dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2006; Atkinson and Massari, 2011). Nevertheless, this appears to be the easiest way of solving the lack of points problem for detailed scale analysis. Moreover, at such a detailed scale, the landslide source areas can be characterised by various types of environmental conditions that cannot be represented using a single point per landslide. It remains then difficult to suggest one or the other method for sampling the landslide data, since few comparative studies are available, and the results might be variable according to the study area-specific conditions. This attempt of applied research into landslide susceptibility was deliberately conducted using GIS integrated statistical software (ARCSDM,  Kemp et al. , 2001) , which does not allow us to take into account all fundamental aspects of statistical modelling, due to its "black box" functioning on logistic regression.
We use WR paradigm in order to understand the atmospheric situation in summer under anomalous conditions of surface temperature in the Mediterranean basin. We used May to October season for exploring the months before and after the core of the boreal summer June-July-August. We posed the following questions: do the WRs found in the Reanalysis dataset project onto the Atmospheric Model Intercomparison Project (AMIP) simulations? Do the features of the summer WR over the Euro-Atlantic and Mediterranean Sector change from anomalous warm and cold conditions over the Mediterranean Sea? If so, WR changes could help in the future to understand impacts on the region. Therefore, this work is a first attempt to understand the changes in WR from anomalous warm and cold conditions of the Mediterranean basin.
The paper is structured as follows: first the data and the methodology are described in section 2. Section 3 describes the Results. Firstly the projection of SLP from AGCM-AMIP simulations onto the reanalysis WR is assessed. Changes in the frequency 106 for anomalous warm and cold conditions over the Mediterranean Sea are evaluated. A discussion about the implications and proposed further work is addressed in section 4. Finally the main conclusions are sumarized in section 5.
Data and Model We have analysed 5-days mean Sea Level Pressure from ERA-40 reanalysis dataset  (Uppala et al. , 2005)  and the outputs of four AMIP-type simulations with two AGCMs: three integrations with IPSL atmospheric model (LMDz4 atmosphere,  Hourdin et al. , 2006)  and one integration with UCLA v7. 3 model  (Mechoso et al. , 2000; Richter et al. , 2008)  for the period 1957-2000 (see  Mohino et al. , 2011  for details in the simulations). UCLA-AGCM has been run in the UCM institution in the framework of the African Monsoon Multidisciplinary Analyses (AMMA) EU-project, therefore the model will be referred to as UCM-model. IPSL model is part of the CMIP5 exercise and it has been also used for study the atmospheric variability. For instance, in relation with the rainfall over West Africa  (Mohino et al.
, 2011). Besides, it has been shown to be an appropriated tool for evaluating the impacts from sensitivity experiments  (Losada et al. , 2010; Fontaine et al. , 2010). In particular, UCM-model has been shown to capture the observed connection between warmer Mediterranean SST and increased rainfall over the Sahel after 1970s  (Mohino et al. , 2011). The two models have climate resolution; the resolution is 2. 5 x 2 longitudelatitude with 29 vertical sigma levels for UCM and 1. 25 x 2. 5 with 39 vertical levels for IPSL. The UCM model outputs consist of 5-days mean from one simulation where the SST is prescribed from ERSSTv2 dataset (Smith and Reynolds, 2004) as boundary conditions. It has been assumed climatological Sea Ice concentrations. We have used only one UCM simulation because from the ensemble simulations, only one member was available at 5 days time resolution.
Acknowledgements We are very grateful to the editor and the reviewer for their useful comments that helped to substantially improve the manuscript. This study has been partially funded by the  EU-AMMA  project. Based on French initiative, AMMA was built by an international scientific group  and is currently funded by a large number of agencies, especially from France,  UK ,  US  and  Africa. It  has been the beneficiary of a major financial contribution from the  European Community 's  Sixth Framework Research Programme. Detailed information on scientific coordination and funding is available on the AMMA International website http://www. ammainternational. org. Irene Polo has been supported by a postdoctoral fellowship funded by the  Spanish Government. T. Losada is currently supported by the postdoctoral program of the campus  CYTEMA of the ''Universidad de Castilla-La Mancha ''.
, 2007). The ocean and sea-ice initial conditions are taken from the short-range ocean forecasting and data assimilation system. An overview of the ocean and sea-ice data assimilation system is given in section 2. 2. 2. Reanalyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim project are used to initialize the atmosphere and land surface in the hindcast members. The soil moisture in both the forecast and hindcast  are initialised from a land surface reanalysis using the JULES land surface model forced with the Integrated Project Water and Global Change (WATCH) Forcing Data methodology applied to ERA-Interim data (WFDEI;  Weedon et al. , 2011). The forecast soil moisture is initialized from the interannually varying mean climatology of the reanalysis. The hindcast soil moisture uses the time series from the reanalysis.
We have increased the number of ensemble members here in order to get a closer estimation of the skill of the real-time forecast. There is a larger number of members in the summer and winter seasons as these are key periods for many of the extreme events we would like to capture in operational forecasts. A subset of this hindcast data is available through the WMO World Climate Research Program Climatesystem Historical Forecast Project (CHFP; http://www. wcrpclimate. org/wgsip/chfp/; accessed 25 May 2014).
This results in an increase from 0. 76 to 0. 80 in the global pattern correlation of this teleconnection with the observed pattern. The pattern correlations are given for Figure  (3(c-f )). The confidence intervals for significance at the 95% level are calculated using Fisher's test and model results are regridded to the coarser observational grid. The improvement between GloSea4 and GloSea5 is statistically significant. Figure  4  shows the lead time dependence of the anomaly correlation coefficient (ACC) for SST in the Niño 3. 4 region (120 • W-170 • W, 5 • S-5 • N) with the HadISST observational dataset  (Rayner et al. , 2006)  for each of the hindcast start dates. The skill of GloSea5 for the ENSO forecast is very high (the ACC is over 0. 8 for a 5 month lead time). The performance of GloSea5 is slightly higher than GloSea4, although this difference is not statistically significant.
61 for GloSea4. This improvement cannot be attributed to GloSea5's larger ensemble size: random subsamples of the GloSea5 members with nine members per year produce a 98% confidence interval for the correlation score of (0. 75, 0. 84). The  sources of the WNPSH predictability in GloSea5 will be a subject of further research. Summer rainfall over much of East Asia is strongly influenced by the WNPSH (e. g. Lu and Dong, 2001). Figure  8(a)  shows the correlation between JJA 1996-2009 precipitation from the Global Precipitation Climatology Project (GPCP;  Adler et al. , 2003)  dataset and the ERA-Interim WNPSH index. Positive correlations over Eastern China are reproduced by both GloSea4 and GloSea5 (Figure  8(b, c )). These patterns, together with the improved WNPSH index skill in GloSea5, show promise for the development of seasonal rainfall predictions for this region and there are useful levels of skill for user-relevant variables such as precipitation over the Yangtze river basin (not shown).
Tropical storms Predictions of tropical storms are of huge interest to society and industry. On seasonal time-scales, climate models show skill in ‡ (US) National Centers for Environmental Prediction/National Center for Atmospheric Research predicting numbers of Atlantic tropical storms at lead times of up to 1 year  (Vitart et al. , 2007a) ; more recently, multi-year forecasts of tropical storm frequency have been developed  (Smith et al. , 2010). However, probabilistic forecasts of landfall risk have not been demonstrated beyond the medium-range time-scale. We examine the tracks of model tropical storms in GloSea5 over the North Atlantic basin for June-November 1996-2009 using TRACK  (Strachan et al. , 2013, and references therein). Model storms are compared with observations from the revised Atlantic best-track Hurricane Database (HURDAT2;  Landsea and Franklin, 2013)  in Figure  10.
Statistical performance Figures 11-13 show the relative operating characteristics (ROC;  Stanski et al. , 1989)  for near-surface temperature, SST and precipitation for the GloSea5 system in the periods JJA and DJF. The near-surface temperature and precipitation scores were calculated for the Giorgi regions  (Giorgi and Francisco, 2000)  (the Australian region has been divided in two), and the SST scores have been calculated for the Niño and Enhanced Ocean Data Assimilation and Climate Prediction (ENACT; ECMWF, 2005) regions (regions are cut at 55 • S to prevent sea-ice being included in the analysis). The SST results were verified against the Hadley Centre Global Sea Ice and Coverage and Sea Surface Temperature dataset (HadISST;  Rayner et al. , 2003). ERA-Interim is used to verify the near-surface temperatures and the Global Precipitation Climatology Project (GPCP;  Adler et al.
, 2003)  dataset to verify precipitation. Values above 0. 5 denote useful skill compared to climatology. Figure  11  shows the near-surface temperature ROC for upper and lower terciles in JJA and DJF. The plots show useful skill across the globe. There are useful levels of skill over Northern Europe and North America in winter, resulting from the AO/NAO performance. We do find a decrease in skill for the Northern Europe region for the summer period compared to GloSea4. This is due to a low SST bias which develops in the North Atlantic in the new model during the summer months because of a reduced amount of mixing in the upper layers of the ocean. Figure  12  shows the SST ROC for upper and lower terciles in JJA and DJF. Again we find useful skill over the globe. The values are either similar to GloSea4 or are improved.
Acknowledgements This work was supported by the  Joint DECC  and  Defra Integrated Climate Programme ,  DECC/Defra  ( GA01101 ) and received funding from the  EU -funded  SPECS programme  ( FP7-308378 ). NOAA AVHRR data were provided by  GHRSST  and the  US National Oceanographic Data Center. This project was supported in part by a grant from the  NOAA Climate Data Record (CDR)  Program for satellites. The authors wish to thank the numerous  Met Office research  staff who have contributed to make GloSea5 possible.
Consumption data The CPAM (social health care system of Puy-de-Dôme) provided monthly data on subsidized pharmaceutical molecules in Auzon water catchment. This dataset concerns 91% of the total inhabitants of the Auzon basin (43,833 covered by the national social insurance over a total of 47,930 inhabitants) and comprises the number of packages subsidized by the CPAM which have been bought from October 2011 to October 2012 (Table  3 ). Medicines dispensed in hospitals (IFO) and CAF, mainly due to the consumption of coffee, tea and soft drinks instead of its pharmaceutical compound  (Camacho-Munoz et al. , 2010)  are not included in these datasets.
Determination of Fstp (elimination rate in STPs) and environmental parameters The elimination rates of each PhAC for various type of STPs (Table  3 ) were reviewed in the literature  (Besse and Garric, 2008; Besse et al. , 2012; Carballa et al. , 2008; Heberer and Feldmann, 2005; Joss et al. , 2006; Miege et al. , 2009; Oosterhuis et al. , 2013; Ortiz de Garcia et al. , 2013; Soulier et al. , 2011; Vieno et al. , 2007). When no data was available, the most conservative value was taken: the elimination rate was set equal to 0 (i. e. the molecule is not eliminated at all by STP). The daily flow rates of the Allier and Auzon Rivers have been extracted from www. hydro. eaufrance database (French Ministry of Ecology and Sustainable Development).
MECs MEC variations of the sixteen target PhACs have been evaluated during one hydrological cycle, from October 2011 to October 2012. Statistical calculations are presented in Table  4. Vystavna et al. (2012)  show that two river basins with similar hydrologic characteristics and water usage (wastewaters, discharge, irrigation, …) can however present different pollution status according to the different socio-economic characteristics in terms of population density, economic activities, incomes, lifestyles, etc. Then data collected during the present study have been compared in priority with concentrations measured in French watersheds similar to the Allier basin, when information is available. Nine among the sixteen PhACs selected have been positively detected in the Allier River with the following order, taking into account the concentration mean in positive samples: ACE N EPO N CAF NHYD N PRA N FUR-ATE N CAR N DOX.
Comparison between PECs and MECs Annual averages of PEC and MECs have been used to calculate PEC/ MEC ratios (Fig. 3, Table 4 ) to see if PECs and MECs are in the same order of magnitude. An absence of values within the MEC dataset means an absence of PhACs within the Allier River or a concentration below the quantification limit. MECs may then lie anywhere between 0 and LQ for non-detected PhACs. To evaluate the impact of these low values annual averages of MECs are calculated: 1) by considering that an absence of measurement corresponds to a concentration equal to 0 ng•L -1 (MEC with0 ); 2) by considering that an absence of values corresponds to an absence of measurements (MEC without0 , Ter  Laak et al. , 2010) , 3) by attributing to the absent value, a concentration equal to LQ/2 (MEC LQ/2 ,  Choubert et al.
Hydrochlorothiazide HYD's PEC/MEC ratio dataset, varying from 0. 3 to 0. 6, is consistent whatever the method of calculations selected and show a clear underestimation of PECs compared to MECs. Fig. 3e  highlights MEC, PEC min , PEC max and LQ of HYD evolutions. Same peaks are observed on the three concentration curves, PECs presenting the most attenuated evolution. The underestimation of PECs could be explained by a wrong evaluation of the pharmacokinetic parameters. Another hypothesis relies on the persistence of HYD in the environment, badly known. If this PhAC is shown to be highly persistent, a supply by the upper part of the Allier basin could increase MECs compared to PECs and then explain the low ratios observed. Verification on pharmacokinetic parameters and evaluation of the persistence of this PhAC within the environment would allow ameliorating the calculations of PECs for HYD.
Conclusion Sixteen PhACs have been selected for they belong to the priority list of the AFFSAPS and they are a part of the dataset acquired on the Allier River during one year measurements, from October-2011 to October-2012. Nine among the sixteen targeted molecules have been positively detected in the surface waters of the Allier River. Atenolol, carbamazepine and caffeine are the most frequently measured PhACs in relation to both consumption and persistence in the STPs and the environment. Acetaminophen and caffeine, despite their (bio)degradation facility in the STPs and environment, present the highest concentrations of the database due to the fact that these compounds are among the most used in France. Comparison between MECs and PECs has been achieved by using an adaptation of the models proposed by  Heberer and Feldmann (2005)  and EMEA  (2006).
Aknowledgments The study was financially supported by the European Regional Development Fund (ERDF) (Program-0020, contract-34874) through the "Plan Loire Grandeur Nature", the "Agence de l'Eau Loire-Bretagne" (AELB) and the municipality of Clermont-Ferrand. The authors would like to thank CPAM (social health care system of Puy-de-Dôme) for providing the dataset on pharmaceutical consumption. PEC calculation should be used for these PhAC evaluation, without any reserve.
Comparisons between the APi-TOF and the NAIS for our measurements produced a fair agreement as well, so the ion mass spectra obtained by the APi-TOF are, in general, representative of the actual population of small ions and ion clusters. However, a few molecules other than H 2 O may also be lost from some clusters during the sampling, as has also been suggested by comparisons between APi-TOF results and cluster simulations  (Olenius et al. , 2013b; Ortega et al. , 2014). The data obtained from the APi-TOF measurements were processed and analyzed using tofTools, a software package based on MATLAB and under continuous development, mainly at the University of Helsinki. Details on the analysis of APi-TOF data are found elsewhere  (Schobesberger et al. , 2013a).
Acknowledgements. We wish to thank  Tinja Olenius  for running the ACDC model calculations presented in this study, as well as for commenting on the manuscript. Tinja Olenius ,  Joonas Merikanto ,  Theo Kurtén ,  Ilona Riipinen ,  Henning Henschel ,  Oona Kupiainen-Määttä  and  Hanna Vehkamäki  are acknowledged for several useful and insightful discussions. CERN's support of CLOUD with important technical and financial resources and provision of a particle beam from the  Proton Synchrotron  is gratefully acknowledged. This research was funded by the  European Commission   7th Framework Programme (Marie Curie Initial Training Network  " CLOUD-ITN ," grant  215072 ), the  European Research Council (ERC)  Advanced Grant Atmospheric nucleation: from molecular to global scale (ATMNUCLE) (grant  227463 ), the  Academy of Finland  via the  Centre of Excellence Programme  (project  1118615 ) and grant  1133872 , the  Swiss National Science Foundation  (projects  200020_135307  and  206620_141278 ), the  German Federal Ministry of Education and Research  (project  01LK0902A ), the  Austrian Science Fund  (projects  P19546  and  L593 ), the  Portuguese Foundation for Science and Technology  (project  CERN/FP/116387/2010 ), and the  US National Science Foundation  (grants  AGS1136479  and  CHE1012293 ).
Material and methods Data sets: the evaluated farming systems In this paper, as well as in companion papers, the wording 'Pig farming systems' stands for a group of farms within a country, that are similar in terms of objectives, production methods and market orientation. The sustainability of a total of 15 contrasted pig farming systems in five countries (Denmark, France, Germany, the Netherlands and Spain) was evaluated. The 15 systems ranged in size from 2500 to 32 million slaughter pigs per year. They were classified in five categories, on the basis of three parameters: breeding line, targeted market segment and extent of outdoor housing  (Bonneau et al. , 2014) : conventional (n = 5; one per country; conventional breed, standard quality, outdoor index = 0), adapted conventional (n = 5; conventional breed, higher quality, outdoor index = 0), organic (n = 2; conventional breed, higher quality, outdoor index = 3 to 5) and traditional (n = 3; local breed, higher quality or specialty, outdoor index = 2 to 6).
Data sets: the evaluation method The evaluation tool was organised along eight themes (Bonneau et al. , 2014): Animal Welfare (AW), Animal Health (AH), Breeding Programmes (BP), Environment (EN), Meat Safety (MS), Market Conformity (MC), Economy (EC) and Working Conditions (WC). It was mostly based on responses to questionnaires obtained through interviews with farmers and/or their employees; a total of over 500 questions were asked to farmers. The major part of the BP data were obtained from interviews with all nine breeding organisations providing genetic material to the farms included in the study. The MC data were obtained from eight slaughterhouse measurements and from questions asked to chain experts regarding the targeted market. Each system was represented by 3 to 13 farms, depending on the systems (8.
Acknowledgements The authors gratefully acknowledge from the  European Community  financial participation under the  Sixth Framework Programme for Research, Technological Development and Demonstration Activities , for the  Integrated  Project  Q-PORKCHAINS FOOD-CT-2007-036245. The views expressed in this publication are the sole responsibility of the author(s) and do not necessarily reflect the views of the  European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use, which might be made of the information. The information in this document is provided as is and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability.
Trend analyses are standard diagnostics for evaluating stratospheric climate model performance  (Powell et al. , 2013). Observations of stratospheric temperature trends have been regularly assessed as part of the World Meteorological Organisation/United Nations Environment Programme (WMO/UNEP) Scientific Assessments on Ozone Depletion  (WMO, 2007)  and the Intergovernmental Panel on Climate Change (IPCC) assessment of climate changes  (IPCC, 2007). Several different research experiments providing atmospheric temperature have operated on board satellites. However, because their objectives, lifetime and the techniques involved are so different, the time continuity over more than a decade has been difficult to keep. Estimates of global past temperature trends in the middle and upper stratosphere rely primarily on a single dataset derived from the successive operational Stratospheric Sounding Unit (SSU) instruments.
The derived series exhibit substantial uncertainties including change of the vertical weighting functions due to atmospheric CO 2 changes  (Shine et al. , 2008) , radiometric drifts and atmospheric tides  (Nash and Forrester, 1986). Essentially, there are two SSU datasets available for the research community: the first was produced by Nash and colleagues  (Nash and Brownscombe, 1982; Nash and Forrester, 1986; Nash, 1988; Nash and Edge, 1989) , and extensively used for several years as it was the only available SSU analysis (even though details of data processing are largely unknown); and a second, recently released SSU analysis from the National Oceanic and Atmospheric Administration (NOAA) Center for Satellite Applications and Research  (Wang et al. , 2012). The latter takes into account the aforementioned uncertainties and corrects for the limb effect due to varying angles-of-view of the SSU instrument; all procedure is extensively documented in  Wang et al.
(2012) , and the dataset has been cross-evaluated with lidar, Global Positioning System (GPS) radio occultation and Microwave Limb Sounder observations  (Wang and Zou, 2013). Trends derived from these two datasets lead to different trend estimates  (Thompson et al. , 2012; Wang et al. , 2012)  in the middle and upper stratosphere. For example, the old SSU series for the lowest stratospheric channel show that there has been no cooling in the global mean since the early 1990s and cooling is not obvious between the two largest volcanic eruptions  (El Chich ón in 1982 and Pinatubo in 1991). However, trends derived from the new NOAA SSU dataset show strong cooling in the mid-stratosphere for the period 1979-2006, i. e. including the period after 1990  (Wang et al. , 2012). These studies show that adjustments between series are critical to analyse temperature trends.