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 net work (NN) model for predicting the contemporaneous wind stress field from t he upper ocean state. Upper ocean heat content (HC) from a 6-layer ocean mo del was a better predictor of the wind stress than the (observed or modelle d! sea surface temperature (SST). Our results showed that the NN model gene rally had slightly better skills in predicting the contemporaneous wind str ess than the linear regression (LR) model in the off-equatorial tropical Pa cific and in the eastern equatorial Pacific. When the wind stresses from th e 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. Be tter skills for the model HC were found in the western and central equatori al Pacific when the NN winds were used instead of the LR winds. Why NN fail ed to show more significant improvement over LR in the equatorial Pacific f or the wind stress and SST is probably because the relationship between the surface ocean and the atmosphere in the equatorial Pacific over the season al time scale is almost linear.