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Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Emissions and atmospheric concentrations of CH4 continue to increase, maintaining CH4 as the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2). The relative importance of CH4 compared to CO2 for temperature change is related to its shorter atmospheric lifetime, stronger radiative effect, and acceleration in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the factors explaining the well-observed atmospheric growth rate arise from diverse, geographically overlapping CH4 sources and from the uncertain magnitude and temporal change in the destruction of CH4 by short-lived and highly variable hydroxyl radicals (OH). To address these challenges, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to improve, synthesise and update the global CH4 budget regularly and to stimulate new research on the methane cycle. Following Saunois et al. (2016, 2020), we present here the third version of the living review paper dedicated to the decadal CH4 budget, integrating results of top-down CH4 emission estimates (based on in-situ and greenhouse gas observing satellite (GOSAT) atmospheric observations and an ensemble of atmospheric inverse-model results) and bottom-up estimates (based on process-based models for estimating land-surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). We present a budget for the most recent 2010–2019 calendar decade (the latest period for which full datasets are available), for the previous decade of 2000–2009 and for the year 2020. The revision of the bottom-up budget in this edition benefits from important progress in estimating inland freshwater emissions, with better accounting of emissions from lakes and ponds, reservoirs, and streams and rivers. This budget also reduces double accounting across freshwater and wetland emissions and, for the first time, includes an estimate of the potential double accounting that still exists (average of 23 Tg CH4 yr-1). Bottom-up approaches show that the combined wetland and inland freshwater emissions average 248 [159–369] Tg CH4 yr-1 for the 2010–2019 decade. Natural fluxes are perturbed by human activities through climate, eutrophication, and land use. In this budget, we also estimate, for the first time, this anthropogenic component contributing to wetland and inland freshwater emissions. Newly available gridded products also allowed us to derive an almost complete latitudinal and regional budget based on bottom-up approaches. For the 2010–2019 decade, global CH4 emissions are estimated by atmospheric inversions (top-down) to be 575 Tg CH4 yr-1 (range 553–586, corresponding to the minimum and maximum estimates of the model ensemble). Of this amount, 369 Tg CH4 yr-1 or ~65 % are attributed to direct anthropogenic sources in the fossil, agriculture and waste and anthropogenic biomass burning (range 350–391 Tg CH4 yr-1or 63–68 %). For the 2000–2009 period, the atmospheric inversions give a slightly lower total emission than for 2010–2019, by 32 Tg CH4 yr-1 (range 9–40). Since 2012, global direct anthropogenic CH4 emission trends have been tracking scenarios that assume no or minimal climate mitigation policies proposed by the Intergovernmental Panel on Climate Change (shared socio-economic pathways SSP5 and SSP3). Bottom-up methods suggest 16 % (94 Tg CH4 yr-1) larger global emissions (669 Tg CH4 yr-1, range 512–849) than top-down inversion methods for the 2010–2019 period. The discrepancy between the bottom-up and the top-down budgets has been greatly reduced compared to the previous differences (167 and 156 Tg CH4 yr-1 in Saunois et al. (2016, 2020), respectively), and for the first time uncertainty in bottom-up and top-down budgets overlap. The latitudinal distribution from atmospheric inversion-based emissions indicates a predominance of tropical and southern hemisphere emissions (~65 % of the global budget, <30° N) compared to mid (30° N–60° N, ~30 % of emissions) and high-northern latitudes (60° N–90° N, ~4 % of global emissions). This latitudinal distribution is similar in the bottom-up budget though the bottom-up budget estimates slightly larger contributions for the mid and high-northern latitudes, and slightly smaller contributions from the tropics and southern hemisphere than the inversions. Although differences have been reduced between inversions and bottom-up, the most important source of uncertainty in the global CH4 budget is still attributable to natural emissions, especially those from wetlands and inland freshwaters. We identify five major priorities for improving the CH4 budget: i) producing a global, high-resolution map of water-saturated soils and inundated areas emitting CH4 based on a robust classification of different types of emitting ecosystems; ii) further development of process-based models for inland-water emissions; iii) intensification of CH4 observations at local (e.g., FLUXNET-CH4 measurements, urban-scale monitoring, satellite imagery with pointing capabilities) to regional scales (surface networks and global remote sensing measurements from satellites) to constrain both bottom-up models and atmospheric inversions; iv) improvements of transport models and the representation of photochemical sinks in top-down inversions, and v) integration of 3D variational inversion systems using isotopic and/or co-emitted species such as ethane as well as information in the bottom-up inventories on anthropogenic super-emitters detected by remote sensing (mainly oil and gas sector but also coal, agriculture and landfills) to improve source partitioning. The data presented here can be downloaded from https://doi.org/10.18160/GKQ9-2RHT (Martinez et al., 2024). [ABSTRACT FROM AUTHOR]

Riverine dissolved organic carbon (DOC) plays a vital role in regional and global carbon cycles. However, the processes of DOC conversion from soil organic carbon (SOC) and leaching into rivers are insufficiently understood, inconsistently represented, and poorly parameterized, particularly in land surface and earth system models. As a first attempt to fill this gap, we propose a generic formula that directly connects SOC concentration with DOC concentration in headwater streams, where a single parameter, the transformation rate from SOC in the soil to DOC leaching flux, Pr , accounts for the overall processes governing SOC conversion to DOC and leaching from soils (along with runoff) into headwater streams. We then derive a high-resolution Pr map over the contiguous U.S. (CONUS) in five major steps: 1) selecting 2595 headwater catchments where observed riverine DOC data are available with reasonable quality; 2) estimating catchment-average SOC for the 2595 catchments based on high-resolution SOC data; 3) estimating the Pr values for these catchments based on the generic formula and catchment-average SOC; 4) developing a predictive model of Pr with machine learning (ML) techniques and catchment-scale climate, hydrology, geology, and other attributes; and 5) deriving a national map of Pr , based on the ML model. For evaluation, we compare the DOC concentration derived using the Pr map and the observed DOC concentration values at another 3210 headwater gauges. The resulting mean absolute scaled error and coefficient of determination are 0.73 and 0.47, respectively, suggesting the effectiveness of the overall methodology. Efforts to constrain uncertainty and evaluate the sensitivity of Pr to different factors are discussed. To illustrate the use of such a map, we derive a riverine DOC concentration reanalysis dataset for more than two million small catchments over CONUS. The map, robustly derived and empirically validated, lays a critical cornerstone for better simulating the terrestrial carbon cycle in land surface and earth system models. Our findings not only set a foundation for improving our predictive understanding of the terrestrial carbon cycle at the regional and global scales but also hold promises for informing policy decisions related to decarbonization and climate change mitigation. [ABSTRACT FROM AUTHOR]

The AR6 Scenario Database is a vital repository of climate change mitigation pathways used in the latest IPCC assessment cycle. In its current version, several scenarios in the database lack information about the level of gross carbon removal on land, as net and gross removals on land are not always separated and consistently reported across models. This makes scenario analyses focusing on carbon removals challenging. We test and compare the performance of different regression models to impute missing data on land carbon sequestration from available data on net CO2 emissions in agriculture, forestry, and other land use. We find that a gradient boosting regression performs best among the tested regression models and provide a publicly available imputation dataset [ https://doi.org/10.5281/zenodo.10696654 ] (Prütz et al., 2024) on carbon removal on land for 404 incomplete scenarios in the AR6 Scenario Database. We discuss the limitations of our approach, its use cases, and how this approach compares to other recent AR6 data re-analyses. [ABSTRACT FROM AUTHOR]

Methane (CH4) is a significant greenhouse gas in exacerbating climate change. Approximately 25 % of CH4 is emitted from storage tanks. It is crucial to spatially explore the CH4 emission patterns from storage tanks for efficient strategy proposals to mitigate climate change. However, due to the lack of publicly accessible storage tank locations and distributions, it is difficult to ascertain the CH4 emission spatial pattern over a large-scale area. To address this problem, we generated a storage tank dataset (STD) by implementing a deep learning model with manual refinement based on high spatial resolution images (1–2m) from the GaoFen-1, GaoFen-2, GaoFen-6, and Ziyuan-3 satellites over cities in China with officially reported numerous storage tanks in 2021. STD is the first storage tank dataset over 92 typical cities in China. The dataset can be accessed at https://zenodo.org/records/10514151 (Chen et al., 2024). It provides a detailed georeferenced inventory of 14,461 storage tanks, wherein each storage tank is validated and assigned the construction year (2000–2021) by visual interpretation referring to the collected high spatial resolution images, historical high spatial resolution images of Google Earth, and field survey. The inventory comprises storage tanks having various distribution patterns in different cities. Spatial consistency analysis with CH4 emission product shows good agreement with storage tank distributions. The intensive construction of storage tanks significantly induces CH4 emissions from 2005 to 2020, underscoring the need for more robust measures to curb CH4 release and aid in climate change mitigation efforts. Our proposed dataset STD will foster the accurate estimation of CH4 released from storage tanks for CH4 control and reduction and ensure more efficient treatment strategies are proposed to better understand the impact of storage tanks on the environment, ecology, and human settlements. [ABSTRACT FROM AUTHOR]

The quantification and mapping of surficial seabed sediment organic carbon has wide-scale relevance across marine ecology, geology and environmental resource management, with carbon densities and accumulation rates being a major indicator of geological history, ecological function, and ecosystem service provisioning, including the potential to contribute to nature-based climate change mitigation. While global mapping products can appear to provide a definitive understanding of the spatial distribution of sediment carbon, there is inherently high uncertainty when making estimates at this scale. Finer resolution national maps which utilise targeted data syntheses and refined spatial data products are therefore vital to improve these estimates. Here, we report a national systematic review of data on organic carbon content in seabed sediments across Canada and combine this with a synthesis and unification of best available data on sediment composition, seafloor morphology, hydrology, chemistry, geographic setting and sediment mass accumulation rates within a machine learning mapping framework. Predictive quantitative maps of mud content, sediment dry bulk density, and organic carbon content, density and accumulation, were each produced along with cell specific estimates of their 95 % confidence interval (CI) bounds at 200 m resolution across 4,489,235 km² of the Canadian continental margin (92.6 % of the seafloor area above 2,500 m). Fine-scale variation in carbon stocks was identified across the Canadian continental margin, particularly in the Pacific and Atlantic Ocean regions. Carbon accumulation was predicted to be concentrated in coastal areas, with the highest rates in the Gulf of St Lawrence and Bay of Fundy. Overall, we estimate the standing stock of organic carbon in the top 30 cm of surficial seabed sediments across the Canadian shelf and slope to be 10.7 Gt (95 % CI 6.6 – 16.0 Gt), and accumulation at 4.9 Mt per year (95 % CI 2.6 – 9.3 Mt y-1). Increased in-situ sediment data collection and higher precision in spatial environmental data-layers could significantly reduce uncertainty and increase accuracy in these products over time. [ABSTRACT FROM AUTHOR]

Fire carbon emissions contribute to the accumulation of atmospheric CO2 and affect climate change. It is crucial to accurately monitor the dynamics of global fire emissions for fire management and climate change mitigation. However, there are large uncertainties in the existing satellite-based global fire emission products. This study analyzed the uncertainties of six state-of-the-art fire emission products and merged them using the three-corner hat method (TCH), producing a new global fire emission dataset, FiTCH. Our results revealed that satellite-based products such as the Global Fire Assimilation System (GFAS), the Quick Fire Emissions Dataset (QFED), and the Global Fire Emissions Database (GFED) had low uncertainties in fire emissions, while the Fire INventory from National Center for Atmospheric Research (NCAR) (FINN), the Fire Energetics and Emissions Research (FEER), and Xu et al. (2021) data had high uncertainties. The proposed FiTCH dataset presented the lowest uncertainties with a mean annual fire emission of 1978.47 Tg C in 2001–2021. Among biomes, tropical forests and tundra showed higher uncertainties than other biomes such as temperate forests and Mediterranean forests. In drought years, forests showed increased fire emissions, especially in boreal forests, while nonforest regions like grasslands displayed decreased emissions. By integrating the FiTCH data and historical fire emissions in the late 20th century, 1994 was identified as a break year, before which global fire emissions increased significantly and after which the emissions decreased. Global land temperatures and fire emissions have decoupled in the past two decades. However, climate change still causes threats to forest carbon sequestration, especially for boreal forests. This study highlights the importance of forest fire monitoring and management for effective climate mitigation and ecosystem conservation. [ABSTRACT FROM AUTHOR]

Globally, northern peatlands are major carbon deposits with important implications for the climate system. It is therefore crucial to understand their spatial occurrence, especially in the context of peatland degradation by land cover and climate change. This study was aimed at mapping peatlands in the forested landscape of Sweden by modelling soil data against LiDAR-based terrain indices. Machine learning methods were used to produce nation-wide raster maps at 10 m spatial resolution indicating presence-or-not of peatlands. Four different definitions of peatlands were examined: 30, 40, 50 and 100 cm thickness of the organic horizon. Depending on peatland definition, testing with a hold-out dataset indicated Accuracy of 0.89 - 0.91 and Matthew's correlation coefficient of 0.79 - 0.81. The final maps showed a national forest peatland extent of 60,726 - 72,604 km2, estimates which are in range with previous studies employing traditional soil maps. In conclusion, these results emphasize the possibilities of mapping boreal peatlands with LiDAR-based terrain indices. The final peatland maps are publicly available and may be employed for spatial planning, estimating carbon stocks and evaluate climate change mitigation strategies. [ABSTRACT FROM AUTHOR]

Seagrass meadows provide valuable socio-ecological ecosystem services, including a key role in climate change mitigation and adaption. Understanding the natural history of seagrass meadows across environmental gradients is crucial to decipher the role of seagrasses in the global ocean. In this data collation, spatial and temporal patterns in seagrass meadow structure, biomass, production and reproduction data are presented as a function of biotic and abiotic habitat characteristics. The biological traits compiled include measures of meadow structure (e.g., percent cover and shoot density), biomass (e.g., above-ground biomass), production (e.g., shoot production), and reproduction effort (e.g., flowering intensity and seed bank density). Categorical factors include bioregion, geotype (coastal or estuarine), genera and year of sampling. This dataset contains data extracted from peer-reviewed publications published between 1975 and 2020 based on a Web of Science search, and includes 15 data variables across 12 seagrass genera. The top four most studied genera are Zostera, Thalassia, Halophila and Cymodocea (80% of data), and the least studied genera are Phyllospadix, Amphibolis and Thalassodendron (2.3% of data). The data hotspot bioregion is the Tropical Indo Pacific (25% of data), whereas data for the other five bioregions are evenly spread (ranging between 13 and 16% of total data within each bioregion). From the data compiled, 39% related to seagrass biomass, while the least number of data were related to seagrass production (10% of data). This data collation can inform several research fields beyond seagrass ecology, such as the development of nature-based solutions for climate change mitigation, which include readership interested in blue carbon, engineering, fisheries, global change, conservation and policy. [ABSTRACT FROM AUTHOR]

Despite an increasing attention on the role of land in meeting countries' climate pledges under the Paris Agreement, the range of estimates of carbon fluxes from Land Use, Land-Use Change and Forestry (LULUCF) in available databases is very large. A good understanding of the LULUCF data reported by countries under the United Nations Framework Convention on Climate Change (UNFCCC) - and of the differences with other datasets based on country reported data - is crucial to increase confidence in land-based climate change mitigation efforts. Here we present a new data compilation of LULUCF fluxes of carbon dioxide (CO2) on managed land, aiming at providing a consolidated view on the subject. Our database builds on a detailed analysis of data from National Greenhouse Gas Inventories (NGHGIs) communicated via a range of country reports to the UNFCCC, which report anthropogenic emissions and removals based on the IPCC (Intergovernmental Panel on Climate Change) methodology. Specifically, for Annex I countries, data are sourced from annual GHG inventories. For non-Annex I countries, we compiled the most recent and complete information from different sources, including National Communications, Biennial Update Reports, submissions to the REDD+ (Reducing Emissions from Deforestation and Forest Degradation) framework and Nationally Determined Contributions. The data are disaggregated into fluxes from forest land, deforestation, organic soils and other sources (including non-forest land uses). The CO2 flux database is complemented by information on managed and unmanaged forest area as available in NGHGIs. To ensure completeness of time series, we filled the gaps without altering the levels and trends of the country reported data. Expert judgement was applied in a few cases when data inconsistencies existed. Results indicate a mean net global sink of -1.6 Gt CO2/yr over the period 2000-2020, largely determined by a sink on forest land (-6.4 Gt CO2/yr), followed by source from deforestation (+4.4 Gt CO2/yr) and minor fluxes from organic soils (+0.9 Gt CO2/yr) and other land uses (-0.6 Gt CO2/yr). Furthermore, we compare our NGHGI database with two other sets of country-based data: those included in the UNFCCC GHG data interface, and those based on forest resources data reported by countries to FAO and used as inputs into estimates of GHG emissions in FAOSTAT. The first dataset, once gap-filled as in our study, results in a net global LULUCF sink of -5.4 Gt CO2/yr. The difference with the NGHGI database is in this case mostly explained by more updated and comprehensive data in our compilation for non-Annex I countries. The FAOSTAT GHG dataset instead estimates a net global LULUCF source of +1.1 Gt CO2/yr. In this case, most of the difference to our results is due to a much greater forest sink for non-Annex I countries in the NGHGI database than in FAOSTAT. The difference between these datasets can be mostly explained by a more complete coverage in the NGHGI database, including for non-biomass carbon pools and non-forest land uses, and by different underlying data on forest land. The latter reflects the different scopes of the country reporting to FAO, which focuses on area and biomass, and to UNFCCC, which explicitly focuses on carbon fluxes. Bearing in mind the respective strengths and weaknesses, both our NGHGI database and FAO offer a fundamental, yet incomplete, source of information on carbon-related variables for the scientific and policy communities, including under the Global Stocktake. Overall, while the quality and quantity of the LULUCF data submitted by countries to the UNFCCC significantly improved in recent years, important gaps still remain. Most developing countries still do not explicitly separate managed vs. unmanaged forest land, a few report implausibly high forest sinks, and several report incomplete estimates. With these limits in mind, the NGHGI database presented here represents the most up-to-date and complete compilation of LULUCF data based on country submissions to UNFCCC. [ABSTRACT FROM AUTHOR]

Recent climate change mitigation strategies rely on the reduction of methane (CH4) emissions. δ13CCH4 and δ²HCH4 measurements can be used to distinguish sources and thus to understand the CH4 budget better. The CH4 emission estimates by models are sensitive to the isotopic signatures assigned to each source category, so it is important to provide representative estimates of the different CH4 source isotopic signatures worldwide. We present new measurements of isotope signatures of various, mainly anthropogenic, CH4 sources in Europe, which represent a substantial contribution to the global dataset of source isotopic measurements from the literature, especially for δ²HCH4. They improve the definition of δ13CCH4 from waste sources, and demonstrate the use of δ²HCH4 for fossil fuel source attribution. Results from previous studies were combined in a common database that we updated with our new measurements, as well as with additional literature. We found that microbial sources are generally well characterised. The large variability in fossil fuels isotopic compositions requires particular care in the choice of weighting criteria for the calculation of a representative global value. The global dataset could be further improved by measurements from African, South American and Asian countries, as well as more measurements from pyrogenic sources. The final version of the European methane isotope database coupled with a global inventory of fossil and non-fossil δ13CCH4 and δ²HCH4 source signature measurements, is available at: https://doi.org/10.24416/UU01-4PO56T (Menoud et al., 2021a). [ABSTRACT FROM AUTHOR]

The Glastir Monitoring and Evaluation Programme (GMEP) ran from 2013 until 2016, and was probably the most comprehensive programme of ecological study ever undertaken at a national scale in Wales. The programme aimed to (1) set up an evaluation of the environmental effects of the Glastir agri-environment scheme and (2) quantify environmental status and trends across the wider countryside of Wales. The focus was on outcomes for climate change mitigation, biodiversity, soil and water quality, woodland expansion and cultural landscapes. As such, GMEP included a large field survey component, collecting data on a range of elements including vegetation, land cover and use, soils, freshwaters, birds and insect pollinators from up to 300 1 km squares throughout Wales. The field survey capitalised upon the UKCEH Countryside Survey of Great Britain, which has provided an extensive set of repeated, standardised ecological measurements since 1978. The design of both GMEP and the UKCEH Countryside Survey involved stratified-random sampling of squares from a 1 km grid, ensuring proportional representation from land classes with distinct climate, geology and physical geography. Data were collected from different land cover types and landscape features by trained professional surveyors, following standardised and published protocols. Thus, GMEP was designed so that surveys could be repeated at regular intervals to monitor the Welsh environment, including the impacts of agri-environment interventions. One such repeat survey is scheduled for 2021 under the Environment and Rural Affairs Monitoring and Modelling Programme (ERAMMP). Data from GMEP have been used to address many applied policy questions, but there is major potential for further analyses. The precise locations of data collection are not publicly available, largely for reasons of landowner confidentiality. However, the wide variety of available datasets can be (1) analysed at coarse spatial resolutions and (2) linked to each other based on square-level and plot-level identifiers, allowing exploration of relationships, trade-offs and synergies. This paper describes the key sets of raw data arising from the field survey at co-located sites, 2013 to 2016. Data from each of these survey elements are available with the following Digital Object Identifiers. Landscape features, https://doi.org/10.5285/82c63533-529e-47b9-8e78-51b27028cc7f, https://doi.org/10.5285/9f8d9cc6-b552-4c8b-af09-e92743cdd3de, https://doi.org/10.5285/f481c6bf-5774-4df8-8776-c4d7bf059d40; Vegetation plots, https://doi.org/10.5285/71d3619c-4439-4c9e-84dc-3ca873d7f5cc; Topsoil physico-chemical properties, https://doi.org/10.5285/0fa51dc6-1537-4ad6-9d06-e476c137ed09; Topsoil meso-fauna, https://doi.org/10.5285/1c5cf317-2f03-4fef-b060-9eccbb4d9c21; Topsoil particle size distribution https://doi.org/10.5285/d6c3cc3c-a7b7-48b2-9e61-d07454639656; Headwater stream quality metrics, https://doi.org/10.5285/e305fa80-3d38-4576-beef-f6546fad5d45 ; Pond quality metrics, https://doi.org/10.5285/687b38d3-2278-41a0-9317-2c7595d6b882; Insect pollinator and flower data, https://doi.org/10.5285/3c8f4e46-bf6c-4ea1-9340-571fede26ee8; Bird counts, https://doi.org/10.5285/31da0a94-62be-47b3-b76e-4bdef3037360. [ABSTRACT FROM AUTHOR]

The increasing demand of ScenarioMIP is calling for GDP projections of high resolution for the future Shared Socioeconomic Pathways (SSPs) in both socioeconomic development and in climate change of adaption and mitigation research. While to date the global GDP projections for five SSPs are mainly provided at national scales, and the gridded data set are very limited. Meanwhile, the historical GDP can be disaggregated using nighttime light (NTL) images but the results are not open accessed, making it cumbersome in climate change impact and socioeconomic risk assessments across research disciplines. To this end, we produce a set of spatially explicit global Gross Domestic Product (GDP) that presents substantial long-term changes of economic activities for both historical period (2005 as representative) and for future projections under all five SSPs with a spatial resolution of 30 arc-seconds. Chinese population in SSP database were first replaced by the projections under the two-children policy implemented since 2016 and then used to spatialize global GDP using NTL images and gridded population together as fixed base map, which outperformed at subnational scales. The GDP data are consistent with projections from the SSPs and are freely available at http://doi.org/10.5281/zenodo.4350027 (Wang and Sun, 2020). We also provide another set of spatially explicit GDP using the global LandScan population as fixed base map, which is recommended at county or even smaller scales where NTL images are limited. Our results highlight the necessity and availability of using gridded GDP projections with high resolution for scenario-based climate change research and socioeconomic development that are consistent with all five SSPs. [ABSTRACT FROM AUTHOR]

Atmospheric methane (CH4) is a potent greenhouse gas that is strongly influenced by several human activities. China, as one of the major agricultural and energy production countries, e.g., rice cultivation, ruminant feeding and coal production, contributes considerably to the global anthropogenic CH4 emissions. Understanding the characteristics of China's CH4 emissions is necessary for interpreting source contributions and for further climate change mitigation. However, the scarcity of data from some sources or years and spatially explicit information pose great challenges to completing an analysis of CH4 emissions. This study provides a comprehensive evaluation of China's anthropogenic CH4 emissions by synthesizing most of the currently available data (12 inventories). The results show that anthropogenic CH4 emissions differ widely among inventories, with values ranging from 41.9-57.5 Tg CH4 yr-1 in 2010. The discrepancy primarily resulted from the energy sector (27.3-60.0% of total emissions), followed by the agricultural (26.9-50.8%), and waste treatment (8.1-21.2%) sectors. Temporally, emissions among inventories stabilized in the 1990s, but increased significantly thereafter, with annual average growth rates (AAGRs) of 1.8-3.9% during 2000-2010, but slower AAGRs of 0.5-2.2% during 2011-2015. Spatially, the growth of CH4 emissions could be attributed mostly to an increase in emissions from the energy sector (mainly from coal mining) in the northern and central inland regions, followed by waste treatment in the southern and eastern regions. The availability of detailed activity data for sectors or subsectors and the use of region-specific emission factors play important roles in understanding source contributions, and reducing the uncertainty of bottom-up inventories. [ABSTRACT FROM AUTHOR]

The global methane (CH4) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH4 over the past decade. Emissions and concentrations of CH4 are continuing to increase making CH4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH4 sources that overlap geographically, and from the destruction of CH4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (~biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (T-D, exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models, inventories, and data-driven approaches (B-U, including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations). For the 2003-2012 decade, global methane emissions are estimated by T-D inversions at 558 & Tg & CH4 & yr-1 (range [540-568]). About 60 & % of global emissions are anthropogenic (range [50-65 & %]). B-U approaches suggest larger global emissions (736 & Tg & CH4 & yr-1 [596-884]) mostly because of larger natural emissions from individual sources such as inland waters, natural wetlands and geological sources. Considering the atmospheric constraints on the T-D budget, it is likely that some of the individual emissions reported by the B-U approaches are overestimated, leading to too large global emissions. Latitudinal data from T-D emissions indicate a predominance of tropical emissions (~64 & % of the global budget, < & 30° & N) as compared to mid (~32 & %, 30° & N-60° & N) and high northern latitudes (~ & 4 & %, 60° & N-90° & N). T-D inversions consistently infer lower emissions in China (~58 & Tg & CH4 & yr-1 [51-72], -14 & %) and higher emissions in Africa (86 & Tg & CH4 & yr-1 [73-108], +19 & %) than B-U values used as prior estimates. Overall, uncertainties for anthropogenic emissions appear smaller than those from natural sources, and the uncertainties on source categories appear larger for T-D inversions than for B-U inventories and models. The most important source of uncertainty on the methane budget is attributable to emissions from wetland and other inland waters. We show that the wetland extent could contribute for 30-40 & % on the estimated range for wetland emissions. Other priorities for improving the methane budget include: i) the development of process-based models for inland-water emissions, ii) the intensification of methane observations at local scale (flux measurements) to constrain B-U land surface models, and at regional scale (surface networks and satellites) to constrain T-D inversions, iii) improvements in the estimation of atmospheric loss by OH, and iv) improvements of the transport models integrated in T-D inversions. The data presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/Global_Methane_Budget_2016). [ABSTRACT FROM AUTHOR]