dataesr/scientific-paragraphs-categorization huggingface dataset:
However, although scarce, we included other disturbed sites in our CO 2 estimates to account for ecosystem CO 2 losses following disturbances and their different successional stages (e. g. , 4 sites reporting thermokarst;  Virkkala et al. 2022). Sites from the above-mentioned GHG flux datasets were classified into one of the five terrestrial land cover classes using the metadata provided in each of the datasets. More details on how ecosystem flux upscaling was performed can be found in the supporting information. While the focus of this study is the period 2000-2020, we include all in-situ measurements obtained between 1991 and 2020 in order to overcome the limited amount of flux measurements in some of the ecosystems and therefore ensure adequate spatial representation of ecosystem fluxes.
(2021;  https://doi. org/10. 5061/dryad. d7wm37pz9; Dryad) from river water dissolved CO 2 pressure and gas transfer velocity. We combined the monthly fluxes from the start of May to the end of October, assuming that this corresponds to the ice-free season (when waterto-air gas transfer can occur). This time extent (184 days) is nine days longer than the duration  Liu et al. (2022)  cite as the mean ice-free period for Arctic lakes (175 days). This data is delivered as unprojected global grids with a 0. 0083 degree resolution (which is ca 1*0. 2 km pixels in the high Arctic). The global grids were clipped to the extent of BAWLD and then reprojected to an equal area grid at 100*100 m resolution. Calculations from this data yields a total stream and river area of 0. 069*10 6 km 2 , and a total flux of 94 Tg CO 2 -C yr -1.
Assuming the mean river flux (1,370 g C m -2 yr -1 ) can be scaled also to smaller streams and rivers, we applied the area of streams and rivers in BAWLD (0. 12*10 6 km2 These data was reprojected from an original 0. 5 degree unprojected grid to an equal area grid at 1 km resolution and clipped to the BAWLD extent. As the original lake and river surface area was not known, no correction of inland water surface area was made. Uncertainties for river GHG budgets were determined using the standard error and coefficient of variance reported by  Liu et al. (2022) ,  Stanley et al. (2016)  and  Maavara et al. (2019) , respectively, for CO 2 , CH 4 , and N 2 O. • GHG budgets for lakes CH 4 fluxes (diffusion and ebullition) were extracted from the BAWLD-CH4 aquatic ecosystem dataset and classified based on classes (yedoma lakes, peatland ponds, and glacial/post-glacial organic poor lakes and ponds) and sizes, from large (> 10 km 2 ) to midsize (0.
1 to 10 km 2 ) to small lakes (< 0. 1 km 2 )  (Kuhn et al. 2021a ; total area = 1. 255 10 6 km 2 ; Table  S3 ). Notably, no minimum size for lakes was considered in the BAWLD dataset, as the dataset gives an estimate of the overall area covered by lakes in each size-class  (Olefeldt et al. 2021 ). Conceptually, any area which is likely to be inundated >50% of the growing season period (long term average) is considered part of the lake land cover classes. Ice-free days were determined based on averages of reported ice-free days for each lake type and this information was used to determine ice-free season fluxes (supplementary Table  S1 ). In addition to ice-free emissions, spring ice-out emissions (i. e. winter contribution) were considered to be 23% of the annual total (Wik et al.
To estimate lake fluxes of N 2 O, gridded global data of annual flux from  Lauerwald et al. (2019)  were used. This estimate is based on the nitrous oxide (N 2 O) emission model developed by  Maavara et al. (2019)  and the HydroLAKES database and was reprojected from an original 0. 5 degree unprojected grid to an equal area grid at 1 km resolution and clipped to the BAWLD extent. As the original lake and river surface area was not known, no correction of inland water surface area was made. Uncertainties for lake N 2 O were determined using the coefficient of variance reported for regions north of 50 deg latitude in  Lauerwald et al. (2019). Localised, but widespread, disturbances associated with abrupt thaw are thought to contribute significantly to GHG emissions from permafrost  (Abbott and Jones, 2015 , Yang et al.
Therefore, we did not differentiate between vegetated and non-vegetated Permafrost Bog areas when upscaling. N 2 O emissions from Tundra Wetlands were negligible (0. 01 (0. 00, 0. 02) Tg N 2 O-N y -1 ), which can be explained by the lack of nitrate supply as an N 2 O precursor in reduced conditions and reduction of N 2 O to N 2 during denitrification when the water table is high  (Butterbach-Bahl et al. 2011; Voigt et al. 2017). Recent observations not included in the N 2 O review dataset  (Voigt et al 2020)  show that wetlands may also function as net N 2 O sinks in the Arctic  (Schulze et al. 2023).
It is unclear how well post-disturbance dynamics, e. g. post-fire regrowth, is captured in our ecosystem flux upscaling. Updated budgets need to consider new datasets of fire emissions to cover the period post-2016 as well as post-fire recovery processes. Our emissions from fires consider direct GHG emissions but not the indirect and longer-term soil emissions resulting from fire-induced ground thaw. Although carbon losses might be offset by shifts in species composition  (Randerson et al. 2006; Ueyama et al. 2019; Mack et al. 2021) , fires can also initiate further permafrost thaw and degradation  (Genet et al. 2013; Jafarov et al. 2013; Gibson et al. 2018 ). As such, fires can trigger shifts in the landscapes, impacting biogeochemical cycling  (Randerson et al. 2006; Bouskill et al. 2022; Hermesdorf et al.
2. 5 5 Disturbances -fires and abrupt thawMonthly GHG fire emissions were extracted for the study region from the Global Fire Emission Database version 4s (GFED; van der Werf et al. 2017). The GFED4s spans from 1997-2016 and estimates of burned areas are based on remote sensing data at a spatial resolution of 0. 25 degrees (van der Werf et al. 2017). GHG emissions in the GFED4s are derived from the multiplication of burned area and fuel consumption per unit burned area, the latter being the product of modelled fuel loads per unit area and combustion completeness. For our purpose, we extracted mean annual GHG emissions from burned areas for the period 2000-2016 and assumed similar rates for the period 2016-2020.
( 2020). Additional details on the inventory model can be found in Turetsky et al. (2020). Since GHG datasets that we used for ecosystem upscaling partly account for abrupt thaw and to prevent double counting GHG fluxes, CO 2 and CH 4 fluxes from abrupt thaw were added as a sub-flux (not added to the total) of terrestrial and inland water land cover fluxes and their contribution to the total GHG budget is discussed. Due to the lack of in situ observations of abrupt thaw impacts on N 2 O fluxes in the used datasets, no N 2 O budget is presented for abrupt thaw. 2. 6 Lateral fluxes and geological emissionsLateral C and N fluxes from riverine transport and coastal erosion (i. e. DOC and DON losses from the permafrost region to the ocean) are taken from Terhaar et al. (2021) , representative for all land north of 60° N.
They estimated riverine lateral fluxes for the six largest Arctic rivers (Mackenzie, Yukon, Kolyma, Lena, Ob, Yenisei) from the Arctic Great River Observatory (ArcticGRO) dataset and extrapolated to the entire Arctic catchment. Emissions from coastal erosion were calculated by multiplying spatially resolved estimates of coastal erosion rates by estimates of C content in coastal soils provided in Lantuit et al. (2012). Estimates of geological emissions of CH 4 (from subsurface fossil hydrocarbon reservoirs) are taken from an upscaled circumpolar permafrost region estimate for gas seeps along permafrost boundaries and lake beds made by Walter Anthony et al. (2012). We note that there is some risk of double counting such fluxes, especially in sites where eddy covariance flux towers may have unknowingly been placed close to seeps of geological CH 4 emissions.
manuscript submitted to replace this text with name of AGU journal the total N from the region. Atmospheric N 2 O from inland waters was negligible while atmospheric N 2 O from terrestrial ecosystems represented 17% of the total N released in the permafrost region. Atmospheric N 2 O losses due to fires represented 2% of the N in the permafrost region. 3. 5 Main sources of uncertainty and research directions• Limitations in the number of observations A major challenge in the representation of GHG exchange in high-latitude and remote environments relates to limitations in spatial representation, length and quality of observational time series (Pallandt et al. 2022 , Virkkala et al. 2018). The synthesis datasets used here to estimate GHGs fluxes are the most comprehensive ones currently available and have been significantly growing during the past decade.
Acknowledgments This work was supported by public funds received in the framework of  GEOSUDDINAMIS Data TERRA , a project ( ANR-10-EQPX-20 ) of the program " Investissements d'Avenir"  (PIA) managed by the  French National Research Agency. Funding was also provided to the project  PErmafrost and Greenhouse gas dynamics in Siberia (PEGS)  of the  PIA  ( ANR-17-MPGA-0014 ) through the  Make Our Planet Great Again (MOPGA ) initiative. We thank  Jerome Chmeleff  for expert management of the OMP mass spectrometry facilities in Toulouse, France. SMC acknowledges her PhD scholarship and a travel grant from the  University of Ferrara, Italy.
The impact of future climate change on thermal inversions has been addressed in very few papers, for the Po valley basin  (Caserini et al. , 2017)  and for southeast Australia  (Ji et al. , 2019). Caserini et al. (2017)  analyse meteorological data recorded at stations in the Po valley over the 1985-2013 period and outputs from a regional climate model (RCM) over the 1950-2100 period (at resolution 0. 44 • ). Two different future scenarios, associated with different green-house gases emissions, are considered: a "middle-of-the-road" one (SSP2-4. 5) and an extreme one (SSP5-8. 5). Caserini et al. (2017)  find a weak change in PCAPs frequency at the end of the century compared to the 1986-2005 average, of +10% for SSP2-4. 5 and even less for SSP5-8. 5. Ji et al. (2019)  use data from a RCM (with three sets of physical parameterizations) forced by four different general circulation models (GCMs) from the CMIP3 database.
Acknowledgements. This work was performed using HPC resources from  GENCI-IDRIS  (Grant  2021-A0080107161 ). The authors thank  Michael Duda  ( NCAR ) and  Dave Gill  ( NCAR ) for the discussion had at the beginning of this project and for their help to create the WPS intermediate file format. The authors thank also  Milton Gomez  ( University of Lausanne ) for having computed the vertical temperature gradients with the observations. SB thanks the  Laboratory of Geophysical and Industrial Flows (LEGI) in Grenoble  and the  ADEME (PACC-MACS project)  for having funded her scientific stay at the  LEGI.
Data availability. The MAR experiments over the Alpine domain are available on zenodo repositories for a limited number of levels and variables. MAR-MPI-ESM-HR, SSP2: https://doi. org/10. 5281/zenodo. 5834221; MAR-MPI-ESM-HR, SSP5: https://doi. org/10. 5281/zenodo. 5834376. More variables can be accessed by contacting the contact author. WRF outputs can be provided upon request to the contact author as well.
This enabled us first to propose a conceptual model for the hydrodynamic functioning of the basin and then to establish a first set of water level contour sketches for the multi-layer aquifers of Tindouf basin, not available hitherto. The regional groundwater budget over the TAS domain was also obtained using GRACE data and compared to the conceptual hydrodynamic model and to the piezometric analysis. This satellite information (gravity and geographic distribution of sebkhas) made it possible to propose an estimate for the unknown direct phreatic surface evaporation through a groundwater mass balance.
However, a few additional piezometric levels measured in the 2010-2020 decade were provided by Ron  Martin [2020]. By comparing these recent levels with the 1980 data, the temporal evolution of aquifer water levels was estimated, at least in the western part. A decline of a few meters per decade for the Hamadian aquifer is observed while a relative stability of piezometric levels characterizes other aquifers. According to  Lamouroux and Hani [2006] , the Hamadian and Visean aquifers are hydraulically connected at the southern border of the basin, as evidenced by very similar piezometric levels at about 400 m in dedicated piezometers for the two aquifers in the area of Tindouf city. The largest piezometric level data set for the Hamadian aquifer demonstrates a groundwater flow from the west-northwest (Hamada plateau) towards the sebkha of Tindouf, which is therefore a major discharge area by exfiltra- tion and evaporation.
Water levels mapping The piezometric contour map, which is the basic tool for identifying groundwater flow, is lacking here for almost all the aquifers due to water level data scarcity, with the exception of a very restricted area around the city of Tindouf for the Hamadian formation. However, some indirect information makes it possible to compensate for the lack of hydraulic head data. Geomorphological and hydrological features indicative of the piezometric level in this arid context can be comprehensively collected. The locations of springs were primarily considered here since their topographical elevation gives the piezometric levels of the aquifers from which such groundwater outflows. The corresponding aquifer was simply identified by considering the exposed geological strata at the location of the springs.
GRACE-derived groundwater balance of the TAS Before correcting the GRACE-derived TWS data from soil water storage (SWS, see Section 3. 2), the compatibility of the different soil models with the GRACE data needs to be verified for the region of interest  [Gonçalvès et al. , 2013 , Scanlon et al. , 2019]. From this point of view,  Scanlon et al. [2019]  recently reiterated that the consistency of SWS fluctuations (seasonality) with that displayed by the GRACE signal (SWS amplitudes expected to be less than or equal to that of TWS) should be verified. For this purpose, domain average terrestrial and soil water storage time-series were considered first for comparison purposes (Figure  5 ). The VIC and NOAH models show a sharp rise of about 100 mm in soil water storage at the end of 2014 then a roughly stable or declining plateau that does not appear as much in the GRACE data.
Consequently, these two models that are inconsistent with the GRACE data (gravity observations) were rejected and only the CLSM model was considered in the following analysis. Despite a sharp increase also for the CLSM model in 2014, the latter shows the best overall consistency with the GRACE TWS timeseries. It can be noted that the CLSM model is consistent with the soil model considered by  Frappart [2020]  showing a weak interannual storage variation (low trend). Using the CLSM model to identify SWS and to produce the GWS time series (TWS -SWS) leads to an overall groundwater depletion of the TAS ∆GWS = -0. 7 ± 0. 1 mm•yr -1 (Figure  5 ). The regional distribution of GRACE-derived values of ∆GWS shows a western part in quasiequilibrium regarding groundwater mass variations while the eastern part is in clear depletion (Figure  6 ).
2021). At Dumont d'Urville (DDU) station, the evolution of the AR and of the associated landfall was analyzed using radiosonde data following the processing methodology of  Vignon et al. (2019). To characterize the vertical structure of the precipitation during the AR, we used data collected by a Micro Rain Radar (MRR) deployed under a radome at the station in 2015. The MRR provides vertical profiles of K-band (24 GHz) reflectivity and Doppler velocity over the first 3,000 m above ground level (a. g. l. ) with a resolution of 100 m. Data were processed following the processing chain for snow hydrometeors developed by  Maahn and Kollias (2012)  and the attenuation due to the radome was estimated and corrected following  Grazioli et al. (2017). Further details on the instrument deployment and data processing are provided in  Grazioli et al.
d. Satellite observations of Conger Ice Shelf and sea ice We analyzed available satellite imagery to characterize the style and timing of the breakup of the Conger Ice Shelf. Landsat 7 and 8 data provided the primary means to document the progressive retreat of the ice shelf over the last ~20 years. Sentinel 1 radar images were used to assess conditions immediately preceding and following the ice shelf collapse. Likewise, MODIS images were assessed to further constrain the timing of ice shelf collapse. Daily maps of sea-ice concentration were obtained from the National Snow and Ice Data Center. From these, the change in sea-ice extent after the passage of the AR was calculated. Landfast sea ice (fast ice) was manually mapped from composite cloud-free NASA moderateresolution imaging spectroradiometer (MODIS) imagery following  Fraser et al.
In the weeks before the ice shelf collapsed, radar backscatter measurements from Sentinel-1 did not indicate any surface melting on the Conger Ice Shelf or the adjacent sea ice, suggesting collapse was not triggered by surface melt-induced hydrofracturing. Indeed, available Sentinel-1 backscatter indicated surface melting occurring only after the collapse, likely indicating the presence of AR-associated heat at this site on or before March 17. As such, the structural precursor for ice shelf collapse appears to be the dual calving events at the southern margin on Assessment of wave swell and wind data from ERA5 show conditions during these two calving events were not anomalous. However, ERA5 indicates highly anomalous wave swell and easterly winds on March 14, 2022 (Fig. 8 ), the day before the presumed ice shelf collapse (see  Bruno et al.
Acknowledgments. All authors particularly thank the national polar institutes such as  US Antarctic Program , the  French Polar Agency (IPEV) , and the  Australian Antarctic Division  (AAD), without which none of the data used here would be available. P. M. Rowe  and  X. Zou  are grateful for funding from the  NSF Office of Polar Programs  under awards  2127632  and  2229392. R. T. Datta  is grateful for funding from  NSF Office of Polar Programs  award  1952199  and  NASA  award  S000885. The PWRF model is developed and is maintained by the  Polar Meteorology Group, Byrd Polar and Climate Research Center (BPCRC) , The  Ohio State University. PWRF simulations were performed on the San Diego Supercomputing Center's COMET resource through  AR Program , Phase  II 460001361  and  III 4600014294  (State of  California, Department of Water Resources ).
J. D. W. and V. F. acknowledge support from the  French Agence Nationale de la Recherche  projects  ANR-20-CE01-0013  (ARCA). C. Amory  and C. Kittel  thanks the Computational resources have been provided by the  Consortium des Équipements de Calcul Intensif (CÉCI) , funded by the  Fonds de la Recherche Scientifique de Belgique (F. R. S. -FNRS ). M. A. Lazzara  and  D. Mikolajczyk  are grateful for support from the  NSF Office of Polar Programs  under grants # 1924730 ,  1951720 , and  1951603. We also wish to thank  Bella Onsi  for her assistance. This project received grant funding from the  Australian Government  as part of the  Antarctic Science Collaboration Initiative program. I. J. Smith  and  G. H. Leonard  were supported by grant  MFP-UOO1825  from the  Marsden Fund Council  from government funding, administered by the  Royal Society of New Zealand , and the  Antarctic Science Platform  ( MBIE SSIF Programmes Investment  contract number  ANTA1801 ).
C. A. Shields supported by  U. S. Department of Energy, Office of Science, Office of Biological & Environmental Research (BER), Regional and Global Model Analysis (RGMA)   component of the Earth and Environmental System Modeling Program ,  DE-SC0022070 ,  National Science Foundation (NSF )  IA 1947282 , and  NCART NSF Cooperative  Agreement No. 1852977. S. P. Alexander  was supported by the  Australian Antarctic Division 's  Australian Antarctic Science  projects 4387 and 4637. We thank the  Australian Bureau of Meteorology  and the  Australian Antarctic Division  for providing the AWS data from the Australian Antarctic stations. S. González-Herrero  was supported by the research group  ANTALP (Antarctic, Arctic, Alpine Environments ;  2017-SGR-1102 ) funded by the  Agència de Gestió d'Ajuts Universitaris i de Recerca of the Government of Catalonia.
S. -J. Accepted for publication in Journal of Climate. DOI 10. 1175/JCLI-D-23-0176. 1. File generated with AMS Word template 2. 0 Kim and  T. Choi  were supported by the  Korea Polar Research Institute (KOPRI)  project " Understanding of Antarctic climate and environment and assessments of global influence " (PE23030) funded by the  Ministry of Oceans and Fisheries. D. Udy  was supported by an  Australian Research Training scholarship  and an  Australian Research Council (ARC) Centre of Excellence for Climate Extremes  top-up scholarship. T. Vance  acknowledges support from the  Australian Antarctic Program Partnership  ( ASCI000002 ). D. U and T. V also acknowledge support from  ARC DP220100606. I. Ollivier  was supported by the project  DEEPICE. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  955750.
numerous different datasets and expertise to provide a comprehensive analysis/overview of the March 2022 East Antarctic heatwave and place this event in context with other extreme Accepted for publication in Journal of Climate. DOI 10. 1175/JCLI-D-23-0176. 1. Brought to you by UNIVERSITE DE BOURGOGNE | Unauthenticated | Downloaded 11/16/23 07:16 AM UTC File generated with AMS Word template 2. 0.
Here, we describe an integrated ecological and metabolic modelling approach with the goal to delineate metabolically cohesive consortia underlying genes-to-community assembly and ecosystem functioning at global scale  30. We combined co-activity ecological information inferred from metaomics with community metabolic simulations using genome-scale metabolic models to uncover putative biotic interactions mediated by metabolic cross-feedings among marine prokaryotic genomes. Through a multi-omic approach integrating Tara Oceans metagenomic and metatranscriptomic datasets, we inferred a global ocean genome-resolved ecological network from whole-genome transcriptomic activities. We used general genomic scaling laws 31 as a framework to characterise the functional content of co-active environmental genomes, and identified functional gene categories likely driving metabolic dependencies.
Genomic scaling laws reveal features of uncultivated marine prokaryotic genomes To build a comprehensive catalogue of marine prokaryotic genomes, we collected and assembled public whole-genome sequences (WGS) from marine prokaryote isolates 32 , single-amplified genomes  22  (SAGs), as well as previously reconstructed MAGs  21. This novel integrated marine prokaryotic genome database counted 7,658 non-redundant species-level representative genomes (delineated by a 95% ANI threshold over 60% of genome length, see methods). Herein, we only considered genomes meeting sufficient quality standards (n=5,678) as defined by the Genomic Standards Consortium 33 , that is High-Quality (HQ) MAGs (>90% complete with less than 5% contamination) as well as Medium-High-Quality (MHQ) MAGs (>75% complete with less than 10% contamination).