'''DEFINITION''' The OMI_EXTREME_WAVE_IBI_swh_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable significant wave height (swh) measured by in situ buoys. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The percentiles are temporally averaged, and the spatial evolution is displayed, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018). '''CONTEXT''' Projections on Climate Change foresee a future with a greater frequency of extreme sea states (Stott, 2016; Mitchell, 2006). The damages caused by severe wave storms can be considerable not only in infrastructure and buildings but also in the natural habitat, crops and ecosystems affected by erosion and flooding aggravated by the extreme wave heights. In addition, wave storms strongly hamper the maritime activities, especially in harbours. These extreme phenomena drive complex hydrodynamic processes, whose understanding is paramount for proper infrastructure management, design and maintenance (Goda, 2010). In recent years, there have been several studies searching possible trends in wave conditions focusing on both mean and extreme values of significant wave height using a multi-source approach with model reanalysis information with high variability in the time coverage, satellite altimeter records covering the last 30 years and in situ buoy measured data since the 1980s decade but with sparse information and gaps in the time series (e.g. Dodet et al., 2020; Timmermans et al., 2020; Young & Ribal, 2019). These studies highlight a remarkable interannual, seasonal and spatial variability of wave conditions and suggest that the possible observed trends are not clearly associated with anthropogenic forcing (Hochet et al. 2021, 2023). In the North Atlantic, the mean wave height shows some weak trends not very statistically significant. Young & Ribal (2019) found a mostly positive weak trend in the European Coasts while Timmermans et al. (2020) showed a weak negative trend in high latitudes, including the North Sea and even more intense in the Norwegian Sea. For extreme values, some authors have found a clearer positive trend in high percentiles (90th-99th) (Young, 2011; Young & Ribal, 2019). '''COPERNICUS MARINE SERVICE KEY FINDINGS''' The mean 99th percentiles showed in the area present a wide range from 2-3.5m in the Canary Island with 0.1-0.3 m of standard deviation (std), 3.5m in the Gulf of Cadiz with 0.5m of std, 3-6m in the English Channel and the Irish Sea with 0.5-0.6m of std, 4-7m in the Bay of Biscay with 0.4-0.9m of std to 8-10m in the West of the British Isles with 0.7-1.4m of std. Results for this year show slight negative anomalies in the Canary Island (-0.4/0.0m) and in the Gulf of Cadiz (-0.8m) barely out of the standard deviation range in both areas, slight positive or negative anomalies in the West of the British Isles (-0.6/+0.4m) and in the English Channel and the Irish Sea (-0.6/+0.3m) but inside the range of the standard deviation and a general positive anomaly in the Bay of Biscay reaching +1.0m but close to the limit of the standard deviation. '''DOI (product):''' https://doi.org/10.48670/moi-00250

This product displays for Naphthalene, 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.

Moving 6-year analysis and visualization of Water body silicate 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 2.7.9. 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.

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.

This visualization product displays the type of litter in percent per net 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. To calculate percentages for each type, formula applied is: Type (%) = (∑number of particles of each type)*100 / (∑number of particles of all type) When the number of microlitters was not filled or zero, the percentage could not be calculated. Standard vocabularies for microliter types are taken from Seadatanet's H01 library (https://vocab.seadatanet.org/v_bodc_vocab_v2/search.asp?lib=H01) 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 National Oceanographic Data Centre (NODC) for this area.

This product displays for Nickel, positions with percentages of all available data values per group of animals that are present in EMODnet regional contaminants aggregated datasets, v2022. The product displays positions for all available years.

'''DEFINITION''' The indicator of the Kuroshio extension phase variations is based on the standardized high frequency altimeter Eddy Kinetic Energy (EKE) averaged in the area 142-149°E and 32-37°N and computed from the DUACS delayed-time (CMEMS SEALEVEL_GLO_PHY_L4_MY_008_047) and near real-time (CMEMS SEALEVEL_GLO_PHY_L4_NRT _008_046) altimeter sea level gridded products. ""CONTEXT"" The Kuroshio Extension is an eastward-flowing current in the subtropical western North Pacific after the Kuroshio separates from the coast of Japan at 35°N, 140°E. Being the extension of a wind-driven western boundary current, the Kuroshio Extension is characterized by a strong variability and is rich in large-amplitude meanders and energetic eddies (Niiler et al., 2003; Qiu, 2003, 2002). The Kuroshio Extension region has the largest sea surface height variability on sub-annual and decadal time scales in the extratropical North Pacific Ocean (Jayne et al., 2009; Qiu and Chen, 2010, 2005). Prediction and monitoring of the path of the Kuroshio are of huge importance for local economies as the position of the Kuroshio extension strongly determines the regions where phytoplankton and hence fish are located. Unstable (contracted) phase of the Kuroshio enhance the production of Chlorophyll (Lin et al., 2014). ""CMEMS KEY FINDINGS"" The different states of the Kuroshio extension phase have been presented and validated by (Bessières et al., 2013) and further reported by Drévillon et al. (2018) in the Copernicus Ocean State Report #2. Two rather different states of the Kuroshio extension are observed: an ‘elongated state’ (also called ‘strong state’) corresponding to a narrow strong steady jet, and a ‘contracted state’ (also called ‘weak state’) in which the jet is weaker and more unsteady, spreading on a wider latitudinal band. When the Kuroshio Extension jet is in a contracted (elongated) state, the upstream Kuroshio Extension path tends to become more (less) variable and regional eddy kinetic energy level tends to be higher (lower). In between these two opposite phases, the Kuroshio extension jet has many intermediate states of transition and presents either progressively weakening or strengthening trends. In 2018, the indicator reveals an elongated state followed by a weakening neutral phase since then. '''DOI (product):''' https://doi.org/10.48670/moi-00222

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 in four files, the L2_LR_SSH ['WindWave'] is described in this metadata sheet. The 3 other file types (['Basic'], ['Expert'], ['Unsmoothed']) are described by 3 different metadata sheets that can be accessed via the links below. The ['WindWave'] file is intended for users interested in wind and wave information. The ['Basic'] file is intended for users who are interested in SSH measurements and who will use the KaRIn measurements as provided. The ['Expert'] file 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'] file, 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. The ['WindWave'] L2_LR_SSH includes measured significant wave height (SWH), normalized radar cross section (NRCS or backscatter cross section or sigma0), wind speed derived from sigma0 and SWH, wind and wave model information, and quality flags on a 2 km geographically fixed grid. 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.

This visualization product displays the type of litter in percent per net per year from specific protocols different 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 a very specific protocol such as the Volvo Ocean Race (VOR) or Oceaneye. To calculate percentages for each type, formula applied is: Type (%) = (∑number of particles of each type)*100 / (∑number of particles of all type) When the number of microlitters was not filled or zero, the percentage could not be calculated. Standard vocabularies for microliter types are taken from Seadatanet's H01 library (https://vocab.seadatanet.org/v_bodc_vocab_v2/search.asp?lib=H01) 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 National Oceanographic Data Centre (NODC) for this area.

'''This product has been archived''' This dataset provide a times series of gap free map of Sea Surface Temperature (SST) foundation at high resolution on a 0.10 x 0.10 degree grid (approximately 10 x 10 km) for the Global Ocean, every 24 hours. Whereas along swath observation data essentially represent the skin or sub-skin SST, the Level 4 SST product is defined to represent the SST foundation (SSTfnd). SSTfnd is defined within GHRSST as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. It is therefore updated at intervals of 24 hrs. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed. The processing combines the observations of multiple polar orbiting and geostationary satellites, embedding infrared of microwave radiometers. All these sources are intercalibrated with each other before merging. A ranking procedure is used to select the best sensor observation for each grid point. An optimal interpolation is used to fill in where observations are missing. '''DOI (product) :''' https://doi.org/10.48670/mds-00321