dataesr/scientific-paragraphs-categorization huggingface dataset:
Differences and inconsistencies between emission estimates were analyzed, and recommendations were made towards future evaluation of NGHGI data. It is important to remember that, while NGHGIs include uncertainty estimates, the "uncertainty analysis should be seen, first and foremost, as a means to help prioritize national efforts to reduce the uncertainty of inventories in the future, and guide decisions on methodological choice" (Volume 1, Chapter 3, IPCC, 2006) and were therefore not developed to enable comparisons between countries or other datasets. In addition, individual spatially disaggregated research emission datasets often lack quantification of uncertainty. Here, we focus on the mean value and various percentiles (0th, 25th, 75th, 100th) of different research products of the same type to get a first estimate of uncertainty (see Sect.
CO2 data sources and estimation approaches The CO2 emissions and removals in the EU27+UK estimated by inversions and anthropogenic emission inventories resolved at the source category level were analyzed. At the time of this work, data of CO2 fossil emissions and CO2 land 4 emissions and removals (Tables  1  and 2 ) covered the period from 1990 to 2020, with some of the data only available for shorter time periods. Since then, some datasets have been updated to include 2021, but not all, and 4 The IPCC Good Practice Guidance (GPG) for Land Use, Land Use Change and Forestry  (IPCC, 2003)  describes a uniform structure for reporting emissions and removals of greenhouse gasses. This format for reporting can be seen as "land based": all land in the country must be identified as having remained in one of six classes since a previous survey, or as having changed to a different (identified) class in that period.
BU results are compared to NGHGI reported in 2021 (which contain the timeseries for 1990-2019). Data sources are summarized in Tables  1  and 2  with the detailed description of all products provided in Appendices A1-A2. In Appendix A, the harmonized methodology for calculation of uncertainties submitted by Member States to the UNFCCC in their National Inventory Reports (NIRs) is explained. This includes the same 95 % confidence interval as is typically reported, but involved an extensive gap-filling to cover more categories and more years than available in  Petrescu et al. (2021b) , which limited uncertainty estimation to a single year. BU anthropogenic CO2 fossil estimates include global inventory datasets such as the Emissions Database for Global Atmospheric Research (EDGAR v6. 0. ), Statistical Review of World Energy by BP, the Carbon Dioxide Information Analysis Center (CDIAC), the Global Carbon Project (GCP), the Energy Information Administration's (EIA) "International" dataset, and the International Energy Agency (IEA) (see Table  1 ).
These datasets are all described in detail by  Andrew (2020). CO2 land emission estimates are derived from BU biogeochemical models (e. g. DGVMs, bookkeeping models, see Table  2 ). TD approaches include both high spatial resolution regional inversions (CarboScopeReg, EUROCOM  (Monteil et al. , 2020) , inversions based on the CIF-CHIMERE system  (Berchet et al. , 2021)  and LUMIA) and coarser spatial resolution global inversions  (GCP 2021 : Friedlingstein et al. , 2022). Most of the inversions were carried out for CO2 land emissions, with only a single inversion for CO2 fossil emissions (CIF-CHIMERE). Note that CIF-CHIMERE provides estimates for both CO2 land and CO2 fossil from separate simulations. These estimates are described in Sect. 2. 3. The sign of the fluxes is defined from an atmospheric perspective: positive values represent a net source to the atmosphere and negative values a net removal from the atmosphere.
The data is free and can be accessed upon registration  (VERIFY Synthesis Plots, 2022). For the sake of harmonization, we report the mean values of all ensembles. For small sample sizes (e. g. , the regional inversions of CSR with four members), the literature does not give a clear indication on whether the mean or the median is preferred; a preference for one or the other depends on what one wishes to demonstrate. In particular, the median downplays the skewness of the data (outliers). We have taken efforts to exclude outliers from the datasets used to construct ensembles, and consequently the datasets which remain should be randomly distributed. For this reason, we display the mean for all ensembles. As the number of datasets in some ensembles is small (less than five), we display the minimum and maximum annual values for every year (i.
e. , the 0th/100th percentiles) to give an idea of the spread. For ensembles with more than ten members (i. e. , TRENDY), we show the mean and the 0th/100th https://doi. org/10. 5194/essd-2022-412 Preprint. Discussion started: 26 January 2023 c Author(s) 2023. CC BY 4. 0 License. percentiles along with the 25th/75th percentiles in the figures. This combination demonstrates "more likely" and "possible" behavior; as only one ensemble has both bars, displaying them does not overwhelm the reader much more than the standard graphs, and we find the added information to be worth the trade-off. In the text, we report the mean and 0/100th percentiles for small ensembles and mean along with the 25th/75th for larger ensembles. The current work extends  Petrescu et al. (2021b)  by updating the included datasets (both increasing the number of years covered and in some cases updating the model versions), adding datasets, and highlighting changes in terms of mean annual emissions and trends.
CO2 anthropogenic emissions from NGHGI The UNFCCC NGHGI (2021) estimates for the period 1990 to year t-2 (2019), collected for the EU27 and UK, are the basis for this dataset. For historical reasons, a few EU countries provide data for a different base year than 1990 5  , yet it should be noted that regardless of the base year all countries of the EU27+UK bloc are obliged to report estimates for the period 1990 to year t-2. The Annex I Parties to the UNFCCC are required to report annual GHG inventories that include a NIR, with qualitative information on data and methods and a Common Reporting Format (CRF) set of tables that provide quantitative information on GHG emission by category. This annually updated dataset includes anthropogenic emissions and removals. For the land-based sector, the land management proxy is used as a way to report only anthropogenic fluxes  (Grassi et al.
CO2 fossil emissions CO2 fossil emissions occur when fossil carbon compounds are broken down via combustion or other noncombustive industrial processes. Most of these fossil compounds are in the form of fossil fuels, such as coal, oil, and natural gas. Another source category of fossil CO2 emissions is fossil carbonates, such as calcium carbonate and magnesium carbonate, which are used in industrial processes. Because CO2 fossil emissions are largely connected with energy, which is a closely tracked commodity group of high economic importance, 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 the fuel's carbon that is oxidized -lead to methodological differences (Appendix A) resulting in differences of emissions between datasets  (Andrew, 2020).
the remaining + conversions to these classes). The notations "FL-FL", "CL-CL" and "GL-GL" are used to indicate emissions and removals from respective forest, cropland and grassland areas which have remained in the same class from year to year, or in the case of NGHGI lands that have not undergone conversion within the aforementioned transition period (e. g. t-20). The results from sector-specific models reporting carbon fluxes for FL-FL (EFISCEN-Space and CBM), CL and GL (EPIC-IIASA and ECOSSE) are presented separately from the models and datasets including multiple land use categories and simulating land use changes: FAOSTAT (version 2021), the DGVM ensemble TRENDY v10  (Friedlingstein et al. , 2022; Le Quéré et al. , 2009) , the ORCHIDEE and CABLE-POP DGVMs forced by high resolution meteorological data as part of the VERIFY project, and the two bookkeeping approaches of H&N  (Houghton & Nassikas, 2017)  and BLUE  (Hansis et al.
Converted land refers to CO2 emissions from conversions to and from all six classes that occurred in the previous N years. By default, N is equal to 20, although the guidelines recognize that longer times may be necessary in temperate and boreal environments for the dead biomass and soil carbon pools to reach the new equilibrium. Member States have the freedom to select a length of time appropriate to their own circumstances. continuous observations and work at typically higher spatial resolution than the global inversion models (Table  2 ). The prior anthropogenic emissions provided for all regional inversions reported here (i. e. , EUROCOM, EUROCOM drought 2018, VERIFY CSR, VERIFY CIF-CHIMERE, and VERIFY LUMIA) are all based on EDGAR v4. 3, BP statistics, and TNO datasets by generating spatial and temporal distributions through the COFFEE approach  (Steinbach et al.
EFISCEN All of the bottom-up models in this work require external forcing datasets. In the context of the VERIFY project (VERIFY, 2022), an effort was made to provide a single, harmonized version of several kinds of data (meteorological, land use/land cover, and nitrogen deposition) on a high-resolution grid over Europe. These datasets were then made available to all of the modeling groups to use in their simulations. Such a practice is common in model intercomparison projects. However, as the models in Table  2  are not all the same type, data harmonization presented more of a challenge in this work as not all models use the same inputs. All of the datasets described in Appendix A2 were used by at least one modeling group in this work.
Overall NGHGI reported anthropogenic CO2 fluxes In 2019, the UNFCCC NGHGI (2021) net CO2 flux estimates for EU27+UK, accounted for 3. 01 Gt CO2 from all sectors (including LULUCF) and 3. 28 Gt CO2 excluding LULUCF (Fig. B1 ), corresponding to a net sink of Together with waste, in 2019 the emissions from agriculture represent 0. 4 % of the total UNFCCC CO2 emissions in the EU27+UK. An overview of all CO2 fossil and land datasets in this work (Fig. 1 ) leads to a series of conclusions: 1) Regardless of the method used (NGHGI, bottom-up models, top-down models), the timeseries of annual fluxes from fossil CO2 emissions rest almost one order of magnitude higher than removals from CO2 uptake/removal by the land surface and well outside uncertainty estimates; 2) Uncertainties are much larger in the LULUCF estimates than in the fossil CO2 estimates (both for total LULUCF and for individual components of FL, CL, and GL); 3) Interannual variability (IAV) is much more present in non-NGHGI LULUCF datasets than in NGHGI LULUCF datasets or any of the fossil datasets.
Eight bottom-up models produce a 25-75 % percentile which is almost invisible on the scale of the graph (center-top, gray shading). On the other hand, four models estimating Grassland emissions/removals produce an error bar that covers the bottom part of the graph and masks any apparent trend (bottom-center, light green shading). A similar conclusion can be drawn from top-down estimates of LULUCF fluxes (top-right, blue shading). Additional work on reducing the uncertainty of LULUCF fluxes in the EU27+UK is highly welcome. Shaded areas for the other estimates represent the 0th-100th percentiles for groups with fewer than seven members, and the 25th-75th percentile for groups with seven or more members. Ensembles (e. g. , TRENDY v10) are included in the above only their mean values, to avoid more heavily weighting the ensembles compared to the other datasets.
Several caveats remain with this overall synthesis. First, the timeseries were combined rather naively in Fig. 1  by taking the mean of annual timeseries for each dataset discussed below. This leads to, for example, the 15-member TRENDY ensemble being given identical weight as the ORCHIDEE high-resolution simulation over Europe. This was done to weigh more heavily the regional approaches under the assumption that higher resolution simulations and more region-specific input data will lead to more accurate results. While the latter assumption appears reasonable, the first assumption can be disputed. Second, only a single top-down result for fossil CO2 emissions is currently available, preventing an estimate of the uncertainty for this approach. Third, sector models were combined disregarding distinctions between those models estimating "Remain" and "Total" fluxes.
CO2 fossil emissions The inventory-based fossil CO2 estimates from nine data sources (and some subsets) are presented as timeseries (1990-last available year) based on  Andrew (2020)  with the objective to explore differences between datasets and visualize trends (Fig. 2 ). Because the emissions source coverage (also called the "system boundary") of datasets varies, comparing total emissions from these datasets is not a like-for-like comparison. Therefore, some harmonization of system boundaries prior to comparison is needed. This harmonization relies on specifying the system boundary of each dataset and, where possible, removing emission sources to produce a near-common system boundary. For example, if IEA doesn't include any carbonates, then carbonates are removed from all emissions datasets that report these separately.
UNFCCC (CRFs) Energy+IPPU, CDIAC, CEDS, PRIMAP, and GCP include Energy sector plus all fossil fuels in IPPU; EIA, EDGAR and BP include some fossil fuels in IPPU, while EIA and BP include bunker fuels as well. UNFCCC CRFs include Energy total and Energy combustion. Further details on how data sets are harmonized are provided by  Andrew (2020). Because of differing levels of detail provided by datasets, it isn't possible to do this perfectly, but the approximate harmonization gives something closer to a like-for-like comparison, with the legend in Fig. 2    Given the remaining differences in system boundaries after harmonization, most datasets agree well  (Andrew, 2020). In response to inconsistencies identified in this work, the EIA recently corrected some doublecounting of emissions from liquid fuels and has revised its estimates of total emissions down about 10 % for the EU27+UK (pers.
1c  and Fig. B3  (for a single year). The inversion yields plausible and consistent fossil emission estimates compared to nine bottom-up estimates from BU datasets with global coverage (Fig. 1b, c, B3 ). Uncertainties of CIF-CHIMERE inversion estimate have not yet been quantified, however they are likely largely driven by large uncertainties in the input data. The satellite observations of NO2 have large uncertainties, which partly explains the small departure from the prior fluxes during the optimization. Emission ratios between NO2 and CO2 are also uncertain (those from the prior are currently used). The atmospheric residence time of NO2 is another major source of uncertainty. The inversion reports total fossil CO2 emissions calculated from NOx combustion emissions. However, in principle, the derivation of CO2 emissions from the NOx inversions should be restricted to fossil fuel CO2 emissions based on the fossil fuel CO2/NOx ratio from the TNO, as there is a better-established relationship between CO2 and NOx from combustion of fossil fuels.
4 ). The magnitude of the values between EFISCEN-Space and the NGHGI (2021) also agree well, though as noted above the EFISCEN-Space results only vary with the amount of forest area which makes the trend much flatter. Given that all three methods (NGHGI, CBM, and EFISCEN-Space) are heavily based on national forest inventory data, the general agreement between the three is not surprising. Figure  5  presents CO2 land estimates for total Forest land (both remain and convert classes, "FL"). For the total Forest land, the results were simulated with an ecosystem model (ORCHIDEE) and a global dataset (FAOSTAT) as it is not possible for these two approaches to separate out the "remain" and "convert" land use category. This obstacle arises due to the use of net land use/land cover information which does not include detailed information on the nature of the conversions.
The fact that FAOSTAT values are similar to the UNFCCC values points to the primary role of drained organic soils, as this is the only flux included for the FAOSTAT dataset in Fig. 6. Finland and Sweden are of particular importance, as they together account for more than half of the total area of organic soil in Europe. Organic soils are an important source of emissions when they are under management practices that disturb the organic matter stored in the soil. In general, the NGHGI emissions from these soils are reported using country-specific values when they represent an important source within the total budget of GHG emissions. ORCHIDEE also shows a much larger year-to-year variation due to the response of vegetation and respiration fluxes to sub-daily meteorology. EPIC-IIASA and ECOSSE both operate on daily timescales (ECOSSE was updated to daily for this work, though the previous version was monthly).
Bottom-up CO2 estimates from all LULUCF categories This section analyzes CO2 emissions and sinks for the LULUCF sector, including NGHGI categories (from Fig. 3 ) and a suite of different bottom-up approaches. This comparison is challenging due to differences in terms of activities covered in the different estimates, as well as differences in terminology (see, for example,  Petrescu et al. , 2020, Fig. 12 ). To summarize:  Other land categories as in the NGHGIs. They account for the evolution of living biomass, dead biomass, and soil organic carbon for all categories while for NGHGIs reporting is not mandatory for all subcategories depending on the method Tier employed (e. g. , dead organic matter in a Tier 1 method is assumed to be constant). There is significant uncertainty associated with the DGVMs' fluxes both 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) model structural uncertainty (i.
g. , TRENDY v10, ORCHIDEE, and CABLE-POP in Fig. 8 ). Out of the GCP2021 models, CAMS was the model responsible for the lower sinks (data not shown), which may be due partly to changes in the stations assimilated. Table  B2  summarizes the processes included in the CO2 land models presented in this work, as these processes are seen for the moment as the main cause of discrepancies between estimates shown in all the previous figures. According to Table  B2 , no bottom-up model or dataset used here contains all of the 13 LULUCF categories reported in the NGHGIs. A simple analysis of the mean 1990-2020 LULUCF fluxes from the EU27+UK NGHGI (Table  A3  in Appendix A2) shows that six categories account for almost 90 % of the gross flux: Forest land remaining forest land (56 %), Land converted to cropland (7 %), Land converted to forest land (7 %), Grassland remaining grassland (6 %), Harvested wood products (6 %), and Land converted to settlements (6 %).
The impact of both is relatively small compared to interannual variability. This is likely due to at least two reasons. The first reason is that meteorological forcing used in this work has been re-aligned to the CRU observational dataset at 0. 5 degrees and monthly resolution, thus removing large-scale and long-term differences between the original meteorological datasets. In addition, extensive spin-up and transient simulations are run for ORCHIDEE before reaching the point at which the forcing changes (1981 for the meteorological forcing, and 1995 for the nitrogen forcing). Such lengthy simulations enable woody biomass and soil carbon pools to develop a significant amount of inertia in response to additional changes. Greater differences may be seen for models where modified forcing data covers the entire length of the preproduction simulation steps.
Data availability Annual timeseries for the EU27+UK used in creation of the figures in this work for V2019 and V2021 are available for public download at https://doi. org/10. 5281/zenodo. 7365863  (McGrath et al. , 2022). This excludes CO2 fossil data for the IEA, which is subject to license restrictions. The data are reachable with one click (without the need for entering login and password), and downloadable with a second click, consistent with the two click 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. In addition, figures and annual timeseries for EU27+UK as well as other countries and regions are available from VERIFY Synthesis Plots (2022).
BU estimates often share common methodology and input data, and through harmonization, structural differences between BU estimates and NGHGIs can be interpreted. However, the use of common input data restricts the independence between the datasets and, from a verification perspective, may limit the conclusions drawn from the comparisons. On the other hand, TD estimates are constrained by independent atmospheric observations and can serve as an additional, nearly independent quality check The UNFCCC NGHGI CO2 emissions/removals include estimates from five key sectors for the EU27+UK: 1 Energy, 2 Industrial processes and product use (IPPU), 3 Agriculture, 4 LULUCF and 5 Waste. The tiers method a country applies depends on the national circumstances and the individual conditions of the land, which explains the variability of uncertainties among the sector itself as well as among EU countries.
CDIAC The original Carbon Dioxide Information Analysis Center included a fossil CO2 emissions dataset that was long known as CDIAC. This dataset is now produced at Appalachian State University, and has been renamed CDIAC-FF (CDIAC, 2022). It includes emissions from fossil fuels and cement production from 1751 to year n-3. Fossil-fuel emissions are derived from UN energy statistics, and cement emissions from USGS production data.
LUH2v2-ESACCI: " 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 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 fraction and crop fraction from LUH2v2 (for the year 2010) have been subtracted from grass and crop PFTs.
CABLE-POP CABLE-POP  (Haverd et al. , 2018)  is a global terrestrial biosphere model developed around a core biogeophysics module  (Wang & Leuning, 1998 ) and a biogeochemistry module including cycles of nitrogen and phosphorus  (Wang et al. , 2010). Only nitrogen cycling was turned on for the present simulations. The model also includes modules simulating woody demography  (Haverd et al. , 2013)  as well as land use change and land management  (Haverd et al. , 2018). The model distinguishes seven plant functional types which can co-occur in a given grid cell. CABLE-POP does not simulate (natural) dynamic vegetation and the distribution and cover fraction of PFTs is only affected by land use change. Forest demography (establishment, age class distribution, mortality) is accounted for in the simulations, as are natural disturbances and forest management (wood harvest).