Impacts of Initial Zonal Current Errors on the Predictions of Two Types of El Nino Events

Perturbations in the thermocline and surface zonal current (ZC) play crucial roles in the evolutions of the eastern Pacific (EP) and central Pacific (CP) El Nino events, respectively. Whereas numerous studies have examined the influence of initial uncertainties in ocean temperature on the predictability of El Nino, only a few studies investigated the impact of the initial ZC. Using an air-sea coupling model, the conditional nonlinear optimal perturbation (CNOP) approach was employed to investigate the maximum impact of initial ZC errors on the El Nino prediction. The optimal initial ZC errors (denoted as CNOP-Us) that have the severest impact on the El Nino prediction are found to mainly concentrate in the western and central tropical Pacific. The CNOP-Us cause larger errors in the CP El Nino prediction than in the EP El Nino prediction. Additionally, CNOP-Us cause rapid sea surface temperature error growth in spring in the EP El Nino prediction but in summer in the CP El Nino prediction. Dynamically, the former is related to the large uncertainties in the meridional current in spring caused by CNOP-Us, while the latter is related to the strong ZC errors in summer. According to the distributions of CNOP-Us, reducing the initial ZC errors in the western and central tropical Pacific may be vital in weakening the predictability barrier phenomena and improving the predictions of El Nino diversity.

Automated summary

Perturbations in the thermocline and surface zonal current have significant impacts on the evolutions of EP and CP El Nino events. Studies have mainly focused on the influence of initial uncertainties in ocean temperature, while the impact of initial zonal current has been less explored. Using a coupled air-sea model, this study found that the optimal initial zonal current errors with the severest impact on El Nino prediction are mainly concentrated in the western and central tropical Pacific. These errors cause larger prediction errors for CP El Nino than for EP El Nino. By reducing the initial zonal current errors in these regions, the predictability barrier phenomena of El Nino can be weakened and the predictions of El Nino diversity can be improved.