Roles of initial ocean surface and subsurface states on successfully predicting 2006-2007 El Nino with an intermediate coupled model

The 2006-2007 El Nino event, an unusually weak event, was predicted by most models only after the warming in the eastern Pacific had commenced. In this study, on the basis of an El Nino prediction system, roles of the initial ocean surface and subsurface states on predicting the 2006-2007 El Nino event are investigated to determine conditions favorable for predicting El Nino growth and are isolated in three sets of hindcast experiments. The hindcast is initialized through assimilation of only the sea surface temperature (SST) observations to optimize the initial surface condition, only the sea level (SL) data to update the initial subsurface state, or both the SST and SL data. Results highlight that the hindcasts with three different initial states can all successfully predict the 2006-2007 El Nino event 1 year in advance and that the hindcast initialized by both the SST and SL data performs best. A comparison between the various sets of hindcast results further demonstrates that successful prediction is more significantly affected by the initial subsurface state than by the initial surface condition. The accurate initial surface state can trigger the easier prediction of the 2006-2007 El Nino, whereas a more reasonable initial subsurface state can contribute to improving the prediction in the growth of the warm event.