The Impact Of Oceanographic Data Assimilation On Seasonal And Interannual Predictions Of SST

EnKF(Ensemble Kalman filter)is a widely used ocean data assimilation scheme,which uses both the members’ state to represent background error covariance of the model and the observation error covariance to calculate the Kalman gain matrix and add the information of observation to the model’s initial conditions.Data assimilation can effectively improve the climate prediction skill since seasonal-interannual climate prediction is largely influenced by the initial conditions.Based on NUIST-CFS1.0(i.e.,previously SINTEX-F)that had used coupled Sea Surface Temperature(SST)-nudging initialization method,we employ En KF to assimilate SST,altimeter satellite gridded sea level anomalies,in situ temperature and salinity profiles at the end of each month.We assess the differences in the initial fields and climate(including ENSO)prediction skills at lead times of up to 24 months with and without the ocean data assimilation,with the following conclusions:(1)The results show that the initial conditions are improved largely with the ocean data assimilation,and the improvement of the initial fields is getting better with the increase of depth.The global mean prediction skills of SST and subsurface temperature with En KF are improved at 1-24 months lead,and the improved prediction also becomes more obvious in deep layers.(2)However,the prediction skill of Ni?o3.4 index is reduced after the En KF assimilation.This might be due the fact that the existence of the model error;the initial field with En KF is gradually drifted from a state close to the observation to the model’s state.The models’ climate displays a larger cold SST bias in the west and warm bias in the equatorial central-eastern Pacific,compared to the original NUIST-CFS1.0 forecasts with coupled SST-nudging initialization.But the prediction accuracy of ENSO events and different types does not decrease.(3)The inclusion of ocean data assimilation improves the area where the model can effectively produce seasonal and interannual predictions,and greatly improves the prediction performance in North Pacific and North Atlantic regions,and very significantly improves the ocean temperature prediction capability of the regions including the tropical Indian Ocean and the tropical Atlantic Ocean.