'''Short description:''' You can find here the biogeochemistry non assimilative hindcast simulation GLOBAL_REANALYSIS_BIO_001_018 at 1/4° over period 1998 - 2016. Outputs are delivered as monthly mean files with Netcdf format (CF/COARDS 1.5 convention) on the native tripolar grid (ORCA025) at ¼° resolution with 75 vertical levels. This simulation is based on the PISCES biogeochemical model. It is forced offline at a daily frequency by the equivalent of the GLOBAL-REANALYSIS-PHYS-001-009 physics product but without data assimilation. '''Detailed description: ''' There are 8 different datasets: * dataset-global-nahindcast-bio-001-018-no3 containing : nitrate concentration * dataset-global-nahindcast-bio-001-018-po4 containing : phosphate concentration * dataset-global-nahindcast-bio-001-018-si containing : silicate concentration * dataset-global-nahindcast-bio-001-018-o2 containing : dissolved oxygen concentration * dataset-global-nahindcast-bio-001-018-fe containing : iron concentration * dataset-global-nahindcast-bio-001-018-chl containing : chlorophyll concentration * dataset-global-nahindcast-bio-001-018-phyc containing : carbon phytoplankton biomass * dataset-global-nahindcast-bio-001-018-pp containing : primary production The horizontal grid is the standard ORCA025 tri-polar grid (1440 x 1021 grid points). The three poles are located over Antarctic, Central Asia and North Canada. The ¼ degree resolution corresponds to the equator. The vertical grid has 75 levels, with a resolution of 1 meter near the surface and 200 meters in the deep ocean.Biogeochemical and physical simulations start at rest (cold start) in December 1991. The spin-up period consists of 5 years of interannual simulation between 1992 and 1997. The simulation period covers the ocean color era (1998 – 2016).The biogeochemical model used is PISCES (Aumont, in prep). It is a model of intermediate complexity designed for global ocean applications (Aumont and Bopp, 2006) and is part of NEMO modeling platform. It has 24 prognostic variables and simulates biogeochemical cycles of oxygen, carbon and the main nutrients controlling phytoplankton growth (nitrate, ammonium, phosphate, silicic acid and iron). The model distinguishes four plankton functional types based on size: two phytoplankton groups <nowiki>(small = nanophytoplankton and large = diatoms)</nowiki> and two zooplankton groups <nowiki>(small = microzooplankton and large = mesozooplankton).</nowiki>Prognostic variables of phytoplankton are total biomass in C, Fe, Si (for diatoms) and chlorophyll and hence the Fe/C, Si/C, Chl/C ratios are variable. For zooplankton, all these ratios are constant and total biomass in C is the only prognostic variable. The bacterial pool is not modeled explicitly. PISCES distinguishes three non-living pools for organic carbon: small particulate organic carbon, big particulate organic carbon and semi-labile dissolved organic carbon. While the C/N/P composition of dissolved and particulate matter is tied to Redfield stoichiometry, the iron, silicon and carbonate contents of the particles are computed prognostically. Next to the three organic detrital pools, carbonate and biogenic siliceous particles are modeled. Besides, the model simulates dissolved inorganic carbon and total alkalinity. In PISCES, phosphate and nitrate + ammonium are linked by constant Redfield ratio <nowiki>(C/N/P = 122/16/1)</nowiki>, but cycles of phosphorus and nitrogen are decoupled by nitrogen fixation and denitrification. Biogeochemical model PISCES (NEMO3.5) is forced offline by daily fields of the physical model NEMO (OPA module in the NEMO platform) without any assimilation of physical data. The main features of this dynamical ocean are: * NEMO 3.1 * Atmospheric forcings from 3-hourly ERA-Interim reanalysis products, CORE bulk formulation * Vertical diffusivity coefficient is computed by solving the TKE equation * Tidal mixing is parameterized according to the works of Bessières et al. (2008) and Koch-Larrouy et al, (2006). * Sea-Ice model: LIM2 with the Elastic-Viscous-Plastic rheology * Initial conditions: Levitus 98 climatology for temperature and salinity, patched with PHC2.1 for the Arctic regions, and Medatlas for the Mediterranean Sea. A special treatment is done on vertical diffusivity coefficient (Kz): the daily mean is done on Log10(Kz) after a filtering of enhanced convection (Kz increased artificially to 10 m2.s-1 when the water column is unstable). The purpose of this Log10 is to average the orders of magnitudes and to give more weight to small values of vertical diffusivity. The atmospheric forcing fields are daily averages from ERA-Interim reanalysis product (CORE bulk formulation). Boundary fluxes account for nutrient supply from three different sources: Atmospheric deposition (Aumont et al., 2008), rivers for nutrients, dissolved inorganic carbon and alkalinity (Ludwig et al., 1996; Mayorga et al., 2010) and inputs of Fe from marine sediments. Nutrient and freshwater inflows by rivers are colocalized. River and dust inputs are balanced with sediment trapping of NO3, Si and Carbon. An annual and global value of atmospheric carbon dioxide is imposed at sea surface.

IOWAGA sea-states forecast best estimate generated by the wave model WAVEWATCH-III and forced by ECMWF winds on the Mediterranean grid from 2017 to now

A partir des données Corine Land Cover, la carte présente l'évolution de l'urbanisation entre 1990 et 2006. L'objectif est de visualiser et de calculer la part de l'urbain situé près des grands axes routiers traversant le Parc Naturel Régional des Landes de Gascogne. Zone d'étude : L'urbanisation au nord du PNR représente un fort enjeu car la zone étudiée est située entre le Bassin d'Arcachon et l'agglomération bordelaise, et est bien desservie par le réseau viaire. La zone d'étude correspond aux communes du nord du PNR desservies par les routes primaires (définis par la BD TOPO 2009 de l'IGN) Données utilisées : -Corine Land Cover 2006 -Corine Land Cover Evolution 1990-2000 -Corine Land Cover Evolution 2000-2006 -BD TOPO 2009 Routes primaires de l'IGN -GEOFLA Communes de l'IGN -Limites administratives du PNR des Landes de Gascogne Traitement des données : Permet d'identifier : - la part d'urbain situé près des grands axes routiers desservant le PNR des Landes de Gascogne par rapport au bâti de la zone d'étude - la part d'urbain situé à proximité des grands axes routiers desservant le PNR des Landes de Gascogne - l'évolution du bâti depuis 2006 par rapport à 1990 sur la zone d'étude - la part du nouveau bati (entre 1990 et 2006) implantée en bordure des grands axes routiers

The raster dataset (1ºx1º) shows the projected change in relative sea level (in metres) in 2081-2100 compared to 1986-2005 for the medium-low emission scenario RCP4.5, based on an ensemble of Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. Projections consider land movement due to glacial isostatic adjustment but not land subsidence due to human activities. No projections are available for the Black Sea. The dataset has been used as a source for an earlier version of the EEA indicator “Global and European Sea Level”: https://www.eea.europa.eu/data-and-maps/indicators/sea-level-rise-5/assessment.

SeaDataNet Temperature and Salinity historical data collection, including revised quality flags after quality control with ODV. For data access please register at http://www.marine-id.org The dataset format is ODV binary collections. You can read, analyse and export from the ODV application provided by Alfred Wegener institute at http://odv.awi.de/

Ce document présente une synthèse des travaux effectués par les partenaires du projet FORRISK (Réseau pour l'innovation dans les sylvicultures et les systèmes de gestion intégrée des risques en forêt).

A la suite de la tempête Martin du 27 décembre 1999, l'Inventaire forestier national (IFN) a été chargé de cartographier les peuplements atteints et d'estimer leur taux de dégâts. Ces données sont le résultat des travaux menées en Gironde et en Dordogne.

Développement d'une méthode pour inventorier les airiaux, afin de les cartographier à différentes échelles prédéfinies.

IOWAGA sea-states forecast best estimate generated by the wave model WAVEWATCH-III and forced by ECMWF winds on the Global grid from 2017 to now

IOWAGA sea-states forecast best estimate generated by the wave model WAVEWATCH-III and forced by ECMWF winds on the North mediterranean grid from 2017 to now