dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The authors would like to thank the  Goddard Earth Sciences Data and Information Services Center  (http: //grace. jpl. nasa. gov)and the  Center for Space Research  (http: //www2. csr. utexas. edu/grace/RL05_mascons. html) for providing global datasets of the JPL and CSR mascon solutions and the total water content obtained from the  Noah  land hydrology model in the GLDAS v2 during 2002-2017. The authors would also like to thank the  University of East Anglia Climatic Research Unit  for providing the precipitation and temperature datasets, the  European Centre for Medium-Range Weather Forecasts  for providing evapotranspiration data, the  Database for Hydrological Time Series of Inland Water  for providing the sea level data, and the  Climate Prediction Center of the U. S.
, 2013; Noël et al. , 2019). Despite Greenland's importance, water vapor isotope monitoring has only been reported from four locations on the island  (Steen-Larsen et al. , 2013; Bonne et al. , 2014; Kopec et al. , 2014; Bailey et al. , 2015a) , and one record (Summit) has not had its data published. We present here a new multi-year dataset from Thule Air Base in northwest Greenland that has recorded stable oxygen and hydrogen ratios (d 18 O and dD, respectively) of ambient water vapor nearly continuously from 04 August 2017 through 31 August 2019. As a result, this record is the first reported for Greenland that continuously spans over two years, critically giving a second set of observations to derive annual patterns and anomalies. Thule Air Base has been a focus of High Arctic research for many years (e.
g. ,  Schytt, 1955; Mastenbrook, 1968; Sullivan et al. , 2008; Rogers et al. , 2011; Leffler and Welker, 2013; Schaeffer et al. , 2013) , and our observing station on northern Baffin Bay allows a focus on how changing seasonal and interannual sea ice coverage affect the local climate and water vapor isotopes. Our research addresses how interactions between the hydroclimate, cryosphere, and ocean are manifested in water vapor isotopes. The long period of record and very high frequency of isotopic (~1 observation per second, aggregated to 10 min) and meteorological observations (10 min) enable us to study water vapor isotope dynamics across varying temporal scales from diurnal to interannual. This Thule dataset and continuing observations at the base are part of the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) project's Arctic Water Isotope Network  (Welker et al.
Meteorological observations An automated weather station (SMT, for South Mountain), located on the roof of the building that houses the L2130-I, took a reading of air temperature, relative humidity (with respect to the saturation vapor pressure over ice), and station barometric pressure every 10 minutes throughout the duration of isotopic observations  (Muscari, 2018). For analysis at different temporal resolutions, these data were also aggregated into 1 h and daily means. The L2130-i takes a reading of the water vapor mixing ratio with every isotopic observation, and observations of this variable were aggregated to 10 min, hourly, and daily means and added to the SMT datasets. Hourly mean near-surface wind speeds and azimuths (USAF, 2019) were taken at the Thule Airport (THU), aggregated to daily values, and joined to the hourly and daily SMT databases.
While the THU recording station is 170 m lower than SMT, the two stations are less than 1 km apart and their meteorological data agree well (rtemperature = 0. 98, rdew. point = 0. 98, rstation. pressure = 0. 96). Daily climate teleconnection indices for the North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) were downloaded from the US National Weather Service Climate Prediction Center (NOAA, 2019b) and joined with the daily SMT database. Daily sea ice extent for Baffin Bay was obtained through the National Snow and Ice Data Center (NSIDC) Multisensor Analyzed Sea Ice Extent (MASIE) product  (Fetterer et al. , 2010). To reduce issues with the radial nature of azimuth data during correlation analysis, wind azimuths were converted to 'katabatic deviations' where an azimuth of 100° (i.
Statistical analyses Spearman correlations between isotopic and meteorological variables were calculated across the full datasets at all three temporal resolutions. Strong correlations that arise between variables in this analysis, though, may be spurious due to common responses to seasonal change. Likewise, variables that do not have a seasonal cycle, such as relative humidity, may have stronger correlations once the interference from seasonality is removed. To examine this, we removed the strong seasonal cycles of the isotopic values, air temperature, and mixing ratio by sinusoidal curves fitted to the annual cycle of each full data time series. With these seasonally-adjusted data, we calculated a second set of correlations. Data at each temporal resolution were also binned by month, and correlations then calculated for each month to examine seasonal changes in isotope-climate relationships.
General overview of isotopic results The mean annual d 18 O and dD values at Thule are -33. 0 ± 5. 9‰ and -248 ± 41‰, respectively (10 min data, mean ± 1 σ). Over the full dataset, d 18 O and dD have a strong linear relationship with low parameter standard error: dD = 6. 959 ± 0. 003 * d 18 O -18. 07 ± 0. 09‰ (r 2 = 0. 98, n=111138, 10 min data). Overall, this value is comparable to other slopes observed at other high latitude sites, such as 6. 8 at Ivittuut, Greenland,  (Bonne et al. , 2014 ), 6. 5 at NEEM, Greenland, (Steen-Larsen et al. , 2013) , 6. 0-6. 5 at Dome C, Antarctica  (Casado et al. , 2016) , and 6. 95 from the vapor mixing line at Kangerlussuaq, Greenland  (Kopec et al. , 2014). Changes in d 18 O are thus closely mirrored in dD, and most differences are only detectable on very short timescales (i.
The isotopic values also have very wide overall ranges, with d 18 O, dD, and dxs spanning over 30‰ (-49. 5‰ to -17. 5‰) 230‰ (-377‰ to -142‰), and 55‰ (-7. 6‰ to 47. 5‰) respectively, in our 10-minute database. Although diel cycles in isotopes are very small (generally <1‰ for d 18 O and dxs), the magnitude of irregular hourly to weekly variations in isotopic values can approach 30-50% of the entire annual range. Among meteorological variables, mixing ratio has the strongest correlation to all isotopic species (rd18O. 10min = +0. 83, rdD. 10min = +0. 79, rdxs. 10min = -0. 79) followed closely by air temperature (rd18O. 10min = +0. 76, rdD. 10min = +0. 72, rdxs. 10min = -0. 74). Katabatic deviation (i. e. , wind direction) (rd18O. hr = +0. 49, rdD. hr = +0. 46, rdxs. hr = -0. 50) and the NAO (rd18O.
For variables with relatively long-term variability, such as teleconnection indices, data from a single year is likely not long enough to reveal fully robust correlations. In our Thule data, we were fortunate that the synoptic patterns and NAO in 2018 and 2019 were different enough to allow analytical comparisons at monthly and seasonal resolutions. However, even two years of data are not enough to fully clarify the relationships between teleconnections and water vapor isotopes for all months, and we look forward to adding additional years of isotopic data in the future. Moving forward, this isotope and meteorological database offers many opportunities for more in-depth and focused analyses of specific weather events and atmospheric patterns. As previously stated, our identified moisture pulse events are a focus of.
Acknowledgments This project was funded by  NSF   Arctic Observing Network-ITEX 1504141 and Arctic Observing Network-EAGER MOSAiC 1852614  and supported in part by the  Inaugural UArctic Research Chairship  to JMW. We greatly thank the assistance of the  United States Air Force , the 821 st  Air Base Group at Thule Air Base, Vectrus , and  Polar Field Services  for logistical and hosting support throughout this research, and we thank  Giovanni Muscari  for graciously providing the SMT meteorological data. Special thanks go to the 821 st weather squadron for meteorological data collection and to  Shawn Arnett ,  Rich Biggins ,  Devin Brewer ,  Joe Burns ,  Matthew Burns ,  David Craig ,  Jarrod Dodgen ,  David Drainer ,  John Gaston ,  Missa Goldun ,  Pablo Londono , and  Josh Neighbours  for onsite assistance and maintenance.
Thus, Alderney Race sea states are often complex, with superposition of swells and wind-seas. Maisondieu (2016)  performed statistical analyses based on the HO MERE database  (Boudiere et al. 2013)  for the period between 2003 and 2012. The results were : i) about 40% of sea states had at least 3 swells, ii) about 30% of sea states had at least 2 swells and 1 wind-sea, and iii) about 20 % of sea states have at least 1 swell and 1 wind-sea. Furthermore, a chaotic sea was observed when the tidal current and wind direc tions were opposite, with wave heights of about 4 m and wavelengths shorter than 50 metres. Complex sea states also occured and were recorded during the HYD2M experiments, with significant wave heights of about 8 m. Wave breaking is often observed in Alderney Race due to the interactions between waves and the tidal current, leading to the French name 'Raz Blanchard' (In English : 'White Race', named for the frequent white caps in this area).
ADCP was located at 49°40/50. 00" N/2°01/46. 44" W. The estimated mean depth was about 35 m. The bin size was 1 m and the lowest cell was 2 m above the seabed. ADCP data were collected from 14 October 2017 to 26 February 2018, but only days in the period of 21 to 25 November 2017 are considered here. ADCP recorded ocean wave characteristics in addi tion to measurements of vertical profile of the three components of the flow velocity. All ADCP data were 15min-averaged. This means that high frequency variations, particularly due to turbulence, were not taken into account in this study, but were presented in  Furgerot et al. (2018). Data from Met-Office wave buoys (62103 and 62027), available on EMODnet platform (http ://www. emodnet. eu), were also used to validate the wave model, but comparison plots are not shown here.
The simulated mean sea level was tested against measurements of Shom tidal gauges installed in Cherbourg (TG1, recordings from 1943 to now) and Dielette (TG2, recordings from 2015 to now). Data are downloadable via the datashom portal (https ://data. shom. fr). Tidal gauge locations are marked in black on Figure  1a. Wind data were collected by Goury Semaphore, that is located 7km apart ADCP point, at 10 meters above ground level. The studied time period is representative of typical conditions in Alderney Race, except for extreme events. The met-oceanic conditions were : i) a tidal range between 4 and 7 m, ii) a tidal current varying from 0. 2 m/s to 3 m/s, iii) a significant wave height ranging from 0. 5m -4. 5m (with wind-waves and swells), and iv) a wind speed less than 18m/s.
4// W -50°27/0. 0// N/0°43/12. 0// W, and ii) for child grid : 49°04/48. 0// N/2°56/56. 4// W -50°4/12. 0// N/ 1°23/24. 0// W. All MARS simulations are in three dimensions with 12 sigma levels over the vertical. The wave model employs 32 frequencies from 0. 04 Hz to 0. 7678 Hz and 24 directions leading to a directional step of 15 degrees. Open boundaries of MARS are forced with the Shom CST France atlas that uses 114 tidal components  (Leroy and Simon 2003). WW3 utilises wave spectra of the HOMERE and Ifremer databases  (Boudiere et al. 2013 ) at its open boundaries. WW3 is forced by NCEP winds from CFSRR re-analysis. The deployment of child grids requires 2D-wave spectra, water levels and flow velocity from their parent grids at boundaries. All runs are coupled, with a one-way/two-way coupling for parent and child grids.
In addition, the maximum value of 30 min often corresponded to the reverse tide occurring sooner in the in-situ dataset. At this time, waves effects on the current were strongest. To investigate the role of waves and wind, measurements of the near-bed wave orbital velocity are required. Furthermore, time delay could be partially corrected by expressing zm (Eq. (  5 )) as a fraction of the mean depth (D). In this study and after many simulations, the fraction was set to 5%. Some sensitivity tests were performed with zm at 50%  (Uchiyama et al. 2009) , 26. 7%, 11. 4%, 5. 71%, 2. 67% and 1. 33% of D and the related velocities are presented in Figure  11. A one-hour delay was observed, as shown on Figure  11 , between both extreme cases (50% and 1. 33% of D), highlighting the link between the bottom stress formulation and time delay.
Acknowledgments Authors are supported by the  HYD2M  project ( ANR-10-IEED-0006-07 ) funded by the program called " Investissements d'avenir"  for the building of  France Energies Marines. Au thors acknowledge  L. Coquart ,  V. Garnier ,  M. Accensi  and  A. Thevenin  for their technical helps. Authors are grateful to  F. Ardhuin  for providing the WAVEWATCH-III forcing fields.
3-25. 50 m core depth (Table  1 ). The calibration of conventional radiocarbon ages is based on the IntCal13 dataset  (Reimer et al. , 2013 ) using OxCal 4. 2. (Bronk Ramsey, 2009). All ages are expressed in 2-sigma calibrated years BP. The age-depth model was constructed using a smoothing spline function in PAST (PAlaeontological STatisticversion 3. 10 by  Hammer et al. , 2001). Differential sediment compaction was not taken into account, thus sedimentation rates were estimated on the basis of radiocarbon ages.
Statistical analysis Following  Tarasov et al. (1998)  and  Kaniewski et al. (2011) , we performed statistical analyses to highlight pollen-derived vegetation biomization and support the identification of pollen groups. Spermatophyte and Pteridophyte taxa were calculated in PAST and grouped using Cluster Analysis. Correlation was used as similarity method to explore the temporal proximity of taxa in a facies-heterogeneous stratigraphic succession, while Paired group was used as the root by suggesting the local provenance of all identified taxa. In order to streamline the data and obtain the most informative groups, sixty occasional taxa, recorded in less than three samples with percentages lower than 1%, were excluded. The robustness of this choice was tested by running several analyses using different selections of taxa.
Pollen data Pollen data from core EM2 reveal a high floristic richness. Specifically, 189 taxa were identified at different taxonomic levels and raw counts are available at Mendeley Data. Consistent with the organicrich nature of sediments, the state of preservation of pollen grains is good and the number of undeterminable grains varies from 0% to ca. 15%. The relative abundance of secondary grains reworked from older successions is generally lower than 10%.
Two events of Mt expansion (PZs 5 and 3) interrupt a pollen spectrum indicative of a mesic holm oak-mixed oak forest developed laterally to hygrophilous prairies and herbaceous wetlands (PZs 6 and 4). Specifically, PZ 5 exhibits a high concentration of Mt (ca. 26%) showing almost all taxa found in the studied succession (except for Pinus sylvestris) and accompanied by most of broadleaves. A relative increase in montane taxa is also recorded within the PZ 4, but it is not paralleled by a significant drop in Mediterraneans. PZs 2-1 (uppermost 7 m; < 3700 cal. yr BP): pollen data in PZ 2 point to the establishment of a holm oak-mixed oak forest without a significant peak in Mt. A peculiar pollen assemblage indicative of a disturbed environment with a strong reduction in forest cover to the benefit of pm (mainly Cichorioideae) and a remarkable peak in montane taxa are found within PZ 1.
Acknowledgements MC acknowledges funding from the  Marco Polo Programme  ( University of Bologna ) for research mobility and fruitful collaborative research work. This research was possible thanks to a collaborative research project supported by  ExxonMobil Upstream Research Company  (grant to  A. Amorosi ) and to collaboration with the team of palynologists and archaeobotanists at " Centro Agricoltura e Ambiente G. Nicoli" laboratory (Italy). We are grateful to  N. Marriner  for the English language editing and revision, and to  M. Vacchi  for providing the highresolution image of W Mediterranean RSL index points. A special thank goes to  C. Ferrari , who provided several interesting papers on ER regional vegetation, and to  A. Chiarucci , who deepened our knowledge of the PNV concept. We are also very thankful to the reviewers and Editor, who provided careful reviews of the manuscript and greatly improved it through their comments and remarks.
Identifying factors influencing survivorship is key to understanding population persistence. Although satellite telemetry is a powerful tool for studying remote animal ecology and behaviour it is rarely used for demographic studies because distinguishing the death of the animal (individual mortality) from failure of the tag (mechanical tag failure) has proven difficult. Southern elephant seals present an opportunity to separate tag failure from animal mortality thanks to the availability of large tracking datasets, broad knowledge of demographic rates, and because for these large animals, satellite tags are known not to influence mortality rates. A key rationale for investigating satellite telemetry to estimate mortality as compared to using traditional Capture-Mark-Recapture methods is the potential for obtaining spatially and temporally specific information, particularly while the animals are at sea and largely unobservable.
, 2016). Tracking data also showed how the cumulative influence of multiple stressors reduced the rate of successful migration for the sockeye salmon (Oncorhynchus nerka)  (Naughton et al. , 2005). The main obstacle in the use of tracking data to estimate mortality rates is that even in the absence of catastrophic battery failure  (Smith et al. , 2018) , an unknown proportion of tags will stop transmitting simply due to failure of the tag itself, which can be as high as 25%, at least in terrestrial studies  (Hofman et al. , 2019). Southern elephant seals provide an opportunity to investigate whether high spatially and temporally resolved at-sea mortality information can be inferred from satellite tracking data due to the availability of large multi-year tracking datasets  (Hindell et al. , 2016) , broad knowledge of demographic rates (Le  Boeuf and Laws, 1994; McMahon et al.
Data from the tag is transmitted via the ARGOS satellite network, relaying location and behavioural information  (Myers et al. , 2006). Location estimates were filtered using a state-space model with a correlated random walk and a 6-h time step (using R package BSAM), for details see  Jonsen et al. (2019). For each seal position, we attributed a bathymetric depth, based on the ETPOPO1 bathymetric data set, 1  and a temporally explicit sea ice concentration at that location on that day using the blended optimal interpolation Version 2 ECDC AMSR + AVHRR 0. 25degree global ice charts from the NASA Coastwatch site. 2  Only tracks which extended beyond a 30 km radius of the deployment location were included in this study, to remove seals from the analysis which did not begin a foraging trip. There were two possible outcomes for each tag; either, it continued to transmit until it returned to land after the foraging trip (censored), or transmissions terminated at sea (failed).
Surface duration is transmitted by the tag at the completion of dives, and we used this to calculate the mean surface duration per day (individually for each seal). A Linear Mixed-Effects (LME) model  (Lindstrom and Bates, 1988)  with a random variable (individual seals) and CorAR1 autocorrelation term was used to determine whether failed tags had; (i) higher daily transmission rates or (ii) longer mean daily surface duration [lme(surface duration/transmission rate ∼ outcome (failed or censored) * sex, random = ∼1| individual seal, corr = corAR1(), data = d)]. The model was simplified based on results from likelihood model selection using function dredge().
Locations of At-Sea Tag Failure A total of 182 individual tag deployments were included in the analysis. Seventy-six (42%) of these were censored, (i. e. , they transmitted data throughout the deployment period) while the remaining 107 (58%) failed at sea. For our study, sub-adult male seals (n = 75) predominantly used shelf areas on the Kerguelen Plateau (exposure to benthos) or the Antarctic continental shelf (exposure to the benthos and sea-ice). In contrast, female seals (n = 107) predominantly used oceanic regions and performed more deep dives resulting in greater exposure to high pressures (Figure  1 ). The locations of the final transmission of failed tags did not appear to be associated with sea-ice extent (exposure to ice, purple frequency distribution), exposure to the benthos (blue locations) or high pressures (green locations, Figure  1 ).
Transmission Rate and Surface Duration The mean number of transmissions per day was higher (LME: d. f. = 167, p = 0. 003) for tags which failed (Figure  2A ). However, failed tags had the same mean surface durations as the censored tags (d. f. = 169, p = 0. 713, Figure  2B ). No significant relationship was found for sex or the interaction term (sex:outcome) in either transmission rate (p = 0. 223, 0. 535, respectively) or surface duration (p = 0. 075, 0. 060, respectively). We are confident the interaction term (sex:outcome) is not inhibiting interpretation of these results, as the dataset is large, there was no strong evidence for an interaction term contributing to model fit and p-values remained high (p ≥ 0. 06). Testing of the residuals suggested assumptions of normal distribution were met.
resource allocation  (McMahon et al. , 2017)  and reproductive output  (van den Hoff et al. , 2014; McMahon et al. , 2015; Oosthuizen et al. , 2015; Clausius et al. , 2017)  are compromised by poor foraging conditions. The cost of reproduction to females is high, and they lose an average of 35% of their mass over the 24-day lactation period  (Fedak et al. , 1996). The ability of females to subsequently recover these resources will be challenged if available foraging resources are limited (as in years of negative SAM) which may cause high rates of mortality in these years. We suggest that increased tag failure may be indicative of increased mortality in negative SAM years for females, although the high confidence intervals overlapped (Figure  5 ). An accompanying CMR dataset of the Isles Kerguelen southern elephant seal population during the study period would provide further valuable insights into tag failure and mortality.
g. , wind bursts, tropical cyclones) due to so-called barrier layer effect (e. g. ,  Lukas and Lindstrom, 1991; Balaguru et al. , 2012). This suggests that mixed layer salinity can actively impact air-sea interactions from local to synoptic scales. Given its importance for many key ocean and climate processes, the SSS has been recognized as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS) program. Bingham et al. (2002)  examined the global distribution of historical  in situ SSS observations measured at 5 m or less in depth from the World Ocean Database 1998 (WOD98). Their results give a good indication of how poorly SSS was sampled by the end of the 1990s (Fig. 1 ), despite a peak period of ocean sampling (including near surface salinity) during the World Ocean Circulation Experiment  (WOCE, 1990 (WOCE, -1998)).