'''DEFINITION''' The CMEMS NORTHWESTSHELF_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The North-West Shelf Multi Year Product (NWSHELF_MULTIYEAR_PHY_004_009) and the Analysis product (NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013). Two parameters are included on this OMI: * Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2019). * Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2020 percentile. This indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (Pérez Gómez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019). '''CONTEXT''' This domain comprises the North West European continental shelf where depths do not exceed 200m and deeper Atlantic waters to the North and West. For these deeper waters, the North-South temperature gradient dominates (Liu and Tanhua, 2021). Temperature over the continental shelf is affected also by the various local currents in this region and by the shallow depth of the water (Elliott et al., 1990). Atmospheric heat waves can warm the whole water column, especially in the southern North Sea, much of which is no more than 30m deep (Holt et al., 2012). Warm summertime water observed in the Norwegian trench is outflow heading North from the Baltic Sea and from the North Sea itself. '''CMEMS KEY FINDINGS''' The 99th percentile SST product can be considered to represent approximately the warmest 4 days for the sea surface in Summer. Maximum anomalies for 2020 are up to 4oC warmer than the 1993-2019 average in the western approaches, Celtic and Irish Seas, English Channel and the southern North Sea. For the atmosphere, Summer 2020 was exceptionally warm and sunny in southern UK (Kendon et al., 2021), with heatwaves in June and August. Further north in the UK, the atmosphere was closer to long-term average temperatures. Overall, the 99th percentile SST anomalies show a similar pattern, with the exceptional warm anomalies in the south of the domain. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product)''' https://doi.org/10.48670/moi-00273

This metadata describes the ICES data on the temporal development of the Lusitanian/Boreal species ratio in the period from 19657 to 2016. Key message: The ratio between the number of Lusitanian (warm-favouring) and Boreal (cool-favouring) species are significantly increasing in several North-East Atlantic marine areas whereas there is no significant changes in all the southern areas. Changes in ratios are most apparent in the North Sea, Irish Sea and West of Scotland. Furthermore, it seems that Lusitanian species have not spread in all northward directions, but have followed two particular routes, through the English Channel and north around Scotland Blue dots indicates L/B ratios below 1 (dominance of Boreal species) Yellow dots indicates L/B ratios >=1 and <2 (dominance of Lusitanian species) Red dots indicates L/B ratios >=2 (high dominance of Lusitanian species) The dataset is derived from the ICES data portal 'DATRAS' (the Database of Trawl Surveys). DATRAS is an online database of trawl surveys with access to standard data products. DATRAS stores data collected primarily from bottom trawl fish surveys coordinated by ICES expert groups. The survey data are covering the Baltic Sea, Skagerrak, Kattegat, North Sea, English Channel, Celtic Sea, Irish Sea, Bay of Biscay and the eastern Atlantic from the Shetlands to Gibraltar. At present, there are more than 56 years of continuous time series data in DATRAS, and survey data are continuously updated by national institutions. The dataset has been used in the EEA Indicator "Changes in fish distribution in European seas" https://www.eea.europa.eu/data-and-maps/indicators/fish-distribution-shifts/assessment-1. The dataset has been used for this static map: https://www.eea.europa.eu/en/analysis/indicators/changes-in-fish-distribution-in/temporal-development-of-the-ratio

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.

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.

Maisons de la communauté

'''This product has been archived''' '''Short description:''' Arctic sea ice thickness from merged SMOS and Cryosat-2 (CS2) observations during freezing season between October and April. The SMOS mission provides L-band observations and the ice thickness-dependency of brightness temperature enables to estimate the sea-ice thickness for thin ice regimes. On the other hand, CS2 uses radar altimetry to measure the height of the ice surface above the water level, which can be converted into sea ice thickness assuming hydrostatic equilibrium. '''DOI (product) :''' https://doi.org/10.48670/moi-00125

Metagenomic analysis of clams from Sanaga river in Cameroon to describe the virome

The SDC_NAT_CLIM_TS_V2 product contains Temperature and Salinity Climatologies for the North Atlantic Ocean including the seasonal and monthly fields for 7 decades starting from 1950 to 2019. One resolution has been processed : 1/2°. The climatic fields were computed from the integrated North Atlantic Ocean dataset that combines data extracted from the 2 major sources: SeaDataNet infrastructure and Coriolis Ocean Dataset for Reanalysis (CORA). The computation was done with the DIVAnd software.

This metadata refers to a dataset that shows the percentage of cities' administrative area (core city based on the Urban Morphological Zones dataset) inundated by the sea level rise of 2 metres, without any coastal flooding defences present for a series of individual coastal European cities (included in Urban Audit). The dataset has been computed using the CReSIS (Centre for Remote Sensing of Ice Sheets) dataset for 2018.

Sediment average grain size in the Mediterranean was generated from sediment categories. This rough granulometry estimate may be used for habitat models at meso- and large scale.