In recent years, large datasets of in situ marine carbonate system parameters (partial pressure of CO2 (pCO2), total alkalinity, dissolved inorganic carbon and pH) have been collated. These carbonate system datasets have highly variable data density in both space and time, especially in the case of pCO2, which is routinely measured at high frequency using underway measuring systems. This variation in data density can create biases when the data are used, for example for algorithm assessment, favouring datasets or regions with high data density. A common way to overcome data density issues is to bin the data into cells of equal latitude and longitude extent. This leads to bins with spatial areas that are latitude and projection dependent (eg become smaller and more elongated as the poles are approached). Additionally, as bin boundaries are defined without reference to the spatial distribution of the data or to geographical features, data clusters may be divided sub-optimally (eg a bin covering a region with a strong gradient). To overcome these problems and to provide a tool for matching in situ data with satellite, model and climatological data, which often have very different spatiotemporal scales both from the in situ data and from each other, a methodology has been created to group in situ data into ‘regions of interest’, spatiotemporal cylinders consisting of circles on the Earth’s surface extending over a period of time. These regions of interest are optimally adjusted to contain as many in situ measurements as possible. All in situ measurements of the same parameter contained in a region of interest are collated, including estimated uncertainties and regional summary statistics. The same grouping is done for each of the other datasets, producing a dataset of matchups. About 35 million in situ datapoints were then matched with data from five satellite sources and five model and re-analysis datasets to produce a global matchup dataset of carbonate system data, consisting of 287,000 regions of interest spanning 54 years from 1957 to 2020. Each region of interest is 100 km in diameter and 10 days in duration. An example application, the reparameterisation of a global total alkalinity algorithm, is shown. This matchup dataset can be updated as and when in situ and other datasets are updated, and similar datasets at finer spatiotemporal scale can be constructed, for example to enable regional studies. This dataset was funded by ESA Satellite Oceanographic Datasets for Acidification (OceanSODA) project which aims at developing the use of satellite Earth Observation for studying and monitoring marine carbonate chemistry. **This version is now superseded by the version 4 with higher spatial and temporal resolution**

Wave impact is the primary cause of coastal structure failure. While wave impact is widely studied in controlled environments, in situ measurements of wave impact pressure are rare. The results of a campaign to measure wave impact pressure in situ are summarised here. Data were collected from 2016 to 2019 from anchored pressure gauges on the wall of the Artha breakwater in southwestern France. The acquisition frequency is 10 kHz and 10-minute bursts are recorded every hour. Two databases are published, one by burst and one by impact. The burst database summarises the main parameters describing the 10-minute record, while the impact database contains a list of parameters describing each impact.

This dataset consists of metatranscriptomic sequencing reads corresponding to coastal micro-eukaryote communities sampled in Western Europe in 2018 and 2019.

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.

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

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 sur les photos anciennes

The data file present detailed individual congener/compound concentrations  for a large variety of hydrophobic organic contaminants including polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs), legacy and alternative brominated flame retardants (BFRs) and per- and polyfluoroalkyl substances (PFASs) in meso- and bathypelagic organisms collected in the Bay of Biscay, northeast Atlantic, in October 2017. The studied species include 3 crustacean species (Pasiphaea sivado, Sergia robusta, Ephyrina figueirai) and 11 fish species (Xenodermichthys copei, Searsia koefoedi, Myctophum punctatum, Notoscopelus kroeyeri, Lampanyctus crocodilus, Argyropelecus olfersii, Arctozenus risso, Stomias boa, Serrivomer beanii, Chauliodus sloani, Aphanopus carbo). The organisms were collected at night during one single trawling using a 25 m vertical opening pelagic trawl in the deep scattering layer (ca 800 m depth in the water column; 1330 m bottom floor). This dataset was used in the article entitled "A large diversity of organohalogen contaminants reach the meso- and bathypelagic organisms in the Bay of Biscay (northeast Atlantic)" published in Marine Pollution Bulletin.

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