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.

JRA55-do is a surface dataset for driving ocean-sea ice models and used in phase 2 of OMIP (OMIP-2). JRA55-do corrects the atmospheric reanalysis product JRA-55 (Kobayashi et al., 2015) using satellite and other atmospheric reanalysis products. The merits of JRA55-do are the high horizontal resolution (~55 km) and temporal interval (3 h). An assessment by Tsujino et al. (2020) implies that JRA55-do can suitably replace the current CORE/OMIP-1 dataset. This reanalysis of atmospheric variables is provided by the Japanese Meteorological Agency starting in the year 1958 and will be used to drive the coupled NEMO-ERSEM model in the hindcast configuration.

The raster dataset represents the risk of collision of whales with vessels in Europe Seas. The most vulnerable species from ship strikes are cetaceans and turtles, since they go to the surface to breathe. On the other hand, their migration routes can overlap with shipping lanes. The collisions can produce the death or injury of the animals, and are an important threat for the conservation of these species. The dataset has been prepared in the context of the development of the first European Maritime Transport Environmental Report (EMSA-EEA report, 2021: https://www.eea.europa.eu/publications/maritime-transport).

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 4 to 7 decades. Currently data is available from 1950, split into Climate Data Store entries for 1950-1978 (preliminary back extension) and from 1979 onwards (final release plus timely updates, this page). ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. So far this has not been the case and when this does occur users will be notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 monthly mean data on single levels from 1979 to present".

Species distribution models (Maxent) predicting the distribution of two Vulnerable Marine Ecosystems (VME): the reef-forming Scleractinian coral Desmophyllum pertusum and the aggregations forming Hexactinellid sponge Pheronema carpenteri. Both of these species are VME indicator taxa and form habitat that enhance deep-sea diversity (Ross and Howell, 2013). Maps of the likely distribution of the habitat formed by these two species will enable efficient Marine Spatial planning to facilitate their conservation. This work was performed at the University of Plymouth in 2021. A GIS layer is provided for each species.

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.

Coastal zones are presented as a series of 10 consecutive buffers of 1km width each (towards inland). For this dataset, were treated as sea data all areas with class values of 52x (521: coastal lagoons, 522: estuaries, 523: sea and ocean) in Corine Land Cover (details in lineage).

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.

Distribution of unequivocal Vulnerable Marine Ecosystems (VMEs) and VME likelihood based on indicator taxa records, on the North Atlantic (18°N to 76°N and 36°E to 98°W). Several datasets, originating from public databases, literature review and data call to ATLAS partners, were gathered to compute the presence of unequivocal VME habitats in 25km * 25 km cells for the ATLAS work package 3. One layer displays the unequivocal VMEs (value=4) and the assigned high (value=3), medium (value=2) or low (value=1) likelihood of gridsquares to host VMEs, indexed on indicator taxa records from public databases with the method detailed in Morato et al (2018). The second displays the confidence associated to the VME likelihood score, indexed on data quality as detailed in Morato et al (2018) (values for unequivocal VMEs thus 100% confidence=4; high confidence=3; medium confidence=2; low confidence=1). This dataset was built to feed a basin-wide spatial conservation planning exercise, targeting the deep sea of the North Atlantic. The goal of this approach was to identify conservation priority areas for Vulnerable Marine Ecosystems (VMEs) and deep fish species, based on the distribution of species and habitats, human activities and current spatial management.

Coastal zones are presented as a series of 10 consecutive buffers of 1km width each (towards inland). For this dataset, were treated as sea data all areas with a class value of 523 (sea and ocean) in Corine Land Cover (details in lineage).