Specifications of the desirable and recommended product attributes for generating spatial layers of sea level trend for the last 10 years for the Mediterranean basin and for each NUTS3 region along the coast.

The objective of this tender is to examine the current data collection, observation and data assembly programmes in the Meditterranean Sea, identify gaps and to evaluate how they can be optimised.

Description of de desirable and recomended attributes for generating time-series of sea surface annual average temperature for the last 10, 50 and 100 yrs for the Mediterranean basin and for each NUTS region along the coast.

Specification of the desirable and recommended product attributes for generating time series of average annual sea-level rise for the last 50 and 100 yrs.

Suitability index of a wind farm in the NWMed concerning the environmental resources, the natural barriers, human activities, MPA and fisheries.

Level 2 sub-skin Sea Surface Temperature derived from AVHRR on Metop, global and provided in full-resolution swath (1 km at nadir), in GHRSST compliant netCDF format. The satellite input data has successively come from Metop-A, Metop-B and Metop-C level 1 data processed at EUMETSAT. SST is retrieved from AVHRR infrared channels (3.7, 10.8 and 12.0 µm) using a multispectral algorithm and a cloud mask. Atmospheric profiles of water vapor and temperature from a numerical weather prediction model, Sea Surface Temperature from an analysis, together with a radiative transfer model, are used to correct the multispectral algorithm for regional and seasonal biases due to changing atmospheric conditions. The quality of the products is monitored regularly by daily comparison of the satellite estimates against buoy measurements.The product format is compliant with the GHRSST Data Specification (GDS) version 2. Users are advised to use data only with quality levels 3,4 and 5.

Confidence in the full output of the 2016 EUSeaMap broad-scale predictive model, produced by EMODnet Seabed Habitats. Values are on a range from 1 (low confidence) to 3 (high confidence). Confidence is calculated by amalgamating the confidence values of the underlying applicable habitat descriptors used to generate the habitat value in the area in question. Habitat descriptors differ per region but include: Biological zone Energy class Oxygen regime Salinity regime Seabed Substrate Riverine input Confidence in habitat descriptors are driven by the confidence in the source data used to determine the descriptor, and the confidence in the threshold/margin (areas closer to a boundary between two classes will have lower confidence). Confidence values are also available for each habitat descriptor and input data layer.

Auteur(s): Cha Lucie , Analyse des paysages de méga évènements sur des sites internationaux. Historique de l'évolution de expositions géantes. Projet d'aménagement de la ville de Bordeaux qui a posé sa candidature pour l'Exposition universelle de 2025

Description ot the spatial layers attributes of sea level trend for the last 10 years for the Mediterranean basin and for each NUTS3 region along the coast.

Moving 10-years analysis of Phosphate at Northeast Atlantic Ocean for each season: - winter: January-March, - spring: April-June, - summer: July-September, - autumn: October-December Every year of the time dimension corresponds to the 10-year centred average of each season. Decades span : - from 1963-1972 until 2005-2014 (winter) - from 1963-1972 until 2005-2014 (spring) - from 1964-1973 until 2005-2014 (summer) - from 1964-1973 until 2005-2014 (autumn) Observational data span from 1962 to 2014. Depth range (IODE standard depths): -3000.0, -2500.0, -2000.0, -1750, -1500.0, -1400.0, -1300.0, -1200.0, -1100.0, -1000.0, -900.0, -800.0, -700.0, -600.0, -500.0, -400.0, -300.0, -250.0, -200.0, -150.0, -125.0, -100.0, -75.0, -50.0,-40.0, -30.0, -20.0, -10.0, -5.0, -0.0 Data Sources: observational data from SeaDataNet/EMODNet Chemistry Data Network. Description of DIVA analysis: Geostatistical data analysis by DIVA (Data-Interpolating Variational Analysis) tool. GEBCO 1min topography is used for the contouring preparation. Analyzed filed masked using relative error threshold 0.3 and 0.5 DIVA settings. Signal to noise ratio and correlation length were optimized and filtered vertically and a seasonally-averaged profile was used. Logarithmic transformation applied to the data prior to the analysis. Background field: the data mean value is subtracted from the data. Detrending of data: no, Advection constraint applied: no. Units: umol/l