EMODnet Chemistry aims to provide access to marine chemistry datasets and derived data products concerning eutrophication, acidity and contaminants. The importance of the selected substances and other parameters relates to the Marine Strategy Framework Directive (MSFD). This aggregated dataset contains all unrestricted EMODnet Chemistry data on eutrophication and acidity, and covers the Northeast Atlantic Ocean (40W). Data were aggregated and quality controlled by 'IFREMER / IDM / SISMER - Scientific Information Systems for the SEA' in France. ITS-90 water temperature and water body salinity variables have also been included ('as are') to complete the eutrophication and acidity data. If you use these variables for calculations, please refer to SeaDataNet for the quality flags: https://www.seadatanet.org/Products/Aggregated-datasets . Regional datasets concerning eutrophication and acidity are automatically harvested, and the resulting collections are aggregated and quality controlled using ODV Software and following a common methodology for all sea regions ( https://doi.org/10.13120/8xm0-5m67 ). Parameter names are based on P35 vocabulary, which relates to EMODnet Chemistry aggregated parameter names and is available at: https://vocab.nerc.ac.uk/search_nvs/P35/ . When not present in original data, water body nitrate plus nitrite was calculated by summing all nitrate and nitrite parameters. The same procedure was applied for water body dissolved inorganic nitrogen (DIN), which was calculated by summing all nitrate, nitrite, and ammonium parameters. Concentrations per unit mass were converted to a unit volume using a constant density of 1.025 kg/L. The aggregated dataset can also be downloaded as an ODV collection and spreadsheet, which is composed of a metadata header followed by tab separated values. This spreadsheet can be imported to ODV Software for visualisation (more information can be found at: https://www.seadatanet.org/Software/ODV ).

This visualization product displays the cigarette related items abundance of marine macro-litter (> 2.5cm) per beach per year from non-MSFD monitoring surveys, research & cleaning operations without UNEP-MARLIN data. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols and reference lists used on a European scale. Preliminary processings were necessary to harmonize all the data: - Exclusion of OSPAR 1000 protocol: in order to follow the approach of OSPAR that it is not including these data anymore in the monitoring; - Selection of surveys from non-MSFD monitoring, cleaning and research operations; - Exclusion of beaches without coordinates; - Selection of plastic bags related items only. The list of selected items is attached to this metadata. This list was created using EU Marine Beach Litter Baselines, the European Threshold Value for Macro Litter on Coastlines and the Joint list of litter categories for marine macro-litter monitoring from JRC (these three documents are attached to this metadata); - Exclusion of surveys without associated length; - Normalization of survey lengths to 100m & 1 survey / year: in some case, the survey length was not 100m, so in order to be able to compare the abundance of litter from different beaches a normalization is applied using this formula: Number of plastic bags related items of the survey (normalized by 100 m) = Number of plastic bags related items of the survey x (100 / survey length) Then, this normalized number of plastic bags related items is summed to obtain the total normalized number of plastic bags related items for each survey. Finally, the median abundance of plastic bags related items for each beach and year is calculated from these normalized abundances of plastic bags related items per survey. Percentiles 50, 75, 95 & 99 have been calculated taking into account plastic bags related items from other sources data for all years. More information is available in the attached documents. Warning: the absence of data on the map does not necessarily mean that they do not exist, but that no information has been entered in the Marine Litter Database for this area.

Moving 6-year analysis of Water body chlorophyll-a in the NorthEast Atlantic for each season: - winter: January-March, - spring: April-June, - summer: July-September, - autumn: October-December. Every year of the time dimension corresponds to the 6-year centred average of each season. 6-year periods span from 1971/1976 until 2019/2024. Observation data span from 1971 to 2024. High-frequency observation trails were filtered to a 3h temporal resolution. Depth levels (IODE standard depths): [0.0, 5.0, 10.0, 20.0, 30.0, 40.0, 50.0, 75.0, 100.0, 125.0, 150.0, 200.0, 250.0, 300.0]. Data sources: observational data from SeaDataNet/EMODNet Chemistry Data Network. Description of DIVAnd analysis: the computation was done with DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.12, using GEBCO 30 sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps is 0.1 degrees. Horizontal correlation length varies from 200km in open sea regions to 50km at the coast. Vertical correlation length is defined as twice the vertical resolution. Signal-to-noise ratio was fixed to 1 for vertical profiles and 0.1 for time series to account for the redundancy in the time series observations. A logarithmic transformation (DIVAnd.loglin) was applied to the data prior to the analysis. Background field: a vertically-filtered profile of the seasonal data mean value (including all years) is substracted from the data. Detrending of data: no, advection constraint applied: no. Units: mg/m3.

Moving 6-year analysis and visualization of Water body dissolved oxygen concentration in the North Sea. Four seasons (December-February, March-May, June-August, September-November). Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. Description of DIVA analysis: Geostatistical data analysis by DIVAnd (Data-Interpolating Variational Analysis) tool, version 2.7.12. results were subjected to the minfield option in DIVAnd to avoid negative/underestimated values in the interpolated results; error threshold masks L1 (0.3) and L2 (0.5) are included as well as the unmasked field. The depth dimension allows visualizing the gridded field at various depths.

Until recently, classical radar altimetry could not provide reliable sea level data  within 10 km to the coast. However dedicated reprocessing of radar waveform  together with geophysical corrections adapted for the coastal regions now allows  to fill this gap at a large number of coastal sites. In the context of the Climate Change Initiative Sea Level project of the European Space Agency, we have recently performed a complete reprocessing of high resolution (20 Hz, i.e., 350m)  along-track altimetry data of the Jason-1, Jason-2 and Jason-3 missions over  January 2002 to June 2021 along the coastal zones of Northeast Atlantic,  Mediterranean Sea, whole African continent, North Indian Ocean, Southeast Asia,  Australia and North and South America. This reprocessing has provided valid sea  level data in the 0-20 km band from the coast. More than 1000 altimetry-based virtual coastal stations have been selected and sea level anomalies time series  together with associated coastal sea level trends have been computed over the study time span. In the coastal regions devoid from tide gauges  (e.g., African coastlines), these virtual stations offer a unique tool for estimating  sea level change close to the coast (typically up to 3 km to the coast but in many  instances up to 1 km or even closer). Results show that at most of the virtual  stations, the rate of sea level rise at the coast is similar to the rate offshore (15 km away from the coast). However, at some stations, the sea level rate in the last 3-4 km to the coast is either faster or slower than offshore.

'''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 products used include three global reanalyses: GLORYS, C-GLORS, ORAS5 (GLOBAL_MULTIYEAR_PHY_ENS_001_031) and two in situ based reprocessed products: CORA5.2 (INSITU_GLO_PHY_TS_OA_MY_013_052) , ARMOR-3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). Additionally, the time series based on the method of von Schuckmann and Le Traon (2011) has been added. 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

The Arcachon Bay is a unique and ecologically important meso-tidal lagoon on the Atlantic coast of south-west France. The Arcachon Bay has the largest area of dwarf seagrass (Z. noltei) in Europe, the extent of which was stable in their extent between the 1950s and 1990s, but a decline in seagrass was observed in mid-2000. The decline of Zostera (seagrass) may have a significant impact on sedimentation in this coastal ecosystem rich in marine life. Interface cores were collected in September 2022 to determine sediment and mass accumulation rates (SAR, MAR) in the Arcachon Bay. Ten study areas were selected, distributed over most of the areas where seagrass meadows are actually observed. Two sites were visited each time, one with the presence of Zostera noltei in good condition (Healthy) and the other where the sediment was bare (Bare). Maximum water heights during spring tides range from 3.44 m for the deepest site (Garrèche) to 2.09 m for the shallowest site (Fontaines). A total of 20 sediment cores were sampled and carefully extruded every 1 cm from the top to the bottom of the core. The sediment layers were used to determine dry bulk density and selected radioisotope activities: DBD, 210Pb, 226Ra, 137Cs, 228Th and 40K expressed as %K). 

This dataset contains all satellite altimeter wave heights above 9 m, from the following satellite missions: ERS-1, ERS-2, Topex-Poseidon (Topex only), Envisat, SARAL, Jason-1, Jason-2, Jason-3, Sentinel-3A, Sentinel-3B, Sentinel-6A, Cryosat-2, CFOSAT, SWOT. Storm event identification used the DetectHsStorm package developed by M. De Carlo and F. Ardhuin (  https://github.com/ardhuin/) . This data can be combined with modeled storm tracks (see F. Ardhuin, M. De Carlo, Storm tracks based on wave heights from LOPS WAVEWATCH III hindcast and ERA5 reanalysis, years 1991-2024, SEANOE (2025). doi: 10.17882/105148 )

This dataset contains the dynamical outputs of a global ocean simulation coupling dynamics and biogeochemistry at ¼° over the year 2019. The simulation has been performed using the coupled circulation/ecosystem model NEMO/PISCES (https://www.nemo-ocean.eu/), which is here enhanced to perform an ensemble simulation with explicit simulation of modeling uncertainties in the physics and in the biogeochemistry. This dataset is one of the 40 members of the ensemble simulation. This study was part of the Horizon Europe project SEAMLESS (https://seamlessproject.org/Home.html), with the general objective of improving the analysis and forecast of ecosystem indicators.   See Popov et al. (https://os.copernicus.org/articles/20/155/2024/) for more details on the study.

The raster corresponds to the predicted Mediterranean bioregions of megabenthic communities.