This visualization product displays the cigarette related items abundance of marine macro-litter (> 2.5cm) per beach per year from Marine Strategy Framework Directive (MSFD) monitoring surveys 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 MSFD surveys only (exclusion of other monitoring, cleaning and research operations); - Exclusion of beaches without coordinates; - Selection of cigarette 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 referring to the UNEP-MARLIN list: the UNEP-MARLIN protocol differs from the other types of monitoring in that cigarette butts are surveyed in a 10m square. To avoid comparing abundances from very different protocols, the choice has been made to distinguish in two maps the cigarette related items results associated with the UNEP-MARLIN list from the others; - Normalization of survey lengths to 100m & 1 survey / year: in some case, the survey length was not exactly 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 cigarette related items of the survey (normalized by 100 m) = Number of cigarette related items of the survey x (100 / survey length) Then, this normalized number of cigarette related items is summed to obtain the total normalized number of cigarette related items for each survey. Finally, the median abundance of cigarette related items for each beach and year is calculated from these normalized abundances of cigarette related items per survey. Sometimes the survey length was null or equal to 0. Assuming that the MSFD protocol has been applied, the length has been set at 100m in these cases. Percentiles 50, 75, 95 & 99 have been calculated taking into account cigarette related items from MSFD monitoring data (excluding UNEP-MARLIN protocol) 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.

Observations of Sea surface temperature and salinity are now obtained from voluntary sailing ships using medium or small size sensors. They complement the networks installed on research vessels or commercial ships. The delayed mode dataset proposed here is upgraded annually as a contribution to GOSUD (http://www.gosud.org )

EMODnet Chemistry aims to provide access to marine chemistry data sets and derived data products concerning eutrophication, ocean acidification and contaminants. The chemicals chosen 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 potential hazardous substances, despite the fact that some data might not be related to pollution (e.g. collected by deep corer). Temperature, salinity and additional parameters are included when available. It covers the Mediterranean Sea. Data were harmonised and validated by the ‘Hellenic Centre for Marine Research, Hellenic National Oceanographic Data Centre (HCMR/HNODC)’ in Greece. The dataset contains water, sediment and biota profiles and timeseries. The temporal coverage is 1974–2022 for water measurements, 1971–2023 for sediment measurements and 1979-2023 for biota measurements. Regional datasets concerning contaminants are automatically harvested and the resulting collections are harmonised and validated using ODV Software and following a common methodology for all sea regions ( https://doi.org/10.6092/8b52e8d7-dc92-4305-9337-7634a5cae3f4 ). Parameter names are based on P01 vocabulary, which relates to BODC Parameter Usage Vocabulary and is available at: https://vocab.nerc.ac.uk/search_nvs/P01/ . The harmonised dataset can be downloaded as as an ODV spreadsheet, which is composed of a metadata header followed by tab separated values. This spreadsheet can be imported into ODV Software for visualisation (more information can be found at: https://www.seadatanet.org/Software/ODV ). In addition, the same dataset is offered also as a txt file in a long/vertical format, in which each P01 measurement is a record line. Additionally, there are a series of columns that split P01 terms into subcomponents (substance, CAS number, matrix...).This transposed format is more adapted to worksheet applications (e.g. LibreOffice Calc).

This daily High-Resolution (HR) Level 3 gridded wind product is derived from Copernicus Sentinel-1 SAR (Synthetic Aperture Radar) observations, over the North Western Atlantic ("ATL" area). It is based on the European Space Agency (ESA) Level-2 OCN products at the highest available resolution. Although L2-OCN products already contain wind vectors, those are calculated using the CMOD5.n Geophysical Model Function (GMF) applied to the co-polarized (co-pol) VV channel (emitting in Vertical polarization and receiving in Vertical polarization). This VV GMF was mapped from scatterometer sensors (Hersbach et al., 2007) which are only able to use co-pol measurements. However, these co-pol GMF are known to lose sensitivity for wind above 20 m/s. Therefore, wind based on such GMF alone, are known to under-estimate wind speed (Polverari et al., 2022). For the L3 products winds based on SAR, we take advantage of the available cross-polarized (cross-pol) VH channel (emitting in Vertical polarization and receiving in Horizontal polarization) for which GMF were specifically derived based on C-Band SAR (Mouche et al., 2017, Mouche et al., 2019). Winds estimated from the combination of both the co-pol and cross-pol channels are referred to as dual-polarization (or dual-pol) winds. As shown in Mouche et al. (2019), taking advantage of the dual polarization strongly improves the wind estimation for high wind conditions thanks to the much greater VH channel sensitivity compared to VV. These new wind estimations are then gridded with a 0.012 degree resolution (between 0.5 and 1.2 km in zonal direction depending on the latitude and 1.3 km in meridional direction) using a cylindrical equidistant projection, independently for ascending and descending satellite passes and for each satellite (so 4 wind fields are available per day for two satellites). This dataset is generated over all Sentinel-1 mission time series starting from March 2018 and updated in delayed mode with a 4-months delay. It is also produced for 4 other different European areas. This dataset is produced and disseminated in the frame of Copernicus Marine Service.

'''Short description:''' Near-Real-Time mono-mission satellite-based integral parameters derived from the directional wave spectra. Using linear propagation wave model, only wave observations that can be back-propagated to wave converging regions are considered. The dataset parameters includes partition significant wave height, partition peak period and partition peak or principal direction given along swell propagation path in space and time at a 3-hour timestep, from source to land. Validity flags are also included for each parameter and indicates the valid time steps along propagation (eg. no propagation for significant wave height close to the storm source or any integral parameter when reaching the land). The integral parameters at observation point are also available together with a quality flag based on the consistency between each propagated observation and the overall swell field. This product is processed by the WAVE-TAC multi-mission SAR data processing system. It processes near-real-time data from the following missions: SAR (Sentinel-1A and Sentinel-1B) and CFOSAT/SWIM. One file is produced for each mission and is available in two formats depending on the user needs: one gathering in one netcdf file all observations related to the same swell field, and for another all observations available in a 3-hour time range, and for both formats, propagated information from source to land. '''DOI (product) :''' https://doi.org/10.48670/moi-00178

'''DEFINITION''' The temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. 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). The regional thermosteric sea level values are then averaged from 60°S-60°N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). '''CONTEXT''' Most of the interannual variability and trends in regional sea level is caused by changes in steric sea level. At mid and low latitudes, the steric sea level signal is essentially due to temperature changes, i.e. the thermosteric effect (Stammer et al., 2013, Meyssignac et al., 2016). Salinity changes play only a local role. Regional trends of thermosteric sea level can be significantly larger compared to their globally averaged versions (Storto et al., 2018). Except for shallow shelf sea and high latitudes (> 60° latitude), regional thermosteric sea level variations are mostly related to ocean circulation changes, in particular in the tropics where the sea level variations and trends are the most intense over the last two decades. '''CMEMS KEY FINDINGS''' Significant (i.e. when the signal exceeds the noise) regional trends for the period 2005-2023 from the Copernicus Marine Service multi-ensemble approach show a thermosteric sea level rise at rates ranging from the global mean average up to more than 8 mm/year. There are specific regions where a negative trend is observed above noise at rates up to about -5 mm/year such as in the subpolar North Atlantic, or the western tropical Pacific. These areas are characterized by strong year-to-year variability (Dubois et al., 2018; Capotondi et al., 2020). Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00241

This dataset comprises energy density and proximal composition (water, ash, lipid and protein contents) for anchovy (Engraulis encrasicolus) and sardine (Sardina pilchardus) from the Bay of Biscay, the English Channel and the southern North Sea between 2014 and 2017. Fish were sampled throughout various seasons thanks to the PELGAS (May, Bay of Biscay), EVHOE (October-November, Bay of Biscay), CGFS and CAMANOC (September-October, English Channel) surveys conducted by IFREMER on the RV “Thalassa”, to the JUVENA (September-October, Bay of Biscay) survey conducted by AZTI, and from commercial landings within the European Data Collection Framework (DCF) during the CAPTAIN project (France Filière Pêche). During the surveys, pelagic (PELGAS and JUVENA) or demersal (EVHOE and CGFS) trawl hauls are undertaken to identify species and measure individual fish traits. Professional sampling was performed from pelagic trawl or purse-seine catches. From the various surveys, a sub-sampling of the trawls was performed to cover as much as possible the spatial extent of the surveys along the french coast. From the various selected trawls, a sub-sampling of 5 fish per size class (when possible) was performed to cover the size range of each species, based on the following size classes : sardine (1 : <15 cm ; 2 : 15-20 cm ; 3 : >20cm), anchovy (1 : <10cm ; 2 : 10-14 cm ; 3 : >14cm). Each fish was individually measured to the nearest tenth of a centimeter and weighted to the nearest tenth of a gram. These measurements were taken either at sea or later in the laboratory. The collected fish were frozen individually at -20°C before laboratory processing. In the laboratory, maturity stages were determined following ICES guidelines (ICES, 2008) based on macroscopic gonads observation and using a six-stage key as follows: stages 1 & 2 indicate immature and developing individuals, stages 3–5 indicate three steps of increasing gonad development and the spawning period (stage 3: partial pre-spawning; stage 4: spawning (hydrated); stage 5: partial post-spawning), and stage 6 features the final post-spawning period. Fish characterised by maturity stages 3, 4 or 5 were considered as being in an active reproductive period as opposed to fish in stages 1, 2 or 6. Fish were then ground and freeze-dried during at least 48 hours. Water content of the entire fish was determined from dry mass and wet mass ratio. Then, fish were ground again to obtain fine homogeneous dry powder for subsequent analysis. Energy density measurements were performed following the protocols of Gatti et al. (2018). Two subsamples of fish powder were placed in an adiabatic bomb calorimeter (IKA C-4000 adiabatic bomb calorimeter, IKA-WerkeGmbh & co. KG) for energy measurements. The energy density (ED, kJ.g-1 dry mass) was determined by measuring the heat released through the combustion of a small subsample, approximately 200 mg. If the coefficient of variation between the two measurements exceeded 3%, a third measurement was made. Finally, ED subsamples measurements were averaged and assigned to each individual fish. Energy density analyses were conducted on 503 individuals for anchovy and 976 individuals for sardine. Ash content was determined gravimetrically by combusting dried tissue in a muffle furnace at 550°C for six hours. Lipids and proteins were analysed by a certified laboratory (Labocea, Plouzané, France). Protein content was estimated using the Kjeldahl method. It consists in first determining the quantity of nitrogen contained in the sample, and to convert it into protein content using a conversion factor (6.25 here). Lipid content was determined by hydrolysis, using petroleum ether as an organic solvent. Carbohydrates represent less than 1% of fish mass and were thus neglected. Protein, lipid and ash content did not exactly sum to 1 in DW (anchovy: mean = 0.91, sd = 0.04; sardine: mean = 0.90, sd = 0.04). This discrepancy may arise from residual water, measurement uncertainties, or to a lesser extent the exclusion of carbohydrates. Body component contents have been normalised by dividing each component by the sum of lipids, proteins and ash content, to sum to one, enabling comparisons between fishes, assuming proportional errors across the components. A total of 116 and 104 proximate composition analyses were performed for anchovy and sardine, respectively.

This dataset contains the biological 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.

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).

The Arcachon bay is a meso- / macro-tidal (0.8 to 4.6 m), semi-enclosed lagoon of 180 km² located on the South-western coast of France. Three main water masses are described in this bay: (i) the external neritic waters (ENW) directly influenced by the adjacent oceanic waters, (ii) the intermediate neritic waters (ItNW) and (iii) the inner neritic waters (InNW) more influenced by the continental inputs. The watershed of the Arcachon bay, mainly covered by forests, has an area of 3500 km² and the bay is considered as poorly anthropised. It hosts the largest Zostera noltei seagrass meadow in western Europe and is an important site for oyster farming and Manilla clam production. Since 1997, Arcachon Bay waters are monitored for hydrological and bio-geochemical parameters by the “Environnements et Paléoenvironnements Océaniques et Continentaux” (EPOC) Research Unit of the University of Bordeaux-CNRS, first in one single station (Eyrac), then on 2 complementary sites since 2005 (Bouee13 and Comprian). The monitoring is carried out within the national framework of the “SOMLIT” (“Service d’Observation en Milieu Littoral”) which is a French multi-site monitoring network initiated in the mid-1990s. SOMLIT is based on a joint strategy for 19 sites belonging to 12 ecosystems that are distributed over the three maritime facades of mainland France, i.e. the English Channel, the Atlantic Ocean and the Mediterranean Sea. Sampling of surface water samples is performed fortnightly at high tide for a group of 15 parameters (temperature, salinity, dissolved oxygen, pH, nitrate, nitrite, ammonium, phosphate, silicate, suspended matter, chlorophyll a, concentrations and isotopic ratios of particulate organic carbon and nitrogen) and 8 flow cytometry biological variables of pico- and nanoplankton. Vertical profiles of multiparametric probes concerning 4 parameters (temperature, salinity, fluorescence, PAR) are also performed. Given the significant diversity of coastal ecosystems where SOMLIT’s stations are located, strict and joint guidelines with regards to sampling strategy, measurement methods and data qualification and storage are paramount in order to make FAIR data available to users. The whole data acquisition strategy is carried out within the framework of the SOMLIT quality system formalized in 2006-2007 by referring to the ISO 17025: 2017 standard “General requirements for the competence of testing and calibration laboratories”. Unified sampling and analysis protocols are based on recognized disciplinary standards and on the expertise of the research teams. The scientific objectives of SOMLIT are 1) to characterize the multi-decadal evolution of coastal ecosystems; 2) to determine the climatic and anthropogenic forcings and 3) to make data and logistical support available for research activities and other observation activities. SOMLIT is therefore a research tool providing large datasets that also serve as logistical support for related research actions (from seasonal to long-term studies). Two additional national networks operate at the same SOMLIT sites: “COAST-HF” network performs high-frequency measurements (automated in situ measurements every 10 to 20 minutes) and “PHYTOBS-network” provides microphytoplankton biodiversity data. SOMLIT, COAST-HF and PHYTOBS are elementary networks of the Research Infrastructure “Infrastructure Littorale et Côtière” (ILICO) and are National Observation Services (SNO) of the Institut National des Sciences de l'Univers (INSU).