Conditional nonlinear optimal perturbations as the optimal precursors for El Nino–Southern Oscillation events

Abstract

[1] We used the approach of conditional nonlinear optimal perturbation (CNOP) to investigate optimal precursors for El Nino-Southern Oscillation (ENSO) events with a theoretical coupled ocean-atmosphere model. The CNOPs of the annual cycle of the coupled system were computed for different time periods, and the derived CNOPs were compared with the linear singular vectors (LSVs). The results show the existence of the CNOPs of annual cycle and local CNOPs. These CNOPs have the robust optimal patterns, which have opposite polarities in sea surface temperature and thermocline depth anomalies in the eastern equatorial Pacific. We demonstrate that the CNOP (local CNOP), rather than LSVs, has the highest likelihood to develop into an El Nino (La Nina) event; thus the CNOPs (local CNOPs) can be regarded as the optimal precursors for El Nino (La Nina) events. These optimal precursors agrees qualitatively well with the observations of period of 1980-2002. On the basis of the nonlinear oscillation described by the model, the physical mechanism of the optimal precursors for ENSO is discussed.

Comparison of the initial errors most likely to cause a spring predictability barrier for two types of El Niño events

“…In this paper, we use the CNOP approach to study the initial errors that cause a significant SPB most probably for two types of El Niño events. The CNOP approach has already been applied to studies of ENSO predictability (Duan et al 2004;Mu et al 2007a, b;Yu et al 2009Yu et al , 2012. The approach has especially been used to explore the initial errors that cause a significant SPB for EP-El Niño events, despite the El Niño events concerned being model El Niño events .…”

Nonlinear instability and sensitivity of a theoretical grassland ecosystem to finite-amplitude perturbations

“…CNOPs is proposed by Mu et al (2003a), and has been utilized to study ENSO by Mu et al (2003bMu et al ( , 2007a and Duan et al (2004Duan et al ( , 2006, and the ocean's thermohaline circulation problems by Mu et al(2004) and Sun et al (2005). Their work shows that CNOPs is a useful tool in the studies of predictability, sensitivity and nonlinear stability analysis.…”

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).…”