This product displays for Mercury, positions with values counts that have been measured per matrix for each year and are present in EMODnet regional contaminants aggregated datasets, v2022. The product displays positions for every available year.

'''Short description:''' For the Global Ocean - The product contains daily L3 gridded sea surface wind observations from available scatterometers with resolutions corresponding to the L2 swath products: *0.5 degrees grid for the 50 km scatterometer L2 inputs, *0.25 degrees grid based on 25 km scatterometer swath observations, *and 0.125 degrees based on 12.5 km scatterometer swath observations, i.e., from the coastal products. Data from ascending and descending passes are gridded separately. The product provides stress-equivalent wind and stress variables as well as their divergence and curl. The NRT L3 products follow the NRT availability of the EUMETSAT OSI SAF L2 products and are available for: *The ASCAT scatterometers on Metop-A (discontinued on 15/11/2021), Metop-B and Metop-C at 0.125 and 0.25 degrees; *The OSCAT scatterometer on Scatsat-1 (discontinued on 28/02/2021) and Oceansat-3 at 0.25 and 0.5 degrees; *The HSCAT scatterometer on HY-2B, HY-2C and HY-2D at 0.25 and 0.5 degrees In addition, the product includes European Centre for Medium-Range Weather Forecasts (ECMWF) operational model forecast wind and stress variables collocated with the scatterometer observations at L2 and processed to L3 in exactly the same way as the scatterometer observations. '''DOI (product) :''' https://doi.org/10.48670/moi-00182

This product displays positions symbolized per matrix, for all available contaminants measurements for each year present in EMODnet regional contaminants aggregated datasets, v2022. The product displays positions for every available year.

Species distribution models (GAM, Maxent and Random Forest ensemble) predicting the distribution of Syringammina fragilissima fields assemblage in the North East Atlantic. This community is considered ecologically coherent according to the cluster analysis conducted by Parry et al. (2015) on image sample. Modelling its distribution complements existing work on their definition and offers a representation of the extent of the areas of the North East Atlantic where they can occur based on the best available knowledge. This work was performed at the University of Plymouth in 2021.

'''DEFINITION''' The omi_climate_sst_ibi_trend product includes the Sea Surface Temperature (SST) trend for the Iberia-Biscay-Irish areas over the period 1982-2024, 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-CLIMATE-SST-IBI_v3.pdf), which provided the SSTs used to compute the SST trend over the Iberia-Biscay-Irish areas. This reprocessed product consists of daily (nighttime) interpolated 0.05° grid resolution SST maps built from re-processed ESA SST CCI, C3S (Embury et al., 2024). 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. The reference for this OMI can be found in the first and second issue of the Copernicus Marine Service Ocean State Report (OSR), Section 1.1 (Roquet et al., 2016; Mulet et al., 2018). '''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 0.012 ±0.001 °C/year over the period 1982-2024. '''DOI (product):''' https://doi.org/10.48670/moi-00257

The Level-2 Ka-band Radar Interferometer (KaRIn) low rate (LR, ocean) sea surface height (SSH) data product from the Surface Water and Ocean Topography (SWOT) mission, also referenced by the short name L2_LR_SSH, provides ocean topography measurements from the low rate ocean data stream of the KaRIn instrument, spanning 60 km on either side of the nadir altimeter with a nadir gap. The L2_LR_SSH product is available continuously and globally, although different versions of the product may be produced at different latencies and/or through different reprocessing with refined input data. Note that L2_LR_SSH does not include SSH data from the SWOT nadir altimeter. The SWOT L2_LR_SSH product is organized as a collection of four subproducts, the L2_LR_SSH ['Basic'] is described in this metadata sheet. The 3 other file types (['WindWave'], ['Expert'], ['Unsmoothed']) are described by 3 different metadata sheets that can be accessed through the links below. The ['Basic'] file on this metadata sheet, is intended for users who are interested in SSH measurements and who will use the KaRIn measurements as provided. The ['Basic'] L2_LR_SSH includes corrected sea surface height (SSH), sea surface height anomaly (SSHA), data quality flags, geophysical reference fields, and height correction information on a 2-km geographically fixed grid. The ['WindWave'] L2_LR_SSH is intended for users interested in wind and wave information. The ['Expert'] L2_LR_SSH is intended for expert users who are interested in the details of how the KaRIn measurements were derived and who may use detailed information for their own custom processing. The ['Unsmoothed'] L2_LR_SSH, also intended for expert users, is provided on a finer 'native' grid of 250-m (with minimal smoothing applied), and has a significantly larger data volume than the other files. May 2025: v3.0 (version D) Production and distribution of the L2_LR_SSH version D products: - PID0 for forward-processed version D products: from May 6, 2025 onward, - PGD0 for reprocessed version D products: from March 30 to July 10, 2023 (phase CalVal) and from July 26, 2023 to May 19, 2025 (phase Science) is ongoing. August 2024: v2.0 (version D) L2_LR_SSH version C products declared as validated by the SWOT project. March 2024: v2.0 (version C) Production and distribution of the pre-validated L2_LR_SSH version C products: - PIC0 for forward-processed version C products: November 23, 2023 to present, - PGC0 for reprocessed version C products: from March 30 to July 10, 2023 (phase CalVal) and from July 26, 2023 to January 25, 2024 (phase Science) November 2023: v1.0 The beta pre-validated L2_LR_SSH version 1.0 product (summer 2023 reprocessing release) is available only for the 1-day CalVal orbit phase, from March 29 to July 10, 2023, and the 21-day Science orbit phase from September 7 to November 21, 2023.

'''DEFINITION''' OMI_CLIMATE_SST_BAL_area_averaged_anomalies product includes time series of monthly mean SST anomalies over the period 1982-2024, relative to the 1991-2020 climatology, averaged for the Baltic Sea. The SST Level 4 analysis products that provide the input to the monthly averages are taken from the reprocessed product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 with a recent update to include 2023. The product has a spatial resolution of 0.02 in latitude and longitude. The OMI time series runs from Jan 1, 1982 to December 31, 2024 and is constructed by calculating monthly averages from the daily level 4 SST analysis fields of the SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 product . The climatology period from 1991 to 2020 (30 years) is selected according to WMO recommendations (WMO, 2017) and the most recent practice from the U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the OMI product. '''CONTEXT''' Sea Surface Temperature (SST) is an Essential Climate Variable (GCOS) that is an important input for initialising numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change (GCOS 2010). The Baltic Sea is a region that requires special attention regarding the use of satellite SST records and the assessment of climatic variability (Høyer and She 2007; Høyer and Karagali 2016). The Baltic Sea is a semi-enclosed basin with natural variability and it is influenced by large-scale atmospheric processes and by the vicinity of land. In addition, the Baltic Sea is one of the largest brackish seas in the world. When analysing regional-scale climate variability, all these effects have to be considered, which requires dedicated regional and validated SST products. Satellite observations have previously been used to analyse the climatic SST signals in the North Sea and Baltic Sea (BACC II Author Team 2015; Lehmann et al. 2011). Recently, Høyer and Karagali (2016) demonstrated that the Baltic Sea had warmed 1-2 oC from 1982 to 2012 considering all months of the year and 3-5 °C when only July-September months were considered. This was corroborated in the Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018). '''CMEMS KEY FINDINGS''' The basin-average trend of SST anomalies for Baltic Sea region amounts to 0.039±0.003°C/year over the period 1982-2024 which corresponds to an average warming of 1.68°C. Adding the North Sea area, the average trend amounts to 0.026±0.002°C/year over the same period, which corresponds to an average warming of 1.19°C for the entire region since 1982. '''DOI (product):''' https://doi.org/10.48670/moi-00205

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 processing 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 and EU Threshold Value for Macro Litter on Coastlines from JRC (these two 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 doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.

'''Short description:''' For the Global Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05° resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive . The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. '''DOI (product) :''' https://doi.org/10.48670/mds-00329

The Pélagiques Gascogne (PELGAS, Doray et al., 2000) integrated survey aims at assessing the biomass of small pelagic fish and monitoring and studying the dynamics and diversity of the Bay of Biscay pelagic ecosystem in springtime. PELGAS has been conducted within the EU Common Fisheries Policy Data Collection Framework and Ifremer’s Fisheries Information System. The PELGAS survey model has allowed for the establishment of a long-term time-series of spatially-explicit data of the Bay of Biscay pelagic ecosystem since the year 2000. Main sampled components of the targeted ecosystem are: hydrology, phytoplankton, mesozooplankton, fish and megafauna (cetacean and seabirds). This dataset presents gridded maps of standard pelagic ecosystem parameters collected in the main sampled components during the PELGAS survey. Ecosystem parameters were mapped on a 15km x 15km grid by applying a block averaging procedure (Petitgas et al., 2009, 2014). The dataset also includes the ecologically meaningful survey dates proposed by Huret et al. (2017), mapped on the same grid. Details on survey protocols and data processing methodologies can be found in Doray et al., (2014, 2017a). This dataset was used in Authier et al., 2017; Doray et al., 2017b, 2017c, 2017a; Huret et al., 2017; Petitgas et al., 2017.