The upper ocean pycnocline (UOP) monthly climatology is based on the ISAS20 ARGO dataset containing Argo and Deep-Argo temperature and salinity profiles on the period 2002-2020. Regardless of the season, the UOP is defined as the shallowest significant stratification peak captured by the method described in Sérazin et al. (2022), whose detection threshold is proportional to the standard deviation of the stratification profile. The three main characteristics of the UOP are provided -- intensity, depth and thickness -- along with hydrographic variables at the upper and lower edges of the pycnocline, the Turner angle and density ratio at the depth of the UOP. A stratification index (SI) that evaluates the amount of buoyancy required to destratify the upper ocean down to a certain depth, is also included. When evaluated at the bottom of the UOP, this gives the upper ocean stratification index (UOSI) as discussed in Sérazin et al. (2022). Three mixed layer depth variables are also included in this dataset, including the one using the classic density threshold of 0.03 kg.m-3, along with the minimum of these MLD variables. Several statistics of the UOP characteristics and the associated quantities are available in 2°×2° bins for each month of the year, whose results were smoothed using a diffusive gaussian filter with a 500 km scale. UOP characteristics are also available for each profile, with all the profiles sorted in one file per month.

This dataset provides detections of fronts derived from high resolution remote sensing SST observations by SEVIRI L3C from OSISAF over Western Europe region. The data are available through HTTP and FTP; access to the data is free and open. In order to be informed about changes and to help us keep track of data usage, we encourage users to register at: https://forms.ifremer.fr/lops-siam/access-to-esa-world-ocean-circulation-project-data/ This dataset was generated by OceanDataLab and is distributed by Ifremer / CERSAT in the frame of the World Ocean Circulation (WOC) project funded by the European Space Agency (ESA).

Understanding the dynamics of species interactions for food (prey-predator, competition for resources) and the functioning of trophic networks (dependence on trophic pathways, food chain flows, etc.) has become a thriving ecological research field in recent decades. This empirical knowledge is then used to develop population and ecosystem modelling approaches to support ecosystem-based management. The TrophicCS data set offers spatialized trophic information on a large spatial scale (the entire Celtic Sea continental shelf and upper slope) for a wide range of species. It combines ingested prey (gut content analysis) and a more integrated indicator of food sources (stable isotope analysis). A total of 1337 samples of large epifaunal invertebrates (bivalve mollusks and decapod crustaceans), zooplankton, fish and cephalopods, corresponding to 114 species, were collected and analyzed for stable isotope analysis of their carbon and nitrogen content. Sample size varied between taxa (from 1 to 52), with an average of 11.72 individuals sampled per species, and water depths ranged from 57 to 516 m. The gut contents of 1026 fish belonging to ten commercially important species: black anglerfish (Lophius budegassa), white anglerfish (Lophius piscatorius), blue whiting (Micromesistius poutassou), cod (Gadus morhua), haddock (Melanogrammus aeglefinus), hake (Merluccius merluccius), megrim (Lepidorhombus whiffiagonis), plaice (Pleuronectes platessa), sole (Solea solea) and whiting (Merlangius merlangus) were analyzed. The stomach content data set contains the occurrence of prey in stomach, identified to the lowest taxonomic level possible. To consider potential ontogenetic diet changes, a large size range was sampled. The TrophicCS data set was used to improve understanding of trophic relationships and ecosystem functioning in the Celtic Sea. When you use the data in your publication, we request that you cite this data paper. If you use the present data set (TrophicCS) for the majority of the data analyzed in your study, you may wish to consider inviting at least one author of the core team of this data paper to become a collaborator /coauthor of your paper.

This dataset provides Level 4 total current including geostrophy and a data-driven approach for Ekman and near-inertial current, based on a convolution between drifter observation and wind history, to fit empirically a complex and time-lag dependant transfert function between ERA5 wind stress and current The data are available through HTTP and FTP; access to the data is free and open. In order to be informed about changes and to help us keep track of data usage, we encourage users to register at: https://forms.ifremer.fr/lops-siam/access-to-esa-world-ocean-circulation-project-data/ This dataset was generated by Datlas and is distributed by Ifremer / CERSAT in the frame of the World Ocean Circulation (WOC) project funded by the European Space Agency (ESA).

French benthic invertebrates composition and abundance taxa data are collected during monitoring surveys on the English Channel / Bay of Biscay coasts and Mediterranean coast (Quadrige program code : REBENT_FAU, RSL_FAU). Protocols are implemented in the Water Framework Directive.  Data are transmitted in a Seadatanet format (CDI + ODV) to EMODnet Biology european database. 498 ODV files have been generated from period 01/01/2003 to 31/12/2021.

A prerequisite for a successful development of a multi-mission wind dataset is to ensure good inter-calibration of the different extreme wind datasets to be integrated in the product. Since the operational hurricane community is working with the in-situ dropsondes as wind speed reference, which are in turn used to calibrate the NOAA Hurricane Hunter Stepped Frequency Microwave Radiometer (SFMR) wind data, MAXSS has used the latter to ensure extreme-wind inter-calibration among the following scatterometer and radiometer systems: the Advanced Scatterometers onboard the Metop series (i.e., ASCAT-A, -B, and -C), the scatterometers onboard Oceansat-2 (OSCAT) and ScatSat-1 (OSCAT-2), and onboard the HY-2 series (HSCAT-A, -B); the Advanced Microwave Scanning Radiometer 2 onboard GCOM-W1(AMSR-2), the multi-frequency polarimetric radiometer (Windsat), and the L-band radiometers onboard the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP) missions. In summary, a two-step strategy has been followed to adjust the high and extreme wind speeds derived from the mentioned scatterometer and radiometer systems, available in the period 2009-2020. First, the C-band ASCATs have been adjusted against collocated storm-motion centric SFMR wind data. Then, both SFMR winds and ASCAT adjusted winds have been used to adjust all the other satellite wind systems. In doing so, a good inter-calibration between all the systems is ensured not only under tropical cyclone (TC) conditions, but also elsewhere. This dataset was produced in the frame of the ESA funded Marine Atmosphere eXtreme Satellite Synergy (MAXSS) project. The primary objective of the ESA Marine Atmosphere eXtreme Satellite Synergy (MAXSS) project is to provide guidance and innovative methodologies to maximize the synergetic use of available Earth Observation data (satellite, in situ) to improve understanding about the multi-scale dynamical characteristics of extreme air-sea interaction.

New results acquired in south-Brittany (MD08-3204 CQ core: Bay of Quiberon and VK03-58bis core: south Glénan islands) allow depicting Holocene paleoenvironmental changes from 8.5 ka BP to present through a multi-proxy dataset including sedimentological and palynological data. First, grain-size analyses and AMS-14C dates highlight a common sedimentary history for both study cores. The relative sea level (RSL) slowdown was accompanied by a significant drop of the sedimentation rates between ca. 8.3 and 5.7 ka BP, after being relatively higher at the onset of the Holocene. This interval led to the establishment of a shell-condensed level, identified in core VK03-58bis by the “Turritella layer” and interpreted as a marker for the maximum flooding surface. Palynological data (pollen grains and dinoflagellate cyst assemblages) acquired in core MD08-3204 CQ argue for an amplification of the fluvial influence since 5.7 ka BP; the establishment of the highstand system tract (i.e., mixed marine and fluviatile influences on the platform) then accompanying the slowdown of the RSL rise-rates. On the shelf, the amplification of Anthropogenic Pollen Indicators (API) is then better detected since 4.2 ka BP, not only due to human impact increase but also due to a stronger fluvial influence on the shelf during the Late Holocene. Palynological data, recorded on the 8.5–8.3 ka BP interval along an inshore-offshore gradient, also demonstrate the complexity of the palynological signal such as i) the fluvial influence that promotes some pollinic taxa (i.e., Corylus, Alnus) from proximal areas and ii) the macro-regionalization of palynomorph sources in distal cores. In addition, the comparison of palynological tracers, including API, over the last 7 kyrs, with south-Brittany coastal and mid-shelf sites subjected to northern vs. southern Loire catchment areas, allowed discussing a major hydro-climatic effect on the reconstructed palynological signals. Strengthened subpolar gyre dynamics (SPG), combined with recurrent positive North Atlantic Oscillation (NAO) configurations, appear responsible for increased winter precipitations and fluvial discharges over northern Europe, such as in Brittany. Conversely, weakened SPG intervals, associated with negative NAO-like modes, are characterized by intensified winter fluvial discharges over southern Europe. Interestingly, we record, at an infra-orbital timescale, major peaks of API during periods of strengthened (/weakened) SPG dynamics in sites subjects to Brittany watersheds (/Loire watersheds) inputs.

We genotyped 1680 thornback ray Raja clavata sampled in the Bay of Biscay using a DNA chip described in Le Cam et al. (2019). After quality control 4604 SNPs were retained for identifying potential sex-linked SNPs using three methods: i) identification of excess of heterozygotes in one sex, ii) FST outlier analysis between the two sexes and iii) neuronal net modelling. Genotype coding: 0 homozygous for major allele, 1 heterozygous, 2 homozygous for minor allele. Flanking DNA sequences of SNPs identified with methods i) and ii) are also provided.  

The data file present detailed individual congener/compound concentrations  for a large variety of hydrophobic organic contaminants including polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs), legacy and alternative brominated flame retardants (BFRs) and per- and polyfluoroalkyl substances (PFASs) in meso- and bathypelagic organisms collected in the Bay of Biscay, northeast Atlantic, in October 2017. The studied species include 3 crustacean species (Pasiphaea sivado, Sergia robusta, Ephyrina figueirai) and 11 fish species (Xenodermichthys copei, Searsia koefoedi, Myctophum punctatum, Notoscopelus kroeyeri, Lampanyctus crocodilus, Argyropelecus olfersii, Arctozenus risso, Stomias boa, Serrivomer beanii, Chauliodus sloani, Aphanopus carbo). The organisms were collected at night during one single trawling using a 25 m vertical opening pelagic trawl in the deep scattering layer (ca 800 m depth in the water column; 1330 m bottom floor). This dataset was used in the article entitled "A large diversity of organohalogen contaminants reach the meso- and bathypelagic organisms in the Bay of Biscay (northeast Atlantic)" published in Marine Pollution Bulletin.

The CDR-derived Wet Tropospheric Correction (WTC) Product V2 is generated from the Level-2+ along-track altimetry products version 2024 (L2P 2024) distributed by AVISO+ (www.aviso.altimetry.fr). It provides a long-term, homogenized estimation of the wet tropospheric correction based on Climate Data Records (CDRs) of atmospheric water vapour combined with high frequencies MWR data. Two independent CDRs datasets are used: - REMSS V7R2 (coverage until 2022) https://www.remss.com/measurements/atmospheric-water-vapor/tpw-1-deg-product/ - HOAPS V5 precursor CDR from EUMETSAT CM SAF (coverage until 2020) HOAPS V4/V5 data available via https://wui.cmsaf.eu Note: the HOAPS V5 precursor is not yet an official CM SAF product; full validation and public release are pending. The MWR/CDR WTC V2 estimates is derived using spatially varying but temporally constant polynomial coefficients (ai). 1. WTC V2 – Along-track L2P Product Data format: The WTC V2 product is delivered in Level-2+ (L2P) format, along the satellite ground track. Each mission is distributed as a compressed archive (.tar.gz) containing one NetCDF4 CF-1.8 file per mission cycle. Archive naming convention: <mission>_WTC_from_WV_CDR_<version>.tar.gz mission: TP (TOPEX/Poseidon), J1, J2, J3 version: product version (currently V2) File naming convention inside archives: <mission>_C<cycle>.nc cycle: 4-digit cycle index (e.g., C0001) Each NetCDF file contains: 1/ Along-track WTC estimate; 2/ Ancillary information; 3/ Space–time coordinates 2. WTC CDR Uncertainties – Gridded Product: A complementary product is provided, delivering regional trend estimates and associated uncertainties from the WTC Climate Data Record. The uncertainty product is distributed as a single NetCDF4 file: wtc_trend_uncertainties.nc . This file contains global gridded fields of WTC CDR trend and uncertainty parameters. Product content: This is the first dedicated version providing both: WTC CDR (HOAPS) linear trends, and Uncertainty estimates on these trends. Uncertainties are expressed as 1-sigma confidence intervals, and propagated using the methodology described in Section 2.3 of the Product User Manual. The product includes: - Total uncertainty on the WTC trend, propagated from all identified uncertainty sources in the WTC–TCWV regression. - Individual contributions of uncertainty sources (Uncertainties on regression coefficients: a0, a1 and their standard deviations; Uncertainties inherited from the HOAPS TCWV CDR) These fields enable users to assess the relative importance of each uncertainty component and recompute uncertainty propagation with alternative methods. Included regression input variables: To ensure transparency and reproducibility, the product provides: 1/ regression coefficients a0, a1; 2/ their associated uncertainties (std of a0, std of a1); 3/additional diagnostic fields required to recompute uncertainties if needed.