dataesr/scientific-paragraphs-categorization huggingface dataset:
According to the IPCC SRCCL, land covers "the terrestrial portion of the biosphere that comprises the natural resources (soil, near-surface air, vegetation and other biota, and water), the ecological processes, topography, and human settlements and infrastructure that operate within that system". Some communities prefer "biogenic" to describe these fluxes, while others found this confusing as fluxes from unmanaged forests, for example, are biogenic but not included in inventories reported to the UNFCCC. and removals between 1990 and 2018 (or the last available year if the datasets do not extend to 2018) from peerreviewed literature and other data delivered under the VER-IFY project (see description in Appendix A). The detailed data source descriptions are found in Sect. A1 and A2. For the BU anthropogenic CO 2 fossil estimates we used global inventory datasets (Emissions Database for Global Atmospheric Research (EDGAR v5.
0. ), Food and Agriculture Organization Corporate Statistical Database (FAO-STAT), British Petroleum (BP), Carbon Dioxide Information Analysis Center (CDIAC), GCP, Energy Information Administration (EIA), International Energy Agency (IEA); see Table  1 ) described in detail by  Andrew (2020) , while for CO 2 land estimates we used BU research-level biogeochemical models (e. g. , DGVMs TRENDY-GCP, bookkeeping models; see Table  2 ). For TD we used global inversions (from the GCP in  Friedlingstein et al. , 2019)  as well as regional inversions at higher spatial resolution (CarboScopeReg, EURO-COM,  Monteil et al. , 2020; Konovalov et al. , 2016). The values are defined from an atmospheric perspective: positive values represent a source to the atmosphere and negative ones a removal from the atmosphere.
As an overview of potential uncertainty sources, Appendix B presents the use of emission factor (EF) data, activity data (AD), and, whenever available, uncertainty methods used for all CO 2 land data sources used in this study. The referenced data used for the figures' replicability purposes are available for download at https://doi. org/10. 5281/zenodo. 4626578 (Petrescu et al. , 2020a). We focus herein on the EU27 and the UK. Within the VERIFY project, we have in addition constructed a web tool which allows for the selection and display of all plots shown in this paper (as well as the companion paper on CH 4 and N 2 O,  Petrescu et al. , 2021)  not only for the regions shown here but for a total of 79 countries and groups of countries in Europe. The website, located on the VERIFY project website (http://webportals.
CO 2 anthropogenic emissions from NGHGIs UNFCCC NGHGI (2019) emissions are country estimates covering the period 1990-2017. The Annex I Parties to the UNFCCC are required to report emissions inventories annually using the common reporting format (CRF). This annual published dataset includes all CO 2 emissions sources for those countries and for most countries for the period 1990 to t -2. Some eastern European countries' submissions be-As this comparison is central to our work, we decided that "land" as defined by the IPCC was a good compromise. gin in the 1980s. Revisions are made on an irregular basis outside of the standard annual schedule.
CO 2 fossil emissions CO 2 fossil emissions occur when fossil carbon compounds are broken down via combustion or other forms of oxidation or via non-metal processes such as for cement production. Most of these fossil compounds are in the form of fossil fuels, such as coal, oil and natural gas. Another category is fossil carbonates, such as calcium carbonate and magnesium carbonate, which are used as feed stocks in industrial processes and whose decomposition also leads to emissions of CO 2. Because CO 2 fossil emissions are largely connected with energy, which is a closely tracked commodity group, there is a wealth of underlying data that can be used for estimating emissions. However, differences in collection, treatment, interpretation and inclusion of various factors such as carbon contents and fractions of oxidized carbon lead to methodological differences (Appendix A, Table  A1 ) resulting in differences of emissions between datasets  (Andrew, 2020).
Bottom-up estimates by sector At the EU27 + UK level our results show that CO 2 fossil emissions are consistent between UNFCCC NGHGI (2019) and BU inventories from EDGAR v5. 0, CEDS and PRIMAP. EDGAR v5. 0 reports the same sources as the UNFCCC, but CEDS reports emissions from energy (1A+1B), IPPU and waste up to 2014, and PRIMAP reports emissions only for energy and IPPU. All BU datasets show a good match for overlapping sectors, energy and IPPU (Fig. 1 , sum of subsectors 1A and 1B). CO 2 fossil emissions are dominated by the energy sector, which includes emissions from energy use in energy in-8 3G and 3H refer to UNFCCC sector activities, as reported by the standardized common reporting format (CRF) tables, which contain CO 2 emissions from agricultural activities: liming and urea applications.
Bottom-up estimates by source category While Fig. 1  was made to assist explanation of differences between datasets disaggregated by sector (e. g. , energy industry, transport), in Fig. 2  we present CO 2 fossil emissions results from the EU27 + UK split by major source categories (solid, liquid, gas). As in Andrew (2020), we observe good agreement between all data sources and UN-FCCC NGHGI (2019) data at this level of regional aggregation. The figure presents estimates for the year 2014, as that was the most recent year when all sources reported estimates. BP 9  (2018), CEDS (v_2019_12_23) and EDGAR 10  v5. 0 (2020) do not publish emissions split by fuel type at the   2019 ). This figure presents the split per fuel type for year 2014. "Others" represents other emissions in the UNFCCC's IPPU, and international bunker fuels are not usually included in total emissions at the sub-global level.
Neither EDGAR (v5. 0 FT2017) nor CEDS publish a breakdown by fuel type, so only the total is shown. country level, and the latter two are shown as dark grey, while the former is shown separating gas from liquid/solid. While the datasets agree well, there are some differences. The EIA (2020) estimate is higher than others, largely because it includes international bunker fuels in liquid-fuel emissions. The IEA (2019) excludes a number of sources from non-energy use of fuels as well as all carbonates. GCP's total matches the NGHGIs exactly by design but remaps some of the fossil fuels used in non-energy processes from "others" to the fuel types used. BP, CEDS and EDGAR v5. 0 all report total emissions very similar to the UNFCCC NGHGI (2019).
Top-down estimates Figure  3  represents the first attempt to evaluate our single inversion of CO 2 fossil emissions, based on satellite CO and NO 2 measurements, against BU estimates. The particular inversion reported here provides emission totals for the EU11  11 + Switzerland, and these exclude non-fossil fuel emissions  (Konovalov et al. , 2016; Konovalov and Lvova, 2018). This inversion estimate partly relies on information available from the BU emission inventories -EDGAR v4. 3. 2 for 2012 (http://edgar. jrc. ec. europa. eu/overview. php? v=432_GHG, last access: December 2020, http://edgar. jrc. ec. europa. eu/overview. php?v=432_AP, last access: December 2020) and CDIAC for 2012-2014 (http://cdiac. ess-dive. lbl. gov/trends/emis/overview_2014. html, last ac-Figure  3. A first attempt in comparing BU CO 2 fossil estimates from eight datasets with a TD fast-track inversion  (Konovalov and Lvova, 2018).
CO 2 land fluxes This section presents an update to the benchmark data collection by  Petrescu et al. (2020b)  on CO 2 emissions and removals from the LULUCF sector (excluding energy-related emissions but including emissions from land use change, emissions from disturbances on managed land, and the natural sink on managed land), expanding the scope of that work by adding TD estimates from inverse model ensembles and additional BU models run with higher-resolution meteorological forcing data over the EU27 + UK. Land CO 2 fluxes result from CO 2 emissions/removals from one land type converted to another (e. g. , forests cleared for croplands), as well as emissions/removals from land occupied by terrestrial ecosystems (depending on the dataset, this may be from managed or unmanaged land, which complicates comparisons with NGHGIs).
The CO 2 fluxes in Fig. 4  include direct and indirect LU-LUCF on managed land. The total UNFCCC estimates include the total LULUCF emissions and sinks (by the UN-FCCC definition) belonging to all six IPCC land classes and HWPs (see Sect. 2. 3, Appendix B1, Fig. B1b ). We plot these and compare them with fluxes simulated with statistical global datasets, bookkeeping and biosphere models, sectorspecific models, and inversion model ensembles. The error bar represents the variability in model estimates as the min and max values in the ensemble. For all regions and the EU27 + UK, we note considerable disagreement between the BU and TD results. We mostly see that BU (observation-based and process-based) estimates agree well with the NGHGIs, while inversions, in particular EUROCOM, report very strong sinks and high variability of the results compared to the BU estimates.
The forest land estimates, which remain in this class (FL-FL) in Fig. 6 , were simulated with ecosystem models (CBM, ORCHIDEE, EFISCEN) (described in Appendix A2 and Table  B1 ), global datasets (FAOSTAT) and countries' official inventory statistics reported to UNFCCC. The results show  that the differences between models are systematic, with CBM having slightly weaker sinks than EFISCEN and FAO-STAT. Starting with year 2000 and towards 2017, the FAO-STAT reports sinks that strengthen over time. Differences between estimates might be due to the use of different input data; e. g. , CBM and EFISCEN use national forest inventory (NFI) data as the main source of input to describe the current structure and composition of European forest, while FAO-STAT uses input data directly from country submission done under the FAO Global Forest Resources Assessment (FRA,  2015 15 ) (e.
They account for the evolution of living and dead biomass as well as SOC for all categories, while for NGHGIs it is not mandatory for all subcategories (i. e. , dead biomass). Finally, there is significant uncertainty associated with the DGVMs' fluxes from (i) the forcing data, including datasets of land use changes and the coverage of different land use change practices; (ii) model parameters; and (iii) structural uncertainty in models (i. e. , which processes are included and which are not)  (Arneth et al. , 2017). Similar to FAOSTAT, DGVMs typically deal with net land use change emissions, instead of gross land use change as reported in NGHGIs, which may induce significant dif-ferences with coarse-resolution model simulations (i. e. , 0. 5 • or 1 • for the TRENDY ensemble). DGVMs often do not distinguish between managed and unmanaged land, while NGHGIs are for emissions from managed land.
, 2018a). The ORCHIDEE model and the TRENDY v7 ensemble means show much higher inter-annual variability due to the sensitivity of the model fluxes to highly variable meteorological forcing and the models' sub-daily time steps, which allow for much more rapid responses to changing conditions (i. e. , 2003 extreme drought year), as already discussed in the previous sections. The incorporation of variable climate data and the fact that DGVM models simulate explicitly climate impacts on CO 2 fluxes, which inventories and bookkeeping do not, explain these differences. DGVMs estimate net land use emissions as the difference between a run with and a run without land use change, and their estimate includes the loss/gain of additional sink capacity, that is, the sink that favors the environmental changes (e.
Thus, the initially reasonable agreement between the two datasets  (2000) (2001) (2002) (2003) (2004) (2005)  becomes a difference well outside the uncertainty range of the NGHGI in 2017 (290 Tg C difference between the GCP and NGHGI, with an NGHGI uncertainty of only 30 Tg C). Between 2011 and 2018, GCP (-241 Tg C mean) (red bars) shows, as well as large inter-annual variability, an increase in the CO 2 sink. The strongest sink between inversions (mean -381 Tg C) is reported by CarboScopeReg, which, similar to EUROCOM, also shows high fluctuations. This fluctuation partly reflects the fact that all other inversions are results from ensembles of inversion systems each with different inter-annual variations, while CSR is a single inversion system (just a small ensemble with differing prior error structure and different set of atmospheric station data used).
Data availability All raw 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. 4626578 (Petrescu et al. , 2020a). The data we submitted are reachable with one click (without the need for entering login and password) and downloadable with a second click, consistent with the twoclick access principle for data published in ESSD  (Carlson and Oda, 2018). The data and the DOI number are subject to future updates and only refers to this version of the paper. Please also see Tables  1  and 2  for an overview of data sources for CO 2 emissions used in this study.
Summary and concluding remarks The overview and variety of data products described in this study are the first of a series of European CO 2 synthesis papers presenting and investigating differences between UNFCCC NGHGIs, bottom-up data-based inventories, highresolution observation-based BU models, and TD approaches represented by both global and regional inversions. The CO 2 fossil emissions dominate the anthropogenic CO 2 flux in the EU27 + UK. Fossil CO 2 emissions are more straightforward to estimate than ecosystem fluxes. Different BU methods have only minor differences with respect to the NGHGI. These differences can often be attributed to differ-ent definitions or assumptions about activity data or emission factors or by the allocation of fuel types to different sectors (see Fig. 2 , Sect.
3. 2). Currently, TD methods, albeit only a single inversion using CO / NO x proxies to determine CO 2 fossil emissions, show broad agreement with the BU estimates. The TD inversion is not yet capable of verifying the minor differences between the BU estimates. However, a substantial decrease in the level of uncertainty is expected in the near term, with the large-scale deployment of observation networks dedicated to detecting fossil fuel emissions (e. g. , with the launch of the CO 2 M 18  constellation in 2025, Maenhout al. , 2020). In the short term, methodological improvements and the potential co-assimilation of existing CO 2 satellite data are also expected to lead to significant decreases in the uncertainty. The CO 2 land fluxes belong to the LULUCF sector, which is one of the most uncertain sectors in UNFCCC reporting due in part to the fact that these fluxes can be either sinks or sources.
The IPCC Guidelines prescribe methodologies that are used to estimate the CO 2 fluxes in the NGHGI, but differences between countries continue to exist due to the use of specific national circumstances (as permitted under the 2006 IPCC Guidelines). When we analyzed the estimates from multiple BU sources (inventories and models), we observe similar sources of uncertainties: (a) differences due to input data and structural/parametric uncertainty of models  (Houghton et al. , 2012)  and (b) differences in definitions  (Pongratz et al. , 2014; Grassi et al. , 2018b; Petrescu et al. , 2020b). More accurate estimates for LULUCF data will be needed in the post-2020 reporting for the EU27 and UK since the LULUCF sector will now contribute to the EU's 2030 targets. To better assess natural variability and trends we believe a reconciliation of BU and TD estimates should focus on clearly defined activities over a given period (e.
We notice stronger similarities between NGHGIs and models using national forest inventory data (e,g. CBM, EFIS-CEN). For cropland and grassland sector-specific models (ECOSSE, EPIC-IIASA) the differences between their results and the NGHGIs are due to differences in input data, process representation (in particular those linked to soil organic matter decomposition) and management representation. In general, management is one of the main drivers for the carbon balance of croplands and grasslands. However, spatial data on management are scarce and can have high uncertainty. For EPIC-IIASA specifically, the regional carbon simulation results for managed cropland are almost evenly impacted by model parameterization, soil input accuracy and crop management regionalization  (Balkovič et al. , 2020).
bottom-up activity data (e. g. , fuel use and emission factors) and ecosystem measurements (bottom-up models). For CO 2 , a specific effort will be made to separate fossil fuel emissions from ecosystems fluxes. For CH 4 and N 2 O, we will separate agricultural from fossil fuel and industrial emissions. Finally, trends in the budget of the three GHGs will be analyzed in the context of NDC targets. The objectives of VERIFY are as follows. -Objective 1. Integrate the efforts between the research community, national inventory compilers, operational centers in Europe and international organizations towards the definition of future international standards for the verification of GHG emissions and sinks based on independent observation. -Objective 2. Enhance the current observation and modeling ability to accurately and transparently quantify the sinks and sources of GHGs in the land use sector for the tracking of land-based mitigation activities.
UNFCCC NGHGI (2019) The Annex I Parties to the UNFCCC are required to report emissions inventories annually using the common reporting format (CRF). This annual published dataset includes all CO 2 emissions sources for those countries and for most countries for the period 1990 to t -2. Some eastern European countries' submissions begin in the 1980s. Revisions are made on an irregular basis outside of the standard annual schedule. For a complete description see  Andrew (2020). Uncertainties. Annex I Parties quantitatively estimate their uncertainties of data used for all source and sink categories using the methodologies provided in the 2006 IPCC Guidelines. The 2006 IPCC Guidelines stipulate that the determination of uncertainties is a key element of any complete inventory. Uncertainties are quantified for emission factors, activity data and, in some cases, for emissions.
EDGAR v5. 0 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 2015, while for the years beyond 2015 a fast-track (FT) approach is applied based on BP data and other proxies to extrapolate CO 2 emissions until most recent years  (Crippa et al. , 2019 , https://edgar. jrc. ec. europa. eu/ overview. php?v=booklet2019, last access: 14 April 2020). For a complete description see  Andrew (2020). Uncertainties. EDGAR uses emission factors (EFs) and activity data (AD) to estimate emissions. Both EFs and AD are uncertain to some degree, and when combined, their uncertainties need to be combined too.
Uncertainty. An estimate of the uncertainty in the annual budgets of the emissions over the 12 countries is derived from the analyses of uncertainties within the CO and NO x emission inversions (associated with model and data errors) and from an assessment of the uncertainties in the CO 2 / CO and CO 2 / NO x emission ratios (based on their spatial variability). The preliminary results indicate that the uncertainty in the information from the CO inversion is too high to provide reliable estimates of the CO 2 fossil emissions when using CO satellite data only or to provide weight to this information when using CO 2 fossil estimates from both the CO and NO x inversions. The estimates based on NO 2 data are close to EDGAR v4. 3. 2 in 2012. These estimates are quite constant over the 4-year period, while we assume that the CO 2 fossil emissions followed a significant negative trend during this period.
Among other environmental indicators, ORCHIDEE simulates positive and negative CO 2 emissions from plant uptake, soil decomposition and harvests across forests, grasslands and croplands. Activity data are based on land use and land cover maps. For VERIFY, pixel land cover/land use fractions were based on the land use map LUH2v2h and the land cover project of the Climate Change Initiative (CCI) program of the European Space Agency (ESA). The latter is based on purely remotely sensed methods, while the former makes use of national harvest data from the UN Food and Agriculture Organization. ESA-CCI LUH2v2. "We describe here the input data and algorithms used to create the land cover maps specific for our CMIP6 simulations using the historical/future reconstruction of land use states provided as reference datasets for CMIP6 within the land use harmonization database LUH2v2h  (Hurtt et al.
, 2020). More details are provided on the devoted web page https://orchidas. lsce. ipsl. fr/dev/lccci (last access: October 2020) which shows further tabular, graphical and statistical data. The overall approach relies on the combination of the LUH2v2 data with present-day land cover distribution derived from satellite observations for the past decades. The main task consists in allocating the land use types from LUH2v2 in the different PFTs for the historical period and the future scenarios. The natural vegetation in each grid cell is defined as the PFT distribution derived from the ESA-CCI land cover product for the year 2010 to which pasture frac-tion and crop fraction from LUH2v2 (for the year 2010) have been subtracted from grass and crop PFTs. This characterization of the natural vegetation in terms of PFT distribution is assumed invariant in time and is used for both the historical period and the different future scenarios.
" (Lurton et al. , 2020). Uncertainty in the ORCHIDEE model arises from three primary sources: parameters, forcing data (including spatial and temporal resolution) and model structure. Some researchers argue that the initial state of the model (i. e. , the values of the various carbon and water pools at the beginning of the production run, following model spinup) represents a fourth area. However, the initial state of the model is defined by its equilibrium state and therefore a strong function of the parameters, forcing data and model structure, with the only independent choice being the target year of the initial state. Out of the three primarily areas of uncertainty, the climate forcing data are dictated by the VERIFY project itself, thus removing that source from explaining observed differences among the models, although it can still contribute to uncertainty between the ORCHIDEE results and the national inventories.
The  Raymond et al. (2013)  study only provides mean flux densities at the much coarser spatial resolution of the socalled COSCAT regions. All estimates were then downscaled to 0. 1 • using the spatial distribution of European inland water bodies. Note that in contrast to  Hastie et al. (2019)  the areal distribution of lakes was extracted from the HydroLAKES database  (Messager et al. , 2016)  to be consistent with the estimates of inland water N 2 O and CH 4 presented by  Petrescu et al. (2021). Uncertainty. Monte Carlo simulations were performed to constrain uncertainties resulting from both the pCO 2 prediction equation and the choice of the k formulation.