dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019)  within national territories to match national emissions estimates provided by the GCB for the years 1959-2020, which were compiled following the methodology described in Sect. 2. 1 with all datasets available on 14 August 2021 (Robbie Andrew, personal communication, 2021). Small differences between the systems due to for instance regridding to the transport model resolution are corrected for in the latitudinal partitioning we present, to ensure agreement with the estimate of E FOS in this budget. We also note that the ocean fluxes used as prior by five out of six inversions are part of the suite of the ocean process model or f CO 2 data products listed in Sect. 2. 4. Although these fluxes are further adjusted by the atmospheric inversions, it makes the inversion estimates of the ocean fluxes not completely independent of S OCEAN assessed here.
3 , Table  8 ). In this period, 46 % of fossil CO 2 emissions came from coal, 35 % from oil, 14 % from natural gas, 3 % from decomposition of carbonates, and 1 % from flaring. In 1850, the UK accounted for 62 % of global fossil CO 2 emissions. In 1891 the combined cumulative emissions of the current members of the European Union reached and subsequently surpassed the level of the UK. Since 1917 US cumulative emissions have been the largest. Over the entire period 1850-2020, US cumulative emissions amounted to 110 GtC (25 % of world total), the EU's to 80 GtC (18 %), and China's to 60 GtC (14 %). There are three additional global datasets that include all sources of fossil CO 2 emissions: CDIAC-FF  (Gilfillan and Marland, 2021) , CEDS version v_2021_04_21  (Hoesly et al. , 2018; O'Rourke et al.
, 2021) , and PRIMAP-hist version 2. 3. 1  (Gütschow et al. , 2016 (Gütschow et al. , , 2021)) , although these datasets are not independent. CDIAC-FF has the lowest cumulative emissions over 1750-2018 at 437 GtC, GCP has 443 GtC, CEDS 445 GtC, PRIMAP-hist TP 453 GtC, and PRIMAPhist CR 455 GtC. CDIAC-FF excludes emissions from lime production, while neither CDIAC-FF nor GCP explicitly include emissions from international bunker fuels prior to 1950. CEDS has higher emissions from international shipping in recent years, while PRIMAP-hist has higher fugitive emissions than the other datasets. However, in general these four datasets are in relative agreement with total historical global emissions of fossil CO 2.
9 % of the total). Growth rates for these countries from 2019 to 2020 were +1. 4 % (China), -10. 6 % (USA), -10. 9 % (EU27), and -7. 3 % (India), with -7. 0 % for the rest of the world. The per capita fossil CO 2 emissions in 2020 were 1. 2 tC person -1 yr -1 for the globe, and were 3. 9 (USA), 2. 0 (China), 1. 6 (EU27) and 0. 5 (India) tC per person per year for the four highest emitting countries (Fig. 5 ). The COVID-19-induced decline in emissions of -5. 4 % in 2020 is close to the projected decline of -6. 7 %, which was the median of four approaches, published in  Friedlingstein et al. (2020)  (Table  7 ). Of the four approaches, the "GCP" method was closest at -5. 8 %. That method was based on national emissions projections for China, the USA, the EU27, and India using reported monthly activity data when available and projections of gross domestic product corrected for trends in fossil fuel intensity (I FOS ) for the rest of the world.
However, differences still exist, which highlight the need for accurate knowledge of land-use transitions and their spatial and temporal variability. A further caveat is that global land-use change data for model input does not capture forest degradation, which often occurs on small scales or without forest cover changes easily detectable from remote sensing and poses a growing threat to forest area and carbon stocks that may surpass deforestation effects (e. g. Matricardi et al. , 2020; Qin et al. , 2021). Overall, therefore, we assign low confidence to the change towards a decreasing trend of land-use emissions over the last two decades as seen compared to the estimate of the global carbon budget 2020  (Friedlingstein et al. , 2020). Our approach aims at using the most up-to-date data and methods, such as accounting for revisions of living databases of country-level agricultural statistics from FAO or including satellite remote-sensing information for spatial allocation.
, 2009). In these regions, mode, intermediate, and deep-water masses are formed that transport an-thropogenic carbon into the ocean interior, thus allowing for continued CO 2 uptake at the surface. Outgassing of natural CO 2 occurs mostly in the tropics, especially in the equatorial upwelling region, and to a lesser extent in the North Pacific and polar Southern Ocean, mirroring a well-established understanding of regional patterns of air-sea CO 2 exchange (e. g. Takahashi et al. , 2009; Gruber et al. , 2009). These patterns are also noticeable in the Surface Ocean CO 2 Atlas (SOCAT) dataset, where an ocean f CO 2 value above the atmospheric level indicates outgassing (Fig. B1 ). This map further illustrates the data sparsity in the Indian Ocean and the Southern Hemisphere in general. Interannual variability of the ocean carbon sink is driven by climate variability with a first-order effect from a stronger ocean sink during large El Niño events (e.
Model evaluation The evaluation of the ocean estimates (Fig. B2 ) shows an RMSE from annually detrended data of 1. 3 to 2. 8 µatm for the seven f CO 2 -based data products over the globe, relative to the f CO 2 observations from the SOCAT v2021 dataset for the period 1990-2020. The GOBMs RMSEs are larger and range from 3. 3 to 5. 9 µatm. The RMSEs are generally larger at high latitudes compared to the tropics, for both the data products and the GOBMs. The data products have RMSEs of 1. 3 to 3. 6 µatm in the tropics, 1. 3 to 2. 7 µatm in the north, and 2. 2 to 6. 1 µatm in the south. Note that the data products are based on the SOCAT v2021 database; hence the latter are not an independent dataset for the evaluation of the data products. The GOBM RMSEs are more spread across regions, ranging from 2.
Discussion Each year when the global carbon budget is published, each flux component is updated for all previous years to consider corrections that are the result of further scrutiny and verification of the underlying data in the primary input datasets. Annual estimates may be updated with improvements in data quality and timeliness (e. g. to eliminate the need for extrapolation of forcing data such as land use). Of all terms in the global budget, only the fossil CO 2 emissions and the growth rate in atmospheric CO 2 concentration are based primarily on empirical inputs supporting annual estimates in this carbon budget. The carbon budget imbalance, yet an imperfect measure, provides a strong indication of the limitations in observations in understanding and representing processes in models, and/or in the integration of the carbon budget components.
g. heat balance, water balance), and the use of a wider range of biogeochemical observations to better understand the land-ocean partitioning of the carbon imbalance (e. g. oxygen, carbon isotopes). Finally, additional information could also be obtained through higher resolution and process knowledge at the regional level, and through the introduction of inferred fluxes such as those based on satellite CO 2 retrievals. The limit of the resolution of the carbon budget imbalance is yet unclear, but most certainly not yet reached given the possibilities for improvements that lie ahead. Estimates of global fossil CO 2 emissions from different datasets are in relatively good agreement when the different system boundaries of these datasets are considered  (Andrew, 2020a). But while estimates of E FOS are derived from reported activity data requiring much fewer complex transformations than some other components of the budget, uncertainties remain, and one reason for the apparently low variation between datasets is precisely the reliance on the same underlying reported energy data.
Conclusions The estimation of global CO 2 emissions and sinks is a major effort by the carbon cycle research community that requires a careful compilation and synthesis of measurements, statistical estimates, and model results. The delivery of an annual carbon budget serves two purposes. First, there is a large demand for up-to-date information on the state of the anthropogenic perturbation of the climate system and its underpinning causes. A broad stakeholder community relies on the datasets associated with the annual carbon budget including scientists, policy makers, businesses, journalists, and non-governmental organizations engaged in adapting to and mitigating human-driven climate change. Second, over the last decades we have seen unprecedented changes in the human and biophysical environments (e.
g. changes in the growth of fossil fuel emissions, impact of COVID-19 pandemic, Earth's warming, and strength of the carbon sinks), which call for frequent assessments of the state of the planet, a better quantification of the causes of changes in the contemporary global carbon cycle, and an improved capacity to anticipate its evolution in the future. Building this scientific understanding to meet the extraordinary climate mitigation challenge requires frequent, robust, transparent, and traceable datasets and methods that can be scrutinized and replicated. This paper via "living data" helps to keep track of new budget updates.
Data availability The data presented here are made available in the belief that their wide dissemination will lead to greater understanding and new scientific insights into how the carbon cycle works, how humans are altering it, and how we can mitigate the resulting human-driven climate change. Full contact details and information on how to cite the data shown here are given at the top of each page in the accompanying database and summarized in Table  2. The accompanying database includes two Excel files organized in the following spreadsheets: The file Global_Carbon_Budget_2021v1. 0. xlsx includes the following:   Atmospheric CO 2 for simulation A provided by the GCP; converted to pCO 2 temper-ature formulation  (Sarmiento et al. , 1992)   as in  Ilyina et al. (2013)  adapted to comply with OMIP protocol  (Orr et al.
JMA-MLR OS-ETHZ-GRaCER Method Spatio-temporal in-terpolation (update of  Rödenbeck et al. , 2013. Specifically, the sea-air CO 2 fluxes and the pCO 2 field are numerically linked to each other and to the spatio-temporal field of ocean-internal carbon sources/sinks through process pa-rameterizations, and the ocean-internal source/sink field is then fit to the SOCATv2021 pCO 2 data  (Bakker et al. , 2021)   Land-schützer et al. , 2013 Land-schützer et al. , , 2016)). An ensemble of neural network models trained on 100 subsampled datasets from the Sur-face Ocean CO 2 Atlas v2021 (SOCATv2021,  Bakker et al. , 2021). Like the original data, subsamples are distributed after in-terpolation on 1 × 1 grid cells along ship tracks. Sea surface salinity, temperature, sea surface height, mixed layer depth, atmospheric CO 2 mole fraction, chlorophyll a, pCO 2 climatology, latitude, and longitude are used as predictors.
Derived from the SO-CAT(v2021) pCO 2 database, but cor-rected to the subskin temperature of the ocean as measured by satellite, using the methodology described by  Goddijn-Murphy et al. (2015). A correction to the flux calculation is also applied for the cool and salty surface skin. In other respects the product uses in-terpolation of the data using the two-step neural network based on MPI-SOMFFN: in the first step the ocean is divided into a monthly climatology of 16 biogeochemical provinces using a SOM, In the second step a feed-forward neural network establishes non-linear relationships between pCO 2 and SST, SSS, mixed layer depth(MLD) and atmo-spheric xCO 2 in each of the 16 provinces. Further description in  Watson et al. (2020)   (Naegler, 2009).
See  Fay and Gregor et al. (2021). Table  A4. Comparison of the inversion set-up and input fields for the atmospheric inversions. Atmospheric inversions include the full CO 2 fluxes, including the anthropogenic and pre-industrial fluxes. Hence they need to be adjusted for the pre-industrial flux of CO 2 from the land to the ocean that is part of the natural carbon cycle before they can be compared with S OCEAN and S LAND from process models. See  Table  A5. Attribution of f CO 2 measurements for the year 2020 included in SOCATv2021  (Bakker et al. , 2016 (Bakker et al. , , 2021) )   B2. Evaluation of the GOBMs and data products using the root mean squared error (RMSE) for the period 1990 to 2020, between the individual surface ocean f CO 2 mapping schemes and the SOCAT v2021 database. The y axis shows the amplitude of the interannual variability (A-IAV, taken as the standard deviation of a detrended time series calculated as a 12-month running mean over the monthly flux time series,  Rödenbeck et al.
, 2015). Results are presented for the globe, north (> 30 • N), tropics (30 • S-30 • N), and south (< 30 • S) for the GOBMs (see legend circles) and for the f CO 2 -based data products (star symbols). The f CO 2 -based data products use the SOCAT database and therefore are not independent from the data (see Sect. 2. 4. 1). (Saatchi et al. , 2011; and GlobalCarbon unpublished data; Avitabile et al. , 2016) , GPP  (Jung et al. , 2010; Lasslop et al. , 2010) , leaf area index  (De Kauwe et al. , 2011; Myneni et al. , 1997) , net ecosystem exchange  (Jung et al. , 2010; Lasslop et al. , 2010) , ecosystem respiration  (Jung et al. , 2010; Lasslop et al. , 2010) , soil carbon  (Hugelius et al. , 2013; Todd-Brown et al. , 2013 ), evapotranspiration (De Kauwe et al. , 2011) , and runoff  (Dai and Trenberth, 2002).
For each model-observation comparison a series of error metrics are calculated, scores are then calculated as an exponential function of each error metric, and finally for each variable the multiple scores from different metrics and observational datasets are combined to give the overall variable scores shown in (a). Overall variable scores increase from 0 to 1 with improvements in model performance. The set of error metrics vary with dataset and can include metrics based on the period mean, bias, root mean squared error, spatial distribution, interannual variability, and seasonal cycle. The relative skill score shown in (b) is a Z score, which indicates in units of standard deviation the model scores relative to the multi-model mean score for a given variable. Grey boxes represent missing model data.
, 2011b). The consumption-based emissions have the same global total but reflect the trade-driven movement of emissions across the Earth's surface in response to human activities. We estimate consumption-based emissions from 1990-2018 by enumerating the global supply chain using a global model of the economic relationships between economic sectors within and between every country  (Andrew and Peters, 2013; Peters et al. , 2011a). Our analysis is based on the economic and trade data from the Global Trade and Analysis Project (GTAP;  Narayanan et al. , 2015) , and we make detailed esti-mates for the years 1997  (GTAP version 5), 2001 (GTAP6), 2004 , 2007 , and 2011 (GTAP9. 2) , covering 57 sectors and 141 countries and regions. The detailed results are then extended into an annual time series from 1990 to the latest year of the gross domestic product (GDP) data (2018 in this budget), using GDP data by expenditure in the current exchange rate of US dollars (USD; from the UN National Accounts Main Aggregrates Database; UN, 2021) and time series of trade data from GTAP (based on the methodology in  Peters et al.
C1. 5 Emissions projection for E FOS To gain insight into emission trends for 2021, we provide an assessment of global fossil CO 2 emissions, E FOS , by combining individual assessments of emissions for China, USA, the EU, and India (the four countries and regions with the largest emissions), and the rest of the world. We provide full year estimates for two datasets: IEA (2021b) and our own analysis. This approach differs from last year where we used four independent estimates including our own, because of the unique circumstances related to the COVID-19 pandemic. This year's analysis is more in line with earlier budgets. Previous editions of the Global Carbon Budget (GCB) have estimated year-to-date (YTD) emissions, and performed projections, using sub-annual energy consumption data from a variety of sources depending on the country or region.
World The global total is the sum of each of the countries and regions. A1 ). Some of these activities lead to emissions of CO 2 to the atmosphere, while others lead to CO 2 sinks. E LUC is the net sum of emissions and removals due to all anthropogenic activities considered. Our annual estimate for 1960-2020 is provided as the average of results from three bookkeeping approaches (Sect. C2. 1 below): an estimate using the Bookkeeping of Land Use Emissions model  (Hansis et al. , 2015;  hereafter BLUE) and one using the compact Earth system model OSCAR  (Gasser et al. , 2020) , both BLUE and OSCAR being updated here to new land-use forcing covering the time period until 2020, and an updated version of the estimate published by Houghton and Nassikas (2017) (hereafter updated H&N2017). All three datasets are then extrapolated to provide a projection for 2021 (Sect.
, 2017). In contrast, the BLUE and OSCAR models include sub-grid-scale transitions between all vegetation types. Furthermore, the updated H&N2017 assume conversion of natural grasslands to pasture, while BLUE and OS-CAR allocate pasture proportionally on all natural vegetation that exists in a grid cell. This is one reason for generally higher emissions in BLUE and OSCAR. Bookkeeping models do not directly capture carbon emissions from peat fires, which can create large emissions and interannual variability due to synergies of land-use and climate variability in Southeast Asia, particularly during El-Niño events, nor emissions from the organic layers of drained peat soils. To correct for this, the updated H&N2017 includes carbon emissions from burning and draining of peatlands in Indonesia, Malaysia, and Papua New Guinea (based on the Global Fire Emission Database, GFED4s; van der  Werf et al.
The updated H&N2017 base their estimates directly on the Forest Resource Assessment of the FAO which provides statistics on forest-area change and management at intervals of 5 years currently updated until 2020  (FAO, 2020). The data are based on country reporting to FAO and may include remote-sensing information in more recent assessments. Changes in land use other than forests are based on annual, national changes in cropland and pasture areas reported by  FAO (FAOSTAT, 2021). On the other hand, BLUE uses the harmonized land-use change data LUH2-GCB2021 covering the entire 850-2020 period (an update to the previously released LUH2 v2h dataset;  Hurtt et al. , 2017 Hurtt et al. , , 2020)) , which was also used as input to the DGVMs (Sect. C2. 2). It describes land-use change, also based on the FAO data as described in Sect.
C2. 2 as well as the HYDE3. 3 dataset  (Klein Goldewijk et al. , 2017a, b) , but provided at a quarterdegree spatial resolution, considering sub-grid-scale transitions between primary forest, secondary forest, primary nonforest, secondary non-forest, cropland, pasture, rangeland, and urban land  (Hurtt et al. , 2020; Chini et al. , 2021). LUH2-GCB2021 provides a distinction between rangelands and pasture, based on inputs from HYDE. To constrain the models' interpretation on whether rangeland implies the original natural vegetation to be transformed to grassland or not (e. g. browsing on shrubland), a forest mask was provided with LUH2-GCB2021; forest is assumed to be transformed to grasslands, while other natural vegetation remains (in case of secondary vegetation) or is degraded from primary to secondary vegetation  (Ma et al.
Due to an artefact in the HYDE3. 3 dataset expressed as an abrupt shift in the pattern of pastures and rangelands in 1960, the year 1960 exhibits much larger gross transitions between natural vegetation and pastures and rangelands than prior and subsequent years. Although these gross transitions cancel out in terms of net area changes causing large abrupt transitions, an unrealistic peak in emissions occurs around 1960 in BLUE and OSCAR. To correct for this, we replace the estimates for 1959-1961 by the average of 1958 and 1962 in both BLUE and OSCAR. Abrupt transitions will immediately influence gross emissions, which have a larger instantaneous component. Processes with longer timescales, such as slow legacy emissions and regrowth, are inseparable from the carbon dynamics due to subsequent land-use change events.
DGVMs that do not simulate sub-grid-scale transitions (i. e. net land-use emissions; see Table  A1 ) used the HYDE land-use change dataset  (Klein Goldewijk et al. , 2017a, b) , which provides annual (1700-2019), half-degree, fractional data on cropland and pasture. The data are based on the available annual FAO statistics of change in agricultural land area available until 2015. The new HYDE3. 3 cropland and grazing land dataset which now in addition to having FAO country-level statistics is constrained spatially based on multi-year satellite land-cover maps from ESA CCI LC. Data from HYDE3. 3 are based on a FAO which includes yearly data from 1961 up to and including the year 2017. After the year 2017 HYDE extrapolates the cropland, pasture, and urban data linearly based on the trend over the previous 5 years, to generate data until the year 2020.
HYDE also uses satellite imagery from ESA-CCI from 1992-2018 for more detailed yearly allocation of cropland and grazing land, with the ESA area data scaled to match the FAO annual totals at country level. The 2018 map is also used for the 2019-2020 period. The original 300 m resolution data from ESA were aggregated to a 5 arcmin resolution according to the classification scheme as described in Klein  Goldewijk et al. (2017a). DGVMs that simulate sub-grid-scale transitions (i. e. gross land-use emissions; see Table  A1 ) also use the LUH2-GCB2021 dataset, an update of the more comprehensive harmonized land-use dataset  (Hurtt et al. , 2020) , that further includes fractional data on primary and secondary forest vegetation, as well as all underlying transitions between land-use states  (850-2020; Hurtt et al.
, 2011 Hurtt et al. , , 2017 Hurtt et al. , , 2020;; Chini et al. , 2021; Table   A1 ). This new dataset is of quarter-degree fractional areas of land-use states and all transitions between those states, including a new wood harvest reconstruction and new representation of shifting cultivation, crop rotations, and management information including irrigation and fertilizer application. The land-use states include five different crop types in addition to the pasture-rangeland split discussed before. Wood harvest patterns are constrained with Landsat-based tree cover loss data  (Hansen et al. , 2013). Updates of LUH2-GCB2021 over last year's version (LUH2-GCB2020) are using the most recent HYDE/FAO release (covering the time period up to 2021 included). We also use the most recent FAO wood harvest data for all years from 1961 to 2019.
This sum is expected to be conceptually more comparable to LULUCF than simply E LUC. E LUC data are taken from bookkeeping models, in line with the global carbon budget approach. To determine S LAND on managed forest, the following steps were taken: Spatially gridded data of "natural" forest NBP (net biome productivity) (S LAND , i. e. due to environmental change and excluding land-use change fluxes) were obtained with S2 runs from DGVMs up to 2019 from the TRENDY v9 dataset. Results were first masked with the Hansen forest map  (Hansen et al. , 2013) , with a 20 % tree cover and following the FAO definition of forest (isolated pixels with maximum connectivity less than 0. 5 ha are excluded), and then further masked with the "intact" forest map for the year 2013, i. e. forest areas characterized by no remotely detected signs of human activity  (Potapov et al.