A neural network atmospheric model for hybrid coupled modelling

 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. Upper ocean heat content (HC) from a 6-layer ocean model was a better predictor of the wind stress than the (observed or modelled) sea surface temperature (SST). Our 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. When the wind stresses from the NN and LR models were used to drive the ocean model, slightly better SST skills were found in the off-equatorial tropical Pacific and in the eastern equatorial Pacific when the NN winds were used instead of the LR winds. Better skills for the model HC were found in the western and central equatorial Pacific when the NN winds were used instead of the LR winds. Why NN failed to show more significant improvement over LR in the equatorial Pacific for the wind stress and SST is probably because the relationship between the surface ocean and the atmosphere in the equatorial Pacific over the seasonal time scale is almost linear. Received: 2 March 1999 / Accepted: 13 July 2000