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. Monitoring sea surface salinity (SSS) is important for understanding andforecasting the ocean circulation. It is even crucial in the context of theintensification of the water cycle. Until recently, SSS was one of the lessobserved essential ocean variables. Only sparse in situ observations, mostlycloser to 5 m depth than the surface, were available to estimate the SSS.The recent satellite ESA Soil Moisture and Ocean Salinity (SMOS), NASAAquarius SAC-D and Soil Moisture Active Passive (SMAP) missions have made it possible for the first time to measure SSS from space and can bring avaluable additional constraint to control the model salinity. Nevertheless,satellite SSS still contains some residual biases that must be removed priorto bias correction and data assimilation. One of the major challenges of thisstudy is to estimate the SSS bias and a suitable observation error for thedata assimilation system. It was made possible by modifying a 3D-Var biascorrection scheme and by using the analysis of the residuals and errors withan adapted statistical technique. This article presents the design and the analysis of an observing systemexperiment (OSE) conducted with the 0.25 ∘ resolution Mercator Oceanglobal analysis and forecasting system during the El Nino 2015/16 event.The SSS data assimilation constrains the model to be closer to thenear-surface salinity observations in a coherent way with the other data setsalready routinely assimilated in an operational context. This also shows thatthe overestimation of E – P is corrected by data assimilation throughsalting in regions where precipitations are higher. Globally, the SMOS SSSassimilation has a positive impact in salinity over the top 30 m.Comparisons to independent salinity data sets show a small but positiveimpact and corroborate the fact that the impact of SMOS SSS assimilation islarger in the Intertropical Convergence Zone (ITCZ) and South Pacific Convergence Zone (SPCZ) regions. There is little impact on the seasurface temperature (SST) and sea surface height (SSH) error statistics.Nevertheless, the SSH seems to be impacted by the tropical instability wave(TIW) propagation, itself linked to changes in barrier layer thickness(BLT). Finally, this study helped us to progress in the understanding of the biasesand errors that can degrade the SMOS SSS data assimilation performance.

Data assimilation of Soil Moisture and Ocean Salinity (SMOS) observations into the Mercator Ocean operational system: focus on the El Niño 2015 event