Maisons de la communauté

MISSION ATLANTIC assesses the whole Atlantic, and ecosystem components at risk from natural hazards and the consequences of human activities, including individual regional Case Studies, and their interconnectivity. To do this, Mission Atlantic develops IEAs for seven regional Case Studies, in sub-Arctic and Tropical regions of the Atlantic Ocean, ranging from shelf seas to the mid-Atlantic Ridge: 1) Norwegian Sea 2) Celtic Sea 3) Canary Current 4) North Mid Atlantic Ridge 5) South Mid Atlantic Ridge 6) Benguela Current 7) South Brazilian Shelf

'''This product has been archived''' '''DEFINITION''' The temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The regional thermosteric sea level values are then averaged from 60°S-60°N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). '''CONTEXT''' Most of the interannual variability and trends in regional sea level is caused by changes in steric sea level. At mid and low latitudes, the steric sea level signal is essentially due to temperature changes, i.e. the thermosteric effect (Stammer et al., 2013, Meyssignac et al., 2016). Salinity changes play only a local role. Regional trends of thermosteric sea level can be significantly larger compared to their globally averaged versions (Storto et al., 2018). Except for shallow shelf sea and high latitudes (> 60° latitude), regional thermosteric sea level variations are mostly related to ocean circulation changes, in particular in the tropics where the sea level variations and trends are the most intense over the last two decades. '''CMEMS KEY FINDINGS''' Significant (i.e. when the signal exceeds the noise) regional trends for the period 2005-2019 from the Copernicus Marine Service multi-ensemble approach show a thermosteric sea level rise at rates ranging from the global mean average up to more than 8 mm/year. There are specific regions where a negative trend is observed above noise at rates up to about -8 mm/year such as in the subpolar North Atlantic, or the western tropical Pacific. These areas are characterized by strong year-to-year variability (Dubois et al., 2018; Capotondi et al., 2020). Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00241

'''This product has been archived''' '''Short description''': You can find here the OMEGA3D observation-based quasi-geostrophic vertical and horizontal ocean currents developed by the Consiglio Nazionale delle RIcerche. The data are provided weekly over a regular grid at 1/4° horizontal resolution, from the surface to 1500 m depth (representative of each Wednesday). The velocities are obtained by solving a diabatic formulation of the Omega equation, starting from ARMOR3D data (MULTIOBS_GLO_PHY_REP_015_002 which corresponds to former version of MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012) and ERA-Interim surface fluxes. '''DOI (product) :''' https://doi.org/10.25423/cmcc/multiobs_glo_phy_w_rep_015_007

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).

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

The GEBCO_2020 Grid was released in May 2020 and is the second global bathymetric product released by the General Bathymetric Chart of the Oceans (GEBCO) and has been developed through the Nippon Foundation-GEBCO Seabed 2030 Project. The GEBCO_2020 Grid provides global coverage of elevation data in meters on a 15 arc-second grid of 43200 rows x 86400 columns, giving 3,732,480,000 data points. Grid Development The GEBCO_2020 Grid is a continuous, global terrain model for ocean and land with a spatial resolution of 15 arc seconds. The grid uses as a ‘base’ Version 2 of the SRTM15+ data set (Tozer et al, 2019). This data set is a fusion of land topography with measured and estimated seafloor topography. It is augmented with the gridded bathymetric data sets developed by the four Seabed 2030 Regional Centers. The Regional Centers have compiled gridded bathymetric data sets, largely based on multibeam data, for their areas of responsibility. These regional grids were then provided to the Global Center. For areas outside of the polar regions (primarily south of 60°N and north of 50°S), these data sets are in the form of 'sparse grids', i.e. only grid cells that contain data were populated. For the polar regions, complete grids were provided due to the complexities of incorporating data held in polar coordinates. The compilation of the GEBCO_2020 Grid from these regional data grids was carried out at the Global Center, with the aim of producing a seamless global terrain model. In contrast to the development of the previous GEBCO grid, GEBCO_2019, the data sets provided as sparse grids by the Regional Centers were included on to the base grid without any blending, i.e. grid cells in the base grid were replaced with data from the sparse grids. This was with aim of avoiding creating edge effects, 'ridges and ripples', at the boundaries between the sparse grids and base grid during the blending process used previously. In addition, this allows a clear identification of the data source within the grid, with no cells being 'blended' values. Routines from Generic Mapping Tools (GMT) system were used to do the merging of the data sets. For the polar data sets, and the adjoining North Sea area, supplied in the form of complete grids these data sets were included using feather blending techniques from GlobalMapper software version 11.0, made available by Blue Marble Geographic. The GEBCO_2020 Grid includes data sets from a number of international and national data repositories and regional mapping initiatives. For information on the data sets included in the GEBCO_2020 Grid, please see the list of contributions included in this release of the grid (https://www.gebco.net/data_and_products/gridded_bathymetry_data/gebco_2020/#compilations).

This dataset provides extreme waves (Hs: significant wave height, Hb:breaking wave height, a proxy of the wave energy flux) simulated with the WWIII model, and extracted along global coastlines. Two simulations, including or not Tropical Cyclones (TCs) in the forcing wind field, are provided.

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.