With a simple 3DVar assimilation algorithm, a new scheme of assimilating sea surface temperature (SST) observations is proposed in this paper. In this new scheme, the linear relation between any two neighboring depths was derived using singular value decomposition technique and then was applied to estimate the temperatures at deeper levels using the temperature analyses at shallower levels. The estimated temperatures were assimilated into an ocean model, and the procedure was run iteratively at each time step from the surface to a depth of 250 m. The oceanic analyses show that the new scheme can more effectively adjust oceanic thermal and dynamical fields and lead to a more realistic subsurface thermal structure when compared with the control run and another scheme that is usually used for SST assimilation. An ensemble of predictions for the Niño-3 region SST anomalies was performed to test the new scheme. It was found that the new scheme can improve fairly well ENSO prediction skills at all lead times, in particular for anomalous warm events, and for lead times of 4-7 months.
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