The ESA Sea State Climate Change Initiative (CCI) project has produced global daily merged multi-sensor time-series of along-track satellite altimeter significant wave height data (referred to as Level 3 (L3) data) with a particular focus for use in climate studies. This dataset contains the Version 3 Remote Sensing Significant Wave Height product, which provides along-track data at approximately 6 km spatial resolution. It has been generated from upstream Sea State CCI L2P products, edited and merged into daily products, retaining only valid and good quality measurements from all altimeters over one day, with simplified content (only a few key parameters). This is close to what is delivered in Near-Real Time by the CMEMS (Copernicus - Marine Environment Monitoring Service) project. It covers the date range from 2002-2021. The altimeter data used in the Sea State CCI dataset v3 come from multiple satellite missions (Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A), therefore spanning over a shorter time range than version 1.1. Unlike version 1.1, this version 3 involved a complete and consistent retracking of all the included altimeters. Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band).

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.

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.

Reef-building species are recognized as having an important ecological role and as generally enhancing the diversity of benthic organisms in marine habitats.  However, although these ecosystem engineers have a facilitating role for some species, they may exclude or compete with others. The honeycomb worm Sabellaria alveolata (Linnaeus, 1767) is an important foundation species, commonly found from northwest Ireland to northern Mauritania (Curd et al., 2020), whose reef structures increase the physical complexity of the marine benthos, supporting high levels of biodiversity. Local patterns and regional differences in taxonomic and functional diversity were examined in honeycomb worm reefs from ten sites along the northeastern Atlantic to explore variation in diversity across biogeographic regions and the potential effects of environmental drivers. To characterize the functional diversity at each site, a biological trait analysis (BTA) was conducted (Statzner et al., 1994). Here we present the functional trait database used for the benthic macrofauna found to live in association with honeycomb worm reefs. Eight biological traits (divided into 32 modalities) were selected (Table 1), providing information linked to the ecological functions performed by the associated macrofauna. The selected traits provide information on: (i) resource use and availability (by the trophic group of species, e.g. Thrush et al. 2006); (ii) secondary production and the amount of energy and organic matter (OM) produced based on the life cycle of the organisms (including longevity, maximum size and mode of reproduction, e.g. (Cusson and Bourget, 2005; Thrush et al., 2006) and; (iii) the behavior of the species in general [i.e. how these species occupy the environment and contribute to biogeochemical fluxes through habitat, movement, and bioturbation activity at different bathymetric levels, e.g. (Solan et al., 2004; Thrush et al., 2006; Queirós et al., 2013). Species were scored for each trait modality based on their affinity using a fuzzy coding approach (Chevenet et al., 1994), where multiple modalities can be attributed to a species if appropriate, and allowed for the incorporation of intraspecific variability in trait expression. The information concerning polychaetes was derived primarily from Fauchald et al (1979) and Jumars et al (2015). Information on other taxonomic groups was obtained either from databases of biological traits (www.marlin.ac.uk/biotic) or publications (Naylor, 1972; King, 1974; Caine, 1977; Lincoln, 1979; Holdich and Jones, 1983; Smaldon et al., 1993; Ingle, 1996; San Martín, 2003; Southward, 2008; Gil, 2011; Leblanc et al., 2011; Rumbold et al., 2012; San Martín and Worsfold, 2015; Jones et al., 2018). Map indicating the locations of the 10 study sites in the UK, France and Portugal within the four biogeographic provinces defined by Dinter (2001). (All sites were sampled in 8 different stations, except for UK4 where 5 stations were sampled).

The SARWAVE project is developing a new sea state processor from SAR images to be applied over open ocean, sea ice, and coastal areas, and exploring potential synergy with other microwave and optical EO products.

The SARWAVE project is developing a new sea state processor from SAR images to be applied over open ocean, sea ice, and coastal areas, and exploring potential synergy with other microwave and optical EO products.

The Level 4 merged microwave wind product is a complete set of hourly global 10-m wind maps on a 0.25x0.25 degree latitude-longitude grid, spanning 1 Jan 2010 through the end of 2020. The product combines background neutral equivalent wind fields from ERA5, daily surface current fields from CMEMS, and stress equivalent winds obtained from several microwave passive and active sensors to produce hourly surface current relative stress equivalent wind analyses. The satellite winds include those from recently launched L-band passive sensors capable of measuring extreme winds in tropical cyclones, with little or no degradation from precipitation. All satellite winds used in the analyses have been recalibrated using a large set of collocated satellite-SFMR wind data in storm-centric coordinates. To maximize the use of the satellite microwave data, winds within a 24-hour window centered on the analysis time have been incorporated into each analysis. To accomodate the large time window, satellite wind speeds are transformed into deviations from ERA5 background wind speeds interpolated to the measurement times, and then an optical flow-based morphing technique is applied to these wind speed increments to propagate them from measurement to analysis time. These morphed wind speed increments are then added to the background wind speed at the analysis time to yield a set of total wind speeds fields for each sensor at the analysis time. These individual sensor wind speed fields are then combined with the background 10-m wind direction to yield vorticity and divergence fields for the individual sensor winds. From these, merged vorticity and divergence fields are computed as a weighted average of the individual vorticity and divergence fields. The final vector wind field is then obtained directly from these merged vorticity and divergence fields. Note that one consequence of producing the analyses in terms of vorticity and divergence is that there are no discontinuities in the wind speed fields at the (morphed) swath edges. There are two important points to be noted: the background ERA5 wind speed fields have been rescaled to be globally consistent with the recalibrated AMSR2 wind speeds. This rescaling involves a large increase in the ERA5 background winds beyond about 17 m/s. For example, an ERA5 10 m wind speed of 30 m/s is transformed into a wind speed of 41 m/s, and a wind speed of 34 m/s is transformed into a wind speed of about 48 m/s. Besides the current version of the product is calibrated for use within tropical cyclones and is not appropriate for use elsewhere. This dataset was produced in the frame of ESA 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.

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).

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.  

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.