ENSO PREDICTION USING A DYNAMICAL OCEAN MODEL COUPLED TO STATISTICAL ATMOSPHERES

The predictability of El Nino/Southern Oscillation (ENSO) events is ad dressed by means of statistical and dynamical schemes. The statistical schemes are based on principal oscillation pattern (POP) analysis of various observed and model ocean fields: these statistical predictions establish a lower limit for the predictability of such a system. For the dynamical predictions, an ocean model of intermediate complexity i s coupled to several statistical surface wind stress models. In these coupled models, the atmospheric anomalies are a linear response to the oceanic fields: several combination of fields are considered, such as SST and heat content. The spatial features of predictability are disc ussed. Predictions seem to be better in the central Pacific. In the we stern and eastern Pacific, the predictability skill scores are poorer, possibly due to deficiencies in the ocean thermodynamics and in the c oupling. The model predictions exhibit a pronounced seasonal dependenc e, with spring and summer being less predictable. Best results are obt ained with seasonally-dependent predictors.