dataesr/scientific-paragraphs-categorization huggingface dataset:
1 ). We, therefore, defined the study site as corresponding to an area < 2000 m deep and north of 56°S, hereafter referred to as the Kerguelen-Heard shelf (Fig. 1 ). Dive data returned from CTD-SRDLs and tracks (derived from SSM analysis) were aligned in time to assign a geographical position (longitude, latitude) to each dive. Bathymetry was extracted under each location and compared to the corresponding maximum dive depth to calculate the percentage of total bathymetric depth covered by the seal (%Bathymetry -Depth). We used bathymetry at 500 m resolution generated by Geosciences Australia to define the Kerguelen Plateau within 47° to 56°S, 68° to 80°E. The associated bathymetric depth of each inter polated dive location beyond the boundary of the high-resolution bathymetry data set (i. e.
S1  in the Supplement at www. int-res. com/ articles/ suppl/ m502p281_ supp. pdf). We tested the significance of these dive strategy groups using analysis of similarities (R Development Core Team, function anosim;  Oksanen et al. 2013). Modelled utilisation distributions (UDs) of dive data were investigated qualitatively by calculating horizontal kernel density maps at 10 equally spaced depth intervals. The maximum depth for each dive was merged to each individual's track data using linear interpolation between the SSM-derived 6 h location estimates and combined for all individuals. The resulting 3-dimensional cloud of points was then divided into 10 equally spaced horizontal slices, and a kernel density was calculated for each slice. Finally, we calculated two 3-dimensional iso-surfaces (density envelopes) that enclosed the 50 and 90 percentile components for these kernels.
Sea-surface variables We used sea-surface temperature (SST, °C), seasurface height anomalies (SSHa, cm) and sea-surface chlorophyll a concentration (chl a, mg m -3 ). The SST was derived from OI-Daily v2 data  (Reynolds et al. 2007) , obtained from www. ncdc. noaa. gov; SSHa was derived from AVISO weekly composites, with a spatial resolution of 0. 25° (www. aviso. oceanobs. com); and chl a was derived from MODIS monthly composites, with a nominal resolution of 0. 05 mg m -3 and a spatial resolution of 0. 25°  (Feldman & McClain 2006) , obtained from http:// oceancolor. gsfc. nasa. gov/ cgi/l3. The mean monthly chl a dataset was used specifically because of the patchy nature of chlorophyll data at higher temporal resolutions due to cloud cover.
Subsurface variables Temperature data collected by the CTD-SRDLs were used to create vertical and horizontal temperature profiles across the Kerguelen-Heard shelf. We used a 24 h temporal resolution for these analyses to maximize temperature-depth coverage. Firstly, COnstrained B-Splines (COBS) using nonparametric regression quantiles  (Ng & Maechler 2011)  were fitted to the combined daily depth and temperature profile data for each seal, to derive interpolated temperature records at 25 m intervals from 0 to 500 m, providing a single temperature-depth profile per day. This was then combined with the mean daily locations for that seal. The spline tended to not fit points deeper than 500 m very well due to the relative scarcity of points at these deeper depths. All temperature profiles were applied to hierarchical cluster analysis based on Euclidean distance to investigate spatial patterns  (Bestley et al.
To compare the relative degree of foraging per cell with all the environmental variables we fitted a series of generalised linear mixed-effect models (GLMMs) using the R software package nlme (R Development Core Team, function lme;  Pinheiro et al. 2013)  with  Zuur et al. (2009). Firstly, we assessed using the 'full' (most complex) model, with and without the random intercept term, to ensure that individual seals were contributing to the model fit. Next, we explored the optimal structure of the random effects comparing random intercept models (seal only) with random intercept and slope models (seal and each environmental variable in turn), retaining the slope terms that improved the models. Next, we assessed the effect of inclusion of an autocorrelation term in the resulting optimal model.
Diving behaviour Diving data described how seals used the water column on the Kerguelen-Heard shelf when in search mode. A total of 29 688 dives made were identified as searches: 19 865 (67%) of these dives were benthic, 5730 (19%) were demersal, 2884 (10%) were meso-pelagic, while only 1209 (4%) were shallow dives. Fewer dives were made in transit (n = 4081), although a similar pattern existed, with most dives being benthic (n = 2840; 70%), followed by demersal (n = 642; 16%), meso-pelagic (n = 427; 11%), and the least were shallow dives (n = 172; 4%). Of the shallow search dives 83% were in the top 100 m of the water column, and approximately 70% of the meso-pelagic search dives were between 100 and 400 m depth, nearly half of which were between the 200 and 300 m depth layers (Fig. 2 ). Most demer-Fig.
1999). However, the effects of climate change on environmental factors, such as the water temperature profile  (Jaud et al. 2012 ), coupled with the potential impact of fisheries, could result in decreased foraging and demographic performance. Indeed, it has been well documented that climatic perturbations influence species' distributions  (Walther et al. 2002 , Parmesan 2006). Investigations into predator use of the shelf and the in situ environment will help understand these changes and perhaps help mitigate negative impacts. Specifically, larger sample sizes and further dietary analyses are needed for a more objective and predictive analysis of future datasets.
Acknowledgements. Thanks to Myriax for providing an  Eonfusion scholarship  to  Malcolm O'Toole , particularly to  Hugh Pederson  and  Francis Chui. Ben Raymond  at the  Australian Antarctic Division  provided all remotely sensed satellite and bathymetric datasets. The study was conducted under  Australian Antarctic Division Animal Ethics Committee (AEC)  Project Number  2794  and  University of Tasmania 's  AEC A8523  and  A10361.
, 2007a, b; Wu et al. , 2008; Agudelo et al. , 2011; Tang et al. , 2011; Curier et al. , 2012). One approach employed to construct a representative background ensemble was to perturb the main model parameters affecting the ozone error variance and correlation  (Hanea et al. , 2004). Another strategy employed in data assimilation is to adjust the BECM iteratively using diagnostics such as Desroziers diagnostics, which derive background errors intrinsically from the assimilation procedure  (Desroziers et al. , 2005; Schwinger and Elbern, 2010). The rCTM CHIMERE that is used in this study has already been successfully coupled to a local EnKF square root scheme  (Evensen, 2004)  in order to assimilate tropospheric ozone columns derived from the IASI instrument  (Coman et al. , 2012). In this paper, we assess the setup of the CHIMERE-ENKF in the context of the assimilation of surface ozone data.
The ensemble Kalman filter assimilation setup, including a description of the rCTM CHIMERE and the a posteriori diagnostics is described in Sect. 2. We present the classification of the surface ozone observations in Sect. 3. Section 4 shows the evaluation of the CHIMERE reference run. Different assimilation experiments of a short period are presented in Sect. 5, while an ozone analysis for a longer summer period is evaluated in Sect. 6. The conclusion and future directions are given in Sect. 7. 2 The CHIMERE-EnKF data assimilation setup and diagnostics.
The EnKF algorithm We implemented the EnKF that was first introduced by  Evensen (1994). This sequential filter allows a relatively simple implementation of a sophisticated data assimilation scheme appropriate for a large three-dimensional model such as CHIMERE. We created an ensemble of N = 20 perturbed model states using a Monte Carlo method. They evolve forward in time in order to obtain a forecast x f i,k from the time step (k -1) to the time step k. x f i,k = M x a i,k-1 + q i,k-1 x a i,k-1 where q i,k-1 = sd * η i,k-1. (1) Here x f i,k represents the vector of forecasted ozone concentrations where the subscript i indicates the ensemble number. Ozone concentrations at the next time step are simulated using the CHIMERE model M. The noise q i,k-1 is the product of spatially correlated fields η i,k-1 and a tunable coefficient with a relative standard deviation (SD).
, 2009)  and provides global trace gas compositions on an hourly basis for O 3 , CO, HCHO, NO x and SO 2. In this way, we obtain three-hourly boundary conditions that are temporally and spatially interpolated on the CHIMERE grid. Ozone observations used in this study are operated by national and regional networks across Europe and collected through the European Environment Agency (EEA) in the Airbase database (http://acm. eionet. europa. eu/databases/ airbase/). A classification of ozone measurement sites corresponding to their representativeness is crucial for their use within the data assimilation procedure (cf. Sect. 5. 1), either for assimilating observed information (assimilation sites) or for evaluating analyzed fields (validation sites). Rather than relying on a station type classification of EEA (see for instance the European Union directive 2008/50/CE), we derive here a classification defined by a specific criterion related to ozone concentrations.
Also, as seen from Table  2 , results obtained with the FLEM05 approach are often consistent with the Airbase classification: around 90 % of the remote stations (FLEM05) are rural (Airbase) and more than 80 % of the suburban stations (FLEM05) are also "suburban" or "urban" in Airbase. However, discrepancies are found in the ozone categorization with respect to the standard classification. For instance, the effect of the urban environment on ozone concentrations for many "urban" Airbase sites is small enough so that these sites are still representative of a larger environment: these sites constitute around 40 % of our (FLEM05) total rural sites. Thus, the classification procedure applied here allows us to obtain a significantly larger observational database than the initial classification proposed by Airbase.
Assuming no observational error correlation, the OECM matrix is defined by the observation error variance (diagonal terms). As highlighted above (Sect. 2), the main observational error source is the representativeness error (and thus is dependent on the model resolution). The random part of the observation error can be defined as an average value of the standard deviation of observations within a grid cell. For the city of Paris, Blond    2003 ) determined an observation error of 5 ppb at 15:00 UTC for a 6 km model resolution. Using observations from the AIRNOW database in the USA,  Chai et al. (2006)  evaluated the ozone standard deviation inside a model grid cell (60 km horizontal resolution) and got on average a daily range between 5 ppb and 13 ppb (at night). Finally, in their assimilation framework, they assumed an observation error of 8 ppb.
Conclusions In this paper we present a data assimilation system based on the rCTM CHIMERE in an EnKF framework and using surface ozone observations provided by the European Airbase database. These stations were classified based on their diurnal ozone variability. 350 stations labelled as remote, rural and suburban were selected for assimilation with an average closest distance of 61 km as compared to 37 km for the entire set of almost 700 stations. The system is based on a local analysis computed using a deterministic square root filter and is applied to an ensemble of 20 perturbed CHIMERE rCTM simulations. The assimilation algorithm is well suited to an operational framework; an analysis of one day (including the control run) takes 5 h where only CHIMERE simulations are parallelized (and not yet the assimilation procedure).
For future work, the evaluation of the system performance should be made over a longer time period covering different seasons. Also, ideally, to be physically consistent, the BECM should rely more on the perturbation of uncertain physical parameters instead of the ozone concentrations. This would also allow assessing of the impact of an ozone correlation on other chemical variables by including and updating them in the state vector  (Brunner et al. , 2012; Miyazaki et al. , 2012). In addition to the model parameter perturbations already implemented, or by replacing them, a new ensemble should be designed taking the model inputs as emissions, meteorological forcing, and chemical boundary conditions from different sources (models). Finally, due to its robustness, the present system already appears suitable for implementation in operational systems such as the one supported by the European FP7 MACC project.
Acknowledgements. This work has been supported by the  MACC  and  MACC-II (Monitoring Atmosphere and Climate Change ) projects from the  European Community 's  Seventh Framework Programme  ( FP7 THEME [SPA. 2011. 1. 5-02 ])under grant agreement no. 283576  and by the  French LEFE  project  ADOMOCA  ( Assimilation de Données pour les Modèles de Chimie Atmosphérique). This work was performed using HPC resources from  GENCI-CCRT  (Grant  2013-t2013016695 ). This study has been also supported by  CNES  in the framework of the  IASI-TOSCA  project. Most of the charts have been made using the Python plotting library Matplotlib (Hunter 2007). B. Gaubert's PhD was supported by a grant from the  French Ministry of Higher Education and Research. Edited by:  H. Garny The publication of this article is financed by  CNRS-INSU.
M. Crippa et al. : Organic aerosol components derived from 25 AMS datasets Abstract. Organic aerosols (OA) represent one of the major constituents of submicron particulate matter (PM 1 ) and comprise a huge variety of compounds emitted by different sources. Three intensive measurement field campaigns to investigate the aerosol chemical composition all over Europe were carried out within the framework of the European Integrated Project on Aerosol Cloud Climate and Air Quality Interactions (EUCAARI) and the intensive campaigns of European Monitoring and Evaluation Programme (EMEP) during 2008 (May-June and September-October) and 2009  (February-March). In this paper we focus on the identification of the main organic aerosol sources and we define a standardized methodology to perform source apportionment using positive matrix factorization (PMF) with the multilinear engine (ME-2) on Aerodyne aerosol mass spectrometer (AMS) data.
Our source apportionment procedure is tested and applied on 25 data sets accounting for two urban, several rural and remote and two high altitude sites; therefore it is likely suitable for the treatment of AMS-related ambient data sets. For most of the sites, four organic components are retrieved, improving significantly previous source apportionment results where only a separation in primary and secondary OA sources was possible. Generally, our solutions include two primary OA sources, i. e. hydrocarbon-like OA (HOA) and biomass burning OA (BBOA) and two secondary OA components, i. e. semi-volatile oxygenated OA (SV-OOA) and low-volatility oxygenated OA (LV-OOA). For specific sites cooking-related (COA) and marine-related sources (MSA) are also separated. Finally, our work provides a large overview of organic aerosol sources in Europe and an interesting set of highly time resolved data for modeling purposes.
Ng et al. (2010)  provided an overview of OA sources in the Northern Hemisphere, including a broader spatial domain than Europe and a wide range of locations affected by different aerosol sources. Moreover, their major focus was the investigation of the secondary oxygenated components and their aging. Lanz et al. (2010)  provided an overview of the aerosol chemical composition and OA sources in central Europe focusing on Switzerland, Germany, Austria, France, and Liechtenstein. In all of these studies, the major fraction of PM 1 was often represented by organics which consisted, for most of the locations, of oxygenated OA; however the contribution of primary sources (like traffic and biomass burning) was not always identified especially in rural and remote places. Our work includes 17 measurement sites all over Europe, comprising 25 unit mass resolution AMS data sets, and represents therefore an unprecedented overview of OA sources in Europe.
, 2011   (Kulmala et al. , 2009). Nemitz et al. (2014) , where a detailed discussion about measurements setup and data processing is also provided. The average concentrations of PM 1 chemical components as measured by the AMS are also reported in Table S1 in the Supplement. In this paper we focus on the organic aerosols (OA) component which represents the major fraction of submicron particulate matter for most of the sites, ranging between 20 and 63 % of PM 1 (concentration range: 0. 6-8. 2 µg m -3 ), consistent with the values found by  Jimenez et al. (2009)  and  Ng et al. (2010). Table  1  summarizes the average organic concentration for each site and all the seasons and the relative OA contribution to NR-PM 1 as measured by the AMS. Figure  2  represents an overview of the EUCAARI measurement field campaigns focusing on the organic aerosol sources (an analogous plot for the PM 1 chemical composition is reported in  Nemitz et al.
In our work, a constant a value is applied to the entire constrained mass spectra (MS); however a softer constraining technique is provided by the pulling approach  (Paatero and Hopke, 2009; Brown et al. , 2012) , which is available within the SoFi package  (Canonaco et al. , 2013)  and which is explained in more detail in  Canonaco et al. (2014b). The ME-2 solver is here successfully applied to the time series of the unit mass resolution organic mass spectra measured by the AMS, including for most of the sites m/z up to 200 (for a few sites the analysis was performed only up to m/z 150 due to the low signal to noise ratio (SNR) observed). Data are first averaged to 15-30 minute time resolution and after performing the source apportionment analysis they are averaged to 1 hour for modeling purposes.
M. Crippa et al. : Organic aerosol components derived from 25 AMS datasets 3. 2 A standardized source apportionment strategy In this section we provide technical guidelines to perform a standard source apportionment analysis on AMS data in order to identify well known organic aerosol sources, like hydrocarbon-like (HOA), biomass burning (BBOA), cooking (COA) and oxygenated components (OOA), but also sitespecific sources. Our approach is particularly relevant for rural locations where the temporal variability of emission sources is less distinct and where the aging processes are dominant compared to fresh emissions. A detailed description of the main features of these OA sources (source profile, diurnal patterns, etc. ) is presented in Sect. 4. Here, we define first our source apportionment strategy and then we provide details about the application of this methodology on the EUCAARI-EMEP data.
Application to the EUCAARI-EMEP data The previously mentioned procedure is successfully applied on the 25 available organic AMS data sets and therefore it provides a consistent methodology. Deploying the ME-2 solver allows the discrimination of traffic and biomass burning within the primary sources and of two OOA components for the secondary fraction for most of the sites. In order to perform a value runs within the ME-2 solver, it is necessary to select reference mass spectra to be constrained in the model. Due to the similar features of the HOA and COA mass spectra, in our work we choose the HOA and COA mass spectra identified in Paris by  Crippa et al. (2013)  as a reference, because of the significant contribution of the cooking source to the total OA mass and its strong diurnal pattern at that site.
As a second step, this MSA MS is used as input to the algorithm for the two other field campaigns  (Finokalia 2008 and Mace Head 2009). The separation of this factor is a challenge for the unconstrained PMF, due to reduced biological activity during early spring in Mace Head and weak marine influence for the site in Crete. Finally, in order to provide a complete overview of the EUCAARI 2008-2009 data, the PMF solution described in this paper for the Finokalia 2009 campaign is reported in this work. Our standardized procedure could not be applied to this data set due to specifics of the data pre-treatment and the presence of unusual local sources.
Primary and secondary OA source contributions The standardized source apportionment strategy introduced in Sect. 3. 2 is systematically applied to the 25 available AMS data sets, consisting of the AMS matrices with the organic mass spectra over time and the corresponding errors. Table  S2  reports the comparison of the number of OA components identified with the a value approach in comparison with the unconstrained run (PMF)  (Ulbrich et al. , 2009) , highlighting in red the constrained source mass spectra. The unconstrained PMF often cannot provide a clear separation of OA sources in rural and remote sites  (Zhang et al. , 2007; Jimenez et al. , 2009). This may also happen at urban background sites where the effect of meteorology can still be dominant compared to the source temporal variability  (Lanz et al.
, 2008; Canonaco et al. , 2013). In our 25 data sets, unconstrained PMF allows mainly the identification of POA (often including HOA and BBOA in one factor typically characterized by high signal at m/z 44 and 60) and SOA sources (refer to Table  S2 ). Even when HOA is identified, it is often not clean due to the high contribution of m/z 44 (which should be rather small for primary traffic emissions). In some cases it is only possible to separate two secondary oxygenated components, but no primary source is retrieved, although expected. On the contrary, with our approach, primary and secondary OA sources are retrieved for all the analyzed data sets, including traffic (HOA), cooking (COA) and biomass burning (BBOA) as primary sources, and methane sulfonic acid (MSA), semi-volatile and low-volatility oxygenated OA (SV-OOA and LV-OOA) as secondary components.
For the Cabauw 2008 and Finokalia 2009 data sets the contribution of local (site specific) sources is also observed. The ME-2 solution for the Cabauw 2008 campaign includes a site specific factor (named here LOA, local organic aerosol) which is interpreted to be hulis-related (humic-like substances), as widely explained in  Paglione et al. (2013). These results represent a great improvement in the source apportionment field, since we demonstrate the possibility to identify several pri- ) is also reported. Site specific sources are classified as LOA (local organic aerosols) and include HULIS-related OA (humic-like substances) for Cabauw  (Paglione et al. , 2013) , while amines and local sources for Finokalia  (Hildebrandt et al. , 2011). mary and secondary OA components even at rural locations where the POA factors often contribute less than 10 % of the OA.
, 2013). However, in absolute terms, HOA concentrations in Puijo are rather low (0. 2 µg m -3 ), while at sites with high total OA concentrations the absolute HOA amount is higher compared to rural sites. Biomass burning is identified in 22 data sets and is associated with a mix of domestic heating during cold periods and open fires (e. g. , agricultural, forest and gardening waste) and barbecuing activities etc. The BBOA contribution to the total OA mass varies on average between 5 and 27 % (corresponding to 0. 1-0. 8 µg m -3 ) with an average relative contribution to the total OA mass equal to 12 ± 5 %. At 3 sites (Melpitz spring 2008, Puijo fall 2008 and Finokalia spring 2008) f 60 is close to or below the background values and the BBOA is regarded as negligible. Major contribution to total OA derives from secondary sources, which are classified here based on their degree of oxygenation as SV-OOA and LV-OOA and which contribute on average 34 ± 11 % and 50 ± 16 % to the total OA mass, respectively.