dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This study was supported by the  LEFE/CLAPA programme (Institut National des Sciences de l'Univers). This work was partially supported by funding by the  ICE2SEA programme from the European Union 7 framework programme , grant number  226 375. Ice2Sea contribution number  176. Funding for this project was provided by a grant from la  Région Rhône-Alpes. We are grateful to the  Observatoire des Sciences de l'Univers de Grenoble (OSUG)  which provided fundings for visits in France and in the USA. Parts of this research by  N. B. W. and T. L. were performed at the  University of Wisconsin-Madison  for the  Jet Propulsion Laboratory, California Institute of Technology , sponsored by the  National Aeronautics and Space Administration. CloudSat data were obtained from the  CloudSat Data Processing Center  (http://www.
The MUST wind tunnel data set served as a validation case for obstacle-resolving micro-scale models in the COST Action 732 "Quality Assurance and Improvement of Micro-Scale Meteorological Models". The code used for the numerical simulation is code CHENSI, simulations carried out showed a certain degree of agreement between the experimental results and those of the numerical simulation, they highlight the need for proceeding to an experimental campaign but with more measurements and the need for having a good control of determining factors in the exploitation of its results. The aim is to explain the experimental data obtained by atmospheric wind on the physical model. The site company of Mock Urban Setting Test (MUST) was selected to be simulated by the code CEN CHENSI developed by the team of Dynamique of l'atmosphere Habitee of LME/ECN.
The code was based on (K-İ) model of (Launder and Spalding). For the integration of the PDE (Potential Dimensional equations) constitute the mathematical model, the finite volume method of (Ferziger and Peric) was used within the decade disposition of unknowns MAC of (Harlow and Welck) for the discretisation of PDE terms. The boundary conditions were imposed according to the wall laws (In ground and on buildings) or within Dirichlet condition (Inlet boundary) or of Newman (Outlet boundary or top limit). The numerical domain used was comparable to the one of the atmospheric wind experiences within a three-dimensional Cartesian mesh. Numerical results presented in this study for the mean flow field, turbulent kinetic energy in the direction of wind incidence 0 o. For an objective comparison of the CHENSI model performances within other European codes used for MUST configuration simulation.
Introduction The aim of the work is to provide data analysis about the use of CHENSI for modeling atmospheric flows, with the help of data obtained in the COST 732 framework (Quality Assurance and Improvement of Micro-scale Meteorological Models) over a mock urban setting (Michael Schatzmann et al 2009). The investigations were carried out with CHENSI, compared to wind tunnel. The work is relevant for the CHENSI community because it deals with environmental problems, with which CFD and CHENSI might be a good tool to deal. As I can see from the forum it is an interesting topic for several users. It also validates CHENSI data with wind tunnel measurements. COST is an Intergovernmental European framework for international cooperation between nationally funded research activities. The COST Action 732 (Quality Assurance and Improvement of Micro-scale Meteorological Models) program was carried out to perform proper quality assurance method for micro-scale meteorological models which can model urban pollution dispersion and therefore are used in environmental impact studies and decisions with economic and political consequences.
CFD code validation The code CHENSI has been validated for a series of 13 reference flows predicting either of the physical characteristics that are important in the lower urban atmospheric flows: diffusive transport in round and plane jets, and in plumes spreading in a uniform or stratified atmosphere; recirculations in isothermal boundary layers over backward-and forward-facing steps, 2-D and 3-D rectangular blocks; inhomogeneous dynamical and thermal developments in internal plane boundary layers over roughness and temperature steps. These validation tests have been extensively presented and analysed by  Sini (1986) ,  Sini and Dekeyser (1987, 1989) , Lévi  Alvarès et al. (1990) , Lévi Alvarès (1991),  Zhang (1991)  and  Mestayer et al (1993). The results show that the standard k-İ model gives good to excellent predictions for all the mean flow fields and relatively good prediction of the turbulent diffusion, with no systematic deficiencies except in the flow zones where coherent structures, clearly non-isotropic, are of importance in transport processes.
De novo sequencing and annotation of Tiso transcriptomes Transcriptomes of two Tiso strains, the reference strain (Tiso-Wt) and a selected strain (Tiso-S2M2), were sequenced with Illumina HiSeq 2000 technology. Read pairs obtained for each strain were filtered and assembled into 44,983 and 44,564 transcripts for Tiso-Wt and Tiso-S2M2, respectively (Table  1 ). These two datasets were clustered to obtain a final consensus transcriptome of 46,687 transcripts. Of these, 34,374 were unique, representing a total length of 44. 4 Mb. At least two isoforms were detected for the remaining  (12, 313)  transcripts. The origin of these non-unique transcripts could be the sequencing of mature and non-mature RNA, alternatively spliced or the sequencing of duplicated genes  [27]. The consensus transcriptome was annotated and 14,790 transcripts were associated with at least one Gene Ontology (GO) term.
Their respective expression was confirmed by a RT-qPCR experiment (Data S1) and we consider them as excellent candidates for future studies. Moreover, homologous gene of TisoTranscripts-288 was identified in E. huxleyi (Emihu-456684), P. tricornutum (Phatr-45510) and C. merolae (CMG-147C). However, TisoTranscripts-160 was only one homologous identified in E. huxleyi (Emihu-213608).
Given their putative functions, a potential implication of these latter two genes in the lipid over- accumulation observed in Tiso-S2M2 is not obvious, but they should be kept in mind for further studies. The targeted phenotypic character of the selected strain is lipid over-accumulation and we therefore focused on the annotated genes of lipid pathways. Transcripts with putative annotation linked to lipid metabolism and containing a positional polymorphism or differential expression level were selected. Two candidate genes were identified (TisoTranscripts-288 and TisoTranscripts-160) with differential expression between the strains. These published results rely on RNAseq dataset and because the implementation of this technique is however rather new, we confirmed the results using the well-established RT-qPCR technique (data S1) in a biological replicates.
). Approximately 4-5 mg of cDNA were used for library construction, undertaken by the Genoscope platform (http://www. genoscope. cns. fr). Sequencing was performed using the paired-ends method with a read length of 100 bases, producing an average of 183 Million of read pairs per transcriptome. Read pairs obtained were analyzed with FastQC software developed by S. Andrews in the Babraham Institute (www. bioinformatics. bbsrc. ac. uk) in order to validate run qualities (read number, quality score of nucleotide sequenced in Q-phred scale  [58] , composition of reads). We sequenced 35 and 37 Gb for Tiso-Wt and Tiso-S2M2 cDNA libraries respectively (Table  1 ) with a mean sequencing quality score per read at Q35. Sequence data for this article have been deposited in the National Center for Biotechnology Information and are accessible in: SRR823264 for Tiso-S2M2 and SRR824147 for Tiso-Wt.
Construction of the reference transcriptome for Tiso Read pairs obtained for each sample were filtered to select only read pairs with the correct quality to be assembled. First, reads containing Illumina sequencing adapter and Illumina control sequences were eliminated with CutAdapt software  [59]. In a second step, reads were filtered on a quality score of the last nucleotides for each read because quality of sequencing decreases proportionally with read length  [60, 61]. The last nucleotides were eliminated until detection of a nucleotide with a quality score of Q25. In a third step, reads were filtered on their length. Reads inferior to 75 bp were eliminated. In a final step, reads were filtered on a mean quality score of all nucleotides. Reads with a quality score inferior to Q25 were eliminated.
After these different screens, 79. 1% and 82. 4% total read pairs for Tiso-Wt and Tiso-S2M2 respectively were conserved (Table  1 ). Read pairs of each library were assembled with the Trinity pipeline  [62]  with the parameters advised by Zhao Q-Y et al. ,  [63]. The putative transcripts assembled for each library were clustered with CD-hit-EST software with 90% identity  [64]  to obtain the consensus transcripts considered as the reference transcriptome of Tiso (Table  1 ). Transcriptome datasets of each strain (Tiso-Wt and Tiso-S2M2) were aligned using the reference transcriptome with MosaikAssembler software (Wan-Ping Lee and Michael Stro ¨mberg, Marth lab). On average, 72. 7% (s = 1. 4) read pairs were aligned on unique locus, 20. 5% (s = 1. 6) aligned on multiple loci and 5. 4% (s = 1.
Research of homologous genes from several reference microalgae and comparison of codon bias Transcriptome data from Tiso were compared with data from 8 other reference microalgae. Data for 5 of these (the haptohyte Emiliana huxleyi  [65] , the diatoms Phaeodactylum tricormutum  [66]  and Thalassiosira pseudonana  [67] , the chlorophytes Chlorella variabilis  [68]  and Chlamydomonas reinhardii  [69] ) were produced by the US Department of Energy Joint Genome Institute (JGI http://www. jgi. doe. gov/) and are publically available in the web site. The other reference microalga used were the dinoflagellate Alexandrium ostenfeldii  [70] , the glaucophyte Cyanophora paradoxa  [71]  and the rhodophyte Cyanidioschyzon merolae  [72]. Homologous transcripts were detected by BLAST analysis  [73]  between the transcriptome data of reference microalgae and Tiso (tblastx with alignment length greater than 100 amino acids and identity score greater than 30%).
Codon usage bias was calculated with the Sequence Manipulation Suite software  [74]  on coding regions of each reference taxon. For Tiso, the putative coding regions of each transcript were identified with ORF-predictor  [75]. A similarity tree of codon bias was built from correlation scores obtained between microalgae and drawn with the Darwin software (http:// darwin. cirad. fr/) using the hierarchical clustering method WPGMA  [76].
Annotation of Tiso trancriptome Research of putative function was undertaken by BLAST analysis  [73]  on the NCBI database (nr bank; blastx with alignment length greater than 100 amino acids and identity score greater than 30%). Transcripts were sorted automatically for major cell functions. For the transcripts showing a polymorphism between Tiso-Wt and Tiso-S2M2, expert annotation was done. A second BLAST on SwissProt database (v. May2012) and functional domain was performed with InterProScan  [77]. Consensus of annotation results was manually attributed for putative function for each transcript.
Differential expression analysis After read alignment for each strain on the reference transcriptome, to search differential expression we normalized the libraries themselves as a function of mean depth per putative transcript (3246 for Tiso-Wt and 3896 for Tiso-S2M2). The number of read pairs aligned per transcript was compared between strains. Differential expression was considered if the difference between the 2 strains was up to 20-fold (log2) and if for both the depth was superior to 50 reads. Differential expressions were also confirmed with the recent GFOLD algorithm  [78]  with the p-value fixed at 0. 001.
Positional polymorphism identification and estimation of genetic diversity Positional polymorphisms, i. e. Single Nucleotide Polymorphism (SNP) and short insertion/deletions (indel), were identified between transcripts of Tiso-Wt and Tiso-S2M2. We used Freebayes software (Erik Garrison, Marth lab) and validated polymorphisms if depth was superior to 506 for each individual and if a minority of alleles had a depth superior to 106. Genetic diversity was measured for each strain (Tiso-Wt and Tiso-S2M2). Polyallelic loci were detected in transcriptomes of each strain. A locus was considered polyallelic if the depth of reads was greater than 506. For each polyallelic locus, frequency of each allele was measured (number of alleles divided by sum of total alleles). Comparison of global genetic diversity between the both strains was estimate with D of Jost  [31]  using SPADE program  [79].
2011; Cesana et al. 2012; Barton et al. 2012; Cesana and Chepfer 2012). Arctic basinwide top-of-atmosphere (TOA) radiative fluxes are available from the latest version of the Clouds and the Earth's Radiant Energy System (CERES;  Wielicki et al. 1996)  Energy Balanced and Filled (EBAF) data product  (Loeb et al. 2009). Comparison of all-sky net TOA shortwave (SW) radiation and outgoing longwave (LW) radiation (OLR) between CAM5 and CERES-EBAF can identify overall energy balance biases in the model. In addition, comparisons of clear-sky and cloudforcing radiative fluxes can help identify the contributions of cloud and surface albedo biases to all-sky biases. The contribution of cloud amount biases to cloudforcing biases can be studied by comparing modeled and observed cloud amount. Recent studies have emphasized the utility of active remote sensing for observing Arctic cloud amount.
For example, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO)-GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP)  (Chepfer et al. 2010) , which is a part of the A-Train (L'Ecuyer and Jiang 2010), is particularly useful  (Cesana et al. 2012; Kay et al. 2012; Barton et al. 2012). Because of differences between modeled clouds and clouds observed by instruments, simulator packages such as the Cloud Feedback Model Intercomparison Project (CFMIP)  (Bony et al. 2011)  Observation Simulator Package (COSP)  (Bodas-Salcedo et al. 2011) , including a lidar simulator  (Chepfer et al. 2008) , are now commonly utilized to enable a more direct comparison of modeled clouds to observations. Assumptions are often made in the parameterizations that apply the simulator algorithms to model output, however, and there continues to be opportunities to improve the accuracy of using instrument simulators on model output.
The paper is organized as follows: Section 2 describes methods for models, datasets, and analysis. In section 3, we discuss CAM5 cloud amount using the CALIPSO simulator including the impacts of a correction for an error in the treatment of snow crystal size. In section 4, CAM5 net TOA radiative flux biases are quantified along with contributions from clouds and surface albedos. Section 5 presents the impacts of two modified mixed-phase ice nucleation schemes on Arctic clouds, radiation, and distribution of weather states. Section 6 provides further discussion and conclusions.
2014). The aerosol number and size distribution are calculated from the aerosol module in CAM5  (Liu et al. 2012) , and the aerosol surface properties are derived by fitting to respective laboratory data of mineral dust and black carbon  (Hoose et al. 2010). c. TOA radiative fluxes (CERES-EBAF 2. 7) CERES-EBAF is the only available source of basinwide TOA fluxes in the Arctic, and newer versions have advanced to distinguish clouds from underlying highalbedo sea ice and snow cover by utilizing cloud radiances from the collocated Moderate Resolution Imaging Spectroradiometer (MODIS) and sea ice concentration fields from the National Snow and Ice Data Center (NSIDC)  (Hollinger et al. 1990). We analyze 11 yr of data (2000-10) from CERES-EBAF version 2. 7, released in June 2013, which further improves calculation of clear-sky fluxes by including CERES partly cloudy data points in the calculation.
In contrast, CAM5 generally has positive SW cloud-forcing biases (Figs. 5g-i ), meaning clouds let too much SW into the Arctic system, which is due to insufficient Arctic cloud amount in CAM5 (Fig. 1 ). Other biases are possible including cloud LWP, cloud droplet size, or cloud optics. The COSP CALIPSO simulator allows the user to specify spherical or nonspherical ice particle shape. We specify spherical shape, which is the default and also what is assumed in the CAM5 cloud optics code and the cloud microphysics code (i. e. , fall speeds). This suggests that CAM5 has compensating biases between surface albedo and cloud forcing, which result in a lower bias when comparing TOA SW all sky. Assuming no significant atmospheric contributions to SW clear-sky radiative fluxes, clear-sky biases are assumed to be largely due to surface albedo biases.
Acknowledgments. Support for J. M. English was provided by  NASA  Award  NNX09AJ05G. Support for X. Liu was provided by the  DOE Office of Science Atmospheric System Research (ASR)   Program and Earth System Modeling Program. Support for Y. Zhang was provided by the  Earth System Modeling program  of the  U. S. Department of Energy. Thanks to NOAA and Rutgers for snow-cover data and to  Mark Flanner  for providing them in a convenient format. Thanks to  Gijs de Boer  for providing SHEBA data in a convenient format, to  David Bailey  for insightful conversations regarding the sea ice model, to  Dave Lawrence  for insightful conversations regarding the land model, and to  Neil Barton  for providing new cloud plots using the new lidar code. Thanks to NASA and CNES for CALIOP and CERES data. The  Pacific Northwest National Laboratory  (PNNL) is operated for the DOE by  Battelle Memorial Institute  under Contract  DE-AC06-76RLO 1830.
Data We used daily 4-km, version 5 Advanced Very High Resolution Radiometer pathfinder  SST (1982 SST ( -2012) )  available at http://www. nodc. noaa. gov/sog/pathfinder4km/. Chl data were taken from the National Aeronautics and Space Administration's daily 4-km level-3 Moderate Resolution Imaging Spectroradiometer daily data set (2002-2010) available at http://oceancolor. gsfc. nasa. gov/. We extracted weekly 1/3u (i. e. , about 33 km at these latitudes) Ssalto/Duacs sea level anomalies and geostrophic velocity anomalies (u,v) computed and distributed by Aviso (1992-2012), with support from the Centre National d'E ´tudes Spatiales (http:// www. aviso. oceanobs. com/duacs/). The bathymetry of the Mediterranean basin was extracted from the ETOPO1 database hosted on the National Oceanic and Atmospheric Administration's website at ,4 km resolution using the getNOAA.
Oceanographic indices Several features were derived by further processing the remotely-sensed data. SST and chl fronts were computed with the gradient method, using a common sobel operator (e. g. ,  [35, 36] ). These continuous values indicate the frontal intensity between water masses. Using geostrophic velocity anomalies, we calculated several indicators of mesoscale ocean features. These data were used to derive backward-calculated FSLEs, which measure the horizontal mixing and dispersion in the ocean  [37]  and help to detect mesoscale Lagrangian coherent structures of ecological significance (e. g. ,  [38] ). FSLEs are defined as l(x,t,d 0 ,d f ) = (1/t)log(d 0 / d f ) where l(x,t,d 0 ,d f ) is the FSLE at position x and time t with an initial separation distance from x of d 0 and final separation distance from x of d f.
A. -E. N. was supported by a joint grant from  France Filiere Peche  (http://www. francefilierepeche. fr/) and  Institut Francais de Recherche pour l'Exploitation de la Mer  (http://wwz. ifremer. fr/). V. R. acknowledges support from  Ministerio de Ciencia e Innovacion  (http://www. idi. mineco. gob. es/portal/site/MICINN/) and  FEDER  (http://www. europe-en-france. gouv. fr /Configuration-Generale-Pages-secondaires/FEDER) through the  ESCOLA  project ( CTM2012-39025-C02-01 ). S. B. was supported by the  Agence nationale de la recherche SEAS-ERA   MERMAID  project ( ANR-12-SEAS-0003 ) and the  French National Research  project  INSU-MERMEX. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
, 1996 Jacobson et al. , , 1997a, b;, b; WRF-Chem: Grell et al. , 2005 ; GEM-AQ:  Kaminski et al. , 2007; WRF-CMAQ: Mathur et al. , 2008; Yu et al. , 2011; GEM-MACH: Moran et al. , 2010). The European Centre for Medium Range Weather Forecasting (ECMWF) also noted the relevance of online chemistry  (Hollingsworth et al. , 2008)  and several national weather services (such as DMI, UK MetOffice) and research institutions (such as KIT, ISAC, UniHH) are developing online models. For operational or climate applications the cost of online integration is still an issue. Therefore, it is important to deter-mine which feedbacks have the largest impact, and what the minimum requirements are for accurately representing them in an online integrated model. The atmospheric model database initiated within the COST Action 728 (http://www.
Enviro-HIRLAM for NWP and long-term simulations is developing a highly simplified chemical scheme based on the ECHAM chemistry. For climate modelling, chemistry of greenhouse gases and aerosols become very important, however, for long-lived GHGs, online integration of full scale short-term chemical reactions are not critically needed. For CWF and predictions of atmospheric composition in a changing climate, more advanced and comprehensive chemical mechanisms are much more important. It will be important to quantify the impacts of these approaches on the quality of the predictions. Future model intercomparisons would greatly benefit from the establishment of a central mechanisms database, to which mechanism owners could upload their schemes and provide further updates as necessary. This would allow true versioning and openness for chemical mechanisms.
g. Tingey et al. , 1980; Guenther et al. , 1991; Staudt et al. , 1997). Most models, therefore, calculate these emissions online, based usually on the Model of Emissions of Gases and Aerosols from Nature (MEGAN) emission scheme  (Guenther et al. , 1993 (Guenther et al. , , 1995 (Guenther et al. , , 2006 (Guenther et al. , , 2012) )  or its variants  (Vogel et al. , 1995) , or a tree species database, but effects of stress like high O 3 concentrations or drought are usually not taken into account. BVOC emissions contribute to O 3 formation and can also give a significant contribution to the formation of SOA, which in turn may affect radiation and hence feedback on BVOC emissions in an online coupled model. Other biogenic emissions of potential relevance for air quality simulations and chemistry-meteorology interactions are NO emissions from soils  (Yienger and Levy II, 1995)  and emissions of bacteria, fungal spores and pollen which have been reported to act as efficient ice nuclei (e.
Nevertheless, the most commonly used mechanisms have converged in terms of the state of the science included in their formulation. Modifications of the chemical mechanisms, which not only affect gas phase chemistry but also the coupling with aqueous-phase and aerosol mechanisms, have faced practical difficulties in the past, requiring significant reprogramming. Methods of updating chemical mechanisms make updates much easier as illustrated in the MECCA module  (Sander et al. , 2005). Therefore, the following actions are recommended: -Create a unified central database of chemical mechanisms, where mechanism owners can upload relevant codes and provide updates as necessary. Versions should be numbered and chemical mechanisms should be open. -Enable interfacing of this database using, e. g. the Kinetic Pre-Processor (KPP) to develop a set of box model intercomparisons including evaluation against smog chamber data (e.