dataesr/scientific-paragraphs-categorization huggingface dataset:
The area of each sub-Arctic and Arctic sea is also indicated. (e. g. ,  Antoine and Morel, 1996; Uitz et al. , 2006). We were not successful in applying this simple approach to the Arctic Ocean, most probably because Arctic phytoplankton communities are exposed to very pronounced seasonality associated with light conditions, sea-ice cover, and nutrient avail-ability  (Harrison and Cota, 1991; Grebmeier et al. , 1995) , and because the mixed layer depth and vertical stratification are controlled by salinity in several Arctic regions rather than temperature  (Carmack, 2007). Here, we first partitioned our dataset according to bathymetry, season, and region. In our procedure, each vertical Chl a profile was linearly interpolated at 1 m resolution, normalized (see Eqs. 2 and 3), grouped by bins, and averaged within each bin.
Description of the empirical model On the basis of a preliminary analysis to break down the dataset in a synthetic manner (see Fig. 3 ), the stations were partitioned according to the following three criteria: i Shallow (≤ 50 m) and deep (> 50 m) stations were considered separately. The former category represents 17. 6 % of the total number of stations (Fig. 2c ) and 18. 3 % of the total area of Arctic and sub-Arctic seas (Fig. 1 ). This was a crucial step to avoid multiple biases on the characterization of the vertical Chl a profiles and the assessment of the occurrence of SCM. A second normalization was needed for the shallow stations, which consists in division of the geometrical depth, z (m), by the bottom depth Z BOT (m): ζ = z Z BOT. ( 4 ) ii The dataset was partitioned into the following three separate time periods: pre-bloom (February-April), postbloom (May-September) and winter period (October-December).
This threshold corresponds to the approximate length of photoperiod below which the SCM is no more observed within any bin (data not shown). iii Finally, a regionalization of the dataset was needed for the post-bloom period when the occurrence of SCM is significantly deeper compared to the two other periods (Fig. 2f ). Using a revised regional partition proposed initially by  Spalding et al. (2007)  and the WWF (World Wildlife Fund) agency, 10 sub-Arctic and Arctic seas were defined to assess regional differences in the depth, shape, and magnitude of SCM (Fig. 1 ). The empirical model of the vertical Chl a profiles was thus developed for sub-Arctic and Arctic seas (C categories > 50 m; see Table  3 ) and Arctic shelves (CC categories ≤ 50 m; see Table  5 ), and for three distinct periods (i.
, 2007) ; semi-analytical method for estimating K d  (Lee et al. , 2005). (4) a NAP , a CDOM , a ϕ : empirical relationships using Chl a concentration  (Wang et al. , 2005) ; semi-analytical method for estimating K d  (Lee et al. , 2005). (5) Empirical relationship between K d and Chl a  (Morel and Maritorena, 2001). The validation of the PP algorithm and the results of parameterizations of the absorption coefficients to estimate Z EU are presented in Appendix A and Table  A1. Two annual depth-integrated PP estimates were then determined for the entire Arctic Ocean with respect to a high (i. e. , 1998) and a low (i. e. , 2007) annual sea-ice cover (Table 6). The sea-ice climatologies were estimated from the F13 special sensor microwave imager (SSMI) and were obtained from the National Snow and Ice Data Center (NSIDC).
We regionalized the C1 and C2 bins of the database in order to detect possible spatial structure in terms of depth, shape, and magnitude of the SCM (Fig. 7  and Table 4 ). When regions were defined as in Fig. 1   Beaufort, and Barents seas as well as in Hudson Bay occurred at the average depth of 42, 36, 48, 51, and 46 m, respectively (Fig. 7 ). In Baffin Bay, the Canadian Archipelago, and the central Arctic Ocean, less pronounced SCMs were found at the average depth of 44, 38, and 36 m, respectively. The Russian seas, despite the few data available, seem to be equally characterized by a SCM, at 49 m on average. The SCMs have been already described as common features in ice-free Arctic waters during late summer and early fall  (Hill et al. , 2005; Tremblay et al. , 2008; Martin et al. , 2010; McLaughlin and Carmack, 2010).
Annual time series of vertical chlorophyll a profiles All Chl a surf data from the different years were pooled to generate a typical annual time series for each sub-Arctic and Arctic sea, and for the Arctic shelves (i. e. , Canadian, Eurasian, and Chukchi-Bering shelves; Fig. 9 ). Time series were smoothed using a LOESS polynomial fit. The calculated Chl a surf time series were then used to derive time series of the vertical Chl a profile with the empirical models illus- CC1-CC2 (0-0. 3) 0. 9965 0. 5444 0. 7487 0. 8438 0. 2959 trated in Figs. 5, 7 and 8 (red lines), using the parameters listed in Tables 3-5 (Russian seas were not considered due to data paucity; Fig. 2a ). Temporal coverage was not complete for Hudson Bay and the Eurasian Shelf since the pre-bloom and winter periods are not represented in the database.
Conclusions Empirical models developed for temperate and tropical oceans have proven to be useful for estimating vertical Chl a profiles and for improving PP estimates based on OC  (Morel and Berthon, 1989; Uitz et al. , 2006; Platt et al. , 2008). Based on a large in situ dataset, we proposed here a novel empirical model of the vertical Chl a distribution specifically tuned for the Arctic Ocean. Our analysis reveals regional and seasonal regimes that are best addressed with specific sub-models. Over an annual cycle, the errors in regional PP estimates caused by ignoring the vertical variations in Chl a are relatively small. Also, overestimates (i. e. , during pre-bloom, post-bloom for Chl a > 0. 7 mg m -3 , and the winter period) somewhat compensate partially for underestimates (i. e. , during post-bloom for Chl a < 0.
Validation of the depth-integrated PP algorithm To our knowledge, few evaluations of depth-integrated PP algorithms have been conducted in direct comparison with in situ PP measurements in Arctic waters. Our depth-integrated PP algorithm was validated using unpublished and published PP and Chl a concentration data collected in diverse regions and seasons in the Arctic Ocean (Table  2 ). All the PP measurements were made using the 24 h (or from dawn to sunset) 14 C-uptake method  (Knap et al. , 1996; Gosselin et al. , 1997) , under in situ or simulated in situ conditions (i. e. , light and temperature). Data from a total of 320 stations were gathered. In addition, when available, Z EU (defined as 1 % of surface irradiance) was determined to examine different parameterizations of the absorption coefficients.
a CDOM (λ, z) values measured in the Hudson Bay system by  Granskog et al. (2007)  were particularly high (i. e. a CDOM (355, z) ≈ 3-3. 5 m -1 at 25 PSU) resulting in an underestimation of Z EU for our val- Note: Columns are the mean (M), standard deviation (SD), root mean square error (RMSE) and absolute percent deviation (APD) of the log-difference error. The geometric mean and one-sigma range of the ratio (F = Z EU (alg) /Z EU (meas)) are respectively given by F med , F min , and F max , as defined in the Appendix A. idation dataset. Parameterization  [1]  shows the best agreement with in situ measurements. Besides the parameterization of a ϕ (λ, z) and a NAP (λ, z)  (Matsuoka et al. , 2011) , the proposed parameterization of a CDOM (λ, z) based on empirical relationships  (Bélanger et al.
Matrai et al. (2013))g the expeditions and databases where chlorophyll a (Chl a) and primary production (PP in situ ) data were obtained for the present study. Expeditions and databases from the ARCSS-PP database(Matrai et al. , 2013)are italicized. Processing methods are indicated for Chl a (F represents in vitro fluorometry; H, high performance liquid chromatography, HPLC) and for PP in situ (PC = DI 14 C uptake; PN = 15 NO 3 uptake). *SeeMatrai et al. (2013)for further details on the methods and quality controls of ARCSS-PP database. The different sub-Arctic and Arctic seas are labeled as follows: 1: Greenland-Norwegian seas, 2: Bering Sea, 3: Chukchi Sea, 4: Barents Sea, 5: Canadian Archipelago, 6: Beaufort Sea, 7: Baffin Bay, 8: central Arctic Ocean, 9: Hudson Bay, 10: Russian seas.
Acknowledgements. This project was supported by grants from the  Canada Excellence Research Chair (CERC) in Remote Sensing of Canada's New Arctic Frontier , the  Takuvik Joint International Laboratory (CNRS and Université Laval) , the  Canadian Network Centre of Excellence ArcticNet , the  Natural Sciences and Engineering Research Council of Canada , and  Québec-Océan  funded by the  Fonds de recherche du Québec -Nature et technologies. M. Ardyna  received a postgraduate scholarship from the  CERC Remote Sensing of Canada's New Arctic Frontier  and stipends from  ArcticNet and Québec-Océan. We gratefully acknowledge  M. Benoît-Gagné ,  S. C ¸izmeli , and  J. Uitz  for programming assistance and precious advice;  J. Sahlin  for geospatial analyses; and  D. Christiansen-Stowe  for language support.
We gratefully acknowledge  P. Matrai  and the  NOAA-NODC  agencies for the availability of the ARCSS-PP database, described in detail on the website (http: //www. nodc. noaa. gov/cgi-bin/OAS/prd/accession/details/63065). We are also especially indebted to  M. Blais ,  S. Brugel ,  F. Bruyant ,  H. Claustre ,  S. Demers ,  J. Ferland ,  A. Forest ,  M. Granskog ,  Y. Gratton ,  H. Hodal ,  A. Lapoussière ,  J. Martin ,  C. Nozais ,  N. Price ,  P. Raimbault ,  J. Ras ,  M. Reigstad ,  M. Starr , and  A. Sundfjord  for sharing data. We also thank particularly  A. Forest ,  M. H. Forget ,  A. Baya ,  T. Lacour  and the whole Takuvik team for constructive comments on initial versions of the manuscript, and  E. Boss  and two anonymous reviewers for constructive and insightful reviews. This is a contribution to the research programs of the  CERC in Remote Sensing of Canada's New Arctic Frontier, Takuvik Joint International Laboratory, ArcticNet, Institut des sciences de la mer de Rimouski , and  Québec-Océan.
Expedition No. of stations Method Sub-Arctic and Reference/Investigator or database Chl a PP in situ Arctic seas AOS 19 F 3,8 Gosselin et al. (1997) ArcNut 1337/2662 * 1,3,4,5,6,7 * ArcticNet 186 82 F & PC 5,6,7,9 Tremblay et al. (2009); Ardyna et al. (2011); Tremblay et al. (2011); M. Gosselin (unpubl. data) BAAS 2000 127/383 * 1,4,10 * BPD 20/193 * 3,6,7,8 * BioChem 137/921 * 5,7 * CABANERA 12 12 F & PC 4,8 Hodal et al. (2008) CASES 129 23 F & PC 5,6 Brugel et al. (2009); Tremblay et al. (2011) CFL 61 7 F & PC 5,6,8 Mundy et al. (2009); Sallon et al. (2011); M. Gosselin (unpubl. data) ICES 636/2932 * 1,4,5,8,10 * JAMSTEC 228 15 F & PC 3,6,8 Nishino et al. (2011a, b) Malina 42 8 H & PN 6 H. Claustre (unpubl. data); P. Raimbault (unpubl. data) MERICA 50 18 F & PC 5,9 Ferland et al. (2011); Estrada et al.
(2012); Lapoussière et al. (2013) NEW 126 10 F & PC 1,8 Pesant et al. (1996) NSIDC 175/208 * 2,3 * NODC 1014/3090 * 1,3,4,5,6,7,8,10 * NOW 104 56 F & PC 7 Klein et al. (2002) PANGEA 551/671 * 1,4,8,10 * SBI 38/100 90 H & PC 3,6,8 Hill et al. (2005) SeaBASS 150/935 * 1,5,6,7 * Rey and Loeng (1985) 6/8 * 4 * Vedernikov et al. (1995) 3/32 * 10 * Vedernikov et al. (2001) 6/39 * 10 * Hill V. database 14/38 * 3,5,6,8 * Matrai P. database 34/43 * 4,8 * Chl a Z base.
Sea level during Xynthia Sea level measurements during the Xynthia storm at La Pallice tide gauge (Fig. 1 ) were collected from the REFMAR (www. refmar. shom. fr) database. The maximum sea level reached at this tide gauge during the storm was about 4. 5 m NGF (Fig. 2 ). In order to investigate the spatial variations of the maximum sea level during the Xynthia storm, a new modeling system was developed and implemented over the northeast Atlantic Ocean. This modeling system realizes the coupling in two horizontal dimensions between the circulation model SELFE  (Zhang and Baptista, 2008)  and the spectral wave model WaveWatch III  (Tolman, 2009). SELFE uses a combination of finite volume and finite element methods to solve the shallow water equations and employs a Lagrangian method to treat the advective terms, which guaranties good stability even when using large time steps.
Topographic and bathymetric datasets The high resolution topographic datasets originate from both LiDAR and RTK-GPS (Real-Time Kinematic -Global Positioning System) measurements. LiDAR is a mapping technology that is increasingly used for coastal geomorphologic studies. This technology is appropriate for such analyses since it provides spatially dense and accurate topographic data  (Chust et al. , 2008; Goff et al. , 2009; Haile and Rientjes, 2005; Mazzanti et al. , 2009; Poulter and Halpin, 2008; Webster, 2010; Young et al. , 2011). The LiDAR is a laser altimeter that measures the range from a platform with a position and altitude determined from GPS and an inertial measurement unit (IMU). Basically, it uses a scanning device that determines the distance from the sensor to a set of ground points roughly perpendicular to the direction of flight  (Chust et al.
, 2008). In 2010, the French National Geographic Institute (IGN) carried out the topographic mapping of the entire coastal area of the Pertuis Charentais four months after Xynthia, using the LiDAR technology. The aerial flights were carried out between low-and mid-tide. A terrestrial DEM was generated from the LiDAR data with a resolution of 1 m and a vertical accuracy of 0. 15 m (root mean square error, hereafter RMSE) in low vegetated and gently sloping areas. The accuracy was assessed by IGN in test zones, using GPS receivers with RTK system. In this study, a ground (bare-earth, i. e. excluding objects such as buildings, trees, and shrubs) DTM obtained from the DEM was used. In order to better represent some key topographic features such as dykes, levees and seawalls, additional measurements based on RTK-GPS were included.
The theoretical vertical accuracy of our devices (Topcon hyperpro) is 0. 02 m, but the operational accuracy, which includes uncertainties related to the measurement, would rather be of the order of 0. 05 m. This data could locally improve the reliability of the LiDAR DTM, as shown by  Gallien et al. (2011). The bathymetric datasets shown in Fig. 1  is a combination from different sources. The bathymetry of intertidal areas was determined using the LiDAR technology, between lowand mid-tide. For subtidal areas, the bathymetry originates from the SHOM (French Navy Hydrographic and Oceanographic Service) and was measured with echo sounders. In areas where there was a lack of data between the intertidal LiDAR data and the subtidal bathymetry, complementary bathymetric measurements were carried out by our team using a single beam echo sounder mounted with the same RTK-GPS as used for topographic surveys.
Observed flooded areas related to the Xynthia storm The area flooded by Xynthia in the northern part of the study area, i. e. , marsh no. 27, northward of the Sèvre Niortaise Estuary, was determined using flood inundation maps from the SERTIT (regional service of image processing and remote sensing), combining images from SPOT 4 (10 m resolution, taken two days after the storm), ENVISAT ASAR (12. 5 m resolution, taken two days after the storm), and RADARSAT 2 (6 m resolution, taken 4 days after the storm) satellites. For all other flooded areas, field observations were carried out by SOGREAH, a French consulting agency  (DDTM-17, 2011). In situ limits of storm deposits, physical marks or markers, and damages to vegetation were observed to determine horizontal and vertical water limits. By compiling all these data in a GIS, the polygons of the inundated areas (Fig.
1 ) were then obtained. Considering the delay between the storm and the satellite images, it is not possible to assess the polygon extension accuracy for the northern part of the marsh no. 27. Nevertheless, SERTIT inundation maps were compared with SOGREAH field observations for areas where both datasets were available. These comparisons showed a good agreement between the two datasets. Considering this difficulty to accurately assess the horizontal accuracy of maximum water limits, we arbitrarily set it to 10 m for urbanized flooded areas and 100 m for marshes without any structures. These polygons were considered as the observed flooded areas for Xynthia and were used to evaluate the simulated flooded areas.
Static flood modeling (methods SM1 and SM2) The first step of the static flood modeling was to isolate the 27 marshes by extracting DTM cells below a 5 m NGF limit. For each of the 27 obtained DTM, two "water surface rasters" were created: (1) a first based on the maximum water level value measured at La Pallice tide gauge (SM1) and (2) a second based on space-varying maximum water levels retrieved from the storm surge modeling system (SM2). To compute differences between marsh DTMs and their associated water surface rasters, the Environmental Systems Research Institute's (ESRI's) ArcGIS 10 software along with the Spatial Analyst extension was used. The raster calculator function was used to compute cell by cell the differences between marshes DTMs and water surface rasters. From these resulting rasters, polygons surrounding the negative value regions were then created and only those directly connected to the open sea were kept, representing the flooded areas identified from static flood modeling.
, 2009). We arbitrarily defined good fit measurements for F-values ≥ 0. 7, intermediate fit measurements for 0. 5 ≤ F-values < 0. 7 and bad fit measurements for F-values < 0. 5. A multiple linear regression analysis (MLRA) was carried out in order to investigate the relationship between morphological parameters and land uses and the F-values. Five parameters that seemed to be a priori the most relevant were chosen: (1) the maximum distance between the coastline and the landward boundary of the marsh (D); (2) the surface area of the marsh; (3) the mean topography of the marsh; (4) the urbanization rate, computed for each marsh using the Corine land cover database (www. eea. europa. eu); and (5) a land reclamation rate since 1824, calculated using a coastline dating from 1824.
Acknowledgements. This work was supported by  FEDER   34146-2010  and  Conseil Général de Charente-Maritime. We would like to thank  Frédéric Pouget ,  Fredéric Rousseaux ,  Cécilia Pignon-Mussaud ,  Dorothée James  and  Jérôme Faucillon  for their help on GIS. Thanks also go to  Nicolas Bruneau  for his help on the extraction of sea level elevations from the storm surge numerical model. We also would like to thank  Clément Poirier  for his support on statistical analysis. IGN is thanked for 2010 LiDAR data. SONEL (www. sonel. org)and REFMAR are thanked for sea level tide gauge measurements. Finally, the authors appreciated the comments of Guillem Chust as well as those of the anonymous reviewer, which greatly improved this manuscript. Edited by: S. Tinti Reviewed by:  G. Chust  and three anonymous referees.
A simplified variant of the Kalman filter is used to assimilate ocean color data during a 9-year period. In this frame, two experiments are carried out, with and without anamorphic transformations of the state vector variables. Data assimilation efficiency is assessed with respect to the assimilated data set, nitrate of the World Ocean Atlas database and a derived climatology. Along the simulation period, the non-linear assimilation scheme clearly improves the surface analysis and forecast chlorophyll concentrations, especially in the North Atlantic bloom region. Nitrate concentration forecasts are also improved thanks to the assimilation of ocean color data while this improvement is limited to the upper layer of the water column, in agreement with recent related literature. This feature is explained by the weak correlation taken into account by the assimilation between surface phytoplankton and nitrate concentrations deeper than 50 meters.
, 9, 37-56, 2013 www. ocean-sci. net/9/37/2013/ (ii) to produce a multi-year reanalysis of the North Atlantic biogeochemistry using SeaWiFS satellite chlorophyll data from the period 1998-2006, which could eventually supersede the biogeochemical climatologies available today, and (iii) to investigate more specifically how information on superficial chlorophyll concentration can be projected onto non-observed variables. Here, non-observed variables mean both chlorophyll where no satellite data are available and other unmeasured biogeochemical components (e. g. , nutrients). The strategy adopted for this work relies on assimilation of the SeaWiFS data set because this mission consists of the longest time series of ocean color data to date (September 1997 to December 2010). The model domain is the North Atlantic, which exhibits highly contrasted seasonal and spatial biogeochemical behavior as well as many ocean circulation features found in other ocean basins.
Initialization of the coupled model and simulation strategy The simulations described in this paper were performed on a 9-year period from 1 January 1998 to 1 January 2007. Physical variables were initialized following a 16-year spin-up period starting from the Levitus monthly climatology  (Levitus et al. , 1998)  for temperature and salinity fields, while velocity fields were null. Biogeochemical variables were initialized following a 2-year spin-up period. This short spin-up was initialized using nitrate concentrations from the World Ocean Atlas climatology  (Garcia et al. , 2010) , while homogeneous values were assigned to the other biogeochemical variables (see  Ourmières et al. , 2009). The coupled model is subsequently run without data assimilation; this simulation will be referred hereafter as the "free" run.
Validation strategy A data assimilation experiment involves at least three sets of information: the free simulation, the data to be assimilated, and the simulation with data assimilation. While intercomparisons between these three information sets is necessary to assess the method efficiency, it will never be totally conclusive since the three information sets are intertwined  (Gregg et al. , 2009 ). An independent data set of unassimilated variables is required for an objective determination. We use here the nitrate database extracted from the historical World Ocean Atlas 2009 (WOA09) as an independent data set to validate the assimilation process. The historical nutrient measurements available in this atlas were obtained from the National Oceanographic Data Center and World Data Center archive, including all data gathered as a result of the Global Oceanographic Data Archeology and Rescue (GO-DAR) and the World Ocean Database (WOD) project  (Boyer et al.
Surface patterns of chlorophyll and nitrate concentrations Figure  2  shows the surface chlorophyll maps obtained after time-averaging the simulation results over successive 60-day periods during the year 2006 (first row: days 1 to 60; second row: days 61 to 120; etc. ). Figure  3  is organized the same way but for the surface nitrate distributions. These maps are shown for (a) the free run, (b) the SeaWiFS data or climatology, (c) the linear run and (d) the anamorphosis run. The free run shows some significant differences with the SeaW-iFS data (Fig. 2 ). The chlorophyll bloom starts slightly later in the free run than observed (second row, corresponding to March-April). An elongated structure centered at ∼35°N appears along the southern flank of the Gulf Stream, while it is not present in the data (third row, corresponding to May-June).