The SDC_MED_DP1 consists of Mixed Layer Depth (MLD) monthly climatology at 1/8 of degree for the Mediterranean Sea computed from an integrated dataset of collocated temperature and salinity profiles which combines data extracted from SeaDataNet infrastructure (SDC_MED_DATA_TS_V1, https://doi.org/10.12770/2698a37e-c78b-4f78-be0b-ec536c4cb4b3) and the Coriolis Ocean Dataset for Reanalysis (CORA), version 5.2 (https://archimer.ifremer.fr/doc/00595/70726/). The products comprehends three versions of MLD climatology over the 1955-2017 time period obtained computing the MLD from three different methods. A MLD climatology for the time span 1987-2017 computed with the fixed density criteria is also available. The analysis was done with the DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.6.1.

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

The Ifremer Wind and Wave Operation Center (IWWOC) runs daily the WaveWatch III (WW3) model to provide surface wave colocations with both SCAT and SWIM instruments onboard CFOSAT. CFOSAT (Chinese French Ocean SATellite) is a french-chinese mission launched in 2018, whose aim is to provide wind (SCAT instrument) and wave (SWIM instrument) measurements over the sea surface. Directional wave spectra are calculated over SWIM sensing geometries over each measurement, thanks to the dedicated toolbox (WAVERUN) which was developed by IFREMER for the colocation of WW3 and satellite remote sensing products. The current Ifremer WW3 run is global, hourly and at 0.25° spatial resolution. Two different colocation product are generated: - WW3 with CWWIC L2 provides WW3 directional spectra over the CWWIC SWIM L2 geometry, meaning a colocated valid is provided for each box defined in CWWIC L2 product. - WW3 with IWWOC L2S provides a WW3 directional spectra over IWWOC SWI_L2S__ product. For each of these products, a colocation product is provided respetively for each input file from CWWIC SWI_L2___ and IWWOC SWI_L2S (for each incidence in the later one). It contains the modelled spectral density and all forcing fields: current, wind, friction velocity, air sea temperature difference. Other parameters can be added in the future. The SWIM and WW3 colocation product is generated and distributed by Ifremer / CERSAT in the frame of the Ifremer Wind and Wave Operation Center (IWWOC) co-funded by Ifremer and CNES and dedicated to the processing of the delayed mode data of CFOSAT mission. Note: colocations with SCAT instrument onboard CFOSAT are also within the SWISCA L2S product also available at IWWOC. It provides WW3 directional spectra over SCAT L2A geometry, meaning a model value is calculated for each Wind Vector Cell (WVC) of L2A/L2B types of SCAT product.

This study gathers multi-year environmental sequencing datasets generated within the French ROME pilot observatory network. It includes eDNA metabarcoding and RNA-based analyses from water samples, oyster tissues, and viral fractions collected across four French estuarine ecosystems between 2020 and 2023, supporting integrated monitoring of coastal microbiomes and microbial hazards.

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.

The SeaDataCloud TS historical data collection V2 for the North Atlantic Ocean, includes open access in situ data on temperature and salinity of water column in the North Atlantic Ocean from 10°N to 62°N, including the Labrador Sea, The data were retrieved from the SeaDataNet infrastructure at summer 2019. The dataset format is Ocean Data View (ODV - http://odv.awi.de/) binary collection. The quality control of the data has been performed with the help of ODV software. Data Quality Flags have been revised and set up using the elaborated by SeaDataNet2 project QC procedures in conjunction with the visual expert check. The final number of the Temperature and Salinity profiles (stations) in the collection is 10119755. For data access please register at http://www.marine-id.org/.

Level 3, four times a day, sub-skin Sea Surface Temperature derived from AVHRR on Metop satellites and VIIRS or AVHRR on NOAA and NPP satellites, over North Atlantic and European Seas and re-projected on a polar stereographic at 2 km resolution, in GHRSST compliant netCDF format. This catalogue entry presents NOAA-20 North Atlantic Regional Sea Surface Temperature. SST is retrieved from infrared channels 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.

This data set corresponds to the global offshore wind farm boundaries with the following attributes for each project: + WindfarmId (ID of the windfarm) + Name (Name of the windfarm) + Country (Country code) + Status (Status code) + WindfarmStatus (Windfarm Status or Project Status) + StatusComments (Comments on the Windfarm Status or Project Status) + CapacityMWMin (Capacity of the windfarm - Min) + CapacityMWMax (Capacity of the windfarm - Max) + NoTurbinesMin (Number of turbines - Min) + NoTurbinesMax (Number of turbines - Max) + Comments (Comments) + TurbineMWMin (Capacity of the turbine (set-up in the windfarm) - Min) + TurbineMWMax (Capacity of the turbine (set-up in the windfarm) - Max) + OtherNames (Other name of the windfarm) + CountryName (Country where the windfarm is set) + Lat (Geographic coordinate - centre latitude) + Lon (Geographic coordinate - centre longitude) + IsEstimatedLocation (This is where we know that a project exists but we don't know its exact location.) + IsOnHold + Developers (Developer(s) of the windfarm) + Owners (Owner of the windfarm) + Operators (Operator of the windfarm) + OffshoreConstructionStarts The frequency of the database release is monthly. This data set corresponds to the release of January 2020. This data set is strictly for internal EEA use as is subjected to a commercial license. Given the limited user subscriptions available, interested users should contact the SDI Team (sdi@eea.europa.eu) to be granted access to the data set.

'''DEFINITION''' The CMEMS IBI_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 Iberia-Biscay-Ireland Multi Year Product (IBI_MULTIYEAR_PHY_005_002) and the Analysis product (IBI_ANALYSISFORECAST_PHY_005_001). Two parameters have been considered for 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 (1993-2021). • Anomaly of the 99th percentile in 2022: The 99th percentile of the year 2022 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2022 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''' The Sea Surface Temperature is one of the essential ocean variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. While the global-averaged sea surface temperatures have increased since the beginning of the 20th century (Hartmann et al., 2013) in the North Atlantic, anomalous cold conditions have also been reported since 2014 (Mulet et al., 2018; Dubois et al., 2018). The IBI area is a complex dynamic region with a remarkable variety of ocean physical processes and scales involved. The Sea Surface Temperature field in the region is strongly dependent on latitude, with higher values towards the South (Locarnini et al. 2013). This latitudinal gradient is supported by the presence of the eastern part of the North Atlantic subtropical gyre that transports cool water from the northern latitudes towards the equator. Additionally, the Iberia-Biscay-Ireland region is under the influence of the Sea Level Pressure dipole established between the Icelandic low and the Bermuda high. Therefore, the interannual and interdecadal variability of the surface temperature field may be influenced by the North Atlantic Oscillation pattern (Czaja and Frankignoul, 2002; Flatau et al., 2003). Also relevant in the region are the upwelling processes taking place in the coastal margins. The most referenced one is the eastern boundary coastal upwelling system off the African and western Iberian coast (Sotillo et al., 2016), although other smaller upwelling systems have also been described in the northern coast of the Iberian Peninsula (Alvarez et al., 2011), the south-western Irish coast (Edwars et al., 1996) and the European Continental Slope (Dickson, 1980). '''CMEMS KEY FINDINGS''' In the IBI region, the 99th mean percentile for 1993-2021 shows a north-south pattern driven by the climatological distribution of temperatures in the North Atlantic. In the coastal regions of Africa and the Iberian Peninsula, the mean values are influenced by the upwelling processes (Sotillo et al., 2016). These results are consistent with the ones presented in Álvarez Fanjul (2019) for the period 1993-2016. The analysis of the 99th percentile anomaly in the year 2023 shows that this period has been affected by a severe impact of maximum SST values. Anomalies exceeding the standard deviation affect almost the entire IBI domain, and regions impacted by thermal anomalies surpassing twice the standard deviation are also widespread below the 43ºN parallel. Extreme SST values exceeding twice the standard deviation affect not only the open ocean waters but also the easter boundary upwelling areas such as the northern half of Portugal, the Spanish Atlantic coast up to Cape Ortegal, and the African coast south of Cape Aguer. It is worth noting the impact of anomalies that exceed twice the standard deviation is widespread throughout the entire Mediterranean region included in this analysis. '''DOI (product):''' https://doi.org/10.48670/moi-00254