'''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

This visualization product displays plastic bags density per trawl. 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. In cases where the wingspread and/or number of items were/was unknown, it was not possible to use the data because these fields are needed to calculate the density. Data collected before 2011 are concerned by this filter. When the distance reported in the data was null, it was calculated from: - the ground speed and the haul duration using the following formula: Distance (km) = Haul duration (h) * Ground speed (km/h); - the trawl coordinates if the ground speed and the haul duration were not filled in. The swept area was calculated from the wingspread (which depends on the fishing gear type) and the distance trawled: Swept area (km²) = Distance (km) * Wingspread (km) Densities were calculated on each trawl and year using the following computation: Density of plastic bags (number of items per km²) = ∑Number of plastic bags related items / Swept area (km²) Percentiles 50, 75, 95 & 99 were calculated taking into account data for all years. The list of selected items for this product is attached to this metadata. Information on data processing and calculation is 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.

Rapid changes in ocean circulation and climate have been observed in marine-sediment and ice cores over the last glacial period and deglaciation, highlighting the non-linear character of the climate system and underlining the possibility of rapid climate shifts in response to anthropogenic greenhouse gas forcing. To date, these rapid changes in climate and ocean circulation are still not fully explained. One obstacle hindering progress in our understanding of the interactions between past ocean circulation and climate changes is the difficulty of accurately dating marine cores. Here, we present a set of 92 marine sediment cores from the Atlantic Ocean for which we have established age-depth models that are consistent with the Greenland GICC05 ice core chronology, and computed the associated dating uncertainties, using a new deposition modeling technique. This is the first set of consistently dated marine sediment cores enabling paleoclimate scientists to evaluate leads/lags between circulation and climate changes over vast regions of the Atlantic Ocean. Moreover, this data set is of direct use in paleoclimate modeling studies.

'''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.he 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

EMODnet Chemistry aims to provide access to marine chemistry data sets and derived data products concerning eutrophication, ocean acidification, contaminants and litter. The chosen parameters are relevant for the Marine Strategy Framework Directive (MSFD), in particular for descriptors 5, 8, 9 and 10. The datasets contain standardized, harmonized and validated data collections from seafloor litter. Datasets concerning seafloor litter data are loaded in a central database after a semi-automated validation phase. Once loaded, a data assessment is performed in order to check data consistency and potential errors are corrected thanks to a feedback loop with data originators. EMODnet seafloor litter data and database are hosted and maintained by ‘Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, Division of Oceanography (OGS/NODC)’ from Italy. For seafloor litter, the harmonized datasets contain all unrestricted EMODnet Chemistry data on seafloor litter data. Data are formatted following Guidelines and forms for gathering marine litter data, which can be found at: https://dx.doi.org/10.6092/15c0d34c-a01a-4091-91ac-7c4f561ab508 The updated vocabularies of admitted values are available at: https://vocab.seadatanet.org/search https://vocab.ices.dk/ The harmonized datasets can be downloaded as EMODnet Sea-floor litter data format version 1.0, which is a csv file, tab separated values.

Mesoscale eddy detection from 2000 to 2021 are computed using the AMEDA algorithm applied on AVISO L4 absolute dynamic topography at 1/8th degree. Eddy numbers correspond to tracks referenced in the DYNED atlas (https://doi.org/10.14768/2019130201.2). Detection is based on AVISO delyed-time product from 2000 to 2019 and on day+6 near-real-time altimetry from 2020 to 2021. Colocalisation is then made with available in situ profiles from Coriolis Ocean Dataset for Reanalysis (CORA) delayed-time data (113486 profiles) and Copernicus near-real-time profiles (43567).

Long-term time series of coliform bacteria concentration (fecal coliform or Escherichia coli) in shellfish in four submarine areas (North Sea/Channel, Britany, Atlantic, Mediterranean).

As part of the marine water quality monitoring of the “Pertuis” and the “baie de l’Aiguillon” (France), commissioned by the OFB and carried out by setec énergie environnement, three monitoring stations were installed. Two of them were set up at the mouths of the Charente and Seudre rivers on February 6 and 27, 2019, respectively, while a third was deployed in the Bay of Aiguillon on March 24, 2021. The dataset presented here concerns the station installed in the Bay of Aiguillon. Measurements are organized into .csv files, with one file per year. Data is collected using a WiMO multiparameter probe, which records the following parameters: •    Temperature (-2 to 35 °C) •    Conductivity (0 to 100 mS/cm) •    Pressure (0 to 30 m) •    Turbidity (0 to 4000 NTU) •    Dissolved Oxygen (0 to 23 mg/L & 0 to 250 %) •    Fluorescence (0 to 500 ppb)  

This visualization product displays the total abundance of marine macro-litter (> 2.5cm) per beach per year from non-MSFD monitoring surveys, research & cleaning operations. 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; - Some categories & some litter types like organic litter, small fragments (paraffin and wax; items > 2.5cm) and pollutants have been removed. 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 items (normalized by 100 m) = Number of litter per items x (100 / survey length) Then, this normalized number of items is summed to obtain the total normalized number of litter for each survey. Finally, the median abundance for each beach and year is calculated from these normalized abundances per survey. Percentiles 50, 75, 95 & 99 have been calculated taking into account 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.

The Southern Ocean plays a fundamental role in regulating the global climate. This ocean also contains a rich and highly productive ecosystem, potentially vulnerable to climate change. Very large national and international efforts are directed towards the modeling of physical oceanographic processes to predict the response of the Southern Ocean to global climate change and the role played by the large-scale ocean climate processes. However, these modeling efforts are greatly limited by the lack of in situ measurements, especially at high latitudes and during winter months. The standard data that are needed to study ocean circulation are vertical profiles of temperature and salinity, from which we can deduce the density of seawater. These are collected with CTD (Conductivity-Temperature-Depth) sensors that are usually deployed on research vessels or, more recently, on autonomous Argo profilers. The use of conventional research vessels to collect these data is very expensive, and does not guarantee access to areas where sea ice is found at the surface of the ocean during the winter months. A recent alternative is the use of autonomous Argo floats. However, this technology is not easy to use in glaciated areas. In this context, the collection of hydrographic profiles from CTDs mounted on marine mammals is very advantageous. The choice of species, gender or age can be done to selectively obtain data in particularly under-sampled areas such as under the sea ice or on continental shelves. Among marine mammals, elephant seals are particularly interesting. Indeed, they have the particularity to continuously dive to great depths (590 ± 200 m, with maxima around 2000 m) for long durations (average length of a dive 25 ± 15 min, maximum 80 min). A Conductivity-Temperature-Depth Satellite Relay Data Logger (CTD-SRDLs) has been developed in the early 2000s to sample temperature and salinity vertical profiles during marine mammal dives (Boehme et al. 2009, Fedak 2013). The CTD-SRDL is attached to the seal on land, then it records hydrographic profiles during its foraging trips, sending the data by satellite ARGOS whenever the seal goes back to the surface.While the principle intent of seal instrumentation was to improve understanding of seal foraging strategies (Biuw et al., 2007), it has also provided as a by-product a viable and cost-effective method of sampling hydrographic properties in many regions of the Southern Ocean (Charrassin et al., 2008; Roquet et al., 2013).