This metadata refers to the EEA marine assessment grid, to which all data and assessment results have been spatially mapped in order to ensure that data can be compared in a uniform way across the European regional seas. The marine assessment grid is based on the EEA reference grid system. The EEA reference grid is based on ERTS89 Lambert Azimuthal Equal Area projection with parameters: latitude of origin 52° N, longitude of origin 10° E, false northing 3 210 000.0 m, false easting 4 321 000.0 m. All grid cells are named with a unique identifier containing information on grid cell size and the distance from origin in meters (easting and northing). An important attribute of the EEA reference grid system is that by using an equal area projection all grid cells are having the same area for the same grid size. In this marine assessment grid, two grid sizes are used: * 100 x 100 km in offshore areas (> 20 km from the coastline) * 20 x 20 km in coastal areas (<= 20 km from the coastline) The grid sizes were choosen after an evaluation of data availability versus the need for sufficient detail in the resulting assessment. The resulting assessment grid is a combination of two grid sizes using the EEA reference grid system. The overall area of interest used in the grid is based on the marine regions and subregions under the Marine Strategy Framework Directive (MSFD). Additionally, Norwegian (Barent Sea and Norwegian Sea) and Icelandic waters (’Iceland Sea’) have been added (see Surrounding seas of Europe). Note that, within the North East Atlantic region, only the subregions within EEZ boundaries (~200 nm) have been included.

En France et en Europe, des logos officiels permettent de reconnaître des produits qui bénéficient d'un signe officiel de la qualité et de l'origine.

'''DEFINITION''' Based on daily, global climate sea surface temperature (SST) analyses generated by the Copernicus Climate Change Service (C3S) (product SST-GLO-SST-L4-REP-OBSERVATIONS-010-024). Analysis of the data was based on the approach described in Mulet et al. (2018) and is described and discussed in Good et al. (2020). The processing steps applied were: 1. The daily analyses were averaged to create monthly means. 2. A climatology was calculated by averaging the monthly means over the period 1991 - 2020. 3. Monthly anomalies were calculated by differencing the monthly means and the climatology. 4. An area averaged time series was calculated by averaging the monthly fields over the globe, with each grid cell weighted according to its area. 5. The time series was passed through the X11 seasonal adjustment procedure, which decomposes the time series into a residual seasonal component, a trend component and errors (e.g., Pezzulli et al., 2005). The trend component is a filtered version of the monthly time series. 6. The slope of the trend component was calculated using a robust method (Sen 1968). The method also calculates the 95% confidence range in the slope. '''CONTEXT''' Sea surface temperature (SST) is one of the Essential Climate Variables (ECVs) defined by the Global Climate Observing System (GCOS) as being needed for monitoring and characterising the state of the global climate system (GCOS 2010). It provides insight into the flow of heat into and out of the ocean, into modes of variability in the ocean and atmosphere, can be used to identify features in the ocean such as fronts and upwelling, and knowledge of SST is also required for applications such as ocean and weather prediction (Roquet et al., 2016). '''CMEMS KEY FINDINGS''' Over the period 1982 to 2024, the global average linear trend was 0.012 ± 0.001°C / year (95% confidence interval). 2024 is nominally the warmest year in the time series. Aside from this trend, variations in the time series can be seen which are associated with changes between El Niño and La Niña conditions. For example, peaks in the time series coincide with the strong El Niño events that occurred in 1997/1998 and 2015/2016 (Gasparin et al., 2018). '''DOI (product):''' https://doi.org/10.48670/moi-00242

'''Short description: ''' For the '''Atlantic''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor. * Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''multi''' products, and S3A & S3B only for the '''olci''' products. * Variables: Chlorophyll-a ('''CHL'''), Gradient of Chlorophyll-a ('''CHL_gradient'''), Phytoplankton Functional types and sizes ('''PFT'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS'''). * Temporal resolutions: '''daily'''. * Spatial resolutions: '''1 km''' and a finer resolution based on olci '''300 meters''' inputs. * 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 the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''GlobColour'''. '''DOI (product) :''' https://doi.org/10.48670/moi-00284

GO-SHIP, the Global Ocean Ship-Based Hydrographic Investigations Program, is conducting repeat hydrography with high accuracy high precision reference measurements of a variety of EOVs through the whole water column. A selection of continent-to-continent full depth sections are repeated at roughly decadal intervals. The data archive for CTD data and bottle data is currently at CCHDO, although the CTD data from European cruises are available at Seadatanet as well.

This is the FAO Fishery and Aquaculture Reference Data repository: Codes and reference data for fishing gear, species, currencies, commodities, countries and others.

The mean and time-varying position of the main Southern Ocean polar fronts has been calculated from a combination of gridded altimetric sea level anomalies and a new climatology of mean sea level constructed from historical data and ARGO T / S profiles.

The EEA coastline for analysis is created for highly detailed analysis, e.g. 1:100 000, for geographical Europe. The coastline is a hybrid product obtained from projects using satellite imagery as data source: EUHYDRO (https://land.copernicus.eu/imagery-in-situ/eu-hydro) and GSHHG (http://www.soest.hawaii.edu/pwessel/gshhg/). The defining criteria was altitude level = 0 from EUDEM (https://land.copernicus.eu/pan-european/satellite-derived-products/eu-dem/view). Outside the coverage of the EUDEM, the coastline from GSHHG was used without modifications. A few manual amendments to the dataset were necessary to meet requirements from EU Nature Directives, Water Framework Directive and Marine Strategy Framework Directive. In 2015, several corrections were made in the Kalogeroi Islands (coordinates 38.169, 25.287) and two other Greek little islets (coordinates 36.766264, 23.604318), as well as in the peninsula of Porkkala (around coordinates 59.99, 24.42). In this revision (v3, 2017), 2 big lagoons have been removed from Baltic region, because, according to HELCOM, are freshwater lagoons.

'''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