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Agricultural, fisheries, forestry and agro-processing activities produce large quantities of residues, by-products and waste materials every year. Inefficient use of these resources contributes to greenhouse gas emissions and non-point pollution, imposing significant environmental and economic burdens to society. Since many nations do not keep statistics of these materials, it has not been possible to accurately quantify the amounts produced and potentially available for recycling. Therefore, the objectives of the present work were to provide: (1) definitions, typologies and methods to aid consistent classification, estimation and reporting of the various residues and by23 products; (2) a global organic matter database (OMD) of residues and by-products from agriculture, fisheries, forestry and related industries; and (3) preliminary estimates of residues and by-products potentially available for use in a circular bio-economy. To the best of our knowledge, the OMD is the first of its kind consolidating quantities and nutrient concentrations of residues and by-products from agriculture, fisheries, forestry and allied industries globally. The OMD and its associated products will be continuously updated as new production data are published in FAOSTAT, and this information is expected to contribute to evidence-based policies and actions in support of sustainable utilization and the transition towards a circular economy. The estimates in OMD are available only at the national level. Due to the lack of uniform methodology and data across countries, it was difficult to accurately estimate the quantities of all agricultural, fisheries and forestry residue and by-products. Therefore, we strongly recommend investment in the inventory of agricultural, fisheries and forestry residues, by-products and wastes for use in a circular bio35 economy and as amendments. [ABSTRACT FROM AUTHOR]

Nutrient budgets help to identify excess or insufficient use of fertilizers and other nutrient sources in agriculture. They allow calculation of indicators such as the nutrient balance (surplus or deficit) and nutrient use efficiency that help in monitoring of agricultural productivity and sustainability across the world. We present a global database of country-level budget estimates for nitrogen (N), phosphorus (P) and potassium (K) in cropland. The database, disseminated in FAOSTAT, is meant to provide a global reference, synthesizing and continuously updating the state-of-the-art on this topic. The database covers 205 countries and territories, as well as regional and global aggregates, for the period 1961 to 2020. Results highlight the wide range in nutrient use and use efficiencies across geographic regions, nutrients, and time. For the year 2020, the data show regional average N surpluses that range from about 10 kg N ha-1 year-1 in Africa to more than 90 kg N ha-1 year-1 in Asia. Furthermore, they highlight P and K deficits in Africa in 2020 and K deficits for the Americas. This study introduces improvements over previous work in relation to key nutrient coefficients affecting nutrient budgets and use efficiency estimates, especially for nutrient removal in crop products, manure nutrient content, atmospheric deposition and crop biological N fixation rates. We conclude by discussing future research directions, highlighting the need to align statistical definitions across research groups, as well as to further refine plant and livestock coefficients and expand estimates to all agricultural land, including nutrient flows in meadows and pastures. [ABSTRACT FROM AUTHOR]

We describe a new dataset of cropland area circa the year 2020, with global coverage, with data for 221 countries and territories and 34 regional aggregates. Data are generated from geospatial information on the agreement-disagreement characteristics of six open access high-resolution cropland maps derived from remote sensing. The cropland area mapping dataset (CAM) provides information on: i) mean cropland area and its uncertainty; ii) cropland area by six distinct cropland agreement classes; and iii) cropland area by specific combinations of underlying land cover product. The data indicated that world cropland area is 1500 ± 400 million hectares (Mha) (mean and 95% confidence interval), with a relative uncertainty of 25% that increased across regions. It was 50% in Central Asia (40 ± 20 Mha), South America (180 ± 80 Mha), and Southern Europe (40 ± 20 Mha); up to 40% in Australia and New Zealand (50 ± 20 Mha), South-eastern Asia (80 ± 30 Mha) and Southern Africa (16 ± 6 Mha). Conversely, cropland area was estimated with better precision, i.e. smaller uncertainties in the range 10%-25% in Southern Asia (230 ± 30 Mha), Northern America (200 ± 40 Mha), Northern Africa (40 ± 10 Mha), Eastern and Western Europe (40 ± 10 Mha). The new data can be used to investigate coherence of information across the six underlying products, as well as to explore important disagreement features. Overall, 70% or more of the estimated mean cropland area globally and by region corresponded to good agreement of underlying land cover maps-four or more. Conversely, in Africa cropland area estimates found significant disagreement, highlighting mapping difficulties in complex landscapes. Finally, the new cropland area data were consistent with FAOSTAT in out of 18 world regions, and for 114 out of 182 countries with a cropland area above 10 kha. By helping to highlight features of cropland characteristics and underlying causes for agreement/disagreement across land cover products, the CAM dataset can be used as a tool to assess quality of country statistics and help guide future mapping efforts towards improved agricultural monitoring. Data are publicly available at: https://doi.org/10.5281/zenodo.7987515 (Tubiello et al., 2023a). [ABSTRACT FROM AUTHOR]

Despite recent advancements in cloud processing and modelling and the increasing availability of high spectral- and temporalresolution satellite imagery, mapping the spatial distribution of crop types remains a challenging task. Here, we present CROPGRIDS - a comprehensive global, geo-referenced dataset providing information on areas for 173 crops circa the year 2020, at a resolution of 0.05° (~5.55 km at the equator). It represents a major update of the Monfreda et al. (2008) dataset, the most widely used geospatial dataset previously available, covering 175 crops with reference year 2000 at 10 km spatial resolution. CROPGRIDS updates Monfreda et al. (2008) through the careful evaluation of 26 published gridded datasets covering more recent crop information at regional, national, and global levels, largely over the period 2015 - 2020. The new product successfully updates the area extent for 80 of the 175 crops originally covered, representing an update to 1.2 billion hectares of crop area (i.e., 81% of the total cropland included in CROPGRIDS). CROPGRIDS carries forward the crop type maps originally in Monfreda et al. (2008) for 93 crops as more recent information for these crops is not available. We compared CROPGRIDS harvested area of individual crops against independent national and subnational data from 36 National Statistical Offices (NSOs), national-level crop area data for more than 180 countries and territories from FAOSTAT, as well as geospatially, against a newly available high-resolution (30 m) cropland agreement map (Tubiello et al., 2023). Results indicated robustness against the available independent information, with CROPGRIDS world total harvested and crop areas around 1.5 billion hectares. To the best of our knowledge, CROPGRIDS represents the most comprehensive update of previous work on the subject area, offering a new benchmark of global gridded harvested and crop area data for the year circa 2020. CROPGRIDS dataset can be downloaded at https://doi.org/10.6084/m9.figshare.22491997 (Tang et al., 2023). [ABSTRACT FROM AUTHOR]

The combustion of woodfuel for residential use is often not considered to be a source of greenhouse gas (GHG) emissions in households since emissions from woodfuel combustion can be offset by the CO2 absorbed by the growth of the forest as a carbon sink (IPCC, 2006). However, this only applies to wood that is harvested in a renewable way, i.e., at a rate not exceeding the regrowth rate of the forest from which it is harvested (Drigo et al., 2002). This paper estimates the share of GHG emissions attributable to non-renewable woodfuel harvesting for use in residential food activities. It adds to a growing research base estimating GHG emissions from across the entire agri-food value chain, from the manufacture of farm inputs, through food supply chains, and finally to waste disposal (Tubiello et al., 2021). Country-level information is generated from United Nations Statistics Division (UNSD) and International Energy Agency (IEA) data on woodfuel use in households. We find that, in 2019, annual emissions from non-renewable woodfuel use in household food consumption were about 745 million tonnes (Mt CO2eq yr-1), with uncertainty ranging from -20 % to + 22 %, having increased 6% from 1990. Overall, global trends were a result of counterbalancing effects: the emission increases were largely fuelled from countries in Sub-Saharan Africa, Southern Asia, and Latin America while significant decreases were seen in countries in Eastern Asia and South-eastern Asia. The Food and Agriculture Organisation of the United Nations (FAO) has developed and regularly maintains a database covering GHG emissions from the various components of the agri-food sector, including pre- and post-production activities, by country and world regions. The dataset is developed according to International Panel on Climate Change guidelines (IPCC, 2006), which avoids overlaps across AFOLU and energy components. It relies mainly on UNSD Energy Statistics data, which are used as activity data for the calculation of the GHG emissions (Tubiello et al., 2022). The information used in this work is available as open data with DOI https://doi.org/10.5281/zenodo.7310932 (Flammini et al., 2022). [ABSTRACT FROM AUTHOR]

Excessive anthropogenic nitrogen (N) inputs to the biosphere have disrupted the global nitrogen cycle. To better quantify the spatial and temporal patterns of anthropogenic N enrichments, assess their impacts on the biogeochemical cycles of the planet and other living organisms, and improve nitrogen use efficiency (NUE) for sustainable development, we have developed a comprehensive and synthetic dataset for reconstructing the History of anthropogenic N inputs (HaNi) to the terrestrial biosphere. The HaNi dataset takes advantage of different data sources in a spatiotemporally consistent way to generate a set of high-resolution gridded N input products from the preindustrial to present (1860-2019). The HaNi dataset includes annual rates of synthetic N fertilizer, manure application/deposition, and atmospheric N deposition in cropland, pasture, and rangeland at a spatial resolution of 5-arcmin. Specifically, the N inputs are categorized, according to the N forms and land uses, as ten types: 1) NH4+-N fertilizer applied to cropland, 2) NO3-N fertilizer applied to cropland, 3) NH4 +-N fertilizer applied to pasture, 4) NO3-N fertilizer applied to pasture, 5) manure N application on cropland, 6) manure N application on pasture, 7) manure N deposition on pasture, 8) manure N deposition on rangeland, 9) NHx-N deposition, and 10) NOy-N deposition. The total anthropogenic N (TN) inputs to global terrestrial ecosystems increased from 29.05 Tg N yr-1 in the 1860s to 267.23 Tg N yr-1 in the 2010s, with the dominant N source changing from atmospheric N deposition (before the 1900s) to manure N (the 1910s-2000s), and to synthetic fertilizer in the 2010s. The proportion of synthetic NH4 +-N fertilizer increased from 64% in the 1960s to 90% in the 2010s, while synthetic NO3-N fertilizer decreased from 36% in the 1960s to 10% in the 2010s. Hotspots of TN inputs shifted from Europe and North America to East and South Asia during the 1960s-2010s. Such spatial and temporal dynamics captured by the HaNi dataset are expected to facilitate a comprehensive assessment of the coupled human-earth system and address a variety of social welfare issues, such as climate-biosphere feedback, air pollution, water quality, and biodiversity. [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]

We present results from the FAOSTAT agri-food systems emissions database, relative to 236 countries and territories and over the period 1990-2019. We find that in 2019, world-total food systems emissions were 16.5 billion metric tonnes (Gt CO2eq yr-1), corresponding to 31 % of total anthropogenic emissions. Of the agri-food systems total, global emissions within the farm gate -from crop and livestock production processes including on-farm energy use--were 7.2 Gt CO2eq yr-1; emissions from land use change, due to deforestation and peatland degradation, were 3.5 Gt CO2eq yr-1; and emissions from pre- and post-production processes -manufacturing of fertilizers, food processing, packaging, transport, retail, household consumption and food waste disposal--were 5.8 Gt CO2eq yr-1. Over the study period 1990-2019, agri-food systems emissions increased in total by 17 %, largely driven by a doubling of emissions from pre- and post-production processes. Conversely, the FAO data show that since 1990 land use emissions decreased by 25 %, while emissions within the farm gate increased only 9 %. In 2019, in terms of single GHG, pre- and post-production processes emitted the most CO2 (3.9 Gt CO2 yr-1), preceding land use change (3.3 Gt CO2 yr-1) and farm-gate (1.2 Gt CO2 yr-1) emissions. Conversely, farm-gate activities were by far the major emitter of methane (140 Mt CH4 yr-1) and of nitrous oxide (7.8 Mt N2O yr-1). Pre-and post-processes were also significant emitters of methane (49 Mt CH4 yr-1), mostly generated from the decay of solid food waste in landfills and open-dumps. The most important trend over the 30-year period since 1990 highlighted by our analysis is the increasingly important role of food-related emissions generated outside of agricultural land, in pre- and post-production processes along food supply chains, at all scales from global, regional and national, from 1990 to 2019. In fact, our data show that by 2019, food supply chains had overtaken farm-gate processes to become the largest GHG component of agri-food systems emissions in Annex I parties (2.2 Gt CO2eq yr-1). They also more than doubled in non-Annex I parties (to 3.5 Gt CO2eq yr-1), becoming larger than emissions from land-use change. By 2019 food supply chains had become the largest agri-food system component in China (1100 Mt CO2eq yr-1); USA (700 Mt CO2eq yr-1) and EU-27 (600 Mt CO2eq yr-1). This has important repercussions for food-relevant national mitigation strategies, considering that until recently these have focused mainly on reductions of non-CO2 gases within the farm gate and on CO2 mitigation from land use change. The information used in this work is available as open data at: https://zenodo.org/record/5615082 (Tubiello et al., 2021d). It is also available to users via the FAOSTAT database (FAO, 2021a), with annual updates. [ABSTRACT FROM AUTHOR]

Reliable quantification of the sources and sinks of atmospheric carbon dioxide (CO2), including that of their trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Kyoto Protocol and the Paris Agreement. This study provides a consolidated synthesis of estimates for all anthropogenic and natural sources and sinks of CO2 for the European Union and UK (EU27 + UK), derived from a combination of state-of-the-art bottom-up (BU) and top-down (TD) data sources and models. Given the wide scope of the work and the variety of datasets involved, this study focuses on identifying essential questions which need to be answered to properly understand the differences between various datasets, in particular with regards to the less-well characterized fluxes from managed ecosystems. The work integrates recent emission inventory data, process-based ecosystem model results, data-driven sector model results, and inverse modelling estimates, over the period 1990-2018. BU and TD products are compared with European national GHG inventories (NGHGI) reported under the UNFCCC in 2019, aiming to assess and understand the differences between approaches. For the uncertainties in NGHGI, we used the standard deviation obtained by varying parameters of inventory calculations, reported by the Member States following the IPCC guidelines. Variation in estimates produced with other methods, like 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. In comparing NGHGI with other approaches, a key source of uncertainty is that related to different system boundaries and emission categories (CO2 fossil) and the use of different land use definitions for reporting emissions from Land Use, Land Use Change and Forestry (LULUCF) activities (CO2 land). At the EU27 + UK level, the NGHGI (2019) fossil CO2 emissions (including cement production) account for 2624 Tg CO2 in 2014 while all the other seven bottom-up sources are consistent with the NGHGI and report a mean of 2588 (± 463 Tg CO2). The inversion reports 2700 Tg CO2 (± 480 Tg CO2), well in line with the national inventories. Over 2011-2015, the CO2 land sources/sinks from NGHGI estimates report -90 Tg C yr-1 ± 30 Tg C while all other BU approaches report a mean sink of -98 Tg yr-1 (± 362 Tg C from DGVMs only). For the TD model ensemble results, we observe a much larger spread for regional inversions (i.e., mean of 253 Tg C yr-1 ± 400 T g C yr-1). This concludes that a) current independent approaches are consistent with NGHGI b) their uncertainty is too large to allow a verification because of model differences and probably also because of the definition of CO2 flux obtained from different approaches. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4288883 (Petrescu et al., 2020). [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]

Trends in global, regional and national CO2 emissions and removals from forest for the period 1990-2020, are estimated for the first time using data from the Forest Resources Assessment (FRA) 2020, providing new information with respect to the previous FRA 2015. Estimates indicate significant reduction of deforestation emissions over the study period, albeit more slowly than previously assessed, from an average of 4.3 Gt CO2 yr-1 during 1991-2000, to an average of 2.9 Gt CO2 yr-1 during 2016-2020. Remaining forest land was a significant net carbon sink globally and over the entire period, albeit decreasing in strength, from -3.4 Gt CO2 yr-1 in 1991-2000 to -2.5 Gt CO2 yr-1 during 2016-2020. The overall net contribution of forests to atmospheric CO2(i.e., the combined effect of deforestation and forest emissions/removals) was an overall emission source of roughly 0.4 Gt CO2 yr-1 on average during 1991-2020, more than one-third less than previously estimated. Remarkably, the new data also suggest an overall net sink of about -0.7 Gt CO2 yr-1 during 2011-2015, never reported before. Forest emissions/removals data independently reported by countries to the United Nations Framework on Climate Change were in excellent agreement with the FAO estimates over the entire period 1990-2020, confirming a large sink on forest land estimated for 2011-2015. Data are made available as open access via the Zenodo portal (Tubiello, 2020), with DOI 10.5281/zenodo.3941973. [ABSTRACT FROM AUTHOR]

Drainage of large areas with organic soils was conducted over the past century to free land for agriculture. A significant acceleration of such trends was observed in recent decades in South-East Asia, largely driven by drainage of tropical peatlands, an important category of organic soils, for cultivation of oil palm. This work presents methods and main results of a new methodology developed for FAOSTAT, whereby the overlay of dynamic maps of land cover and the use of information on histosols allows the production of a global annual dataset of drained area and emissions over a time series, covering the period 1990-2019. This is an improvement over the existing FAO approach, which had produced only a static map of drained organic soils for the year 2000. Results indicate that drained area and emissions increased by 13 percent globally since 1990, reaching in 2019 24 million ha of drained organic soils, with world total emissions of 830 million tonnes of carbon dioxide (CO2) equivalent. Of these totals, the largest contribution was from the drainage of tropical peatlands in South-East Asia, generating nearly half of global emissions. Results were validated against national data reported by countries to the UN Climate Convention and to well established literature. Overall, the validation yielded a good agreement with these sources. FAOSTAT estimates explained about 60 percent of the variability in official country reported data. The predicted emissions were virtually identical - with over 90 percent of explained variability - to official data from Indonesia, currently the top emitting country by drained organic soils. Also, calculated emissions factors for oil palm plantations in Indonesia and Malaysia were in the same range and very close to emissions factors derived from detailed field measurements. This validation suggests that the FAO estimates may be a useful and sound reference in support of countries reporting needs. Data are made available as open access via the Zenodo portal (Tubiello and Conchedda, 2020) with DOI https://doi.org/10.5281/zenodo.3942370. [ABSTRACT FROM AUTHOR]

Emission of greenhouse gases (GHG) and removals from land, including both anthropogenic and natural fluxes, require reliable quantification, along with estimates of their inherent uncertainties, in order to support credible mitigation action under the Paris Agreement. This study provides a state-of-the-art scientific overview of bottom-up anthropogenic emissions data from agriculture, forestry and other land use (AFOLU) in Europe. The data integrates recent AFOLU emission inventories with ecosystem data and land carbon models, covering the European Union (EU28) and summarizes GHG emissions and removals over the period 1990–2016, of relevance for UNFCCC. This compilation of bottom-up estimates of the AFOLU GHG emissions of European national greenhouse gas inventories (NGHGI) with those of land carbon models and observation-based estimates of large-scale GHG fluxes, aims at improving the overall estimates of the GHG balance in Europe with respect to land GHG emissions and removals. Particular effort is devoted to the estimation of uncertainty, its propagation and role in the comparison of different estimates. While NGHGI data for EU28 provides consistent quantification of uncertainty following the established IPCC guidelines, uncertainty in the estimates produced with other methods will need to account for both within model uncertainty and the spread from different model results. At EU28 level, the largest inconsistencies between estimates are mainly due to different sources of data related to human activity which result in emissions or removals taking place during a given period of time (IPCC 2006) referred here as activity data (AD) and methodologies (Tiers) used for calculating emissions/removals from AFOLU sectors. [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]