[03-Dec-2012] Tranchant, B., Greiner, E., Legalloudec, O., Lellouche, J-M., Ferry, N., and Guinehut, S.
Presented at the 2012 AGU Fall MeetingIn the framework of the European project GMES/Myocean, Mercator Ocean has designed a hierarchy of ocean analysis and forecasting operational systems delivering weekly and daily services in real time. The ocean and sea ice models are based on the NEMO/LIM codes. The data assimilation algorithm relies on a reduced-order Kalman filter with a 3D multivariate modal decomposition of the forecast error. Altimeter data, satellite sea surface temperature (SST) and in situ temperature and salinity vertical profiles are jointly assimilated.An important issue for ocean forecasting systems is the occurrence of sea surface salinity (SSS) bias at various scales which comes from ocean model errors and forcing uncertainties, mainly precipitations. Moreover, it is not possible using Argo network data to fully remove SSS biases because the data are too scarce and do not correctly sample the near surface salinity (depth < 5m). Recently, SSS from space (SMOS and Aquarius) have become available in near real time and can possibly fill this data gap. In this context, Mercator Ocean investigates the best way to deal with this new SSS data by taking into account different errors associated with different scales. Early results with the global 1/4#176; system show to what extent it is possible to correct the large scale bias and the meso-scale background errors by assimilating this new SSS data.