Hauteurs significatives de vagues (SWH) et vitesse du vent, mesurées le long de la trace par les satellites altimétriques CFOSAT (nadir), Sentinel-3A et Sentinel-3B, Jason-3, Saral-AltiKa, Cryosat-2 et HY-2B, en temps quasi-réel (NRT), sur une couverture globale (-66°S/66+N pour Jason-3, -80°S/80°N pour Sentinel-3A et Saral/AltiKa). Un fichier contenant les SWH valides est produit pour chaque mission et pour une fenêtre de temps de 3 heures. Il contient les SWH filtrées (VAVH), les SWH non filtrées (VAVH_UNFILTERED) et la vitesse du vent (wind_speed). Les mesures de hauteurs de vagues sont calculées à partir du front de montée de la forme d'onde altimétrique. Pour Sentinel-3A et 3B, elles sont déduites de l'altimètre SAR.

"'Short description: ''' Global Ocean - This delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for ocean surface and subsurface currents. Current data from 5 different types of instruments are distributed: * The drifter's near-surface velocities computed from their position measurements. In addition, a wind slippage correction is provided from 1993. Information on the presence of the drogue of the drifters is also provided. * The near-surface zonal and meridional total velocities, and near-surface radial velocities, measured by High Frequency (HF) radars that are part of the European HF radar Network. These data are delivered together with standard deviation of near-surface zonal and meridional raw velocities, Geometrical Dilution of Precision (GDOP), quality flags and metadata. * The zonal and meridional velocities, at parking depth (mostly around 1000m) and at the surface, calculated along the trajectories of the floats which are part of the Argo Program. * The velocity profiles within the water column coming from Acoustic Doppler Current Profiler (vessel mounted ADCP, Moored ADCP, saildrones) platforms * The near-surface and subsurface velocities calculated from gliders (autonomous underwater vehicle) trajectories '''DOI (product) :''' https://doi.org/10.17882/86236

'''Short description:''' For the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding to the L2 swath products: *0.5 degrees grid for the 50 km scatterometer L2 inputs, *0.25 degrees grid based on 25 km scatterometer swath observations, *and 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products. Data from ascending and descending passes are gridded separately. The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The MY L3 products follow the availability of the reprocessed EUMETSAT OSI SAF L2 products and are available for: The ASCAT scatterometer on MetOp-A and Metop-B at 0.125 and 0.25 degrees; The Seawinds scatterometer on QuikSCAT at 0.25 and 0.5 degrees; The AMI scatterometer on ERS-1 and ERS-2 at 0.25 degrees; The OSCAT scatterometer on Oceansat-2 at 0.25 and 0.5 degrees; '''DOI (product) :''' https://doi.org/10.48670/moi-00183

'''DEFINITION''' The Copernicus Marine IBI_OMI_seastate_extreme_var_swh_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Significant Wave Height (SWH) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (IBI_MULTIYEAR_WAV_005_006) and the Analysis product (IBI_ANALYSISFORECAST_WAV_005_005). Two parameters have been considered for this OMI: * Map of the 99th mean percentile: It is obtained from the Multi-Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged in the whole period (1980-2023). * Anomaly of the 99th percentile in 2024: The 99th percentile of the year 2024 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile to the percentile in 2024. This indicator is aimed at monitoring the extremes of annual significant wave height and evaluate the spatio-temporal variability. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This approach was first successfully applied to sea level variable (Pérez Gómez et al., 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (Pérez Gómez et al 2018 and Álvarez-Fanjul et al., 2019). Further details and in-depth scientific evaluation can be found in the CMEMS Ocean State report (Álvarez- Fanjul et al., 2019). '''CONTEXT''' The sea state and its related spatio-temporal variability affect dramatically maritime activities and the physical connectivity between offshore waters and coastal ecosystems, impacting therefore on the biodiversity of marine protected areas (González-Marco et al., 2008; Savina et al., 2003; Hewitt, 2003). Over the last decades, significant attention has been devoted to extreme wave height events since their destructive effects in both the shoreline environment and human infrastructures have prompted a wide range of adaptation strategies to deal with natural hazards in coastal areas (Hansom et al., 2015). Complementarily, there is also an emerging question about the role of anthropogenic global climate change on present and future extreme wave conditions (Young and Ribal, 2019). The Iberia-Biscay-Ireland region, which covers the North-East Atlantic Ocean from Canary Islands to Ireland, is characterized by two different sea state wave climate regions: whereas the northern half, impacted by the North Atlantic subpolar front, is of one of the world’s greatest wave generating regions (Mørk et al., 2010; Folley, 2017), the southern half, located at subtropical latitudes, is by contrast influenced by persistent trade winds and thus by constant and moderate wave regimes. The North Atlantic Oscillation (NAO), which refers to changes in the atmospheric sea level pressure difference between the Azores and Iceland, is a significant driver of wave climate variability in the Northern Hemisphere. The influence of North Atlantic Oscillation on waves along the Atlantic coast of Europe is particularly strong in and has a major impact on northern latitudes wintertime (Gleeson et al., 2017; Martínez-Asensio et al. 2016; Wolf et al., 2002; Bauer, 2001; Kushnir et al., 1997; Bouws et al., 1996; Bacon and Carter, 1991). Swings in the North Atlantic Oscillation index produce changes in the storms track and subsequently in the wind speed and direction over the Atlantic that alter the wave regime. When North Atlantic Oscillation index is in its positive phase, storms usually track northeast of Europe and enhanced westerly winds induce higher than average waves in the northernmost Atlantic Ocean. Conversely, in the negative North Atlantic Oscillation phase, the track of the storms is more zonal and south than usual, with trade winds (mid latitude westerlies) being slower and producing higher than average waves in southern latitudes (Marshall et al., 2001; Wolf et al., 2002; Wolf and Woolf, 2006). Additionally, a variety of previous studies have uniquevocally determined the relationship between the sea state variability in the IBI region and other atmospheric climate modes such as the East Atlantic pattern, the Arctic Oscillation, the East Atlantic Western Russian pattern and the Scandinavian pattern (Izaguirre et al., 2011, Martínez-Asensio et al., 2016). In this context, long‐term statistical analysis of reanalyzed model data is mandatory not only to disentangle other driving agents of wave climate but also to attempt inferring any potential trend in the number and/or intensity of extreme wave events in coastal areas with subsequent socio-economic and environmental consequences. '''CMEMS KEY FINDINGS''' The climatic mean of 99th percentile (1980-2023) reveals a north-south gradient of Significant Wave Height with the highest values in northern latitudes (above 8m) and lowest values (2-3 m) detected southeastward of Canary Islands, in the seas between Canary Islands and the African Continental Shelf. This north-south pattern is the result of the two climatic conditions prevailing in the region and previously described. The 99th percentile anomalies in 2024 show that during this period, virtually the entire IBI region was affected by positive anomalies in maximum SWH, which exceeded the standard deviation of the historical record in the waters west of the Iberian Peninsula, the Spanish coast of the Bay of Biscay, and the African coast south of Cape Ghir. Anomalies reaching twice the standard deviation of the time series were also observed in coastal regions of the English Channel. '''DOI (product):''' https://doi.org/10.48670/moi-00249

'''This product has been archived''' '''DEFINITION''' The linear change of zonal mean subsurface temperature over the period 1993-2019 at each grid point (in depth and latitude) is evaluated to obtain a global mean depth-latitude plot of subsurface temperature trend, expressed in °C. The linear change is computed using the slope of the linear regression at each grid point scaled by the number of time steps (27 years, 1993-2019). A multi-product approach is used, meaning that the linear change is first computed for 5 different zonal mean temperature estimates. The average linear change is then computed, as well as the standard deviation between the five linear change computations. The evaluation method relies in the study of the consistency in between the 5 different estimates, which provides a qualitative estimate of the robustness of the indicator. See Mulet et al. (2018) for more details. '''CONTEXT''' Large-scale temperature variations in the upper layers are mainly related to the heat exchange with the atmosphere and surrounding oceanic regions, while the deeper ocean temperature in the main thermocline and below varies due to many dynamical forcing mechanisms (Bindoff et al., 2019). Together with ocean acidification and deoxygenation (IPCC, 2019), ocean warming can lead to dramatic changes in ecosystem assemblages, biodiversity, population extinctions, coral bleaching and infectious disease, change in behavior (including reproduction), as well as redistribution of habitat (e.g. Gattuso et al., 2015, Molinos et al., 2016, Ramirez et al., 2017). Ocean warming also intensifies tropical cyclones (Hoegh-Guldberg et al., 2018; Trenberth et al., 2018; Sun et al., 2017). '''CMEMS KEY FINDINGS''' The results show an overall ocean warming of the upper global ocean over the period 1993-2019, particularly in the upper 300m depth. In some areas, this warming signal reaches down to about 800m depth such as for example in the Southern Ocean south of 40°S. In other areas, the signal-to-noise ratio in the deeper ocean layers is less than two, i.e. the different products used for the ensemble mean show weak agreement. However, interannual-to-decadal fluctuations are superposed on the warming signal, and can interfere with the warming trend. For example, in the subpolar North Atlantic decadal variations such as the so called ‘cold event’ prevail (Dubois et al., 2018; Gourrion et al., 2018), and the cumulative trend over a quarter of a decade does not exceed twice the noise level below about 100m depth. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00244

'''Short description:''' For the Baltic Sea- The DMI Sea Surface Temperature reprocessed analysis provides daily gap-free sea surface temperature fields, referred as L4 product, at 0.02deg. x 0.02deg. horizontal resolution. It is produced by the DMI Optimal Interpolation (DMIOI) system (Høyer and She, 2007) to provide a high resolution (1/50deg. - approx. 2km grid resolution) daily analysis of the daily average sea surface temperature (SST) at 20 cm depth. It uses satellite data from infra-red radiometers, from the ESA SST_cci v3.0 (Embury et al., 2024) and Copernicus C3S projects, namely L2P data from (A)ATSRs, SLSTR and AVHRR for the period 1982-2021, L3U data from SLSTR and AVHRR for 2022-July 19 2024 and L2P data from SLSTR and AVHRR from July 20 2024 onward. For the Sea Ice Concentration it uses the Baltic high resolution sea ice concentration data from the Copernicus Marine Service SI TAC. '''DOI (product) :''' https://doi.org/10.48670/moi-00156

'''Short description:''' The product MULTIOBS_GLO_PHY_SSS_L4_MY_015_015 is a reformatting and a simplified version of the CATDS L4 product called “SMOS-OI”. This product is obtained using optimal interpolation (OI) algorithm, that combine, ISAS in situ SSS OI analyses to reduce large scale and temporal variable bias, SMOS satellite image, SMAP satellite image, and satellite SST information. '''DOI (product) :''' https://doi.org/10.48670/mds-00369

'''Short description:''' MEDSEA_MULTIYEAR_WAV_006_012 is the multi-year wave product of the Mediterranean Sea Waves forecasting system (Med-WAV). It contains a Reanalysis dataset and a monthly climatological dataset (reference period 1993-2016). The Reanalysis dataset is a multi-year wave reanalysis starting from January 1985, composed by hourly wave parameters at 1/24° horizontal resolution, covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. The dataset is extended every year as well as on a monthly basis through one-month extensions in interim mode, reaching one month before present. The Med-WAV modelling system is based on wave model WAM 4.6.2 and has been developed as a nested sequence of two computational grids (coarse and fine) to ensure that swell propagating from the North Atlantic (NA) towards the strait of Gibraltar is correctly entering the Mediterranean Sea. The coarse grid covers the North Atlantic Ocean from 75°W to 10°E and from 70° N to 10° S in 1/6° resolution while the nested fine grid covers the Mediterranean Sea from 18.125° W to 36.2917° E and from 30.1875° N to 45.9792° N with a 1/24° resolution. The modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins. The wave system also includes an optimal interpolation assimilation scheme assimilating significant wave height along track satellite observations available through CMEMS and it is forced with daily averaged currents from Med-Physics and with 1-h, 0.25° horizontal-resolution ERA5 reanalysis 10m-above-sea-surface winds from ECMWF. ''DOI (Product)'': https://doi.org/10.48670/mds-00376 ''DOI (Interim dataset)'': https://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_MEDWAM3I ''DOI (climatological dataset)'': https://doi.org/10.25423/ CMCC/MEDSEA_MULTIYEAR_WAV_006_012_CLIM

'''Short description:''' MEDSEA_ANALYSISFORECAST_WAV_006_017 is the nominal wave product of the Mediterranean Sea Forecasting system, composed by hourly wave parameters at 1/24º horizontal resolution covering the Mediterranean Sea and extending up to 18.125W into the Atlantic Ocean. The waves forecast component (Med-WAV system) is a wave model based on the WAM Cycle 6. The Med-WAV modelling system resolves the prognostic part of the wave spectrum with 24 directional and 32 logarithmically distributed frequency bins and the model solutions are corrected by an optimal interpolation data assimilation scheme of all available along track satellite significant wave height and 10m wind speed observations. The atmospheric forcing is provided by the operational ECMWF Numerical Weather Prediction model and the wave model is forced with hourly averaged surface currents and sea level obtained from MEDSEA_ANALYSISFORECAST_PHY_006_013 at 1/24° resolution. The model uses wave spectra for Open Boundary Conditions from GLOBAL_ANALYSIS_FORECAST_WAV_001_027 product. The wave system includes 2 forecast cycles providing twice per day a Mediterranean wave analysis and 10 days of wave forecasts. '''DOI (product)''': https://doi.org/10.48670/mds-00373

'''Short description:''' For the Global Ocean - The product contains hourly Level-4 sea surface wind and stress fields at 0.125 degrees horizontal spatial resolution. Scatterometer observations for Metop-B and Metop-C ASCAT and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) operational model variables are used to calculate temporally-averaged difference fields. These fields are used to correct for persistent biases in hourly ECMWF operational model fields. The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The applied bias corrections, the standard deviation of the differences (for wind and stress fields) and difference of variances (for divergence and curl fields) are included in the product. '''DOI (product) :''' https://doi.org/10.48670/moi-00305