EMODnet (European Marine Observation and Data Network) is the long term marine data initiative supported by the European Commission since 2009 to ensure that European marine data will become easily accessible, interoperable, and free on restrictions on use. EMODnet Chemistry provides access to standardized, harmonized and validated chemical data collections for water quality evaluation at a regional scale, as defined by the Marine Strategy Framework Directive (MSFD). The data portal has adopted and adapted SeaDataNet standards and services, establishing interoperability between the data sets from the many different providers (more than 60 in EMODnet Chemistry network). Concentration maps of nutrients, chlorophyll-a and dissolved oxygen are computed on a standard grid, providing information at a regular time interval, per season and over several vertical layers, including the deepest one. Dedicated OGC standard services for browsing, viewing and downloading chemistry observation, data and data products for the European waters have been developed, and are actively maintained and monitored.

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''DEFINITION''' The time series are derived from the regional chlorophyll reprocessed (REP) product as distributed by CMEMS. This dataset, derived from multi-sensor (SeaStar-SeaWiFS, AQUA-MODIS, NOAA20-VIIRS, NPP-VIIRS, Envisat-MERIS and Sentinel3A-OLCI) Rrs spectra produced by CNR using an in-house processing chain, is obtained by means of the Mediterranean Ocean Colour regional algorithms: an updated version of the MedOC4 (Case 1 (off-shore) waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 (coastal) waters, Berthon and Zibordi, 2004). The processing chain and the techniques used for algorithms merging are detailed in Colella et al. (2021). Monthly regional mean values are calculated by performing the average of 2D monthly mean (weighted by pixel area) over the region of interest. The deseasonalized time series is obtained by applying the X-11 seasonal adjustment methodology on the original time series as described in Colella et al. (2016), and then the Mann-Kendall test (Mann, 1945; Kendall, 1975) and Sens’s method (Sen, 1968) are subsequently applied to obtain the magnitude of trend. '''CONTEXT''' Phytoplankton and chlorophyll concentration as a proxy for phytoplankton respond rapidly to changes in environmental conditions, such as light, temperature, nutrients and mixing (Colella et al. 2016). The character of the response depends on the nature of the change drivers, and ranges from seasonal cycles to decadal oscillations (Basterretxea et al. 2018). Therefore, it is of critical importance to monitor chlorophyll concentration at multiple temporal and spatial scales, in order to be able to separate potential long-term climate signals from natural variability in the short term. In particular, phytoplankton in the Mediterranean Sea is known to respond to climate variability associated with the North Atlantic Oscillation (NAO) and El Niño Southern Oscillation (ENSO) (Basterretxea et al. 2018, Colella et al. 2016). '''CMEMS KEY FINDINGS''' In the Mediterranean Sea, the trend average for the 1997-2020 period is slightly negative (-0.580.62% per year). Due to the change in processing techniques and chlorophyll retrieval, this trend estimate cannot be compared directly to those previously reported. The observations time series (in grey) shows minima values have been quite constant until 2015 and then there is a little decrease up to 2020, when an absolute minimum occurs with values lower than 0.04 mg m-3. Throughout the time series, maxima are variable year by year (with absolute maximum in 2015, >0.14 mg m-3), showing an evident reduction since 2016. In the last years of the series, the decrease of chlorophyll concentrations is also observed in the deseasonalized timeseries (in green) with a marked step in 2020. This attenuation of chlorophyll values in the last years results in an overall negative trend for the Mediterranean Sea. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00259

Annual time series of salmon recruitement biomass (2005-2014): • Time series of atlantic salmon recruitment • Location and Long Term Average (LTA) of atlantic salmon recruitment per Management Unit, that could be a river, basin district, a region or a whole country.

The challenge attempts to collect data on landings for the North Atlantic sea basin (i.e. north of the equator, excluding Caribe, Baltic, North Sea and Artic) and to compute: mass and number of discards by species and year. In addition, by-catch of fish, mammals, reptiles and seabirds. Data are presented in an Excel spreadsheet.

'''DEFINITION''' Heat transport across lines are obtained by integrating the heat fluxes along some selected sections and from top to bottom of the ocean. The values are computed from models’ daily output. The mean value over a reference period (1993-2014) and over the last full year are provided for the ensemble product and the individual reanalysis, as well as the standard deviation for the ensemble product over the reference period (1993-2014). The values are given in PetaWatt (PW). '''CONTEXT''' The ocean transports heat and mass by vertical overturning and horizontal circulation, and is one of the fundamental dynamic components of the Earth’s energy budget (IPCC, 2013). There are spatial asymmetries in the energy budget resulting from the Earth’s orientation to the sun and the meridional variation in absorbed radiation which support a transfer of energy from the tropics towards the poles. However, there are spatial variations in the loss of heat by the ocean through sensible and latent heat fluxes, as well as differences in ocean basin geometry and current systems. These complexities support a pattern of oceanic heat transport that is not strictly from lower to high latitudes. Moreover, it is not stationary and we are only beginning to unravel its variability. '''CMEMS KEY FINDINGS''' The mean transports estimated by the ensemble global reanalysis are comparable to estimates based on observations; the uncertainties on these integrated quantities are still large in all the available products. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00245

PCI vecteur

This raster dataset represents the Sea Surface Temperature (SST) anomalies, i.e. changes of sea temperatures, in the European Seas. The dataset is based on the map "Mean annual sea surface temperature trend in European seas" by Istituto Nazionale di Geofisica e Vulcanologia (INGV), which depicts the linear trend in sea surface temperature (in °C/yr) for the European seas over the past 25 years (1989-2013). Since all changes of sea temperatures can be considered to have an impact on the marine environment, the pressure layer includes absolute values of SST anomalies, i.e. negative/decreasing temperature trends were changed to positive values so that they represent a pressure. The original data was in a 1° grid format but was converted to a 100 km resolution, adapted to the EEA 10 km grid and clipped with the area of interest. This dataset has been prepared for the calculation of the combined effect index, produced for the ETC/ICM Report 4/2019 "Multiple pressures and their combined effects in Europe's seas" available on: https://www.eionet.europa.eu/etcs/etc-icm/etc-icm-report-4-2019-multiple-pressures-and-their-combined-effects-in-europes-seas-1.

This raster dataset represents input of impulsive anthropogenic sound in Europe Seas. Impulsive sounds are typically brief with a rapid rise time, i.e. a great change in amplitude over a short period of time. The main anthropogenic sources of impulsive underwater noise are typically impact pile driving for inshore and offshore construction, seismic exploration with airguns, explosions and sonar systems. The dataset was created by combining pulse-block-days (PBD) data from the ICES Registry (for HELCOM and OSPAR areas) and ACCOMBAS (for the Mediterranean Sea), resampled using the EEA 10 km grid. The dataset does not include the Black Sea. The temporal reference of this dataset is the period 2014-2016. The cell values have been transformed into a logarithmic scale (log10). This dataset has been prepared for the calculation of the combined effect index, produced for the ETC/ICM Report 4/2019 "Multiple pressures and their combined effects in Europe's seas" available on: https://www.eionet.europa.eu/etcs/etc-icm/etc-icm-report-4-2019-multiple-pressures-and-their-combined-effects-in-europes-seas-1.

This raster dataset presents the number of different hydrographical pressures per grid cell along the European coastlines. Hydrographical pressures are human activities that cause changes in hydrological conditions, i.e. changes to freshwater input, salinity, seawater flows, waves, currents, and temperature. Examples of such activities include riverine or coastal dams, offshore infrastructure, and outflows from power plants. The layer has been created using the Water Framework Directive (WFD) reported data on hydrographical pressures joined with the water body polygon features for the reference year 2016. The dataset was then rasterized into the EEA 10 km grid, and the cell values assigned with the number of different hydrographical pressures in the area covered by the cell. This dataset has been prepared for the calculation of the combined effect index, produced for the ETC/ICM Report 4/2019 "Multiple pressures and their combined effects in Europe's seas" available on: https://www.eionet.europa.eu/etcs/etc-icm/etc-icm-report-4-2019-multiple-pressures-and-their-combined-effects-in-europes-seas-1.

This product attempt to follow up on the sea level rise per stretch of coast of the North Atlantic, over past 100 years as follows: • Characterization of absolute sea level trend at annual resolution, along the coasts of EU Member States (including Outermost Regions), Canada, Faroes, Greenland, Iceland, Mexico, Morocco, Norway and USA; The stretchs or coast are defined by the administrative regions of the Atlantic Coast: • from NUTS3** administrative division for EU countries (see Eurostat), and • from GADM*** administrative divisions for non-EU countries. ** Third level of Nomenclature of Territorial Units for Statistics *** Global Administrative Areas For absolute sea level trend for 100 years we extract the information from grided sea level reconstruction datasets (using a combination of satellite and tide gauges) and extrapolate it to the nearest strecth of coast. The product is Provided in tabular form and as a map layer.