dataesr/scientific-paragraphs-categorization huggingface dataset:
The atmospheric corrections in the solar domain were based on the inversion of an atmospheric radiative transfer model by exploiting the differential behavior of TOA reflectances in time and space depending on the variations in aerosol content of the atmosphere and the variations of surface properties. Hagolle et al. (2008) , both with simulated and experimental data (Formosat-2 images), showed that the method worked well, in particular when the aerosol optical thickness varied significantly with time. This was particularly true over our area where aerosol optical thickness and surface reflectances were retrieved with a good accuracy. The adaptation of the method, originally designed for sensors with a revisit of only few days (as Formosat-2 or VENµS in the future), to ETM+ did not degrade the accuracy of the atmospheric correction significantly  (Hagolle et al.
Radiosonde data were downloaded from (WWW3). TOC brightness temperatures were obtained by considering land surface emissivity equals to 1. Conversely to land surface temperature, TOC brightness temperature is not depending on any assumption on the definition of land surface emissivity. Thus, it can be used in a variety of applications including the assimilation in land surface models that generate thermal signals from coupled energy balance -radiative transfer parameterization  (Olioso et al. , 1999)  or the evaluation of thermal infrared emission models such as the SCOPE model (  Van der Tol et al. , 2009; Duffour et al. , 2015). In the present study, it was used to derive land surface temperature assuming specific estimations of land surface emissivity (see below). In the present study, 27 Landsat-7 ETM+ images acquired at about 10:15 UTC between 2007 and 2010 were used (i.
Albedo estimation We estimated albedo (α) from spectral reflectances (ρ j ) using the NTB conversion method: ¦ n j j j 1 0. U E E D (2) where subscript j refers to the spectral band number and n to the number of bands, β j is the weighting coefficients, and E 0 is the offset. We considered thirteen coefficient sets from the literature that can be applied to ETM+ spectral bands. They were labeled as m1-m13 and are summarized in Table  1. They were originally obtained by calibrating the linear combination model using either experimental data (m1 to m7), dataset simulated using radiative transfer models (m8, m9, m11 to m13), or theoretical consideration on the representativity of each spectral band (m10) -see associated references in Table  1. Coefficient sets m1 and m4 to m7 were obtained after calibration over datasets acquired in the same area as our study.
This was observed at the Avignon (site 1). Such spatial variability may have an impact on the quality of the albedo retrievals since a Landsat pixel is around 1. 5 to 4. 5 larger than the footprint of the ground pyranometers. [Insert Fig. 2  about here] The evaluation of the albedo computed from each coefficient sets against ground data is presented in Table  4. RMSE A ranged from 0. 025 to 0. 033 (14 % to 19 % in terms of RMSE R ). In most cases, a significant negative bias, between -0. 010 and -0. 024 was obtained, in particular for estimations using middle infrared bands: m2, m3, m8 and m10. After subtracting the bias, performances of most of the coefficient sets improved. Similar performances were obtained for coefficient sets which did not include middle infrared bands (RMSE A between 0.
↓ ). Thus, considering the uncertainties in incident radiations, the evaluation of estimated Rn was clearly optimistic. This explained that net radiation evaluations in Fig. 7  always laid well inside of the level of net radiation uncertainties (45 Wm -2 to 85 Wm -2 from winter to summer). This was not the case for albedo (Fig. 2 ) and surface temperature (Fig. 4 ). Estimations of albedo from Landsat data were obtained by applying the NTB conversion method using different coefficient sets. Albedo were within a large range of values (~0 to ~0. 6) indicating a large spatial variability depending on land use. When evaluated against ground data, the albedo obtained with any of the coefficient sets used in the NTB conversion, except one, were showing a significant negative bias. This bias was higher than uncertainties related to the calibration of the remote sensor (evaluated to 5 % in spectral reflectances  (Chander et al.
2009) ). Underestimation of albedo estimated from Landsat data were also recently reported by  Shuai et al. (2011) ,  Roman et al. (2013), and Franch et al. (2014). The latter demonstrated that it was possible to reduce the bias by improving both 1) the way anisotropy of surface reflectance is accounted in the albedo calculation and 2) the atmospheric corrections by introducing local measurements of atmospheric parameters (from the AERONET network). This raises the question of the evaluation of the accuracy of operational spectral reflectance products (or pre-operational products as those used in our study). These products are usually based on more systematic estimation of atmospheric parameters which are less accurate. This also militates in favor of the development of detailed albedo products based on the merging of Landsat nadir reflectance and BRDF information from other sensors such as MODIS or PROBA-V.
After unbiasing, albedo estimates were within the range of performances observed over independent experimental data sets (RMSE A ~0. 022-0. 024), and as expected the best results were obtained for albedo coefficient sets that were derived for Landsat sensors. This indicated the necessity of accounting for the spectral characteristics of the sensors in the derivation of albedo models. It is interesting to notice that while the different coefficient sets were derived for Landsat sensors using different methods, the estimated albedo remained in a narrow range as shown by the low uncertainty related to the choice of coefficient sets (Fig. 8g ): calibration against surface reflectance models  (Liang 2000) , against ground measurements  (Dubayah (1992) ;  Duguay and Ledrew (1992) ;  Bsaibes et al.
Error analysis based on RMSE calculations also considers an averaged impact over datasets which have usually a limited number of individual data. The analysis we performed in our study provides more generic information as uncertainties are mostly independent of the data used for computing RMSE. We believe that the uncertainty evaluations presented in this study can be easily transferred to the analysis of mapping net radiation from other space or airborne sensors. In any cases, the uncertainties related to the estimation of incident radiations will be the main source of uncertainties in the estimation of net radiation. This has to be considered deeper (   measurements over soil samples from Alpilles  (Coll et al. , 2001 (Coll et al. , , 2002) )  and La Crau  (Labed and Stoll, 1991) ; filled rectangle indicates the range of values corresponding to experimental measurements over surfaces with high NDVI  (Coll et al.
Acknowledgements This work was made through different projects funded by the  National Centre for Space Studies, CNES (TOSCA)  and the  European SIRRIMED  ( Sustainable use of irrigation water in the Mediterranean )  FP7  project (www. sirrimed. org). Mira was endorsed by a postdoctoral contract within the  VALi+d program  from  Generalitat Valenciana (Spain) , followed by a CNES postdoctoral contract and a " Juan de la Cierva " postdoctoral contract from the  Spanish Ministry of Economy and Competitiveness. Gallego-Elvira was endorsed by a postdoctoral fellowship from the  Ramon Areces Foundation.
, 1998  data set with modifications in the Arctic Ocean based on  Steele et al. , 2001 ). The NCAR, FSU-HYCOM, Kiel-ORCA05, NOC, and FSU-HYCOMv2 models use, however, PHC2 rather than PHC3 for their initial hydrography. The sea-ice models are initialized from sea ice states obtained from previous ocean sea ice coupled simulations  ( Danabasoglu et al. , 2014 ). Typically, semi-equilibrium states are obtained after two model cycles in terms of both the Arctic Ocean sea ice state and the sea ice export fluxes (Q. Wang, personal communication). The MRI-A assimilation model was run for 70 years starting from model year 231 of the MRI-F integration. The first 10 years are treated as spin-up and the remaining 60 years (associated with the period of CORE-II forcing) are used for the analysis. Wang et al.
Anna Through outflow ( Fig. 4    the Arctic Ocean. All models but MOM-0. 25 and MRI-A shows positive biases in the Canadian Basin. MRI-A, has a bias of 1-2 psu due to use of inadequate error statistics for assimilating coastal data, which is a problem specific to the data assimilation scheme. Fig. 6  shows horizontal maps of time averaged salinity biases at 400 m depth. The PHC3. 0 climatology field ( Fig. 6 (a )) shows a gradually disappearing high-salinity signature as the AW propagates into the Arctic interior. All models but MRI-A have a fresh bias in the Canadian Basin, with the MRI-F, GFDL-GOLD, CMCC, NOC and FSU-HYCOM showing the largest bias in this region. The NCAR, GFDL-MOM and MOM-0. 25 models are comparable and close to the climatological value in the Eurasian Basin.
In CERFACS, GFDL-MOM, AWI-FESOM, MRI-F, Kiel-ORCA05, CMCC and FSU-HYCOM the temperature signal of the AW is substantially weakened after the St. Anna Trough. It could be that cold shelf water is indeed blending with the AW in these models but in a more vertically homogeneous manner than BERGEN, GFDL-GOLD, CNRM and NOC. 2014 ) employed all available observational data except ITP data to compute winter and summer MLDs in Arctic basins. They showed that maximum MLD in winter is found in the Barents Sea with values about 110 m. The MLD in the Eurasian Basin is second deepest, while that in the Makarov Basin is the third deepest (about 50 m). In the following, the MLD is set to the depth where the density difference between the surface density and the sub-surface potential density (referenced to the surface) exceeds 0.
Acknowledgments M. Ilicak is supported by  Ice2Ice  project that has received funding from the  European Research Council  under the European Community's  Seventh Framework Programme  (  FP7/2007-2013 ) /  ERC  grant agreement no. 610055. M. Bentsen ,  H. Drange  and  M. Ilicak  acknowledge support from the  Research Council of Norway  through the project  EVA  (grants  229771  , nn2345k , ns2345k ). The  WCRP/CLIVAR Ocean Model Development Panel (OMDP)  is responsible for organizing the Coordinated Ocean-sea ice Reference Experiments, with support from the international CLIVAR and  U. S. CLIVAR  project offices. We are grateful for the efforts of modelers who have contributed to the simulation and processing of the CORE-II experiments.
2009  ). As the chapters in this book demonstrate, the development of new methods and their applications are very much tuned into human impacts and sustainability issues. Thus, red list ings, drivers of extinction, and changes in spatial and temporal distribution of phylogenetic diversity are common elements of these studies. All this promotes the incorporation of phylogenetic diversity in the international conservation agenda. These prospects are magnifi ed by the remarkable facilities for obtaining entire or large parts of genomes or other molecular sequences of any kind of organisms, and by the sheer magnitude of biological (gene sequences, trait databases, species occurrences, red list s) and environmental data (climate layers for past, present and future interpolated to very fi ne spatial scale s; land-use layers, spatial data indicating particular important risks such as fi res, fl oods, and so on) now available in the public domain.
At the point we are now, systematics is therefore able to offer a comprehensive picture of diversity , linking species, their Fig. 1  The traditional data processing in systematics, beginning with the sampling of specimens and the characterization and description of species. Specimens were then specifi cally sampled for phylogenetic characters, allowing to build phylogenetic trees and to compute phylogenetic diversity. In parallel, other biological knowledge was obtained separately. Note that in this framework, the number of species is obtained at the early step of species characterization (the so-called "morphospecies" may be obtained before description if necessary) before the phylogenetic analysis (Drawings by Agathe Haevermans) relationships and their characters to conservation biology  (Funk et al.
2002 ; Wilson 2003 ; Faith et al. 2010  ; Lean and MacLaurin chapter " The Value of Phylogenetic Diversity "). Obviously, this new framework enhanced by molecular biology and metagenomics will maintain biological signifi cance and usefulness as long as molecular proxies will remain related to species concepts, taxon names and classifi cations linking to the wider biological knowledge  (Mace 2004  ; German National Academy of Sciences Leopoldina 2014 ). The peril to invest only in an isolated and blind molecular database was already keenly emphasized by many taxonomists at the occasion of the rise of the barcoding initiative (e. g. ,  Will et al. 2005 ). Building the network between names, biological knowledge and molecular data is from far the biggest challenge of present-day systematics and other sciences of diversity , much beyond the molecular technical tour de force  (Grandcolas et al.
Due to the fragility of snow cores, the first 4 m were only analyzed on a single core (Fig. 1 ). We will thus not discuss the variability of the Pinatubo and Agung eruptions present in these first 4 m. Concentration data are deposited in the public domain and are made freely available at the NOAA National Centers for Environmental Information, Paleoclimatology Data (https://www. ncdc. noaa. gov/ data-access/paleoclimatology-data).
Core synchronization and dating Core 1 was entirely dated with respect to the recently published volcanic ice core database  (Sigl et al. , 2015)  using the Analyseries 2. 0. 8 software (http://www. lsce. ipsl. fr/Phocea/ Page/index. php?id=3), and it covers the time period of 588 BCE to 2010 CE. Figure  2  shows the age-depth profile obtained for this core. A total of 13 major volcanic well-dated eruptions were used as time markers to set a timescale (bold date in Table  1 ). Core 1 was entirely dated through linear interpolation between these tie points. The dated core 1 was then used as a reference to synchronize the remaining four cores, using the same tie points and 10 additional peaks (nonbold date in Table  1 ), presenting characteristic patterns common to each core. In total, 23 points were therefore used to synchronize the cores.
17 ). From the reconstructed paleo-latitude for each million year time slice, we computed the 95th percentile of the paleo-latitude at which corals were living to inform on the latitudinal border of tropical oceans across time. By combining reconstructed shelfal areas with tropical limits, we generated 1 map per million years of tropical shallow marine habitats for the last 140 Myr (Supplementary Fig. 1 ). Mapping current and past biodiversity patterns. Current biodiversity patterns were extracted from a global-scale distribution database  3, 44  for tropical reef fishes and from the IUCN (http://www. iucnredlist. org/technical-documents/spatial-data) 14 ). The distribution of these two labrid lineages may still show a relict signal of those origins, with a high proportion of hypsigenyines and pseudocheilines along the coast of Australia.
More specifically, we calculated the NODF index among sites (hereafter NODF sites ) that ranges from 0 (cells are not nested) to 100 (cells are perfectly nested). To determine the probability that nestedness could be obtained by chance, we compared the observed NODF sites value to those obtained by a null model where species-presence matrices are randomly drawn (n ¼ 9,999) under the sequential swap algorithm  53. Last, we extracted the rank order of sites in the maximally packed matrix (that is, maximally ordered) for simulated occurrence data under the parapatric and sympatric models and observed fish occurrence data. We assessed the level of congruency between observed and predicted rank orders using a Spearman's rank correlation test. Because differences in species composition between places may result from both nestedness and species turnover (that is, the replacement of some species by others between assemblages), we also quantified the levels of species turnover across the Central Indo-Pacific region using a pairwise turnover metric (b jtu ) that is independent of species richness differences  54.
We quantified the level of congruency between observed and predicted distance matrices using a Mantel test (999 permutations). To visualize observed and predicted patterns of species turnover, we plotted the corresponding distance matrices along reduced axes using the non-metric multidimensional scaling neighbour-joining algorithm. The ordination results were then plotted and mapped by assigning a colour to each grid cell according to its position in the ordination space. Phylogenetic reconstruction from molecular sequences. We reconstructed the phylogenetic relationships of species occurring in tropical reefs of the Labridae family together with outgroups according to previous phylogenies  55. Molecular sequences from published sources were downloaded on GenBank focusing on the most frequent loci (Supplementary Data 1).
Due to the lack of knowledge of the sister family of the Labridae, we confined our biogeographic analyses to within the Labridae. Based on a global-scale distribution database  3, 44  , we classified the distribution of the species belonging to the Labridae into three main areas, namely Atlantic, Western Indian Ocean and Central Indo-Pacific. We performed ancestral biogeographic estimation on the reconstructed empirical phylogeny using the R package BioGeoBEARS  65. BioGeoBEARS is designed to perform inference of biogeographic history on time-calibrated phylogenies using models of dispersal, extinction and cladogenesis (DEC)  66  with the addition of several other biogeographic parameters that can be combined to form other biogeographic models (for example, DIVA, BayAREA) in a likelihood framework.
In addition, it allows the inclusion of founder-event speciation scenarios through the inclusion of the 'Jump' ( þ J) parameter. Alternate models can be compared using Akaike information criterion (AIC). We estimated the biogeographic range evolution under a DEC model which allowed many alternative biogeographic scenarios. In addition we compared the inclusion of the ' þ J' parameter for the empirical data using AIC. Species ranges were categorized into three separate biogeographic regions -Atlantic, West Indian Ocean and Central Indo-Pacific. To reflect the tectonic history of marine habitat we allowed a fourth area to only exist in the past -the Tethys. Lineages were only permitted to occupy, or include the Tethys region as part of its range before 12 Ma, a time that signifies the final closure of the Tethys seaway  67.
Diversification rates. To compare to the simulated diversification rate to empirical patterns, we estimated extinction and speciation rates shaping diversification for the coral family Acroporidae using fossils occurrences following the method (PyRate) proposed by Silvestro et al. 71  This method includes the probability of preservation and sampling of fossils to estimate the lifespan of each lineage. We first removed all undetermined taxa from the data set. Due to the lack of fossil occurrences before 60 Ma, we only estimated diversification rates during the last 60 Myr. We first ran 10,000,000 BDMCMC generations both assuming constant and Gamma distributed preservation rate to test the heterogeneity of preservation rate. We then generated 10 randomization of the age of fossils occurrences and performed 10 independent BDMCMC analyses to estimate the diversification rates through time.
Observational analysis We compared the 0. 25 • E-OBS analysis 1950-now  (Haylock et al. , 2008)  and 0. 5 • CPC analysis 1979-now http://www. cpc. ncep. noaa. gov/products/Global_Monsoons/gl_obs. shtml and for Germany the DWD station data obtained via ECA&D. As French precipitation data were not available in real time, the analyses there are based on a relatively sparse subset of stations. We checked that the decorrelation scales of 3-day precipitation are large enough that this is not a problem by comparing with the ERA-interim reanalysis for the past. Satellite-derived products do not perform well in this region and season. The CPC analysis, which is updated daily, shows the highest sums over 29-31 May 2016: the 3-day sum was 55,mm/3dy averaged over the Seine basin and 47 mm/3dy for the Loire basin, see Fig.
We can improve the estimate of the return time by setting ξ=0, i. e. , assuming a Gumbel distribution. This gives a best estimate of 180 yr, with still a wide 95% uncertainty range of 50 to 3000 yr. The 3-day rainfall in the Loire basin was less exceptional (Figs 4bd), with a return time of the order of 100 yr in April-June (2. 5% lower bound 15 yr). The observations also show a positive trend that is not significantly different from zero. A Gumbel fit gives a return time of 35 yr (10. 300 yr). For Germany we first use station data from the DWD, obtained via the ECA&D dataset. We selected all stations in the study area (extended to 13. 5 stations would introduce a spurious downward trend, as the probability of an extreme shower hitting a rain gauge decreases over time. We therefore analyse all April-June maxima together as in  Vautard et al.
5 (0. 4 to 0. 8), so the spatial heterogeneity does not affect this result. We also fitted the spatial maximum of E-OBS to Eq, 1. This dataset has 30% lower extremes due to the gridding with a decorrelation scale that is smaller than the size of a thunderstorm. The ration σ/µ and the shape parameter ξ agree with the station data. This fit gives a return time in 2016 of 9 to 300 yr, in agreement with the station analysis. The trend is positive but not significantly different from zero. The result from the station analysis of a negative trend is well within the uncertainty range. The re-analysis is compatible with this, but the CPC analysis, although at a lower resolution, does not have the expected bias towards lower extremes. We have not yet investigated the reason for this. The simulations were made as an ensemble of 15 realizations for the same forcings.
CORDEX The EURO-CORDEX ensemble  (Jacob et al. , 2014)  was designed to provide a coordinated set of climate projections at a relatively high resolution (12 km), over Europe and part of the North Atlantic. We used a subset of the EURO-CORDEX. These all have different biases, these are corrected for to first order by a simple scaling to a common mean. Note that the common driving GCMs and RCMs imply that these experiments are not all independent. The same moving block procedure employed for the station data found that there are only 4 to 5 degrees of freedom in these 8 ensemble members. The uncertainties take this into account. The basin averages over the Seine and Loire have a shape that resembles the observed distribution well enough after the multiplicative bias correction. For the Seine we find a return time of about 1000 yr (>250 yr) with a risk ratio of 1.