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Nitrous oxide (N2O) emissions from livestock manure contribute significantly to the growth of atmospheric N2O, a powerful greenhouse gas and dominant ozone‐depleting substance. Here, we estimate global N2O emissions from livestock manure during 1890–2020 using the tier 2 approach of the 2019 Refinement to the 2006 IPCC Guidelines. Global N2O emissions from livestock manure increased by ~350% from 451 [368–556] Gg N year−1 in 1890 to 2042 [1677–2514] Gg N year−1 in 2020. These emissions contributed ~30% to the global anthropogenic N2O emissions in the decade 2010–2019. Cattle contributed the most (60%) to the increase, followed by poultry (19%), pigs (15%), and sheep and goats (6%). Regionally, South Asia, Africa, and Latin America dominated the growth in global emissions since the 1990s. Nationally, the largest emissions were found in India (329 Gg N year−1), followed by China (267 Gg N year−1), the United States (163 Gg N year−1), Brazil (129 Gg N year−1) and Pakistan (102 Gg N year−1) in the 2010s. We found a substantial impact of livestock productivity, specifically animal body weight and milk yield, on the emission trends. Furthermore, a large spread existed among different methodologies in estimates of global N2O emission from livestock manure, with our results 20%–25% lower than those based on the 2006 IPCC Guidelines. This study highlights the need for robust time‐variant model parameterization and continuous improvement of emissions factors to enhance the precision of emission inventories. Additionally, urgent mitigation is required, as all available inventories indicate a rapid increase in global N2O emissions from livestock manure in recent decades. [ABSTRACT FROM AUTHOR]

Livestock contributes approximately one‐third of global anthropogenic methane (CH4) emissions. Quantifying the spatial and temporal variations of these emissions is crucial for climate change mitigation. Although country‐level information is reported regularly through national inventories and global databases, spatially explicit quantification of century‐long dynamics of CH4 emissions from livestock has been poorly investigated. Using the Tier 2 method adopted from the 2019 Refinement to 2006 IPCC guidelines, we estimated CH4 emissions from global livestock at a spatial resolution of 0.083° (~9 km at the equator) during the period 1890–2019. We find that global CH4 emissions from livestock increased from 31.8 [26.5–37.1] (mean [minimum−maximum of 95% confidence interval) Tg CH4 yr−1 in 1890 to 131.7 [109.6–153.7] Tg CH4 yr−1 in 2019, a fourfold increase in the past 130 years. The growth in global CH4 emissions mostly occurred after 1950 and was mainly attributed to the cattle sector. Our estimate shows faster growth in livestock CH4 emissions as compared to the previous Tier 1 estimates and is ~20% higher than the estimate from FAOSTAT for the year 2019. Regionally, South Asia, Brazil, North Africa, China, the United States, Western Europe, and Equatorial Africa shared the majority of the global emissions in the 2010s. South Asia, tropical Africa, and Brazil have dominated the growth in global CH4 emissions from livestock in the recent three decades. Changes in livestock CH4 emissions were primarily associated with changes in population and national income and were also affected by the policy, diet shifts, livestock productivity improvement, and international trade. The new geospatial information on the magnitude and trends of livestock CH4 emissions identifies emission hotspots and spatial–temporal patterns, which will help to guide meaningful CH4 mitigation practices in the livestock sector at both local and global scales. [ABSTRACT FROM AUTHOR]

Reliable quantification of the sources and sinks of atmospheric carbon dioxide (CO 2), 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 CO 2 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 modeling estimates over the period 1990–2018. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported under the UNFCCC in 2019, aiming to assess and understand the differences between approaches. For the uncertainties in NGHGIs, 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), arises from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. In comparing NGHGIs with other approaches, a key source of uncertainty is that related to different system boundaries and emission categories (CO 2 fossil) and the use of different land use definitions for reporting emissions from land use, land use change and forestry (LULUCF) activities (CO 2 land). At the EU27 + UK level, the NGHGI (2019) fossil CO 2 emissions (including cement production) account for 2624 Tg CO 2 in 2014 while all the other seven bottom-up sources are consistent with the NGHGIs and report a mean of 2588 (± 463 Tg CO 2). The inversion reports 2700 Tg CO 2 (± 480 Tg CO 2), which is well in line with the national inventories. Over 2011–2015, the CO 2 land sources and sinks from NGHGI estimates report -90 Tg C yr -1 ± 30 Tg C yr -1 while all other BU approaches report a mean sink of -98 Tg C yr -1 (± 362 Tg of C from dynamic global vegetation models 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 Tg C yr -1). This concludes that (a) current independent approaches are consistent with NGHGIs and (b) their uncertainty is too large to allow a verification because of model differences and probably also because of the definition of "CO 2 flux" obtained from different approaches. The referenced datasets related to figures are visualized at 10.5281/zenodo.4626578 (Petrescu et al., 2020a). [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 modeling estimates over the period 1990–2017. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported to the UN climate convention UNFCCC secretariat in 2019. For uncertainties, we used for NGHGIs the standard deviation obtained by varying parameters of inventory calculations, reported by the member states (MSs) following the recommendations of the IPCC Guidelines. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model-specific uncertainties when reported. In comparing NGHGIs with other approaches, a key source of bias is the activities included, e.g., anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011–2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 TgCH4yr-1 (EDGAR v5.0) and 19.0 TgCH4yr-1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 TgCH4yr-1. The estimates of TD total inversions give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher-resolution atmospheric transport models give a mean emission of 28.8 TgCH4yr-1. Coarser-resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 TgCH4yr-1) and surface network (24.4 TgCH4yr-1). The magnitude of natural peatland emissions from the JSBACH–HIMMELI model, natural rivers and lakes emissions, and geological sources together account for the gap between NGHGIs and inversions and account for 5.2 TgCH4yr-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 TgN2Oyr-1 , respectively, agreeing with the NGHGI data (0.9 ± 0.6 TgN2Oyr-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 TgN2Oyr-1 , respectively. The TD and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at the EU + UK scale and at the national scale. The referenced datasets related to figures are visualized at 10.5281/zenodo.4590875 (Petrescu et al., 2020b). [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]

Emission of greenhouse gases (GHGs) and removals from land, including both anthropogenic and natural fluxes, require reliable quantification, including estimates of uncertainties, 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 the European Union (EU28 We refer to EU28 as communicated by EUROSTAT, including the UK: https://ec.europa.eu/eurostat/help/faq/brexit (last access: February 2020). As of 1 February 2020, the UK is no longer part of the European Union.). The data integrate recent AFOLU emission inventories with ecosystem data and land carbon models and summarize GHG emissions and removals over the period 1990–2016. This compilation of bottom-up estimates of the AFOLU GHG emissions of European national greenhouse gas inventories (NGHGIs), 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. Whenever available, we present uncertainties, its propagation and role in the comparison of different estimates. While NGHGI data for the EU28 provide consistent quantification of uncertainty following the established IPCC Guidelines, uncertainty in the estimates produced with other methods needs to account for both within model uncertainty and the spread from different model results. The largest inconsistencies between EU28 estimates are mainly due to different sources of data related to human activity, referred to here as activity data (AD) and methodologies (tiers) used for calculating emissions and removals from AFOLU sectors. The referenced datasets related to figures are visualized at 10.5281/zenodo.3662371 (Petrescu et al., 2020). [ABSTRACT FROM AUTHOR]

Croplands are the single largest anthropogenic source of nitrous oxide (N2O) globally, yet their estimates remain difficult to verify when using Tier 1 and 3 methods of the Intergovernmental Panel on Climate Change (IPCC). Here, we re-evaluate global cropland-N2O emissions in 1961–2014, using N-rate-dependent emission factors (EFs) upscaled from 1206 field observations in 180 global distributed sites and high-resolution N inputs disaggregated from sub-national surveys covering 15593 administrative units. Our results confirm IPCC Tier 1 default EFs for upland crops in 1990–2014, but give a ∼15% lower EF in 1961–1989 and a ∼67% larger EF for paddy rice over the full period. Associated emissions (0.82 ± 0.34 Tg N yr–1) are probably one-quarter lower than IPCC Tier 1 global inventories but close to Tier 3 estimates. The use of survey-based gridded N-input data contributes 58% of this emission reduction, the rest being explained by the use of observation-based non-linear EFs. We conclude that upscaling N2O emissions from site-level observations to global croplands provides a new benchmark for constraining IPCC Tier 1 and 3 methods. The detailed spatial distribution of emission data is expected to inform advancement towards more realistic and effective mitigation pathways. [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]