ENSO simulation and prediction using hybrid coupled models with data assimilation

The possibility of using a nonlinear empirical atmospheric model for hybrid coupled atmosphere-ocean modelling has been examined by using a neural network (NN) model for predicting the contemporaneous wind stress field from the upper ocean state in the tropical Pacific. Upper ocean heat content (HC) from a 6-layer dynamic ocean model was a better predictor of the wind stress than the (observed or modelled) sea surface temperature (SST). The results showed that the NN model generally had slightly better skills in predicting the contemporaneous wind stress than the linear regression (LR) model in the off-equatorial tropical Pacific and in the eastern equatorial Pacific.; Next, the NN and LR atmospheric models were separately coupled to the dynamic ocean model, yielding respectively a hybrid coupled model with a nonlinear atmosphere (NHCM) and one with a linear atmosphere (LHCM).; ENSO prediction skills in the two hybrid coupled models have also been investigated under two initialization schemes.; The impact of assimilating different types of data on ENSO simulations and predictions was investigated by separately assimilating the SST, sea surface height anomalies (SSHA), upper ocean heat content anomalies (HCA), and wind stress, with the 3-D Var (variational assimilation) technique.; In summary, this thesis has initiated the fusion of neural network techniques into dynamical models. Using an NN model for the atmosphere, it has produced the first HCM with a nonlinear empirical atmospheric component, and showed that the nonlinear atmosphere could have advantages over a linear atmosphere in modelling ENSO variability and in ENSO prediction. This study has introduced NN for the assimilation of non-prognostic variables (e.g. HCA) by using NN to relate the non-prognostic variable to prognostic variables, which are then assimilated into the model. While the full 4-D Var HCM is beyond the scope of this thesis, a neural-dynamical hybrid approach under 4-D Var has been developed to study the simple Lorenz system. (Abstract shortened by UMI.)...