Abstract
Non-Gaussianity and nonlinearity have been shown to be ubiquitous characteristics of El Niño Southern Oscillation (ENSO) with implication on predictability, modelling, and assessment of extremes. These topics are investigated through the analysis of third-order statistics of El Niño 3.4 index in the period 1870–2018, namely bicovariance and bispectrum. Likewise, the spectral decomposition of variance, the bispectrum provides a spectral decomposition of skewness. Positive and negative bispectral contributions identify modes contributing respectively to La Niñas and El Niños, mostly in the period range 2–6 years. The ENSO bispectrum also shows statistically significant features associated with nonlinearity. The analysis of bicovariance reveals a nonlinear correlation between the Boreal Spring and following Winter, coming from an asymmetry of the persistence of El Niño, contributing hence to a reduction of Spring Predictability Barrier. The positive skewness and main features of the ENSO bicovariance and bispectrum are shown to be well reproduced by fitting a bilinear stochastic model. This model shows improved forecasts, with respect to benchmark linear models, especially of the amplitude of extreme El Niños. This study is relevant, particularly in a changing climate, to better characterize and predict ENSO extremes coming from non-Gaussianity and nonlinearity.
Highlights
The present study aims to perform a systematic and thorough analysis of third-order statistics, both in the time and spectral domains to infer and improve the understanding of El Nin~o Southern Oscillation (ENSO) non-Gaussianity through skewness, nonlinearity (e.g. Hinich, 1982; Cox, 1991), and nonlinear predictability on time scales ranging from seasons to years
El Nin~o Southern Oscillation (ENSO) is one of the most important coupled atmosphere–ocean system, exhibiting time scales ranging from seasons to decades and beyond, with a worldwide teleconnection
Using different stochastic and/or dynamic approaches, most studies have emphasized and shown its intrinsic complexity, nonlinearity and non-Gaussianity. Most of those studies limited their investigations to the second order statistics in addition to skewness and/or kurtosis
Summary
Earth’s weather and climate vary on a wide range of spa- that sea surface temperature (SST) has non-Gaussian tio-temporal scales. Sura and Hannachi (2015) provide a detailed account of the different sources and mechanisms contributing to the observed non-Gaussianity of the atmospheric large-scale and lowfrequency variability. They discussed, in particular, nonlinear regime dynamics, multiplicative noise, cross-frequency coupling, jet stream meandering and nonlinear boundary layer drags. The present study aims to perform a systematic and thorough analysis of third-order statistics, both in the time and spectral domains to infer and improve the understanding of ENSO non-Gaussianity through skewness, nonlinearity
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
