'''DEFINITION''' Significant wave height (SWH), expressed in metres, is the average height of the highest third of waves. This OMI provides global maps of the seasonal mean and trend of significant wave height (SWH), as well as time series in three oceanic regions of the same variables and their trends from 2002 to 2020, calculated 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 SWH for the selected period and region. The 95th percentile is the value below which 95% of the data points fall, indicating higher than normal wave heights. The mean and 95th percentile of SWH (in m) 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). Trends have been obtained using linear regression and are expressed in cm/yr. For the time series, the uncertainty around the trend was obtained from the linear regression, while the uncertainty around the mean and 95th percentile was bootstrapped. For the maps, if the p-value obtained from the linear regression is less than 0.05, the trend is considered significant. '''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, regionally and globally. '''KEY FINDINGS''' From 2002 to 2020, positive trends in both Significant Wave Height (SWH) and extreme SWH are mostly found in the southern hemisphere (a, b). The 95th percentile of wave heights (q95), increases faster than the average values, indicating that extreme waves are growing more rapidly than average wave height (a, b). Extreme SWH’s global maps highlight heavily storms affected regions, including the western North Pacific, the North Atlantic and the eastern tropical Pacific (a). In the North Atlantic, SWH has increased in summertime (July August September) but decreased in winter. Specifically, the 95th percentile SWH trend is decreasing by 2.1 ± 3.3 cm/year, while the mean SWH shows a decrease of 2.2 ± 1.76 cm/year. In the south of Australia, during boreal winter, the 95th percentile SWH is increasing at 2.6 ± 1.5 cm/year (c), with the mean SWH increasing by 0.5 ± 0.66 cm/year (d). Finally, in the Antarctic Circumpolar Current, also in boreal winter, the 95th percentile SWH trend is 3.2 ± 2.14 cm/year (c) and the mean SWH trend is 1.7 ± 0.84 cm/year (d). These patterns highlight the complex and region-specific nature of wave height trends. Further discussion is available in A. Laloue et al. (2024). '''DOI (product):''' https://doi.org/10.48670/mds-00352

Rocch, the french "mussel watch", provides chemical data for marine quality management. Once a year, trace metals, organic compounds (chlorinated, PAH, brominated flame retardants, perfluorinated compounds, organotins ...) are analysed in molluscs tissues to check chemical quality according to European Framework Directives and to Regional Seas Convention (OSPAR).

As part of the European Horizon Europe FOCCUS project (https://foccus-project.eu/), the metadata inventory of European coastal platforms has been extracted. The inventory was based on the following History and Latest products, downloaded from the CMEMS website (https://marine.copernicus.eu/fr/acces-donnees) at: 1) Global Ocean-In-Situ Near-Real-Time Observation, 2) Atlantic Iberian Biscay Irish Ocean-In-Situ Near Real Time Observations, 3) Mediterranean Sea-In-Situ Near Real Time Observations, 4) Atlantic-European North West Shelf-Ocean In-Situ Near Real Time Observations. To carry out this inventory, it was decided to target only coastal platforms, located less than 200km from the coast and at a depth of less than 400m. For mobile platforms, it was also decided to focus only on the first position in the file. This data must be located within 200 km of the coast and at a depth of less than 400 m. In this inventory, FerryBox platforms have all been considered as coastal platforms. The following platforms were extracted from the products: BO (Bottles), CT (CTD), DB (Drifting Buoys), FB (Ferry Box), GL (Gliders), HF (High Frequency Radar), MO (Mooring), PF (Profiling Float), TG (Tide Gauge) and XB (XBT). Once the metadata had been extracted from the files, duplicates were removed (files with the same names). Duplicate platforms of type _TS_ and _WS_ were merged (date and parameters). Latest‘ files have been merged with ’History" files. Missing metadata have been replaced in the Excel file by ‘Missing Data’. Some old dates were also revised by hand because they had been badly extracted, as well as some institution names that included special characters. Platforms located on estuaries/rivers/lakes/ponds have also been removed by hand. This inventory identified a total of 10,479 coastal platforms.

This visualization product displays the spatial distribution of the sampling effort over the six-years' period 2017-2022. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of seafloor litter collected by international fish-trawl surveys 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 (OSPAR and MEDITS protocols) and reference lists used on a European scale. Moreover, within the same protocol, different gear types are deployed during bottom trawl surveys. The spatial distribution was determined by calculating the number of times each cell was sampled during the period 2017-2022. The corresponding total distance (kms) sampled in each cell is also provided in the attribute table. Information on data processing and calculation are detailed in the attached methodology document. 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. This work is based on the work presented in the following scientific article: O. Gerigny, M. Brun, M.C. Fabri, C. Tomasino, M. Le Moigne, A. Jadaud, F. Galgani, Seafloor litter from the continental shelf and canyons in French Mediterranean Water: Distribution, typologies and trends, Marine Pollution Bulletin, Volume 146, 2019, Pages 653-666, ISSN 0025-326X, https://doi.org/10.1016/j.marpolbul.2019.07.030.

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). 

210Pb, 226Ra and 137Cs were measured by non-destructive gamma spectrometry on marine sediment cores, collected during RIKEAU 2002 cruise on board r/v Thalia, on the shelf of the Bay of Biscay

This visualization product displays the density of floating micro-litter per net normalized per m³ per year from research and monitoring protocols. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Before 2021, there was no coordinated effort at the regional or European scale for micro-litter. Given this situation, EMODnet Chemistry proposed to adopt the data gathering and data management approach as generally applied for marine data, i.e., populating metadata and data in the CDI Data Discovery and Access service using dedicated SeaDataNet data transport formats. EMODnet Chemistry is currently the official EU collector of micro-litter data from Marine Strategy Framework Directive (MSFD) National Monitoring activities (descriptor 10). A series of specific standard vocabularies or standard terms related to micro-litter have been added to SeaDataNet NVS (NERC Vocabulary Server) Common Vocabularies to describe the micro-litter. European micro-litter data are collected by the National Oceanographic Data Centres (NODCs). Micro-litter map products are generated from NODCs data after a test of the aggregated collection including data and data format checks and data harmonization. A filter is applied to represent only micro-litter sampled according to research and monitoring protocols as MSFD monitoring. Densities were calculated for each net using the following calculation: Density (number of particles per m³) = Micro-litter count / Sampling effort (m³) When the number of micro-litters was not filled, it was not possible to calculate the density. Percentiles 50, 75, 95 & 99 have been calculated taking into account data for all years. 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 National Oceanographic Data Centre (NODC) for this area.

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 Greater North Sea and Celtic Seas. Data were aggregated and quality controlled by 'Aarhus University, Department of Bioscience, Marine Ecology Roskilde' in Denmark. 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 ).

Moving 6-year analysis of Water body dissolved inorganic nitrogen 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. 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, 400.0, 500.0, 600.0, 700.0, 800.0, 900.0, 1000.0, 1100.0, 1200.0, 1300.0, 1400.0, 1500.0, 1750.0, 2000.0]. 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 400km 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: umol/l.

Seasonal climatology of Water body phosphate for Loire river for the period 1950-2024 and for the following seasons: - winter: January-March, - spring: April-June, - summer: July-September, - autumn: October-December. Observation data span from 1950 to 2024. Depth levels (m): [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0, 120.0, 130.0]. 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 15 sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps is 0.01 degrees. Horizontal correlation length is defined seasonally (in meters): 230000 (winter), 264000 (spring), 140000 (summer), 135000 (autumn). Vertical correlation length was optimized and vertically filtered and a seasonally-averaged profile was used (DIVAnd.fitvertlen). 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: the vertically-filtered data mean profile is substracted from the data. Detrending of data: no, advection constraint applied: no. Units: umol/l.