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.

scRNA-seq reads from a Pacific oyster (Crassostrea gigas) hemocyte preparation. Hemocytes were isolated from a unique immunologically naive animal (Ifremer Standardized Animal, 18 months) and single-cell drop-seq technology was applied to 3,000 individual hemocytes.

Land-sea continuum microbiome analyses in 4 coastal French sites and in oysters aimed at evaluating human impact on coastal ecosystems and new potentiel microbiological sanitary risks.

L'orthophotographie de précision planimétrique de classe A (arrêté du 16 septembre 2003) et produit en RVB (couleurs : Rouge, Vert, Bleu) constitue la composante image du géostandard PCRS. Un PCRS constitue le socle commun topographique minimal de base décrivant à très grande échelle les limites apparentes de la voirie. Il est limité aux objets les plus utiles et n'aborde aucune des logiques "métiers" par ailleurs traitées chez les gestionnaires de réseaux. Le PCRS est destiné à servir de support topographique à un grand nombre d'applications requérant la meilleure précision possible. Il répond essentiellement aux exigences de la réglementation dite "anti-endommagement" ou réforme DT-DICT portant sur les travaux à proximité des réseaux, notamment sous la forme d'un fond de plan utilisable dans le cadre des échanges entre gestionnaires et exploitants. Conçu pour facilité les échanges entre les plans de type DAO et les SIG des collectivité et exploitants, les objets du PCRS gèrent peu d'attributs autres que ceux liés à la généalogie de leur acquisition, majoritairement par levé topographique.

The SDC_GLO_CLIM_O2_AOU product contains two different monthly climatology for dissolved Oxygen and Apparent Oxygen Utilization, SDC_GLO_CLIM_O2 and SDC_GLO_CLIM_AOU respectively from the World Ocean Data (WOD) database. Only basic quality control flags from the WOD are used. The first climatology, SDC_GLO_CLIM_O2, considers Dissolved Oxygen profiles casted together with temperature and salinity from CTD, Profiling Floats (PFL) and Ocean Station Data (OSD) for time duration 2003 to 2017. The second climatology, SDC_GLO_CLIM_AOU, apparent Oxygen utilization, is computed as a difference of dissolved oxygen and saturation O2 profiles. The gridded fields are computed using DIVAnd (Data Interpolating Variational Analysis) version 2.3.1.

Assessments run at AFWG provide the scientific basis for the management of cod, haddock, saithe, redfish, Greenland halibut and capelin in subareas 1 and 2. Taking the catch values provided by the Norwegian fisheries ministry for Norwegian catches1 and raising the total landed value to the total catches gives an approximate nominal first-hand landed value for the combined AFWG stocks of ca. 20 billion NOK or ca. 2 billion EUR (2018 estimates).

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 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.

'''DEFINITION''' The CMEMS MEDSEA_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 (MEDSEA_MULTIYEAR_PHY_006_004) and the Analysis product (MEDSEA_ANALYSISFORECAST_PHY_006_013). 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 (1987-2019). * Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Near Real Time 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''' 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. In recent decades (from mid ‘80s) the Mediterranean Sea showed a trend of increasing temperatures (Ducrocq et al., 2016), which has been observed also by means of the CMEMS SST_MED_SST_L4_REP_OBSERVATIONS_010_021 satellite product and reported in the following CMEMS OMI: MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies and MEDSEA_OMI_TEMPSAL_sst_trend. The Mediterranean Sea is a semi-enclosed sea characterized by an annual average surface temperature which varies horizontally from ~14°C in the Northwestern part of the basin to ~23°C in the Southeastern areas. Large-scale temperature variations in the upper layers are mainly related to the heat exchange with the atmosphere and surrounding oceanic regions. The Mediterranean Sea annual 99th percentile presents a significant interannual and multidecadal variability with a significant increase starting from the 80’s as shown in Marbà et al. (2015) which is also in good agreement with the multidecadal change of the mean SST reported in Mariotti et al. (2012). Moreover the spatial variability of the SST 99th percentile shows large differences at regional scale (Darmariaki et al., 2019; Pastor et al. 2018). '''CMEMS KEY FINDINGS''' The Mediterranean mean Sea Surface Temperature 99th percentile evaluated in the period 1987-2019 (upper panel) presents highest values (~ 28-30 °C) in the eastern Mediterranean-Levantine basin and along the Tunisian coasts especially in the area of the Gulf of Gabes, while the lowest (~ 23–25 °C) are found in the Gulf of Lyon (a deep water formation area), in the Alboran Sea (affected by incoming Atlantic waters) and the eastern part of the Aegean Sea (an upwelling region). These results are in agreement with previous findings in Darmariaki et al. (2019) and Pastor et al. (2018) and are consistent with the ones presented in CMEMS OSR3 (Alvarez Fanjul et al., 2019) for the period 1993-2016. The 2020 Sea Surface Temperature 99th percentile anomaly map (bottom panel) shows a general positive pattern up to +3°C in the North-West Mediterranean area while colder anomalies are visible in the Gulf of Lion and North Aegean Sea . This Ocean Monitoring Indicator confirms the continuous warming of the SST and in particular it shows that the year 2020 is characterized by an overall increase of the extreme Sea Surface Temperature values in almost the whole domain with respect to the reference period. This finding can be probably affected by the different dataset used to evaluate this anomaly map: the 2020 Sea Surface Temperature 99th percentile derived from the Near Real Time Analysis product compared to the mean (1987-2019) Sea Surface Temperature 99th percentile evaluated from the Reanalysis product which, among the others, is characterized by different atmospheric forcing). Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00266

The present data set concerne metabarcoding raw reads produced using 4 different PCR targeting polymerase or capside coding region of the genoyupe I and II of norovirus. Test samples of norovirus with serial dilutions in pure water and after a bio-accumulation in oysters. Sequencing was made after VirCapSeq-VERT approach.