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:''' 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. Kolodziejczyk Nicolas, Hamon Michel, Boutin Jacqueline, Vergely Jean-Luc, Reverdin Gilles, Supply Alexandre, Reul Nicolas (2021). Objective analysis of SMOS and SMAP Sea Surface Salinity to reduce large scale and time dependent biases from low to high latitudes. Journal Of Atmospheric And Oceanic Technology, 38(3), 405-421. Publisher's official version : https://doi.org/10.1175/JTECH-D-20-0093.1, Open Access version : https://archimer.ifremer.fr/doc/00665/77702/ '''DOI (product) :''' https://doi.org/10.48670/mds-00369

'''Short description:''' This product consists of vertical profiles of the concentration of nutrients (nitrates, phosphates, and silicates) and carbonate system variables (total alkalinity, dissolved inorganic carbon, pH, and partial pressure of carbon dioxide), computed for each Argo float equipped with an oxygen sensor. The method called CANYON is based on a neural network trained using nutrient data (GLODAPv2 database) '''DOI (product) :''' https://doi.org/10.48670/moi-00048

'''DEFINITION''' Estimates of Arctic sea ice extent are obtained from the surface of oceans grid cells that have at least 15% sea ice concentration. These values are cumulated in the entire Northern Hemisphere (excluding ice lakes) and from 1993 up to the year 2019 aiming to: i) obtain the Arctic sea ice extent as expressed in millions of km square (106 km2) to monitor both the large-scale variability and mean state and change. ii) to monitor the change in sea ice extent as expressed in millions of km squared per decade (106 km2/decade), or in sea ice extent loss since the beginning of the time series as expressed in percent per decade (%/decade; reference period being the first date of the key figure b) dot-dashed trend line, Vaughan et al., 2013). These trends are calculated in three ways, i.e. (i) from the annual mean values; (ii) from the March values (winter ice loss); (iii) from September values (summer ice loss). The Arctic sea ice extent used here is based on the “multi-product” (GLOBAL_MULTIYEAR_PHY_ENS_001_031) approach as introduced in the second issue of the Ocean State Report (CMEMS OSR, 2017). Five global products have been used to build the ensemble mean, and its associated ensemble spread. '''CONTEXT''' Sea ice is frozen seawater that floats on the ocean surface. This large blanket of millions of square kilometers insulates the relatively warm ocean waters from the cold polar atmosphere. The seasonal cycle of the sea ice, forming and melting with the polar seasons, impacts both human activities and biological habitat. Knowing how and how much the sea ice cover is changing is essential for monitoring the health of the Earth as sea ice is one of the highest sensitive natural environments. Variations in sea ice cover can induce changes in ocean stratification, in global and regional sea level rates and modify the key rule played by the cold poles in the Earth engine (IPCC, 2019). The sea ice cover is monitored here in terms of sea ice extent quantity. More details and full scientific evaluations can be found in the CMEMS Ocean State Report (Samuelsen et al., 2016; Samuelsen et al., 2018). '''CMEMS KEY FINDINGS''' Since the year 1993 to 2023 the Arctic sea ice extent has decreased significantly at an annual rate of -0.57*106 km2 per decade. This represents an amount of -4.8 % per decade of Arctic sea ice extent loss over the period 1993 to 2023. Over the period 1993 to 2018, summer (September) sea ice extent loss amounts to -1.18*106 km2/decade (September values), which corresponds to -14.85% per decade. Winter (March) sea ice extent loss amounts to -0.57*106 km2/decade, which corresponds to -3.42% per decade. These values slightly exceed the estimates given in the AR5 IPCC assessment report (estimate up to the year 2012) as a consequence of continuing Northern Hemisphere sea ice extent loss. Main change in the mean seasonal cycle is characterized by less and less presence of sea ice during summertime with time. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00190

'''Short description:''' For the Atlantic Ocean - The product contains daily Level-3 sea surface wind with a 1km horizontal pixel spacing using Synthetic Aperture Radar (SAR) observations and their collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model outputs. Products are processed homogeneously starting from the L2OCN products. '''DOI (product) :''' https://doi.org/10.48670/mds-00339

'''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

'''Short description:''' The Mean Dynamic Topography MDT-CMEMS_2020_MED is an estimate of the mean over the 1993-2012 period of the sea surface height above geoid for the Mediterranean Sea. This is consistent with the reference time period also used in the SSALTO DUACS products '''DOI (product) :''' https://doi.org/10.48670/moi-00151

'''Short description:''' For the '''Mediterranean Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR – Rome, Italy), is providing '''Bio-Geo_Chemical (BGC)''' regional datasets: * '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific algorithms (Case 1 waters: Volpe et al., 2019, with new coefficients; Case 2 waters, Berthon and Zibordi, 2004) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm (Di Cicco et al. 2017) * '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS) * '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) (for '''""multi'''"" observations achieved via region-specific algorithm, Volpe et al., 2019) * '''''optics''''' including the IOPs (Inherent Optical Properties) such as absorption and scattering and particulate and dissolved matter (ADG, APH, BBP), via QAAv6 model (Lee et al., 2002 and updates) '''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''""multi""''' products, and OLCI-S3A & S3B for the '''""olci""''' products '''Temporal resolution''': daily '''Spatial resolutions''': 1 km for '''""multi""''' and 300 meters for '''""olci""''' To find this product in the catalogue, use the search keyword '''""OCEANCOLOUR_MED_BGC_L3_NRT""'''. '''DOI (product) :''' https://doi.org/10.48670/moi-00297

'''Short description:''' IBI Seas - near real-time (NRT) in situ quality controlled observations, hourly updated and distributed by INSTAC within 24-48 hours from acquisition in average '''DOI (product) :''' https://doi.org/10.48670/moi-00043

'''Short description:''' Multi-Year mono-mission satellite-based along-track significant wave height. Only valid data are included, based on a rigorous editing combining various criteria such as quality flags (surface flag, presence of ice) and thresholds on parameter values. Such thresholds are applied on parameters linked to significant wave height determination from retracking (e.g. SWH, sigma0, range, off nadir angle…). All the missions are homogenized with respect to a reference mission and in-situ buoy measurements. Finally, an along-track filter is applied to reduce the measurement noise. This product is based on the ESA Sea State Climate Change Initiative data Level 3 product (version 2) and is formatted by the WAVE-TAC to be homogeneous with the CMEMS Level 3 Near-real-time product. It is based on the reprocessing of GDR data from the following altimeter missions: Jason-1, Jason-2, Envisat, Cryosat-2, SARAL/AltiKa and Jason-3. CFOSAT Multi-Year dataset is based on the reprocessing of CFOSAT Level-2P products (CNES/CLS), inter-calibrated on Jason-3 reference mission issued from the CCI Sea State dataset. One file containing valid SWH is produced for each mission and for a 3-hour time window. It contains the filtered SWH (VAVH) and the unfiltered SWH (VAVH_UNFILTERED). '''DOI (product) :''' https://doi.org/10.48670/moi-00176