dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2013)  to find the geostrophic component of shear, expressed as Ri g = f 2 N 2 |∇ h b| 2 , (2) where b = -gρ ρ 0 is the buoyancy (with g the gravitational constant, ρ the density, and ρ 0 a reference density), f is the Coriolis parameter, and ∇ h is the horizontal components of the gradient operator. In this paper, we characterized the flow as submesoscale when Ri g reached this value of 1. The Ri g diagnostic was originally designed for instability criteria. Here, we are not searching for instabilities. The fact that Ri g solely looks at shear due to buoyancy gradients is useful for considering submesoscale features. In order to more fully investigate the instabilities that are possible in a given dataset, the relative vorticity is required in addition to Ri g (see Fig. 1  in  Thomas et al.
, 2013;  for example, sufficient cyclonic vorticity can make a water column stable to SI below Ri = 1), which is out of the scope of this paper. The determination of Ri required combining the MVP data for the numerator and SADCP data for the denominator and so is limited in resolution by the SADCP bins. Therefore, in calculating N 2 , 8 m bins were produced by averaging the central finite differences from the 1 m density profile. Since we wished to characterize submesoscale phenomena, the horizontal gradient required for Eq. (  2 ) was left as is, without further re-binning. The calculation of Ri g was also calculated at the same 8 m resolution. By assuming balanced flow, we can estimate the horizontal scale of density structures in a layer with the first internal Rossby radius of deformation, approximated by  (Pedlosky, 2013)  as R D = N H πf , ( 3 ) where H is the depth of the water column (for non-constant stratification, this becomes an integral over depth).
Upward and downward oscillations of isopycnals indicated the presence of near-inertial oscillations, which was reflected in the SADCP time series that is investigated further in  de Verneil et al. (2017, this special issue). The MLD, though not formally tied to an isopycnal value, also largely reflected these oscillations. Nitracline depth, which in our formulation was tied to the 24. 34 isopycnals, was consistently found between 100 and 120 m throughout the dataset, well below the chl a max and general bloom area, even when the DCM distribution began to appear towards the end of LDB.
In situ Ri and Ri g The possible presence of balanced, submesoscale currents in the in situ MVP and SADCP datasets was determined by comparing Ri and Ri g. Since both Ri and Ri g span several orders of magnitude, log 10 Ri and log 10 Ri g during T4 are shown in Fig. 5. All the transects contained similar distributions, and T4 was chosen because of the sharp gradients in chl a. The majority of observations of log 10 Ri and log 10 Ri g were above 0, or 1 in normal space, and so here we diagnosed that the circulation was largely not submesoscale in its dynamics. Log 10 Ri (Fig. 5a ) values were similar to but slightly lower than log 10 Ri g (Fig. 5b ), demonstrated by a preponderance of saturated shading in Fig. 5b. Larger log 10 Ri g values were found at depth, with the smallest log 10 Ri g found near the surface.
Satellite chl a time series, altimetry-derived FSLE, and ARIANE trajectories The remotely sensed distribution of surface chl a, calculated FSLEs, and  in Fig. 6. FSLEs and particles are shaded gray and red, respectively, with 10 % of the particles randomly selected for plotting in all sub-panels. The date 25 December was chosen as the starting point by visually examining the chl a dataset for a pre-bloom period, with a bloom "source" region identified on 13 January centered at 186 • E, 20 • S. The temporal evolution of FSLEs and of the Lagrangian particles superimposed on SST is reproduced in Fig. S4  of the Supplement. Additional chl a data, between 10 and 31 January 2015, are also provided in Fig. S6. The 25 December start date showed a modest chl a region oriented north-south (N-S) near 186 • E (Fig.
Discussion 4. 1 Chl a bloom processes, physical forcing, and collapse The bloom sampled in this study has a number of distinguishing features that differentiate it from the surrounding water sampled during OUTPACE. Together with physical measurements, these characteristics allow for a determination of what combination of biogeochemical processes and physical forcing was responsible for the bloom and its temporal evolution. Since this study focuses on a surface bloom, there must be some process responsible for the relative accumulation of chl a. Ideally, in situ biological rate measurements would have been measured before and during the chl a increase. In addition to the pragmatic difficulty of obtaining all these measurements for all time and space, the satellite data suggest the bloom was already 2 months old by the time of sampling at station LDB, precluding direct observation of its initial conditions.
Therefore, one must start with the assumption that the starting conditions for the bloom were similar to surrounding regions (e. g. the chl a distribution pre-bloom on 25 December; Fig. 6a ). We can infer, then, that the local increase of chl a in the bloom was linked to a source of new production. For the WTSP, the two main mechanisms to consider are, first, nitrate delivery from below due to advective fluxes and/or diapycnal mixing and, second, N 2 fixation by diazotrophs. The data from stations SD12, LDB, and SD13 support the role of N 2 fixation in creating the bloom and reject nitrate delivery from below. Firstly, the high chl a concentrations in the bloom were located in the top 40 m, as shown by the in situ MVP surveys (Fig. 2 ) and station LDB CTD time series (Fig. 3 ). The nitracline, one potential source for new production, was consistently at least 60 m deeper in the water column in the CTD time series than the bloom.
(2011)  (Fig. S7 ). As further comparison, climatological data from station ALOHA in that study place phosphate reservoirs for N 2 fixation at 40 m depth, shallower than the depths observed during OUTPACE. These results, in addition to the lack of SST gradients one would expect (Fig. S4 ), make this mechanism unlikely. Another mechanism is strong wind forcing, such as that provided by tropical cyclones. These storms have been shown to fertilize blooms in oligotrophic waters  (Law et al. , 2011). Using the value-added altimetry dataset with wind component, the impact of wind was evaluated and found to be relatively small (Fig. S8 ) and could not create deep mixing. By contrast, another region in the OUTPACE domain witnessed the passage of Cyclone Pam in early March 2015. The satellite imagery before and after its passage corroborate the fertilizing effect of storms in this region (Fig.
Surface circulation, chl a advection, and the formation of gradients The major focus of this study, apart from diagnosing the bloom's biogeochemical makeup, is to identify which circulation patterns explained the horizontal distribution of the bloom once it was formed, namely submesoscale or mesoscale currents. Comparing the in situ datasets with satellite altimetry currents, the relevant scales of surface motion in the WTSP can be deduced. As mentioned in the introduction, though the SEC is the mean current advecting water westward from the northern limb of the subtropical gyre, much more kinetic energy is typically found at the mesoscale. Interest has mounted in recent decades in the possibility that submesoscale currents, intensified at the surface, may play a role in affecting nutri-ent delivery into the euphotic zone, as well as redistributing biological tracers, including in N 2 fixation zones  (Calil and Richards, 2010).
The values of Ri and Ri g present within the top 50 m of the MVP transects were predominantly greater than 1, with the majority larger than 10 (0 and 1 in log space, respectively). Considering these two independent measurements of shear agreed in magnitude, and that both occurred outside the dynamical range of consideration, the data thus provided little support for the presence of submesoscale circulation, perfectly balanced or otherwise. Combining the strong surface stratification, general lack of isopycnal outcroppings (i. e. no strong horizontal density gradients), and absence of correspondence between chl a and density gradients, it should perhaps not be surprising that buoyancy-driven submesoscale circulation was not readily discernable even at the surface in this dataset. If most submesoscale structures are expected to be in the mixed layer, which throughout this dataset was near 20 m, then the subkilometer Rossby radius R D (here found to be < 200 m) would not have been resolved by the MVP survey since MVP horizontal resolution is ∼ 2 km.
The possibility remains that during other periods of the year such as winter, with a deeper mixed layer than that observed during the cruise, the horizontal spatial scales of these features would increase and possibly impact the advection of such a bloom. The question thus becomes whether these bloom events occur during winter periods. If not, then the mutually exclusive timing of bloom events and stratification favorable for submesoscale turbulence would preclude its influence on bloom evolution for the region. The natural horizontal scales of the bloom were more in line with the R D of the entire water column, ∼ 60 km, which is impacted by the mesoscale regime. In our dataset, satellite altimetry-derived currents represented the mesoscale circulation. Whereas the MVP T4 transect revealed no submesoscale source of horizontal shear to create a chl a gradient, the mesoscale circulation did via an FLSE structure present near MVP T4 in Fig.
6g. While FSLEs may not provide the exact regions where chl a gradients form in the station LDB bloom, they help define dynamical boundaries that provide more explanation than the in situ data for why the chl a bloom appeared as it did. The positions of Lagrangian particles also allow for a better representation of where bloom water advected, as shown in Fig. 7. Firstly, most particles were present in the defined region of interest throughout the time series (Fig. 7a ), providing additional support (beyond visual inspection) that the chl a dataset sufficiently covered bloom water relative to the chosen initial time of integration, 6 March. If the chl a satellite time series therefore encompasses most of the bloom water of interest, then the fraction of particles in high chl a water throughout their trajectories, as represented in Fig.
Indeed, both the SEC and the mesoscale structures responsible for currents in the altimetry dataset propagate westward, reflected by the migration of N-S-oriented FSLE ridges the bloom encountered in Fig. 6b-d  and f-g. Therefore, the complex circulation evoked by a westwardmoving mesoscale field allowed for the counterintuitive eastward advection of water with enhanced biological production at its surface. This was possible due to the bloom occurring in water not associated with the coherent elliptic structures that move west. Instead, the bloom occurred in water outside these structures, with tortuous trajectories hyperbolic in nature  (Kirwan et al. , 2003; Rypina et al. , 2010). Eventually, as the bloom collapses, more particles move west with the mean circulation, as shown in Fig. 6h-j.
The circulation responsible for advecting the bloom at the surface was satisfactorily represented by altimetry-derived mesoscale currents, with physical structures corresponding to biological gradients. The in situ density and velocity data, in contrast, did not have structures that correspond with the sharp gradients in chl a. Additionally, the dynamical nature of the in situ data did not fall within the submesoscale regime. In the ongoing debate between the seasonal and geographic distribution of submesoscale turbulence, this dataset posits the first-order importance of mesoscale circulation in the WTSP summer. The complex trajectories that mesoscale currents create in hyperbolic regions outside of eddies can be of prime importance in advecting blooms, sometimes in counterintuitive directions such as the eastward transport presented here.
Future studies in the region will be necessary to resolve residing questions. For instance, the particular biogeochemical conditions igniting the bloom, here hypothesized to be island-derived iron, cannot be confirmed within this dataset and will require further in situ sampling. Additionally, the presence of submesoscale structures during other parts of the year, such as wintertime with deeper mixed layers, remains unanswered. However, armed with the information that not only summertime blooms fueled by N 2 fixation occur in the WTSP, but that their advection at the surface may be represented by the value-added, high-resolution satellite products such as those produced by CLS/CNES, future studies may begin to quantify the combined interannual impact of N 2 fixation and mesoscale transport in distributing new production in this region.
Acknowledgements. We would like to thank the two anonymous reviewers and Associate Editor who helped in making this a better article. This is a contribution of the  OUTPACE (Oligotrophy to UlTra-oligotrophy PACific Experiment ) project (Moutin and Bonnet, 2015) funded by the  French national research agency  ( ANR-14-CE01-0007-01 ), the  LEFE-CyBER program  ( CNRS-INSU) , the  GOPS program  (IRD), and  CNES  ( BC T23 ,  ZBC 4500048836 ). The OUTPACE cruise was managed by  MIO (OSU Institut Pytheas, AMU)  from  Marseilles (France). The authors thank the crew of the  RV L'Atalante  for outstanding shipboard operations. Gilles Rougier ,  Marc Picheral , and  Lucio Bellomo  are warmly thanked for their help in CTD rosette and MVP deployment and data processing. We also thank  Audrey Gimenez  for T DIP measurements and  Sandra Helias-Nunige  for nutrient values.
Catherine Schmechtig  is thanked for LEFE-CyBER database management. Satellite SST, chl a, and altimetry data have been provided by  CLS  in the framework of CNES funding; we thank  Isabelle Pujol  and  Guillaume Taburet  for their support in providing these data. Aurelia Lozingot  is acknowledged for administrative aid for the  OUTPACE  project. Edited by:  Kelvin Richards Reviewed  by: two anonymous referees.
Data availability. All data and metadata are available at the French INSU/CNRS LEFE CYBER database (scientific coordinator: Hervé Claustre; data manager and webmaster: Catherine Schmechtig) at the following web address: http://www. obs-vlfr. fr/proof/php/ outpace/outpace. php (INSU/CNRS LEFE CYBER, 2017). The Supplement related to this article is available online at https://doi. org/10. 5194/bg-14-3471-2017-supplement.
2012) were then evaluated. Climate projections for two representative concentration pathways (RCPs,  Moss et al. 2010 ) are available within the Med-CORDEX dataset (RCP4. 5 and RCP8. 5) and are discussed in this paper. In addition, the wind speeds during Mistral and Tramontane events are discussed. This paper is structured as follows. The observation and simulation data are discussed in Sect. 2. Then, the methods used are explained in Sect. 3, followed by the results and discussion in Sect. 4. The last section contains a summary of this work and the conclusions.
ERA-Interim The reanalysis dataset ERA-Interim  (Dee et al. 2011 ) is used as a forcing for the evaluation runs of the RCMs in this study. It is calculated with a grid resolution of about 80 km. Sea-level pressure fields from ERA-Interim are used together with the observational Mistral and Tramontane time series to train the classification algorithm. ERA-Interim data were provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) database.
Regional climate models (RCMs) The regional climate simulations investigated in this study were prepared in the Med-CORDEX framework  (Ruti et al. 2015)  and HyMeX program  (Drobinski et al. 2014) , and were performed on the Med-CORDEX domain (encompassing the Mediterranean and Black Sea, as well as the surrounding land areas). Data from five different combinations of GCMs and atmosphere-only RCMs are available in the MedCORDEX database on 0. 44 • grids. In this study, the simulations are identified by the name of the RCM for ERA-Interim driven simulations. GCM driven simulations are identified by the GCM's name followed by the name of the RCM applied. Simulations with the COSMO-CLM (CCLM) model (see  Rockel et al. 2008 ) were performed at two institutions: CMCC and Goethe University, Frankfurt (GUF).
GCM constraint on RCMs To estimate the influence of the driving GCM on the M/T representation in RCMs, the PC score of the two setups MPI-ESM-LR-CCLM4-8-18 and CMCC-CM5-CCLM4-8-19 with the driving GCM as reference is used. These runs provide the longest time series of the models in this study, and both RCP4. 5 and RCP8. 5 simulations are available. The PC scores are given in Table  8. The PC score between the RCM simulations and the driving GCMs is about 80%. This shows that the RCMs are constrained by the driving GCM, consistent with the results of  Sanchez-Gomez et al. (2009) , who found the RCMs ALADIN, CCLM, and RegCM to reproduce similar weather regimes as the driving dataset in about 70-90% of the cases. When looking at Mistral days only, the proportion of correct score reaches higher values than for Tramontane winds.
The majority of these genes are located in genomic regions (1 kb upstream and 1 kb downstream) exhibiting deficiency in genome coverage (Additional file 2). Amplification of those targeted genes suggested that those genes are actually absent from the 6 genomes (data not shown). All of these 13 genes were discarded for downstream analysis. The locations of genetic variations were investigated (Table  1 ). Variants were called on the basis of a variation compared to the sequence of the reference genome (RRES v4. 0). Variant calling was fine-tuned to detect preferentially short size variants, i. e. , Single Nucleotide Polymorphisms (SNPs) and short Insertions or Deletions (InDels), and obtained a final dataset of 242,756 highlyconfident variants, all strains considered, consisting of 234,151 SNPs (96%) and 8,605 InDels (Table  1 , Additional file 3).
1. 19 [81]. Valid alignments were annotated using Picard tools (v1. 88, http://broadinstitute. github. io/picard/) for further processing with the GATK suite (v2. 4, [82] ). Identification of genomic regions that could not be mapped or called for variants considering our sequencing set up was conducted using the CallableLoci module of GATK. Alignments on the last 1. 38 Mb of the end of chromosome IV systematically showed poor quality scores and were removed from the analysis. This result arises from the large number of rRNA-encoding repetitive sequences units  [41]  which lengths approximate the length of the sequenced reads resulting in non-unique alignments that are excluded from the analysis. This specific region, however, does not contain known protein-coding genes. The six BAM files were simultaneously used to discover SNPs and InDels using GATK and the Unified Genotyper walker in haploïd mode  [82].
Depth of read coverage per 100 kb-long non-overlapping bins was calculated using the bamCoverage tool of the Bamtools suite (version 1. 5. 9. 1. 0). Positions of InDels were recalibrated using the RealignerTargetCreator and the IndelRealigner modules of GATK with standard parameters. A variant was called when observed on at least 80% of the reads aligned at the position with a read coverage more than 5 reads, and a confidence threshold greater than 250 (SelectVariants module of GATK, v2. 4). Other parameters were standards. Variant densities for 100 kblong non-overlapping windows and other statistics on the resulting Variant Call Format (VCF) files were obtained using Vcftools (v0. 1. 12a, [83] ). Regions longer than 200 kb showing at least a two-fold increase in variant density compared to the genome-wide median density where considered as polymorphic islands.
Functional variant annotation Genetic variants from specific genomic compartments (i. e. , exons, introns, and intergenic regions) were annotated and their effect predicted using snpEff  [66]  and a custom database constructed from the reference genome (RRes v4. 0) and the GTF file associated. Briefly, the software classifies variants found within genes (exons and introns) according to their downstream effect at the protein level (under the standard genetic code). A full list of mutation event classification is available (http://snpeff. sourceforge. net/SnpEff_manual. html). Using this classification, four categories of genes were distinguished: i) the category "Non-functional", i. e. , genes containing variant(s) predicted to lead to a loss of the function of the protein; ii) the category "Modified Protein", i.
5 (corresponding to a two-fold increase of the genome-wide median number). PROVEAN software  [84]  was finally used to estimate the functional importance of missense mutations found in genes coding for secreted proteins (as described in King et al. ,  [41] ), genes expressed in wheat, barley and/or maize  [50] , and genes predicted to belong to secondary metabolite pathways (retrieved from Sieber et al. ,  [48] ).
Acknowledgements We would like to acknowledge the  Genotoul bioinformatics platform Toulouse Midi-Pyrénées and Sigenae group  for providing help along the bioinformatics analysis and for providing computing and storage resources (http://bioinfo. genotoul. fr/). We are grateful to  Vessela Atanasova-Pénichon ,  Pauline Lassere-Züber  and  Richard Blanc  for their help in strain characterizations.
Availability of data and materials Strains are available from the International Center for Microbial Resources (CIRM, (http://www6. inra. fr/cirm)), with codes 1974, 1977, 1981, 1988, 1999  and 2013  for strains INRA-156, INRA-159, INRA-164, INRA-171, INRA-181 and INRA-195 respectively. The entire set of raw reads that was generated in this project has been deposited in fastq format at the GenBank Sequence Read Archive under the accession number SRP064374. The description and annotation of variant supporting the conclusions of this article is accessible in Additional file 3: Table  S3.
langsethiae] IPR017853, IPR026891, IPR026892 FGRRES_17022 - II 1,652,257 1,656,378 Uncharacterized - - muc1-extracellular protein alpha--glucan glucosidase [F. langsethiae] FGRRES_16333 - II 4,194,219 4,196,939 Uncharacterized - - - protein FGRRES_03274 FGSG_03274 II 4,695,334 4,698,042 Uncharacterized IPR029167 Mug117 - protein FGRRES_03521 FGSG_03521 II 5,366,512 5,367,123 Uncharacterized IPR009327, RmlC-like cupin putative cupin family protein IPR011051, domain protein [Diaporthe IPR014710 ampelina] FGRRES_03612 FGSG_03612 II 5,604,284 5,605,254 Uncharacterized IPR001087, Lipase_GDSL, gdsl lipase protein IPR013830 SGNH hydrolase- acylhydrolase type esterase [F. langsethiae] domain FGRRES_12405_M FGSG_12405 II 5,622,275 5,622,943 Uncharacterized IPR003609 Pan_apple - protein FGRRES_03944 FGSG_03944 II 6,465,510 6,466,808 Uncharacterized IPR011042 Six-bladed beta- serum paraoxonase protein propeller, TolB-like arylesterase [F.
langsethiae] FGRRES_03972 FGSG_03972 II 6,548,953 6,550,914 Uncharacterized IPR006094, flavin adenine 6-hydroxy-d- protein IPR012951, dinucleotide nicotine oxidase IPR016166, linked oxydase; [F. langsethiae] IPR016169 Berberine & berberine-like; CO dehydrogenase flavoprotein-like FGRRES_04429 FGSG_04429 II 7,989,077 7,992,064 Uncharacterized IPR003609 Pan_apple - protein FGRRES_12210 FGSG_12210 II 8,620,515 8,622,358 Uncharacterized IPR003609 Pan_apple - protein FGRRES_04646_M FGSG_04646 II 8,655,498 8,656,180 Uncharacterized - - - protein FGRRES_04689 FGSG_04689 II 8,765,660 8,767,148 Rhamnogalacturonase IPR000743, Glycoside probable A IPR011050, hydrolase, family rhamnogalacturonase IPR012334 28; Pectin lyase A precursor [Fusarium fujikuroi IMI 58289] FGRRES_05719 FGSG_05719 III 3,177,333 3,180,794 Uncharacterized IPR029167 Meiotically - protein up-regulated gene 117 protein.