ENSO hindcast skill of the IAP-DecPreS near-term climate prediction system: comparison of full-field and anomaly initialization

Model initialization is a key process of climate predictions using dynamical models. In this study, the authors evaluated the performances of two distinct initialization approaches—anomaly and full-field initializations—in ENSO predictions conducted using the IAP-DecPreS near-term climate prediction system developed by the Institute of Atmospheric Physics (IAP). IAP-DecPreS is composed of the FGOALS-s2 coupled general circulation model and a newly developed ocean data assimilation scheme called ‘ensemble optimal interpolation-incremental analysis update’ (EnOI-IAU). It was found that, for IAP-DecPreS, the hindcast runs using the anomaly initialization have higher predictive skills for both conventional ENSO and El Niño Modoki, as compared to using the full-field initialization. The anomaly hindcasts can predict super El Niño/La Nina 10 months in advance and have good skill for most moderate and weak ENSO events about 4–7 months in advance. The predictive skill of the anomaly hindcasts for El Niño Modoki is close to that for conventional ENSO. On the other hand, the anomaly hindcasts at 1- and 4-month lead time can reproduce the major features of large-scale patterns of sea surface temperature, precipitation and atmospheric circulation anomalies during conventional ENSO and El Niño Modoki winter.