dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2010) , the new version CB05 with updated toluene chemistry  (Whitten et al. , 2010)  and a new aerosol module AERO6. These are to be used with CAM and RRTMG (two radiation schemes options in WRF-CMAQ) to calculate the aerosol extinction, single scattering albedo, and asymmetry factor for shortwave radiation and aerosol extinction for longwave (LW) radiation. The latest version of the two-way coupled WRF-CMAQ model has also included aerosol indirect effects  (Yu et al. , 2001). The aerosol chemical species calculated by CMAQ are combined into five groups: watersoluble, insoluble, sea-salt, black carbon, and water. The refractive indices for these species are taken from the OPAC database  (Hess et al. , 1998)  using linear interpolation to the central wavelength of the CAM and RRTMG wavelength intervals  (Wong et al.
Continued. Aerosol Robotic Network, http://aeronet. gsfc. nasa. gov/ BSRN, Baseline Surface Radiation Network, http://www. bsrn. awi. de/ CALIPSO, Cloud-Aerosol Lidar and Infrared Pathfinder, http://www-calipso. larc. nasa. gov/ Cloudnet, http://www. cloud-net. org/index. html EEA Airbase, Air quality database of European Environmental Agency, http://acm. eionet. europa. eu/databases/airbase/ EMEP, European Monitoring and Evaluation Program, http://www. emep. int/index. html, http://ebas. nilu. no ESA CCI soil moisture, http://www. esa-soilmoisture-cci. org/ EUCAARI, European Integrated Project on Aerosol Cloud Climate Air Quality Interactions, http://www. atm. helsinki. fi/eucaari/ EUSAAR, European Supersites for Atmospheric Aerosol Research, http://www. eusaar. net/, http://ebas. nilu. no GPCP, Global Precipitation Climatology Project, http://www.
gewex. org/gpcp. html, http://gpcc. dwd. de MARGA, Monitor for aerosols and gases in air, http://products. metrohm. com/prod-MARGA. aspx MODIS, Moderate Resolution Imaging Spectroradiometer, http://modis. gsfc. nasa. gov/ RAOB, WMO radiosonde observations network, http://www. esrl. noaa. gov/raobs/ Network/database acronyms and websites: AERONET, Aerosol Robotic Network, http://aeronet. gsfc. nasa. gov/ BSRN, Baseline Surface Radiation Network, http://www. bsrn. awi. de/ CALIPSO, Cloud-Aerosol Lidar and Infrared Pathfinder, http://www-calipso. larc. nasa. gov/ Cloudnet, http://www. cloud-net. org/index. html EEA Airbase, Air quality database of European Environmental Agency, http://acm. eionet. europa. eu/databases/airbase/ EMEP, European Monitoring and Evaluation Program, http://www. emep.
Chemical species. Acknowledgements. This work was realised within and supported by the COST Action ES1004 EuMetChem. Several authors of this article are grateful for the support received from the following projects: EC FP7 TRANSPHORM and PEGASOS, Nordic En-sCLIM, the RF SOL No 11. G34. 31. 0078 and No 14. B37. 21. 0880 at RSHU, the US NSF EaSM program (AGS-1049200) at NCSU, the German Science Foundation grant No SCHL-499-4, the Spanish projects "Supercomputación y eCiencia" (CSD2007-0050) and CGL2010-19652, Estonian grants ETAG 7895 and SF0180038s08, and the NASA's Air Quality Science Team (AQAST) programme.
Acknowledgements go to  Ralf Wolke  ( IfT Leipzig ,  COSMO-MUSCAT) ;  Kristian Nielsen ,  Ashraf Zakey  and  Roman Nuterman  ( DMI, Enviro-HIRLAM ),  Jacek Kaminski ,  Joanna Struzewska  ( GEM-AQ) ;  Fabien Solmon  ( ICTP, RegCM ),  Maud Leriche ,  Christine Lac  (Meso-NH),  Malte Uphoff  ( Univ. Hamburg, M-SYS), Erik van Meijgaard (KNMI ,  RACMO2 ),  Claas Teichmann  ( CSC, REMO-HAM ),  Bärbel Langmann  ( Univ. Hamburg, REMOTE ),  Rohit Mathur ,  Jon Pleim ,  Christian Hogrefe  ( US EPA, WRF-CMAQ ) for providing detailed information on the named model systems and for valuable comments,  Carlos Pérez García-Pando  for permission of his figure use,  Sabine Ehrenreich  for a technical support with the manuscript revision, and all  COST ES1004  members for collaboration and productive discussions during COST meetings.
nilu. no GPCP, Global Precipitation Climatology Project, http://www. gewex. org/gpcp. html, http://gpcc. dwd. de MARGA, Monitor for aerosols and gases in air, http://products. metrohm. com/prod-MARGA. aspx MODIS, Moderate Resolution Imaging Spectroradiometer, http://modis. gsfc. nasa. gov/ RAOB, WMO radiosonde observations network, http://www. esrl. noaa. gov/raobs/ Network/database acronyms and websites: AERONET, Aerosol Robotic Network, http://aeronet. gsfc. nasa. gov/ BSRN, Baseline Surface Radiation Network, http://www. bsrn. awi. de/ CALIPSO, Cloud-Aerosol Lidar and Infrared Pathfinder, http://www-calipso. larc. nasa. gov/ Cloudnet, http://www. cloud-net. org/index. html EEA Airbase, Air quality database of European Environmental Agency, http://acm. eionet. europa. eu/databases/airbase/ EMEP, European Monitoring and Evaluation Program, http://www.
Lidars/Ceilometers 100 RAOB twice D LP, 2D - - - - cloud cover, cloud top, SEVIRI satellite H 2D cloud optical depth, cloud MODIS satellite twice D 2D base AVHRR satellite D 2D CALIPSO Lidar satellite I Cloudnet 3 Cloudnet H in-situ, LP Selected Lidars, Ceilometers - - H in-situ cloud liquid water path AERONET 60-80 AERONET H in-situ, LP Cloudnet 3 Cloudnet H in-situ, LP soil moisture satellite soil moisture satellite ESA CCI D Network/database acronyms and websites: AERONET, A.
$=49! 2$@$2$-+$! '(. $)! \A$--$44! ,,4$. ! ,4! 49$! P$&4$2-'(&4! =,24! (@! 49$! %&),-. 7! 2>-&! @2('! #9$2>12>1! (-! 49$! &(>49$2-! +(,&4! 4(! A,. ,',! (-! 49$! -(249$2-! +(,&4! \d%*;! L1];! _9$! R(9(!. %&+(-4%->%45!(++>2&!,4!WXC!N'!1$-$,49!49$!&(>49$2-!$-. !(@!=2(@%)$!TOM!,-. !49$-We gratefully acknowledge Alex Brisbourne, Victoria Lane and David Hawthorn (NERC Seis-UK equipment pool), the French Embassy in Yemen (J. G. Sarkis, J. Dechezlepretre and C. Bousquet), local governers and the people of the Yemen governerates for their help during the field work. We deeply thank Dr I. Al Ganad from GSMRB during the deployment. We thank C. Tiberi, J. Vergne, G Hetenyi for providing software. The work was funded by the Agency National de la Recherche ANR-07-BLAN-0135 YOCMAL project and Rift2ridge project, CNRS-INSU-PICS Yemen, GSMRB Yemen and Actions Marges.
The facilities of SEIS-UK are supported by the Natural Environment Research Council (NERC) under Agreement R8/H10/64. DK is supported by NERC grant NE/L013932/1. ,. 7! Q;! ,-. ! M,&>5,>7! 6;7! Xc"C;! 62>&4,)! &42>+4>2$! (@! 49$! 2%@4$. ! 3()+,-%+! ',2*%-&! ,-. ! >=)%@4$. ! =),4$,>! (@! U$&4$2-! :$'$-! @2('! 2$+$%3$2! @>-+4%(-! ,-,)5&%&;!S$(=95&;!r;!Q-4;!. (%h"c;"ckCb*a%b**4cJX;+4%(-&;!G,249!g),-$4;!#+%;!K$44;7!XC"7!"JJY"kZ;! T)%7!R;:;7!U,44&7!T;M;7!Xc"C;!#>1&%. $-+$!9%&4(257!+2>&4,)!&42>+4>2$7!,-. !$3()>4%(.
In particular, the aerosol size distribution, as well as the morphology of the particles, may be affected by these modifications mainly due to the pressing during pellet production. Moreover, the fact of dispersing a very small quantity of aerosol (0. 1%) in the KBr matrix determines a strong suppression of the aerosol scattering signal, which becomes almost negligible compared to that from the matrix itself. As a consequence, mainly the absorption component of the extinction is measured with the pellet technique, with a strong underestimation of the scattering fraction. This aspect is particularly important for dust particles, for which scattering is estimated to significantly contribute to the atmospheric infrared extinction  (Dufresne et al. , 2002). Despite these limitations and uncertainties, nonetheless, pellet spectroscopy data represent nowadays the only available references for dust infrared optical properties, and are used as the main basis for global aerosol databases such as OPAC (Optical Properties of Aerosols and Clouds;  Hess et al.
The mean aerosol optical depth (τ ) at 0. 50 µm and the Ångstrom exponent (α, calculated between 0. 44 and 0. 87 µm) obtained from AERONET (Aerosol Robotic Network) measurements during filter sampling for the different cases are also reported; no data are available for the SOP1-8, when the solar zenith angle at the start of the sampling was > 70   1 ) varied between a few hours to 1-2 days, depending on the aerosol concentration and the duration of the event. Dust particles deposited on filters have been analysed to obtain their bulk mineralogical composition. Then the infrared transmission spectra have been measured on the samples of collected dust to estimate their complex refractive index. The number size distribution of dust particles in the diameter range 0. 3-20 µm was measured during filter sampling by means of a Grimm optical particle counter (OPC, model 1.
Optical calculations have been performed using the Mie theory for spherical particles. The complex refractive index was set at 1. 53-0. 002i, within the range of values available in the literature for Saharan dust (e. g. Osborne et al. , 2008; Petzold et al. , 2009). After refractive index correction the diameter measurements range for the Grimm 1. 108 became 0. 38-28. 9 µm. The average of the size distribution data measured corresponding to each of the five dust filter samplings has been calculated. Continuous Grimm data were available for the three Banizoumbou SOP dust events, whereas Tamanrasset measurements were acquired only at the end of the dust episode for N32 (26 July 2006 from 10:00 to 14:17 UTC) and at the beginning for N93 (5 October 2006 between 01:00 and 08:07 UTC).
Comparison with literature data and the OPAC database The results of our analysis have been compared with other studies providing direct estimates (pellet spectroscopy data on dust collected at different sites, i. e. Germany,  Barbados, and Niger; Volz, 1972 Volz, , 1973;; Fouquart et al. , 1987)  and mineralogy-based calculations  (Longtin et al. , 1988)  of the dust infrared refractive index, as well as syntheses of available literature data  (Carlson and Benjamin, 1980; Sokolik et al. , 1993). These studies have been selected as they serve as the main basis for models for aerosol optical properties (e. g. Toon and Pollack, 1976; Shettle and Fenn, 1976, 1979; WMO, 1986; D'Almeida, 1991)  and global aerosol databases, such as OPAC and GADS, all extensively used in a wide variety of radiative and climatological studies.
Besides, the OPAC aerosol database plays a very important role in satellite remote sensing as it is the most used reference for dust properties in the retrieval algorithms of several sensors measuring in the thermal infrared (MODIS, SEVIRI, AVHRR, HIRS, AIRS, IASI) (see Table  1  in  Klüser et al. , 2012). Owing to its widespread utilization and relevance for atmospheric application, the complex refractive index of the desert aerosol model from the OPAC database is also considered for comparison with our results. The results of the comparison are shown in Fig. 7. For both the real and the imaginary parts, our estimates of the dust refractive index fall, over the whole considered spectrum, within the range of variability reported in literature data. For the imaginary part, a similar spectral behaviour is observed in the 8-12 µm window region between our data and the different literature curves, with the only exception of  Longtin et al.
(  2   4 )  Carlson and Benjamin (1980) ,  Sokolik et al. (1993)  (indicated as CA80): mineral dust model, as used in the OPAC and GADS databases (based on a synthesis of measurements on Saharan dust or generally on desert aerosols). (  5 )  Longtin et al. (1988)  (indicated as LO88): dust sand, i. e. modelled as an internal mixture of hematite (10 % by volume) and quartz. In the internal mixing hypothesis the dust refractive index is calculated as the volume average of the refractive indices of individual minerals. (6) OPAC desert model: modelled as an internal mixture of a water soluble component at 80 % relative humidity (5 % by volume), and mineral dust in nucleation (3 % by volume), accumulation (72 % by volume) and coarse (20 % by volume) modes  (Hess et al. , 1998) ; the dust refractive index for the three modes is that reported by CA80.
In this case, the combined effect of refractive index and size distribution variability will emerge from the results. In this study, we have decided to neglect the effect of particle shape  (Kalashnikova and Sokolik, 2002; Nousiainen, 2009) , and dust optical calculations have been performed using Mie theory for spherical particles. The ensemble of calculated dust optical properties is reported in Fig. 8. By comparison, the spectral optical properties obtained using the OPAC refractive index, calculated considering the mean_B size distribution, are also shown in the plot. We consider at first data obtained for the fixed dust size distribution (Fig. 8a ). Calculated optical properties, k ext and ω in particular, follow the spectral signature and variability of the complex refractive index, with the strongest absorption observed in the window region and at wavelengths > 17 µm.
Conclusions In this paper we have presented new experimental estimates of the infrared complex refractive index of African mineral dust. The particle refractive index has been derived from laboratory transmission spectra (2. 5-25 µm) by applying a retrieval algorithm which combines Mie and Lorentz dispersion theories. Spectroscopy measurements have been performed on five natural dust samples collected at the sites of Banizoumbou (Niger) and Tamanrasset (Algeria) during the AMMA campaign in 2006 and which originated in different Western Saharan and Sahelian source regions. Co-located in situ measurements of the dust size distribution and laboratory analyses of particle mineralogy have been considered together with spectroscopy data in order to relate the spectral features of the refractive index to the physico-chemical properties of the particles.
For the N32 and N93 cases, instead, the iron oxide content has been estimated from the elemental Fe assuming the ratio between the iron in the form of oxide and the total elemental Fe to be 0. 59  (Formenti et al. , 2008). The Fe content of the N32 and N93 events has been calculated with a procedure similar to that used for the TDM, i. e. by performing a linear fit (y = dx) of the measured Fe and τ for the four dust samples obtained before and after the two considered events. The result in this case is d = (142 ± 18) µg, with a correlation R 2 = 0. 87. The uncertainty on the iron oxide content estimated through this procedure is ∼ 12 %. For the SOP1-17 sample only, the speciation between hematite and goethite has been also determined through XANES and EXAFS analyses. 3. The mass of clays (kaolinite, illite, smectite, chlorite), which cannot be directly determined from XRD data due to the absence of appropriated calibration standards for these components, has been estimated as the difference between TDM and the total mass of quartz, calcium-rich species, feldspars, and iron oxides.
Acknowledgements. This work was supported by the  Centre National des Etudes Spatiales (CNES)  and by the  OSU EFLUVE (Observatoire des Sciences de l'Univers, Enveloppes Fluides de la Ville à l'Exobiologie). Based on a French initiative, AMMA was built by an international scientific group and is currently funded by a large number of agencies, especially from  France , the  United Kingdom , the  United States , 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 Web site at http://www. amma-international. org. The Tamanrasset AMMA supersite deployment and operation were supported by the  Office National de la Météorologie (ONM) in Algeria , the  Laboratoire de Météorologie Dynamique (LMD) , and the  Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS).
Acknowledgments Data analysis of this work was supported the  NSFC  projects ( D03/41321062  and  D0512/ 0207131138 ) and the  MOST 973 project  ( 2010CB428500 ). The field campaign was supported by the  MOST 973  Project (2005CB422203). Part of the work by  Nie Wei  was supported by the  Hong Kong Polytechnic University. Part of this work was also supported by the  Jiangsu Provincial Science Fund for Distinguished Young Scholars  awarded by  A. Ding  (No. BK20140021 ) and the  Centre of Excellence in Atmospheric Science in Finland. We thank the  Mt. Heng Observatory  for providing the measurement platform for this campaign. The emission inventories were provided by  ECCAD  (The GEIA Database) and  Q. Zhang  at  Tsinghua University , and the CALIPSO data were provided by  LARC of NASA. The authors would like to thank  Douglas R.
Model data The multi-model ensemble (MME) analyzed here includes the contributions to the CMIP5 project produced with the HadCM3, MRI-CGCM3, MIROC4h, MIROC5, CanCM4, Accepted Article climate models. These contributions consist in 10-year long hindcasts initialized from an estimate of the observed climate state every 5 years over the period 1960-2006 between November, 1 st and the following January, 1st. This ensemble is referred to as Init thereafter. Complete calendar years from January to December are considered as forecast years here. Two ranges of forecast years are analyzed in the following: from forecast years 2 to 5 and 6 to 9, which are classically used as target periods for analyses of climate predictions  (Doblas-Reyes et al. , 2013). The MME hindcast set gives 10 predicted values for a specific forecast range.
The forecast fields are interpolated using a conservative method onto the grid of the observational dataset used for verification before any analysis. More details on the model experiments analyzed in this work are provided in Table  1. A sister ensemble has been built from the "historical" coupled simulations and the first years of the climate projections following the CMIP5 RCP4. 5 (Representative Concentration Pathway 4. 5W/m 2 , Taylor et al. , 2012) scenario run with the same models. In these experiments, the simulated natural variability is not constrained to follow the contemporaneous observed one except through the effect of the external radiative forcings, which are exactly the same as in the Init MME. Additionally, errors in the response to the external radiative forcings could accumulate over time.
Observational data For temperature verification, we have used a combination of land surface temperatures from the National Centers for Environmental Prediction (NCEP) GHCN/CAMS dataset  (Fan and van den Dool, 2008) , SST from the NCDC ERSST v3b dataset  (Smith et al, 2008)  and, north of 60°N, the GISTEMP dataset with 1200km decorrelation scale  (Hansen et al 2010). This dataset covers the 1960-2012 period. For precipitation, a land-only monthly dataset is considered because of the sparse observational coverage over the oceans before 1979. We use the Global Precipitation Climatology Centre (GPCC) v5 data  (Rudolf et al 2010)  for the 1960-2012 period. For sea level pressure, we have used the HadSLP2 dataset  (Allan and Ansell, 2006)  for the 1960-2004 period.
Conclusions In this article, we have used the extensive CMIP5 database of multi-model ensemble decadal hindcasts to assess the state-of-the-art skill in predicting the Mediterranean temperature and precipitation and the reasons behind this skill. High and significant skill in predicting 4-year This article is protected by copyright. All rights reserved Accepted Article averaged annual and summer mean temperature is found over most of our study domain. Still positive but slightly lower skill is found during the winter season. Most of the high skill originates from the model accumulated response to the external radiative forcings. The initialization contributes however to the temperature forecast skill over the Mediterranean Sea and surrounding land areas. The high and significant correlations between the observed Mediterranean temperatures and the observed AMO in the summer and annual means which are captured by the multi-model ensemble suggest that this added skill is related to the ability of the multi-model ensemble to predict the AMO.
Acknowledgments This work was supported by the  EU- funded  SPECS  ( FP7-ENV-2012-308378 ),  NACLIM  ( FP7-ENV-2012-308299 ),  QWeCI  ( FP7-ENV-2009-1-243964 ),  CLIM-RUN (FP7-ENV-2010-1-265192 ) projects, the MICINN-funded RUCSS ( CGL2010-20657 ) project, the  Catalan Government  and the  NOAA  grant  NA10OAR4310208. Oriol Mula-Valls  and  Domingo Manubens-Gil  are thoroughly acknowledged for the invaluable technical support.
Statistical analysis We used a principal component analysis performed on mollusc abundance data (%) in order to evaluate changes in community composition and structure. This statistical analyse was extracted from the Statistica package (Statsoft, version 9. 0). The results from Lake Annecy and used below for comparison with those from Lake Bourget have been obtained from similar types of sites (i. e. , sediment cores and samples in littoral zone) and using the same methods as those used for the present study  (Mouthon and Magny, 2004).
Comparison of fossil, 1930s and present mollusc communities A principal component analysis was performed on quantitative data (in percentage) from Lakes Bourget and Annecy to examine temporal changes in mollusc community structure (Fig. 4 ). Unionidae, whose adult shell size is large (>75-100 mm), were excluded from the analysis as they were not found in the core samples, probably due to the small surface areas sampled. The two first axes accounted for 62. 8% of the total mollusc data inertia (F1=37. 9, F2=24. 9%, respectively). The structures of fossil mollusc assemblages at the end of the Holocene in the two lakes (positive scores on axis 1) were close and varied little during the different periods (Fig. 4A ). On the contrary, the structures of contemporary populations (negative scores on axis 1) are very far from those of the Late Holocene and different from each other.
DISCUSSION Data collected from sediment cores and samples taken in the 1930s by Favre and more recently in the 2000s highlight the rapid changes occurring in the mollusc com-  During the late Holocene, the structures of mollusc populations of Lakes Bourget and Annecy remained very similar. In spite of the smaller number of core samples extracted from the littoral zone of Lake Bourget (3 vs 8 at Annecy), the relative abundances of the species were close to those obtained by  Favre (1940) , based on the sampling performed at the top of lacustrine chalk using a drag-net (twelve points from 6 to 20 m depth) and can therefore be considered as representative of the fossil assemblages of this lake. Globally, populations analogous to those in Lakes Bourget and Annecy can be found in the Holocene chalk deposits of a large number of lakes in Switzerland  (Favre, 1935 (Favre, , 1940;; Magny et al.
Acknowledgments The present report has emerged through the meetings as well as many informal discussions face to face and over e-mails for the whole duration of the  COST Action   ES0905. The lead author sincerely thanks to all the COST participants for this reason. Especially, in preparing the manuscript,  Florin Spineanu ,  Linda Schlemmer ,  Alexander Khain  have provided text segments. Parts of the manuscript are carefully proof read by  Peter Bechtold ,  Alexander Bihlo ,  Elsa Cardoso ,  Alan L. M. Grant. Marja Bister  and  Sandra Turner  have provided specific comments. Wim de Rooy  has gone through the whole manuscript several times for critical reading and with suggestions for elaborations. Discussions on turbulence modelling with  Steve M. Tobias  and a reference on TRMM data from  Steve Krueger  are also acknowledged.
Acknowledgements. This research was supported by the  MATH-ACC  Project funded by the  Italian "Programma Nazionale di Ricerche in Antartide " (PNRA contract no. 2010/A2. 11-2753 ). The work of H. A. Dijkstra was also sponsored by the  COMPLEXITY  Project PreKurs funded by the  Netherlands Organization for Scientific Research (NWO). We would like to thank two anonymous reviewers, whose detailed comments helped improve the manuscript. Edited by:  M. Hecht.