dataesr/scientific-paragraphs-categorization huggingface dataset:
S1 ). We seek to reconcile inversions with inventories with a clear framework to process inversion data in order to make them as comparable as possible with inventories. Uncertainties suggested by the spread of different inversion models (min-max range given the small number of inversions) and the causes for discrepancies with inventories are analyzed systematically and on a case-by-case basis, for annual variations and for mean budgets over several years. We specifically address the following questions: (1) how do inversion models compare with NGHGIs for the three gases? (2) What are plausible reasons for mismatches between inversions and NGHGIs? (3) What independent information can be extracted from inversions to evaluate the mean values or the trends of greenhouse gas emissions and removals? (4) Can inversions be used to constrain national CH 4 emissions and separate trends for natural versus anthropogenic sources, and into fossil fuels and agricultural plus waste anthropogenic sources? And (5) can inversions help constrain the importance of emissions and removals for unmanaged lands (not reported by inventories and yet important for linking emissions and removals to the concentration and radiative forcing changes) of the three main greenhouse gases? The paper presents a new global database of national emissions reports for all countries and its grouping into sectors, the global atmospheric inversions used for the study, the processing of fluxes from these inversions to make their results as comparable as possible with inventories (Sect.
Compilation and harmonization of national inventories reported to the UNFCCC All UNFCCC parties "shall" periodically update and submit their national greenhouse gas (GHG) inventories of emissions by sources and removals by sinks to the convention parties. Annex I countries have submitted their national inventory reports (NIRs) and common reporting format (CRF) tables every year with a complete time series starting in 1990. Non-Annex I parties have been required to submit their national communications (NCs) roughly every 4 years after entering the convention and submit biennial update reports (BURs) every 2 years since 2014. Currently, there are in total nearly 400 submissions of NCs and over 100 submissions of BURs (Fig. 1 ). We collected the greenhouse gas emissions data from the national inventories submitted to the UNFCCC.
For Annex I countries, data collection is straightforward, as their reports are provided as Excel files under a common reporting format (CRF). For non-Annex I countries, the data were directly extracted from the original reports provided in Portable Document Format (PDF) files. Data from successive reports for the same country were extracted, except when they relate to the same years, in which case only the latest version is considered. While Annex I countries are required to compile their inventory following IPCC 2006 guidelines and the subdivision between sectors established by a UNFCCC decision (dec. 24/CP. 19), non-Annex I countries are allowed to follow the older 1996 IPCC guidelines, with different approaches and sectors. Consequently, the methods used and the reported sectors may be different among NC and BUR reports.
(2017)  shown in Fig. S1. This approach assumes that nonintact forest represents a reasonably good proxy for managed forest reported in national GHG inventories  (Grassi et al. , 2021). In the absence of a machine-readable definition of the plots considered to be managed in many NGHGIs, this choice remains somewhat arbitrary, and other unmanaged land datasets could have been used  (Ogle et al. , 2018; Chevallier, 2021). We subtracted the same fossil fuel emissions from  Friedlingstein et al. (2020)  from the total CO 2 flux of each inversion to analyze terrestrial CO 2 fluxes, which is equivalent to assuming perfect knowledge of fossil emissions, but note that these values are consistent with the fossil fuel emissions reported in the NGHGIs. This assumption leads to an underestimation of the spread of terrestrial CO 2 fluxes among inversions.
Therefore, we subtracted bottom-up (BU) emissions from wildfires (E BU wildfires ) based on the GFEDv4 dataset (van der  Werf et al. , 2017)   The GFEDv4 dataset also reports separately agricultural and waste fire emissions data, which are obviously anthropogenic and occur on managed lands. We assumed that those fires are reported by NGHGIs, so they were not counted in E BU wildfires. Methods 2, 3/1, and 3/2 The second method is a variant of the first one, which removes the median of all inversions of natural emissions (wetlands and other natural sources in  Saunois et al. , 2020)  from the total sources and applies the same removal of wildfires from the BB category as in method 1. The third method removes natural emissions using only products from bottom-up approaches. This method relies first on the soil uptake (E inv soil ), either prescribed or optimized by each inversion, in order to determine the total methane emissions (anthropogenic + natural).
Natural emissions from lakes were estimated only at a global scale by  Tian et al. (2020)  and represent a small fraction of rivers' emissions. Therefore, they are neglected in this study. The flux E GFED wildfires is based on the GFED4s dataset (van der Werf et al. , 2017) using their reported dry matter burned and N 2 O emission factors. Because the GFED dataset reports specific agricultural and waste fire emissions data and we assume that those fires (on managed lands) are reported by NGHGIs, they were not counted in E GFED wildfires. Note that there could also be a background natural N 2 O emission from soils over managed lands (E soil managed land ). We did not try to subtract this flux from managed land emissions because we assumed that, after a land use change from natural to fertilized agricultural land, background emissions decrease and become very small compared to N-fertilizer-induced anthropogenic emissions.
AIEs arise from anthropogenic nitrogen from fertilizers leached to rivers and anthropogenic nitrogen deposited from the atmosphere to soils. AIEs typically represent 20 % of direct anthropogenic emissions and cannot be ignored in a comparison with inversions. For Annex I countries, AIEs are systematically reported, generally based on ad hoc emission factors since these fluxes cannot be directly measured, and it is assumed that indirect emissions only occur on managed land. For non-Annex I countries, we checked manually from the original NC and BUR documents if AIEs were reported or not by each non-Annex I country. If AIEs were reported by a country, they were used as such to compare NGHGI data with inversion results and grouped into the agricultural sector. If they were not reported or if their values were outside plausible ranges, AIEs were independently estimated by the perturbation simulation of N-fertilizer leaching, CO 2 , and climate on rivers and lakes fluxes in the DLEM  (Yao et al.
Grouping of inventory sectors for comparison with inversion sectors The categories of fluxes estimated by inversions and NGHGI sectors are different (Table  S3 ). The bottom-up inventories are compiled based on activity data (statistics) following the  IPCC 1996 and 2006 guidelines (IPCC, 1997 , 2006) , with detailed information of sub-sectors. But the top-down inversions can only distinguish very few sectors. Thus, in this study, we aggregated sectors into some larger sectors to make inversions and inventories comparable for each GHG gas (Table  1 ). For CO 2 , the inversions are divided into two aggregated sectors: (1) fossil fuel and cement CO 2 emissions and (2) land flux. Inversions use a prior gridded fossil fuel dataset as summarized in Sect. 2. 2; thus, in this study we compare only the land flux between inversions and inventories.
The adjusted land flux (NEE) from each inversion is calculated by subtracting the national total fossil emissions from the total CO 2 flux, where the fossil emissions are from the Global Carbon Project annual dataset  (Friedlingstein et al. , 2020) , consistent with the prior fossil emission maps proposed to inversion modellers and used by them, except for the inversion of  Feng et al. (2009 Feng et al. ( , 2016) )  (see Table  A4  of  Friedlingstein et al. , 2020, for details). For processing NGHGIs, we subtracted fossil emissions of the sectors of energy and industrial processes (or industrial processes and product use) from the total net CO 2 emissions to obtain NEE CO 2 fluxes over managed ecosystems. Note that transportation and residential CO 2 emissions are reported under the energy sector.
In EUR, inversions systematically indicate a larger net CO 2 uptake than NGHGIs, by on average 104 Tg C yr -1 more than NGHGIs yet with a non-significant trend (Mann-Kendall test p = 0. 7), consistent with stable land carbon storage shown by NGHGIs. In the two largest boreal and Arctic countries Canada (CAN) and Russia (RUS), inversions produce a CO 2 sink (average 217 and 325 Tg C yr -1 ) which is systematically larger than that of the NGHGIs (2 and 171 Tg C yr -1 , respectively) during 2000-2019. CAN is one of the few countries that does not capture most of the recent indirect environmental change effects  (Grassi et al. , 2021). The larger Russian sink of inversions is similar to the results of a recent analysis  (Schepaschenko et al. , 2021)  of forest inventory and satellite biomass data estimating a carbon accumulation of 343 Tg C yr -1 from 1988 to 2014.
The Russian carbon sink rate of increase is 6. 0 Tg C yr -2 in the NGHGIs during the 2000s, smaller than the increasing CO 2 sink rate of 16. 4 Tg C yr -2 across inversions. When inversions include all lands in these two countries instead of managed land only, the net land CO 2 sink becomes 40 % larger in CAN and 16 % larger in RUS. Unmanaged lands in our intact forest dataset cover 30 % of CAN and 15 % of RUS total forest area and are associated with CO 2 sink densities of 44 and 29 g C m -2 yr -1 , respectively, nearly identical to 46 and 30 g C m -2 yr -1 in managed lands. It should be noted that in Canada's CRF, removals by forest land are largely diminished by the emissions from harvested wood products in the LULUCF sector. Among the selected large forested tropical countries, Brazil (BRA) is one of the few non-Annex I countries that has provided continuous time series of NGHGIs since 1990.
Note the asymmetric range around the median of inversions for BRA in Fig. 4. The data in Fig. 4  indicate a large spread between inversions, owing to differences in model settings and transport. Differences due to different methods used to separate anthropogenic from natural emissions are smaller than this spread, and we discuss them in Sect. 6. 2. We observed on average a smaller range of GOSAT inversions (number of inversions, n is 11) than in situ inversions (n = 10) in countries emitting more than 10 Tg CH 4 yr -1. The median emissions from GOSAT inversions are systematically lower than from in situ inversions, except in CHN where GOSAT inversions are on average 21 % larger than in situ ones during  2010-2017. Ranges overlap between the two inversion ensembles. Generally, the difference between NGHGIs and inversions is of the same sign based on GOSAT and in situ inversions, which gives us some confidence for evaluating NGHGIs because the GOSAT observations are different and independent from in situ networks.
S5  and 10 ). Note however that RUS wetland emissions are partly concentrated in the region of western Siberia (northern Ob river basin) just to the south of a major basin of gas extraction (Yamal), and these two sources are difficult to separate from each other in global inversions. Note that methane emissions from fires in Russia are smaller than fossil and wetland emissions, and fires cannot explain why inversions give larger emissions than the NGHGIs. In Brazil (BRA), the result that inversions give systematically larger CH 4 emissions than NGHGIs is also robust due to the method used to separate wetlands from anthropogenic emissions (Fig. S5 ), but our inversions did not use in situ data in the interior of the country (Fig. S1 ) and the coverage of GOSAT is sparse due to clouds (Fig. S2 ), especially in the wet season, which makes the estimation of the total CH 4 source uncertain over this country.
In India (IND), both the in situ and the GOSAT inversions also give a higher anthropogenic emission than the NGHGI data and the share of emissions from natural wetlands is much smaller than in RUS and BRA, reducing the risk of aliasing between anthropogenic and natural emissions and suggesting a lower estimation by the NGHGIs. In Indonesia (IDN) the median of inversions is slightly higher than the NGHGI data with the separation method used in Fig. 4  but close to NGHGIs with other methods (Figs. 10  and S5 ). All inversions give a large positive anomaly of CH 4 emissions during the 2015 El Niño, when abnormal peat fire emissions occurred  (Heymann et al. , 2017; Yin et al. , 2016) , with a biomass burning event be-ing attributed to an anthropogenic source by our aggregation of inversion results (Sect.
The emitting countries in the Persian Gulf area have no atmospheric CH 4 in situ station coverage, while KAZ has two stations. In contrast, the sampling of atmospheric column CH 4 by GOSAT is rather dense in all those countries, thanks to frequent cloud-free conditions. Thus GOSAT inversions could be viewed as more accurate than in situ inversions for IRN, SAU, and KAZ, and we note that GOSAT inversions give lower emissions than in situ inversions. We also compared inversions and NGHGIs with annual CH 4 emissions data compiled by PRIMAP-hist  (Gütschow et al. , 2016)  for the energy sector and found that this dataset produces much larger emissions than both NGHGIs and the median of inversions for GULF and KAZ&TKM (Fig. S6 ). The methodology of PRIMAP-hist interpolates and extrapolates UNFCCC values using trends of EDGAR v4.
2, an inventory which is known to overestimate fossil CH 4 emissions  (Thompson et al. , 2015; Patra et al. , 2016; Ganesan et al. , 2017). The higher values of PRIMAP-hist may thus be due to the extrapolation of temporally sparse national inventories for those countries, and this dataset should not be considered similar to NGHGIs for the fossil fuel CH 4 sector. In Nigeria (NGA) and Venezuela (VEN), where nearly half of the oil and gas industry is offshore or near the coast (NAPIMS, 2021), we found that fossil CH 4 emissions are smaller in inversions than in the NGHGIs. This result should be considered with caution as those countries have a small size and thus emissions that are difficult to constrain by global inversions. The presence of clouds further reduces the number of GOSAT soundings, and anthropogenic and natural CH 4 emissions are collocated with fossil ones.
7 , using FAOSTAT data. Those indirect emissions represent 18 % of direct mineral-fertilizer-induced anthropogenic emissions from cropland soils in EUR and 16 % in the USA for instance and thus make NGHGI data systemati-cally lower than inversions for countries that did not include them. According to the data in Table  S2 , indirect emissions represent 5 % to 10 % of anthropogenic emissions in most of the non-Annex I countries shown in Fig. 7. In consequence, their omission cannot explain the whole mismatch between NGHGIs and inversions. We also compared in Table  S2  indirect emissions data from inventories with those provided by the FAOSTAT database (FAO, 2021).
The only regional N 2 O inversion over Europe is also about 2 times higher than the median of global inversions. 7 Data availability GHG (CO 2 , CH 4 , N 2 O) data from inverse models and UNFCCC national inventories are available at https://doi. org/10. 5281/zenodo. 5089799 (Deng et al. , 2021). This dataset contains five data files, including GHG data from inverse models and UNFCCC national inventories in the top emitter countries. -CO 2 _inversion_1990-2019. This contains annual CO 2 flux data from 6 inversion models in three sectors: • "land flux (all land)", i. e. , land flux from all land; • "land flux (managed land)", i. e. , land flux from managed land; • "land flux (managed land + lateral adjustment)", i. e. , land flux from managed land by adjusting the lateral flux. -CH 4 _inversion_2000-2017.
version data were used. All harvest is seen by NGHGIs as a loss of carbon stock in forest. Then, the wood that remains in the country enters the harvested wood products (HWP) pool (where gains and losses are recorded). What is "invisible" in NGHGIs is the wood that enters the HWP pool of a foreign country. CH 4 and N 2 O emissions have been even less explored for a systematic comparison of inversions with inventories. For these two gas species, we improved the processing of inversion gridded fluxes to separate anthropogenic fluxes from the total emissions in order to provide estimates that can be compared with NGHGIs for policy implementation. For CH 4 , we proposed three methods to remove the signal of natural CH 4 emissions and found that their robustness is country dependent, the separation of natural emissions to retrieve anthropogenic emissions being more difficult in countries that have both large natural and anthropogenic emissions and few atmospheric stations, like RUS or BRA.
We certainly recommend here to reduce the uncertainty in prior estimates and improve estimations of natural sources using, e. g. , better bottom-up datasets of wetland area, rivers, and lakes and their CH 4 emissions rates in order to make further in-depth comparisons between these methods. For CH 4 , a second notable result is that despite the large spread of inversions, both in situ and GOSAT inversions show valid differences from NGHGI anthropogenic emissions. We also found that Kazakhstan and Turkmenistan in central Asia and the Gulf countries in the Middle East, characterized by oiland gas-producing industries, report much lower CH 4 emissions than atmospheric inversions. It is fair to say that in this region, there are few ground stations, and inversions could depend on their prior fluxes, but the fact that GOSAT and in situ data point to NGHGI emissions being underestimated suggests areas for future research to constrain the emissions of these countries.
, 2020; Table S6 in Saunois et al. , 2020; and Table 18  in the Supplement of  Tian et al. , 2020). They will likely soon have to go below 1 • on a global scale to remain competitive for this type of study, despite the high computational challenge posed by the atmospheric inversion of long-lived tracers. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/essd-14-1639-2022-supplement. Author contributions. ZD, PC, MS, and FC designed and coordinated the study. PC, MS, CQ, RLT, HT, YH, and FC designed the framework of atmosphere inversion data processing. ZD, PC, ZATS, MS, CQ, and FC performed the post-processing and analysis and wrote the paper. ZD, CT, TS, PK, and YC compiled the national greenhouse gas inventories. MS, RLT, HT, and FC gathered the global atmosphere inversion datasets of CO 2 , CH 4 , and N 2 O.
Financial support. This research has been supported by the  European Space Agency Climate Change Initiative  ( ESA CCI) REC-CAP2  project (grant no. ESRIN/4000123002/18/I-NB ) and by the  European Commission Prototype  system for a Copernicus CO 2 service (CoCO 2 , grant no. 958927 ); the " Investissements d'avenir" program , reference  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute ); and the Observation-based system for monitoring and verification of greenhouse gases (VERIFY, grant no. 725546 ). Review statement. This paper was edited by  David Carlson  and reviewed by two anonymous referees.
The nano-LC-MS/MS analyses were performed as described elsewhere  (25). The raw data were processed into mgf files and proteins identified by means of Mascot licensed software (Matrix Science Boston, USA) using the Homo Sapiens National Center of Biotechnology Information (NCBI) database. The mass tolerance was set at 10 ppm for MS and 0. 6 Da for MS/MS search. Carbamidomethyl (C) was used as fixed modification and Gln->pyro-Glu (N-term Q), Oxidation (M), Pyro-carbamidomethyl (N-term C) as variable modifications. Classification of HBZ interactors was obtained by the Gene Ontology slim (GOslim) classification on the Webgestalt platform (http://www. webgestalt. org).
Functional enrichment analysis All protein lists were analyzed by over-representation analysis using Reactome as the pathway database on the Webgestalt platform (other parameters: reference database, genome protein-coding; False Discovery Rate (FDR) <0. 05 after Benjamini-Hochberg correction; redundancy reduction, "Affinity Propagation" algorithm (  26 )). Pathways were ordered by FDR and only the top 10 enriched pathways are presented.
Network analysis Cytoscape 3. 8. 2 was used to generate a protein-protein interaction (PPI) network  (27). The public database Intact was queried through Cytoscape using the Proteomics Standard Initiative Common QUery InterfaCe (PSICQUIC), by searching the HBZ interactors identified by the MS analysis. The network was filtered for taxonomy ID 9606 (Homo sapiens) to remove homology inferences. All self-loops and duplicated edges were removed. Identifiers (IDs) of the original list have been selected on the whole network and the sub-selection extracted (to visualize known interactions among HBZ identified interactors). The GLay algorithm has been used to cluster the network in subnetworks. Each of them has been analyzed by overrepresentation analysis by the Webgestalt platform.
RNA Seq analysis The RNA-seq analyses were performed with total RNA extracted from ATL-2, Jurkat cells expressing or not HBZ and MOLT-4, in triplicates. Directional (rRNA removal) and mRNA library preparation were generated at Novogene (Hong Kong, China) using Stranded RNA Sample Pre Kit (Illumina) and sequenced by NovaSeq PE150 (15G raw data per sample). Each sample had in average of 6x10 7 paired-end pairs of reads. RNAseq data were analyzed using FaRLine, a computational program dedicated to analyzing alternative splicing with FasterDB database  (29). Gene expression level in each sample was calculated with HTSeq-count (v0. 7. 2), and differential expression between conditions was computed with DESeq2 (v1. 10. 1) (|log2-FoldChange| ≥ 0. 4; p ≤0. 05)  (30, 31).
An overrepresentation analysis of these data by using Reactome as reference database was then performed to get deeper inside into the cellular pathways specifically targeted by nuclear HBZ. Interestingly, among the top 10 significant pathways, the main enriched biological pathway was related to mRNA splicing and processing (Supplementary Data 1-2). We then proceeded to generate the biological network of the 249 identified HBZ interactors by querying the Intact database via Cytoscape. Interestingly, the network depicted 24325 direct associations between 7154 proteins (Supplementary Figure  2 ). The experimentally identified HBZ-interacting proteins were then extrapolated from the whole original network (Figure  2B ). Of relevance, most of the nodes (241 out of 249) resulted to be interconnected (1957 edges) and part of a predominant group.
Firstly, we evaluated the total impact of HBZ expression on host cellular splicing machinery by statistically computing splicing events using FARLine pipeline. We mainly focused on 5 alternative splicing events: exon skipping, alternative acceptor site, alternative donor site, mutually exclusive exons, and multiple exons skipping (Figure  4A ), which we quantified as Percent Spliced In (PSI) considering positive the splicing events with |deltaPSI|>0. 1 and p<0. 05. Our analysis identified 2118 and 3191 ASE corresponding to 1408 and 1987 genes in ATL-2 (Figure  4B , left panel) and Jurkat-HBZ cells (Figure  4B , right panel), respectively (Supplementary Data 3). In both cells most of the alternative splicing events correspond to exon skipping, either single (38% in ATL-2 and 40% in Jurkat-HBZ) or multiple exon skipping (18% in ATL-2 and 17% in Jurkat-HBZ).