I., Thanwerdas, J., Broquet, G., Chevallier, F., Aalto, T., Bergamaschi, P., Brunner, D., Engelen, R., Fortems-Cheiney, A., Gerbig, C., Groot Zwaaftink, C., Haussaire, J.-M., Henne, S., Houweling, S., Karstens, U., Kutsch, W. L., Luijkx, I. T., Monteil, G., Palmer, P. I., van Peet, J. C. A., Peters, W., Peylin, P., Potier, E., Rödenbeck, C., Saunois, M., Scholze, M., Tsuruta, A., and Zhao, Y.: The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2020-407, in review, 2020.

We use the OMI-QA4ECV-v1.1 NO2 tropospheric columns over the 10-yr 2008-2017 period to confront satellite-based trends in NO2 concentrations to those from the state-of-the-art regional chemistry-transport model CHIMERE and to evaluate the bottom-up anthropogenic and biogenic NOx emissions in Europe. A focus is made for the 30 most populated urban areas in Europe. Over urban areas in Western Europe, except for coastal cities, OMI confirm the drop in the simulated CHIMERE NO2 tropospheric columns based on the latest country emission official reporting. OMI does not show significant decreasing trends over Central and Eastern Europe urban areas. Increasing biogenic emissions helps reconciling CHIMERE and OMI trends over urban areas in Central Europe and over rural areas, confirming the importance of accounting for non-anthropogenic emissions to assess long-term trends. Over Eastern Europe, our results question emission reductions estimated for particular sectors and in particular the road transport, public power and industrial emissions.

Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27 + UK). We integrate recent emission inventory data, ecosystem process-based model results and inverse modeling estimates over the period 1990-2017. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported to the UN climate convention UNFCCC secretariat in 2019. For uncertainties, we used for NGHGIs the standard deviation obtained by varying parameters of inventory calculations, reported by the member states (MSs) following the recommendations of the IPCC Guidelines. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model-specific uncertainties when reported. In comparing NGHGIs with other approaches, a key source of bias is the activities included, e.g. anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011-2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr-1 (EDGAR v5.0) and 19.0 Tg CH4 yr-1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr-1. The estimates of TD total inversions give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher-resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr-1. Coarser-resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4 yr-1) and surface network (24.4 Tg CH4 yr-1). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions, and geological sources together account for the gap between NGHGIs and inversions and account for 5.2 Tg CH4 yr-1. For N2O emissions, over the 2011-2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr-1, respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr-1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr-1, respectively. The TD and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at the EU+UK scale and at the national scale. The referenced datasets related to figures are visualized at. (Petrescu et al. 2020b)., SCOPUS: re.j, info:eu-repo/semantics/published

Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface greenhouse gas (GHG) fluxes. In the past decade, the research community has developed various inversion softwares, mainly using variational or ensemble Bayesian optimization methods, with various assumptions on uncertainty structures and prior information and with various atmospheric chemistry-transport models. Each of them can assimilate some or all of the available observation streams for its domain area of interest: flask samples, in-situ measurements or satellite observations. Although referenced in peer-reviewed publications and usually accessible across the research community, most systems are not at the level of transparency, flexibility and accessibility needed to provide the scientific community and policy makers with a comprehensive and robust view of the uncertainties associated with the inverse estimation of GHG fluxes. Furthermore, their development, usually carried out by individual research institutes, may in the future not keep pace with the increasing scientific needs and technical possibilities. We present here a Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is primarily a programming protocol to allow various inversion bricks to be exchanged among researchers. In practice, the ensemble of bricks makes a flexible, transparent and open-source python-based tool to estimate the fluxes of various GHGs both at global and regional scales. It will allow running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow a comprehensively assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and demonstrate how it operates in a simple academic case.