Assessment of the confidence limits of the data base by means of evaluation of the two involved numerical models: The wave model WAM (Parameter: Significant wave height Hs) and the Atmospheric model SKIRON (Parameter: Wind Speed 10m)

Assess whether the MPA network constitutes a representative and coherent network as described in article 13 of the Marine Strategy Framework Directive 3 products were specified to achieve the second objectif of the challenge: ATLANTIC_CH02_Product_2 / Quantitative analyse of MPA coherency The product comprises 4 components: Distribution of vulnerable marine habitats : Shape represent the distribution of different vulnérable habitats Distribution biologically or ecologically significant areas (EBSAs) Critical areas of vulnerable species Distribution of indicator species The method used computes the percentage coverage between : Vulnerable habitats like carbon sinks, reef, kelp... Ecologically or biologically significant area Life critical area (feeding , breeding, migratory routes, spawning, dispersal larvea, nursery…) for indicator species Distribution of indicator species in the study area and MPA network location.

Phyto plankton Abundance: Identify the 3 most abundant phytoplankton species in the North Atlantic and calculate a timeseries of their abundance within the basin.

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.

Tronçons linéaires de voies de l'Aquitaine romaine - projet Aquitaviae

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.

PCI vecteur

'''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''DEFINITION''' The time series are derived from the regional chlorophyll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

Maisons éclusières sur les départements de la Gironde et du Lot-et-Garonne.

This dataset represents the regions for levels 1, 2 and 3 of the Nomenclature of Territorial Units for Statistics (NUTS) for 2016. The NUTS nomenclature is a hierarchical classification of statistical regions and subdivides the EU economic territory into regions of four different levels (NUTS , 1, 2 and 3, moving respectively from larger to smaller territorial units). NUTS 1 is the most aggregated level. An additional Country level (NUTS 0) is also available for countries where the the nation at statistical level does not coincide with the administrative boundaries. For example Mt Athos in Greece and Mellum and Minsener Ogg in Germany. The NUTS classification has been officially established through Regulation (EC) No 2016/2066 of the European Parliament and of the Council and its amendments. A non-official NUTS-like classification has been defined for the EFTA countries and candidate countries. An introduction to the NUTS classification is available here: http://ec.europa.eu/eurostat/web/nuts/overview. This dataset has been created mainly from the EuroBoundary Map v 12 (Eurogeographics) and geographic information from TurkStat for Turkey. The public dataset is available under the Download link indicated below. Available scales are 1M, 3M, 10M, 20M, 60M). The full dataset is available via the EC restricted download link under GISCO.NUTS_2016. Here six scale ranges (100K, 1M, 3M, 10M and 20M, 60M) are available. Coverage is the economic territory of the EU, EFTA countries and candidate countries as in 2013.