The analysis was performed per season using DIVA software tool (Data-Interpolating Variational Analysis). The analyses products are stored as NetCDF CF files and made available as WMS layers for easy browsing and adding. Every step of the time dimension corresponds to a 6-year moving average from 1983 to 2016. The depth dimension spans from surface to 1000 m, with 21 vertical levels. The boundaries and overlapping zones between these regions were filtered to avoid any unrealistic spatial discontinuities. This combined water body dissolved oxygen concentration product is masked using the relative error threshold 0.5. Units: µmol/l Created by 'University of Liège, GeoHydrodynamics and Environment Research (ULiège-GHER)'. The data used as input for DIVA have been extracted from the EMODnet Chemistry Download Service: https://emodnet-chemistry.maris.nl/search Intermediate regional data products: Mediterranean Sea - DIVA 4D 6-year analysis of Water body chlorophyll-a 1990/2017 v2018, Arctic Ocean - DIVA 4D 6-year analysis of Water body chlorophyll-a 1980/2017 v2018, Black Sea - DIVA 4D 6-year analysis of Water body chlorophyll-a 1990/2016 v2018, North East Atlantic Ocean - DIVA 4D 6-year analysis of Water body chlorophyll-a 1960/2017 v2018, North Sea - DIVA 4D 6-year analysis of Water body chlorophyll-a 1980/2017 v2018, Baltic Sea - DIVA 4D 6-year analysis of Water body chlorophyll-a 1980/2016 v2018

'''This product has been archived'''                For operationnal and online products, please visit https://marine.copernicus.eu '''DEFINITION''' The ibi_omi_tempsal_sst_trend product includes the Sea Surface Temperature (SST) trend for the Iberia-Biscay-Irish Seas over the period 1993-2019, i.e. the rate of change (°C/year). This OMI is derived from the CMEMS REP ATL L4 SST product (SST_ATL_SST_L4_REP_OBSERVATIONS_010_026), see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-ATL-SST.pdf), which provided the SSTs used to compute the SST trend over the Iberia-Biscay-Irish Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05° grid resolution SST maps built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. Trend analysis has been performed by using the X-11 seasonal adjustment procedure (see e.g. Pezzulli et al., 2005), which has the effect of filtering the input SST time series acting as a low bandpass filter for interannual variations. Mann-Kendall test and Sens’s method (Sen 1968) were applied to assess whether there was a monotonic upward or downward trend and to estimate the slope of the trend and its 95% confidence interval. '''CONTEXT''' Sea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). '''CMEMS KEY FINDINGS''' Over the period 1993-2021, the Iberia-Biscay-Irish Seas mean Sea Surface Temperature (SST) increased at a rate of 0.011 ± 0.001 °C/Year. '''DOI (product):''' https://doi.org/10.48670/moi-00257

This product displays the stations where naphthalene has been measured and the values present in EMODnet Chemistry infrastructure are always below the limit of detection or quantification (LOD/LOQ), i.e quality values found in EMODnet validated dataset can be equal to 6 or Q. It is necessary to take into account that LOD/LOQ can change with time. These products aggregate data by station, producing only one final value for each station (above, below or above/below). EMODnet Chemistry has included the gathering of contaminants data since the beginning of the project in 2009. For the maps for EMODnet Chemistry Phase III, it was requested to plot data per matrix (water,sediment, biota), per biological entity and per chemical substance. The series of relevant map products have been developed according to the criteria D8C1 of the MSFD Directive, specifically focusing on the requirements under the new Commission Decision 2017/848 (17th May 2017). The Commission Decision points to relevant threshold values that are specified in the WFD, as well as relating how these contaminants should be expressed (units and matrix etc.) through the related Directives i.e. Priority substances for Water. EU EQS Directive does not fix any threshold values in sediments. On the contrary Regional Sea Conventions provide some of them, and these values have been taken into account for the development of the visualization products. To produce the maps the following process has been followed: 1. Data collection through SeaDataNet standards (CDI+ODV) 2. Harvesting, harmonization, validation and P01 code decomposition of data 3. SQL query on data sets from point 2 4. Production of map with each point representing at least one record that match the criteria The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols. Preliminary processing were necessary to harmonize all the data : • For water: contaminants in the dissolved phase; • For sediment: data on total sediment (regardless of size class) or size class < 2000 μm • For biota: contaminant data will focus on molluscs, on fish (only in the muscle), and on crustaceans • Exclusion of data values equal to 0

The raster dataset represents the risk of collision of whales with vessels in Europe Seas. The most vulnerable species from ship strikes are cetaceans and turtles, since they go to the surface to breathe. On the other hand, their migration routes can overlap with shipping lanes. The collisions can produce the death or injury of the animals, and are an important threat for the conservation of these species. The dataset has been prepared in the context of the development of the first European Maritime Transport Environmental Report (EMSA-EEA report, 2021: https://www.eea.europa.eu/publications/maritime-transport).

This product displays the stations where DDT has been measured and the values present in EMODnet Chemistry infrastructure are either above or below the limit of detection or quantification (LOD/LOQ), i.e for the substance, in that station, quality values found in EMODnet validated dataset can be equal to 6, Q or 1. It is necessary to take into account that LOD/LOQ can change with time. These products aggregate data by station, producing only one final value for each station (above, below or above/below). EMODnet Chemistry has included the gathering of contaminants data since the beginning of the project in 2009. For the maps for EMODnet Chemistry Phase III, it was requested to plot data per matrix (water,sediment, biota), per biological entity and per chemical substance. The series of relevant map products have been developed according to the criteria D8C1 of the MSFD Directive, specifically focusing on the requirements under the new Commission Decision 2017/848 (17th May 2017). The Commission Decision points to relevant threshold values that are specified in the WFD, as well as relating how these contaminants should be expressed (units and matrix etc.) through the related Directives i.e. Priority substances for Water. EU EQS Directive does not fix any threshold values in sediments. On the contrary Regional Sea Conventions provide some of them, and these values have been taken into account for the development of the visualization products. To produce the maps the following process has been followed: 1. Data collection through SeaDataNet standards (CDI+ODV) 2. Harvesting, harmonization, validation and P01 code decomposition of data 3. SQL query on data sets from point 2 4. Production of map with each point representing at least one record that match the criteria The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols. Preliminary processing were necessary to harmonize all the data : • For water: contaminants in the dissolved phase; • For sediment: data on total sediment (regardless of size class) or size class < 2000 μm • For biota: contaminant data will focus on molluscs, on fish (only in the muscle), and on crustaceans • Exclusion of data values equal to 0

The project’s purpose is to introduce new low trophic species, products and processes in marine aquaculture value chains across the Atlantic. Low trophic species are those organisms low on the food chain as sea urchins or mussels. The five chosen value chains of AquaVitaeinclude macroalgae, Integrated Multi-Trophic Aquaculture (IMTA), echinoderm species (e.g. sea urchins), shellfish and finfish. IMTA is a process that farms several species together using waste from one species as feed for another. One of the main expected results of the project would be the creation of real and meaningful collaborative links between researchers, industry and other aquaculture stakeholders in the Atlantic area. AquaVitae will contribute to the Belém Statement, the joint Declaration on Atlantic Ocean Research and Innovation Cooperation between the European Union, Brazil, and South Africa through: - Setting up a network for knowledge and research exchange through the Atlantic. - Sustainable use of marine resources with a circular economy approach. - Better monitoring of aquaculture activities through new and emerging technologies. - Contributing to the well-being of aquaculture communities. - Enhancing citizen engagement through training and outreach activities. - Setting up student exchanges and industrial apprenticeships.

This vector dataset represents the benthic broad habitat types in Europe seas potentially affected by anchoring. The anchoring of vessels can produce damages to the seafloor and the loss of the habitat where it happens. It takes place in shallow waters, close to ports, where vessels have to wait for a spot in the port so they can load or offload (since ports have a limited amount of dockage). The anchoring areas allow vessels to safely anchor in protection from bad weather or other circumstances. The type of alterations to the habitats resulting from anchoring depend mainly on the dimensions and type of the anchor, as well as its movement resulting from the movement of the vessel due to currents or waves, which can increase the adverse effects. The dataset has been prepared in the context of the development of the first European Maritime Transport Environmental Report (to be published as an EMSA-EEA report in 2021).

'''This product has been archived'''                For operationnal and online products, please visit https://marine.copernicus.eu '''DEFINITION''' The ibi_omi_tempsal_sst_area_averaged_anomalies product for 2021 includes Sea Surface Temperature (SST) anomalies, given as monthly mean time series starting on 1993 and averaged over the Iberia-Biscay-Irish Seas. The IBI SST OMI is built from the CMEMS Reprocessed European North West Shelf Iberai-Biscay-Irish Seas (SST_MED_SST_L4_REP_OBSERVATIONS_010_026, see e.g. the OMI QUID, http://marine.copernicus.eu/documents/QUID/CMEMS-OMI-QUID-ATL-SST.pdf), which provided the SSTs used to compute the evolution of SST anomalies over the European North West Shelf Seas. This reprocessed product consists of daily (nighttime) interpolated 0.05° grid resolution SST maps over the European North West Shelf Iberai-Biscay-Irish Seas built from the ESA Climate Change Initiative (CCI) (Merchant et al., 2019) and Copernicus Climate Change Service (C3S) initiatives. Anomalies are computed against the 1993-2014 reference period. '''CONTEXT''' Sea surface temperature (SST) is a key climate variable since it deeply contributes in regulating climate and its variability (Deser et al., 2010). SST is then essential to monitor and characterise the state of the global climate system (GCOS 2010). Long-term SST variability, from interannual to (multi-)decadal timescales, provides insight into the slow variations/changes in SST, i.e. the temperature trend (e.g., Pezzulli et al., 2005). In addition, on shorter timescales, SST anomalies become an essential indicator for extreme events, as e.g. marine heatwaves (Hobday et al., 2018). '''CMEMS KEY FINDINGS''' The overall trend in the SST anomalies in this region is 0.011 ±0.001 °C/year over the period 1993-2021. '''DOI (product):''' https://doi.org/10.48670/moi-00256

This metadata corresponds to the EUNIS Coastal habitat types, distribution based on vegetation plot data dataset. Coastal habitats are those above spring high tide limit (or above mean water level in non-tidal waters) occupying coastal features and characterised by their proximity to the sea, including coastal dunes and wooded coastal dunes, beaches and cliffs. Includes free-draining supralittoral habitats adjacent to marine habitats which are normally only very rarely subject to any type of salt water, in as much as they may be inhabited predominantly by terrestrial species, strandlines characterised by terrestrial invertebrates and moist and wet coastal dune slacks and dune-slack pools. Supralittoral sands and wracks may be found also in marine habitats (M). Excludes supralittoral rock pools and habitats, the splash zone immediately above the the mean water line, as well the spray zone and zone subject to sporadic inundation with salt water in as much as it may be inhabited predominantly by marine species, which are included in marine (M). The verified coastal habitat samples used are derived from the Braun-Blanquet database (http://www.sci.muni.cz/botany/vegsci/braun_blanquet.php?lang=en) which is a centralised database of vegetation plots and comprises copies of national and regional databases using a unified taxonomic reference database. The geographic extent of the distribution data are all European countries except Armenia and Azerbaijan. The dataset is provided both in Geodatabase and Geopackage formats.

The dataset presents the results of classification of eutrophication status of the European seas using the HEAT+ tool. Eutrophication status is evaluated in five classes, where NPAhigh and NPAgood are recognised as ‘non-problem areas’ and PAmoderate, PApoor and PAbad are recognised as ‘problem areas’. Besides the overall Eutrophication status (HEAT+) are the results shown for three aspects of eutrophication: C1) Nutrient Concentrations C2) Direct Effects C3) Indirect Effects This dataset underpins the findings and cartographic representations published in the report "Nutrient enrichment and eutrophication in Europe's seas" (EEA, 2019): https://www.eea.europa.eu/publications/nutrient-enrichment-and-eutrophication-in.