dataesr/scientific-paragraphs-categorization huggingface dataset:
This is also how CO 2 measurements on National Institute for Environmental Studies (NIES) ships were treated. Entire aircraft profiles in the NOAA light-aircraft profiling network are assumed to consist of vertically correlated measurements, so entire profiles were withheld: we chose 5 % of aircraft profiles to withhold. Most flask sites have measurement sampling protocols intended to ensure independence;  they are often taken at weekly or biweekly intervals during meteorological conditions meant to allow regional background air masses to be sampled. Thus, we chose to withhold 5 % of assimilable flask measurements. We also verified that datasets at the same site were withheld on the same days; aircraft profiles over tower sites were, for instance, withheld on the same days that tower data were withheld.
Lateral carbon fluxes Lateral carbon flux datasets (Table  3 ) include countrylevel F rivers export (Sect. 3. 3. 1), country-level F crop trade and country-level F wood trade  (Sect. 3. 3. 2). Gridded lateral fluxes are estimated using a somewhat different approach and are described in Sect. 3. 3. 3. The DLEM is a process-based terrestrial ecosystem model that couples biophysical, soil biogeochemical, plant physiological and riverine processes with vegetation and landuse dynamics to simulate and predict the vertical fluxes, lateral fluxes, and storage of water, carbon, GHGs, and nutrient dynamics in terrestrial ecosystems and their interfaces with the atmosphere and land-ocean continuum  (Tian et al. , 2010 (Tian et al. , , 2015a)). There are three major processes involved in simulating the export of water, carbon and nutrients from land surface to the coastal ocean: (1) the generation of runoff and leachates; (2) the leaching of water, carbon, and nutrients from land to river networks in the form of overland flow and base flow; and (3) transport of riverine materials along river channels from upstream areas to coastal regions.
Country-level F wood trade and F crop trade Wood and crop products are traded between nations. We estimate the annual lateral fluxes of carbon due to this trade following the approaches of  Deng et al. (2022)  and  Ciais et al. (2021). This approach utilizes crop and wood trade data compiled by the Food and Agriculture Organization of the United Nations (FAO; http://www. fao. org/faostat/en/#data, last access: 6 February 2023). The crop flux was estimated from the annual trade balance of 171 crop commodities calculated for each country. For wood products, we use the bookkeeping model of Mason  Earles et al. (2012)  to calculate the fraction of imported carbon in wood products that is oxidized in each of 270 countries during subsequent years. The 1σ uncertainties in country-level fluxes are assumed to be 30 % of the mean value.
This dataset covers 2015-2019. For 2020, we assume fluxes equal to the 2015-2019 mean. The net crop and wood lateral fluxes and their uncertainties are shown in Fig. S2. 3. 3. 3 1 • × 1 • lateral flux estimates Lateral fluxes at a higher resolution (1 • × 1 • ) follow similar principles to national values but were estimated separately with different implementation choices. Highresolution proxy data (satellite-derived NPP, population or livestock maps, etc. ) enabled sub-national disaggregation. This was done using national totals based on FAO statistics for F wood trade and F crop trade. For F rivers export these estimates were generated from Global NEWS and COSCATs data (DLEM was only used for national totals). For each 1 • × 1 • grid cell, we assume the standard deviation of the mean flux to be 30 % for F wood trade and F crop trade and 60 % for F rivers export.
Estimate of carbon stock loss (∆C loss ) Finally, we calculate C loss using Eq. (  2 ) with the datasets described above. Assuming that the components contributing to C loss are independent, we calculate the uncertainty on C loss by combining the uncertainties (1 standard deviations) from the component fluxes in quadrature: σ 2 C loss = σ 2 NBE + σ 2 F crop trade + σ 2 F wood trade + σ 2 F rivers export. (6).
Evaluation of v10 OCO-2 MIP experiments The performance of top-down CO 2 flux estimates can be impacted by a number of factors, including biases in the assimilated data, model transport, prior constraints and inversion architectures. Therefore, evaluating the performance of v10 OCO-2 MIP fluxes against independent observational datasets is critical for assuring high-quality flux estimates. Here, we evaluate the v10 OCO-2 MIP experiments in two ways. First, we compare the posterior CO 2 fields against independent CO 2 measurements (Sect. 4. 1). Second, we compare the inferred air-sea CO 2 flux against estimates based on surface ocean CO 2 partial pressure (pCO 2 ) measurements (Sect. 4. 2).
Evaluation of posterior CO 2 fields We consider four atmospheric CO 2 datasets: 1. Withheld in situ CO 2 measurements. These are measurements contained in the ObsPack collection described in Sect. 3. 2. 1 but intentionally withheld for evaluation purposes. Independence from the assimilated data is ensured following the steps described in Sect. 3. 2. 1. 2. X CO 2 retrievals from the TCCON. These data are acquired from a network of ground-based Fourier transform spectrometers measuring direct solar spectra from which X CO 2 is retrieved  (Wunch et al. , 2011). For this analysis, we include 30 TCCON sites listed in Table  A1. These data are filtered and aggregated following the method outlined in Appendix C of  Crowell et al. (2019). 3. Withheld OCO-2 land glint and land nadir X CO 2 retrievals.
We find that the IS, LNLG and LNLGIS experiments show good agreement with the NOAA estimate over this period. However, both the OG and LNLGOGIS experiments are found to have a high bias. This suggests that there may be a spurious trend in the v10 OCO-2 ocean glint X CO 2 retrievals of 0. 04-0. 13 ppm yr -1 (OG experiment bias) that impacts flux estimates in both experiments that assimilate ocean glint data. Second, we estimate the overall observation-model agreement as the root mean square error (RMSE) for the withheld in situ CO 2 , TCCON X CO 2 , withheld OCO-2 land X CO 2 and withheld OCO-2 ocean X CO 2 (Fig. 5 ). For the in situ and OCO-2 data, the normalized RMSE is shown, meaning that the observation-model difference is divided by the observational uncertainty (1σ ). Overall, we find reasonably good agreement between the evaluation datasets and poste- rior fields for all experiments.
The OG experiment gives the largest RMSEs against the withheld in situ CO 2 , TCCON X CO 2 and OCO-2 land X CO 2. This provides further evidence that the ocean glint data may have some residual biases that adversely impact the flux estimates. Finally, we examine the mean bias over 2015-2020 for 30 • latitude bins (Fig. 6 ). Similar to previous comparisons, we find that the OG experiment stands out as being more biased against the independent observations relative to the other experiments. In particular, the observation-model difference for the OG experiment tends to be lower (higher modeled CO 2 ) than the evaluation datasets. This is particularly evident in the northern extratropics. Over 30-60 • N, where independent observations are densest, we find that the OG ensemble median is biased by -0.
Overall, this analysis finds that the OG experiment shows the poorest agreement against the evaluation datasets (excluding the withheld ocean glint data). The LNLGOGIS experiment shows the second worst performance against evaluation datasets, while the remaining experiments (IS, LNLG and LNLGIS) all show good agreement against the evaluation data. These results suggest that there may be residual biases in the OCO-2 ocean glint dataset that adversely impact the OG and LNLGOGIS experiments.
, 2021; Gregor and Fay, 2021a). SeaFlux developed a standardized approach to harmonize and extend six air-sea CO 2 flux products from as many surface pCO 2 products: JENA-MLS  (Rödenbeck et al. , 2013) , MPI-SOMFFN  (Landschützer et al. , 2014 (Landschützer et al. , , 2020)) ,  CMEMS-FFN (Denvil-Sommer et al. , 2019; Chau et al. , 2022) , CSIR-ML6  (Gregor et al. , 2019) , JMA-MLR  (Iida et al. , 2021)  and NIES-FNN  (Zeng et al. , 2014). For each pCO 2 product, we examine the mean of three air-sea fluxes obtained using different wind reanalysis datasets to estimate the gas transfer parameterization (ERA5, JRA-55 and CCMP2). The spread among these six estimates provides a measure of uncertainty in the extrapolation of pCO 2 data to a global grid but does not account for errors in the gas transfer velocity formulation nor the uncertainties in the reanalysis winds used as input  (Fay et al.
, 2021). Note that the prior estimates of air-sea CO 2 fluxes in v10 OCO-2 MIP experiments are generally pCO 2 -based flux estimates and therefore not independent from the SeaFlux datasets. Figure  7  shows the 2015-2019 mean air-sea fluxes for each of the six SeaFlux products and for the v10 OCO-2 MIP experiments across 30 • latitude bands and large ocean regions. Over the global ocean, the pCO 2 -based air-sea fluxes tend to give stronger removals (median = -10. 0 Pg CO 2 yr -1 or -2. 7 Pg C yr -1 , range = -9. 2 to -12. 9 Pg CO 2 yr -1 or -3. 5 to -2. 5 Pg C yr -1 ) than the v10 OCO-2 MIP, which range from -7. 9 ± 1. 9 Pg CO 2 yr -1 (-2. 1 ± 0. 5 Pg C yr -1 ) for the IS experiment to -10. 2 ± 1. 28 Pg CO 2 yr -1 (-2. 8 ± 0. 4 Pg C yr -1 ) for the OG experiment. On regional scales, the v10 OCO-2 MIP experiments overlap with the pCO 2 -based estimates except for the northern high latitudes (60-90 • N), where pCO 2 -based estimates suggest systematically larger removals.
In this section, we examine the posterior uncertainty estimates from two contributing inversion systems (CAMS and TM5-4DVar) and compare these estimates to the ensemblebased uncertainty estimate provided with the dataset. Then, we define the FUR metric between the posterior and prior NCE estimates based on the TM5-4DVar model (as CAMS does not estimate uncertainties for the LNLGIS and LNL-GOGIS experiments), which can be used to understand the relative impact of assimilated atmospheric CO 2 data on estimates of country-level NCE and C loss. Both CAMS and TM5-4DVar estimate CO 2 fluxes using four-dimensional variational assimilation (4D-Var) and estimate posterior uncertainty estimates using a Monte Carlo method derived by  Chevallier et al. (2007). The realism of the prior and posterior CAMS uncertainty estimates has already been the topic of several studies (see  Chevallier, 2021 , and references therein).
In fact, the ensemble spread increases with data density in some cases (e. g. , Australia LNLGIS). Thus, overall, we find that the ensemble-based uncertainty estimate is of similar magnitude to the prior/posterior estimate but that the magnitude of pos-terior uncertainty is quite dependent on the assumed prior uncertainty. We now calculate the FUR metric in NCE from the TM5-4DVar Bayesian uncertainties (note that we use TM5-4DVar only because CAMS does not report LNLGIS or LNLGOGIS uncertainties). FUR is calculated from the prior flux standard deviation (σ prior ) and posterior flux standard deviation (σ posterior ) as FUR = 1 - σ posterior σ prior. (8) This quantity ranges between 0 and 1, with larger values indicating that the Bayesian uncertainties have decreased more (relative to the prior) due to the observational constraints from assimilated data.
Dataset description The dataset described in this paper,  Byrne et al. (2022) , provides annual totals of country-level and 1 • × 1 • gridded C loss , NBE, NCE, F rivers export and the combined  F crop trade + F wood trade fluxes, as well as their uncertainties over 2015-2020. In addition, the country-level Z statistic (Eq. 7) and FUR (Eq. 8) metrics are provided to help interpret the flux and stock change estimates. These data are provided for the v10 OCO-2 MIP IS, LNLG, LNLGIS and LNL-GOGIS experiments. The OG experiment is excluded due to poor evaluation against independent CO 2 measurements and pCO 2 -based air-sea fluxes, likely due to residual X CO 2 biases in the OCO-2 ocean glint X CO 2 retrievals (Sect. 4). We note that biases in ocean glint X CO 2 retrievals will also adversely impact flux estimates from the LNLGOGIS and caution against using these data when they show differences from the IS, LNLG and LNLGIS experiments.
Future improvements to the OCO-2 X CO 2 retrievals are expected to reduce residual X CO 2 biases, and thus the quality of the LNL-GOGIS experiment is expected to improve in future OCO-2 MIP experiments. For the 1 • × 1 • gridded dataset, we emphasize that caution is needed in interpreting these data. As discussed in Sect. 1. 3, atmospheric CO 2 inversion analyses provide the best constraints on the largest spatial scales (e. g. , continentalto-global). The confidence in these top-down estimates decreases at smaller spatial scales. The minimum spatial resolution for robust flux estimates is dependent on the density and precision of the measurements and is challenging to quantify. However, scales smaller than France or Germany in geographic extent are unlikely to be meaningfully constrained.
Thus, we recommend only using 1 • × 1 • CO 2 fluxes aggregated to larger spatial scales. In aggregating, we recommend propagating uncertainties by assuming first 100 % correlation (sum of the 1 • × 1 • uncertainties) and then 0 % correlation (square root of the sum of the squared uncertainties) between grid cells. We strongly encourage contacting the authors before using the gridded 1 • × 1 • dataset. These data are available for download from the Committee on Earth Observation Satellites' (CEOS) website: https://doi. org/10. 48588/npf6-sw92 (Byrne et al. , 2022). The country-level data are available for download as commaseparated values (CSV), Network Common Data Form (NetCDF) and Microsoft Excel worksheet files. The 1 • × 1 • gridded dataset is available as a NetCDF file.
Characteristics of the dataset Globally, over 2015-2020, we report FF emissions of 35. 79 ± 1. 50 Pg CO 2 yr -1 (9. 76 ± 0. 41 Pg C yr -1 ), F rivers export of -3. 35 ± 0. 59 Pg CO 2 yr -1 (-0. 91 ± 0. 16 Pg C yr -1 ), and globally balanced F crop trade and F wood trade. Table  4  gives the global annual mean changes in the atmospheric burden of CO 2 , C gain and ocean sequestration. Across the experiments, the median fraction of fossil fuel emissions remaining in the atmosphere is 55 %-56 %, while 32 %-36 % is sequestered by the ocean and 9 %-13 % is sequestered by terrestrial ecosystems. Note that this omits land-use change (LUC) emissions of ∼ 3. 85 Pg CO 2 yr -1 (∼ 1. 05 Pg C yr -1 ,  Friedlingstein et al. , 2022) , which are compensated for by additional carbon uptake by land. Of the combined FF+LUC emissions, 50 % remains in the atmosphere, 29 %-33 % is sequestered by the ocean and 18 %-21 % is sequestered by terrestrial ecosystems.
5. 2), model transport errors  (Stephens et al. , 2007; Schuh et al. , 2019 Schuh et al. , , 2022)) , and residual retrieval biases in the OCO-2 X CO 2 retrievals  (Peiro et al. , 2022). Unfortunately, the regions showing the largest differences in fluxes generally have few independent atmospheric CO 2 measurements for validation, limiting our ability to distinguish between different causes. Thus, we believe that NCE and C loss estimates are most reliable when agreement is found across the v10 OCO-2 MIP experiments. We will now show examples of carbon budgets for four countries from this dataset. Figure  13  shows the 2015-2020 mean FF, F rivers export , F crop trade , F wood trade , C loss and  NCE fluxes for the USA, India, Indonesia and Australia. All of the CO 2 fluxes on the left of the dashed line combine to give the NCE flux constrained by the v10 OCO-2 MIP experiments.
Comparison with national inventories We demonstrate how the dataset presented here can be compared with NGHGIs reported under the UNFCCC, which were downloaded from https://di. unfccc. int/flex_annex1 (last access: 6 February 2023). We also refer the reader to    2022 ) can be compared with the combined emissions from the energy and IPPU (En-ergy+IPPU) categories. In both cases, these estimates account for anthropogenic CO 2 emissions from the burning of fossil fuels and production of cement and other materials. We expect these estimates to generally be in good agreement, as they are similarly based on bottom-up accounting for national totals. However, the estimates may diverge when there are missing activity data, particularly in non-Annex 1 countries and more recent years  (Andrew, 2020).
, 2019). It is worth noting that NGHGIs require estimates of turnover times for wood products in producing countries, as these can have lifetimes of decades to centuries (see Appendix 3a. 1 of  Penman et al. , 2003). No such estimate is needed for the top-down methods, as emissions from decaying wood products will be implicitly incorporated in NCE. Therefore, top-down methods only need to account for the lateral movement of wood products from the region where the carbon is sequestered to the region where the wood products are used and decompose. For this analysis, we compare NGHGIs and our dataset for three entities: the USA, European Union plus the United Kingdom (EU27+UK) and Australia. These were chosen for two reasons. First, NCE is better constrained by atmospheric CO 2 data over these relatively large regions.
Expanding observations An expanding network of CO 2 observing systems provides an opportunity to reduce uncertainties in top-down estimates of NCE. Across much of the globe, country-level estimates of NCE have been limited by the observational coverage of in situ CO 2 measurements and X CO 2 retrievals. However, there are a number of planned expansions in observing systems that will help fill data gaps. The first generation of space-based CO 2 systems currently in operation (GOSAT, GOSAT-2, OCO-2, OCO-3, TanSat) were designed primarily as proof-of-concept missions to demonstrate that space-based measurements could yield X CO 2 retrievals with the precision and accuracy required to quantify emissions and removals of CO 2. Planned future missions will expand and improve upon current observing systems.
lsce. ipsl. fr/, last access: 6 February 2023). These projects are developing and refining inversion systems to estimate anthropogenic fossil fuel emissions, as well as emissions and removals from the agriculture, LULUCF and waste categories. Future planned projects include developing approaches to utilize co-emitted species and auxiliary observations ( 14 C, solar-induced fluorescence, carbonyl sulfide and others) in order to isolate some of the CO 2 budget components and improve our understanding of the carbon cycle. For example, multiple data streams could be used together to optimize the dynamic global vegetation model parameters (e. g. ,  Peylin et al. , 2016). In contrast to recent European efforts, there is no mandate for an operational top-down carbon-flux-attribution system in the USA.
Data availability Top-down CO 2 budgets are available from the Committee on Earth Observation Satellites' (CEOS) website: https://doi. org/10. 48588/npf6-sw92 (Byrne et al. , 2022). Gridded NBE and air-sea fluxes from the OCO-2 MIP are available at https://gml. noaa. gov/ccgg/OCO2_ v10mip/  (Baker et al. , 2023). Fossil fuel emissions prescribed in the inversions can be downloaded from https://doi. org/10. 5281/zenodo. 4776925 (Basu and Nassar, 2021). The ODIAC2020 emission data product can be downloaded from the Global Environmental Database hosted by the Center for Global Environmental Research at NIES (https://doi. org/10. 17595/20170411. 001, Oda and Maksyutov, 2015). SeaFlux pCO 2 -based air-sea fluxes were downloaded from https://doi. org/10. 5281/zenodo. 5482547, (Gregor and Fay, 2021b).
Conclusions We introduced a pilot top-down CO 2 budget dataset  (Byrne et al. , 2022)  intended to start a dialogue between research communities and to identify ways that top-down flux estimates can inform country-level carbon budgets. This dataset provides annual country-level and 1 • × 1 • gridded top-down NCE and C loss over 2015-2020, in addition to bottom-up FF and lateral fluxes. These data are provided for four experiments from the v10 OCO-2 MIP that differ in the data used in the assimilation: IS, LNLG, LNLGIS and LNLGOGIS. In addition, we provide two metrics for interpreting countrylevel estimates: (1) the Z statistic (Sect. 5. 1), which quan-tifies the agreement between IS and LNLG NCE estimates, and (2) the FUR (Sect. 5. 2), which quantifies the impact of atmospheric CO 2 data in reducing flux uncertainties.
The accuracy of top-down NCE estimates was characterized through comparisons against independent atmospheric CO 2 datasets and through comparisons against pCO 2 -based air-sea CO 2 fluxes. Overall, the IS, LNLG and LNLGIS were found to show the best agreement against independent CO 2 measurements, and we recommend using these experiments for analysis. Poorer agreement was found for experiments assimilating OCO-2 ocean glint X CO 2 retrievals, suggesting that residual retrieval biases adversely impact the LNL-GOGIS experiment, and we urge caution in interpreting these data. For future GSTs, top-down NCE estimates will be refined as new space-based X CO 2 observing systems expand and retrieval algorithms are improved. Complementary expansions of ground-based and aircraft-based CO 2 measurements in undersampled regions will similarly fill critical observational gaps in regions with large uncertainties and susceptibility to retrieval biases.
Acknowledgements. Part of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  (grant no. 80NM0018D004 ). The v10 OCO-2 MIP activity was supported by the  NASA   OCO science team program. Brendan Byrne  and  Junjie Liu  were supported by the  NASA  OCO-2 https://doi. org/10. 5194/essd-15-963-2023Earth Syst. Sci. Data, 15,  963-1004 ,  2023 992  B. Byrne et al. : Top-down CO 2 budgets and  OCO-3 science team program  (grant no. NNH17ZDA001N-OCO2 ). Andrew R. Jacobson ,  Andrew Schuh  and  David F. Baker  were funded by  NASA  (grant no. 80NSSC21K1080 ). Andrew Schuh  was also supported by  NASA  (grant no. NNX15AG93G ). Sean Crowell acknowledges support from the  NASA OCO science team  (grant no.
CAMS73 ) and by the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement number  958927  (prototype system for a Copernicus CO 2 service). It was granted access to the HPC resources of TGCC under the allocation A0110102201 made by  GENCI. Sha Feng  at  PNNL  is supported by the  NASA   Carbon Monitoring Program  (grant no. 80HQTR21T0069 ). The  Pacific Northwest National Laboratory  is operated by  Battelle Memorial Institute  under contract  DE-AC05-76RL01830. Ning Zeng  acknowledges support from  NOAA  (grant no. NA18OAR4310266 ) and  NASA  (grant no. 80NSSC18K0908 ). Prabir K. Patra  is partly supported by the  Environment Research and Technology Development Fund  (grant no. JPMEERF21S20800 ) of the  Environmental Restoration and Conservation Agency of Japan.
Scot M. Miller  was supported by the  NASA  grants  80NSSC18K0976  and  80NSSC21K1073. Nicholas M. Deutscher  was funded by the  ARC Future Fellowship  (grant no. FT180100327 ). The  TCCON Nicosia  site has received additional support from the  European Union 's  Horizon 2020 Research and Innovation Programme  (grant no. 856612/EMME-CARE ), the  Cyprus Government  and the  University of Bremen. The TCCON Anmyeondo site has been funded by the  Korea Meteorological Administration Research and Development Program ,  Developing Technology for Integrated Climate Change Monitoring and Analysis , under grant  KMA2018-00320. TCCON sites at Tsukuba, Rikubetsu and Burgos are supported in part by the  GOSAT  series project. Burgos is supported in part by the  Energy Development Corp. , Philippines. The Eureka TCCON measurements were made at the  Polar Environment Atmospheric Research Laboratory (PEARL)  by the  Canadian Network for the Detection of Atmospheric Change (CANDAC) , primarily supported by the  Natural Sciences and Engineering Research Council of Canada, Environment and Climate Change Canada , and the  Canadian Space Agency.