Assimilating satellite sea-surface salinity data from SMOS, Aquarius and SMAP into a global ocean forecasting system

Measuring Sea-Surface Salinity (SSS) from space is a relatively recent technique that relies on L-band radiometry that has evolved to a point where useful information is provided every few days. The impact of assimilating satellite SSS data is investigated using the global FOAM ocean forecasting system. This system assimilates daily satellite SSS products from the ESA Soil Moisture and Ocean Salinity (SMOS), NASA Aquarius and Soil Moisture Active Passive (SMAP) missions equatorward of 40 degrees N/S, in addition to other observing systems. The data are assimilated using a 3D-Var scheme that includes an observation bias correction scheme to estimate spatially and temporally varying bias estimates for each SSS satellite. The SSS assimilation is tested over 2 years and 3 months covering the 2015/2016 El Nino with assessment focussed on the tropical regions, particularly in the Pacific. Consistent reductions in root-mean-square errors are found in all tropical regions from each of the satellite SSS datasets. The largest improvements of up to about 8%. are found in the tropical Pacific from assimilating both the SMOS and SMAP datasets together. The largest impact is in the intertropical convergence zone (ITCZ) i n the central Pacific during 2015 and early 2016 where the surface salinity is reduced by about 0.03 pss on average, correcting for too little precipitation. A smaller magnitude, large-scale reduction in the SSS is also seen in the tropical Pacific that increases the modelled surface stratification and leads to reduced vertical mixing. Changes to the SSS in the ITCZ lead to changes in the meridional gradients of SSS that affects the sea surface height and surface currents, both of which are improved in the SMOS assimilation experiment compared to externally produced observation-based datasets. The results suggest that assimilation of SMOS and SMAP satellite data is now of an appropriate quality for operational implementation.