The 'spring predictability barrier' for ENSO predictions and its possible mechanism: results from a fully coupled model

Using predictions for the sea surface temperature (SST) generated by a Flexible Global Ocean-Atmosphere-Land System model of IAP/LASG (FGOALS-g), the season-dependent predictability of SST anomalies for El Nino/La Nina events is investigated by analyzing the forecast error growth in an imperfect model scenario. The results indicate that, for the predictions through the spring season in the growth phase of El Nino events, the prediction errors induced by both initial errors and model errors tend to have a prominent season-dependent evolution and yield a prominent spring predictability barrier (SPB). For the decay-phase predictions of El Nino events, a less prominent season-dependent evolution of prediction errors and then a less prominent SPB are observed. For the growth- and decay-phase predictions of La Nina events, the prediction errors do not exhibit a significant season-dependent evolution and yield a less prominent SPB phenomenon. These results indicate that the SPB phenomenon depends remarkably on the ENSO events themselves, particularly the phases of the El Nino/La Nina events. We also report that the initial SST errors that correspond to a significant SPB for El Nino events tend to have the dominant modes in a large-scale dipolar pattern with negative anomalies in the equatorial central-western Pacific and positive anomalies in the eastern Pacific, or vice versa. We further demonstrate that the error growth related to a significant SPB for El Nino prediction generated by the FGOALS-g model can result from two dynamical mechanisms: in one case, the prediction errors grow in a manner similar to El Nino; in the other, the prediction errors develop with a tendency opposite to El Nino. Copyright (c) 2012 Royal Meteorological Society