Contrasting the skills and biases of deterministic predictions for the two types of El Niño

The tropical Pacific has experienced a new type of El Niño, which has occurred particularly frequently during the last decade and is referred to as the central Pacific (CP) El Niño. Various coupled models with different degrees of complexities have been used to make real-time El Niño predictions, but large uncertainties still exist in the forecasts. It is still not yet known how much of the uncertainty is specifically related to the new CP type of El Niño and how much is common to both this type and the conventional Eastern Pacific (EP) type of El Niño. In this study, the deterministic performance of an El Niño-Southern Oscillation (ENSO) ensemble prediction system (EPS) is examined for these two types of El Niño. Ensemble hindcasts are performed for the nine EP El Niño events and twelve CP El Niño events that have occurred since 1950. The results show that (1) the skill scores for the EP events are significantly better than those for the CP events at all lead times; (2) the systematic forecast biases come mostly from the prediction of the CP events; and (3) the systematic error is characterized by an overly warm eastern Pacific during the spring season, indicating a stronger spring prediction barrier for the CP El Niño. Further improvements of coupled atmosphere-ocean models in CP El Niño prediction should be recognized as a major challenge and high-priority task for the climate prediction community.