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The ongoing development of the Global Carbon Project (GCP) global methane (CH 4 ) budget shows a continuation of increasing CH 4 emissions and CH 4 accumulation in the atmosphere during 2000-2017. Here, we decompose the global budget into 19 regions (18 land and 1 oceanic) and five key source sectors to spatially attribute the observed global trends. A comparison of top-down (TD) (atmospheric and transport model-based) and bottom-up (BU) (inventory- and process model-based) CH 4 emission estimates demonstrates robust temporal trends with CH 4 emissions increasing in 16 of the 19 regions. Five regions-China, Southeast Asia, USA, South Asia, and Brazil-account for >40% of the global total emissions (their anthropogenic and natural sources together totaling >270 Tg CH 4  yr -1 in 2008-2017). Two of these regions, China and South Asia, emit predominantly anthropogenic emissions (>75%) and together emit more than 25% of global anthropogenic emissions. China and the Middle East show the largest increases in total emission rates over the 2000 to 2017 period with regional emissions increasing by >20%. In contrast, Europe and Korea and Japan show a steady decline in CH 4 emission rates, with total emissions decreasing by ~10% between 2000 and 2017. Coal mining, waste (predominantly solid waste disposal) and livestock (especially enteric fermentation) are dominant drivers of observed emissions increases while declines appear driven by a combination of waste and fossil emission reductions. As such, together these sectors present the greatest risks of further increasing the atmospheric CH 4 burden and the greatest opportunities for greenhouse gas abatement., (© 2021 The Authors. Global Change Biology published by John Wiley & Sons Ltd. This article has been contributed to by US Government employees and their work is in the public domain in the USA.)

The Arctic and boreal regions are experiencing a rapid increase in temperature, resulting in a changing cryosphere, increasing human activity, and potentially increasing high-latitude methane emissions. Satellite observations from Sentinel-5P TROPOMI provide an unprecedented coverage of a column-averaged dry-air mole fraction of methane (XCH4) in the Arctic, compared to previous missions or in situ measurements. The purpose of this study is to support and enhance the data used for high-latitude research through presenting a systematic evaluation of TROPOMI methane products derived from two different processing algorithms: the operational product (OPER) and the scientific product (WFMD), including the comparison of recent version changes of the products (OPER, OPER rpro, WFMD v1.2, and WFMD v1.8). One finding is that OPER rpro yields lower XCH4 than WFMD v1.8, the difference increasing towards the highest latitudes. TROPOMI product differences were evaluated with respect to ground-based high-latitude references, including four Fourier Transform Spectrometer in the Total Carbon Column Observing Network (TCCON) and five EM27/SUN instruments in the Collaborative Carbon Column Observing Network (COCCON). The mean TROPOMI–TCCON GGG2020 daily median XCH4 difference was site-dependent and varied for OPER rpro from −0.47 ppb to 22.4 ppb, and for WFMD v1.8 from 1.2 ppb to 19.4 ppb with standard deviations between 13.0 and 20.4 ppb and 12.5–15.0 ppb, respectively. The TROPOMI–COCCON daily median XCH4 difference varied from −26.5 ppb to 5.6 ppb for OPER rpro, with a standard deviation of 14.0–28.7 ppb, and from −5.0 ppb to 17.2 ppb for WFMD v1.8, with a standard deviation of 11.5–13.0 ppb. Although the accuracy and precision of both TROPOMI products are, on average, good compared to the TCCON and COCCON, a persistent seasonal bias in TROPOMI XCH4 (high values in spring; low values in autumn) is found for OPER rpro and is reflected in the higher standard deviation values. A systematic decrease of about 7 ppb was found between TCCON GGG2014 and GGG2020 product update highlighting the importance of also ensuring the reliability of ground-based retrievals. Comparisons to atmospheric profile measurements with AirCore carried out in Sodankylä, Northern Finland, resulted in XCH4 differences comparable to or smaller than those from ground-based remote sensing. [ABSTRACT FROM AUTHOR]

Wetland methane responses to temperature and precipitation were studied in a boreal wetland-rich region in Northern Europe using ecosystem process models. Six ecosystem models (JSBACH-HIMMELI, LPX-Bern, LPJ-GUESS, JULES, CLM4.5 and CLM5) were compared to multi-model mean of ecosystem models and atmospheric inversions from the Global Carbon Project and up-scaled eddy covariance flux results for their temperature and precipitation responses and seasonal cycles of the regional fluxes. Two models with contrasting response patterns, LPX-Bern and JSBACH-HIMMELI, were used as priors in atmospheric inversions with Carbon Tracker Europe – CH4 in order to find out how the inversion attempts to change the prior fluxes in the posterior and how this alters the interpretation of the flux responses to temperature and precipitation. The inversion attempted to move emissions of both models in posterior towards co-limitation by temperature and precipitation. In general high temperature and/or high precipitation periods often resulted in high posterior emissions. This was not the case for the warm and dry period of summer 2018. The process models showed strong temperature as well as strong precipitation responses for the region (51–91 % of the variance explained by both), and the month of maximum emissions varied from May to September. However, multi-model means, inversions and up-scaled eddy covariance flux observations agreed on the month of maximum emissions, and had rather balanced temperature and precipitation responses. The set-up of different emission components (peatland emissions, mineral land fluxes) had a significant role in building up the response patterns. Considering the significant differences among the models, it is essential to pay more attention to the magnitude, composition, annual cycle and climate driver responses of wetland emissions in different regions. [ABSTRACT FROM AUTHOR]

Monitoring the spatial distribution and trends in surface greenhouse gas (GHG) fluxes, as well as flux attribution to natural and anthropogenic processes, is essential to track progress under the Paris Agreement and to inform its Global Stocktake. This study updates earlier syntheses (Petrescu et al., 2020, 2021, 2023) and provides a consolidated synthesis of CH4 emissions using bottom-up (BU) and top-down (TD) approaches for the European Union (EU) and seven additional countries with large anthropogenic and/or natural emissions (USA, Brazil, China, India, Indonesia, Russia, and the Democratic Republic of Congo (DR Congo)). The work utilizes updated National GHG Inventories (NGHGIs) reported by Annex I Parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2023 and the latest available Biennial Update Reports (BURs) reported by non-Annex I Parties. The NGHGIs are considered in an integrated analysis that also relies on independent flux estimates from global inventory datasets, process-based models, inverse modeling and, when available, respective uncertainties. Whenever possible, it extends the period to 2021. Comparing NGHGIs with other approaches reveals that differences in the emission sources that are included in the estimate is a key source of divergence between approaches. A key system boundary difference is whether both anthropogenic and natural fluxes are included and, if they are, how fluxes belonging to these two sources are grouped/partitioned. Additionally, the natural fluxes are sensitive to the prior geospatial distribution of emissions in atmospheric inversions. Over the studied period, the total CH4 emissions in the EU, USA, and Russia show a steady decreasing trend since 1990, while for the non-EU emitters analyzed in this study, Brazil, China, India, Indonesia, and DR Congo, CH4 emissions have generally increased. In the EU, the anthropogenic BU approaches are reporting relatively similar mean emissions over 2015 to 2020 of 18.5 ± 2.7 Tg CH4 yr-1 for EDGAR v7.0, 16 Tg CH4 yr-1 for GAINS and 19 Tg CH4 yr-1 for FAOSTAT, with the NGHGI estimates of 15 ± 1.8 Tg CH4 yr-1. Inversions give higher emission estimates as they include natural emissions. Over the same period, the three high-resolution regional inversions report a mean emission of 21 (19-25) Tg CH4 yr-1, while the mean of six coarser-resolution global inversions results in emission estimates of 24 (23-25) Tg CH4 yr-1. The magnitude of BU natural emissions (peatland and mineral soils, lakes and reservoirs, geological and biomass burning) accounts for 6.6 Tg CH4 yr-1 (Petrescu et al., 2023a) and explains the differences between the TD inversions and the BU estimates of anthropogenic emissions (including NGHGIs). For the other Annex I Parties in this study (USA and Russia), over 2015 to 2020, the mean of the four anthropogenic BU approaches reports 18.5 (13-27.9) Tg CH4 yr-1 for Russia and 29.1 (23.5- Tg CH4 yr-1 for the USA, against total TD mean estimates of 37 (30-43) Tg CH4 yr-1 and 43.4 (42-48) Tg CH4 yr-1, respectively. The averaged BU and TD natural emissions account for 16.2 Tg CH4 yr-1 for Russia and 14.6 Tg CH4 yr-1 for the USA, partly explaining the gap between the BU anthropogenic and total TD emissions. For the non-Annex I Parties, anthropogenic CH4 estimates from UNFCCC BURs show large differences with the other global inventory-based estimates and even more with atmospheric-based ones. This poses an important potential challenge to monitoring the progress of the global CH4 pledge and the Global Stocktake, not only from the availability of data but also its accuracy. By systematically comparing the BU with TD methods, this study provides recommendations for more robust comparisons of available data sources and hopes to steadily engage more Parties in using observational methods to complement their UNFCCC inventories, as well as considering their natural emissions. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, future development needs to resolve knowledge gaps in both BU and TD approaches and to better quantify remaining uncertainty. Consequently, TD methods may emerge as a powerful tool for verifying emission inventories for CH4, and other GHGs and informing international climate policy. The referenced datasets related to figures are available at https://doi.org/10.5281/zenodo.10276087 (Petrescu et al., 2023b). [ABSTRACT FROM AUTHOR]

Climate change mitigation requires countries to report their annual greenhouse gas (GHG) emissions and sinks, including those from land use, land use change, and forestry (LULUCF). In Finland, the LULUCF sector plays a crucial role in achieving net-zero GHG emissions, as the sector is expected to be a net sink. However, accurate estimates of LULUCF-related GHG emissions, such as methane (CH 4 ), remain challenging. We estimated LULUCF-related CH 4 emissions in Finland in 2013–2020 by combining national land cover and remote-sensed surface wetness data with CH 4 emissions estimated by an inversion model. According to our inversion model, most of Finland's CH 4 emissions were attributed to natural sources such as open pristine peatlands. However, our research indicated that forests with thin tree cover surrounding open peatlands may also be a significant source of CH 4 . Unlike open pristine peatlands and pristine peatlands with thin tree cover, surrounding transient forests are included in the Finnish GHG inventory if they meet the criteria used for forest land. The current Finnish national GHG inventory may therefore underestimate CH 4 emissions from forested organic soils surrounding open peatlands, although more precise methods and data are needed to verify this. Given the potential impact on net GHG emissions, CH 4 emissions from transitional forests on organic soils should be further investigated. Furthermore, the results demonstrate the potential of combining atmospheric inversion modelling of GHGs with diverse data sources and highlight the need for methods to more easily combine atmospheric inversions with national GHG inventories. [ABSTRACT FROM AUTHOR]

Drivers of natural high-latitude biogenic methane fluxes were studied by combining atmospheric inversion modelling results of methane fluxes (CTE-CH4 model) with datasets on permafrost (ESA Permafrost CCI), climate (Köppen–Geiger classes) and wetland classes (BAWLD) and seasonality of soil freezing (ESA SMOS F/T) for the years 2011–2019. The highest emissions were found in the southern parts of the study region, while areas with continuous permafrost, tundra climate, and tundra wetlands had the lowest emissions. The magnitude of the methane flux per wetland area followed the order of permafrost zones excluding non-permafrost, continuous permafrost having the smallest flux and sporadic the largest. Fens had higher fluxes than bogs in the thaw period, but bogs had higher fluxes in the colder seasons. The freezing period when the soil status is between complete thaw and frozen contributed to annual emissions more in the warmest regions studied than in other regions. In the coldest areas, freezing period fluxes were lower and closer to wintertime values than elsewhere. Emissions during freezing periods were smaller than those during winter periods, but were of comparable magnitude in warm regions. The contribution of the thaw period to the total annual emission varied from 86% in warmest areas to 97% in the coldest areas, suggesting that the longest winter periods did not contribute significantly to the annual budget. [ABSTRACT FROM AUTHOR]

This study investigates atmospheric δ 13 CH 4 trends, as produced by a global atmospheric transport model using CH 4 inversions from CarbonTracker-Europe CH 4 for 2000–2020, and compares them to observations. The CH 4 inversions include the grouping of the emissions both by δ 13 CH 4 isotopic signatures and process type to investigate the effect, and to estimate the CH 4 magnitudes and model CH 4 and δ 13 CH 4 trends. In addition to inversion results, simulations of the global atmospheric transport model were performed with modified emissions. The estimated global CH 4 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 CH 4 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 δ 13 CH 4 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. [ABSTRACT FROM AUTHOR]

Recent advances in satellite observations of methane provide increased opportunities for inverse modeling. However, challenges exist in the satellite observation optimization and retrievals for high latitudes. In this study, we examine possibilities and challenges in the use of the total column averaged dry-air mole fractions of methane ( XCH 4 ) data over land from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite in the estimation of CH 4 fluxes using the CarbonTracker Europe- CH 4 (CTE- CH 4 ) atmospheric inverse model. We carry out simulations assimilating two retrieval products: Netherlands Institute for Space Research's (SRON) operational and University of Bremen's Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS). For comparison, we also carry out a simulation assimilating the ground-based surface data. Our results show smaller regional emissions in the TROPOMI inversions compared to the prior and surface inversion, although they are roughly within the range of the previous studies. The wetland emissions in summer and anthropogenic emissions in spring are lesser. The inversion results based on the two satellite datasets show many similarities in terms of spatial distribution and time series but also clear differences, especially in Canada, where CH 4 emission maximum is later, when the SRON's operational data are assimilated. The TROPOMI inversions show higher CH 4 emissions from oil and gas production and coal mining from Russia and Kazakhstan. The location of hotspots in the TROPOMI inversions did not change compared to the prior, but all inversions indicated spatially more homogeneous high wetland emissions in northern Fennoscandia. In addition, we find that the regional monthly wetland emissions in the TROPOMI inversions do not correlate with the anthropogenic emissions as strongly as those in the surface inversion. The uncertainty estimates in the TROPOMI inversions are more homogeneous in space, and the regional uncertainties are comparable to the surface inversion. This indicates the potential of the TROPOMI data to better separately estimate wetland and anthropogenic emissions, as well as constrain spatial distributions. This study emphasizes the importance of quantifying and taking into account the model and retrieval uncertainties in regional levels in order to improve and derive more robust emission estimates. [ABSTRACT FROM AUTHOR]

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 CH 4 and N 2 O 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 CH 4 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 CH 4 yr -1 (EDGARv6.0, last year 2018) and 18.4 Tg CH 4 yr -1 (GAINS, last year 2015), close to the NGHGI estimates of 17.5±2.1 Tg CH 4 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 CH 4 yr -1. Coarser-resolution global-scale TD inversions result in emission estimates of 23 and 24 Tg CH 4 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 CH 4 yr -1. For N 2 O emissions, over the 2015–2019 period, both BU products (EDGARv6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9 Tg N 2 O yr -1 , close to the NGHGI data (0.8±55 % Tg N 2 O yr -1). Over the same period, the mean of TD global and regional inversions was 1.4 Tg N 2 O 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 CH 4 and N 2 O 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 CH 4 and N 2 O, 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 CH 4 and N 2 O 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 CH 4 , N 2 O and other GHGs. The referenced datasets related to figures are visualized at 10.5281/zenodo.7553800 (Petrescu et al., 2023). [ABSTRACT FROM AUTHOR]

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 v6.0, 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 v6.0 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). [ABSTRACT FROM AUTHOR]

This study investigates the contribution of different CH4 sources to the seasonal cycle of δ 13 C during 2000–2012 by using the TM5 atmospheric transport model, including spatially varying information on isotopic signatures. The TM5 model is able to produce the background seasonality of δ 13 C, but the discrepancies compared to the observations arise from incomplete representation of the emissions and their source-specific signatures. Seasonal cycles of δ 13 C are found to be an inverse of CH4 cycles in general, but the anti-correlations between CH4 and δ 13 C are imperfect and experience a large variation ( p = −0.35 to −0.91) north of 30° S. We found that wetland emissions are an important driver in the δ 13 C seasonal cycle in the Northern Hemisphere and Tropics, and in the Southern Hemisphere Tropics, emissions from fires contribute to the enrichment of δ 13 C in July–October. The comparisons to the observations from 18 stations globally showed that the seasonal cycle of EFMM emissions in the EDGAR v5.0 inventory is more realistic than in v4.3.2. At northern stations (north of 55° N), modeled δ 13 C amplitudes are generally smaller by 12–68%, mainly because the model could not reproduce the strong depletion in autumn. This indicates that the CH4 emission magnitude and seasonal cycle of wetlands may need to be revised. In addition, results from stations in northern latitudes (19–40° N) indicate that the proportion of biogenic to fossil-based emissions may need to be revised, such that a larger portion of fossil-based emissions is needed during summer. [ABSTRACT FROM AUTHOR]

We present inverse modelling (top down) estimates of European methane (CH4) emissions for 2006-2012 based on a new quality-controlled and harmonised in situ data set from 18 European atmospheric monitoring stations. We applied an ensemble of seven inverse models and performed four inversion experiments, investigating the impact of different sets of stations and the use of a priori information on emissions. The inverse models infer total CH4 emissions of 26.8 (20.2-29.7) TgCH4 yr-1 (mean, 10th and 90th percentiles from all inversions) for the EU-28 for 2006-2012 from the four inversion experiments. For comparison, total anthropogenic CH4 emissions reported to UNFCCC (bottom up, based on statistical data and emissions factors) amount to only 21.3 TgCH4 yr-1 (2006) to 18.8 TgCH4 yr-1 (2012). A potential explanation for the higher range of top-down estimates compared to bottom-up inventories could be the contribution from natural sources, such as peatlands, wetlands, and wet soils. Based on seven different wetland inventories from the Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP), total wetland emissions of 4.3 (2.3-8.2) TgCH4 yr-1 from the EU-28 are estimated. The hypothesis of significant natural emissions is supported by the finding that several inverse models yield significant seasonal cycles of derived CH4 emissions with maxima in summer, while anthropogenic CH4 emissions are assumed to have much lower seasonal variability. Taking into account the wetland emissions from the WETCHIMP ensemble, the top-down estimates are broadly consistent with the sum of anthropogenic and natural bottom-up inventories. However, the contribution of natural sources and their regional distribution remain rather uncertain. Furthermore, we investigate potential biases in the inverse models by comparison with regular aircraft profiles at four European sites and with vertical profiles obtained during the Infrastructure for Measurement of the European Carbon Cycle (IMECC) aircraft campaign.We present a novel approach to estimate the biases in the derived emissions, based on the comparison of simulated and measured enhancements of CH4 compared to the background, integrated over the entire boundary layer and over the lower troposphere. The estimated average regional biases range between -40 and 20% at the aircraft profile sites in France, Hungary and Poland. [ABSTRACT FROM AUTHOR]

The northern wetland methane emission estimates have large uncertainties. Inversion models are a qualified method to estimate the methane fluxes and emissions in northern latitudes but when atmospheric observations are sparse, the models are only as good as their a priori estimates. Thus, improving a priori estimates is a competent way to reduce uncertainties and enhance emission estimates in the sparsely sampled regions. Here, we use a novel way to integrate remote sensing soil freeze/thaw (F/T) status from SMOS satellite to better capture the seasonality of methane emissions in the northern high latitude. The SMOS F/T data provide daily information of soil freezing state in the northern latitudes, and in this study, the data is used to define the cold season in the high latitudes and, thus, improve our knowledge of the seasonal cycle of biospheric methane fluxes. The SMOS F/T data is implemented to LPX-Bern DYPTOP model estimates and the modified fluxes are used as a biospheric a priori in the inversion model CarbonTracker Europe-CH 4 . The implementation of the SMOS F/T soil state is shown to be beneficial in improving the inversion model's cold season biospheric flux estimates. Our results show that cold season biospheric CH 4 emissions in northern high latitudes are approximately 0.60 Tg lower than previously estimated, which corresponds to 17% reduction in the cold season biospheric emissions. This reduction is partly compensated by increased anthropogenic emissions in the same area (0.23 Tg), and the results also indicates that the anthropogenic emissions could have even larger contribution in cold season than estimated here. [ABSTRACT FROM AUTHOR]

We employed a global high-resolution inverse model to optimize the CH4 emission using Greenhouse gas Observing Satellite (GOSAT) and surface observation data for a period from 2011–2017 for the two main source categories of anthropogenic and natural emissions. We used the Emission Database for Global Atmospheric Research (EDGAR v4.3.2) for anthropogenic methane emission and scaled them by country to match the national inventories reported to the United Nations Framework Convention on Climate Change (UNFCCC). Wetland and soil sink prior fluxes were simulated using the Vegetation Integrative Simulator of Trace gases (VISIT) model. Biomass burning prior fluxes were provided by the Global Fire Assimilation System (GFAS). We estimated a global total anthropogenic and natural methane emissions of 340.9 Tg CH4 yr−1 and 232.5 Tg CH4 yr−1, respectively. Country-scale analysis of the estimated anthropogenic emissions showed that all the top-emitting countries showed differences with their respective inventories to be within the uncertainty range of the inventories, confirming that the posterior anthropogenic emissions did not deviate from nationally reported values. Large countries, such as China, Russia, and the United States, had the mean estimated emission of 45.7 ± 8.6, 31.9 ± 7.8, and 29.8 ± 7.8 Tg CH4 yr−1, respectively. For natural wetland emissions, we estimated large emissions for Brazil (39.8 ± 12.4 Tg CH4 yr−1), the United States (25.9 ± 8.3 Tg CH4 yr−1), Russia (13.2 ± 9.3 Tg CH4 yr−1), India (12.3 ± 6.4 Tg CH4 yr−1), and Canada (12.2 ± 5.1 Tg CH4 yr−1). In both emission categories, the major emitting countries all had the model corrections to emissions within the uncertainty range of inventories. The advantages of the approach used in this study were: (1) use of high-resolution transport, useful for simulations near emission hotspots, (2) prior anthropogenic emissions adjusted to the UNFCCC reports, (3) combining surface and satellite observations, which improves the estimation of both natural and anthropogenic methane emissions over spatial scale of countries. [ABSTRACT FROM AUTHOR]

We present a global 0.1° × 0.1° high-resolution inverse model, NIES-TM-FLEXPART-VAR (NTFVAR), and a methane emission evaluation using the Greenhouse Gas Observing Satellite (GOSAT) satellite and ground-based observations from 2010–2012. Prior fluxes contained two variants of anthropogenic emissions, Emissions Database for Global Atmospheric Research (EDGAR) v4.3.2 and adjusted EDGAR v4.3.2 which were scaled to match the country totals by national reports to the United Nations Framework Convention on Climate Change (UNFCCC), augmented by biomass burning emissions from Global Fire Assimilation System (GFASv1.2) and wetlands Vegetation Integrative Simulator for Trace Gases (VISIT). The ratio of the UNFCCC-adjusted global anthropogenic emissions to EDGAR is 98%. This varies by region: 200% in Russia, 84% in China, and 62% in India. By changing prior emissions from EDGAR to UNFCCC-adjusted values, the optimized total emissions increased from 36.2 to 46 Tg CH4 yr−1 for Russia, 12.8 to 14.3 Tg CH4 yr−1 for temperate South America, and 43.2 to 44.9 Tg CH4 yr−1 for contiguous USA, and the values decrease from 54 to 51.3 Tg CH4 yr−1 for China, 26.2 to 25.5 Tg CH4 yr−1 for Europe, and by 12.4 Tg CH4 yr−1 for India. The use of the national report to scale EDGAR emissions allows more detailed statistical data and country-specific emission factors to be gathered in place compared to those available for EDGAR inventory. This serves policy needs by evaluating the national or regional emission totals reported to the UNFCCC. [ABSTRACT FROM AUTHOR]

Methane ( CH 4 ) is a potent greenhouse gas with a large temporal variability. To increase the spatial coverage, methane observations are increasingly made from satellites that retrieve the column-averaged dry air mole fraction of methane ( XCH 4 ). To understand and quantify the spatial differences of the seasonal cycle and trend of XCH 4 in more detail, and to ultimately help reduce uncertainties in methane emissions and sinks, we evaluated and analyzed the average XCH 4 seasonal cycle and trend from three Greenhouse Gases Observing Satellite (GOSAT) retrieval algorithms: National Institute for Environmental Studies algorithm version 02.75, RemoTeC CH 4 Proxy algorithm version 2.3.8 and RemoTeC CH 4 Full Physics algorithm version 2.3.8. Evaluations were made against the Total Carbon Column Observing Network (TCCON) retrievals at 15 TCCON sites for 2009–2015, and the analysis was performed, in addition to the TCCON sites, at 31 latitude bands between latitudes 44.43°S and 53.13°N. At latitude bands, we also compared the trend of GOSAT XCH 4 retrievals to the NOAA's Marine Boundary Layer reference data. The average seasonal cycle and the non-linear trend were, for the first time for methane, modeled with a dynamic regression method called Dynamic Linear Model that quantifies the trend and the seasonal cycle, and provides reliable uncertainties for the parameters. Our results show that, if the number of co-located soundings is sufficiently large throughout the year, the seasonal cycle and trend of the three GOSAT retrievals agree well, mostly within the uncertainty ranges, with the TCCON retrievals. Especially estimates of the maximum day of XCH 4 agree well, both between the GOSAT and TCCON retrievals, and between the three GOSAT retrievals at the latitude bands. In our analysis, we showed that there are large spatial differences in the trend and seasonal cycle of XCH 4 . These differences are linked to the regional CH 4 sources and sinks, and call for further research. [ABSTRACT FROM AUTHOR]

Atmospheric methane (CH4) is a greenhouse gas strongly influenced by human activities. Ithas increased in recent years as rapidly as in the end of 20th century. The cause of the rapidincrease and its interannual variability are still under discussion due to lack of fluxinformation and modelling complexity. Global CH4 budgets are fairly well understood, butthe regional estimates still vary between models. In order to obtain further understandingespecially on the regional budgets, we present global and regional methane (CH4)emission estimates from CarbonTracker Europe-CH4 (CTE-CH4) atmospheric inversemodel that assimilates 1) the global network of ground-based surface atmosphericCH4 observations, and 2) column averaged dry-air mole fractions of CH4 (XCH4)retrieved from GOSAT TANSO-FTS. Emissions from anthropogenic and natural(wetlands and other soils, biofuel and biomass burning, termites, ocean and geological)sources are taken into account, and among those, emissions from anthropogenicsources and wetlands and other soils are optimized simultaneously based on theensemble Kalman filter. In the GOSAT inversion, XCH4 zonal mean differences at 5∘latitudinal bands between posterior 3D atmospheric CH4 fields from the surfaceinversion and the GOSAT retrievals were removed before the inversion. The twoinversions estimate similar global total CH4 emissions for 2010-2017 (540-545 Tg CH4yr−1), with increasing trends in emissions during 2004-2008 and 2013-2016. Theseasonal cycle of the emissions was different in the two inversions in the SouthernHemisphere extratropics, and the summer emissions in Northern Hemisphere temperateregions were greater in the GOSAT inversion. The results are evaluated by comparingestimated atmospheric CH4 with the assimilated and non-assimilated observations,and the emission estimates from other inverse models and process-based models. [ABSTRACT FROM AUTHOR]

To estimate anthropogenic methane emissions localized around large cities, we use theLagrangian particle dispersion model FLEXPART to model local tracer transport at 0.1˚ spatial resolution and compare the model simulations of local methane enhancements toGOSAT observations made in 2009-2015. The satellite-observed methane columnenhancements are aggregated according to discrete simulated enhancement levels. Theobserved and simulated enhancements compare well for the global domain and showdifferences for several large regions such as the US and East Asia. To extend the analysis andaccount for large scale transport and influence of natural fluxes, we perform globalhigh-resolution methaneflux inversion to estimate global methane emissions usingatmospheric methane data collected at global in-situ network, which is archivedat WDCGG, and GOSAT satellite retrievals. FLEXPART is coupled to a globalatmospheric tracer transport model (NIES-TM). Prior fluxes at 0.1˚ resolution wereprepared for anthropogenic emissions (EDGAR 4.3.2), biomass burning (GFAS), andwetlands (VISIT). The inverse model NIES-TM-FLEXPART-VAR (NTFVAR)applies variational optimization to two categories of fluxes: anthropogenic andnatural (wetlands). Bi-weekly emissions are estimated for years 2009 to 2017. Toreduce GOSAT retrieval biases, the monthly mean difference between GOSAT dataand the inversion-optimized forward simulation is estimated for each 5º latitudeband and then it is subtracted from GOSAT retrievals before including them in theinversion. The bias correction is designed to remove large scale biases in GOSATretrievals, while retaining local scale variability that contains most information onanthropogenic emissions. Estimated anthropogenic emissions over large regions (US,China, India) are comparable to GCP-CH4 top-down estimates. The sensitivity of theestimated emissions to prior fluxes is checked by making inverse modeling with prioremissions adjusted to match national reports to UNFCCC for selected large countries. [ABSTRACT FROM AUTHOR]

Novel Earth Observations of atmospheric greenhouse gases and the cryosphere have the potential to fundamentally increase our understanding of the carbon cycle at high Northern latitudes. We will present our on-going ESA project on quantifying methane (CH4) emissions in the Northern Hemisphere, and investigating their connection to the soil freezing and thawing at boreal latitudes. We combine methods for the quantification of CH4 emissions by applying data from Earth Observing (EO) satellites and global atmospheric methane inversion model estimates. The EO data consist of a global soil freeze/thaw estimate obtained from the ESA Soil Moisture and Ocean Salinity (SMOS) mission as well as retrievals of atmospheric column-averaged methane obtained from the Greenhouse Gases Observing Satellite (GOSAT) and Sentinel 5 Precursor TROPOMI (S5P-TROPOMI) observations. These EO data will be used in global atmospheric methane inversion model, CarbonTracker Europe – CH4, simulations, focusing on (1) the identification of CH4 sources in the Northern Hemisphere and (2) providing analysis on the trends and seasonal variability of methane emissions. EO data will be used to assess the spatial variability of the emissions and to better quantify the contributions from regions dominated by anthropogenic emissions and by natural emissions, especially those from wetlands. Further, EO data will be used to create proxies of the seasonality of the natural methane emissions, focusing on the timing and length of the autumn freezing period and springtime melting period. [ABSTRACT FROM AUTHOR]