dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2012). Details and quality assessment regarding both datasets can be found in  Bartalis et al. (2007 ) and Naeimi et al. (2009b , 2012)  The surface soil moisture retrieval algorithm was developed at the Vienna University of Technology (TU Wien) based on a change detection method  (Wagner et al. , 1999 , Naeimi et al. , 2009b) ; WARP5. 2 is the version of the algorithm used to produce the afore-mentioned SSM dataset. In this algorithm, components of the backscattered microwave signal showing seasonal or lower-than-seasonal frequency variations are successively subtracted from the original signal. These components include surface roughness (assumed to be temporally invariant), vegetation canopy and vegetation water content (assumed to vary within several days to weeks). The residual signal then relates to the uppermost soil moisture content, which ranges from 1 to 5 cm depth depending on surface state and soil water content.
The TU Wien Freeze-Thaw retrieval algorithm was used to create the ASCAT SSF dataset. This algorithm distinguishes between regions where the backscatter coefficient is higher in winter than in summer (due to higher liquid precipitation in winter, leading to wetter soils) and opposite (due to frozen surface having a generally lower backscatter than moderately wet soils). Distinct decision trees are applied in both regions, based on locally calibrated backscatter thresholds for freeze-thaw transition and snow melt. The final SSF product describes the surface status as frozen, unfrozen, melting snow or unknown. There are known limitations to both datasets affecting their accuracy over our study area. Concerns have been raised as to the accuracy of the ASCAT SSF products in the area North of the Caspian and Black Seas; this issue is currently under investigation.
At this location, the model overestimates the April SWE by 85 mm (e. g. ∼ 25 cm snow depth assuming a snow density of 300 kg m -3 ) with respect to CMC data. There is much uncertainty in the solid precipitation accounted for in the atmospheric forcing datasets and especially over Siberia, as a result of the lower density of meteorological stations there and the necessity of applying corrections for gauge undercatch (e. g. Sheffield et al. , 2006; Weedon et al. , 2011; Burke et al. , 2013). The possibility of an overestimation of solid precipitation in the forcing data cannot be ruled out, resulting in an overestimated snow water equivalent in the model and a longer-than-observed modelled melting period. The atmospheric forcing we used in this study relies on NCEP reanalyses corrected by the CRU precipitation dataset.
Other precipitation datasets like the GPCC dataset (Global Precipitation Climatology Centre; http://gpcc. dwd. de; Fuchs et al. , 2009)  display a denser observational network at high latitudes. The WATCH forcing dataset  (Weedon et al. , 2011 ) makes use of GPCC data for precipitation corrections. However, ancillary simulations performed with the WATCH atmospheric forcing over the study area revealed similar SWE overestimation (resp. underestimation) patterns for the year 2007 with respect to CMC data. Another bias towards a delayed snowmelt may originate from the snow albedo parameterization currently in use in the model ORCHIDEE (Chalita, 1992), which does not account for the substantial decay of the snow albedo during the melting period. Also, the parameterization of snow albedo decay with time over vegetated areas needs revision  (Gouttevin, 2012)  and may explain the delayed timing of surface thaw over the forested areas of Central Siberia.
This situation is illustrated at location 6 in the Central Siberian Plateau (Fig. A2c ): the model is not able to reproduce the extent of the observed mid-July soil moisture depletion. Soil freezing or snow-covered events responsible for the absence of ASCAT SSM data for a short period in mid-July are also not captured by the model. Finally, the June low ASCAT SSM may be induced by failures of the AS-Figures.
Daily correlations The modelled and remotely-sensed SSM exhibit spatially variable correlation at the daily timescale (Fig. 6 ). Correlation is distinctively high in Western Siberian regions and low in North-Eastern Siberian regions, with contrasted results over the unmasked mountainous areas. In each case, the modelled SSM responds closely and consistently to precipitation events reported by the forcing data (Fig. A2 ; in this figure the modelled unscaled SSM is the real-time modelled saturation degree of the uppermost 5 cm of soil, and reacts consistently to precipitation events occurring in the atmospheric forcing). Two types of anti-correlation periods can be distinguished: periods with ASCAT SSM increasing while modelled SSM decreases, and the exact opposite. In terms of surface soil moisture, the former periods corresponds to undue surface drying in the model with respect to observations; the latter indicate undue surface moistening.
Back Close Full inated for these moderate performances; we synthesize them below and in Table  2 , along with possible ways to circumvent them. i. In the model, standing waters and the lateral redistribution of excess surface runoff within a grid cell are not accounted for. Instead, excess surface water is routed towards the rivers; no floodplains are accounted for in the version of the model used for this study. This shortens the duration of saturated surface conditions in the model and limits the model's ability to represent the spring surface soil moisture signal. In other versions of ORCHIDEE, modules representing floodplains and ponding water have been developed  (Vivant, 2003; d'Orgeval, 2006) ; their use could improve the performance of our hydrological modelling in flat areas like the Western Siberian Lowlands.
ii. The hydrological module of ORCHIDEE theoretically allows different soil types to coexist over the same grid-cell: in this case, separate hydrological balances are computed for each soil type, representative of specific hydrological properties. However, the soil map used by ORCHIDEE (Sect. 2. 3) exhibits a very limited spatial variability of soil types over the study area. Medium textured soils dominate, with coarse soils sporadically appearing in the Western Siberian Lowlands only. As stated in Sect. 3, this may be not representative of the hydrological properties of soils over large areas of our study domain, with Central and Eastern Siberian landscapes featuring large extents of rocky, or gravely, barren soils. Since the compilation of soil texture data into a five-classes map by Zobler (1986), there has been progress in soil science and knowledge even at high latitudes (e.
, 2009; Scipal et al. , 2008). Though ORCHIDEE includes a physicallybased representation of soil moisture, our results suggest that the spatial resolution and the spatially-explicit soil parameters in use in this study, combined with the hydrological processes accounted for, still hamper the representation of a volumetric soil moisture content of entire physical meaning by the model. This diagnostic of the model performances proceeds from several coherent lines of evidence and distinct identified regional patterns, hence its robustness. However, identified limitations of our methodological framework and of the satellite dataset can additionally contribute to the diagnosed model-to-data discrepancies: i. In the WARP5 algorithm used to infer the remotely-sensed SSM from the original backscatter signal, the surface soil wet and dry references are computed as averages of maximum (resp.
Temperature The analysis of the relationship between the NH-ifl spring snow cover and global average air temperature was carried out using the HadCRUT4 temperature dataset  (Morice et al. , 2012). Spatially distributed NH land surface air temperatures from the CRUTEMP4  (Jones et al. , 2012)  dataset, which were also used in the HadCRUT4 combined landocean dataset, were used for the study of global mean surface air temperature trends and for the analysis of the relationship between the NH-ifl spring snow cover and spring NH-ifl surface air temperatures north of 50 • N.
Snowfall Simulated NH snowfall rates are compared to the spatially distributed WATCH dataset  (Weedon et al. , 2011)  for the 1979-2001 period. The WATCH dataset is essentially based on GPCCv4  (Schneider et al. , 2008)  precipitation data, but it uses CRU  (New et al. , 1999 (New et al. , , 2000) )  number of wet days and ERA-40  (Uppala et al. , 2005)  data for determining the proportion of snowfall to the total precipitation rate.
Boreal spring data We mainly restricted our analysis of the evolution of NHifl snow cover to the boreal spring because snow cover is most sensitive to temperature during the transition seasons  (Brown, 2000)  and because long-term snow observations have good spatial and temporal coverage during that time of the year  (Brown, 2000; Brown and Robinson, 2011). We thus used average data for the months of March and April. However, the full annual cycle of snow cover is also analyzed.
, 2007) , is not reproduced by the models either. Among other less obvious causes, this misfit can be due to either a wrong simulation of the temperature trend over the Northern Hemisphere ice-free land during the melting season, or a wrong simulation of the snowfall trend (which is in part of course linked to the temperature trend, because temperature directly determines the partitioning between snowfall and rainfall). Indeed, the recent NH extratropical March-April land surface air temperature trends are substantially underestimated (see also Fig. 5c  and d   The spatial distribution of snowfall trends (not shown) is difficult to evaluate because the observational data set is rather noisy, certainly because the selected period  is very short. However, average snowfall rates are clearly overestimated in the models: the WATCH dataset suggests an average annual snowfall rate of about 163 kg m -2 yr -1 on ice-free land north of 50 • N, while the models simulate an average of 307 kg m -2 yr -1.
As stated before, the average observed spring (March-April) temperature trend on ice-free land north of 50 • N in the CRUTEM4 dataset is T NH-ifl > 50 • N, obs = 0. 66 • C per decade, while it is only T NH-ifl > 50 • N, mod = 0. 38 • C per decade in the multi-model mean. In contrast, the global and annual average surface air temperature trend simulated over the 1979-2005 period by the available models is T global, mod = 0. 20 • C per decade, which, given the relatively short time span, compares very well to the HadCRUT4 trend of T global, obs = 0. 18 • C per decade. Therefore, the observed boreal (north of 50 • N) spring land surface air temperature change amplification is A obs = T NH-ifl > 50 • N, spring, obs / T global, annual, obs = 3. 7, while for the CMIP5 models used here this amplification is A mod = 1.
for 1979-2001): the average observed March-April temperature trend on ice-free land north of 50 • N in the CRUTEM4 dataset is T NH-ifl > 50 • N, obs = 0. 66 • C per decade for the period 1979-2005 (0. 79 • C per decade for 1979-2001), while it is only T NH-ifl > 50 • N, mod = 0. 38 • C per decade (0. 32 • C per decade for 1979-2001) in the multi-model mean; less than 20 % of the more than 100 individual model runs exhibit a spring surface air temperature trend T NH-ifl > 50 • N, mod in excess of the observed value of 0. 66 • C per decade. This ob-viously contributes to the underestimate of the spring snow cover trend.
Acknowledgements. We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. For CMIP, the  US Department of Energy 's  Program for Climate Model Diagnosis and Intercomparison  provided coordinating support and led development of software infrastructure in partnership with the  Global Organization for Earth System Science Portals. We are grateful to the  Rutgers University Global Snow Lab  (http://climate. rutgers. edu/snowcover)for making their distributed snow cover data available. This work was supported by  Agence Nationale de la Recherche  under contracts Paprika and Classique and by funding from the  European Union   7th Framework Programme under projects Page21  (grant  282700 ) and LIFE SNOWCARBO.
Acknowledgements This study was conducted as part of: 1) the  Malina Scientific Program  (http:// malina. obs-vlfr. fr)funded by  ANR (Agence nationale de la recherche) ,  INSU-CNRS  ( Institut national des sciences de l'univers, Centre national de la recherche scientifique ),  CNES  ( Centre national d'etudes spatiales)  and  ESA  ( European Space Agency) ; 2) the  Circumpolar Flaw Lead (CFL)  study (http://www. ipy-api. gc. ca/pg_IPYAPI_029-eng. html)funded by the  Government of Canada  (IPY # 96 ); 3) the Impacts of Climate on the  Eco-Systems and Chemistry of the Arctic Pacific Environment (ICESCAPE) program  (http://www. espo. nasa. gov/icescape/)funded by  NASA (National Aeronautics and Space Administration) ; 4) the  Nansen and Amundsen Basin Observational System (NABOS)  project (http://nabos.
iarc. uaf. edu/index. php)funded by the  US-NSF  ( National Science Foundation ),  NOAA (National Oceanographic and Atmospheric Administration) , the  ONR (Office of Naval Research)  and  JAMSTEC (Japan Marine Science and Technology Center). This work was directly supported by  NSF  grants  0713915  and  0850653  to  R. B. ,  NSF  grants  0229302 ,  0425582 ,  0713991  to R. A. , and  NASA  funding to S. H. We thank the following people: the crews of the  USCGC Healy ,  CCGS Amundsen  and  Russian icebreaker Kapitan Dranitsyn  for assistance with sample collection,  Dr. Jolynn Carroll  for collecting the Gulf-of-Ob samples,  Vanessa Wright ,  Joaquin Chavez  and  Aimee Nealy  for assisting S. H. with collection and analysis of Malina and ICESCAPE data, and  Yuan Shen  for DOC analysis of Malina samples.
This model, henceforth called Correspondence and likelihood analysis (COaLA), makes use of a correspondence analysis to reduce the number of parameters to be optimized through maximum likelihood, focusing on most of the compositional variation observed in the data. The model was thoroughly tested on both simulated and biological data sets to show its high performance in terms of data fitting and CPU time. COaLA efficiently estimates ancestral amino acid frequencies and sequences, making it relevant for studies aiming at reconstructing and resurrecting ancestral amino acid sequences. Finally, we applied COaLA on a concatenate of universal amino acid sequences to confirm previous results obtained with a nonhomogeneous Bayesian model regarding the early pattern of adaptation to optimal growth temperature, supporting the mesophilic nature of the Last Universal Common Ancestor.
Ancestral sequence reconstruction and resurrection is a powerful approach to characterize ancient molecular properties, to highlight the complex relationship between sequence, structure, and function, or to infer past lifestyle conditions  (Harms and Thornton 2010; Boussau and Gouy 2012). The widely applied protocol for ancestral sequence reconstruction starts with the choice of one of the time-reversible Markov models that ModelTest  (Posada and Crandall 1998; Posada 2008)  considers. Then, this model is used in the PAML package  (Yang 2007)  to compute the most likely ancestral sequence at each internal node of a maximum-likelihood (ML) tree for the gene under consideration. However, the variation of the substitution process through time or among sites is not accounted for, even when billions of years separate all sequences from their common ancestor  (Gaucher et al.
Model Availability The COaLA model is implemented in the Bio++ libraries  (Dutheil et al. 2006 ), which are a set of freely available C++ libraries dedicated, among other things, to evolutionary biology. The model can be employed with the BppML program, available in the bppSuite series of programs  (Dutheil and Boussau 2008). BppML is a general program to optimize a large set of homogeneous/stationary or nonhomogeneous/nonstationary models in the ML framework for several types of data sets (e. g. , DNA, codons, and proteins). Information on the model and on how to download and install the libraries can be found at http://pbil. univ-lyon1. fr/software/COaLA/.
Sequence Simulations All simulations of amino acid sequences with nonhomogeneous models were performed with BppSeqGen, from the bppSuite series of programs  (Dutheil and Boussau 2008). To simulate these nonhomogeneous amino acid sequences, we considered the 5000 trees used by  Guindon and Gascuel (2003)  to test the performance of PhyML and which are available at http://www. atgcmontpellier. fr/phyml/datasets. php. These trees contain 40 species. We randomly removed 20 of these 40 species for each of the 5000 trees. Branch lengths were increased to allow different parts of the tree to have sufficient time to diverge in terms of compositions. Thus, the height of the tree, defined as the maximum distance between a leaf and the root, was set to a minimum of 0. 8 substitutions/site and all other branches were scaled up accordingly.
Finally, we compared these newly simulated alignments with the original alignments: for each of the 20 sequences per alignment, the amino acid frequencies were computed and compared with the amino acid frequencies observed in the original alignments. We also investigated the influence of the alignment size on the estimation of equilibrium frequencies (see Supplementary Fig. S2  that can be found in the Dryad data repository [doi:10. 5061/dryad. 7h66k]). We simulated 1000 alignments containing either 100 or 200 amino acids, with the same trees and sets of parameters as previously. This approach was motivated by two main reasons. First, it is not obvious whether NH-NS COaLA is able to generate accurate parameter estimates for short single-gene alignments. Second, in short alignments, some amino acids, especially rare amino acids such as tryptophan or cystein, may never be observed in any sequences.
Phylogenomic Alignments The COaLA model was tested on four previously published phylogenomic data sets (see below). For each data set, rate heterogeneity across sites was modeled with a discretized distribution with four categories  (Yang 1994). Yeast data set. -This data set is a concatenation of 106 genes belonging to eight yeast species  (Rokas et al. 2003). This alignment contains 42 342 amino acids and the species tree presented in Figure  4  of the corresponding paper is used to estimate evolutionary parameters and compute the likelihood. The G+C content of third codon positions is heterogeneous among the eight species, ranging from 0. 28 in Candida albicans to 0. 45 in Saccharomyces kluyveri, possibly influencing the composition at the amino acid level. In line with this, 46% of the pairwise Bowker tests performed on the protein concatenate are statistically significant (according to Holm correction for multiple comparisons).
Archaea data set. -These data are a concatenation of 72 protein-coding genes sampled in 35 archaeal species and 10 bacterial species  (Groussin and Gouy 2011). We removed bacteria from the alignment, as well as the two uncultured thaumarchaeal species, for which only one protein sequence was present in the alignment. The final alignment of 9387 amino acids contains 33 archaeal species. We used the topology presented in figure  3  of  Groussin and Gouy (2011)  to determine the best evolutionary model with BppML. These sequences are compositionally highly heterogeneous since 86% (after correction for multiple tests) of the pairwise Bowker tests significantly rejected the stationarity or homogeneity hypotheses. Eocyte data set. -Cox et al. (2008)  used 45 genes to build a universal alignment of 5521 sites and 40 species.
7  (Edgar 2004) , internally used by Guidance v1. 1  (Penn et al. 2010)  with its default parameters. Guidance is a program allowing users to evaluate the reliability of alignments by taking into account the uncertainty of the guide tree used to align sequence positions with a bootstrap procedure. The resulting alignments were then treated by Gblocks  (Castresana 2000)  to eliminate ambiguous regions (default parameters with the authorization to conserve gap sites were used). The final gene alignments were concatenated and the sites with more than 50% of gaps were removed to eventually obtain an alignment of amino acids with 6269 sites.
Single Gene Alignments To evaluate both the ability to fit the data and the accuracy of ancestral sequence reconstruction on singlegene alignments with NH-NS COaLA in comparison with a homogeneous model, gene alignments were constructed from 24 methanogenic archaeal genomes (15 Methanococcales, 8 Methanobacteriales, and 1 Methanopyrale, see Supplementary Table  S2 ). This data set presents two advantages: these species do not have extreme rates of evolution  (Brochier-Armanet et al. 2011)  and are adapted to different optimal growth temperatures (OGTs), leading to compositional variability  (Groussin and Gouy 2011). All genome sequences were retrieved from GenBank. The software package SiLiX  (Miele et al. 2011 ) was employed to cluster amino acid sequences into homologous gene families. Unicopy gene families containing at least 80% of the 24 species were conserved, leading to 535 gene families.
Accordingly, Supplementary Figure  S2  shows that the optimization of ancestral root frequencies for amino acid aligments with 100 sites is less accurate than what can be obtained with a H-S model: the sums of squared differences between the estimated frequencies and the true frequencies are equal to 7. 64 and 6. 72, respectively (P < 0. 001). However, for amino acid aligments with 200 sites, NH-NS COaLA becomes better than a homogeneous model (4. 55 and 5. 27, respectively [P < 0. 001], data not shown). NH-COaLA accurately estimates branch lengths. -The ability of NH-NS COaLA to accurately estimate branch lengths was assessed (See Supplementary Information). Supplementary Figure  S3  shows that NH-NS COaLA has similar performances to an H-S LG+F obs model without any bias. NH-COaLA efficiently fits data.
S1b ). In the eocyte data set, the first three axes account, respectively, for 46%, 24%, and 9% (Supplementary Fig. S1c ), while their contribution is 43%, 28%, and 10% (Supplementary Fig. S1d ), respectively, in the archaea data set. These variation axes are strongly linked to biological properties that influence the global amino acid composition of proteomes. We observed that the first axis of the COA highly correlates with the G+C content of third codon positions (GC3) of each yeast species (r =-0. 89). In the eocyte data set, the first factor discriminates eukaryotic from archaeal/bacterial species. The second factor highly correlates with the genomic G+C content (r = 0. 9) and the third factor is strongly linked to OGT (r = 0. 88). Finally, in the archaeal data set, the first and second axes highly correlate with the genomic G+C content (r = 0.
Finally, we found that using AIC instead of BIC for model selection (see "Materials and Methods" section) systematically leads to the choice of overparameterized models, illustrating the property of 533 BIC to more heavily penalize parameter-rich models. For instance, with the archaea data set, AIC selects the NH-NS LG+COaLA[7] model, where the seventh axis of the COA only represents 1. 4% of the total compositional variance of the data. With respect to the number of parameters involved, the COaLA model strongly reduces the dimension of the evolutionary model. Consequently, COaLA is fast and saves a large amount of computing time: with the yeast data set containing eight species, 5 h 32 min were necessary to compute the likelihood with 19 equilibrium frequencies per branch in comparison with 2 h 38 m for the NH-NS COaLA[1] model and with 16 min 14 s for the H-NS COaLA[1] model.