Water body phosphate - Monthly Climatology for the European Seas for the period 1960-2020 on the domain from longitude -45.0 to 70.0 degrees East and latitude 24.0 to 83.0 degrees North. Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. 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. Horizontal correlation length and vertical correlation length vary spatially depending on the topography and domain. Depth range: 0.0, 5.0, 10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 55.0, 60.0, 65.0, 70.0, 75.0, 80.0, 85.0, 90.0, 95.0, 100.0, 125.0, 150.0, 175.0, 200.0, 225.0, 250.0, 275.0, 300.0, 325.0, 350.0, 375.0, 400.0, 425.0, 450.0, 475.0, 500.0, 550.0, 600.0, 650.0, 700.0, 750.0, 800.0, 850.0, 900.0, 950.0, 1000.0, 1050.0, 1100.0, 1150.0, 1200.0, 1250.0, 1300.0, 1350.0, 1400.0, 1450.0, 1500.0, 1550.0, 1600.0, 1650.0, 1700.0, 1750.0, 1800.0, 1850.0, 1900.0, 1950.0, 2000.0, 2100.0, 2200.0, 2300.0, 2400.0, 2500.0, 2600.0, 2700.0, 2800.0, 2900.0, 3000.0, 3100.0, 3200.0, 3300.0, 3400.0, 3500.0, 3600.0, 3700.0, 3800.0, 3900.0, 4000.0, 4100.0, 4200.0, 4300.0, 4400.0, 4500.0, 4600.0, 4700.0, 4800.0, 4900.0, 5000.0, 5100.0, 5200.0, 5300.0, 5400.0, 5500.0 m. Units: umol/l. The horizontal resolution of the produced DIVAnd analysis is 0.25 degrees.

Moving 6-year analysis and visualization of Water body chlorophyll-a 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.

This dataset contains bio-optical measurements and environmental parameters associated with  Deep Chlorophyll Maxima acquired by BGC-Argo profiling floats and matched with mesoscale eddies location from the Ocean Eddy Detection and Tracking Algorithms (TOEeddies) Atlas. For each BGC-Argo profile the data files includes the World Meteorological Organistion (WMO) and profile numbers, geographical position (LON and LAT), the date of the profile in Julian Day (JULD); the qualification of the vertical profile (CARAC_BIO) as Deep Biomass Maximum (DBM), Deep photoAcclimation Maximum (DAM), or presenting no DCM (NO); at the depth of the maximum (DCM_DEPTH), the chlorophyll a concentration (CHLA_DCM, mg chla m-3 ) and the backscattering coefficient (BBP_DCM, m-1); the Mixed Layer Depth (MLD, m), the nitracline depth (NCLINE, m), the mean daily Available PAR in the Mixed Layer Depth (MIPAR_MLD, E m -1 d -1), the daily Available PAR at the nitracline depth (IPAR_NCLINE, E m-2 d-1); the location of the profile (CARAC_EDDY) as being inside the core/periphery (IN/_EN) of a cyclonic/anticyclonic eddy (DEP_/P_), or outside eddy influence (OUT); the processing level (MODE) of the ADT maps used for the TOEddies detection, either Near Real Time (-1), or Delayed Mode (1). The qualification and processing of the BGC-Argo profiles, as well as the DCM detection (DAM/DBM/NO) and the estimation of the environmental parameters, were applied as described from Cornec, M., Claustre, H., Mignot, A., Guidi, L., Lacour, L., Poteau, A., D'Ortenzio, F., Gentili, B., Schmechtig, C.  (2021). Deep chlorophyll maxima in the global ocean: Occurrences, drivers and characteristics. Global Biogeochem Cycles 35. https://doi.org/10.1029/2020GB006759 Data relative to mesoscale eddies were produced by processing daily 0.25°x0.25° AVISO Absolute Dynamical Topography (maps produced by Ssalto/Duacs and distributed by Copernicus-Marine Environment Services) with the TOEddies algorithm (Laxenaire, R., Speich, S., Blanke, B., Chaigneau, A., Pegliasco, C., & Stegner, A. (2018). Anticyclonic Eddies Connecting the Western Boundaries of Indian and Atlantic Oceans. Journal of Geophysical Research: Oceans, 123(11), 7651–7677. https://doi.org/10.1029/2018JC014270)

Input data, TMB (C++) and R code for close-kin mark recapture (CKMR) abundance estimation for two subpopulations of thornback ray (Raja clavata) in the central Bay of Biscay (offshore and Gironde estuary, see figure). Samples were taken between 2015 to 2020. Parent-offspring pairs were identified using SNP genotype information. Birth years of individuals were estimated using length information and growth curves by sex.

The anchovy (Engraulis encrasicolus) and sardine (Sardina pilchardus) populations in the Bay of Biscay are jointly surveyed each year in May since 2000 and in September since 2003 by means of acoustic surveys. The integrated survey PELGAS (Doray et al., 2018) is run in May by France and covers the French shelf of the Bay of Biscay. Its objectives are to monitor the Bay of Biscay pelagic ecosystem in springtime and assess the biomass of its small pelagic fish species, including sardine and anchovy. Amongst many information on the ecosystem, the survey PELGAS provides knowledge on the adults of anchovy and sardine during their spawning in spring. The survey JUVENA (Boyra et al., 2013) is run in September by Spain. It has a larger spatial coverage than PELGAS, including part of the Spanish coast and open ocean outside the shelf because it targets juvenile anchovy. It also provides knowledge on sardine as well as other pelagic species. Both surveys are coordinated by the ICES Working Group on Acoustic and Egg Surveys for Small Pelagic Fish (WGACEGG), together with other pelagic surveys in ICES areas 7, 8 and 9. Survey protocoles are detailed in Doray et al. (2021). Briefly, fish backscatter data are recorded along the survey transect lines and pelagic trawl hauls are undertaken opportunistically to identify the echotraces to species and collect fish samples for biometric data. The trawl haul catches have provided the anchovy and sardine length data, from which the maps presented here are derived. At each trawl haul, the catch is sorted by species and weighted. A subsample by species is measured to estimate the species’ length distribution. The four maps presented here correspond to the average maps of anchovy and sardine length distributions in May and September, derived from the PELGAS and JUVENA trawl haul data series. The maps were obtained by kriging, following the procedure explained in Petitgas et al. (2011) for mapping functions instead of variables. For each species, the experimental length distribution at each haul was fitted by a linear combination of Legendre polynomials, the coefficients of which were co-kriged. The number of polynomials varied from 15 to 22 depending on the survey and species, with a higher number for sardine and in autumn. The length histogram at each grid node was then deduced from the mapped coefficients. When the length distribution at a given haul was estimated with less than 40 individual fish, the haul was not taken into account for mapping. This threshold defined presence and absence of the species in the haul data sets. The trawl hauls from 2000 to 2019 were pooled for the PELGAS series (1965 stations) and from 2003 to 2020 for JUVENA (852 stations). The mapping was performed on the same grid for both PELGAS and JUVENA and both species, and with similar moving kriging neighbourhoods. The grid has a mesh size of 0.25 x 0.25 decimal degree square. In addition to mapping the length distribution, presence/absence was mapped for each species by ordinary kriging on the same grid and with the same neighbourhoods as previously. The computations were performed in R (version 4.0.5) with the RGeostats package (version 13.0.1) freely available at http://rgeostats.free.fr. The map data files comprise for each species the following information: the geographical coordinates of the grid points, the probability of presence and the probability of each length class of width 0.5 cm ranging from 2.5 to 27 cm.

This product displays for DDT, DDE, and DDD, 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 sea level ocean monitoring indicator has been presented in the Copernicus Ocean State Report #8. The ocean monitoring indicator on regional mean sea level is derived from the DUACS delayed-time (DT-2024 version, “my” (multi-year) dataset used when available) sea level anomaly maps from satellite altimetry based on a stable number of altimeters (two) in the satellite constellation. These products are distributed by the Copernicus Climate Change Service and the Copernicus Marine Service (SEALEVEL_GLO_PHY_CLIMATE_L4_MY_008_057). The time series of area averaged anomalies correspond to the area average of the maps in the Irish-Biscay-Iberian (IBI) Sea weighted by the cosine of the latitude (to consider the changing area in each grid with latitude) and by the proportion of ocean in each grid (to consider the coastal areas). The time series are corrected from regional mean GIA correction (weighted GIA mean of a 27 ensemble model following Spada et Melini, 2019). The time series are adjusted for seasonal annual and semi-annual signals and low-pass filtered at 6 months. Then, the trends/accelerations are estimated on the time series using ordinary least square fit.The trend uncertainty is provided in a 90% confidence interval. It is calculated as the weighted mean uncertainties in the region from Prandi et al., 2021. This estimate only considers errors related to the altimeter observation system (i.e., orbit determination errors, geophysical correction errors and inter-mission bias correction errors). The presence of the interannual signal can strongly influence the trend estimation considering to the altimeter period considered (Wang et al., 2021; Cazenave et al., 2014). The uncertainty linked to this effect is not considered. ""CONTEXT "" Change in mean sea level is an essential indicator of our evolving climate, as it reflects both the thermal expansion of the ocean in response to its warming and the increase in ocean mass due to the melting of ice sheets and glaciers (WCRP Global Sea Level Budget Group, 2018). At regional scale, sea level does not change homogenously. It is influenced by various other processes, with different spatial and temporal scales, such as local ocean dynamic, atmospheric forcing, Earth gravity and vertical land motion changes (IPCC WGI, 2021). The adverse effects of floods, storms and tropical cyclones, and the resulting losses and damage, have increased as a result of rising sea levels, increasing people and infrastructure vulnerability and food security risks, particularly in low-lying areas and island states (IPCC, 2022a). Adaptation and mitigation measures such as the restoration of mangroves and coastal wetlands, reduce the risks from sea level rise (IPCC, 2022b). In IBI region, the RMSL trend is modulated by decadal variations. As observed over the global ocean, the main actors of the long-term RMSL trend are associated with anthropogenic global/regional warming. Decadal variability is mainly linked to the strengthening or weakening of the Atlantic Meridional Overturning Circulation (AMOC) (e.g. Chafik et al., 2019). The latest is driven by the North Atlantic Oscillation (NAO) for decadal (20-30y) timescales (e.g. Delworth and Zeng, 2016). Along the European coast, the NAO also influences the along-slope winds dynamic which in return significantly contributes to the local sea level variability observed (Chafik et al., 2019). ""KEY FINDINGS "" Over the [1999/02/20 to 2025/10/18] period, the area-averaged sea level in the IBI area rises at a rate of 5.0 ± 0.8 mm/yr with an acceleration of 0.29 ± 0.06 mm/yr². This trend estimation is based on the altimeter measurements corrected from global GIA correction (Spada et Melini, 2019) to consider the ongoing movement of land. The TOPEX-A is no longer included in the computation of regional mean sea level parameters (trend and acceleration) with version 2024 products due to potential drifts, and ongoing work aims to develop a new empirical correction. Calculation begins in February 1999 (the start of the TOPEX-B period). '''DOI (product):''' https://doi.org/10.48670/moi-00252

Water body chlorophyll-a - Monthly Climatology for the European Seas for the period 1960-2020 on the domain from longitude -45.0 to 70.0 degrees East and latitude 24.0 to 83.0 degrees North. Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. 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. Horizontal correlation length and vertical correlation length vary spatially depending on the topography and domain. Depth range: 0.0, 5.0, 10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 55.0, 60.0, 65.0, 70.0, 75.0, 80.0, 85.0, 90.0, 95.0, 100.0, 125.0, 150.0, 175.0, 200.0, 225.0, 250.0, 275.0, 300.0, 325.0, 350.0, 375.0, 400.0, 425.0, 450.0, 475.0, 500.0, 550.0, 600.0, 650.0, 700.0, 750.0, 800.0, 850.0, 900.0, 950.0, 1000.0, 1050.0, 1100.0, 1150.0, 1200.0, 1250.0, 1300.0, 1350.0, 1400.0, 1450.0, 1500.0, 1550.0, 1600.0, 1650.0, 1700.0, 1750.0, 1800.0, 1850.0, 1900.0, 1950.0, 2000.0, 2100.0, 2200.0, 2300.0, 2400.0, 2500.0, 2600.0, 2700.0, 2800.0, 2900.0, 3000.0, 3100.0, 3200.0, 3300.0, 3400.0, 3500.0, 3600.0, 3700.0, 3800.0, 3900.0, 4000.0, 4100.0, 4200.0, 4300.0, 4400.0, 4500.0, 4600.0, 4700.0, 4800.0, 4900.0, 5000.0, 5100.0, 5200.0, 5300.0, 5400.0, 5500.0 m. Units: mg/m3. The horizontal resolution of the produced DIVAnd analysis is 0.25 degrees.

Water body dissolved inorganic nitrogen (DIN) - Monthly Climatology for the European Seas for the period 1960-2020 on the domain from longitude -45.0 to 70.0 degrees East and latitude 24.0 to 83.0 degrees North. Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. 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. Horizontal correlation length and vertical correlation length vary spatially depending on the topography and domain. Depth range: 0.0, 5.0, 10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 55.0, 60.0, 65.0, 70.0, 75.0, 80.0, 85.0, 90.0, 95.0, 100.0, 125.0, 150.0, 175.0, 200.0, 225.0, 250.0, 275.0, 300.0, 325.0, 350.0, 375.0, 400.0, 425.0, 450.0, 475.0, 500.0, 550.0, 600.0, 650.0, 700.0, 750.0, 800.0, 850.0, 900.0, 950.0, 1000.0, 1050.0, 1100.0, 1150.0, 1200.0, 1250.0, 1300.0, 1350.0, 1400.0, 1450.0, 1500.0, 1550.0, 1600.0, 1650.0, 1700.0, 1750.0, 1800.0, 1850.0, 1900.0, 1950.0, 2000.0, 2100.0, 2200.0, 2300.0, 2400.0, 2500.0, 2600.0, 2700.0, 2800.0, 2900.0, 3000.0, 3100.0, 3200.0, 3300.0, 3400.0, 3500.0, 3600.0, 3700.0, 3800.0, 3900.0, 4000.0, 4100.0, 4200.0, 4300.0, 4400.0, 4500.0, 4600.0, 4700.0, 4800.0, 4900.0, 5000.0, 5100.0, 5200.0, 5300.0, 5400.0, 5500.0 m. Units: umol/l. The horizontal resolution of the produced DIVAnd analysis is 0.25 degrees.

Moving 6-year analysis of Water body dissolved oxygen concentration 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 1971-1976 until 2017-2022. Data Sources: observational data from SeaDataNet/EMODNet Chemistry Data Network. 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 27 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.,1750.,2000.]. 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.,500.,500.,500.]. 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 (1971-2022) 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.