Improving the CFSv2 prediction of the Indian Ocean Dipole based on a physical-empirical model and a deep-learning approach

Despite significant advances in seasonal climate forecasts, the reliability of both dynamical and empirical models for the Indian Ocean Dipole (IOD) prediction is still limited to a lead time of one season or less. In this study, the skill of the NCEP Climate Forecast System version 2 (CFSv2) for the IOD prediction during the period 1982-2014 is evaluated. The results indicate that the model performance for the IOD prediction is the worst in spring among the four seasons, which is manifested in the fact that a skilful prediction of spring IOD event is limited to a lead time of only about 1-2 months. To improve the forecast of spring IOD events, a physical-empirical (PE) model and a convolutional neural network (CNN) model are established in the present study. The IOD in April-May-June (AMJ) is taken as the predictand, and the CFSv2-predicted sea surface height (SSH) in AMJ and the observed Laptev sea ice in the preceding December are used as the two predictors. The original CFSv2-predicted IOD time series has an insignificant correlation with the observed IOD time series with a temporal correlation coefficient (TCC) of 0.03; the PE model (CNN model) can largely improve the IOD prediction with a TCC of 0.74 (0.77) between the PE-model-predicted (CNN-model-predicted) IOD and the observed IOD during AMJ. Thus, the PE model and the CNN model developed in the present study can be applied to improve the IOD predictions from numerical models in the future.