Bispectral analysis of nonlinear interaction, predictability and stochastic modelling with application to ENSO

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...

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Autores principales:	Carlos A. L. Pires, Abdel Hannachi
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	enso spring predictability barrier bispectrum el niño skewness nonlinear predictability bilinear models Oceanography GC1-1581 Meteorology. Climatology QC851-999
Acceso en línea:	https://doaj.org/article/79aa80fd004a40f99ca779e601c66983
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spelling	oai:doaj.org-article:79aa80fd004a40f99ca779e601c669832021-12-01T14:40:58ZBispectral analysis of nonlinear interaction, predictability and stochastic modelling with application to ENSO1600-087010.1080/16000870.2020.1866393https://doaj.org/article/79aa80fd004a40f99ca779e601c669832021-01-01T00:00:00Zhttp://dx.doi.org/10.1080/16000870.2020.1866393https://doaj.org/toc/1600-0870Non-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.Carlos A. L. PiresAbdel HannachiTaylor & Francis Grouparticleenso spring predictability barrierbispectrumel niño skewnessnonlinear predictabilitybilinear modelsOceanographyGC1-1581Meteorology. ClimatologyQC851-999ENTellus: Series A, Dynamic Meteorology and Oceanography, Vol 73, Iss 1, Pp 1-30 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	enso spring predictability barrier bispectrum el niño skewness nonlinear predictability bilinear models Oceanography GC1-1581 Meteorology. Climatology QC851-999
spellingShingle	enso spring predictability barrier bispectrum el niño skewness nonlinear predictability bilinear models Oceanography GC1-1581 Meteorology. Climatology QC851-999 Carlos A. L. Pires Abdel Hannachi Bispectral analysis of nonlinear interaction, predictability and stochastic modelling with application to ENSO
description	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.
format	article
author	Carlos A. L. Pires Abdel Hannachi
author_facet	Carlos A. L. Pires Abdel Hannachi
author_sort	Carlos A. L. Pires
title	Bispectral analysis of nonlinear interaction, predictability and stochastic modelling with application to ENSO
title_short	Bispectral analysis of nonlinear interaction, predictability and stochastic modelling with application to ENSO
title_full	Bispectral analysis of nonlinear interaction, predictability and stochastic modelling with application to ENSO
title_fullStr	Bispectral analysis of nonlinear interaction, predictability and stochastic modelling with application to ENSO
title_full_unstemmed	Bispectral analysis of nonlinear interaction, predictability and stochastic modelling with application to ENSO
title_sort	bispectral analysis of nonlinear interaction, predictability and stochastic modelling with application to enso
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/79aa80fd004a40f99ca779e601c66983
work_keys_str_mv	AT carlosalpires bispectralanalysisofnonlinearinteractionpredictabilityandstochasticmodellingwithapplicationtoenso AT abdelhannachi bispectralanalysisofnonlinearinteractionpredictabilityandstochasticmodellingwithapplicationtoenso
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Bispectral analysis of nonlinear interaction, predictability and stochastic modelling with application to ENSO

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