'''Short description:''' This product consists of vertical profiles of the concentration of nutrients (nitrates, phosphates, and silicates) and carbonate system variables (total alkalinity, dissolved inorganic carbon, pH, and partial pressure of carbon dioxide), computed for each Argo float equipped with an oxygen sensor. The method called CANYON is based on a neural network trained using nutrient data (GLODAPv2 database) '''DOI (product) :''' https://doi.org/10.48670/moi-00048

This product displays for Hexachlorobenzene, median values of the last 6 available years that have been measured per matrix and are present in EMODnet regional contaminants aggregated datasets, v2022. The median values ranges are derived from the following percentiles: 0-25%, 25-75%, 75-90%, >90%. Only "good data" are used, namely data with Quality Flag=1, 2, 6, Q (SeaDataNet Quality Flag schema). For water, only surface values are used (0-15 m), for sediment and biota data at all depths are used.

'''DEFINITION''' The OMI_EXTREME_SL_NORTHWESTSHELF_slev_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 sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_northwestshelf_slev_mean_and_anomaly_obs, 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''' Sea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990’s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one metre by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. The North West Shelf area presents positive sea level trends with higher trend estimates in the German Bight and around Denmark, and lower trends around the southern part of Great Britain (Dettmering et al., 2021). '''COPERNICUS MARINE SERVICE KEY FINDINGS''' The completeness index criteria is fulfilled by 33 stations in 2023, one less than in 2022 (32). The mean 99th percentiles present a large spatial variability related to the tidal pattern, with largest values found in East England and at the entrance of the English channel, and lowest values along the Danish and Swedish coasts, ranging from the 3.08 m above mean sea level in Immingan (East England) to 0.45 m above mean sea level in Tregde (Norway). The standard deviation of annual 99th percentiles ranges between 2-3 cm in the western part of the region (e.g.: 2 cm in Harwich, 3 cm in Dunkerke) and 7-8 cm in the eastern part and the Kattegat (e.g. 8 cm in Stenungsund, Sweden). The 99th percentile anomalies for 2023 show overall slightly negative values except in the Kattegat (Eastern part), with maximum significant values of +11 cm in Hornbaek (Denmark), and +10 cm in Ringhals (Sweden). '''DOI (product):''' https://doi.org/10.48670/moi-00272

Species distribution models (Random Forest) predicting the distribution of mixed cold-water coral community (Coral Garden) assemblage in the Celtic Sea. 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.

Moving 6-year analysis of Water body silicate 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 1950/1955 until 2016/2021. Observation data span from 1950 to 2021. 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. Descrption of DIVAnd analysis: the computation was done with DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.4, 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 300km 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.Anam.loglin) was applied to the data prior to the analysis to avoid unrealistic negative values. 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.

This visualization product displays the total abundance of marine macro-litter (> 2.5cm) per beach, per 100m & to 1 survey aggregated over the period 2001 to 2020 from Marine Strategy Framework Directive (MSFD) monitoring surveys. 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 MSFD surveys only (exclusion of other 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 (total abundance list). 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); - Normalization of survey lengths to 100m & 1 survey / year: in some cases, 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 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, a median is calculated over the entire period among all these total numbers of litter per 100m calculated for each 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. The size of each circle on this map increases with the calculated median number of marine litter per beach, per 100m & to 1 survey. The median litter abundance values displayed in the legend correspond to the 50 and 99 percentiles and the maximum value. 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. - This map was created to give an idea of the distribution of beach litter between 2001 and 2021 in a synthetic manner. NOT ALL BEACHES MAY HAVE DATA FOR THE ENTIRE PERIOD, SO IT IS NOT POSSIBLE TO MAKE A COMPARISON BETWEEN BEACHES.

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 Northeast Atlantic Ocean (40W). Data were aggregated and quality controlled by 'IFREMER / IDM / SISMER - Scientific Information Systems for the SEA' in France. 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).

The data sets presented here result from the long-term monitoring of individual growth patterns in anchovy and sardine in the Bay of Biscay, from 2000 to 2018. They derived from the PELGAS survey series (Doray et al., 2018), which monitors annually the Bay of Biscay pelagic ecosystem since 2000. The survey is performed in May during the peak spawning of anchovy and main spawning of sardine. Among the many data collected, anchovy and sardine populations are assessed by combining acoustic records with pelagic trawl hauls catches and ICES survey protocoles are used, as detailed in Doray et al. (2021). Briefly, fish acoustic backscatter are recorded along survey transect lines and pelagic trawl hauls undertaken opportunistically to identify echotraces to species and collect fish samples for acquiring biometric data. At each trawl haul and for each species, a random subsample of individuals is taken to establish the species’ length distributions. For anchovy and sardine, this subsample is further subsampled, spanning the whole length range, to take individual fish measurements. These amount to extracting otoliths and measuring individuals’ age, length, weight, sexual maturity and other parameters. Individual measurements are taken on fourty individuals of anchovy and sardine when the species are present in the catch. For each individual fish, the two otolith sagittae are extracted on board, mounted in leukit for age reading on board when permitting and/or on land in the laboratory. Growth patterns in the otoliths were analysed on land with a binocular stereomicroscope under reflected natural light. For anchovy, otoliths’ growth was measured for all individuals in all the hauls. For sardine, trawl hauls were selected and all individual otoliths were measured in each selected haul. The selection was made using the geographical stratification defined in Petitgas et al. (2018) based on the ecosystem spatial structure. An average of two to three hauls in each of the four strata were selected per year. The otoliths mounted in leukit were imaged and growth-at-age in the otoliths was measured with the software TNPC (Traitement numérique des pièces calcifiées: Mahé et al., 2009). Under the binocular microscope and natural light, the otoliths showed hyaline (aragonite-poor) rings corresponding to winter periods of low growth and between the rings, white opaque (aragonite-rich) portions corresponding to annual growth periods. The annual ring determination, the age assignment and the measurement of annual ring diameters followed ICES protocoles and guidelines for anchovy and sardine (ICES, 2010; 2011). The age was estimated as the number of hyaline rings. If the edge was hyaline, it was counted as a ring as a hyaline edge observed within the first half of the year is assumed to represent the last winter. The diameter of each annual ring was measured from middle of the hyaline ring on one side to the middle of the ring on the opposite side along the major elongated axis of the otolith and passing through its centre. The distance was expressed in mm after calibration of the stereomiscroscope and the pixel images. Such diameter corresponded to growth-at-age. Ages 0 to 4 were considered (diameters R1 to R5). The total diameter of the otolith was also measured. The data sets span 19 years, from 2000 to 2018 and comprise 20,186 and 8,624 individual fish analyzed at 535 and 235 trawl hauls for anchovy and sardine, respectively. These data sets were used by Boëns et al. (2021 and 2023) to analyse changes in growth patterns and growth-selective mortality at age in anchovy and sardine under environmental and fishing pressures. References: Doray, M., Boyra, G. and Van Der Kooij, J. (eds) (2021). ICES Survey Protocols – Manual for acoustic surveys coordinated under ICES Working Group on Acoustic and Egg Surveys for Small Pelagic Fish (WGACEGG). 1st Edition. ICES Techniques in Marine Environmental Sciences, 64. https://doi.org/10.17895/ices.pub.7462  Doray, M., Petitgas, P., Romagnan, J.-B., Huret, M., Duhamel, E., Dupuy, Ch., Spitz, J., Authier, M., Sanchez, F., Berger, L., Doremus, G., Bourriau, P., Grellier, P. and Masse, J. (2018). The PELGAS survey: ship-based integrated monitoring of the Bay of Biscay pelagic ecosystem. Progress In Oceanography, 166, 15-29. https://doi.org/10.1016/j.pocean.2017.09.015 ICES (2010). Report of the Workshop on Age reading of European anchovy (WKARA), 9-13 November 2009, Sicily, Italy. ICES CM 2009/ACOM: 43. 122 pp. https://doi.org/10.17895/ices.pub.19280525 ICES (2011). Report of the Workshop on Age Reading of European Atlantic Sardine (WKARAS), 14-18 February 2011, Lisbon, Portugal. ICES CM 2011/ACOM:42. 91 pp. https://doi.org/10.17895/ices.pub.19280855 Petitgas, P., Huret, M., Dupuy, Ch., Spitz, J., Authier, M., Romagnan, J.-B. and Doray, M. (2018). Ecosystem spatial structure revealed by integrated survey data. Progress In Oceanography, 166, 189-198. https://doi.org/10.1016/j.pocean.2017.09.012 Mahe, K., Bellail, R., Dufour, J.-L., Boiron-Leroy, A., Dimeet, J., Duhamel, E., Elleboode, R., Felix, J., Grellier, P., Huet, J., Labastie, J., Le Roy, D., Lizaud, O., Manten, M.-L., Martin, S., Metral, L., Nedelec, D., Verin, Y. and Badts, V. (2009). Synthèse française des procédures d'estimation d'âge / French summary of age estimation procedures. https://archimer.ifremer.fr/doc/00000/7294/ Boëns, A., Grellier, P., Lebigre, Ch. and Petitgas, P. (2021). Determinants of growth and selective mortality in anchovy and sardine in the Bay of Biscay. Fisheries Research, 239, 105947. https://doi.org/10.1016/j.fishres.2021.105947 Boëns, A., Ernande, B., Petitgas, P. and Lebigre, Ch. (2023). Different mechanisms underpin the decline in growth of anchovies and sardines of the Bay of Biscay. Evolutionary Applications, 16: 1393–1411. https://doi.org/10.1111/eva.13564  

'''DEFINITION''' The OMI_EXTREME_SL_IBI_slev_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 sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_ibi_slev_mean_and_anomaly_obs, 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''' Sea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990’s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one meter by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. The Iberian Biscay Ireland region shows positive sea level trend modulated by decadal-to-multidecadal variations driven by ocean dynamics and superposed to the long-term trend (Chafik et al., 2019). '''COPERNICUS MARINE SERVICE KEY FINDINGS''' The completeness index criteria is fulfilled by 62 stations in 2023, five more than those available in 2022 (57), recently added to the multi-year product INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053. The mean 99th percentiles reflect the great tide spatial variability around the UK and the north of France. Minimum values are observed in the Irish eastern coast (e.g.: 0.66 m above mean sea level in Arklow Harbour) and the Canary Islands (e.g.: 0.93 and 0.96 m above mean sea level in Gomera and Hierro, respectively). Maximum values are observed in the Bristol Channel (e.g.: 6.25 and 5.78 m above mean sea level in Newport and Hinkley, respectively), and in the English Channel (e.g.: 5.16 m above mean sea level in St. Helier). The annual 99th percentiles standard deviation reflects the south-north increase of storminess, ranging between 1-3 cm in the Canary Islands to 15 cm in Hinkley (Bristol Channel). Negative or close to zero anomalies of 2023 99th percentile prevail throughout the region this year, reaching < -20 cm in several stations of the UK western coast and the English Channel (e.g.: -22 cm in Newport; -21 cm in St.Helier). Significantly positive anomaly of 2023 99th percentile is only found in Arcklow Harbour, in the eastern Irish coast. '''DOI (product):''' https://doi.org/10.48670/moi-00253

Species distribution models (GAM, Maxent and Random Forest ensemble) predicting the distribution of Solitary Scleractinian fields assemblage in the Celtic Sea. 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.