Abstract
Abstract. The 2006–2007 El Niño 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 Niño prediction system, roles of the initial ocean surface and subsurface states on predicting the 2006–2007 El Niño event are investigated to determine conditions favorable for predicting El Niño 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 Niño 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 Niño, whereas a more reasonable initial subsurface state can contribute to improving the prediction in the growth of the warm event.
Highlights
El Niño–Southern Oscillation (ENSO), which is one of the most striking interannual variabilities in the tropical Pacific Ocean, has been studied for several decades
The latter is important in the ocean because the memory for ENSO resides in the ocean (e.g., Neelin et al, 1998), and the importance of ocean data in making ENSO predictions has been demonstrated in a number of studies (e.g., Ji and Leetmaa, 1997; Ji et al, 2000; Alves et al, 2004; Keenlyside et al, 2005; Zheng et al, 2006, 2007; Zheng and Zhu, 2008; Yang et al, 2010; Zhu et al, 2012)
This type of zonal sea surface temperature (SST) gradient is convenient for generating stronger westerly winds over the equatorial Pacific (Fig. 3b) at the initial time, which results in the triggering of a considerable El Niño event
Summary
El Niño–Southern Oscillation (ENSO), which is one of the most striking interannual variabilities in the tropical Pacific Ocean, has been studied for several decades. Many climate models from operational centers have routinely been used to make ENSO predictions in real time (Latif et al, 1998; Kirtman et al, 2002), the skill of sea surface temperature (SST) forecasts in the equatorial Pacific is strongly model dependent and widely divergent across various prediction systems (Jin et al, 2008; Barnston et al, 2012). Zhu: Roles of initial ocean states on predicting 2006 El Niño from operational centers only predicted the event after the warming had already become apparent and was basinwide (McPhaden, 2008). On the basis of the ensemble Kalman filter (EnKF) algorithm (e.g., Evensen, 2009), the roles of the initial ocean surface and subsurface states on the 2006–2007 El Niño predictions were examined in three sets of retrospective forecast experiments. The forecast differences in the three sets of the retrospective experiments with initializations by the three separate data assimilation schemes were examined to isolate the effects of the various initial states on predicting the warm event
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