dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgement The authors would like to thank  Edwige Pons-Branchu ,  Eric Douville ,  Lorna Foliot  for advices on the chemistry and mass spectrometry development and  Nadine Laborde  and  François Thil  for the opportunist sampling of Vienne sample. Philippe Bonté  kindly provided the "mare du Rusquec" sample. We acknowledge all the sampling team on board the Marion Dufresne during the HYDROSED cruise and Bonus GoodHope cruise. We thank  Fabien Pointurier  for his thorough review of the manuscript. This work benefited from the  French government  support managed by the  ANR  under the " Investissement d'avenir" programme  [ ANR-11-IDEX-0004-17-EURE-0006 ], supported by the  COMUE Paris Saclay University. J o u r n a l P r e -p r o o f.
(2011)  database. Comparison of APIFLAMEv1's CO emissions with those from three other BB emission inventories often used in the scientific community, GFEDv3 (Global Fire Emissions Database, version 3. 0 -  Giglio and Randerson, 2010; Werf et al. , 2010) , FINNv1 (Fire INventory from NCAR, version 1. 0 -  Wiedinmyer et al. , 2011)  and GFASv1 (Global Fire Assimilation System, version 1. 0 -  Kaiser et al. , 2012) , showed good spatial-temporal correlations of 0. 9  (Turquety et al. , 2014). These three global emission inventories also rely on MODIS observations for fire detection. The validation study of API-FLAMEv1 conducted by  Turquety et al. (2014)  showed uncertainties of a factor of 2 to 4 on the emitted amounts of carbonaceous species linked in particular to the wide variability of emission factors by vegetation type.
02 from 490 to 865 nm compared to those retrieved by POLDER-3/AERO-AC. Regarding the spectral behaviour of the ACSSA, the WRF-Chem optimized configuration (red curve, d) simulates an upward trend from 300 to 600 nm due to the presence of BrOC in the BBA plumes and then a slightly decreasing trend in the near-infrared with increasing wavelength. However, the lack of data above clouds in the ultraviolet spectrum with POLDER-3/AERO-AC does not allow us to assess the reliability of the increase in absorption below 490 nm due to the addition of BrOC to the WRF-Chem optimized configuration. In the near-infrared, the decrease in ACSSA values is more pronounced in the POLDER-3/AERO-AC retrieval, with a positive average bias of 0. 03 at 865 nm between the WRF-Chem optimized configuration and the POLDER-3/AERO-AC retrieval.
11 above clouds over the SEAO. These values are also in good agreement with previous in situ measurements over this region, such as the SAFARI-2000 airborne campaign  (Formenti et al. , 2003). In addition, the absorbing fraction of OA in the BBA plumes, i. e. BrOC, is estimated at 2 %-3 % for the studied period. This value could vary geographically and temporally. Importantly, this BrOC contribution allows the increase in absorption retrieved by POLDER-3/GRASP at the shortest wavelengths of the visible spectrum, characteristic of the presence of BrOC in the BBA plumes, to be well simulated with WRF-Chem over the BB source areas. However, the lack of available POLDER-3 data for the SSA in the blue and in the ultraviolet spectrum above clouds did not enable us to constrain with certainty the BrOC content in cases of BBA plumes transported over stratocumulus off the coasts of Namibia and Angola.
To progress on this aspect, a synergy between satellite observations provided by the POLDER-3 and the Ozone Monitoring Instrument (OMI) space sensors would allow us to expand the retrieval of the SSA of aerosols to the ultraviolet range, a spectral domain particularly sensitive to the presence of BrOC. Another outlook will consist in applying our methodology to the more recent French AEROCLO-sA airborne campaign that took place in the SEA region in August-September 2017, using other satellite datasets than POLDER-3, for which observations ended in 2013. Other international measurement campaigns have also taken place in this region (CLARIFY, LASIC and ORACLES) in recent years  (Zuidema et al. , 2016b). They will provide detailed reference observations on the BBA properties bringing additional constraints on their radiative and climate effects.
This work has been supported by the  CaPPA (Chemical and Physical Properties of the Atmosphere)  project, which is funded by the  French National Research Agency (ANR)  through the  Programme d'Investissement d'Avenir (PIA)  under contract  R-LABEX0-20-007-CAPPA  and by the  Regional Council of Hauts-de-France  and the  European Regional Development Fund (ERDF) ; the  University of Lille (France) ; the  Programme National de Télédétection Spatiale  (PNTS, https://programmes. insu. cnrs. fr/transverse/pnts,last access: 30 November 2021, grant no. PNTS-2013-10 ); and by the  French Centre National d'Etudes Spatiales (CNES). The authors also thank the  Hauts-de-France region , the  Ministère de l'Enseignement Supérieur et de la Recherche  ( CPER Climibio ), and the  ERDF  for their financial support.
MODIS Daily Snow Product Snow cover maps were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products MOD10A1 and MYD10A1 (MODIS Snow Cover Daily L3 Global 500 m SIN GRID V006), respectively, generated from the Terra and Aqua MODIS satellites, and acquired from the National Snow and Ice Data Center (NSIDC)  (Hall and Riggs, 2016). The MODIS snow products from the Terra and Aqua satellites are available since February 2000 and February 2002, respectively. The datasets are produced in a Geographic projection and were re-projected to the World Geodetic System 1984 (WGS84) Universal Transverse Mercator (UTM) coordinate system using the data transformation options available at NSIDC. A total of 4734 MOD10A1 images and 4760 MYD10A1 images, covering 13 years from September 2003 to August 2016, were used in this study.
Since there are no stations with reliable precipitation data above the CAF station (2612 m) to constrain this value, this parameter was subjected to a calibration/validation procedure against the MODIS SCA maps for all melt models. Z max was calibrated on odd years within a 300-1800 m range and validated on even years for each model in order to reduce the climate dependency of the calibration period  (Arsenault et al. , 2018). The validation was done using the optimal parameter for each model as well as with the mean of the optimal parameters of each model (mean Z max ). The potential, clear-sky direct solar radiation was calculated as a function of solar geometry, topography and a constant vertical atmospheric transmissivity following  Hock (1999) , and includes topographic shading (Equation 4b in Table  2 ).
, 2015; Baba et al. , 2019). Confusion matrices were used to assess the classification accuracy of the simulated SCA maps relative to MODIS SCA. Confusion matrices are two-dimensional contingency tables that display the discrete joint distribution of simulated and observed data frequencies  (Zappa, 2008). Model skill scores were derived from the confusion matrix (Table  4 ). The Heidke Skill Score (HSS)  (Heidke, 1926)  which is equivalent to the Kappa coefficient proposed by  Cohen (1960) , measures the classification accuracy relative to that expected by chance and has been extensively used for imbalanced datasets such as snow remote-sensing data (e. g. ,  Zappa, 2008; Notarnicola et al. , 2013; Baba et al. , 2019). The HSS was thus the preferred global metrics used for model assessment.
FUNDING This research was funded by the  Natural Sciences and Engineering Council of Canada , grant number  RGPIN-2015-03844  and the  Canada Research Chair program , grant number  231380  to CK, as well as by a Ph. D. scholarship from the  CNRST (Centre National pour la Recherche Scientifique et Technique of Morocco)  to HB. AC, AB, and LH are supported by the research program "MorSnow-1",  International Water Research Institute (IWRI) ,  Mohammed VI Polytechnic University  ( UM6P ), Morocco (Accord spécifique n • 39 entre  OCP S. A et UM6P ).
Next, we define the element N + 1 as the couple (jpni, jpnj) that gives the smallest number of subdomains (jpni×jpnj). It rarely happens that several couples (jpni, jpnj) correspond to this definition. If the situation arises, we decide to keep the couple with the smaller subdomain perimeter to minimize the volume of data exchanged during the communications. This choice is quite arbitrary as NEMO scalability is limited by the number of communications but not by their volume. Limiting the subdomain perimeter should therefore have a very limited effect. We stop the iteration process once there is no longer a couple with a subdomain size smaller than the one selected at rank N. Once this list of the best domain decomposition sorted from 1 to Nsub max subdomains is established, it remains only to follow the third step of our algorithm to get the best subdomain decomposition (see Sect.
Mutual benefit of source terms and of assimilation The evaluation of the different simulations is carried out against VACOS measurements using a similar approach to that of  Plu et al. (2021). VACOS and MOCAGE data are regridded at a 0. 2 • resolution on the domain shown in Fig. 1. A grid point is considered to be contaminated by ash if ash load is above 0. 2 g m 2 (VACOS lower detection limit). Figure  6  shows some diagnostics about the detection of ash by MOCAGE simulations compared to the VACOS measurements: hits (the number of contaminated grid points in both MOCAGE and VACOS) and false alarms (number of grid points that are contaminated in MOCAGE and not in VA-COS), for all MOCAGE simulations. Detection is performed on the same 0. 2 • resolution grid, but the grid points where VACOS ash detection (due to meteorological water clouds for instance) was not possible are excluded from the analysis, even for the model outputs.
Material and methods 3. 1. Field data 3. 1. 1. Long term monitoring data: discharge and nitrate streamwater time series To inform the main hydro-biogeochemical processes occurring at the catchment scale, long time series of stream discharge and nitrate concentrations observed in the three catchment rivers were gathered. First, monthly river discharge from 1998 to 2010 is presented (Fig. 2 ). These streamflow data were initially collected by measuring the daily river height (http://hydro. eaufrance. fr/). Average observed streamflow values are given on Table  1  for the 1998-2010 period. Annual discharge standard deviation, representative of the interannual streamflow variability, is on average equal to 0. 17 m/year for the three catchments to compare with the average discharge of 0. 52 m/year.
Secondly, monthly nitrate concentrations in rivers from 1998 to 2015 are presented (Fig. 3 ). These data were initially collected at bimonthly time step by local basin agencies. Average nitrate concentration in rivers is around 32 mg/L for the three catchments over the 1998-2015 period (Table  1 ). For all catchments, concentrations are  decreasing during this period (on average -0. 6 mg/L/year), with highest decrease for the Kerharo river (-0. 92 mg/L/year). In addition, nitrate concentrations were occasionally measured between 1976 and 1984 in the Ris river (black dots in Fig. 3 ). These data come from NAÏA-DES French data base (http://www. naiades. eaufrance. fr/). This data was added to the 1998-2015 nitrate monitoring datasets as they provide an interesting opportunity to assess the long-term behavior of the catchment.
Recharge and nitrogen inputs To estimate recharge and nitrate inputs over the 1955-2015 period, we merged two modelled datasets providing either nitrate and/or groundwater recharge inputs over different time scales. First, we used the TNT2-STICS model providing monthly recharge rates and nitrate concentrations in this recharge water over the 1997-2010 period  (Beaujouan et al. , 2002). This period is constrained by nitrate measures in river and the monitoring of agricultural practices. TNT2-STICS is a spatially distributed hydrological and nitrogen model running at daily time step and taking into account several soil layers for water transfers. Crop development and the associated soil/plant nitrogen transformation are physically represented along with heterotrophic denitrification in soils and in often saturated areas like humid zones.
Flow and nitrate transport models are implemented within the well-known MODFLOW and MT3D software suite through the Python Flopy interface  (Bakker et al. , 2016; Bedekar et al. , 2016; Harbaugh, 2005; McDonald and Harbaugh, 1984; Zheng and Wang, 1999). They are presented in details in Supplementary material. The modelled mean transit times are computed by dividing the groundwater volume by the mean recharge. They are compared to the mean transit time derived from the CFCs (Section 3. 1. 2). Groundwater nitrate inputs, as well as recharge rates, are provided by the TNT2-STICS model. Thus, we only consider here nitrate transport in the aquifer, without taking into Fig. 5. Sketch of the 2D hillslope model. The morphology of the hillslope is defined by its length (L) (here 1000 m), its characteristic thickness (E) (here 80 m) and the slope of its surface (here 0.
Acknowledgments This study was funded by the  "Région Bretagne" authorities, the "DREAL" Department (Ministry of Environment)  and the  Water Agency "Agence de l'Eau Loire-Bretagne " through the  MORAQUI (MOdélisation de la Réactivité des AQUIfères dans les bassins algues vertes ) project. We thank the  Public offices "EPA Baie de Douarnenez" and the "Communauté de Communes de Morlaix" , and the  Water Regional Technical Center "CRESEB"  for data collection and field work as well as the " Condate-Eau " ( OSU-Univ. Rennes 1) analytical platform for the chemical and dissolved gases analysis.
The resulting PCR products were used for the preparation of libraries for Illumina sequencing. High-throughput sequencing of the libraries was performed with MiSeq Reagent Micro Kit v2 (300-cycles) MS-103-1002 (Illumina, USA) on a MiSeq sequencer (Illumina, USA) according to the manufacturer's instructions. The raw reads were processed as described in  Gavrilov et al. (2019). All the reads of the V4 region of 16S rRNA gene obtained in two replicates for each sample were analyzed using the SILVAngs service with default parameters (https://ngs. arb-silva. de/silvangs/) and SILVA138. 1 SSU database. NCBI BLASTn (https://blast. ncbi. nlm. nih. gov/Blast. cgi) with various parameters, and databases were used for manual curation of taxonomy of the sequences of interest. The current version of the SILVAngs (as of August 2021) uses the Silva taxonomy that is, in turn, based on the Genome Taxonomy Database (GTDB, https://gtdb.
Microbial community composition To identify the microbial community associated with Tegulaherpia sp. , the amplicons of the V4 region of the 16S rRNA (SSU) genes were sequenced from the total DNA, isolated from the whole bodies of three individual mollusks. The representatives of bacterial phyla Bacteroidetes (26-47%), Proteobacteria (10-32%), Nitrospirae (8-30%), as well as of the archaeal phylum Thaumarchaeota (11-15%), predominated in all samples (Fig. 4 ). Bacteroidetes were represented almost exclusively by an uncultured bacterium of the family Saprospiraceae (93-95% of Bacteroidetes and 25-44% of the total community). Results of manual BLAST of this most abundant OTU, belonging to Saprospiraceae, using the nr/nt database with ''type material'' limitation resulted in Haliscomenobacter and Lewinella members among the best hits (≤89% of sequence identities with the query).
ACKNOWLEDGEMENTS We are grateful to  Professor Elena S. Lobakova  and  Professor Martin V. Sørensen  for the fruitful discussions. We express our gratitude to  Laurent Vanbostal  and  Dr. Alexander Ereskovsky  for the help in sampling. Logistics and field assistance were provided by  IMBE  and  OSU Institut Pythéas in Marseille, France , through the diving and laboratory facilities.
Upon publication at the ORNL DAAC, a formal citation, including the authors, title, and date of publication, and a unique Digital Object Identifier (DOI) was issued for each data product. The data citation and DOI provide a convenient and traceable identity for each specific data set that can be cited in the scholarly literature and linked to subsequent research efforts and products. In order to increase the visibility and maximize the scientific impact of NASA data products, the ORNL DAAC provides searchable metadata to a variety of relevant data catalogs, advertises the data online through email, news, and the DAAC website, and provides user support services. Throughout the ACT-America project, data files were revised, recalibrated, and updated, as necessary. Close communication, and automated monitoring of data availability, were essential for keeping the LaRC data repository and the holdings at ORNL DAAC in sync.
In the winter, fossil fuel influence was broadly observed throughout the entire observational domain. Feng et al. (2021)  documented biases in biological models of summertime CO 2 fluxes and compared inferences from the ACT-America flight data to NOAA tall tower and AmeriFlux flux towers. Rigorous investigation of atmospheric GHG modeling systems is also underway. ACT-America airborne data are being used to evaluate OCO-2 Model Intercomparison Project (MIP) estimated CO 2 fluxes. Gaudet et al. (2020) , for example, evaluated the skill of 10 global CO 2 inversion models from the OCO-2 MIP using 148 airborne vertical profiles of CO 2 for frontal cases from the ACT-America Summer 2016 campaign. High-resolution models  (Hu et al. , 2021; Samaddar et al. , 2021)  of the elevated CO 2 band observed along the frontal boundary shown in  Pal et al.
(2020a)  have been evaluated using the ACT-America airborne data, as has a newly developed global CO 2 transport model  (Zheng et al. , 2020). As noted previously, OCO-2 XCO 2 variability at local to synoptic scales has been evaluated using ACT-America lidar and in situ observations  (Bell et al. , 2020). Davis et al. (2021)  provides a broad discussion of the scientific design of the mission and the applications of ACT-America data products.
Large-Scale Waves Planetary waves, Kelvin waves, and tides larger than our nightly lidar datasets (four to nine hours) could also have a significant influence  [28, 39]. Unfortunately, we cannot replicate the 10-day continuous lidar study conducted by Baumgarten et al. [40] , which was able to assess the contribution of longer-period waves. The Monge lidar does not yet have the capacity for daylight observations. In this article, we will restrict our focus to smaller-scale waves over the course of a few consecutive nights.
9 km  [50]. The ICON-MIGHTI data discussed below are within a 1000 km radius of the launch site for all dates. Recent validation studies for ICON-MIGHTI thermospheric winds conducted using ground-based Fabry-Pérot Interferometers Makela et al. [51]  and specular meteor radars Harding et al. [52]  have demonstrated the utility of ICON winds for comparison with ground-based or ship-borne measurements.
Merged Temperatures and Winds In Figure  7 , we have used the data from the Monge lidar, local radiosondes, ERA5, and ICON to attempt to reconstruct approximate nightly average profiles of the temperature, local Brunt-Väisälä frequency, and zonal and meridional winds. For temperature, we took the radiosonde profile closest to the middle of the lidar measurement from the sea surface to 15 km. We then merged the Monge lidar Raman, low-gain Rayleigh, and high-gain Rayleigh to extend the temperatures from 15 to 110 km. The static stability is shown to indicate the transition between the troposphere and stratosphere, the presence of strong MILs, possible regions where instabilities may be found (N 2 < 0), and the transition from the mesosphere to the thermosphere. The zonal and meridional components of the winds were taken from the radiosondes from the sea surface to the balloon burst altitude, ERA5 winds were then taken up to 80 km, ICON winds were generally available above 90 km, and the region between ERA5 and ICON was interpolated using a cubic spline.
On the ordinate of each scalogram subplot is the period of the wave (the scale is log(2)), and the abscissa corresponds to the number of minutes since the start of the lidar measurement at 00:23 local time. The grayed-out region of each scalogram is the Cone of Influence (CoI), which represents the period-time spaces that are influenced by the edges of the dataset (e. g. , near the top of each sub-figure, more space is grayed out, as fewer wavelets of periods greater than 120 min can be fitted into a 9 h dataset). The color scale of the plots represents the amplitudes of the temperature perturbations of each wave. N. B. the scale of the perturbations increased with altitude. The temperature perturbations at 90 km can be an order of magnitude larger than the temperature perturbations at 50 km.
Discussion We have presented a series of a few high-resolution lidar profiles taken by one of the world's most powerful Rayleigh lidars aboard the ship Monge and used supporting information from the new ICON satellite to help in our analysis of a case study. The limited nature of the available datasets us to make several assumptions regarding the nature of the GWs and winds under consideration. We recognize that we were not able to fully evaluate all the GW parameters (intrinsic wave speed, wave propagation direction, realistic energy dissipation rates, etc. ); however, some reasonable discussion of the high-resolution vertical results can still take place. Starting with the night of D+0 in Figure  3 , we see rapid variations in the 15 min integrated temperature profiles between 60 and 75 km associated with the lower MIL.
Furthermore, concerning the lower MIL, we see drastically different pictures of this layer in Figures  3  and 4. On D-2, D-1, D+0, and D+1, SABER measured an inversion layer near 70 km that we can refer to as a lower MIL. These are "snapshot" measurements and generally correspond to the nightly average lidar profile. The exception is the night of D+0, where the nightly average lidar profile was averaging over a highly variable region of the atmosphere and shows a "smooth bump" rather than the sharper layer seen by SABER. This observational mismatch raises concerns over MIL identification, MIL variability, and optimal data integration when comparing MIL datasets created by satellite and ground-based observatories. We can also raise questions as to what constitutes a MIL: How long must it persist? Over what horizontal and vertical range must it extend? What is a sufficient bottom-side temperature gradient? What is the range of phase speeds that separates a MIL from faster waves? How much variation in MIL amplitude due to the superposition of shorter-period waves is acceptable? Is the wind gradient a better metric than the temperature gradient for defining a lower MIL? Above 85 km, the wind measurements from the ICON satellite reproduce many of the medium-scale structures we see in the temperature data.
CO 2 -LULUCF therefore is the net sum of emissions and removals from all human-induced land-use changes and land management. , 2006, 2019)  as well as relevant GHGs. Note that EDGAR v6 distinguishes between biogenic CO 2 and CH 4 sources with a "bio" label, with all other sectors "fossil" by default, even if that source is not related to fossil fuel activities. The fossil/bio label is hence not descriptive in nature. Two HCFC gases (denoted with * ) are included in the dataset, despite being neither PFCs nor HFCs (and hence regulated under Montreal). This is to preserve consistency with current and previous versions of EDGAR, which include these gases. Their total warming effect is low (∼ 10 MtCO 2 eq. in 2019), and the major HCFC sources are not included. tial CO 2 emissions as compared to land-use changes such as clearing or regrowth of natural vegetation.