The systematic swath bathymetric mapping of the Mediterranean Sea started in the mid-nineties. This mapping has considerably modified our understanding of the morphology of the Mediterreanean Sea and of the various active geological processes (sedimentary, tectonic, volcanic, bio-geochemical processes) which participate to the seafloor morphology.

Until recently, classical radar altimetry could not provide reliable sea level data  within 10 km to the coast. However dedicated reprocessing of radar waveform  together with geophysical corrections adapted for the coastal regions now allows  to fill this gap at a large number of coastal sites. In the context of the Climate Change Initiative Sea Level project of the European Space Agency, we have recently performed a complete reprocessing of high resolution (20 Hz, i.e., 350m)  along-track altimetry data of the Jason-1, Jason-2 and Jason-3 missions over  January 2002 to June 2021 along the coastal zones of Northeast Atlantic,  Mediterranean Sea, whole African continent, North Indian Ocean, Southeast Asia,  Australia and North and South America. This reprocessing has provided valid sea  level data in the 0-20 km band from the coast. A total of 1189 altimetry-based  virtual coastal stations have been selected and sea level anomalies time series  together with associated coastal sea level trends have been computed over the study time span. In the coastal regions devoid from tide gauges  (e.g., African coastlines), these virtual stations offer a unique tool for estimating  sea level change close to the coast (typically up to 3 km to the coast but in many  instances up to 1 km or even closer). Results show that at most of the virtual  stations, the rate of sea level rise at the coast is similar to the rate offshore (15 km away from the coast). However, at some stations, the sea level rate in the last 3-4 km to the coast is either faster or slower than offshore.

Satellite altimetry missions provide a quasi-global synoptic view of sea level over more than 25 years. The satellite altimetry constellation is used to build sea level maps and regional sea level indicators such as trends and accelerations. Estimating realistic uncertainties on these quantities is crucial to address some current climate science questions such as climate change detection and attribution or regional sea level budget closure for example. Previous studies have estimated the uncertainty for the global mean sea level (GMSL), but no uncertainty information is available at regional scales. In this study we estimate a regional satellite altimetry error budget and use it to derive maps of confidence intervals for local sea rise rates and accelerations. We analyze 27 years of satellite altimetry maps and derive the satellite altimetry error variance-covariance matrix at each grid point, prior to the estimation of confidence intervals on local trends and accelerations at the 90% confidence level using extended least squares estimators. Over 1993–2019, we find that the average local sea level trend uncertainty is 0.83 mm.yr-1 with local values ranging from 0.78 to 1.22 mm.yr-1. For accelerations, uncertainties range from 0.057 to 0.12 mm.yr-2, with a mean value of 0.063 mm.yr-2.   Change history: - 2020/07/08: initial dataset submission over 1993-2018 - 2020/10/21: 1993-2019 update and addition of error levels

This dataset is composed by the climatological seasonal field of the Ocean Salinity Stratification as defined from the Brunt-Vaisala frequency limited to the upper 300 m depth. The details are given in Maes, C., and T. J. O’Kane (2014), Seasonal variations of the upper ocean salinity stratification in the Tropics, J. Geophys. Res. Oceans, 119, 1706–1722, doi:10.1002/2013JC009366.

The binned Sea Surface Salinity, Temperature and Density data set covers regularly sampled ship-of-opportunity lines. It is based on data collected from 1993 to 2018 from Voluntary Observing Ships subsequently validated. This monthly product is binned with monthly total values deviations and then deviations from climatology in each bin, with additional 1-2-1 time filter applied on the averages. Along B-AX01, some gaps were filled with additional data located one degree north or south of the grid boxes.

The gridded Sea Surface Salinity (SSS) data set covers the region between 95°W – 20°E and 50°N – 30°S in the Atlantic Ocean. It is based on available data collected from 1970 to 2016 mostly from Voluntary Observing Ships, PIRATA moorings and Argo profilers, and subsequently validated. This monthly SSS product is gridded using an objective mapping at the spatial resolution 1° x 1°. It is distributed with its associated error fields. It is an update of the SSS product presented in Reverdin et al (2007).

Gironde estuary environmental parameters and SPM maps generated from 41 Landsat-8/OLI and Sentinel-2/MSI images acquired over the period 2013-2018. Except bathymetry and daily river discharge data, that are accessible on public platforms, the dataset includes all of the time seris used in the publication: Analysis of suspended sediment variability in a large highly-turbid estuary using a 5-year-long remotely-sensed data archive at high resolution, Journal of Geophysical Research: Oceans, DOI:10.1029/2019JC015417.

The annually binned Sea Surface Salinity data set covers a large part of the Atlantic Ocean (75°W-10°E, 20°S-70°N). It is based on salinity near-surface data collected since December 1895 to 2016 from all data sources, including Voluntary Observing Ships subsequently validated. This annual product presents Atlantic SSS 32 grid box anomaly and error time-series (for boxes 1 to 26, year 2016 not smoothed 1-2-1), as well as Atlantic SSS grid box coordinates and March-May mean climatology. The SSS binned time series are an update from the 1896-2013 time series (Friedman et al., 2017).

Global mean sea level is an integral of changes occurring in the climate system in response to unforced climate variability as well as natural and anthropogenic forcing factors. Its temporal evolution allows detecting changes (e.g., acceleration) in one or more components. Study of the sea level budget provides constraints on missing or poorly known contributions, such as the unsurveyed deep ocean or the still uncertain land water component. In the context of the World Climate Research Programme Grand Challenge entitled “Regional Sea Level and Coastal Impacts”, an international effort involving the sea level community worldwide has been recently initiated with the objective of assessing the various data sets used to estimate components of the sea level budget during the altimetry era (1993 to present). These data sets are based on the combination of a broad range of space-based and in situ observations, model estimates and algorithms. Evaluating their quality, quantifying uncertainties and identifying sources of discrepancies between component estimates is extremely useful for various applications in climate research. This effort involves several tens of scientists from about sixty research teams/institutions worldwide (www.wcrp-climate.org/grand-challenges/gc-sea-level). The results presented in this paper are a synthesis of the first assessment performed during 2017-2018. We present estimates of the altimetry-based global mean sea level (average rate of 3.1 +/- 0.3 mm/yr and acceleration of 0.1 mm/yr2 over 1993-present), as well as of the different components of the sea level budget. We further examine closure of the sea level budget, comparing the observed global mean sea level with the sum of components. Ocean thermal expansion, glaciers, Greenland and Antarctica contribute by 42%, 21%, 15% and 8% to the global mean sea level over the 1993-present. We also study the sea level budget over 2005-present, using GRACE-based ocean mass estimates instead of sum of individual mass components. Results show closure of the sea level budget within 0.3 mm/yr. Substantial uncertainty remains for the land water storage component, as shown in examining individual mass contributions to sea level.

Sea Surface Salinity (SSS) is an essential climate variable that requires long term in situ observation. The French SSS Observation Service (SSS-OS) manages a network of Voluntary Observing Ships equipped with thermosalinographs (TSG) since 2002. The network is global though more concentrated in the tropical Pacific and North Atlantic oceanic basins. The acquisition system is autonomous with real time transmission and is regularly serviced at harbor calls. The high resolution data retrieved from the acquisition system during ship calls is processed through a dedicated software (freely available) for attribution of data quality flags by visual inspection, and correction of TSG time series by comparison with climatology, onboard daily water samples and collocated Argo data. Details can be found in the reference below. The validated delayed time data collected from TSG, together with some bucket samples mostly collected before 2002, are made available for educational and research purposes through an interactive web interface.