'''Short description:''' For the Mediterranean Sea - 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-00342

'''DEFINITION''' Ocean heat content (OHC) is defined here as the deviation from a reference period (1993-2014) and is closely proportional to the average temperature change from z1 = 0 m to z2 = 700 m depth: OHC=∫_(z_1)^(z_2)ρ_0 c_p (T_yr-T_clim )dz [1] with a reference density of = 1030 kgm-3 and a specific heat capacity of cp = 3980 J kg-1 °C-1 (e.g. von Schuckmann et al., 2009). Time series of annual mean values area averaged ocean heat content is provided for the Mediterranean Sea (30°N, 46°N; 6°W, 36°E) and is evaluated for topography deeper than 300m. '''CONTEXT''' Knowing how much and where heat energy is stored and released in the ocean is essential for understanding the contemporary Earth system state, variability and change, as the oceans shape our perspectives for the future. The quality evaluation of MEDSEA_OMI_OHC_area_averaged_anomalies is based on the “multi-product” approach as introduced in the second issue of the Ocean State Report (von Schuckmann et al., 2018), and following the MyOcean’s experience (Masina et al., 2017). Six global products and a regional (Mediterranean Sea) product have been used to build an ensemble mean, and its associated ensemble spread. The reference products are: • The Mediterranean Sea Reanalysis at 1/24 degree horizontal resolution (MEDSEA_MULTIYEAR_PHY_006_004, DOI: https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R1, Escudier et al., 2020) • Four global reanalyses at 1/4 degree horizontal resolution (GLOBAL_MULTIYEAR_PHY_ENS_001_031): GLORYS, C-GLORS, ORAS5, FOAM • Two observation based products: CORA (INSITU_GLO_PHY_TS_OA_MY_013_052) and ARMOR3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). Details on the products are delivered in the PUM and QUID of this OMI. '''CMEMS KEY FINDINGS''' The ensemble mean ocean heat content anomaly time series over the Mediterranean Sea shows a continuous increase in the period 1993-2022 at rate of 1.38±0.08 W/m2 in the upper 700m. After 2005 the rate has clearly increased with respect the previous decade, in agreement with Iona et al. (2018). '''DOI (product):''' https://doi.org/10.48670/moi-00261

'''Short description:''' Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) sampling. It serves in delayed-time applications. This product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, Jason-1, Jason-2, Topex/Poseidon, ERS-1, ERS-2, Envisat, Geosat Follow-On, HY-2A, HY-2B, etc). The system exploits the most recent datasets available based on the enhanced GDR/NTC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the European Sea area. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). The product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details) “’Associated products”’ A time invariant product https://resources.marine.copernicus.eu/product-detail/SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033/INFORMATION describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document. '''DOI (product):''' https://doi.org/10.48670/moi-00139

'''Short description:''' For the Baltic Sea- The DMI Sea Surface Temperature L3S aims at providing daily multi-sensor supercollated data at 0.03deg. x 0.03deg. horizontal resolution, using satellite data from infra-red radiometers. Uses SST satellite products from these sensors: NOAA AVHRRs 7, 9, 11, 14, 16, 17, 18 , Envisat ATSR1, ATSR2 and AATSR. '''DOI (product) :''' https://doi.org/10.48670/moi-00154

'''Short Description:''' The ocean biogeochemistry reanalysis for the North-West European Shelf is produced using the European Regional Seas Ecosystem Model (ERSEM), coupled online to the forecasting ocean assimilation model at 7 km horizontal resolution, NEMO-NEMOVAR. ERSEM (Butenschön et al. 2016) is developed and maintained at Plymouth Marine Laboratory. NEMOVAR system was used to assimilate observations of sea surface chlorophyll concentration from ocean colour satellite data and all the physical variables described in [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009 NWSHELF_MULTIYEAR_PHY_004_009]. Biogeochemical boundary conditions and river inputs used climatologies; nitrogen deposition at the surface used time-varying data. The description of the model and its configuration, including the products validation is provided in the [https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-011.pdf CMEMS-NWS-QUID-004-011]. Products are provided as monthly and daily 25-hour, de-tided, averages. The datasets available are concentration of chlorophyll, nitrate, phosphate, oxygen, phytoplankton biomass, net primary production, light attenuation coefficient, pH, surface partial pressure of CO2, concentration of diatoms expressed as chlorophyll, concentration of dinoflagellates expressed as chlorophyll, concentration of nanophytoplankton expressed as chlorophyll, concentration of picophytoplankton expressed as chlorophyll in sea water. All, as multi-level variables, are interpolated from the model 51 hybrid s-sigma terrain-following system to 24 standard geopotential depths (z-levels). Grid-points near to the model boundaries are masked. The product is updated biannually, providing a six-month extension of the time series. See [https://documentation.marine.copernicus.eu/PUM/CMEMS-NWS-PUM-004-009-011.pdf CMEMS-NWS-PUM-004-009_011] for details. '''Associated products:''' This model is coupled with a hydrodynamic model (NEMO) available as CMEMS product [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_PHY_004_009 NWSHELF_MULTIYEAR_PHY_004_009]. An analysis-forecast product is available from: [https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=NWSHELF_MULTIYEAR_BGC_004_011 NWSHELF_MULTIYEAR_BGC_004_011]. '''DOI (product) :''' https://doi.org/10.48670/moi-00058

'''Short description:''' For the Global - Arctic and Antarctic - Ocean. The OSI SAF delivers five global sea ice products in operational mode: sea ice concentration, sea ice edge, sea ice type (OSI-401, OSI-402, OSI-403, OSI-405 and OSI-408). The sea ice concentration, edge and type products are delivered daily at 10km resolution and the sea ice drift in 62.5km resolution, all in polar stereographic projections covering the Northern Hemisphere and the Southern Hemisphere. The sea ice drift motion vectors have a time-span of 2 days. These are the Sea Ice operational nominal products for the Global Ocean. '''DOI (product) :''' https://doi.org/10.48670/moi-00134

'''Short description:''' Arctic L3 sea ice product providing concentration, stage-of-development and floe size information retrieved from Sentinel-1 and RCM SAR imagery and GCOM-W AMSR2 microwave radiometer data using a deep learning algorithm and delivered on a 0.5 km grid. '''DOI (product) :''' https://doi.org/10.48670/mds-00343

'''Short description:''' The product MULTIOBS_GLO_PHY_SSS_L3_MYNRT_015_014 is a reformatting and a simplified version of the CATDS L3 product called “2Q” or “L2Q”. it is an intermediate product, that provides, in daily files, SSS corrected from land-sea contamination and latitudinal bias, with/without rain freshening correction. '''DOI (product) :''' https://doi.org/10.48670/mds-00368

'''Short description:''' The Med MFC physical multiyear product is generated by a numerical system composed of an hydrodynamic model, supplied by the Nucleous for European Modelling of the Ocean (NEMO) and a variational data assimilation scheme (OceanVAR) for temperature and salinity vertical profiles and satellite Sea Level Anomaly along track data. The model horizontal grid resolution is 1/24˚ (ca. 4-5 km) and the unevenly spaced vertical levels are 141. The datasets are extended every year as well as on a monthly basis through one-month extensions in interim mode, reaching one month before present. ''DOI (Product)'': https://doi.org/10.48670/mds-00375 ''DOI (Interim dataset)'': https://doi.org/10.48670/mds-00375

'''This product has been archived''' '''DEFINITION''' Significant wave height (SWH), expressed in metres, is the average height of the highest one-third of waves. This OMI provides time series of seasonal mean and extreme SWH values in three oceanic regions as well as their trends from 2002 to 2020, computed from the reprocessed global L4 SWH product (WAVE_GLO_PHY_SWH_L4_MY_014_007). The extreme SWH is defined as the 95th percentile of the daily maximum of SWH over the chosen period and region. The 95th percentile represents the value below which 95% of the data points fall, indicating higher wave heights than usual. The mean and the 95th percentile of SWH are calculated for two seasons of the year to take into account the seasonal variability of waves (January, February, and March, and July, August, and September) and are in m while the trends are in cm/yr. '''CONTEXT''' Grasping the nature of global ocean surface waves, their variability, and their long-term interannual shifts is essential for climate research and diverse oceanic and coastal applications. The sixth IPCC Assessment Report underscores the significant role waves play in extreme sea level events (Mentaschi et al., 2017), flooding (Storlazzi et al., 2018), and coastal erosion (Barnard et al., 2017). Additionally, waves impact ocean circulation and mediate interactions between air and sea (Donelan et al., 1997) as well as sea-ice interactions (Thomas et al., 2019). Studying these long-term and interannual changes demands precise time series data spanning several decades. Until now, such records have been available only from global model reanalyses or localised in situ observations. While buoy data are valuable, they offer limited local insights and are especially scarce in the southern hemisphere. In contrast, altimeters deliver global, high-quality measurements of significant wave heights (SWH) (Gommenginger et al., 2002). The growing satellite record of SWH now facilitates more extensive global and long-term analyses. By using SWH data from a multi-mission altimetric product from 2002 to 2020, we can calculate global mean SWH and extreme SWH and evaluate their trends. '''KEY FINDINGS''' Over the period from 2002 to 2020, positive trends in both Significant Wave Height (SWH) and extreme SWH are mostly found in the southern hemisphere. The 95th percentile of wave heights (q95), increases more rapidly than the average values, indicating that extreme waves are growing faster than the average wave height. In the North Atlantic, SWH has increased in summertime (July August September) and decreased during the wintertime: the trend for the 95th percentile SWH is decreasing by 2.1 ± 3.3 cm/year, while the mean SWH shows a decreasing trend of 2.2 ± 1.76 cm/year. In the south of Australia, in boreal winter, the 95th percentile SWH is increasing at a rate of 2.6 ± 1.5 cm/year (a), with the mean SWH increasing by 0.7 ± 0.64 cm/year (b). Finally, in the Antarctic Circumpolar Current, also in boreal winter, the 95th percentile SWH trend is 3.2 ± 2.15 cm/year (a) and the mean SWH trend is 1.4 ± 0.82 cm/year (b). This variation highlights that waves evolve differently across different basins and seasons, illustrating the complex and region-specific nature of wave height trends. A full discussion regarding this OMI can be found in A. Laloue et al. (2024). '''DOI (product):''' https://doi.org/10.48670/mds-00352