dataesr/scientific-paragraphs-categorization huggingface dataset:
Rather, anomalies are amplified intermittently compared to observations or one reanalysis departs for several years from the ensemble. At Amundsen Scott Station (South Pole), observations exhibit variations of higher amplitude than the reanalyses. The extreme climate in our only inland station must have been a test to both measurements and reanalyses. We now examine the long term evolution of moisture transport in terms of linear trends, this time analysed along the horizontal dimension (Fig. 10 ). Most longitudes show no statistically significant change in the advection. Among those that do, even fewer display an agreement between datasets. Mirniy station and its surroundings to the east has seen less moisture travelling inland according to observations, NCEP CFSR and MERRA 2. The other reanalyses agree in sign but not in significance.
The sign of the linear trend for moisture convergence is not consistent. In any case, changes are not driven by precipitable water increases. The scarce situ observations and the unreliable satellite observations  (Bouchard et al. , 2010)  make Antarctica a fitting object of study for reanalyses. The differences we found between the datasets remind their familiar limitations : model dependence in the absence of observations and unreliable time series  (Bromwich et al. , 2011; Nicolas and Bromwich, 2011). Additionally, the non-closure of their atmospheric hydrological budget is an issue to estimate accumulation over the ice-sheet. Fortunately, the gap between moisture convergence and net precipitation is reduced by 4D-Var assimilation. Although radiosondes are assimilated by reanalyses, lower resolution models have difficulties to take into account observations near the surface and on irregular terrain.
Generally speaking, the ensemble standard deviation is proportional to the mean convergence, being highest in the Peninsula and along the coast (Fig. 3. b). The inter-dataset spread is disproportionately high in the Transantarctic Mountains and the East Antarctic Plateau. The 100 km scale inhomogeneities on the plateau are due to JRA 55. Neither the precipitation nor the evaporation exhibit these features (not shown). Unlike the other datasets, in JRA 55, evaporation is on average negative (net deposition) above 2000 m.
The resulting time averages are shown as dots in Figure 5. As expected, there is a gap between profiles 4 The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2018-156 Manuscript under review for journal The Cryosphere Discussion started: 27 August 2018 c Author(s) 2018. CC BY 4. 0 License. and dots at South Pole Station, a measure of the sampling bias. Same goes for Mirniyj as well as Novolazarevskaja in the case of NCEP CFSR. Otherwise, the subsampled reanalyses are quite representative of the year-round values including the summer-only data at Mario Zuchelli Station surprisingly.
a), ERA Interim, JRA 55 and MERRA 2 are grouped together especially after 2000. NCEP DOE R2 is once again the outlier. It is the only dataset not to peak in 1980. Surprisingly, NCEP CFSR diverges from the other modern reanalyses in the 1980s. When we restrict the reanalyses to the locations and times of radisonde launches, they display a more consistent behaviour (Fig. 11, b) : a downward trend in the 1980s and an upward trend in the two following decades. Observations follow the same pattern with comparatively higher fluxes in the 1980s and 2000s. During the same period, precipitable water above the launch sites has remained constant both in observations and analyses (not shown). The interdecadal pattern in transport is therefore a consequence of variable winds rather than thermodynamics.
Acknowledgements. The reanalysis datasets were available by courtesy of  ECMWF, NASA, NCEP  and  JMA. The IGRA sounding data were made available by the  NOAA National Centers for Environmental Information (NCEI). This work was supported by the project " Mechanisms of moisture advection in high latitudes in the present and future climate " funded by  CNRS ,  IGE  and  UGA  and partly through the  BELMONT Forum Fund ARCTIC-ERA  project funded by  ANR. OZ also benefited from the project  14. 613. 21. 0083 (ID RFMEFI61317X0083 ) funded by the  Ministry of Education and Science of Russia. Finally , we thank the Météo France team during the 2014 winter-over at  Dumont d'Urville  for sharing their field experience and particularly  Didier Lacoste  for investigating the station's present weather archives on our behalf.
Post-breeding females leave the colony only for 2 or 3 months in order to replenish their body reserve before coming back on land to molt, allowing for tag retrieval  (Hindell & Perrin, 2009). Monitoring durations will thus be shorter for adult females compared to juveniles. Their diving and foraging behavior is used in this study as a reference in order to better understand how juvenile foraging and diving skills change in comparison with the general aptitudes of adult females. Adult males were not used because they are difficult to equip due to their large size (up to 3,700 kg whereas females only weight between 400 and 800 kg,  Hindell & Perrin, 2009) , and so no accelerometer datasets are available for male Kerguelen elephant seals. Moreover, they tend to display extreme diving skills that are much less comparable to juveniles than those of adult females  (Hindell & Perrin, 2009).
| Tag specifications and diving parameters The DSA tags used for juveniles enabled a new method of collecting, abstracting, and transmitting accelerometer and dive data via the Argos satellite system (Collect Localisation Satellites-CLS, Toulouse, France). The advantages of this system are that real-time diving data could be transmitted (c. 5 dives daily) negating the need for tag retrieval. Tags used on adult females were recovered when they returned to shore for their annual molt (January). The sampling rates of the juvenile tags were programed to record one dive every  2 hr, whereas for the adults, continuous dive records were obtained. During recording periods, for both juveniles and adults, pressure was recorded at a rate of 1 Hz and triaxial acceleration at 16 Hz. A full description of DSA tag specifications and on-board processing algorithms can be found in  Cox et al.
(2017). So that the two datasets would be comparable, high-resolution datasets from the adults were processed using the same abstraction methods as implemented on-board the DSA tags that the juveniles were equipped with (see  Cox et al. , 2017). Following this, all data were formatted to create a dive profile summary across three dive segments: descent, bottom, and ascent. In this study, the following dive metrics were analyzed: maximum dive depth (m), total dive duration (s), surface duration (s), descent and ascent swimming efforts (scaled by duration and used as an index of energy expenditure, in m/s 3 ), and prey catch attempt rates during the bottom phase of a dive (PrCA; time spent catching prey in the bottom phase scaled by its corresponding duration). Total swimming efforts (before scaling by duration), reflected the sum of all accelerations (m/s 2 ) on the lateral axis.
| Changes in diving behavior parameters: juveniles vs adults-Aim (2) & (3) In order to study nonlinear responses of diving parameters relative to time since departure (Figure  2 ) or other diving parameters, we fitted generalized additive mixed-effects models (GAMMs) via the mgcv package  (Wood, 2017). Smooths were applied to the co-variables (time since departure and diving parameters) in an interaction with age classes (adult vs. juvenile) to test differences. Smooths were fitted using cubic regressions splines with extra shrinkage to avoid over-fitting. We included a corAR1 structure in all models to account for temporal autocorrelation  (Zuur, Ieno, Walker, Saveliev, & Smith, 2009). A random effect of individual was included. To satisfy normality of residuals, response variables were transformed using Box-Cox power transformation when needed  (Fox & Weisberg, 2011).
| Changes in dive duration and drift rate-Aim (2) & (3) In order to estimate juvenile rates of change in dive duration and DR, we separated the monitoring period into 3 phases: departure, central, and return  (Biuw et al. , 2003). Rates of change were investigated during the first phase (departure) by fitting linear relationships (see Supporting information Figures  A2  and A3 ). Individual coefficients (intercepts and slopes) for changes in dive duration or DR with time were retrieved using linear mixed-effects models (LME) with a Gaussian error distribution via the lme4 package  (Bates et al. , 2017). A random effect on the intercept and slope of individual was included  (Zuur et al. , 2009). For each LME, we used the MuMIn package  (Barton, 2009).
DATA ACCE SS I B I LIT Y All diving data are available in the Dryad public data at https://datadryad. org/ https://doi. org/10. 5061/dryad. qq6mb07 O RCI D Florian Orgeret https://orcid. org/0000-0002-1940-7797 Sam L. Cox https://orcid. org/0000-0002-5409-4458 R E FE R E N C E S.
This study was funded by an Advanced Grant from the European Research Council as part of the EARLYLIFE program under the European Commission's Seventh Framework Programme (grant agreement FP7/2007-2013/ERC-2012-ADG_20120314; PI Dr Henri Weimerskirsch) and was supported by IPEV (program 109; PI Dr Henri Weimerskirsch). We are grateful to Joffrey Jouma'a, Yves.
, 1997; NEEM community members, 2013). Proxy data derived from coral reefs show a global mean sea level at least 4 m above the present level  (Overpeck et al. , 2006; Kopp et al. , 2013; Dutton et al. , 2015). Several studies have investigated the Eemian GrIS. Nevertheless, there is no consensus on the extent to which the GrIS retreated during the Eemian. Scientists have applied ice core reconstructions (e. g. ,  Letréguilly et al. , 1991; Greve, 2005)  and global climate models (GCMs) of various complexities (e. g. ,  Otto-Bliesner et al. , 2006; Stone et al. , 2013 ) combined with regional models (e. g. ,  Robinson et al. , 2011; Helsen et al. , 2013)  to create Eemian temperature and precipitation forcing over Greenland. Based on these reconstructed or simulated climates, different models have been used to calculate the surface mass balance (SMB) in Greenland for the Eemian.
2. The estimated ice sheet extent and the volume loss (expressed as sea level rise contribution) vary strongly between the studies. All models show large ice loss in the southwest, and several studies show a separation of the ice sheet into a northern and a southern dome. Additionally, some studies also exhibit extensive ice loss in the north, while this northern ice loss is absent in other studies. Overall, the estimated Eemian sea level rise contribution from Greenland remains uncertain due to the big differences between these studies. However, it is important to emphasize that the early studies partly lacked proxy data to constrain model results (i. e. , ice core records), which were available to more recent studies. As an example,  Otto-Bliesner et al. (2006)  assumed an ice-free Dye-3 location during the Eemian as an evaluation criterion for their sim-ulations.
We use a horizontal resolution of 25×25 km covering the Greenland domain (6600 grid points; stereographic oblique projection with its origin at 40 • W and 70. 5 • N) from 60 to 20 • W and from 58 to 81 • N. The model has 24 atmospheric layers from the surface to an altitude of 16 km. SISVAT has 30 layers to represent the snowpack (with a depth of at least 20 m over the permanent ice area) and seven levels for the soil in the tundra area. The snowpack initialization is described in  Fettweis et al. (2005). MAR has often been validated against in situ observations, e. g. , in  Fettweis (2007) ;  Fettweis et al. (2013 Fettweis et al. ( , 2017)). Lateral boundary conditions can be provided either by reanalysis datasets (such as ERA-Interim or the National Centers for Environmental Prediction -NCEP) to reconstruct the recent GrIS climate (1900-2015)  (Fettweis et al.
Experiment terminology We force the PDD model with monthly near-surface air temperature and precipitation fields from NorESM and MAR, and refer to the resulting SMBs as NorESM-PDD and MAR-PDD, respectively. MAR has a full SEB model implemented and its derived SMB is referred to as MAR-SEB. Additionally, we force the intermediate complexity SMB model, BESSI, with daily NorESM and MAR near-surface air temperature, precipitation, and the downward shortwave radiation, and call its output NorESM-BESSI and MAR-BESSI, respectively. An overview of the experimental design is shown in Fig. 3   For lack of observational data with a comprehensive coverage, we use the most complex model, MAR-SEB, as our reference SMB model. The standard PDD experimental setup (see Table  1 ) is tuned to present-day Greenland.
Code availability. The NorESM model code can be obtained upon request. Instructions on how to obtain a copy are given at https://wiki. met. no/noresm/gitbestpractice (last access: 17 October 2018). The PDD Python script is available at https://github. com/juseg/pypdd (last access: 17 October 2018). BESSI is under active development. For more information, contact Andreas Born (andreas. born@uib. no). The MAR code is available at http://mar. cnrs. fr (last access: 17 October 2018). Data availability. The full set of NorESM model data will be made publicly available through the Norwegian Research Data Archive at https://archive. norstore. no (last access: 17 October 2018) upon publication. The MAR, BESSI, and PDD experiment simulations are available upon request from the corresponding author.
Simulated stratospheric temperatures over the period 1979-2016 in models from the Chemistry-Climate Model Initiative are compared with recently updated and extended satellite data sets. The multimodel mean global temperature trends over 1979-2005 are À0. 88 ± 0. 23, À0. 70 ± 0. 16, and À0. 50 ± 0. 12 K/decade for the Stratospheric Sounding Unit (SSU) channels 3 (~40-50 km), 2 (~35-45 km), and 1 (~25-35 km), respectively (with 95% confidence intervals). These are within the uncertainty bounds of the observed temperature trends from two reprocessed SSU data sets. In the lower stratosphere, the multimodel mean trend in global temperature for the Microwave Sounding Unit channel 4 (~13-22 km) is À0. 25 ± 0. 12 K/decade over 1979-2005, consistent with observed estimates from three versions of this satellite record.
Thompson et al. (2012)  also reported differences between stratospheric temperature trends in the two versions of the SSU record and model simulations from the fifth Coupled Model Intercomparison Project and the Chemistry-Climate Model Validation project. On average, the models showed weaker long-term cooling in the upper stratosphere and lower mesosphere (SSU channel 3, ~40-50 km) of around 0. 8-0. 9 K/decade compared to the cooling trend in both SSU data sets (~1. 2 K/ decade). In the middle to upper stratosphere (SSU channel 2, ~35-45 km), the modeled cooling trends (~0. 6-0. 7 K/decade) lay between the best estimates of the cooling trends from the Met Office and NOAA/STAR data sets (~0. 4 and 0. 9 K/decade, respectively). In the middle stratosphere (SSU channel 1, ~25-35 km), the simulated temperature trends (~0.
CCMI Models The analysis uses zonal and monthly mean temperature output on pressure levels from the CCMI models listed in Table  1  (see  Morgenstern et al. , 2017, for further details). Data were downloaded from the CCMI Database hosted at the British Atmospheric Data Centre. Data are used from the following experiments  (Eyring et al. , 2013) : refC2, senC2fODS, and senC2fGHG. All experiments span the period 1960-2100 though only the period 1979-2016 is analyzed here. The models performed a 10-year spin-up from 1950 to 1959 before beginning the simulations from 1960. Concentrations of greenhouse gases (CO 2 , N 2 O, and CH 4 ) are prescribed from observations to 2005 and then follow the representative concentration pathway 6. 0 scenario  (Eyring et al. , 2013). Concentrations of ODSs are prescribed according to the WMO (2011) A1 scenario.
10. 1029/2018GL078035 To produce output from the CCMI models that is comparable to the satellite data sets, the pressure level output is sampled using the vertical global weighting functions for the three SSU channels (ftp://ftp. star. nesdis. noaa. gov/pub/smcd/emb/mscat/data/SSU/SSU_v3. 0) and the MSU4/AMSU9 channel covering the upper troposphere/lower stratosphere (C. Z. Zou, personal communication, July 2017). Trends are calculated using least squares linear regression, and confidence intervals on trends are estimated using the standard error of the fit accounting for the effect of lag-1 autocorrelation in the residuals  (Santer et al. , 2000). To examine the effects of internal variability on stratospheric temperature trends, multiple ensemble members from each model are used where available (see Table  1 ).
In the refC1 experiment (see Figure  S1 ), the CCMI multimodel mean temperature anomalies show better agreement with the satellite data sets in the periods around the major volcanic eruptions, though this reflects averaging across a large spread, with some models not simulating any stratospheric heating from volcanic eruptions and some models strongly overestimating the response. In Figure  1 , the CCMI multimodel mean shows larger differences from the Met Office data set in SSU channels 2 and 3. In the low to middle stratosphere (SSU channel 1), the two SSU data sets are in better agreement, but the CCMI multimodel mean shows slightly weaker long-term cooling, on average, compared to the satellite data sets. Of the individual models, 10. 1029/2018GL078035 ULAQ-CCM shows a much stronger quasi-decadal temperature variability than observed, particularly in the upper stratosphere, which suggests an over estimation of the effect of the 11-year solar cycle on stratospheric temperatures.
(2012). The subsequent analyses in this study focus on the extended NOAA/STAR SSU data set since the Met Office data set is provided as global averages and ends in 2005, when the SSU stopped making measurements, meaning an assessment of temperature trends over the recent past and of the spatial pattern of trends is not possible. For the MSU4 channel, we also show for simplicity only the NOAA/STAR data set, which has similar long-term trends to the RSS data set but somewhat weaker trends than the UAH record. Figure  3  shows vertical profiles of global mean stratospheric temperature trends over the periods 1979-2016 (Figure  3a ), 1979-1997 (Figure  3b ), and 1998-2016 (Figure  3c ) in the CCMI simulations and satellite data sets. The trends over these periods in the SSU and MSU4 channels for all the data sets along with their 95% confidence intervals are given in Table  2.
25 ± 0. 12 (MSU4, ~13-22 km), À0. 50 ± 0. 12 (SSU channel 1, ~25-35 km), À0. 70 ± 0. 16 (SSU channel 2, ~35-45 km), and À 0. 88 ± 0. 23 (SSU channel 3, ~40-50 km) K/decade. These are within the estimated uncertainty ranges of the trends derived from the most recent versions of the satellite data sets. The models simulate weaker global stratospheric cooling in the SSU channels over 1998-2016 compared to 1979-1997 and nonsignificant temperature trends in the lower stratosphere (MSU4) over the latter period, comparable to observations  (Ferraro et al. , 2015; Khaykin et al. , 2017). However, in the tropics one should keep in mind that the MSU4 integrates over the lower stratosphere and upper troposphere and hence includes some effects of tropospheric warming  (Ladstädter et al. , 2011; Randel et al.
Altimetry data The radar altimetry data used in this study were acquired by ERS-2, ENVISAT, Jason-1 and 2 and SARAL. Altimetry data used in this study consist of high frequency measurements, sampled at 18 to 40 Hz depending on the mission, of the acquisition time and geographical location (longitude and latitude) as well as the backscattering coefficient at Ku-band for all the missions except SARAL and at Ka-band for SARAL. Backscattering coefficients are derived from the Offset Center of Gravity (OCOG) or Ice-1 retracking algorithm following earlier studies  [5] ,  [6] ,  [8] ,  [10]  present in the Geophysical Data Records from the space agencies for all the missions except ERS-2 and from the reatrcking performed by Centre de Topographie des Océans et de l'Hydrosphère (CTOH) for ERS-2  [11].
Soil map The Harmonized World Soil Database v 1. 2 provides the composition in terms of soil units and the characterization of selected soil parameters (organic Carbon, pH, water storage capacity, soil depth, cation exchange capacity of the soil and the clay fraction, total exchangeable nutrients, lime and gypsum contents, sodium exchange percentage, salinity, textural class and granulometry) at a spatial resolution of 30''  [14]. It result of a collaboration between the FAO with IIASA, ISRIC-World Soil Information, Institute of Soil Science, Chinese Academy of Sciences (ISSCAS), and the Joint Research Centre of the European Commission (JRC).
CONCLUSION A good correlation was observed between radar altimetry coefficients and SSM from ORCHIDEE LSM over sandy areas without open water (temporary and permanent ponds) in the Gourma region of Mali (R²=0. 71) in spite of the difference of spatial resolution between the two datasets. A method for inverting SSM from radar altimetry backscattering coefficients was developped. This method will be applied to backscattering coefficients from  ERS-2 (1995 ERS-2 ( -2003) ).
The model also shows that the roughening process is balanced by two inherent parameters -surface and elastic energies. This theory was corroborated by numerical models based on a similar approach  (Koehn et al. , 2007; Ebner et al. , 2009a ; see also Section 3). The thickness of the residual was shown to positively correlate with the amount of dissolution  (Railsback, 1993; Tada and Siever, 1989). A positive correlation between stylolite amplitude and residual thickness was reported by  Heald (1955)  and  Kaplan (1976) , whereas  Peacock and Azzam (2006)  observed only a very weak correlation between amplitude and residual thickness for a large dataset on dolomite and limestone. The positive relationship between residual thickness and stylolite amplitude seems to be limited to relatively clean rocks, because comparatively thick clay seams have a negative influence on the stylolite amplitude  (Park and Schot, 1968).
Acknowledgments: We would like to thank  Pascale Talour  ( CNRS and University of Grenoble Alpes ), for her help to search for old scientific literature on stylolites. We thank  K. L. Milliken,  H. Fossen  and  W. Dunne  for their valuable and careful reviews. We thank  ANDRA  for giving us access to valuable borehole samples from the  Underground Research Laboratory in Bure-sur-Meuse. We acknowledge support from the  EU   FP7 ITN FlowTrans network , from the  CNRS INSU ALEAS , from  NEEDS MIPOR , and from the  University of Strasbourg  IDEX "Espoirs". This work was partly supported by the  LIA France-Norway D-FFRACT ,.
Data and methods One measurement per hour of Tair and RH were obtained from 17 Tinytag TGP-4500 data loggers (www. geminidataloggers. com/data-loggers/tinytag-plus-2/tgp-4500) evenly placed in an area of 12 km 2 within the Formigal ski resort (Fig. 1 ); these provided data for 331 days during the three seasons of the study. The manufacturer's stated accuracy for the sensors is ± 0. 01°C for Tair and ± 0. 3% for RH. The data loggers were hung from the eaves of the roofs of chair lift sheds, at approximately 2. 5 m height above the bare ground, and were protected by radiation shields (DataMate TM ). The lowest sensor was at 1554 m a. s. l. and the highest was at 2305 m a. s. l. , at the top of the ski area. At least one sensor was placed in each 100-m elevation band in the two main valleys of the ski area.