dataesr/scientific-paragraphs-categorization huggingface dataset:
Study area and sampling Samples analyzed in the present study originated from brown bear populations sampled within the Scandinavian Brown Bear Research Project. We used data from two large areas in Sweden. The southern study area consisted of ,13,000 km 2 centered on 61 ˚N, 15 ˚E and the northern area consisted of ,8,000 km 2 centered on 67 ˚N, 17 ˚E. All captures were approved by the Swedish Ethical Committee on Animal Research in Uppsala (application numbers C212/9 and C47/9) and the Swedish Environmental Protection Agency (Dnr 412-7327-09 Nv). The capture and sampling protocols are described thoroughly in Arnemo et al. 2011; http://www. bearproject. info/uploads/publications/2011%20Biomedical% 20Protocols%20Carnivores. pdf. Genetic samples were obtained from yearlings captured with their mothers, because we did not capture young-of-the-year for ethical reasons  [52].
This means that we did not obtain samples from young that died during their first year of life. Cub mortality is, however, mostly explained by sexually selected infanticide. Indeed cub mortality is much higher in the southern area (35%), where male turnover is high due to hunting, but usually (68% of the cases) results in the loss of whole litters  [51] , so such cases were not entered into our dataset. In the more socially-stable northern population, mortality is lower (4%). Thus overall, cub mortality had little effect on paternity assignment. The study was carried out on both private and public land in Sweden. The Swedish Environmental Protection Agency's capture permit is general and valid for all of Sweden, including public and private lands. Thus, prior permission from private landowners was not required.
We used estimates of paternity, relatedness, and heterozygosity, obtained by Bellemain et al. [47]  on the basis of a set of 18 microsatellite loci, to discriminate between successful and unsuccessful males and to control for genome-wide similarity between mating partners and the genome-wide heterozygosity of males. Of the 114 males that sired progeny (henceforth: successful males), two shared paternity with other males, and the remaining males sired all of the cubs in the litter. The final dataset consisted of genotypes for 912 female-potential mate pairs, 114 of which were female-successful mate pairs, and 788 were female-unsuccessful potential mate pairs.
Statistics To analyse the effect of MHC similarity on the probability that a male sired the young of a given female, we used generalized linear mixed models implemented in the MCMCglmm package in R  [57] , with paternity as a binomial response variable, MHC similarity (either AA distance or proportion of shared alleles) as a predictor, relatedness, and body size as covariates, and female id, male id, and year as random factors. Similar models were run to check whether the number of MHC alleles affected male paternity, but with male multi-locus heterozygosity at 18 microsatellite alleles as a covariate. We also entered a quadratic relationship between the number of alleles possessed by a male and mating success to test if males with an intermediate number of alleles achieved the highest mating success.
Results We found 43 MHC class I alleles, including 11 alleles belonging to pseudogene clusters and 4 alleles from putative non-classical loci. Six alleles have not been reported previously (GenBank accession no KM242064-KM24206). Only 28 putatively expressed alleles from classical MHC I genes were included in our analyses. There were 2-7 such alleles per individual. For DRB, we found 17 alleles in our sample, including 1 allele that had not been reported previously (File S1). Individual bears carried 2-4 DRB alleles. The proportion of alleles shared between females and potential mates ranged between 0 and 33% for MHC I and 0 and 50% for MHC II DRB. AA distances ranged between 0. 100 and 0. 189 for MHCI and 0. 037 and 0. 239 for DRB. Mean proportions of MHC I alleles shared between a female and her potential partners are shown in Table  1.
This work was supported by grant  N N303 457338  from the  Polish Ministry of Science and Higher Education  to JR. The  Scandinavian Brown Bear Research Project (SBBRP)  is funded by the  Swedish Environmental Protection Agency ,  Norwegian Directorate for Nature Management, Swedish Association for Hunting and Wildlife Management , the  Austrian Science Foundation , and the  Research Council of Norway. SPYGEN provided support in the form of salaries for authors EB and EV, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the 'author contributions' section.
Since September 2011, a wavelength-scanned cavity ring-down spectroscopy analyser has been remotely operated in Ivittuut, southern Greenland, providing the first record of surface water vapour isotopic composition based on continuous measurements in South Greenland and the first record including the winter season in Greenland. The comparison of vapour data with measurements of precipitation isotopic composition suggest an equilibrium between surface vapour and precipitation. δ 18 O and deuterium excess are generally anti-correlated and show important seasonal variations, with respective amplitudes of ∼ 10 and ∼ 20 ‰, as well as large synoptic variations. The data depict small summer diurnal variations. At the seasonal scale, δ 18 O has a minimum in November-December and a maximum in June-July, while deuterium excess has a minimum in May-June and a maximum in November.
In recent years, new types of infrared laser spectrometers have been developed, allowing high-frequency in situ measurements of water vapour isotopic composition. If an appropriate measurement and calibration protocol is applied  (Tremoy et al. , 2011; Aemisegger et al. , 2012; Steen-Larsen et al. , 2013) , these instruments reach a precision close to classical instruments based on isotope-ratio mass spectrometry. In the Arctic region, data have been reported for Siberia, showing large-magnitude synoptic events  (Gribanov et al. , 2013). A recent monitoring effort has been implemented at the NEEM Greenland ice core drilling site. During summer, precipitation, surface water vapour and surface snow were sampled for isotopic measurements  (Steen-Larsen et al. , 2011 , 2013 , 2014a) , in order to better understand the postdeposition processes affecting the ice core signals.
Meteorological data High-frequency (around 1 Hz) meteorological measurements have been obtained at Ivittuut station since September 2007. Temperature and relative humidity are measured by a Vaisala humidity and temperature Probe HMP155 (situated 1. 8 m above ground), wind direction and speed by a Young 05603B sensor (situated on the roof top, about 3. 5 m above ground), and pressure by a Druck RPT 410 sensor (1. 8 m above ground). A failure of the sensor acquisition card led to several gaps in the acquisition of the data during the observation period (see Sect. 3). Complementary historical meteorological information is available from the measurements at Grønnedal with the framework of GNIP/IAEA, from 1961 to 1974 (available at http://nucleus. iaea. org/CIR/CIR/GNIPIHIS. html, 2013).
Precipitation stable water isotopes data and equilibrium vapour isotopic calculations Monthly historical isotope observations of precipitation in Grønnedal are available through the GNIP/IAEA network from 1961 to 1974 inclusive. In September 2011, two rain gauges were installed, one at Ivittuut and one at Grønnedal. Water is sampled manually at an approximately weekly frequency for the Grønnedal rain gauge and an approximately bi-weekly frequency for Ivittuut rain gauge. Water samples are stored in glass bottles closed with plastic caps and packed in cellophane film after sampling. They are then shipped and analysed at LSCE using a mass spectrometer or Picarro liquid water isotope analyser, with accuracies of 0. 7 ‰ for δD p and 0. 025 ‰ for δ 18 O p (resulting in 0. 9 ‰ for d p ). The type of precipitation (liquid or snow) is recorded, depending of the water phase in the collector at the time of sampling, as well as the precipitation amount.
Water vapour isotope monitoring A Picarro brand model L2120-i wavelength-scanned cavity ring-down spectroscopy (WS-CRDS) analyser, hereafter Picarro, was installed in September 2011 at Ivittuut station, in the basement of the building to ensure a better temperature stability in the room. The experimental design and data treatment procedure are similar to those described in  Gribanov et al. (2013). Ambient air is sampled through a 10 m long and 3/8 inch diameter O'Brien analytical True Tube EPS (inox 316L chromium electropolished), a material chosen to limit wall effects between the inner tube and the water vapour in the air sample. Air is sampled about 5 m a. g. l. The sampling line is temperature autoregulated (Wheatstone bridge) and protected against rain, snow and insects by a hard cover and a net.
Independent humidity measurements The Picarro humidity mixing ratio values are compared to those derived from the meteorological sensor relative humidity and temperature measurements. Hereafter, the humidity measurements are reported in water vapour molar mixing ratio (in mmol water mol -1 dry air ). As seen in Fig. 4 , hourly averaged data from those two independent sensors over the full data sets from 21 September 2011 to 31 May 2013 are fitted by the following second-order polynomial function with a high determination coefficient (R 2 = 0. 97): y = 0. 013 x 2 + 0. 77 x -0. 25, (4) where y gives meteorological sensor mixing ratio and x the Picarro mixing ratio. In the 1-18 mmol mol -1 range, our comparison shows a nonlinear relationship between the Picarro instrument and the meteorological sensor.
The model has a uniform resolution of 3. 75 • in longitude and 2. 5 • in latitude and 19 vertical levels. The simulation was nudged by three-dimensional fields of horizontal winds of ECMWF operational analyses for the period after 2002, and of ERA-40 for the period 1961-1974, ensuring realistic synoptic and long-term variability. Risi et al. (2010a)  observed no noticeable discontinuity linked to the change of nudging atmospheric data. Daily outputs, for comparison with Ivittuut observations, are provided for the September 2011 to December 2012 period. Monthly outputs over the 1961-1974 period are used for comparison with GNIP/IAEA data presented in Supplement Sect. A. Hereafter, grid cell coordinates correspond to the western border of the cell for longitudes, and to the center of the cell for latitudes (LMDZiso model convention).
Concerning precipitation isotopic measurements, δ 18 O p values are in the range of -20 to -5 ‰, with quite low values compared to the GNIP/IAEA mean seasonal cycle (described in Supplement Sect. A). d p varies from about -5 to +20 ‰, with higher values for snow samples in autumn 2011, compared to the climatological average. During winter, snow precipitation samples show generally higher d p than liquid precipitation. If the liquid (snow) samples correspond to liquid (ice) clouds, this d p difference between snow and liquid samples might reflect the different equilibrium fractionation coefficients for solid or for liquid phases. Unfortunately, no direct cloud observations are available at Ivittuut. However, this hypothesis is consistent with remote sensing data suggesting the predominance of low-altitude clouds in this area  (Liu et al.
For δ 18 O v , our data show that its variability is strongly driven by changes in local air temperature and humidity, as expected from Rayleigh distillation. However, we observe relatively small δ 18 O v -T slope (0. 37 ‰ • C -1 ) and changing relationships through seasons, likely due to seasonal shifts of moisture sources. Temperature variations only explain at best (in autumn) half of the δ 18 O v variance. We report specifically a divergence between δ 18 O v and temperature during the summer 2012 heat wave, associated with long-distance transport from an exceptional atmospheric river. The comparison between the isotopic measurements at Ivittuut with those acquired above the ice sheet (e. g. NEEM,  Steen-Larsen et al. , 2013)  will offer an isotopic benchmark to test the parameterizations of atmospheric models for this event.
The response function of the measurements against humidity suggests effects due to the use of DRIERITE in our calibration introduction system, for low humidity levels. While this does not impact the results reported here, further studies would benefit from an alternative calibration method using e. g. dry air cylinders. Testing the robustness of our findings (such as the d v peak in autumn) over longer time periods motivates an extension of the surface water vapour monitoring in Ivittuut over several years. This would make it possible to investigate the local fingerprint of changes in weather regimes and explore inter-annual variations. The mismatch between LMDZiso simulated d v and our data should be further investigated. The fact that LMDZiso simulates too small d v variations at Ivittuut could be linked either to an underestimation of the variability of d v at the moisture source and/or to an inadequate representation of the moisture sources mixing.
Acknowledgements. Thanks to partnerships established with local authorities ( Kommuneqarfik Sermersooq ) and the  Danish navy (Grønland Kommando, GLK) , the atmospheric observatory has been installed with logistical support from both sides. Precious logistic help has been provided to the station by Grønland Kommando since 2007. We want to thank them greatly for this very fruitful collaboration. We also acknowledge  Sonia Falourd  and  Bénédicte Minster  for the isotopic measurements of precipitation samples. We finally warmly acknowledge the helpful comments and suggestions of  Amaelle Landais ,  Guillaume Tremoy  and  Françoise Vimeux. LMDZ simulations were performed on the NEC supercomputer of the IDRIS computing centre. This research was funded by the  ANR CEPS Green Greenland  project (grant number  ANR-10-CEPL-0008 ).
Wetlands are a major emission source of methane (CH 4 ) globally. In this study, we evaluate wetland emission estimates derived using the UK community land surface model (JULES, the Joint UK Land Earth Simulator) against atmospheric observations of methane, including, for the first time, total methane columns derived from the SCIAMACHY instrument on board the ENVISAT satellite. Two JULES wetland emission estimates are investigated: (a) from an offline run driven with Climatic Research Unit-National Centers for Environmental Prediction (CRU-NCEP) meteorological data and (b) from the same offline run in which the modelled wetland fractions are replaced with those derived from the Global Inundation Extent from Multi-Satellites (GIEMS) remote sensing product. The mean annual emission assumed for each inventory (181 Tg CH 4 per annum over the period 1999-2007) is in line with other recently published estimates.
of methane. We obtain improved agreement with the surface concentration measurements, especially at high northern latitudes, compared to previous HadGEM2 runs using the wetland emission data set of  Fung et al. (1991). Although the modelled monthly atmospheric methane columns reproduce the large-scale patterns in the SCIAMACHY observations, they are biased low by 50 part per billion by volume (ppb). Replacing the HadGEM2 modelled concentrations above 300 hPa with HALOE-ACE assimilated TOM-CAT output results in a significantly better agreement with the SCIAMACHY observations. The use of the GIEMS product to constrain the JULES-derived wetland fraction improves the representation of the wetland emissions in JULES and gives a good description of the seasonality observed at surface sites influenced by wetlands, especially at high latitudes.
Wetland emissions are particularly sensitive to climate change  (O'Connor et al. , 2010; Melton et al. , 2013). Gedney et al. (2004)  concluded that the wetlands model used in the Joint UK Land Earth Simulator (JULES, the UK community land surface model) would lead to a doubling of CH 4 emissions from wetlands by 2100 for the IPCC IS92a scenario considered. As a major emission source of CH 4 which responds strongly to climate change, it is vital that the description of wetlands and the associated emissions of CH 4 used in land surface and climate models reflects current understanding and the implications of emerging data sets. In this paper, we use atmospheric observations of CH 4 (surface concentrations and total columns derived from the SCIAMACHY instrument) to evaluate simulations of the Hadley Centre's Global Environmental Model (HadGEM2,  Collins et al.
Land surface module JULES is a physically based model that describes the water, energy and carbon balances and includes temperature, moisture and carbon stores  (Best et al. , 2011; Clark et al. , 2011). JULES can be run as a stand-alone model using appropriate driving meteorological data or as the land surface component in UK climate or earth system models (note that HadGEM2 strictly uses the Met Office Surface Exchange System, an earlier version of JULES, as the land surface component). JULES uses a tiled approach to describe subgrid-scale heterogeneity. Nine surface types are used, of which five are vegetation-related. The fractions of surface types within each land-surface grid box can either be modelled or prescribed. Air temperature, humidity, wind speed and incident radiation above the surface and soil temperatures and moisture contents below the surface are treated as homogeneous across a grid cell; other parameters are calculated for each surface type.
Active microwave backscattering at 5. 25 GHz from the ASCAT scatterometer (the original method used the scatterometer on board the European Remote Sensing (ERS) satellite) is very sensitive to vegetation density  (Prigent et al. , 2001a). These measurements are used to assess vegetation contributions and to quantify the fraction of inundation within the pixel. The GIEMS data set is now available on a monthly basis from 1993 to 2007 globally, and mapped on an equal area grid of 773 km 2 (equivalent to 0. 25 • × 0. 25 • at the equator)  (Prigent et al. , 2012). This and the earlier data sets have been thoroughly evaluated by comparison with other static estimates of wetland extent. This product is the only dynamic estimate available. It has also been compared with related hydrological variables such as rain rate, river gauges and river heights  (Prigent et al.
SCIAMACHY atmospheric column methane Atmospheric column-averaged CH 4 dry-air mixing ratios (XCH 4 in ppb) are available from the SCIAMACHY instrument on the ENVISAT satellite  (Schneising et al. , 2009 (Schneising et al. , , 2011)). The SCIAMACHY data product used in this study was retrieved from nadir measurements using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS) processing algorithm (version 2. 3, WFMDv2. 3). WFMDv2. 3 is an improved version of WFMDv2. 0. 2  (Schneising et al. , 2011 (Schneising et al. , , 2012)) , using a correction factor depending on simultaneously retrieved water vapour abundance (from the same fitting window as CO 2 , which is used as a proxy for the light path) to account for spectroscopic interferences. The WFM-DOAS algorithm is one of the algorithms currently being compared in the European Space Agency (ESA) project: Greenhouse Gases Climate Change Initiative (GHG-CCI;  Buchwitz et al.
, 2013). The SCIAMACHY data set has been validated and its relative accuracy, a quality measure quantifying regional biases, is 7. 8 ppb  (Dils et al. , 2014). The SCIAMACHY XCH 4 data set was provided on a 0. 5 • × 0. 5 • grid at monthly intervals for the time period 2003-2009. It was regridded to the spatial resolution of the HadGEM2 model to enable direct comparison with the model.
HALOE-ACE assimilated TOMCAT The Halogen Occultation Experiment (HALOE,  Russell et al. , 1993)  provides solar occultation observations of a range of trace gases including CH 4  (Park et al. , 1996)  from September 1991 until November 2005. Observations were obtained at about 15 sunrise and sunset locations per day. The Atmospheric Chemistry Experiment (ACE,  Bernath et al. , 2005)  was launched onboard SCISAT-1 in August 2003 and since then has been providing solar occultation observations of trace gases including CH 4  (De Mazière et al. , 2008). Despite the geographical sparseness of these data sets, the long atmospheric lifetime of CH 4 means that these solar occultation data are sufficient to constrain a stratospheric chemical transport model (CTM) through data assimilation (see  Chipperfield et al.
Wetland methane emissions For their CH 4 wetland emissions, O'Connor et al. (  2014 ) aggregate the wetlands, bogs, swamps and tundra components in the data set of  Fung et al. (1991) , available from http://data. giss. nasa. gov/ch4_fung/. This aggregated wetland emission data set (totally 181 Tg CH 4 yr -1 ), together with the other CH 4 emission sources used, was found to give very reasonable atmospheric CH 4 lifetimes and burdens and global mean concentrations, and reasonably good comparisons with in situ surface atmospheric observations. One of the runs undertaken in this study made use of this inventory (denoted FUNG below). We now believe our use of the data set to be incorrect. The components in the data set represent two different emission scenarios with different assumptions on seasonality  (Fung et al.
, 1991). We also use the version of the Fung inventory produced for the TRANSCOM-CH 4 study  (Patra et al. , 2009 (Patra et al. , , 2011)) , denoted TRANSCOM-FUNG below. This was rescaled to give a global annual emission flux of 181 Tg CH 4 yr -1 , as this was the nominal total wetland emission used in previous work. The other runs reported here use methane wetland emissions derived from an offline global run of the JULES land surface model (see Sect. 2. 1. 3), driven with CRU-NCEP meteorological data  (Viovy and Ciais, 2009) , for 0. 5 • ×0. 5 • terrestrial grid squares (denoted JULES). A second emission estimate is derived from this offline JULES run by replacing the modelled wetland fraction in Eq. (  1 ) with the wetland fraction derived from the regridded GIEMS product (denoted JULES-GIEMS).
Other emissions We generate year-and month-specific emission data sets for the period from 1997 to 2009 for the emitted species in the UKCA standard tropospheric chemistry scheme (see Sect. 2. 1. 2). The approach adopted varies depending on the source sector: -Anthropogenic: year-and month-specific emission data sets are derived from the decadal-averaged emission inventories compiled by  Lamarque et al. (2010) , by scaling the emission totals for the different years and source sectors using sector and species-specific scaling factors based on the annual trends given in various EDGAR time series. -Biomass burning: year-specific emission inventories are available from the Global Fire Emissions Database (GFED, v3. -Other: data on sources such as termites and hydrates for CH 4 and oceanic emissions of CH 4 and other volatile organic compounds are taken from various sources, as described in O'  Connor et al.
Acknowledgements. This work was supported from a number of sources: (a) the  European Space Agency through its Support to Science Element initiative  ( ALANIS Methane ),  Climate Change Initiative on Greenhouse Gases (GHG-CCI ) and CARBONGASES; (b) the  Centre for Ecology and Hydrology 's  Science Budget Programme ; (c) the  UK National Centre for Earth Observation ; and (d) the  Joint DECC/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ). We gratefully acknowledge the support of these funding bodies. We are grateful to  N. Viovy  and  P. Ciais  for providing the  CRU-NCEP  meteorological data set used to drive the JULES model. We are also grateful to  P. Bousquet  for providing the inverse emission estimates. G. D. Hayman  acknowledges the assistance provided by  L. Abraham  from the  UKCA team at the University of Cambridge  with the HadGEM model and ancillary data sets.