Surface current data measured by the CNR-ISMAR HF radar network are made available in graphical format for the last 48 hours and in real time and delayed mode via a THREDDS catalog which provides metadata and data access. The web site gives information on HF radar technology, sites position and operational parameters, and links to the THREDDS catalog. The catalog offers different remote-data-access protocols such as Open-source Project for a Network Data Access Protocol (OpenDaP), Web Coverage Service (WCS), Web Map Service (WMS) (OGS standards), as well as pure HTTP or NetCDF-Subsetter. They allow for metadata interrogation and data download (even sub-setting the dataset in terms of time and space) while embedded clients, such as GODIVA2, NetCDF-JavaToolsUI and Integrated Data Viewer, grant real-time data visualization directly via browser and allow for navigating within the plotted maps, saving images, exporting-importing on Google Earth, generating animations in selected time intervals. The data on the THREDDS catalog are organized in two folders, collecting the hourly current files of the last five days and grouping all the historical data. The two folders are accessible both in aggregated and in non-aggregated configuration. The data set consists of maps of radial and total velocity of the sea water surface current collected by the HF radars within the Italian Coastal Radar Network established in the framework of the Italian flagship project RITMARE. Surface ocean velocities estimated by HF Radar are representative of the upper 0.3-2.5 meters of the ocean. The radar sites are operated according to Quality Assessment procedures and data are processed for Quality Control. Data access tools are compliant to Open Geospatial Consortium (OGC), Climate and Forecast (CF) convention and INSPIRE directive. The use of netCDF format allows an easy implementation of all the open source services developed by UNIDATA.

Monthly time series of Total Nitrogen [mg/l] from model data

Wave forecasting models allow the representation of sea states based on a spectral resolution at the global scale or at the scale of ocean basins. This code calculates the evolution of the sea state by decomposing it into a wave spectrum that propagates in different directions and with different periods. During the propagation, the wave energy is increased or decreased by the effects of wind, breaking waves and energy exchanges between the different components. The wave forecasts available on data.shom.fr are calculated with 2 different types of models: MFWAM for the offshore domain (resolution from 0.5° to 0.1°) and Wavewatch III ® (WW3) for the coastal domain (resolution from 2' to 200m). MFWAM is a sea state forecasting model (wind wave and swell) derived from the third generation WAM code (WAMDI Group, 1988). Wavewatch III ® (WW3) is developed in a collaboration between the United States Weather Service (NOAA/NCEP), Shom, the University of Darmstadt in Germany, and other partners. The forecasts published on data.shom.fr are issued from the parameterization carried out and optimized by the Shom and Météo-France within the framework of the Homonim project (national coastal flood/wave/storm warning system).

The technologies developed will expand our knowledge of the ocean’s interconnected systems and provide tangible benefits to the industries that rely on them, such as fisheries and aquaculture. The data generated will also support conservation initiatives and provide vital information to policy makers. The future impact of these valuable technologies relies on their accessibility. Therefore, TechOceanS technology pilots will be low-cost and place minimal demands on existing infrastructure, allowing them to be made available for use by all countries regardless of resources. TechOceanS will also work with the IOC-UNESCO to develop “ocean best practices” standards for training and monitoring of metrology and ocean systems.

A final aggregated vulnerability index was obtained by combining all the partial indices belonging to each of the five vectors with V4 scores multiplied by −1 since Vector 4 indicators are of “resilience” rather than of “vulnerability”. Figures 6a and 6b show respectively map and cartogram of the geographical distribution obtained for this vector. As can be seen, except for most of Ireland, the Atlantic European coast ap- pears in redish colours corresponding to higher values of vulnerability.

One product and 3 components were developed in order to fulfill the third objectif ATLANTIC_CH02_Product_5 / Distribution of ocean monitoring systems to assess climate change existing into the MPA network • Physical parameter monitoring • Chemical parameter monitoring • Biological parameter monitoring The aim of the product is the identification of ocean monitoring systems to assess climate change in MPAs.

Today's normative and regulatory requirements to assess the producible energy from wind rely on in situ measurements (mast with anemometric sensors), which are extremely costly to Implement offshore. However, proof should be provided that hindcast model results are highly reliable, in order to provide an equivalent assessment. Very high resolution models is also the key issue in decision making for a proper siting that is relaying on the consistency of all datasets provided in the assessment. In this tender the products of the FP7 MARINA project will be used. 10-year (2001-2010) highresolution atmospheric, wave, tidal and ocean current simulations will be used. The model outputs are at high resolution (0.05x0.05 degree horizontal resolution, 1-hour time resolution, 5-vertical levels at 10,40,80,120,180 m). The wave parameters are co-located with the meteorological output fields. Satellite altimetry data from ENVISAT and JASON satellites have been assimilated in the system. Other wind and wave satellite data sets will be also analyzed (Synthetic Aperture Radars-SAR for example). At the same co-located points the tidal and ocean current data together with bathymetry are available. For preselected points in the North Western Mediterranean (Spain-France-ltaly areas) directional wave spectra data have been saved and are available. From SKIRON meteorological model available parameters are: WIND SPEED (m/s), WIND DIRECTION (deg), AIR PRESSURE (hPa), AIR DENSITY (Kgr/m3), TEMPERATURE (K), MODEL SEAMASK From the wave model available parameters: SIGNIFICANT WAVE HEIGHT (m), MEAN WAVE DIRECTION (deg), WAVE MEAN PERIOD (s), PEAK WAVE PRERIOD (s), SWELL WAVE HEIGHT (m), MEAN SWELL PERIOD (s), MEAN DIRECTIONAL SPREAD, WINDSEA MEAN DIRECTIONAL SPREAD, SWELL MEAN DIRECTIONAL SPREAD, MAXIMUM WAVE HEIGHT (m)

This visualization product displays the spatial distribution of seafloor litter density per trawl. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of seafloor litter collected by international fish-trawl surveys 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 (OSPAR and MEDITS protocols) and reference lists used on a European scale. Moreover, within the same protocol, different gear types are deployed during fishing bottom trawl surveys. In cases where the wingspread and/or number of items were unknown, data could not be used because these fields are needed to calculate the density. Data collected before 2011 are affected by this filter. When the distance reported in the data was null, it was calculated from: - the ground speed and the haul duration using this formula: Distance (km) = Haul duration (h) * Ground speed (km/h); - the trawl coordinates if the ground speed and the haul duration were not filled in. The swept area is calculated from the wingspread (which depends on the fishing gear type) and the distance trawled: Swept area (km²) = Distance (km) * Wingspread (km) Densities have been calculated on each trawl and year using the following computation: Density (number of items per km²) = ∑Number of items / Swept area (km²) Then a grid with 30km x 30km cells is used to calculate the weighted mean of densities in each cell from the formula : Weighted mean (number of items per km²) = ∑ (Distance (km) * Density (number of items per km²)) / ∑ Distance (km) Percentiles 50, 75, 95 & 99 have been calculated taking into account data for all years. More information on data processing and calculation are detailed in the document attached. 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 work is based on the work presented in the following scientific article: O. Gerigny, M. Brun, M.C. Fabri, C. Tomasino, M. Le Moigne, A. Jadaud, F. Galgani, Seafloor litter from the continental shelf and canyons in French Mediterranean Water: Distribution, typologies and trends, Marine Pollution Bulletin, Volume 146, 2019, Pages 653-666, ISSN 0025-326X, https://doi.org/10.1016/j.marpolbul.2019.07.030.