Conversion into the EMODnet format of the published grid for the Capbreton Canyon in 2007: http://dx.doi.org/10.12770/72e2f750-c255-11df-a9b6-005056987263

'''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 products used include three global reanalyses: GLORYS, C-GLORS, ORAS5 (GLOBAL_MULTIYEAR_PHY_ENS_001_031) and two in situ based reprocessed products: CORA5.2 (INSITU_GLO_PHY_TS_OA_MY_013_052) , ARMOR-3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). Additionally, the time series based on the method of von Schuckmann and Le Traon (2011) has been added. 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''' The global mean sea level is reflecting changes in the Earth’s climate system in response to natural and anthropogenic forcing factors such as ocean warming, land ice mass loss and changes in water storage in continental river basins. Thermosteric sea-level variations result from temperature related density changes in sea water associated with volume expansion and contraction (Storto et al., 2018). Global thermosteric sea level rise caused by ocean warming is known as one of the major drivers of contemporary global mean sea level rise (Cazenave et al., 2018; Oppenheimer et al., 2019). '''CMEMS KEY FINDINGS''' Since the year 2005 the upper (0-2000m) near-global (60°S-60°N) thermosteric sea level rises at a rate of 1.3±0.3 mm/year. Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00240

'''DEFINITION''' The global annual chlorophyll anomaly is computed by subtracting a reference climatology (1997-2014) from the annual chlorophyll mean, on a pixel-by-pixel basis and in log10 space. Both the annual mean and the climatology are computed employing ESA Ocean Colour Climate Change Initiative (ESA OC-CCI, Sathyendranath et al., 2018a) global products (i.e. using the standard OC-CCI chlorophyll algorithms, OCI) as distributed by CMEMS. '''CONTEXT''' Phytoplankton – and chlorophyll concentration as a proxy for phytoplankton – respond rapidly to changes in their physical environment. Some of those changes are seasonal and are determined by light and nutrient availability (Racault et al., 2012). By comparing annual mean values to a climatology, we effectively remove the seasonal signal, while retaining information on potential events during the year. Chlorophyll anomalies can be correlated to climate indexes in particular regions, such as the ENSO index in the equatorial Pacific (Behrenfeld et al. 2006; Racault et al., 2012) and the IOD index in the Indian Ocean (Brewin et al., 2012). It is important to study chlorophyll anomalies in consonance with sea surface temperature and sea level anomalies, as increases in chlorophyll are generally consistent with decreases in SST and sea level anomalies, suggesting an increase in mixing and vertical nutrient transport (von Schuckmann et al., 2016). '''CMEMS KEY FINDINGS''' The average global chlorophyll anomaly 2019 is -0.02 log10(mg m-3), with a maximum value of 1.7 log10(mg m-3) and a minimum value of -3.2 log10(mg m-3). That is to say that, in average, the annual 2019 mean value is slightly lower (96%) than the 1997-2014 climatological value. The positive signals reported in 2016 and 2017 (Sathyendranath et al., 2018b) in the southern Pacific Ocean could still be observed in the 2019 map, while the significant negative anomalies in the tropical waters of the northern Pacific Ocean were also detected to a lesser extent. Areas showing a change of anomaly sign from 2019 include the southern coast of Japan (no anomaly to positive) and the tropical Atlantic (anomalies close to zero for 2019). A marked increase in chlorophyll concentration was observed during 2019 in the Great Australian Bight, while negative anomalies became stronger in the Guatemala Basin and the region south of the Gulf of Guinea and, with values of chlorophyll reaching as low as 30% of the climatological value (anomaly < -0.5 log10(mg m-3)). The persistent positive anomalies in the higher latitudes of the North Atlantic (> 40°) match the cooling observed in the 2018 and previous years SST anomaly maps.

IREMARE (Marine Renewable Energie Resource Information) is a project funded by ADEME (Agency for the Environment and Energy Control, French Public Institution), convention n°1505C0027. It is dedicated to the production and dissemination of high level information about Marine Renewable Energy (MRE) resource. The information produced during IREMARE project covers the western coast of France (Atlantic, English Channel and North Sea) and can be used for national down to local scale studies. IREMARE-MED (Informations sur la Ressource pour les Energies MArines REnouvelables en MEDiterranée/Marine Renewable Energie Resource Information in the Mediterranean) is a project funded by ADEME (Agence de l'Environnement et de la Maitrise de l'Energie/Agency for the Environment and Energy Control, French Public Institution), convention n°1705C0016. It is dedicated to the production and dissemination of high level information about Marine Renewable Energy (MRE) resource. The data comes from the HOMERE database (Boudiere et al. 2013) for the zone Atlantic, Channel and North Sea and from the ANEMOC-2 dataset (Tiberi-Wadier et al. 2016) for the Mediterranean Sea.

La loi du 27 février 2002 relative à la démocratie de proximité fixe comme premier objectif du recensement de la population la publication tous les ans des chiffres des populations légales : population municipale, population comptée à part et population totale. Ces chiffres sont calculés pour la France, toutes ses communes et circonscriptions administratives. La population municipale comprend les personnes ayant leur résidence habituelle sur le territoire de la commune. Elle inclut les personnes sans abri ou résidant habituellement dans des habitations mobiles recensées sur le territoire de la commune ainsi que les détenus dans les établissements pénitentiaires de la commune. C'est la population statistique comparable à la population sans double compte des précédents recensements. La population comptée à part comprend certaines personnes dont la résidence habituelle est dans une autre commune mais qui gardent un lien de résidence avec la commune. Elle comprend, par exemple, les élèves ou étudiants majeurs qui logent pour leurs études dans une autre commune mais dont la résidence familiale est située sur le territoire de la commune ou les personnes résidant dans une maison de retraite située dans une autre commune mais qui ont conservé une résidence familiale sur le territoire de la commune. Il est important de dénombrer à part de telles situations, d'abord pour clarifier quelle est véritablement la commune de résidence mais aussi pour ne pas produire des doubles comptes entre deux communes quand on additionne leurs populations. La population totale est la somme de la population municipale et de la population comptée à part. Les populations légales millésimées "n" sont diffusées fin décembre "n+2" pour les communes, cantons, arrondissements, départements et régions de France. Les résultats statistiques du recensement "n" sont diffusés au cours du second semestre "n+3".

Les ministères chargés de l'écologie (Meeddm) et de l'agriculture (Maap) ont confié au Gip Ecofor une mission d'expertise collective scientifique et technique à visée prospective sur « l'avenir du massif forestier des Landes de Gascogne ». Son objectif est de mobiliser la connaissance autour d'options envisageables pour assurer l'avenir du massif forestier landais et de la partager avec l'ensemble des parties intéressées. Les document disponibles sont les rapports finaux des groupes de travail et d'experts.

Il s'agit du diagnostic du SCOT et de l’état initial de l'environnement validé en 2014.

TSG-QC is a tool for interactive analysis and validation of sea surface temperature and salinity data acquired from a Thermosalinograph (TSG) installed on research or commercial ships. It has been developed under Matlab. It allows: • Visualization of TSG variables: Temperature, salinity and ship speed • Interactive comparison with climatological values (WOA and ISAS) • Automatic quality control using selected threshold criteria • Data validation and adjustment with external measurements (water samples, collocated Argo data, CTD, ...) • Quantitative estimation of sensor drift. The software can deal with different input data formats: ASCII, Labview, Seabird, GOSUD NetCDF... The use of TSG-QC from sources requires a valid Matlab license. A compiled version is available free of charge for users who do not have a Matlab license.

This dataset provide a times series of daily multi-sensor composite fields of Sea Surface Temperature (SST) foundation at ultra high resolution (UHR) on a 0.02 x 0.02 degree grid (approximately 2 x 2 km) over Mediterranean Sea, every 24 hours. Whereas along swath observation data essentially represent the skin or sub-skin SST, the L3S SST product is defined to represent the SST foundation (SSTfnd). SSTfnd is defined within GHRSST as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. It is therefore updated at intervals of 24 hrs. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed. The processing combines the observations of multiple polar orbiting and geostationary satellites, embedding infrared of microwave radiometers. All these sources are intercalibrated with each other before merging. A ranking procedure is used to select the best sensor observation for each grid point. The processing is the same (minus the optimal interpolation step) as for the Atlantic Near Real Time (NRT) L3S dataset available on Copernicus Marine Service [SST_ATL_PHY_L3S_NRT_010_037 dataset] and users can refer to the user manual and quality information documents available there for more details. This dataset is generated daily within a 24 delay and is therefore suitable for assimilation into operational models.