dataesr/scientific-paragraphs-categorization huggingface dataset:
Total terrestrial water storage (TWS) TWS is the integrated water amount stored on and below the land surface. In this study, we use the monthly terrestrial water storage variations from the GRACE data set, which are derived from a satellite mission mapping the earth's gravitational field. We use the latest solution RL05 "DSTvSCS1401" produced by the University of Texas at Austin/Center for Space Research (CSR). To compare the TWS simulated by ORCHIDEE to GRACE data, we calculate from ORCHIDEE outputs the sum of soil moisture, snowpack (negligible in Amazonia), water on the canopy and the free water stored in the four water routing reservoirs. GRACE data cover the 12-year period April 2002-October 2013 and are expressed as the difference in water depth equivalent from the 5-year average for 2003-2007.
Basin-scale water budget Precipitation (P ) A precipitation data set for the Amazon Basin has recently been collected and harmonized by the ORE (Environmental Research Observatory) HYBAM (Geodynamical, hydrological and biogeochemical control of erosion/alteration and material transport in the Amazon Basin -http://www. ore-hybam. org). This data set is independent from the one produced by  Sheffield et al. (2006). Daily in situ rain gauge observations from the meteorological services of Amazonian countries have been interpolated at 1 • × 1 • resolution over the basin. The correction of CRU-NCEP precipitation by the ORE HYBAM data set contributed to significant improvements in river discharge simulation with ORCHIDEE  (Guimberteau et al. , 2012a).
River discharge (Q) River discharge data have been collected and harmonized by the ORE HYBAM project  (Cochonneau et al. , 2006). The same database used by  Guimberteau et al. (2012a)  is updated up to 2011. Six river-gauging stations (Table  3 ), representative of the main sub-basins of the Amazon Basin (Fig. 1 ), are used to evaluate river discharge simulated by ORCHIDEE. Óbidos (OBI) is the last gauging station before the mouth of the Amazon and is thus the most representative station to assess the average simulated river flow over the whole basin. The station Fazenda Vista Alegre (FVA) measures the discharge of the Madeira sub-basin, in the southern part of the Amazon Basin. The Madeira sub-basin has the largest contributing area and provides nearly 15 % of the total river flow measured at Óbidos.
4. 5. The simulated ET is poorly correlated with the MTE-ET data set but the monthly correlation is higher with the 11LAY (0. 49) than with the 2LAY (0. 33). The low correlation can largely be attributed to the dry season ET simulation, as correlation is higher when the JJA period is removed from the time series (0. 63 and 0. 47 according to the 11LAY and 2LAY, respectively). The ET increase during the dry season, relative to the annual value, is much higher in the simulations (up to +0. 85 mm d -1 ) than MTE-ET estimation (up to +0. 20 mm d -1 ). In the wet season, the 2LAY produces an early maximum soil water storage (January), which remains constant until June (Fig. 7c ), whereas the 11LAY produces higher anomalies, a longer period of soil water recharge (until March) and stores more water in the soil (see Fig.
Acknowledgements. This work was financially supported by the  EU-FP7  AMAZALERT (Raising the alert about critical feedbacks between climate and long-term land-use change in the Amazon) project. We acknowledge the  ORE HYBAM team , who made available their precipitation and river flow data sets for the  Amazon Basin  (http://www. ore-hybam. org). We gratefully acknowledge  Martin Jung  for access to the ET and GPP data set and  Bertrand Decharme  for providing Sheffield's forcing data set including precipitation correction by GPCC. GRACE land data (available at http://grace. jpl. nasa. gov)processing algorithms were provided by  Sean Swenson , and supported by the  NASA   MEaSUREs Program. Simulations with ORCHIDEE were performed using computational facilities of the  Institut du Développement et des Ressources en Informatique Scientifique (IDRIS, CNRS, France ).
Acknowledgements. We thank  Nathalie Bardet ,  Eric Buffetaut  and  Rainer Schoch  for discussion on Machimosaurus and sharing specimen photographs,  Marine Fau  and  Michel Laurin  for discussion on crocodylomorph dermatocranium ornamentation,  Nils Knötschke  (DFMMh),  Eliza Howlett  (OUMNH) and  Sylvia Humphrey (TWCMS)  for collections access, Simão Mateus who photographed the Portuguese isolated teeth,  Ron Blakey  (http://cpgeosystems. com/) for providing the palaeomaps,  Sarah Dylewski  ( Musée de la Princerie ) for arranging specimen photography and  Dmitry Bogdanov  for creating the life reconstructions. We also thank  NHMUK  image resources for digitizing photograph negatives, photographing and photoshopping the NHMUK specimens. Finally, we thank  Andrea Cau  and an anonymous reviewer for their constructive comments to an earlier version of this paper.
, 2011; Simberloff et al. , 2013; Katsanevakis et al. , in press). Understanding the role of biological invasions in modifying biodiversity patterns and the functionality of ecosystems is a major challenge for marine ecosystems ecology  (Borja, 2014). In the Mediterranean Sea, despite the variability in monitoring and reporting effort among countries and the gaps in our knowledge of alien species distribution, there is an enormous amount of information scattered in various databases, institutional repositories, and the literature (including single-or multi-species reviews). By harmonizing and integrating information that has often been collected based on different protocols and is distributed in various sources  (Gatto et al. , 2013) , the needed knowledge basis to assess the distribution and status of marine alien species can be built.
Recently, the European Alien Species Information Network (EASIN;  Katsanevakis et al. , 2012)  increased the accessibility to alien species spatial information by creating a network of interoperable web services through which data in distributed sources is accessed. Integrated distribution maps of single species or species aggregations can be easily produced with EASIN's freely available mapping tools. Here, we utilize information on alien species distribution from EASIN to investigate the distribution patterns of marine alien species in the Mediterranean Sea, in relation to the main pathways of introduction. We investigate how specific human activities (opening of the Suez Canal, shipping, aquaculture) may shape the patterns of alien species distribution and consequently the overall biodiversity patterns in the Mediterranean Sea.
In the absence of a permanent monitoring network and of a biased effort favoring specific locations (e. g. , ports, marinas, and aquaculture facilities), some uncertainty remains (especially for category 2 species). All alien species were included in all other analyses (non-pathway-specific). The "Species Search/Mapping By Multiple Criteria" tool of EASIN was used to select and map species introduced in the Mediterranean by the three major pathways of introduction, i. e. , (1) through the Suez Canal; (2) by shipping; and (3) by aquaculture (Figure  1 ). The spatial data used herein through EASIN originate from the following sources: (1) the CIESM Atlas of Exotic Species (http://www. ciesm. org/online/atlas/index. htm); (2) the Global Biodiversity Information Facility (GBIF; http://www. gbif.
Permutational multivariate analysis of variance (Permanova;  Anderson, 2001)  was used to test for differences among ecoregions, using type III sum of squares and 999 random permutations of the appropriate units. The software Primer 6 was used for multivariate analysis  (Clarke and Warwick, 2001 ) and Permanova+ v. 1. 0. 3 for the PERMANOVA analysis. We also included available data regarding the spatial distribution of native fish and invertebrate species described in the Mediterranean Sea  (Coll et al. , 2010 (Coll et al. , , 2012) )  to compare the spatial patterns of species richness between native and alien species. For fish species we used data available from the "Fishes of the Northern Atlantic and Mediterranean" (FNAM atlas;  Whitehead et al. , 1986 ) updated and integrated by Ben Rais Lasram and  Mouillot (2009)  and  Coll et al.
(2012). Data on invertebrates were compiled from the Food and Agriculture Organization of the United Nations (FAO: www. fao. org/fishery/species/distribution) and the Sea Around Us (www. seaaroundus. org) databases  (Coll et al. , 2012). To estimate the distribution of native species richness, we grouped all the species as the sum of the species co-occurring by overlapping distribution maps at fine-scale resolution (10 × 10 km). We assessed the spatial congruence of native and alien species by calculating the correlation coefficient between the native and alien raster layers, i. e. , the ratio of the covariance between the two layers divided by the product of their standard deviations. Only the cells adjacent to the coastline were included in this analysis, as alien species are generally concentrated in coastal and shelf waters (otherwise the overabundance of zero values in the offshore cells would mask any significant correlation).
In the eastern Mediterranean (Levantine Sea), this high richness of alien species is highly reflected also in terms of total biomass and community FIGURE 6 | Top panel: The 37 "sampling" sites in the seven Mediterranean ecoregions (sensu  Spalding et al. , 2007). Presence/ absence data for these sites were retrieved from EASIN. Site 18: Venice lagoon; site 24: Thau lagoon. Bottom panel: nMDS graph of the 37 sites, based on Jaccard similarity. The underlying dataset is available as a Supplementary File. structure. The proportion of alien fish in trawl catches along the Levantine continental shelf has been increasing, reaching 54% in abundance and 55% in biomass (84 and 66%, respectively for the 15-30 m depth stratum)  (Edelist et al. , 2013). Most of the alien species that are established in the Mediterranean Sea were introduced in the last decades.
Acknowledgments J. M. , M. A. K. and M. B. E. acknowledge support from  USDA Hatch  and  USDA-NRI 2006-03645  grant awarded to J. M. S. L. acknowledges support from the  European Research Council  (project  TEEMBIO , FP7/2007-2013 Grant Agreement no. 281422 ), and from  ANR  projects  SCION  ( ANR-08-PEXT-03 ) and  EVORANGE  ( ANR-08-PEXT-11 ) projects. S. R. K. acknowledges support from  National Science Foundation   IOS-1238885. This is contribution number  4869  from the  University of Maryland Center for Environmental Science.
Funding Information Funding provided by  USDA Hatch  and  USDA-NRI 2006-03645  grant awarded to J. M. , the  European Research Council  (project  TEEMBIO , FP7/2007-2013 Grant Agreement no. 281422 ), and from  ANR  projects  SCION  ( ANR-08-PEXT-03 ) and  EVORANGE  ( ANR-08-PEXT-11 ) projects to S. L and support from  National Science Foundation   IOS-1238885  to S. R. K.
Throughout the paper, results computed from a series of dispersion experiments  [Rudd et al. , 2012]  conducted in a meteorological wind tunnel, under neutral condition, are provided for illustrative purposes. Those data were obtained at the Environmental Flow Research Centre (ENFLO), in the frame of the DYCE project  [Lepley et al. , 2011]. In all the trials, a gas mixture of 1. 5% propane in air was released as a tracer over a period of 15 min. The concentrations were measured by four fast-response flame ionization detectors (FFIDs) at the frequency response of 200 Hz. The height of the sources and of the samplers was 0. 1H, where H is the boundary layer height. Eleven different configurations for the position of the FFIDs were tested with the same wind speed (U H 5 2. 5 m s 21 ) and direction (aligned with the x axis).
, 2007]  scales. Since the ultimate proof for any STE algorithms is the practical validation, the discrete renormalization method has to be ''tested'' and ''validated'' against new Journal of Advances in Modeling Earth Systems 10. 1002/2014MS000385 Atmospheric and Tracer Data (ATD) sets or against data obtained from research studies from the past 50 years at local, regional, continental or global scale. In a future paper, the discrete renormalization technique will be evaluated against the results of the recent dispersion experiment  ''Fusion Field Trial 2007 '' [Storwald, 2007].
Acknowledgments All the data supporting Figures 1 and2 have been published in a research article [Rudd et al. , 2012] http://dx. doi. org/10. 1007/s10546-012-9712-y. They are from the  DYCE dispersion experiments [Lepley et al. , 2011] conducted in the wind tunnel of the  Environmental Flow Research Centre (ENFLO) ,  University of Surrey ,  U. K. We  thank  A. G. Robins , Director of  EnFlo , who has authorized their use. Journal of Advances in Modeling Earth Systems 10. 1002/2014MS000385.
The latter depends on the overlying geologic formations. If the depth of the shallow aquifer increases above the user-defined threshold value, it is assumed that groundwater discharge occurs and contributes to the reach. Upward flow movement to the overlaying unsaturated soil layers is simulated by routing water in the shallow aquifer storage component to the soil by capillary pressure or by direct absorption by the plant roots. This water removal process is termed "revap". The model computes evaporation from soils and plants separately. Potential evapotranspiration can be modeled with three options available in SWAT, that is, the Penman-Monteith, Priestley-Taylor and Hargreaves methods  (Neitsch et al. , 2005) , depending on data availability. Potential soil water evaporation is estimated as a function of potential ET and leaf area index.
Acknowledgements. This work was funded by the  IDB (Islamic Development Bank)  under its  Ph. D. Merit Scholarship program. The work has been conducted in the context of the European project  CLIMB (Climate induced changes on the hydrology of Mediterranean Basins , http://www. climb-fp7. eu/home/home. php). Authors thank  Egis eau , the  Syndicat Mixte du Bassin du Thau and Ifremer  for providing most of the data. Edited by:  A. Shamseldin.
Discharge and flood volumes The selected time series is the Loire river discharge daily series at Rieutord, from 01/09/1983 to 31/12/2002. The duration of the data is K = 19. 33 yr, and the size of the time series is n = 7062 data. The discharge series is plotted in Fig. 5. The local modulus of the river is estimated around 2. 7 m 3 s -1. A physical threshold of u p = 10 m 3 /s based on an expert judgment has been applied. The inter-event duration has been set to 10 days. That leads to an average number of flood events per year λ T = 3. 6, which is physically acceptable and coherent with expert prior knowledge based on physical characteristics of the discharge phenomenon. N T = 70 flood events are thus retained. Their mean duration is around 103 h. 50 % of the flood peaks last only one day, while 9 % last two days and 14 % last three days.
, 2004; Masson-Delmotte et al. , 2005; Huber et al. , 2006; Wolff et al. , 2010  T and CH 4 are derived from ice-core reconstructions. The LGM case is included for comparison with the work of  Hopcroft et al. (2011). GCM Greenland anomalies are averaged over 60-20 • W by 70-80 • N. LPJ-SDGVM shows the difference between LPJ-WHyMe and SDGVM over the region ≥ 45 • N. The range encompasses two scenarios of peat area (standard and with extra peat in Europe and North America). the CO 2 , CH 4 and N 2 O mixing ratios are prescribed based on Vostok and European Project for Ice Coring in Antarctica (EPICA) ice-core data  (Petit et al. , 1999; Spahni et al. , 2005) ; and insolation is modified according to the orbital parameters of  Berger and Loutre (1991). The vegetation distribution that is prescribed in FAMOUS is based on the pre-industrial (PI) but accounts for changes in land area and ice-sheet distribution.
Acknowledgements. This work was funded by the  joint UK and France QUEST-INSU DESIRE  (Dynamics of the Earth System and the  Ice-core Record ) project, and partly by a  NERC  UK grant Earth System Modelling of  Abrupt Climate Change. We thank  James Levine ,  Bruno Ringeval  and  Eric Wolff  for discussions on many aspects of the CH 4 cycle and abrupt climate change. This work was carried out using the computational facilities of the  Advanced Computing Research Centre, University of Bristolhttp ://www. bris. ac. uk/acrc/. Edited by:  H. Fischer.
Triacylglycerols were analyzed by a high-performance liquid chromatography (HPLC) system where a separation in a flow rate linear gradient according to the specific carbon composition. Triacylglycerol-specific identification was carried out with the help of standards and by comparison with literature data. A linear discriminant analysis (LDA) applied to the resulting dataset (given as a % of the total composition) allowed a good discrimination between samples of French olive oils from different areas. Ollivier and co-workers studies demonstrated that it was possible to characterize olive oils from the same country using their fatty acid and triacylglycerol compositions, but only samples with different varietal compositions were perfectly differentiated. Samples with varieties in common were misclassified implying that fatty acid and triacylglycerol composition of olive oils cannot be used as a single geographical "marker as evidenced by other studies on fatty acid composition"  [39, 41].
In other words, using projections of the dataset, it is possible to demonstrate similarities and differences by determining at the same time, which variables are principally involved  [28, 51, 63]. (2) The cluster analysis (CA): the objective is to group the objects in clusters by measuring the distance or the similarities between them, with the assumption that the nearness of objects is correlated with the similarity of their properties  [45]. (3) The analysis of variance (ANOVA): the objective is to analyze the differences between two or more group means  [71]. The most common targeted procedures used are: (1) The LDA: is based on the assumption that the samples distribution is multivariate normal, and the covariance matrix of each category is not significantly different from one case to another.
In other words, the samples are assigned to the category with the lowest differences in the hyperspace of the previously identified variables. The limit between two categories is a linear function  [42, 65, 72]. (2)  The partial least square (PLS): is an asymmetric method establishing a correlation analysis between two dataset and permitting to predict one set from another by estimating the values of one variable from a set of independent variables  [18, 52]. All these methods have been applied to discriminate olive oil samples from different geographical origin. They may be applied on volatiles, organic or trace elements profiles, as well as stable isotope ratios.
There exist also observations of depleted ozone in Mt. St. Helen's plume  (Hobbs et al. , 1982)  that are now believed oxidants). These may reflect differences in the model representations and modelling uncertainties or demonstrate volcano-specific differences in the plume chemistry. Navigating the vast model parameter space of volcanic plume chemistry is challenging due to the non-linear controls on the plume chemistry of multiple inter-dependent parameters including volcanic aerosol, rate of horizontal dispersion, rate of vertical dispersion, wind-speed, volcanic gas flux, bromine in the emission, and high-temperature radical formation. Limited observational datasets are available to compare to the models, and the available data do not fully constrain the high-and low-temperature plume chemistry.
In this manner, the reaction modifies the rapid conversion of volcanic HBr into reactive bromine compared to an earlier HSC-initialised PlumeChem model  (Roberts et al. , 2009)  that predicted extremely rapid (within ∼ 2 min) conversion of HBr into reactive bromine. This rapid rate of HBr conversion is somewhat slowed by the inclusion of the reaction Br + BrONO 2 in the PlumeChem model chemistry scheme used here, which nevertheless remains fast due to the HSC initialisation whose radicals accelerate the onset of the reactive bromine cycles. It is noted that this reaction is neither included in the IUPAC Kinetics nor JPL Data evaluation databases, thus is not necessarily included "as standard" in all atmospheric models of reactive halogen chemistry. BrNO 2 was suggested by von Glasow (2010) to be an important reservoir for Br in the near-downwind plume, because formation of BrNO 2 from volcanic NO x and Br radicals exceeds the rate of BrNO 2 loss via photolysis.
Back Close Full abundances. Figure  1a  shows slant SO 2 column abundance in Mt. Etna's plume over  2004-2005, reported  from DOAS (Differential Optical Absorption Spectroscopy) observations from  Oppenheimer et al. (2006)  and  Bobrowski et al. (2007). The data show a general decline with distance downwind, with the exception of two very near source measurements, which may have been underestimated in the very strong near-source plume, see  Bobrowski and Guiffrida (2012)  for improved SO 2 evaluation. Also shown in Fig. 1a  is the model downwind plume SO 2 column abundance calculated for the plume in the vertical. The decline in modelled SO 2 column abundance with distance (or time) downwind is largely due to dispersion, given the slow rate of in-plume SO 2 oxidation. The rate of dispersion depends on plume depth, width, gas flux and windspeed during each DOAS measurement, which are not fully constrained by available observations.
Available observational datasets only partially constrain the volcano plume chemistry model parameter space. Few simultaneous observations exist to both constrain plume conditions and quantify BrO formation and plume impacts e. g. on ozone under specific volcanological and meteorological conditions. Extremely few observations are available to constrain the near-vent plume composition. The study has shown that more than one model solution can exist that reproduces reported volcanic plume BrO/SO 2. Whilst acknowledging these limitations, we summarize the following recommendations based on the parameter space investigated in this study: The volcanic aerosol loading at Etna is likely significantly lower than that suggested by  Roberts et al. (2009) , and the simulations here found ∼ 10 -11 µm 2 molec SO -1 2 to be suitable.
ACPD  ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD Acknowledgements. This study was financed by  LABEX VOLTAIRE (VOLatils -Terre Atmosphère Interactions -Ressources et Environnement )  ANR-10-LABX-100-01  ( 2011-20 ) and an NSINK career development allowance that enabled HSC software purchase. RSM acknowledges  Christ's College, Cambridge  for a research fellowship. Contributions of authors: TJR designed and performed the PlumeChem model experiments and HSC calculations and wrote the manuscript. RSM advised on HSC methodology and contributed to manuscript writing. LJ advised on scientific scope and contributed to manuscript writing. ACPD  ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD ACPD.
Mortality data Mortality data were extracted from the French National Cattle Register, managed by the French Ministry of Agriculture. This register gathers information about cattle herds (identification number, location) and animal movements (date and nature of the movement, e. g. entry/exit; reason of the movement, e. g. birth/ purchase for entries, death/slaughter/sale for exits). This database was used to determine the daily number of living or dead cattle at a herd scale. The analysis focused on unplanned farm deaths or euthanasia between January 1 st 2003 and December 31 st 2006 in mainland France, except for the greater Paris region where there were too few cattle. In all, about 45 million cattle were registered and 4. 9 million died from unforeseen causes during the 2003-2006 period.