On the “spring predictability barrier” for strong El Niño events as derived from an intermediate coupled model ensemble prediction system

Abstract

Using predictions for the sea surface temperature anomaly (SSTA) generated by an intermediate coupled model (ICM) ensemble prediction system (EPS), we first explore the “spring predictability barrier” (SPB) problem for the 2015/16 strong El Nino event from the perspective of error growth. By analyzing the growth tendency of the prediction errors for ensemble forecast members, we conclude that the prediction errors for the 2015/16 El Nino event tended to show a distinct season-dependent evolution, with prominent growth in spring and/or the beginning of the summer. This finding indicates that the predictions for the 2015/16 El Nino occurred a significant SPB phenomenon. We show that the SPB occurred in the 2015/16 El Nino predictions did not arise because of the uncertainties in the initial conditions but because of model errors. As such, the mean of ensemble forecast members filtered the effect of model errors and weakened the effect of the SPB, ultimately reducing the prediction errors for the 2015/16 El Nino event. By investigating the model errors represented by the tendency errors for the SSTA component, we demonstrate the prominent features of the tendency errors that often cause an SPB for the 2015/16 El Nino event and explain why the 2015/16 El Nino was under-predicted by the ICM EPS. Moreover, we reveal the typical feature of the tendency errors that cause not only a significant SPB but also an aggressively large prediction error. The feature is that the tendency errors present a zonal dipolar pattern with the west poles of positive anomalies in the equatorial western Pacific and the east poles of negative anomalies in the equatorial eastern Pacific. This tendency error bears great similarities with that of the most sensitive nonlinear forcing singular vector (NFSV)-tendency errors reported by Duan et al. and demonstrates the existence of an NFSV tendency error in realistic predictions. For other strong El Nino events, such as those that occurred in 1982/83 and 1997/98, we obtain the tendency errors of the NFSV structure, which cause a significant SPB and yield a much larger prediction error. These results suggest that the forecast skill of the ICM EPS for strong El Nino events could be greatly enhanced by using the NFSV-like tendency error to correct the model.