| Summary: | European Geosciences Union (EGU) General Assembly 2018, 8-13 April 2018, Vienna, Austria.-- 1 page The Mediterranean Sea is a hot spot for climate change. The water balance in the basin is characterized by anexcess of evaporation over precipitation and river runoff, which is compensated by the entrance of fresher waterfrom the Atlantic. This Atlantic water, AW, which spreads through the Mediterranean Sea, determines the surfacecirculation.In the Algerian Basin, AW forms an unstable current that generates fresh-core coastal eddies that propa-gate downstream, eventually offshore. The eddy activity in the region enhances the mixing of the recently enteredAW with the saltier resident water, strongly affecting the spatial distribution of salinity and, therefore, playinga major role in the surface circulation of the Mediterranean Sea. Besides, during winter, in the North WesternMediterranean, deep water convection occurs under the influence of dry and cold northerly winds.In such a context, data from Soil Moisture and Ocean Salinity (SMOS) European Space Agency (ESA)’smission spanning more than 8 years can help to gain a better understanding of the Sea Surface Salinity (SSS)dynamics in the Mediterranean Sea. Unfortunately, this critical area is strongly affected by Radio FrequencyInterference and systematic biases due to the coast contamination (also called Land-Sea contamination). Botheffects impair SMOS SSS retrieval in these areas.A new methodology using a combination of debiased non-Bayesian retrieval, DINEOF (Data InterpolatingEmpirical Orthogonal Functions) and multifractal fusion has been used to improve to improve SMOS SSS fieldsover the North Atlantic Ocean and the Mediterranean Sea. The debiased non-Bayesian retrieval mitigates thesystematic errors produced by the Land-Sea contamination. Besides, this retrieval improves the coverage bymeans of multiyear statistical filtering criteria. This methodology allows obtaining SMOS SSS fields in theMediterranean Sea. However, the resulting SSS suffers from a ...
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