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).

The SDC_MED_CLIM_TS_V2 product contains Temperature and Salinity Climatologies for Mediterranean Sea: monthly and seasonal fields for time periods 1955-2018, 1955-1984 and 1985-2018 and seasonal fields for 6 decades covering the time period 1955 to 2018. The climatic fields were computed from an integrated Mediterranean Sea data set that combines data extracted from SeaDataNet infrastructure (SDC_MED_DATA_TS_V2, https://doi.org/10.12770/2a2aa0c5-4054-4a62-a18b-3835b304fe64) and Coriolis Ocean Dataset for Reanalysis (CORA5.2) distributed by the Copernicus Marine Service (INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b). The computation was done with the DIVAnd (Data-Interpolating Variational Analysis), version 2.4.0.

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.

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.

'''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

The SDC_MED_DP2 product contains 55 sliding decadal temperature fields (1955-1964, 1956-1965, 1957-1966, …, 2009-2018) at 1/8° horizontal resolution obtained in the 0-2000m layer and two derived OHC annual anomaly estimates for the 0-700m and the 0-2000m layers. Sliding decades of annual Temperature fields were obtained from an integrated Mediterranean Sea dataset covering the time period 1955-2018, which combines data extracted from SeaDataNet infrastructure at the end of July 2019 (SDC_MED_DATA_TS_V2, https://doi.org/10.12770/3f8eaace-9f9b-4b1b-a7a4-9c55270e205a) and the Coriolis Ocean Dataset for Reanalysis (CORA 5.2, accessed in July 2020, https://archimer.ifremer.fr/doc/00595/70726/). The resulting annual OHC anomaly time series span the 1960-2014 period. The analysis was performed with the DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.6.1.

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

Périmètre de la CAPB

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

Itinéraires de randonnée et pistes cyclables du Département des Landes. Le Département des Landes propose 3 500 km d’itinéraires inscrits au Plan départemental des itinéraires de promenade et de randonnée (PDIPR) et près de 2 500 km d’itinéraires cyclables. Ces circuits sont entretenus et balisés avec des niveaux de difficultés mentionnés sur chaque parcours.