Suitability of satellite sea surface salinity data for use in assessing and correcting ocean forecasts

Abstract

Abstract Near-surface salinity data from the Forecasting Ocean Assimilation Model (FOAM) system are used to understand various characteristics of satellite sea surface salinity (SSS) data from SMOS. The errors in the model fields are first estimated by comparing them to near-surface Argo salinity measurements, with RMS errors of less than 0.2 pss over most of the global oceans, except for regions of high variability in SSS such as boundary current regions and areas of large precipitation or river run-off. Regional biases are generally less than 0.05 pss but some regions such as the Antarctic Circumpolar Current and the region to the north of the Gulf Stream extension have larger biases. Various different processing versions of the SMOS data are assessed, including different temporal and spatial averaging, and the daily 1° resolution SMOS differences to FOAM are approximately 3–10 times larger than Argo-FOAM differences. The spatial information in the SMOS data is also assessed by comparing spatial gradients in the satellite SSS data with those calculated from other datasets including satellite sea surface temperature (SST) and satellite altimeter sea surface height (SSH) data, as well as with the model's gradients. The SMOS data contain information about the underlying ocean dynamics in the summer months, in agreement with the SSH data, which are not present in the satellite SST data or in the model's SSS fields. This demonstrates that the data contains useful information which could be used to correct the model through data assimilation.