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

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.

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).

'''This product has been archived''' '''Short description:''' Arctic sea ice thickness from merged SMOS and Cryosat-2 (CS2) observations during freezing season between October and April.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

Cette couche recense les Zones d’Activités Economiques (ZAE) présentes sur le département de la Charente. Initialement crée par Charente Développement, il s'agit d'un surfacique qui permet d'identifier précisément le contour de ces zones en se calant sur les données du Cadastre.

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.

Périmètre de la CAPB

Maisons de la communauté

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 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