This daily High-Resolution (HR) Level 3 gridded wind product is derived from Copernicus Sentinel-1 SAR (Synthetic Aperture Radar) observations, over the North Western Atlantic ("ATL" area). It is based on the European Space Agency (ESA) Level-2 OCN products at the highest available resolution. Although L2-OCN products already contain wind vectors, those are calculated using the CMOD5.n Geophysical Model Function (GMF) applied to the co-polarized (co-pol) VV channel (emitting in Vertical polarization and receiving in Vertical polarization). This VV GMF was mapped from scatterometer sensors (Hersbach et al., 2007) which are only able to use co-pol measurements. However, these co-pol GMF are known to lose sensitivity for wind above 20 m/s. Therefore, wind based on such GMF alone, are known to under-estimate wind speed (Polverari et al., 2022). For the L3 products winds based on SAR, we take advantage of the available cross-polarized (cross-pol) VH channel (emitting in Vertical polarization and receiving in Horizontal polarization) for which GMF were specifically derived based on C-Band SAR (Mouche et al., 2017, Mouche et al., 2019). Winds estimated from the combination of both the co-pol and cross-pol channels are referred to as dual-polarization (or dual-pol) winds. As shown in Mouche et al. (2019), taking advantage of the dual polarization strongly improves the wind estimation for high wind conditions thanks to the much greater VH channel sensitivity compared to VV. These new wind estimations are then gridded with a 0.012 degree resolution (between 0.5 and 1.2 km in zonal direction depending on the latitude and 1.3 km in meridional direction) using a cylindrical equidistant projection, independently for ascending and descending satellite passes and for each satellite (so 4 wind fields are available per day for two satellites). This dataset is generated over all Sentinel-1 mission time series starting from March 2018 and updated in delayed mode with a 4-months delay. It is also produced for 4 other different European areas. This dataset is produced and disseminated in the frame of Copernicus Marine Service.

Moving 6-year analysis of Water body dissolved inorganic nitrogen in the NorthEast Atlantic for each season: - winter: January-March, - spring: April-June, - summer: July-September, - autumn: October-December. Every year of the time dimension corresponds to the 6-year centred average of each season. 6-year periods span from 1971/1976 until 2019/2024. Observation data span from 1971 to 2024. Depth levels (IODE standard depths): [0.0, 5.0, 10.0, 20.0, 30.0, 40.0, 50.0, 75.0, 100.0, 125.0, 150.0, 200.0, 250.0, 300.0, 400.0, 500.0, 600.0, 700.0, 800.0, 900.0, 1000.0, 1100.0, 1200.0, 1300.0, 1400.0, 1500.0, 1750.0, 2000.0]. Data sources: observational data from SeaDataNet/EMODNet Chemistry Data Network. Description of DIVAnd analysis: the computation was done with DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.12, using GEBCO 30 sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps is 0.1 degrees. Horizontal correlation length varies from 400km in open sea regions to 50km at the coast. Vertical correlation length is defined as twice the vertical resolution. Signal-to-noise ratio was fixed to 1 for vertical profiles and 0.1 for time series to account for the redundancy in the time series observations. A logarithmic transformation (DIVAnd.loglin) was applied to the data prior to the analysis. Background field: a vertically-filtered profile of the seasonal data mean value (including all years) is substracted from the data. Detrending of data: no, advection constraint applied: no. Units: umol/l.

This dataset contains the dynamical outputs of a global ocean simulation coupling dynamics and biogeochemistry at ¼° over the year 2019. The simulation has been performed using the coupled circulation/ecosystem model NEMO/PISCES (https://www.nemo-ocean.eu/), which is here enhanced to perform an ensemble simulation with explicit simulation of modeling uncertainties in the physics and in the biogeochemistry. This dataset is one of the 40 members of the ensemble simulation. This study was part of the Horizon Europe project SEAMLESS (https://seamlessproject.org/Home.html), with the general objective of improving the analysis and forecast of ecosystem indicators.   See Popov et al. (https://os.copernicus.org/articles/20/155/2024/) for more details on the study.

The BioSWOT-Med campaign (Doglioli et al., 2023) was conducted aboard R/V L’Atalante from April 20 to May 15, 2023 in the Northwestern Mediterranean Sea, in the region of the North Balearic Front (NBF) to study interactions between fine-scale oceanic circulation and biogeochemical processes.  Three water masses were sampled across the NBF, northern ('A'), southern ('B'), and frontal ('F'). Each Lagrangian station consisted of a 24-hour sampling period following the displacement of a water parcel (Doglioli et al., 2024). Vertical profiles down to 500 m were collected every 6 hours at 06:00 ('T1'), 12:00 ('T2'), 18:00 ('T3'), and 00:00 ('T4') UTC, for a total of 28 Lagrangian stations: first between April~24-28 (A1, F1, B1), and again between May~4-7 (B2, F2, A2), with a final station in southern waters (B3) on May~12-13. B2 and B3 stations were located inside an anticyclonic eddy. Hydrological profiles were obtained using a Sea-Bird CTD, with data averaged to a 1~m vertical resolution, they include potential temperature (°C), practical salinity, fluorescence-derived chlorophyll-a (µg/L) and oxygen (µmol/kg).  Samples for nitrate + nitrite and phosphate (µM) were collected from Niskin bottles and analyzed onboard within 2-12~hours using a segmented flow analyzer (AAIII HR Seal Analytical) following (Aminot et al., 2007). Quantification limits (QL) were 0.05 µM for nitrate and 0.02 µM for phosphate. Phosphate concentrations at a nanomolar level analyses were performed in the laboratory using a high-sensitivity method combining a 1 m Liquid Waveguide Capillary Cell (LWCC) and an auto-analyzer (Zhang et al. 2002), achieving a detection limit of 0.002µM.  A BGC-Argo float (WMO: 1902605 - Provor CTS4 SUNA) equipped with a CTD and SUNA nitrate sensor was deployed near station B2 and sampled the anticyclonic eddy. To better resolve the photic and nutricline layers, the standard sampling cycle was modified to a 6-hour frequency, reaching depths of 300-400~m. The BGC-Argo float nitrate dataset spans May~2-16 and includes 55~profiles, with a 0.5 µM limit of quantification. It passed through a nitrate calibration procedure against 8 ship-made  profiles at B2 and B3. Data export in NetCDF format - Dataset at the 7 Lagrangian stations (28 vertical profiles for each variable, 4 at each station):  ‘BioSWOT-Med_LS_Date_Time.nc’ (with day, time, longitude and latitude);  ‘BioSWOT-Med_LS_Nutrients.nc’ (with nitrate, phosphate and phosphate at nanomolar level concentrations and depths);  'BioSWOT-Med_LS_CTD.nc' (with temperature in situ, practical salinity, chlorophyll-a and oxygen concentrations, photosynthetically active radiations and depth). - Dataset of the BGC-Argo float including 55 vertical profiles recorded between May 2 and 16:  'BioSWOT-Med_BGC-Argo' (with day and time, longitude, latitude; nitrate concentrations with associated depth; temperature in situ and practical salinity associated depth; chlorophyll-a concentrations with associated predepthssure; and oxygen concentrations with associated depth). Contact list  Aude Joël (aude.joel@mio.osupytheas.fr), Sandra Nunige (sandra.nunige@mio.osupytheas.fr, for ship-made nutrient dataset), Riccardo Martellucci (rmartellucci@ogs.it, for the BGC-Argo float dataset) and Andrea Doglioli (andrea.doglioli@mio.osupytheas.fr, for the BioSWOT-Med cruise). References Aminot, A., & Kérouel, R. 2007. Dosage automatique des nutriments dans les eaux marines: méthodes en flux continu. Méthodes d’analyse en milieu marin. Ifremer. Doglioli, A.M., & Gregori, G. 2023. BioSWOT-Med cruise, RV L’Atalante. doi:10.17600/18002392. Doglioli, A., Grégori, G., D’Ovidio, F., Bosse, P. E., A., Carlotti, F., Lescot, M.,. . . Waggonet, E. (2024). Bioswot med. biological applicati.ons of the satellite surface water and ocean topography in the mediterranean. ref. rapport de campagne. université aix-marseille. (doi:10.13155/100060) Zhang, J.Z., & Chi, J. 2002. Automated analysis of nanomolar concentrations of phosphate in natural waters with liquid waveguide. Environ Sci Technol., 1;36(5), 1048–53. doi: 10.1021/es011094v.  

Seasonal climatology of Water body dissolved oxygen concentration for Loire river for the period 1950-2024 and for the following seasons: - winter: January-March, - spring: April-June, - summer: July-September, - autumn: October-December. Observation data span from 1950 to 2024. Depth levels (m): [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0, 120.0, 130.0]. Data sources: observational data from SeaDataNet/EMODNet Chemistry Data Network. Description of DIVAnd analysis: the computation was done with DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.12, using GEBCO 15 sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps is 0.01 degrees. Horizontal correlation length is defined seasonally (in meters): 150000 (winter), 100000 (spring), 70000 (summer), 115000 (autumn). Vertical correlation length was optimized and vertically filtered and a seasonally-averaged profile was used (DIVAnd.fitvertlen). Signal-to-noise ratio was fixed to 1 for vertical profiles and 0.1 for time series to account for the redundancy in the time series observations. A logarithmic transformation (DIVAnd.loglin) was applied to the data prior to the analysis. Background field: the vertically-filtered data mean profile is substracted from the data. Detrending of data: no, advection constraint applied: no. Units: umol/l.

Moving 6-year analysis of Water body chlorophyll-a in the NorthEast Atlantic for each season: - winter: January-March, - spring: April-June, - summer: July-September, - autumn: October-December. Every year of the time dimension corresponds to the 6-year centred average of each season. 6-year periods span from 1971/1976 until 2019/2024. Observation data span from 1971 to 2024. High-frequency observation trails were filtered to a 3h temporal resolution. Depth levels (IODE standard depths): [0.0, 5.0, 10.0, 20.0, 30.0, 40.0, 50.0, 75.0, 100.0, 125.0, 150.0, 200.0, 250.0, 300.0]. Data sources: observational data from SeaDataNet/EMODNet Chemistry Data Network. Description of DIVAnd analysis: the computation was done with DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.12, using GEBCO 30 sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps is 0.1 degrees. Horizontal correlation length varies from 200km in open sea regions to 50km at the coast. Vertical correlation length is defined as twice the vertical resolution. Signal-to-noise ratio was fixed to 1 for vertical profiles and 0.1 for time series to account for the redundancy in the time series observations. A logarithmic transformation (DIVAnd.loglin) was applied to the data prior to the analysis. Background field: a vertically-filtered profile of the seasonal data mean value (including all years) is substracted from the data. Detrending of data: no, advection constraint applied: no. Units: mg/m3.

The flat oyster Ostrea edulis is a European native species that once covered vast areas in the North Sea, on the Atlantic coast and in other European coastal waters including the Mediterranean region. All these populations have been heavily fished by dredging over the last three centuries. More recently, the emergence of parasites combined with the proliferation of various predators and many human-induced additional stressors have caused a dramatic decrease in the last remaining flat oyster populations. Today, this species has disappeared from many locations in Europe and is registered on the OSPAR (Oslo-Paris Convention for the Protection of the Marine environment of the North-East Atlantic) list of threatened and/or declining species (see https://www.ospar.org/work-areas/bdc/species-habitats/list-of-threatened-declining-species-habitats). In that context, since 2018, the Flat Oyster REcoVERy project (FOREVER) has been promoting the reestablishment of native oysters in Brittany (France). This multi-partner project, involving the CRC (Comité Régional de la Conchyliculture), IFREMER (Institut Français de Recherche pour l’Exploitation de la Mer), ESITC (École Supérieure d’Ingénieurs des Travaux de la Construction) Caen and Cochet Environnement, has consisted of (1) inventorying and evaluating the status of the main wild flat oyster populations across Brittany, (2) making detailed analysis of the two largest oyster beds in the bays of Brest and Quiberon to improve understanding of flat oyster ecology and recruitment variability and to suggest possible ways of improving recruitment, and (3) proposing practical measures for the management of wild beds in partnership with members of the shellfish industry and marine managers. the final report of this project is available on Archimer : https://doi.org/10.13155/79506. This survey is part of the task 1 of the FOREVER, which took place between 2017-2021. Some previous data, acquired with the same methodology and within the same geographic area have been also added to this dataset. These data were collected during 30 intertidal and diving surveys in various bays and inlets of the coast of Bretagne. The localization of these surveys has been guided by the help of historical maps. In the field, the methodology was simple enough to be easily implemented regardless of the configuration of the sampled site. The intertidal survey was conducted at very low tide (tidal range > 100) to sample the 0-1m level. Sampling was carried out randomly or systematically following the low water line. Where possible (in terms of visibility and accessibility), dive surveys were also carried out (0-10m depth), along 100m transects, using the same methodology of counting in a 1m2 quadrat. As often as possible, geo-referenced photographs were taken to show the appearance, density and habitat where Ostrea edulis was present. All these pictures are available in the image bank file. Overall, this dataset contains a total of 300 georeferenced records, where flat oysters have been observed. The dataset file contains also information concerning the surrounding habitat description and is organized according the OSPAR recommendations. This publication gives also a map, under a kml format showing each occurrence and its characteristics. This work was done in the framework of the following research project: " Inventaire, diagnostic écologique et restauration des principaux bancs d’huitres plates en Bretagne : le projet FOREVER. Contrat FEAMP 17/2215675".

Moving 6-year analysis of Water body dissolved oxygen concentration in the Mediterranean Sea for each season: - winter: January-March, - spring: April-June, - summer: July-September, - autumn: October-December. Every year of the time dimension corresponds to the 6-year centered average of the season. 6-years periods span from 1970-1975 until 2019-2024. Description of DIVA analysis: The computation was done with the DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.12, using GEBCO 30sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps grids is dx=dy=0.125 degrees (around 13.5km and 10.9km accordingly). The vertical resolution is 27 depth levels: [0.,5.,10.,20.,30.,50.,75.,100.,125.,150.,200.,250.,300.,400.,500.,600.,700.,800.,900.,1000.,1100.,1200.,1300.,1400.,1500.,1750.,2000.]. The horizontal correlation length is 200km. The vertical correlation length (in meters) was set twices the vertical resolution: [10.,10.,20.,20.,40.,50.,50.,50.,50.,100.,100.,100.,200.,200.,200.,200.,200.,200.,200.,200.,200.,200.,200.,200.,500.,500.,500.]. Duplicates check was performed using the following criteria for space and time: dlon=0.001deg., dlat=0.001deg., ddepth=1m, dtime=1hour, dvalue=0.1. The error variance (epsilon2) was set equal to 1 for profiles and 10 for time series to reduce the influence of close data near the coasts. An anamorphosis transformation was applied to the data (function DIVAnd.Anam.loglin) to avoid unrealistic negative values: threshold value=200. A background analysis field was used for all years (1970-2024) with correlation length equal to 600km and error variance (epsilon2) equal to 20. Quality control of the observations was applied using the interpolated field (QCMETHOD=3). Residuals (differences between the observations and the analysis (interpolated linearly to the location of the observations) were calculated. Observations with residuals outside the minimum and maximum values of the 99% quantile were discarded from the analysis. Originators of Italian data sets-List of contributors: - Brunetti Fabio (OGS) - Cardin Vanessa, Bensi Manuel doi:10.6092/36728450-4296-4e6a-967d-d5b6da55f306 - Cardin Vanessa, Bensi Manuel, Ursella Laura, Siena Giuseppe doi:10.6092/f8e6d18e-f877-4aa5-a983-a03b06ccb987 - Cataletto Bruno (OGS) - Cinzia Comici Cinzia (OGS) - Civitarese Giuseppe (OGS) - DeVittor Cinzia (OGS) - Giani Michele (OGS) - Kovacevic Vedrana (OGS) - Mosetti Renzo (OGS) - Solidoro C.,Beran A.,Cataletto B.,Celussi M.,Cibic T.,Comici C.,Del Negro P.,De Vittor C.,Minocci M.,Monti M.,Fabbro C.,Falconi C.,Franzo A.,Libralato S.,Lipizer M.,Negussanti J.S.,Russel H.,Valli G., doi:10.6092/e5518899-b914-43b0-8139-023718aa63f5 - Celio Massimo (ARPA FVG) - Malaguti Antonella (ENEA) - Fonda Umani Serena (UNITS) - Bignami Francesco (ISAC/CNR) - Boldrini Alfredo (ISMAR/CNR) - Marini Mauro (ISMAR/CNR) - Miserocchi Stefano (ISMAR/CNR) - Zaccone Renata (IAMC/CNR) - Lavezza, R., Dubroca, L. F. C., Ludicone, D., Kress, N., Herut, B., Civitarese, G., Cruzado, A., Lefèvre, D.,Souvermezoglou, E., Yilmaz, A., Tugrul, S., and Ribera d'Alcala, M.: Compilation of quality controlled nutrient profiles from the Mediterranean Sea, doi:10.1594/PANGAEA.771907, 2011.

'''DEFINITION''' The temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The products used include three global reanalyses: GLORYS, C-GLORS, ORAS5 (GLOBAL_MULTIYEAR_PHY_ENS_001_031) and two in situ based reprocessed products: CORA5.2 (INSITU_GLO_PHY_TS_OA_MY_013_052) , ARMOR-3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). Additionally, the time series based on the method of von Schuckmann and Le Traon (2011) has been added. The regional thermosteric sea level values are then averaged from 60°S-60°N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). '''CONTEXT''' The global mean sea level is reflecting changes in the Earth’s climate system in response to natural and anthropogenic forcing factors such as ocean warming, land ice mass loss and changes in water storage in continental river basins. Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction (Storto et al., 2018). Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (Cazenave et al., 2018; Oppenheimer et al., 2019). '''CMEMS KEY FINDINGS''' Since the year 2005 the upper (0-2000m) near-global (60°S-60°N) thermosteric sea level rises at a rate of 1.3±0.3 mm/year. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00240