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.

Businesses, policymakers, and local communities need to access reliable weather and climate information to safeguard human health, wellbeing, economic growth, and environmental sustainability. However, important changes in climate variability and extreme weather events are difficult to pinpoint and account for in existing modelling and forecasting tools. Moreover, many changes in the global climate are linked to the Arctic, where climate change is occurring rapidly, making weather and climate prediction a considerable challenge. Blue-Action evaluated the impact of Arctic warming on the northern hemisphere and developed new techniques to improve forecast accuracy at sub-seasonal to decadal scales. Blue-Action specifically worked to understand and simulate the linkages between the Arctic and the global climate system, and the Arctic’s role in generating weather patterns associated with hazardous conditions and climatic extremes. In doing so, Blue-Action aimed to improve the safety and wellbeing of people in the Arctic and across the Northern Hemisphere, reduce the risks associated with Arctic operations and resource exploitation, and support evidence-based decision-making by policymakers worldwide.

Cette donnée représente l'ensemble des ERP géolocalisés du Lot-et-Garonne. Le périmètre de production concerne les ERP de catégories 1, 2, 3, 4, et 5 avec local à sommeil soit 1069 ERP. Seuls les ERP de catégorie 5 sans local à sommeil ne sont pas classés. Tous les types d'ERP sont représentés sauf les ERP type CTS. Etant donné les changements fréquents dont les ERP sont sujets, la mise à jour de ces données ne peut être permanente.

Description of spatial layers attributes of sea-level trend (units: mm/year) from tide gauges over periods of 50 years (1963-2012) and 100 years (1913-2012), to characterize and assess average annual sea-level rise at the coast.

This dataset presents the estimated multiplication factor by which the frequency of flooding events of a given height in European tide gauges will change between 2010 and 2100, due to projected regional sea relative level rise under the Representative Concentration Pathways (RCP) 4.5 scenario. Values larger than 1 indicate an increase in flooding frequency. This dataset is derived from the Figure 13.25(b) of the Working Group I contribution to the IPCC Fifth Assessment Report (http://www.climatechange2013.org/images/report/WG1AR5_ALL_FINAL.pdf). This dataset also contributes to an earlier version of the EEA Indicator "Global and European sea-level": https://www.eea.europa.eu/data-and-maps/indicators/sea-level-rise-5/assessment.

Description of the attributes for the 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 temperature at mid-water and sea bottom for the last 10 yrs.

Moving 10-years analysis of Ammonium 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 1984-1993 until 2005-2014 (winter) - from 1980-1989 until 2005-2014 (spring) - from 1980-1989 until 2005-2014 (summer) - from 1980-1989 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

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.

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.