dataesr/scientific-paragraphs-categorization huggingface dataset:
Wavelength λ = 440 nm. The equal presence of prolate and oblate spheroids with the same aspect ratios was still assumed for dust aspect ratio distribution as adopted in the retrieval algorithm [7], while only prolate spheroids were considered for the measured aspect ratio distribution. The measured aspect ratio distribution was applied in modeling light scattering assuming a mixture of spheroids. Influences of different aerosol aspect ratio distributions on scattering matrix elements were simulated by the spheroid kernels software package  [7]. The distribution of aspect ratios was assumed as independent of particle size in this simulation. This assumption was also adopted in the sun/sky radiometer retrieval  [7]. From Fig. 4 , it is obvious that elements of the scattering matrix (except F 11 ) simulated with the real-measured aspect ratio distribution are distinct from those simulated with sphere and the priori fixed aspect ratio distribution of dusts employed in AERONET retrieval  [7].
Acknowledgements We thank  Jim P. Grover  and one anonymous reviewer for their constructive review. This work was funded by national research grants  CGL2013-41256-P (MARES)  and  CTM2014-54926-R (SUAVE ) from the  Spanish government. S. M. V. and P. C. are supported by  «Ramon y Cajal» (RyC)  contracts from the  Spanish government. S. D. was supported by  NSF  (grant  1434007 ) from the  United States government. M. L. and J. M. M. are supported by the  French Laboratory of Excellence  project  «TULIP»  ( ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 ). M. L. was also supported by the  BIOSTASES Advanced Grant , funded by the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  666971 ) and J. M. M. by the  Region Midi-Pyrenees project  ( CNRS 121090 ).
Study area and sampling Data were collected during 101 weeks from January 2009 to December 2010 at Pt. B station (43˚41' 10" N, 7˚19' 00" E) NW Mediterranean Sea (Fig  1 ). The seawater was collected at 6 depths (surface, 10, 20, 30, 50 and 75 m) with Niskin bottles and placed in 10 dm 3 polypropylene bottles. Sampling was conducted weekly between 9:00-10:00 am in order to limit shortterm variability in group abundances. This site is routinely monitored since 1957 and the long-term series of physical and biogeochemical variables are gathered and maintained by the.
Flow cytometry Sub-samples (2 ml) of seawater from each depth were immediately fixed in glutaraldhehyde (1% final concentration), flash-frozen in liquid nitrogen and stored at -80˚C until analysis in the laboratory  [39]. Single cell analysis was processed through a Becton Dickinson, FACSCalibur flow cytometer with a maximum flow rate of 65 mm 3 min -1 , equipped with a 488 nm argon laser. Each cell was characterized by 5 optical signals, forward and side scatter signals, green (530/30 nm), orange (585/42 nm) and red (> 670 nm) fluorescence. Data acquisition and treatment were processed with the CellQuest-Pro (BD-Biosciences) software. The sample flow rate was calibrated by adding to each sample fluorescent 1μm beads (Polyscience Inc. , Europe) of known concentration. HP abundance was determined as described by Gasol and Del Giorgio  [40].
Statistical analysis A Principal Components Analysis (PCA) was used to assess the relationships between microbial and ultraphytoplankton community structures and their hydrological environment. The dataset consisted of 101 week values, from January 6 2009 to December 28 2010, described by 42 biological active variables: concentrations (cells cm -3 ) of HP, Synechococcus, Prochlorococcus, autotrophic picoeukaryotes and nanoeukaryotes, Chl a (μg dm -3  ; Si(OH) 4 ) at the same 6 depths and POC and PON concentrations (at surface and 50m) are illustrative variables transformed into rows (N = 7420). They were also projected in the factorial space, in order to improve interpretation of the results. The missing 8 values of the active data were estimated using two different methods: when a single value was missing between two existing ones, a linear interpolation was done.
In the case where several successive values were missing an iterative approach using a PCA was performed. The multifactorial treatments were made using R language with FactoMineR and missDT libraries packages. The statistical significance of PCA axes was tested by the method of Boostraped data and the method of Broken-Stick model  [50]. Moreover, potential relationships among integrated variables were tested by Spearman correlation coefficients (p). The intraset correlation coefficients of the different variables as well as the eigenvalues of axes were selected at significance level, P <0. 05. Differences in POC, bacterial concentrations related to depth and to hydrological periods were tested by one-way analysis of variance (ANOVA) followed by a Post Hoc comparison using Tukey's and least significant difference (LSD) test.
Interannual variations and link with environmental parameters Coastal areas are complex ecosystems, hydrology and winds can lead to changes in nutrient availability altering species competition, the microbial assemblage and the associated carbon cycle. The ACP highlighted a significant interannual variability in the microbial community structure during MIX period that likely resulted from the hydrological features. Although end of February of both years corresponded to the onset of the phytoplankton bloom, the year 2009 was characterized by warmer winter-spring temperature compared to 2010 and to the median year defined by the 1995-2008 dataset (Fig  2 ). These conditions likely affected the intensity of the mixing period, the transition towards spring conditions and the length of the stratification period.
Acknowledgments The present study was supported by  Service d'Observation en Milieu Littoral (SOMLIT) French program , the  Service d'Observation de la rade (SORADE) of the Observatory of Villefranche sur Mer (OOV)  and the  French Ministry of Research (CNRS-INSU). We thank the  Service d'Observation Rade de Villefranche (SORADE)  especially  A. M. Corre  for hydrological sampling and analysis,  M. E. Kerros  for graph editing,  John Dolan  for English editing and the crew of the boat Sagitta 2. We thank  Michel Denis  for constructive comments that helped improve the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
2009; Smith and Marshall 2009; Naveira Garabato et al. 2011; Bates et al. 2014) , a detailed understanding of the physical implications of three-dimensionally varying K for the large-scale overturning circulation is still lacking. In this study, we seek to characterize and quantify the influence of storm tracks and the suppression of the eddy diffusivity by the mean flow on the Southern Ocean overturning circulation. We will explore the impact of geostrophic mean flow on the MOC, and in particular the impact of the strong currents of the ACC in modulating the eddy overturning. To achieve our goals, we reconstruct the overturning circulation from a large observational dataset that combines hydrographic data from Argo floats, oceanographic cruises, and instrumented elephant seals, with sea surface height altimetry.
The observational datasets are used to develop a direct estimate of the Eulerian-mean overturning due to ageostrophic Ekman currents. In addition, we produce a three-dimensional estimate of K based on the theory of  Ferrari and Nikurashin (2010)  that allows a reconstruction of an eddy overturning streamfunction from the downgradient diffusion of potential vorticity  (Treguier et al. 1997 ). We will investigate the influence of spatial variation of K and the suppressing influence of the background flow on the reconstructed eddy and residual streamfunctions. By reconstructing C + from observations, we show that this mixing suppression has a significant impact on the eddy overturning and therefore plays a key role in shaping the residual overturning circulation. In tandem with this reconstruction, we employ a simple conceptual model, based on the TEM approach of  Marshall and Radko (2003, 2006) , to guide our interpretation of the observational-based reconstruction.
The remainder of this paper is organized as follows: The theoretical framework used for building the reconstruction of the MOC from observations, including the procedure for estimating the horizontal (isopycnal) diffusivity K, will be presented in section 2. The observational dataset we employ will be described in section 3. Our estimate of the threedimensional eddy diffusivity will be presented in section 4, and our estimated MOC reconstruction, along with a comparison of the results obtained with and without the influence of the background flow in section 5. The influence of a vertically varying K on the overturning will then be elucidated using a conceptual model in section 6. Finally, we will bring together the observational and theoretical results of this study in section 7.
However,  McCaffrey et al. (2015)  and  Chapman and Sallée (2017, manuscript  submitted to Ocean Modell. ) have shown that it is possible to use Argo floats to measure the statistics of the mesoscale turbulence. The Argo array has a limited number of observations along the southern border of the ACC and along the seasonal sea ice edge. To supplement the Argo data, we additionally employ hydrographic profiles obtained from various research cruises, assembled in the World Ocean Database (WOD;  Boyer et al. 2009 ) and 223 426 profiles collected from 513 instrumented southern elephant seals  (Roquet et al. 2013 (Roquet et al. , 2014)). These animals forage throughout the Southern Ocean but prioritize regions that are generally farther south of those sampled by the Argo floats. Data coverage of Argo, WOD, and the instrumented seals are shown in Fig.
1. The combined dataset samples all of the major water mass classes within the Southern Ocean, as shown in Fig. 2. This figure shows histograms of the deepest profile depth (Fig. 2a ) and densest profile neutral density (Fig. 2b ) sampled by each of the three data sources. The majority of Argo profiles sample to 2000 m and to about g 5 27. 8 kg m 23 ; the majority of the instrumented seals' profiles sample to around 500-m depth, with some profiles deeper than 1000 m and to about g 5 28. 0 kg m 23. We note that although waters denser than g 5 28. 0 kg m 23 (Antarctic Bottom Water) are sampled in this dataset, the coverage is patchy, being sampled primarily in the Atlantic sector. It can be seen in Fig. 2c  that the addition of instrumented seals to the database significantly improves winter data coverage.
For all datasets, only profiles that have passed quality control checks are used. Additional quality control was carried out using an automated outlier detection algorithm based on an interquartile range filter and density inversion filter, as in  Schmidtko et al. (2013). Data from 2006 to 2014 are used in this study, as there is insufficient data in preceding years to provide coverage of the entire Southern Ocean, as shown in Fig. 2c.
b. Satellite data In addition to the hydrographic data, we also employ satellite-derived estimates of sea surface dynamic topography in order to provide a reference velocity. The AVISO ADT is then calculated at each of the hydrographic profile locations and sampling times by three-dimensional linear interpolation (i. e. , spatially and temporally). Thus, for every hydrographic profile we have an associated estimate of the ADT. Profiles obtained in regions or at times where ADT data are not available (which occurs frequently in winter in the far south of the domain) are flagged and excluded from the analysis.
d. Climatology of the Southern Ocean Using the hydrographic and satellite data products described above, we develop a climatology of the Southern Ocean. In particular, from the temperature, salinity, and pressure profiles, we compute the neutral density g, the isopycnal potential vorticity (IPV) q, and the absolute geostrophic streamfunction M. Profiles of g are computed from our hydrographic database using the software described by  Jackett and McDougall (1997). To compute the IPV, we make the planetary geostrophic approximation, which is a good approximation of the Ertel PV in the Southern Ocean interior  (Thompson and Naveira Garabato 2014) : q ' f r 0 ›g ›z. ( 20 ) To compute profiles of isopycnal streamfunction, defined in  McDougall and Klocker (2010)  [see Eq. (  8 )], we use version 3 of the International Thermodynamic Equation Of Seawater-2010 (TEOS-10) software  (McDougall and Barker 2011).
We note that although this isopycnal is well represented in our dataset (see Fig. 2b ), it is deeper than 2000 m over much of region north of the ACC. Figure  3b  shows IPV on the same isopycnal, which increases poleward, as expected. However, it is worth noting that the IPV structure is not zonally homogeneous, and there are regions of stronger and weaker meridional gradients, which, according to Eq. (  9 ), can indicate regions of enhanced eddy volume transport. Finally, Fig. 3c  shows the geostrophic current speed computed from the gradient of the absolute geostrophic streamfunction. The currents appear realistic: they form jets and show steering by topography. The strength of these mean currents is important for the suppression of eddy volume fluxes, as will become apparent in sections 4 and 5.
We compute the RMS of the geostrophic streamfunction by first computing the streamfunction fluctuations by subtracting the mean geostrophic streamfunction M from each of the instantaneous profiles of M. The square of M 0 is then computed for each profile and mapped using the CARS-LOWESS software on a regular longitude/ latitude grid (see Fig. 4a ). A highly zonally asymmetric field is produced, with elevated streamfunction variance To compute the suppression factor in the cross-stream direction, we again follow  Klocker and Abernathey (2014)  and compute the required estimate of the timemean current velocity u, the eddy phase velocity c p , the eddy decorrelation time scale t eddy , and the eddy wavenumber k eddy. The time-mean current velocity u is obtained from the gradient of the absolute geostrophic streamfunction M, as described in section 3d.
There is the hint of a weak counterclockwise cell, consistent with the deep abyssal cell, although our dataset does not sufficiently sample waters denser than 28 kg m 23 to completely resolve the northward abyssal cell. In comparing C res K? and C res K 0 with previous studies using numerical models it is clear that C res K? more closely resembles the canonical structure of the MOC's upper branch. Without taking into account the suppression of the mean flow, the counterclockwise eddy-induced overturning is likely far too strong, which results in an overcompensation of the Eulerian overturning and the shallowing of the clockwise overturning cell (seen most clearly in Fig. 11c ). Additionally, the diabatic downwelling present in C res K 0 that arises due to the intensity of the inferred eddy flux is likely not a real physical effect.
Here, we use the Archiving, Validation, and Interpretation of Satellite Oceanographic data (AVISO) daily gridded absolute dynamic topography (ADT) from SSALTO/ DUACS, downloaded from Copernicus Marine Services (http://marine. copernicus. eu/web/69-interactive-catalogue. php; Ssalto/Duacs 2014; Pujol et al. 2016). We use delayedmode dynamic topography provided on a 1/48 Mercator grid, obtained by optimally interpolating the along-track data series based on the REF dataset, which uses two satellite missions [Ocean Topography Experiment (TOPEX)/Poseidon/European Remote Sensing Satellite (ERS) or Jason-1/Envisat or Jason-2/Envisat] with consistent sampling over the 21-yr period.
Acknowledgments. The authors thank  Dhruv Balwada ,  Jessica Masich ,  Andreas Klocker , and  Christopher Roach  for useful discussions and  Christopher Roach  for helpfully providing the diffusivity data from Roach et al. (2016) for comparison with our calculations. We also thank two anonymous reviewers for their comments on an earlier version of this manuscript. C. C. was supported by an  NSF Division of Ocean Sciences postdoctoral fellowship  Grant  1521508. J. B. S. received support from  Agence Nationale de la Recherche (ANR)   ANR-12-PDOC-0001.
Data Availability All interpolated fields used in this study, including the estimates of the suppressed and unsuppressed eddy diffusivity, the neutral density, the approximate isopycnal geostrophic streamfunction and its variance, and the isopycnal potential vorticity, are available online for download in NetCDF format (available online at https://dx. doi. org/10. 5061/dryad. 55bp8). The output is provided annually. Additionally, the first two seasonal harmonics are estimated and are included in the output.
1-0. 9 µg m -3 ) were observed while significant levels of biogenic species (peaking at ppb level) were measured. Furthermore, secondary oxygenated VOCs (OVOCs) largely dominated the VOC speciation during the campaign, while Organic Matter (OM) dominated the aerosol chemical composition (55% of the total mass of non-refractory submicron aerosol on average). Second, Positive Matrix Factorization (PMF) and Concentration Field (CF) analyses were performed on a database containing 42 VOCs (or grouped VOCs), including OVOCs, to identify co-variation factors of compounds that are representative of primary emissions, or chemical transformation processes. A six-factor solution was found for the PMF analysis, including a primary and secondary biogenic factor, both correlated to temperature and exhibiting a clear diurnal profile.
Aerosol measurements Online measurements of organic aerosols (PILS-IC, PILS-TOC, OCEC Sunset Field Instruments, Q-ACSM) have been available since beginning of June 2013, but data reported here are restricted to the ChArMEx SOP2 period (15/07-05/08/2013) for which VOC measurements have been performed. In addition, Black carbon (BC) has been continuously monitored, during the same extended period, using a 7-wavelength aethalometer model AE-31 (MAGEE Scientific®) at a time resolution of 15 min.
Based on IC settings, the detection limit (2) for ions was typically 0. 1 ppb, which corresponds to an atmospheric concentration of ~1 ng m -3. The overall uncertainty associated with PILS-IC measurements includes variability in air sampling flow rate, liquid flow rate, calibration, and collection efficiency and was estimated to be of the order of 25%. Time resolutions were typically of 24 min for anions (including light organics) and 12 min for cations. Because this study focuses on organics in the atmosphere, only MSA and oxalate data will be presented and discussed here. A total of 761 and 996 valid data points of MSA and Oxalate were obtained, respectively, with concentrations ranging for MSA from 4 to 59 ng m -3 (21 ng m -3 on average) and ranging for oxalate from 1 to 24 ng m -3 (10 ng m -3 on average).
OCEC Sunset Field instrument Semi-continuous (2-h time resolution) concentrations of elemental carbon (EC) and organic carbon (OC) in PM 2. 5 were obtained in the field from an OCEC Sunset field instrument (Sunset Laboratory, Forest Grove, OR, USA;  Bae et al. , 2004)  running at 8 L min - 1. A denuder provided by the manufacturer was set upstream in order to remove possible adsorption of VOCs onto the filter used to collect fine aerosols in the instrument. Measurement uncertainty given by the OCEC Sunset field instrument is poorly described in literature and an estimate of 20% for this uncertainty was taken here following  Peltier et al. (2007). This instrument has been running continuously for the whole duration of the campaign (15/07-05/08/2013) with 252 valid EC and OC data points obtained.
It is noteworthy that the slope observed for our dataset (0. 65, see Figure  3 ) is significantly lower than the theoretical ones calculated for an isolated air mass experiencing a selective oxidation by OH (2. 50) or by Cl (1. 97). The lack of concordance with theoretical slopes has often been observed (e. g. Parrish et al. , 1992; McKeen et al. , 1996)  and has been attributed to the mixing between air parcels of different histories and origins during longrange transport  (Parrish et al. , 2007  and references therein). 3 Source-receptor models.
The Positive Matrix Factorization (PMF) In this study, US E. P. A PMF 3. 0 was used to perform the factor analysis. For a detailed presentation of the PMF principle, the reader can refer to the first description made by  Paatero and Tapper (1994)  and to the user's guide written by  Hopke (2000). Briefly, a specific dataset at a receptor site can be viewed as a data matrix X containing i samples and j The minimization of the residual sum of squares Q is performed using eq. (  4 ) to derive the solution of eq. (  3 ).
                    n i m j ij p k kj ik ij n i m j ij ij s f g X s e Q 1 1 2 1 1 1 2 2 (4) Where S ij is the uncertainty matrix associated to the data matrix X ij , estimated as described in section 2. 2. The PMF analysis was conducted on a dataset of 42 species, including NMHCs, and OVOCs measured by the two online GCs and the PTR-ToFMS, and 329 observations, the time resolution being 1h30' (time resolution of the GCs). Measurements by active sampling on sorbent and DNPH cartridges were not included in this dataset due to their low time resolution (3h), which would have resulted in too few observations. Furthermore, compounds were not considered when missing, when more than half of observations were below the detection limit, or when associated to a low signal-to-noise ratio (s/n < 1 in our case).
Missing values and values below the detection limit in the selected dataset were replaced by the geometric mean and half of the detection limit, respectively, following the method used by  Sauvage et al. (2009). To minimize the weight of these observations in the PMF results, the uncertainties of missing values and values below the detection limit were set to 4 times the geometric mean and 5/6 of the detection limit, respectively. PMF also allows the minimization of the contribution of species of low signal-to-noise ratio (s/n < 1. 5 in our case) by declaring these species as "weak" and hence tripling their original uncertainties. Fourteen species have been declared as "weak" in this work. Ethane, methanol and acetone are characterized by high background concentrations at the measurement site.
To minimize the weight of these three species in the PMF results, their estimated background concentrations (500, 1000 and 1200 ppt for ethane, methanol and acetone, respectively) were subtracted to the measured concentrations in the data matrix X. These values were chosen arbitrarily to subtract the background concentrations of these species keeping their variability and avoiding near zero values. The PMF was run following the protocol proposed by  Sauvage et al. (2009)   Based on this approach, we have derived a final solution with 6 factors for a Fpeak of -0. 5. Moreover, the homogeneity of the database built using measurements from different techniques was studied to ensure that all instruments are well-represented in the solutions. This was done by checking that no substantial differences are observed between the scale residuals of the different instruments.
We therefore calculated the mean of the absolute values of scaled residuals for the three instruments         ij ij s e (0. 73, 0. 67 and 0. 75 for the PTR-ToF-MS, GC/FID-FID and GC/FID-MS, respectively). The differences observed between these parameters calculated for the three instruments are lower than 0. 08. This indicates a reasonable homogeneity of the instrument databases (concentrations, uncertainties) since absolute differences below 0. 25 have been determined to be satisfactory to avoid overweighting of the measurements of a particular instrument in PMF solutions  (Crippa et al. , 2013a) ). Therefore, no scaling procedure was performed on the database used in our PMF analysis. Furthermore, 100 bootstrap runs were performed for the 6 factors solution to estimate the stability and uncertainty of this solution.