Serveur wms du projet CHARM II

Serveur wms sur les photos anciennes

Global wave hindcast (1961-2020) at 1° resolution using CMIP6 wind and sea-ice forcings for ALL (historical), GHG (historical greenhouse-gas-only), AER (historical Anthropogenic-aerosol-only), NAT (historical natural only) scenario.

The SARWAVE project is developing a new sea state processor from SAR images to be applied over open ocean, sea ice, and coastal areas, and exploring potential synergy with other microwave and optical EO products.

Serveur wms du projet CHARM III

WGS for Iatlantic projet ( ) for assessing past and present connectivity

The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite altimeter significant wave height data (referred to as Level 4 (L4) data) with a particular focus for use in climate studies. This dataset contains the Version 3 Remote Sensing Significant Wave Height product, gridded over a global regular cylindrical projection (1°x1° resolution), averaging valid and good measurements from all available altimeters on a monthly basis (using the L2P products also available). These L4 products are meant for statistics and visualization. The altimeter data used in the Sea State CCI dataset v3 come from multiple satellite missions spanning from 2002 to 2021 ( Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3A), therefore spanning over a shorter time range than version 1.1. Unlike version 1.1, this version 3 involved a complete and consistent retracking of all the included altimeters. Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band).

The SARWAVE project is developing a new sea state processor from SAR images to be applied over open ocean, sea ice, and coastal areas, and exploring potential synergy with other microwave and optical EO products.

A prerequisite for a successful development of a multi-mission wind dataset is to ensure good inter-calibration of the different extreme wind datasets to be integrated in the product. Since the operational hurricane community is working with the in-situ dropsondes as wind speed reference, which are in turn used to calibrate the NOAA Hurricane Hunter Stepped Frequency Microwave Radiometer (SFMR) wind data, MAXSS has used the latter to ensure extreme-wind inter-calibration among the following scatterometer and radiometer systems: the Advanced Scatterometers onboard the Metop series (i.e., ASCAT-A, -B, and -C), the scatterometers onboard Oceansat-2 (OSCAT) and ScatSat-1 (OSCAT-2), and onboard the HY-2 series (HSCAT-A, -B); the Advanced Microwave Scanning Radiometer 2 onboard GCOM-W1(AMSR-2), the multi-frequency polarimetric radiometer (Windsat), and the L-band radiometers onboard the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP) missions. In summary, a two-step strategy has been followed to adjust the high and extreme wind speeds derived from the mentioned scatterometer and radiometer systems, available in the period 2009-2020. First, the C-band ASCATs have been adjusted against collocated storm-motion centric SFMR wind data. Then, both SFMR winds and ASCAT adjusted winds have been used to adjust all the other satellite wind systems. In doing so, a good inter-calibration between all the systems is ensured not only under tropical cyclone (TC) conditions, but also elsewhere. This dataset was produced in the frame of the ESA funded Marine Atmosphere eXtreme Satellite Synergy (MAXSS) project. The primary objective of the ESA Marine Atmosphere eXtreme Satellite Synergy (MAXSS) project is to provide guidance and innovative methodologies to maximize the synergetic use of available Earth Observation data (satellite, in situ) to improve understanding about the multi-scale dynamical characteristics of extreme air-sea interaction.

Reef-building species are recognized as having an important ecological role and as generally enhancing the diversity of benthic organisms in marine habitats.  However, although these ecosystem engineers have a facilitating role for some species, they may exclude or compete with others. The honeycomb worm Sabellaria alveolata (Linnaeus, 1767) is an important foundation species, commonly found from northwest Ireland to northern Mauritania (Curd et al., 2020), whose reef structures increase the physical complexity of the marine benthos, supporting high levels of biodiversity. Local patterns and regional differences in taxonomic and functional diversity were examined in honeycomb worm reefs from ten sites along the northeastern Atlantic to explore variation in diversity across biogeographic regions and the potential effects of environmental drivers. To characterize the functional diversity at each site, a biological trait analysis (BTA) was conducted (Statzner et al., 1994). Here we present the functional trait database used for the benthic macrofauna found to live in association with honeycomb worm reefs. Eight biological traits (divided into 32 modalities) were selected (Table 1), providing information linked to the ecological functions performed by the associated macrofauna. The selected traits provide information on: (i) resource use and availability (by the trophic group of species, e.g. Thrush et al. 2006); (ii) secondary production and the amount of energy and organic matter (OM) produced based on the life cycle of the organisms (including longevity, maximum size and mode of reproduction, e.g. (Cusson and Bourget, 2005; Thrush et al., 2006) and; (iii) the behavior of the species in general [i.e. how these species occupy the environment and contribute to biogeochemical fluxes through habitat, movement, and bioturbation activity at different bathymetric levels, e.g. (Solan et al., 2004; Thrush et al., 2006; Queirós et al., 2013). Species were scored for each trait modality based on their affinity using a fuzzy coding approach (Chevenet et al., 1994), where multiple modalities can be attributed to a species if appropriate, and allowed for the incorporation of intraspecific variability in trait expression. The information concerning polychaetes was derived primarily from Fauchald et al (1979) and Jumars et al (2015). Information on other taxonomic groups was obtained either from databases of biological traits (www.marlin.ac.uk/biotic) or publications (Naylor, 1972; King, 1974; Caine, 1977; Lincoln, 1979; Holdich and Jones, 1983; Smaldon et al., 1993; Ingle, 1996; San Martín, 2003; Southward, 2008; Gil, 2011; Leblanc et al., 2011; Rumbold et al., 2012; San Martín and Worsfold, 2015; Jones et al., 2018). Map indicating the locations of the 10 study sites in the UK, France and Portugal within the four biogeographic provinces defined by Dinter (2001). (All sites were sampled in 8 different stations, except for UK4 where 5 stations were sampled).