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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), provides a consolidated synthesis of CH 4 emissions using bottom-up (BU) and top-down (TD) approaches for the European Union (EU), and is expanded to include seven additional countries with large anthropogenic and/or natural emissions (the USA, Brazil, China, India, Indonesia, Russia, and the Democratic Republic of the Congo (DR Congo)). Our aim is to demonstrate the use of different emission estimates to help improve national GHG emission inventories for a diverse geographical range of stakeholders. We use 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. Comparing NGHGIs with other approaches highlights that different system boundaries are a key source of divergence. A key system boundary difference is whether anthropogenic and natural fluxes are included and, if they are, how fluxes belonging to these two sources are partitioned. Over the studied period, the total CH 4 emission estimates in the EU, the USA, and Russia show a steady decreasing trend since 1990, while for the non-Annex I emitters analyzed in this study, Brazil, China, India, Indonesia, and DR Congo, CH 4 emissions have generally increased. Quantitatively, in the EU the mean of 2015–2020 anthropogenic UNFCCC NGHGIs (15±1.8 Tg CH 4 yr -1) and the mean of the BU CH 4 emissions (17.8 (16–19) Tg CH 4 yr -1) generally agree on the magnitude, while inversions show higher emission estimates (medians of 21 (19–22) Tg CH 4 yr -1 and 24 (22–25) Tg CH 4 yr -1 for the three regional and six global inversions, respectively), as they include natural emissions, which for the EU were quantified at 6.6 Tg CH 4 yr -1 (Petrescu et al., 2023). Similarly, for the other Annex I parties in this study (the USA and Russia), the gap between the BU anthropogenic and total TD emissions is partly explained by the natural emissions. For the non-Annex I parties, anthropogenic CH 4 estimates from UNFCCC BURs show large differences compared to the other global-inventory-based estimates and even more compared to atmospheric ones. This poses an important potential challenge to monitoring the progress of the global CH 4 pledge and the global stocktake. Our analysis provides a useful baseline to prepare for the influx of inventories from non-Annex I parties as regular reporting starts under the enhanced transparency framework of the Paris Agreement. By systematically comparing the BU and 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 the BU and TD approaches and to better quantify the remaining uncertainty. TD methods may emerge as a powerful tool to help improve NGHGIs of CH 4 emissions, but further confidence is needed in the comparability and robustness of the estimates. The referenced datasets related to figures are available at 10.5281/zenodo.12818506 (Petrescu et al., 2024). [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]

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]

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]

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 modelling estimates over the period 1990-2018. BU and TD products are compared with European National GHG Inventories (NGHGI) reported to the UN climate convention secretariat UNFCCC in 2019. For uncertainties, we used for NGHGI the standard deviation obtained by varying parameters of inventory calculations, reported by the Member States following the IPCC guidelines recommendations. 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 NGHGI 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. TD total inversions estimates 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 CH4yr-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 NGHGI 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, compared to 0.9 Tg N2O yr-1 from the BU data. The TU 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 EU+UK scale and at national scale. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4288969 (Petrescu et al., 2020). [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]

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]