dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , resolution, spatial correlation) compared to previous results, where prior data were coming from observation networks, it is difficult to conclude what effects satellites have on posterior emissions from the 2 years. However, it is interesting to note how satellite data assimilation infers changes on a regional scale. Unlike surface stations, satellite data have more power to constrain northern European emissions than central European emissions. The total UNFCCC estimates that include emissions from all sectors are compared with the fluxes from global datasets, BU models and TD inversions. Relative to 1990, N 2 O emissions in 2019 decreased by a minimum of 26 % (eastern Europe) up to a maximum of 46 % (western Europe) and by 39 % for the EU27 + UK. At the European level, the emissions from BU estimates (anthropogenic NGHGI plus the sum of all natural, 991 kt N 2 O) and the TD total (including natural) regional estimate (1443 kt N 2 O) averaged over 2015-2019 roughly agree within the uncertainty reported by UNFCCC (±59 %).
18, -0. 17, -0. 15 and -0. 11 % yr -1 in NGHGI, EDGARv6. 0, GAINS and FAO-STAT, respectively; for the other sectors the trends differ. The match in agriculture trends reflects that the sources rely on the same basic activity data from FAOSTAT and follow the IPCC EF Tier 1 or 2 approach  (Petrescu et al. , 2020). However, the high reported uncertainty range from the NGHGIs contradicts the match of the BU estimate absolute values and represents an important research question to be further investigated. In contrast, ECOSSE shows lower estimates because it does not use the FAO fertilizer application rate database but instead calculates ideal fertilizer application rates from the nitrogen demand of the crops. ECOSSE uses fertilizer data derived by  Mueller et al. (2012)  and simulates only for winter wheat.
FLEX-PART estimated smaller fluxes compared to the prior and to CHIMERE in northeastern Germany. CHIMERE, on the other hand, remained close to the prior estimates. The total mean emission for the EU27 + UK for the period of 2005-2018 was 1538 kt N 2 O and 1680 kt N 2 O yr -1 for CHIMERE and FLEXPART, respectively, compared to the estimate of 1513 kt N 2 O yr -1 from FLEXPART (Fig. 13 ). Both inversions also found a decreasing trend over 2005-2018, with decreases of 157 and 298 kt N 2 O yr -1 for CHIMERE and FLEXPART, respectively, which was not seen in the prior estimates. Similar to CH 4 , the differences might be owing to the different dataset used for determining the background mixing ratios. Further analysis is ongoing.
Data availability Data files reported in this work which were used for calculations and figures are available for public download at https://doi. org/10. 5281/zenodo. 7553800 (Petrescu et al. , 2023). The data are reachable with one click (without the need for entering login and password) and can be downloaded with a second click, consistent with the two-clickaccess principle for data published in ESSD  (Carlson and Oda, 2018). The data and the DOI number are subject to future updates and only refer to this version of the paper. The raw gridded data, according to the VERIFY consortium governing document, will be made publicly available 12 months after its publication in ESSD.
Five-year means of CH 4 (Fig. 1 ) and N 2 O (Fig. 10 ) emissions for the periods 2011-2015 and 2015-2019 from the different BU and TD datasets have been calculated for the EU27 + UK bloc, as well as for five regions in Europe. These estimates are then compared with respective NGHGI emissions for the same periods and the 1990 base year. Inconsistencies between CH 4 BU estimates and NGHGI data at the EU27 + UK level (Fig. 3 ) are mainly caused by different methodologies in calculating emissions as highlighted in  Petrescu et al. (2020 Petrescu et al. ( , 2021)). Both BU inventories and the NGHGI use similar activity data and, to varying ex- tents, the default EFs reported in the IPCC (  2006 ) guidelines, meaning that the estimates are predestinated to agree rather well. Thus, the spread in all BU estimates may not be indicative of the uncertainty.
For global consistency purposes, EDGARv6. 0 and FAOSTAT mostly use Tier 1 approaches in calculating emissions (and uncertainties), a fact which triggers differences with other data sources using Tier 2 or 3 methods (GAINS for all sectors and CAPRI for agriculture). Within the UNFCCC reporting process, the agriculture sector was the highest contributor to the CH 4 emissions, followed by energy and waste (Fig. 1 ). A reason for the small inconsistencies between datasets is the allocation of emissions to different sectors and that some data sources use updated methods and emission factors from different versions of the IPCC guidelines (e. g. , 1996 versus 2006 or 2019 refinements to the 2006 IPCC guidelines, i. e. , EDGARv6. 0). For N 2 O anthropogenic emissions, all BU data sources show good agreement with the UNFCCC NGHGI (2021) data in both trends and means (Fig.
The overall agreement is generally due to similar sources of input activity data and emission factors (albeit with some aforementioned variations) and is not indicative of the true uncertainties in the respective CH 4 and N 2 O inventories. Indeed, NGHGIs report CH 4 and N 2 O emissions with large uncertainties, and, furthermore, NGHGI estimates generally diverged from the respective TD fluxes. Despite the significant spread in the inversion estimates (due to, e. g. , use of different transport models and/or observation datasets, while priors might be the same; Table  B4 ), TD estimates were generally higher than NGHGI, even when accounting for the (albeit uncertain) natural fluxes. The analysis done here generally compared estimates in terms of long-term trends and averages over 5 or more years and thus provides a working example of how such syntheses could inform future GSTs under the Paris Agreement.
Gap-filling harmonization procedure for NGHGI uncertainties The presented uncertainties in the emission levels of the individual countries and the EU27 + UK bloc were calculated by using the methods and data used to compile the official GHG emission uncertainties that are reported by the EU under the UNFCCC NIRs (UNFCCC NGHGI, 2022). The EU uncertainty analysis reported in the bloc's National Inventory Report (NIR) is based on country-level, Approach 1 uncertainty estimates  (IPCC, 2006, vol. 1, chap. 3) that are reported by EU member states, Iceland and the United Kingdom under Article 7(1)(p) of Regulation (EU) 525/2013 (2020). These country-level uncertainty estimates are typically reported at the beginning of a submission cycle and are not always revised with updated CRF submissions later in the submission cycle.
For each year of the time series, uncertainties in the total and sectoral CH 4 and N 2 O emissions are calculated using Gaussian error propagation, by summing the respective subsectoral uncertainties (expressed in kilotons CH 4 and N 2 O) in quadrature and assuming no error correlation. In contrast, for the EU27 + UK bloc, uncertainties in the total and sectoral CH 4 and N 2 O emissions were calculated to take into A. M. R. Petrescu et al. :  The consolidated European synthesis of CH 4 and N 2 O emissions for the EU27 + UK account error correlations between the respective country estimates at the subsector level. This was done by applying the same methods and assumptions described in the 2022 EU NIR (UNFCCC NGHGI, 2022). The subsector resolution applied for gap-filling allows the routine to access respective data on emission factors from CRF Table "Summary 3" (UN-FCCC NGHGI, 2021) and apply correlation coefficients (r) when aggregating the uncertainties.
EDGARv6. 0 The Emissions Database for Global Atmospheric Research (EDGAR) is an independent global emission inventory of greenhouse gases (GHG) and air pollutants developed by the Joint Research Centre of the European Commission (https://edgar. jrc. ec. europa. eu/index. php, last access: May 2022). The first edition of the Emissions Database for Global Atmospheric Research was published in 1995. The dataset now includes almost all sources of fossil CO 2 emissions, is updated annually, and reports data for 1970 to n -1. Estimates are provided by sector. Emissions are estimated fully based on statistical data from 1970 until 2018 (https://data. jrc. ec. europa. eu/ dataset/97a67d67-c62e-4826-b873-9d972c4f670b, last access: November 2022). For complete description see  Andrew (2020). Uncertainties.
Activity data are mainly based on FAO-STAT and EUROSTAT statistics, and estimation of emissions follows IPCC (2006) methodologies, with a higher or lower level of detail depending on the importance of the emission source. Details on CAPRI methodology for emission calculations is referenced in Table  A1. Then, for each cell, the uncertainty of the disaggregation and that of the process are combined as they are independent (sum of squares; see  Solazzo et al. , 2021) , and then the total uncertainty for the grid cell is aggregated using emission weighted sum of squares. The GAINS model covers all source sectors of anthropogenic methane (CH 4 ) emissions -agricultural sector emissions from livestock, rice cultivation and agricultural waste burning, energy sector emissions from upstream and downstream sources in fossil fuel extraction and use, and emissions from handling and treatment of solid waste and wastewater source sectors.
The resulting emission estimates are well comparable across geographic and temporal scales. The GAINS approach to calculate waste emissions is developed in consistency with the first-order-decay method recommended by  IPCC (2006 and 2019 revision) , applying different decay periods when estimating emissions from flows of different types of organic waste, i. e. , food and garden, paper, wood, textile, and other. Data on waste generation, composition and treatment are taken from EUROSTAT (2019) and complemented with national information from the UNFCCC NGHGI (2019) com-mon reporting format tables on the amounts of waste diverted to landfills of various management levels and to treatment, e. g. , recycling, composting, biodigestion and incineration.
FAOSTAT The Food and Agriculture Organization of the United Nations (FAO) provides CH 4 emission totals per gas/activity from the agriculture and LULUCF sectors available at https: //www. fao. org/faostat/en/#data/GT (last access: April 2022). The FAOSTAT emissions database is computed following Tier 1 of the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (http://www. ipcc-nggip. iges. or. jp/public/2006gl/index. html, last access: January 2022). Country reports to FAO on crops, livestock and agriculture use of fertilizers are the source of activity data. Geospatial data are the source of AD for the estimates from cultivation of organic soils, biomass and peat fires. GHG emissions are provided by country, regions and special groups, with global coverage, relative to the period of 1961-present (with annual updates, currently to 2019) and with projections for 2030 and 2050, expressed as CO 2 eq.
for CH 4 , by underlying agricultural emission sub-domain and by aggregate (agriculture total, agriculture total plus energy, agricultural soils). LULUCF emissions consist of CH 4 (methane) associated with biomass and peat fires. Comparison to the UNFCCC submissions is also provided. Uncertainties were computed by  Tubiello et al. (2013)  but are not available in the FAOSTAT database. A. M. R. Petrescu et al. :  The consolidated European synthesis of CH 4 and N 2 O emissions for the EU27 + UK A1. 2 Top-down anthropogenic CH 4 emission estimates.
Global Carbon Project -Global Methane Budget (GMB) GMB uses an ensemble of 22 top-down global inversions for anthropogenic CH 4 emissions presented in  Saunois et al. (2020)  for the Global Methane Budget. These inversions were simulated by nine atmospheric inversion systems based on various chemistry transport models, differing in vertical and horizontal resolutions, meteorological forcing, advection and convection schemes, and boundary layer mixing. Surface-based inversions were performed over the pe-riod of 2000-2017, while satellite-based inversions cover the GOSAT data availability for 2010-2017. The protocol established for these simulations was not stringent as the prior emission flux dataset was not mandatory, and each group selected their constraining observations. More information can be found in  Saunois et al.
(2020) , in particular in their Tables 6 and S6. Uncertainties. Currently there are no uncertainties reported for the GMB models. This study uses the median and the min/max as uncertainty range estimation from the 22model ensemble. In general, uncertainties might be due to factors like different transport models, physical parametrizations, prior fluxes and observation datasets, among others. The CH 4 estimate from inland waters represents a climatology of diffusive and ebullitive CH 4 emissions from rivers, lakes and reservoirs. It is based on two approaches. The first approach synthesizes existing inland water emission estimates for CH 4  (Rosentreter et al. , 2021; Bastviken et al. , 2011; Del Sontro et al. , 2018; Stavert et al. , 2022; Johnson et al. , 2022 Johnson et al. , , 2021;; Harrison et al.
5×2. 5) lakes are binned into different classes as a function of the key drivers of CH 4 fluxes that are lake size and depth  (Messager et al. , 2016)  and lake trophic status. Then, for each grid and each class, a representative simulation forced by high-resolution local climate forcings extracted from the lake sector of ISIMIP was performed. To carry out the spatial upscaling, the resulting diffusive and ebullitive areal CH 4 fluxes through the water-air interface were multiplied by the surface area of each lake class in each grid of the domain, extracted from the HydroLAKES database  (Messager et al. , 2016).
HIMMELI is driven with soil temperature, water table depth, the leaf area index and anoxic respiration. These parameters are provided to HIM-MELI from JSBACH, which models hydrology, vegetation, and soil carbon input from litter and root exudates. CH 4 emission and uptake of mineral soils are calculated applying the method by  Spahni et al. (2011)  based on soil moisture estimated by JSBACH. The distribution of terrestrial vegetation types in JSBACH-HIMMELI is adopted from CORINE land cover data and from native JSBACH land cover for the areas that CORINE does not cover. The HIMMELI methane model is applied for peatlands and the mineral soil approach for the rest. The map of inland water CH 4 emissions has been combined with the JSBACH-HIMMELI land use map so that the map of inland waters is preserved and JSBACH grid-based fractions of different land use categories are adjusted accordingly.
In order to avoid double counting, the terrestrial CH 4 flux estimates have been normalized by the ratio of the two inland water body distributions. Uncertainties. As in any process modeling the uncertainties of the bottom-up modeling of CH 4 arise from three primary sources: parameters, forcing data (including spatial and temporal resolution) and model structure. An important source of uncertainty in the case of terrestrial CH 4 flux modeling is the spatial distribution of peatlands. The uncertainties of JSBACH-HIMMELI peatland emissions were estimated by comparing the annual totals of measured and simulated methane fluxes at five European observation sites. Two of the sites are located in Finnish Lapland, one in middle Sweden, one in southern Finland and one in Poland. For the sensitivity of mineral soil fluxes,  Spahni et al.
Bottom-up emission estimates at the European level including onshore seepage and geothermal exhalations were proposed 10 years later, by  Etiope (2009) , on the basis of published regional emission estimates, suggesting around 3 Tg yr -1 for geographic, onshore and offshore, Europe (including Azerbaijan; practically corresponding to present EU49). Again 10 years later, thanks to a wider dataset of CH 4 seepage flux from different geological environments in dif-ferent countries and global inventories of geo-CH 4 emission sites, a process-based model using statistically derived emission factors and activity (areas) was developed to derive a global grid map of geo-CH 4 emissions  (Etiope et al. , 2019). The global grid model, developed by ArcGIS at 1 • × 1 • resolution, can be, in theory, used to derive (scale up) geo-CH 4 emissions at a continental or regional scale.
The main purpose of the global gridding was to offer a global spatial distribution of geo-CH 4 sources, with emission potential and methane isotopic values; it could not provide locally precise geo-CH 4 emissions because the datasets, developed for gridding purposes, were not complete and did not contain all the information necessary for improving previous estimates. A refinement of bottom-up estimates was possible only for mud volcanoes and microseepage because their gridding implied a more careful assessment of the spatial distribution and emission factors.
A2. 3 Top-down natural CH 4 emission estimates Global Carbon Project -Global Methane Budget  (Saunois et al. , 2020)  For this study, none of the global inversions were updated. GMB uses an ensemble of 13 monthly gridded estimates of wetland emissions based on different land surface models as calculated for  Saunois et al. (2020). Each model conducted a 30-year spin-up and then simulated net methane emissions from wetland ecosystems over 2000-2017. The models were forced by CRU JRA (University of East Anglia Climatic Research Unit (CRU) Japanese Reanalysis (JRA)) reconstructed climate fields  (Harris, 2019)  and by the remote-sensing-based wetland dynamical area dataset WAD2M (Wetland Area Dynamics for Methane Modeling). This dataset provides monthly global areas over 2000-2017 based on a combination of microwave remote sensing data from  Schroeder et al.
(2015)  and various regional inven-tory datasets. More information is available in  Saunois et al. (2020) , and more details will be presented in a future publication led by  Poulter et al. (2017)  and colleagues. Uncertainty. As described by  Saunois et al. (2020)  uncertainties are reported as minimum and maximum values of the available studies, in brackets. They do not take into account the uncertainty of the individual estimates but rather express the uncertainty as the range of available mean estimates, i. e. , the standard error across measurements/methodologies considered.
DayCent DayCent was designed to simulate soil C dynamics, nutrient flows (N, P, S) and trace gas fluxes (CO 2 , CH 4 , N 2 O, NO x , N 2 ) between soil, plants and the atmosphere at a daily time step. Submodels include soil water content and temperature by layer, plant production and allocation of net primary production (NPP), decomposition of litter and soil organic matter, mineralization of nutrients, N gas emissions from nitrification and denitrification, and CH 4 oxidation in nonsaturated soils. The DayCent modeling application at the EU level is a consolidated model framework running on LUCAS point  (Orgiazzi et al. , 2018) , which was extensively explained in previous works  (Lugato et al. , 2017 (Lugato et al. , , 2018;; Quemada et al. , 2020) , where a detailed description of numerical and geographical datasets and uncertainty estimations is reported.
Information directly derived from LUCAS (2009-2015) included the soil organic carbon content (SOC), particle size distribution and pH. Hydraulic properties and bulk density were also calculated with an empirically derived pedotransfer function. Management information was derived from official statistics (EUROSTAT, 2019) and included crop shares at the NUTS2 level. The amount of mineral N was partitioned ac-cording to the regional crop rotations and agronomic crop requirements. Organic fertilization and irrigated areas were derived from the Gridded Livestock of the World FAO dataset and the FAO-AQUASTAT (2020) product. Meteorological data were downloaded from the E-OBS gridded dataset (http://www. ecad. eu, last access: June 2019) at 0. 1 • resolution. For the climatic projection, the gridded data from CORDEX database (https://esgf-node.
N 2 O emissions from inland waters The N 2 O estimate represents a climatology of average annual N 2 O emissions from rivers, lakes, reservoirs and estuaries at the spatial resolution of 0. 1 •. Based on a spatially explicit representation of water bodies and point and nonpoint sources of N and P, this model quantifies the globalscale spatial patterns in inland water N 2 O emissions in a consistent manner at 0. 5 • resolution, which were then downscaled to 0. 1 • using the spatial distribution of European inland water bodies. The procedure to calculate the cascading loads of N and P delivered to each water body along the river-reservoir-estuary continuum and to topologically connect 1. 4 million lakes (extracted from the HydroLAKES database) is described in  Maavara et al. (2019)  and  Lauerwald et al.
FAOSTAT The Statistics Division of the Food and Agriculture Organization of the United Nations provides N 2 O emissions from agriculture (https://www. fao. org/faostat/en/#data/GT, last access: April 2022) and its sub-domains, as well as N 2 O emissions from land use linked to biomass burning. The FAOSTAT emissions database is computed following Tier 1 of the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (http://www. ipcc-nggip. iges. or. jp/public/2006gl/ index. html, last access: January 2022). Country reports to FAO on crops, livestock and agriculture use of fertilizers are the source of activity data. Geospatial data are the source of AD for the estimates from cultivation of organic soils, biomass and peat fires. N 2 O emissions are provided by country, regions and special groups, with global coverage, relative to the period of 1961-present (with annual updates, currently 2019) and with projections for 2030 and 2050 for agriculture only, expressed in both CO 2 e and N 2 O by underlying agricultural and land use emission sub-domain and by aggregate (agriculture total, agriculture total plus energy, agricultural soils).
The main N 2 O emissions are reported for the following agricultural activities: manure management, synthetic fertilizers, manure applied to the soils, manure left in pasture, crop residues, cultivation of organic soils and burning crop residues. LULUCF emissions consist of N 2 O associated with burning biomass and peat fires, as well as from the drainage of organic soils. Comparison to the UNFCCC submissions is also provided. Uncertainties were computed by  Tubiello et al. (2013)  but are not available in the FAOSTAT database.
Upscaled emission estimates from RECCAP2 Detailed gridded data can be obtained by contacting the data providers: Ronny Lauerwald ronny. lauerwald@inrae. fr Pierre Regnier pierre. regnier@ulb. ac. Akagi et al. (2011)  Uncertainty in the observation space is calculated as the quadratic sum of the measurement and transport uncertainties. For the error in each land grid cell, the maximum magnitude of the flux in the cell of interest and its eight neighbors is used; for ocean grid cells the magnitude of the cell of interest only is used. Competing interests. At least one of the (co-)authors is a member of the editorial board of Earth System Science Data. The peerreview process was guided by an independent editor, and the authors also have no other competing interests to declare. Disclaimer. The views expressed in this publication are those of the author(s) and do not necessarily reflect the views or policies of FAO.
The uncertainty for EDGARv6. 0 is the same as that of v5. 0 and calculated for 2015 as min/max values for the total and each sector (Solazzo et al. , 2021)  and represents the 95 % confidence interval of a lognormal distribution. The mean column represents the common overlapping periods between datasets: 1990-2015 for total EU27 + UK, energy, agriculture and waste; 1990-2018 for IPPU; and 1990-2014 for CAPRI. The last years of the time series of the respective datasets are 2018 (EDGARv6. 0, CAPRI), 2019 (FAOSTAT, UNFCCC) and 2015 (GAINS). After 2014 CAPRI delivered estimates for 2 additional years, 2016 and 2018, as well as uncertainties for 2014 and 2018 (25. 1 %) and 2016 (25. 2 %).