Exploring the initial errors that cause a significant “spring predictability barrier” for El Niño events

Abstract

Within the Zebiak‐Cane model, we identify two types of initial errors that have significant season‐dependent evolutions related to the spring predictability barrier (SPB) for El Niño events. One type includes the sea surface temperature anomaly (SSTA) errors that have a zonal dipolar pattern with positive anomalies in the central equatorial Pacific and negative ones in the eastern equatorial Pacific; the other type consists of the SSTA errors with a spatial structure opposite to that of the former type, the zonal dipolar pattern shows negative anomalies in the central equatorial Pacific and positive anomalies in the eastern equatorial Pacific. The patterns of these two types of errors are nearly opposite of each other. The former causes the El Niño event to be underpredicted, and the latter causes the El Niño event to be overpredicted. For strong El Niño events the former tends to have a larger effect on the predictions than the latter, but for weak El Niño events, it is very difficult to determine which type of initial errors results in worse predictions. It is thought that strong (weak) El Niño events could be affected by strong (weak) nonlinearities. There are also other initial errors; however, they do not yield considerable season‐dependent evolutions nor can a common characteristic be extracted from their patterns. The two types of initial errors suggest two dynamical behaviors of error growth related to the SPB: in one case, the initial errors grow in a manner similar to the El Niño events; in the other case, the initial errors develop with a tendency opposite to the El Niño events. The two types of initial errors may capture the errors that exhibit significant season‐dependent evolutions related to the SPB. In addition, they may provide information regarding the “sensitive area” of ENSO predictions because of their localized regions. Therefore, if these types of initial errors exist in the realistic El Niño–Southern Oscillation (ENSO) predictions and if a data assimilation or a target method can filter them, the ENSO forecast skill may be improved. For ensemble forecast studies, different signs of prediction errors caused by the two types of initial errors could illustrate why the ensemble mean offers a better forecast than a single prediction.

Towards optimal observational array for dealing with challenges of El Niño-Southern Oscillation predictions due to diversities of El Niño

“…Quite a few studies have explored the important role of the initial errors in ENSO prediction uncertainties (Chen et al 1995(Chen et al , 2004Moore and Kleeman 1996;Mu et al 2007;Thompson 1998;Xue et al 1997a, b) or emphasized the importance of the accuracy of initial analysis fields in improving ENSO forecast skill (Keenlyside et al 2005;Zheng et al 2006Zheng et al , 2007Zhu et al 2017). Moreover, recent studies showed that the initial errors with particular spatial structures cause much larger prediction uncertainty for ENSO (Mu et al 2007;Yu et al 2009;Duan et al 2009;Duan and Hu 2016). Specifically, Duan et al (2009) pointed out that the initial errors with a dipole structure of sea surface temperature anomalies (SSTAs) along the tropical Pacific are most likely to cause a significant spring predictability barrier (SPB) phenomenon for El Niño events.…”

“…Moreover, recent studies showed that the initial errors with particular spatial structures cause much larger prediction uncertainty for ENSO (Mu et al 2007;Yu et al 2009;Duan et al 2009;Duan and Hu 2016). Specifically, Duan et al (2009) pointed out that the initial errors with a dipole structure of sea surface temperature anomalies (SSTAs) along the tropical Pacific are most likely to cause a significant spring predictability barrier (SPB) phenomenon for El Niño events. Duan and Wei (2012) further showed the existence of these initial errors in realistic predictions for El Niño.…”

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

“…This clarification illustrates why we emphasize that a significant SPB entails not only a large prediction error but also a prominent seasonality of error growth. With this description of the 'significant SPB', Duan et al (2009) and Yu et al (2009) used the Zebiak-Cane model (Zebiak and Cane, 1987) to study the spatial characteristics of initial errors that cause 'a significant SPB' for ENSO events by performing perfect model predictability experiments with the approach of conditional nonlinear optimal perturbation (CNOP; Mu et al, 2003;Duan et al, 2004Duan et al, , 2008Mu and Zhang, 2006;Duan and Mu, 2006). CNOP represents the initial error that induces the largest prediction error at the prediction time and has the potential for yielding a significant SPB (Mu et al, 2007a(Mu et al, , 2007bYu et al, 2009).…”

“…CNOP represents the initial error that induces the largest prediction error at the prediction time and has the potential for yielding a significant SPB (Mu et al, 2007a(Mu et al, , 2007bYu et al, 2009). Using the CNOP method, Duan et al (2009) and Yu et al (2009) identified two types of CNOP-type initial errors that cause a significant SPB for El Nino events. One type possesses an SSTA component that has a largescale zonal dipolar pattern with positive anomalies in the central equatorial Pacific and negative anomalies in the eastern equatorial Pacific; it tends to cause El Nino events to be under-predicted through spring.…”

“…The experiments of Duan et al (2009) and Yu et al (2009) are perfect model predictability experiments, although the CNOP method tackles the evolution of finite-amplitude initial perturbation and has led to instructive results. In realistic predictions, there are typically both initial errors and model errors.…”