dataesr/scientific-paragraphs-categorization huggingface dataset:
The iOSG-24 gravimeter of the LSBB was funded by the  EQUIPEX MIGA  ( Matter wave-laser based Interferometer Gravitation Antenna )  ANR-11-EQPX-0028  and by the  European FEDER   2006-2013  " PFM LSBB -Développement des qualités environnementales du LSBB ". We thank the reviewer for his constructive comments.
The cameras are controlled by custom-made intervalometers triggering hourly from a real-time clock (DS1337C) signal (1. 7-s drift per day at 5 • C). We collected data over two seasons. During the first melt season of June and July 2017, we tested the experimental setup to find the optimal configuration and estimated the accuracy of measurements of this method. We collected images irregularly during this season and only present hereafter a direct comparison of the model from 12 July to a dGPS track of the same day. However, during the second melt season from 10 May to 12 June 2018, the cameras continuously recorded hourly images from which we selected midday images for a total of 34 sets to extract snow depth change and snow cover duration. Over this 34-day period, the landscape had undergone significant changes.
The instrument was operated at a 6-second resolution and data were acquired as 3-minute averages. The equivalent black carbon mass concentration (eBC) was determined by the measurement of light-attenuation at 880 nm performed by a spectral aethalometer (Magee Sci. model AE31) operated at a 2-minute time resolution and equipped with a TSP particle inlet. As the evaluation of eBC is used as a qualitative tracer of pollution, the factory mass conversion factor of 16. 6 m 2 g -1 was applied to the raw measurement of attenuation without further corrections. The meteorological measurements (air pressure, temperature, relative humidity, wind direction and speed and precipitation) were collected by a Vaisala Milos 500 station with a sampling rate of 10 minutes. The wind sensor was installed on a 10-m meteorological tower, while the air temperature and humidity were measured at a height of 2 m.
Data analysis 2. 3. 1. Analysis of the cToF-AMS data The cToF-AMS data set was processed using two different software analysis tools. The first makes use of the widely-used and standard Igor Pro package, Squirrel (version 1. 57G). This software processes the raw data and analyzes the unit-mass resolution (UMR) output with a fragmentation table reported in  Aiken et al. (2008). The second method uses a cumulative peak fitting analysis and residual analysis and allows the separation of multiple isobaric peaks not taken into account in the traditional analysis of unit mass resolution squirrel data treatment  (Muller et al. , 2011). Uncertainties in the major chemical species from the cToF-AMS are typically of the order of ±20%  (Drewnick et al. , 2005). The PM1 sea salt concentration was estimated in the cTof-AMS by applying a scaling factor of 102 to the ion fragment at 57.
These factors were compared with ambient organic mass spectra listed in the AMS Spectral  Database (Ulbrich et al. , 2009). The spectra for the OOA factors (see Figure  7 ) were strongly correlated with each other (R > 0. 97) and all three were similar to a "continental" OOA factor observed in a ship campaign in the Artic  (Chang et al. , 2011) , as well as a low-volatility OOA factor identified in Paris  (Crippa et al. , 2013). Despite the similarities in their mass spectra, they exhibited different diurnal trends (Figure  7 ) and time series that were associated with different wind directions and air masses and were therefore not recombined into a single OOA factor. The MO-OOA factor was the most dominant factor during the field campaign (~53% of the total OA mass) and was typical of low-volatile/highly oxidized OOA observed in many other studies, in the Mediterranean, with high contributions of m/z 44 (f44 = 0.
Acknowledgements. This work is part of the  ChArMEx  project supported by  CNRS-INSU ,  ADEME ,  Météo-France  and  CEA  in the framework of the multidisciplinary program  MISTRALS (Mediterranean Integrated Studies aT Regional And Local Scales ; http://mistrals-home. org/). It has also been supported by the  French National Research Agency (ANR)  through the  ADRIMED program  (contract  ANR-11-BS56-0006 ). Measurements at Lampedusa were also supported by the  Italian Ministry for University and Research through the NextData and RITMARE Projects. The AERIS national data infrastructure is acknowledged for maintaining the  ChArMex database. Atmos. Chem. Phys. Discuss. , https://doi. org/10. 5194/acp-2019-192Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 12 March 2019 c Author(s)  2019.
Data availability. Open-access to the data used for this publication is provided to registered users following the data and publication policy of the ChArMEx program (http://mistrals. sedoo. fr/ChArMEx/ Data-Policy/ChArMEx_DataPolicy. pdf). Additional code used in the analysis of data can be obtained upon request from the corresponding or first author. Weekly GDAS1-analysis (Global Data Assimilation System; 1° resolution) trajectory files were downloaded from the Air Resources Laboratory (ARL) of the National Oceanic and Atmospheric Administration (NOAA) archive (ftp://arlftp. arlhq. noaa. gov/archives/gdas1/). 144-hour air-mass backwards trajectories were calculated using the R-package, SplitR (https://github. com/rich-iannone/SplitR). Cluster analyses were performed on these calculated trajectories, using the R-package, OpenAir  (Carslaw and Ropkins, 2012) ;.
ACKNOWLEDGEMENTS We thank  Axel Seifert  for advice concerning the interactive radiation implementation, and  Marcus Klingebiel ,  Hauke Schulz , and two anonymous reviewers for constructive comments on an earlier version of this manuscript. Louise Nuijens wishes to acknowledge the opportunity to spend one year at MIT during this work through the  Reimar Luest Stipendium  and  Max Kade Foundation   Postdoctoral Research Grant. The UCLA LES model is distributed under the  GNU General Public License  and can be downloaded from https://github. com/uclales/uclales. The LES code, input files and analysis scripts are available on request from publications@mpimet. mpg. de. This work stems from the first author's doctoral research at the  University of Hamburg.
Kerguelen southern elephant seals dataset To understand the influence of sub-mesoscale fronts on SES behavior, we use a unique high-resolution physical and ecological data set collected by 12 female SESs from the Kerguelen Islands colony during their journeys in the ACC  (Rivière et al. 2019). The female SES leave the Kerguelen Islands for 2-to 3-month, 3000 km, post-breeding voyages during the southern spring-early summer (October to January) (Fig. 1A ). This period corresponds to the phase when SES females need to regain energy by feeding in the open ocean. Their diet is mostly composed of mesopelagic fish, dominated by Myctophids, found at 300-700 m depth  (Cherel et al. 2008). The SESs were equipped with miniaturized temperature and pressure sensors (Wildlife Computers MK10 Fast-loc time-depth recorders) with a two-second sampling interval (precision of +/-0.
When GPS tags did not record true locations, we estimated likely latitudes and longitudes of dives in between true locations using a linear interpolation between the closest locations before and after these dives. The mean distance covered during dives (Supporting Information Fig. S1 ) is about 1 AE 0. 4 km. The cumulative distance of the 12 horizontal trajectories was more than 20,000 km, totaling 25,378 dives. We estimate the seal horizontal tracking velocity based on the time and distance differences between GPS positions (with relative error less than 0. 2%). Tracking data were filtered by removing the locations that would have implied seals velocities greater than 2. 8 ms -1 according to the algorithm described in  Viviant et al. (2010)  and  Guinet et al. (2014). Velocities larger than this threshold are unrealistic and likely to be due to GPS positioning errors.
Therefore, SST alone can be used for detecting the vast majority of sub-mesoscale fronts. Using this approach, we define the SST as the temperature measured by the seals' logger between 0 and 15 m depth during their ascending phase, to minimize the influence of air temperature. We assume that the drift of the animal during its stay at the surface (about 120 s) is small, as elephant seals are known to remain vertical and motionless with their heads outside the water to breathe during those surface intervals. Moreover, as the smallest horizontal scales (in 2D) of the SST cannot be estimated from the SES data set (limitations are detailed in the discussion), we used the simplest definition of sub-mesoscale fronts based on the strongest along-trajectory SST gradients at the smallest scales. The procedure for the sub-mesoscale front detection is as follows: strong small-scale SST gradients are identified as surface temperature variation between two consecutive surface locations greater than a given threshold (here 0.
Characterization of the SES foraging behavior We quantify SES foraging behavior through the analysis of mouth-opening events detected by the head-mounted three axis accelerometers. Data from the accelerometers were processed according to  Viviant et al. (2010)  and  Gallon et al. (2013)  (see also  Guinet et al. 2014  for details). Processing consisted of a high-pass filter to remove the noise corresponding to swimming movements. Then, a fixed window of 1 s was used to calculate the standard deviation every second along each acceleration axis. Signals were then processed using a moving standard deviation with a window size of 5 s. Last, a two-mean clustering was performed on the final three signals to distinguish a "high state" from a "low state. " A prey catch attempt event is assumed to be occurring when the three axes were simultaneously in a "high state," with a continuous succession of "high states" being considered as a single event.
Our estimates of prey catch attempt events detected by the accelerometers are not exact. However, several recent studies using seal daily mass ingested estimations, or correlation with body-buoyancy changes or microsonar data  (Guinet et al. 2014; Richard et al. 2016; Goulet et al. 2019) , showed that head-mounted accelerometer data provide a robust proxy for actual prey encounters. Here we hypothesize that changes detected by our threshold method are related to changes in foraging behavior (assuming that missed attempts occur in a constant proportion relative to successful attempts-see  Guinet et al. 2014  for a more complete discussion, see also  Richard et al. 2016 or Goulet et al. 2019). Even if no direct measure of energy gain/loss ratio is available during this process, the results we obtained support the hypothesis that inside sub-mesoscale fronts, SES tend to use less energy by (1) diving to shallower depths and (2) gaining more energy by ingesting a greater number of prey per unit time spent diving.
Acknowledgments We thank  Peter Gaube ,  Mike Fedack ,  Peter Franks , and the editor for providing very valuable remarks that considerably ameliorated the manuscript. We also thank  Francesco d'Ovidio  and  Philippe Pondaven  for useful discussions. This work was mostly carried out at  LEMAR  and partly at the  Jet Propulsion Laboratory, California Institute of Technology  under contract with the  National Aeronautics and Space Administration. L. S. is supported by a joint  CNES-Région Bretagne doctoral  grant and is a JPL JVSRP affiliate. P. K. is supported by a  NASA  fellowship and the SWOT mission. Data set was provided by  SNO-MEMO (CNRS, CNES, IPEV , http://www. meop. net/groups/france. html). We acknowledge  Baptiste Picard (CEBC)  for data processing and  Sébastien Hervé  ( IUEM ) for his help in improving the figures.
Pollen data from the DEEP core A drilling campaign within the scope of the International Continental Scientific Drilling Program (ICDP) was carried out as part of the project Scientific Collaboration On Past Speciation Conditions in Lake Ohrid (SCOPSCO) in 2013. Six parallel cores were recovered from the depocentre of the lake at 243 m water depth (DEEP site). A composite sequence representing an overall sediment depth of 569 spanning at least the last 1. 2 million years has been obtained  (Wagner et al. , 2017). According to the age model, the up-permost 247. 8 m of the DEEP core cover the last 637 ka  (Francke et al. , 2016). Palynological data has been published for the upper 200 m of the DEEP pollen record, covering the last 500 ka, with a time resolution of ca. 1600 years  (Bertini et al.
A cross-validation has been performed to determine the right number of components  (Ter Braak and Juggins, 1993). For both methods, we have used a modern pollen dataset containing more than 3088 samples from European and Mediterranean regions  (Peyron et al. , 2013). From this dataset, we have excluded those pollen samples collected in warm to hot steppes in order to improve the climate reconstruction during steppic phases  (Tarasov et al. , 1998). Moreover, Pinus has been excluded due to its overwhelming presence in the DEEP record that potentially masks climatically controlled environmental signals from other taxa. The differences between the two methods probably depend on the lack of present-day European analogues for some glacial vegetation formations. Five climate parameters have been reconstructed for the DEEP pollen record excluding Pinus (Fig.
Climate changes at Lake Ohrid: comparison with independent proxies and other climate reconstructions Our data are in agreement with climate signals depicted in geochemical data from the DEEP site  (Francke et al. , 2016; Wagner et al. , 2017)  and other Lake Ohrid cores (core JO2004 from the south of the lake; Bordon, 2008; core Co1202 from the north-east; Holtvoeth et al. , 2017; see Fig. 1 ;  Vogel et al. , 2010). When comparing our results to the Eemian climate reconstruction of JO2004  (Bordon, 2008) , the trends are similar, while some differences in temperature and precipitation values should be pointed out. They proba-bly result from differences in pollen assemblages due to the different positions of the analysed cores. Core JO2004 was retrieved from the southern part of the lake, closer to the lake shoreline.
Therefore, its pollen assemblages show increased values of local taxa and of those not dispersed over long distances; in contrast, these taxa are found in lower abundance or not at all in the central part of the basin from where the DEEP core was retrieved. Due to the central position of the DEEP and the morphology of the territory around the lake (vegetation organized in altitudinal belts) we assume that our climate reconstruction integrates the palynological signal of the surrounding mountain ranges and, consequently, our data accounts for a regional and not a local climate reconstruction. In Fig. 4 , the temperature and precipitation (PANN and TANN) signals are compared to the TIC and the total organic carbon (TOC) records from the DEEP core and to the TIC and TetraEther indeX of archaeal lipids (TEX 86 ) from core Co1202.
Data availability. Pollen data from the first 500 ka years of the sequence  (Sadori et al. , 2016)  are available through the online database PANGAEA at https://doi. org/10. 1594/PANGAEA. 892362 (Sadori et al. , 2018). Higher resolution pollen data  (Sinopoli et al. , 2018)  and climate reconstruction data (this paper) are not public yet as they will be part of further elaborations by authors of this article. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/cp-15-53-2019-supplement.
5 ± 2. 5 • C  (Landais et al. , 2016)  compared to pre-industrial levels. In spite of this strong warming, total gas content measurements from the Greenland ice cores at the Greenland Ice Sheet Project 2 (GISP2), Greenland Ice Core Project (GRIP), North Greenland Ice Core Project (NGRIP), and NEEM indicate an Eemian surface elevation no more than a few hundred meters lower than present (at these locations). NEEM data indicate that the ice thickness in northwest Greenland decreased by 400 ± 250 m between 128 and 122 ka with a surface elevation of 130 ± 300 m lower than the present at 122 ka, resulting in a modest sea level rise estimate of 2 m (NEEM community members, 2013). Nevertheless, coral-reef-derived global mean sea level estimates show values of at least 4 m above the present level  (Overpeck et al.
Experimental setup All simulations (forced with MAR-SEB and MAR-BESSI) run from 127 to 115 ka following the Paleoclimate Modeling Intercomparison Project (PMIP4;  Otto-Bliesner et al. , 2017)  experimental design and initiating the Eemian simulations at 127 ka with a modern GrIS. The thermal structure is derived using a thermal steady-state simulation with prescribed preindustrial temperature at the ice surface (from the regional climate model simulations) and an enthalpy formulation  (Aschwanden et al. , 2012)  at the base to determine the basal conditions (cold or temperate ice). At the base of the ice sheet, a prescribed geothermal heat flux  (Shapiro and Ritzwoller, 2004)  as provided by the SeaRISE dataset  (Bindschadler et al. , 2013)  is imposed. The basal friction coefficients are kept constant over time and are derived from an inversion of spatially varying, observed surface velocities.
Basal sensitivity experiments with changed basal friction are performed to investigate the importance of uncertainties related to basal friction. In order to minimize the number of 3-D higher-order experiments, a number of test experiments are performed with the simpler 2-D SSA configuration of ISSM to identify the range of basal friction coefficients which yield plausible results. For example, if the basal friction coefficients for the entire ice sheet are reduced by factors of 0. 8 and 0. 5 (in the 2-D SSA test experiments; not shown), the ice surface elevation at the NEEM location shows a late Eemian lowering of 300 and 800 m, respectively. Proxy data indicate a surface lowering of no more than 300 m (NEEM community members, 2013) at this point in time. In order to stay clearly within the proxy reconstructions, the friction for the entire ice sheet is reduced by a factor of 0.
Acknowledgements. The research leading to these results has received funding from the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement  610055  as part of the ice2ice project. The simulations were performed on resources provided by  UNINETT Sigma2 , the  National Infrastructure for High Performance Computing and Data Storage in Norway  ( NN4659k ;  NS4659k ). The publication of this paper was supported by the openaccess funding of the  University of Bergen. Petra M. Langebroek  was supported by the  RISES  project of the  Centre for Climate Dynamics at the Bjerknes Centre for Climate Research. Sébastien Le  clec'h acknowledges the financial support from the  French-Swedish   GIWA project , the  ANR   AC-AHC2 , as well as the ice- MOD  project funded by the  Research Foundation -Flanders (FWO-Vlaanderen ).
We thank  Joshua K. Cuzzone  for assisting in setting up the higher-order ISSM runs,  Michiel M. Helsen  for helping with the SMB gradient method, and  Basile de Fleurian  for helping to resolve technical issues with ISSM. Furthermore, we very much thank  Bas de Boer , an anonymous referee, and the editor  Arjen Stroeven  for their comments which significantly improved the manuscript. Financial support. This research has been supported by the  European Commission  ( ICE2ICE  (grant no. 610055 )). Review statement. This paper was edited by  Arjen Stroeven  and reviewed by  Bas de Boer  and one anonymous referee.
, 2001;  Fettweis et al. , 2006)  are available upon request from the corresponding author. The SeaRISE dataset used is freely available at http://websrv. cs. umt. edu/isis/images/e/e9/ Greenland_5km_dev1. 2. nc (last accessed: 18 October 2018). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/tc-13-2133-2019-supplement.
However, modern taxonomic descriptions of the relatively diverse Viséan faunas of Belgium  (Demanet, 1958)  are lacking, and thus palaeobiogeographical relations may be obscured. The current abence of Kansuella in Belgium and the Rhenish Mountains may be related to absence of sufficiently preserved material, which prevents the identification of this genus. Gigantoproductids are also reported from the western and eastern end of the Northern Variscides. Data from the Ossa Morena in SW Spain  (Martínez Chacón and Legrand-Blain, 1992)  are scarce, and the fauna is relatively poorly diversified. So far, common taxa with the Montagne Noire are Datangia semiglobosa and Balakhonia ostrogensis, although the Spanish record for the latter is slightly questionable (aff. ). The Sudetes, SW Poland, have yielded a medium diverse fauna of large productids.
Acknowledgements Eddy Poty is thanked for participating in brachiopod collecting in the field. Suzanne Jiquel  ( Montpellier University ) furnished photographs of figured P. latissimus in Böhm's collection. Comments and suggestions of  Shuzong Shen ,  Qiao Li ,  Miguel Angel Torres Martinez ,  Gabriela Cisterna ,  Bertrand Lefebvre ,  Gilles Escarguel  and an anonymous reviewer greatly helped to improve the manuscript. M. Legrand-Blain  thanks  Denis Bates  for providing advice on the English language.
The CMEMS IBI reanalysis has been produced by the CMEMS IBI Monitoring and Forecasting Centre (IBI-MFC) and, it is one of the multi-year data products used in the Copernicus Ocean State Report  (von Schuckman et al. , 2016 (von Schuckman et al. , , 2018 , which aims to provide regular and systematic reference information on the physical state, variability, and dynamics of the global ocean and European regional seas. In the framework of its second issue, the IBI-MFC proposed an ocean monitoring indicator to characterize the inter-annual variability in MOW in the IBI region  (Pascual et al. , 2018) ; to that purpose, an analysis of the circulation patterns, transports, and temporal variability in MOW was performed. The present work summarizes part of the results from the analysis done specifically for the design phase of the CMEMS MOW ocean indicator.
According to  Machín and Pelegrí (2009) , the northern limit of AAIW is located around 32 • N near the African continental slope; however, their presence northward of the Canary Islands is subjected to seasonal variability. The mixing processes between both water masses have been studied by  van Aken (2000) ; they conclude that the core of AAIW appears to contribute to the formation of MOW since it is entrained into the overflow near Gibraltar. Such entrainment gives rise to an enhanced concentration of the nutrients in the Mediterranean originated water in the North Atlantic. Several studies have focused on the temporal variability in the MOW and have concluded that their variability is within a wide range of timescales. Prieto et al. (2013)  analysed a dataset of semi-annual hydrographic stations located on the Iberian platform.
Their findings point out that despite a larger inter-annual variability, the seasonal signature represents 20 % of the inter-annual variability. This variability has been found along the continental slopes affected by MOW, and is a consequence of the variability of the slope current responsible of the northwards transport of MOW along the European continental slopes. In summertime, when the narrow jet is trapped along the continental slope, its thermohaline signature is reinforced near the slope and diminished its presence in the open ocean. In wintertime, the slope current weakens and can even be reversed in some areas  (Fricourt et al. , 2007; Prieto et al. , 2013; Pascual et al. , 2018). Variability at longer timescales has also been addressed by several studies. Potter and Lozier (2004)  analysed 40 years of hydrographic data to calculate temperature and salinity trends of the MOW reservoir.