Moving 6-year analysis of Water body dissolved inorganic nitrogen 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 1971/1976 until 2016/2021. Observation data span from 1971 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 400km 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.

'''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

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 Northeast Atlantic Ocean (40W). Data were harmonised and validated by the '‘IFREMER / IDM / SISMER - Scientific Information Systems for the SEA’ in France. The dataset contains water (profiles), sediment (profiles and timeseries) and biota (timeseries). The temporal coverage is 1974–2018 for water measurements, 1966–2014 for sediment measurements and 1979–2021 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).

'''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

Moving 6-year analysis of Water body chlorophyll-a in the Mediterranean Sea 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 centered average of the season. 6-years periods span from 1990-1995 until 2016-2021. Data Sources: observational data from SeaDataNet/EMODNet Chemistry Data Network. Units: mg/m3. Description of DIVA analysis: The computation was done with the DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.9, using GEBCO 30sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps grids is dx=dy=0.125 degrees (around 13.5km and 10.9km accordingly). The vertical resolution is 20 depth levels: [0.,5.,10.,20.,30.,50.,75.,100.,125.,150.,200.,250.,300.,400.,500.,600.,700.,800.,900.,1000.]. The horizontal correlation length is 200km. The vertical correlation length (in meters) was set twices the vertical resolution: [10.,10.,20.,20.,40.,50.,50.,50.,50.,100.,100.,100.,200.,200.,200.,200.,200.,200.,200.,200.]. Duplicates check was performed using the following criteria for space and time: dlon=0.001deg., dlat=0.001deg., ddepth=1m, dtime=1hour, dvalue=0.1. The error variance (epsilon2) was set equal to 1 for profiles and 10 for time series to reduce the influence of close data near the coasts. An anamorphosis transformation was applied to the data (function DIVAnd.Anam.loglin) to avoid unrealistic negative values: threshold value=200. A background analysis field was used for all years (1990-2021) with correlation length equal to 600km and error variance (epsilon2) equal to 20. Quality control of the observations was applied using the interpolated field (QCMETHOD=3). Residuals (differences between the observations and the analysis (interpolated linearly to the location of the observations) were calculated. Observations with residuals outside the minimum and maximum values of the 99% quantile were discarded from the analysis. Originators of Italian data sets-List of contributors: - Brunetti Fabio (OGS) - Cardin Vanessa, Bensi Manuel doi:10.6092/36728450-4296-4e6a-967d-d5b6da55f306 - Cardin Vanessa, Bensi Manuel, Ursella Laura, Siena Giuseppe doi:10.6092/f8e6d18e-f877-4aa5-a983-a03b06ccb987 - Cataletto Bruno (OGS) - Cinzia Comici Cinzia (OGS) - Civitarese Giuseppe (OGS) - DeVittor Cinzia (OGS) - Giani Michele (OGS) - Kovacevic Vedrana (OGS) - Mosetti Renzo (OGS) - Solidoro C.,Beran A.,Cataletto B.,Celussi M.,Cibic T.,Comici C.,Del Negro P.,De Vittor C.,Minocci M.,Monti M.,Fabbro C.,Falconi C.,Franzo A.,Libralato S.,Lipizer M.,Negussanti J.S.,Russel H.,Valli G., doi:10.6092/e5518899-b914-43b0-8139-023718aa63f5 - Celio Massimo (ARPA FVG) - Malaguti Antonella (ENEA) - Fonda Umani Serena (UNITS) - Bignami Francesco (ISAC/CNR) - Boldrini Alfredo (ISMAR/CNR) - Marini Mauro (ISMAR/CNR) - Miserocchi Stefano (ISMAR/CNR) - Zaccone Renata (IAMC/CNR) - Lavezza, R., Dubroca, L. F. C., Ludicone, D., Kress, N., Herut, B., Civitarese, G., Cruzado, A., Lefèvre, D.,Souvermezoglou, E., Yilmaz, A., Tugrul, S., and Ribera d'Alcala, M.: Compilation of quality controlled nutrient profiles from the Mediterranean Sea, doi:10.1594/PANGAEA.771907, 2011.

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.

Moving 6-year analysis of Water body silicate in the Mediterranean Sea 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 centered average of the season. 6-years periods span from 1970-1975 until 2017-2022. Data Sources: observational data from SeaDataNet/EMODNet Chemistry Data Network. Units: umol/l. Description of DIVA analysis: The computation was done with the DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.9, using GEBCO 30sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps grids is dx=dy=0.125 degrees (around 13.5km and 10.9km accordingly). The vertical resolution is 25 depth levels: [0.,5.,10.,20.,30.,50.,75.,100.,125.,150.,200.,250.,300.,400.,500.,600.,700.,800.,900.,1000.,1100.,1200.,1300.,1400.,1500.]. The horizontal correlation length is 200km. The vertical correlation length (in meters) was set twices the vertical resolution: [10.,10.,20.,20.,40.,50.,50.,50.,50.,100.,100.,100.,200.,200.,200.,200.,200.,200.,200.,200.,200.,200.,200.,200.,200.]. Duplicates check was performed using the following criteria for space and time: dlon=0.001deg., dlat=0.001deg., ddepth=1m, dtime=1hour, dvalue=0.1. The error variance (epsilon2) was set equal to 1 for profiles and 10 for time series to reduce the influence of close data near the coasts. An anamorphosis transformation was applied to the data (function DIVAnd.Anam.loglin) to avoid unrealistic negative values: threshold value=200. A background analysis field was used for all years (1970-2022) with correlation length equal to 600km and error variance (epsilon2) equal to 20. Quality control of the observations was applied using the interpolated field (QCMETHOD=3). Residuals (differences between the observations and the analysis (interpolated linearly to the location of the observations) were calculated. Observations with residuals outside the minimum and maximum values of the 99% quantile were discarded from the analysis. Originators of Italian data sets-List of contributors: - Brunetti Fabio (OGS) - Cardin Vanessa, Bensi Manuel doi:10.6092/36728450-4296-4e6a-967d-d5b6da55f306 - Cardin Vanessa, Bensi Manuel, Ursella Laura, Siena Giuseppe doi:10.6092/f8e6d18e-f877-4aa5-a983-a03b06ccb987 - Cataletto Bruno (OGS) - Cinzia Comici Cinzia (OGS) - Civitarese Giuseppe (OGS) - DeVittor Cinzia (OGS) - Giani Michele (OGS) - Kovacevic Vedrana (OGS) - Mosetti Renzo (OGS) - Solidoro C.,Beran A.,Cataletto B.,Celussi M.,Cibic T.,Comici C.,Del Negro P.,De Vittor C.,Minocci M.,Monti M.,Fabbro C.,Falconi C.,Franzo A.,Libralato S.,Lipizer M.,Negussanti J.S.,Russel H.,Valli G., doi:10.6092/e5518899-b914-43b0-8139-023718aa63f5 - Celio Massimo (ARPA FVG) - Malaguti Antonella (ENEA) - Fonda Umani Serena (UNITS) - Bignami Francesco (ISAC/CNR) - Boldrini Alfredo (ISMAR/CNR) - Marini Mauro (ISMAR/CNR) - Miserocchi Stefano (ISMAR/CNR) - Zaccone Renata (IAMC/CNR) - Lavezza, R., Dubroca, L. F. C., Ludicone, D., Kress, N., Herut, B., Civitarese, G., Cruzado, A., Lefèvre, D.,Souvermezoglou, E., Yilmaz, A., Tugrul, S., and Ribera d'Alcala, M.: Compilation of quality controlled nutrient profiles from the Mediterranean Sea, doi:10.1594/PANGAEA.771907, 2011.

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.

Moving 6-year analysis of Water body chlorophyll-a 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 1971/1976 until 2016/2021. Observation data span from 1971 to 2021. High-frequency observation trails were filtered to a 3h temporal resolution. 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]. 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 200km 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: mg/m3.

Classification of the Atlantic Ocean seabed into broad-scale benthic habitats employing a hierarchical top-down clustering approach aimed at informing Marine Spatial Planning. This work was performed at the University of Plymouth in 2021 with data provided by a wide group of partners representing the nations surrounding the Atlantic Ocean. It classifies continuous environmental data into discrete classes that can be compared to observed biogeographical patterns at various scales. It has 3 levels of classification. For ease of use, a layer is provided for each level. Level 1 has 4 classes. Level 2 has 15 classes nested within level 1. Layers indices are 2 digits (1[level1 class index]1[level 2 class index]). Level 3 has 157 classes nested within level 2 and class names have 4 digits (1digit[level1 class index]1[level 2 class index]2[level 3 class index]). Note that the classification was performed for the whole world and thus it has more classes than in the presented layer.