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

Abstract

Using predictions for the sea surface temperature (SST) generated by a Flexible Global Ocean-AtmosphereLand System model of IAP/LASG (FGOALS-g), the season-dependent predictability of SST anomalies for El Nino/La Nina events is investigated by analyzing the forecast error growth in an imperfect model scenario. The results indicate that, for the predictions through the spring season in the growth phase of El Nino events, the prediction errors induced by both initial errors and model errors tend to have a prominent season-dependent evolution and yield a prominent spring predictability barrier (SPB). For the decay-phase predictions of El Nino events, a less prominent season-dependent evolution of prediction errors and then a less prominent SPB are observed. For the growth-and decay-phase predictions of La Nina events, the prediction errors do not exhibit a significant season-dependent evolution and yield a less prominent SPB phenomenon. These results indicate that the SPB phenomenon depends remarkably on the ENSO events themselves, particularly the phases of the El Nino/La Nina events. We also report that the initial SST errors that correspond to a significant SPB for El Nino events tend to have the dominant modes in a large-scale dipolar pattern with negative anomalies in the equatorial central-western Pacific and positive anomalies in the eastern Pacific, or vice versa. We further demonstrate that the error growth related to a significant SPB for El Nino prediction generated by the FGOALS-g model can result from two dynamical mechanisms: in one case, the prediction errors grow in a manner similar to El Niño; in the other, the prediction errors develop with a tendency opposite to El Niño.

The initial errors that induce a significant “spring predictability barrier” for El Niño events and their implications for target observation: results from an earth system model

“…Furthermore, Mu et al (2014) demonstrated that the precursory perturbations that are most likely to develop into El Niño or La Niña events bear a strong resemblance with the initial errors that induce a significant SPB with the ZC model. This indicates that initial anomalies with the structure of type-A and -B initial errors also act as precursory disturbances for El Niño and La Niña events, respectively (Duan and Wei 2012;. In the present study, we also obtain two types of SPB-related initial errors (i.e.…”

“…In terms of the third factor, Mu et al (2007b) used the Zebiak-Cane model (ZC model;Zebiak and Cane 1987) along with the conditional nonlinear optimal perturbation (CNOP) approach (Mu et al 2003) to explore the initial errors that cause a significant SPB. Yu et al (2009Yu et al ( , 2012 further recognized two kinds of CNOP-type initial errors, which show a large-scale zonal dipolar pattern for the sea surface temperature anomaly (SSTA) component and a basin wide deepening or shoaling along the equator for the thermocline depth anomaly, and similar CNOP-like initial errors also exist in realistic ENSO predictions Duan and Wei 2012). All these studies attempt to reveal the initial error that induces a significant SPB for El Niño events most probably, and identify the location in which additional observations should be a priority for improving the El Niño forecast skill.…”

“…This barrier manifests as a sharp drop-off in the monthly persistence of observed oceanic and atmospheric index associated with ENSO across boreal spring, and in numerical prediction models it appears as a sudden decrease (or increase) of the anomaly correlation coefficient (ACC) (or RMSE: root-mean-square error), regardless of the starting month (Webster and Yang 1992;Webster 1995;Torrence and Webster 1998;Luo et al 2008). From the perspective of error growth, a "significant SPB" refers to the phenomenon that ENSO forecasting has a large prediction error; and in particular, a prominent error growth occurs during boreal spring when the prediction is made before and throughout that spring (Mu et al 2007a, b;Duan et al 2009;Duan and Wei 2012).…”

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

“…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. These studies therefore provided a possible way to improve ENSO forecast skill by filtering out the initial errors of particular structures.…”

An improved simulation of the 2015 El Niño event by optimally correcting the initial conditions and model parameters in an intermediate coupled model

“…Indeed, the CNOP approach has been extensively used to study ENSO predictability using the ENSO model developed by Zebiak and Cane (1987). For instance, Duan and Wei (2012) found that the CNOP-like errors exist in the initial fields used to make realistic ENSO predictions and thought that the ENSO forecast skill can be greatly improved if the initial analysis fields are corrected according to the CNOP-like errors. Xu (2006) and Mu et al (2014) found the similarities between optimal precursors for ENSO and the CNOP-type errors derived in ENSO predictions.…”