Document type
Journal articles
Document subtype
Full paper
Title
Bispectral analysis of nonlinear interaction, predictability and stochastic modelling with application to ENSO
Participants in the publication
Carlos A. L. Pires (Author)
Dep. Engenharia Geográfica, Geofísica e Energia
IDL
Abdel Hannachi (Author)
Summary
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.
Editor(s)
Taylor and Francis Online
Date of Submisson/Request
2020-08-26
Date of Acceptance
2020-12-16
Date of Publication
2021-02-07
Institution
Instituto Dom Luiz (IDL), Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749–016, Lisboa, Portugal
Where published
Tellus A: Dynamic Meteorology and Oceanography
Publication Identifiers
ISSN - 1600-0870
Publisher
Informa UK Limited
Number of pages
29
Starting page
1
Last page
30
Document Identifiers
DOI -
https://doi.org/10.1080/16000870.2020.1866393
URL -
http://dx.doi.org/10.1080/16000870.2020.1866393
Rankings
SCIMAGO Q1 (2020) - 1.016 - Oceanography
SCIMAGO Q1 (2018) - 1.153 - Oceanography
Tags
El Niño
Bispectrum
Predictability
Stochastic Modelling
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