'''This product has been archived''' '''DEFINITION''' Estimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60°S-60°N aiming i) to obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change. ii) to monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). Ocean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017). '''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 ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019). '''CMEMS KEY FINDINGS''' Since the year 2005, the upper (0-700m) near-global (60°S-60°N) ocean warms at a rate of 0.6 ± 0.1 W/m2. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00234

'''DEFINITION''' Heat transport across lines are obtained by integrating the heat fluxes along some selected sections and from top to bottom of the ocean. The values are computed from models’ daily output. The mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in PetaWatt (PW). '''CONTEXT''' The ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth’s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth’s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. '''CMEMS KEY FINDINGS''' The mean transports estimated by the ensemble global reanalysis are comparable to estimates based on observations; the uncertainties on these integrated quantities are still large in all the available products. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00245

'''Short description:''' For the '''Global''' Ocean '''Satellite Observations''', Brockmann Consult (BC) is providing '''Bio-Geo_Chemical (BGC)''' products based on the ESA-CCI inputs. * Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP, OLCI-S3A & OLCI-S3B for the '''""multi""''' products. * Variables: Chlorophyll-a ('''CHL'''). * Temporal resolutions: '''monthly'''. * Spatial resolutions: '''4 km''' (multi). * Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY'''). To find these products in the catalogue, use the search keyword '''""ESA-CCI""'''. '''DOI (product) :''' https://doi.org/10.48670/moi-00283

'''This product has been archived''' '''DEFINITION''' Estimates of Ocean Heat Content (OHC) are obtained from integrated differences of the measured temperature and a climatology along a vertical profile in the ocean (von Schuckmann et al., 2018). The regional OHC values are then averaged from 60°S-60°N aiming i) to obtain the mean OHC as expressed in Joules per meter square (J/m2) to monitor the large-scale variability and change. ii) to monitor the amount of energy in the form of heat stored in the ocean (i.e. the change of OHC in time), expressed in Watt per square meter (W/m2). Ocean heat content is one of the six Global Climate Indicators recommended by the World Meterological Organisation for Sustainable Development Goal 13 implementation (WMO, 2017). '''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 ocean shapes our perspectives for the future (von Schuckmann et al., 2020). Variations in OHC can induce changes in ocean stratification, currents, sea ice and ice shelfs (IPCC, 2019; 2021); they set time scales and dominate Earth system adjustments to climate variability and change (Hansen et al., 2011); they are a key player in ocean-atmosphere interactions and sea level change (WCRP, 2018) and they can impact marine ecosystems and human livelihoods (IPCC, 2019). '''CMEMS KEY FINDINGS''' Since the year 2005, the near-surface (0-300m) near-global (60°S-60°N) ocean warms at a rate of 0.4 ± 0.1 W/m2. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00233

'''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

'''DEFINITION''' The indicator of Volume Transport Anomaly in Selected Vertical Sections in the Iberia–Biscay–Ireland (IBI) region (OMI_CIRCULATION_VOLTRANS_IBI_section_integrated_anomalies) is defined as the time series of annual mean volume transport calculated across a set of vertical ocean sections. These sections have been selected to represent the temporal variability of key ocean currents within the IBI domain. The monitored ocean currents include the transport towards the North Sea through the Rockall Trough (RTE) (Holliday et al., 2008; Lozier and Stewart, 2008), the Canary Current (CC) (Knoll et al., 2002; Mason et al., 2011), the Azores Current (AC) (Mason et al., 2011), the Algerian Current (ALG) (Tintoré et al., 1988; Benzohra and Millot, 1995; Font et al., 1998), and the net transport along the 48° N latitude parallel (N48) (see OMI figure). To produce ensemble-based results, six datasets provided by the Copernicus Marine Service have been used: * '''IBI-REA''' & '''IBI-INT''': IBI_MULTIYEAR_PHY_005_002 (reanalysis and interim datasets) * '''GLO-REA''': GLOBAL_MULTIYEAR_PHY_001_030 (reanalysis) * '''ARMOR''': MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 (reprocessed observations) * '''MED-REA''': MEDSEA_MULTIYEAR_PHY_006_004 (reanalysis) * '''NWS-REA''': NWSHELF_MULTIYEAR_PHY_004_009 (reanalysis) The time series displays the ensemble mean (blue line), the ensemble spread (grey shaded area), and the mean transport with reversed sign (red dashed line), which indicates the threshold of anomaly values corresponding to a reversal in the direction of the current transport. In addition, the trend analysis at the 95% confidence level is shown in the bottom-right corner of each diagram. Further details on the product are provided in the corresponding Product User Manual (de Pascual-Collar et al., 2026a) and Quality Information Document (de Pascual-Collar et al., 2026b), as well as in de Pascual-Collar et al., 2024. '''CONTEXT''' The IBI area is a highly complex region characterized by a remarkable variety of ocean currents. Among them, we can highlight those that originate as a result of the closure of the North Atlantic Drift (Mason et al., 2011; Holliday et al., 2008; Peliz et al., 2007; Bower et al., 2002; Knoll et al., 2002; Pérez et al., 2001; Jia, 2000); the subsurface currents flowing northward along the continental slope (de Pascual-Collar et al., 2019; Pascual et al., 2018; Machín et al., 2010; Fricourt et al., 2007; Knoll et al., 2002; Mazé et al., 1997; White & Bowyer, 1997); and the exchange currents occurring in the Strait of Gibraltar and the Alboran Sea (Sotillo et al., 2016; Font et al., 1998; Benzohra & Millot, 1995; Tintoré et al., 1988). The variability of ocean currents in the IBI domain is relevant to the global thermohaline circulation and other climatic and environmental processes. For example, as discussed by Fasullo and Trenberth (2008), subtropical gyres play a crucial role in the meridional energy balance. The poleward salt transport of Mediterranean water, driven by subsurface slope currents, has significant implications for salinity anomalies in the Rockall Trough and the Nordic Seas, as studied by Holliday (2003), Holliday et al. (2008), and Bozec et al. (2011). The Algerian Current serves as the only pathway for Atlantic Water to reach the Western Mediterranean. '''CMEMS KEY FINDINGS''' The volume transport time series reveal periods during which the monitored currents exhibited notably high or low variability. Specifically, the RTE current shows pronounced variability in 2010 and during 2014–2015; the N48 section between 2012 and 2014; the ALG current in 2006 and 2017; the AC current between 2005–2007 and in 2021; and the CC current between 2005–2007. Furthermore, certain periods display anomalies of sufficient magnitude (in absolute value) to indicate a reversal in the net transport direction of the current. This is the case for the ALG current in 2017 and 2024 (with net transport towards the west), and for the CC current in 2010 (with net transport towards the north). Trend analysis over the period 1993–2023 does not reveal any statistically significant trends for the monitored currents. However, the confidence interval for the trend in the ALG section is close to rejecting the null hypothesis of no trend. '''DOI (product):''' https://doi.org/10.48670/mds-00351

'''This product has been archived''' This dataset provide a times series of gap free map of Sea Surface Temperature (SST) foundation at high resolution on a 0.10 x 0.10 degree grid (approximately 10 x 10 km) for the Global Ocean, every 24 hours. Whereas along swath observation data essentially represent the skin or sub-skin SST, the Level 4 SST product is defined to represent the SST foundation (SSTfnd). SSTfnd is defined within GHRSST as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. It is therefore updated at intervals of 24 hrs. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed. The processing combines the observations of multiple polar orbiting and geostationary satellites, embedding infrared of microwave radiometers. All these sources are intercalibrated with each other before merging. A ranking procedure is used to select the best sensor observation for each grid point. An optimal interpolation is used to fill in where observations are missing. '''DOI (product) :''' https://doi.org/10.48670/mds-00321

'''Short Description''' The biogeochemical analysis and forecasts for the Mediterranean Sea at 1/24° of horizontal resolution (ca. 4 km) are produced by means of the MedBFM4 model system. MedBFM4, which is run by OGS (IT), consists of the coupling of the multi-stream atmosphere radiative model OASIM, the multi-stream in-water radiative and tracer transport model OGSTM_BIOPTIMOD v4.6, and the biogeochemical flux model BFM v5.3. Additionally, MedBFM4 features the 3D variational data assimilation scheme 3DVAR-BIO v4.1 with the assimilation of surface chlorophyll (CMEMS-OCTAC NRT product) and of vertical profiles of chlorophyll, nitrate and oxygen (BGC-Argo floats provided by CORIOLIS DAC). The biogeochemical MedBFM system, which is forced by the NEMO-OceanVar model (MEDSEA_ANALYSIS_FORECAST_PHY_006_013), produces one day of hindcast and ten days of forecast (every day) and seven days of analysis (weekly on Tuesday). Salon, S.; Cossarini, G.; Bolzon, G.; Feudale, L.; Lazzari, P.; Teruzzi, A.; Solidoro, C., and Crise, A. (2019) Novel metrics based on Biogeochemical Argo data to improve the model uncertainty evaluation of the CMEMS Mediterranean marine ecosystem forecasts. Ocean Science, 15, pp.997–1022. DOI: https://doi.org/10.5194/os-15-997-2019 ''DOI (Product)'': https://doi.org/10.48670/mds-00358

'''DEFINITION''' Important note to users: These data are not to be used for navigation. The data is 100 m resolution and as high quality as possible. It has been produced with state-of-the-art technology and validated to the best of the producer’s ability and where sufficient high-quality data were available. These data could be useful for planning and modelling purposes. The user should independently assess the adequacy of any material, data and/or information of the product before relying upon it. Neither Mercator Ocean International/Copernicus Marine Service nor the data originators are liable for any negative consequences following direct or indirect use of the product information, services, data products and/or data. Product overview: This is a satellite derived bathymetry product covering the global coastal area (where data retrieval is possible), with 100 m resolution, based on Sentinel-2. This global coastal product has been developed based on 3 methodologies: Intertidal Satellite-Derived Bathymetry; Physics-based optical Satellite-Derived Bathymetry from RTE inversion; and Wave Kinematics Satellite-Derived Bathymetry from wave dispersion. There is one dataset for each of the methods (including a quality index based on uncertainty) and an additional one where the three datasets were merged (also includes a quality index). Using their expertise and special techniques the consortium tried to achieve an optimal balance between coverage and data quality. '''DOI (product):''' https://doi.org/10.48670/mds-00364

'''Short description:''' Arctic L4 sea ice concentration product based on a L3 sea ice concentration product retrieved from Sentinel-1 and RCM SAR imagery and GCOM-W AMSR2 microwave radiometer data using a deep learning algorithm (SEAICE_ARC_PHY_AUTO_L3_MYNRT_011_023), gap-filled with OSI SAF EUMETSAT sea ice concentration products and delivered on a 1 km grid. '''DOI (product) :''' https://doi.org/10.48670/mds-00344