Long-term non-linear predictability of ENSO events over the 20th century

TL;DR

This study demonstrates that the ENSO phenomenon, as described by the SOI index, can be nonlinearly predicted over several years using attractor topology analysis, showing long-term predictability beyond traditional limits.

Contribution

It introduces a nonlinear dynamical system approach to predict ENSO events using attractor topology, extending predictability beyond the spring barrier.

Findings

Nonlinear predictability of ENSO over 2-4 years.

SOI index contains sufficient information for long-term predictions.

Method is simple and adaptable to similar observational data.

Abstract

We show that the monthly recorded history (1878-2013) of the Southern Oscillation Index (SOI), a descriptor of the El Ni\~no Southern Oscillation (ENSO) phenomenon, can be well described as a dynamic system that supports an average nonlinear predictability well beyond the spring barrier. The predictability is strongly linked to a detailed knowledge of the topology of the attractor obtained by embedding the SOI index in a wavelets base state space. Using the state orbits on the attractor we show that the information contained in the Southern Oscillation Index (SOI) is sufficient to provide average nonlinear predictions for time periods of 2, 3 and 4 years in advance throughout the 20th century with an acceptable error. The simplicity of implementation and ease of use makes it suitable for studying non linear predictability in any area where observations are similar to those that describe the ENSO phenomenon.