BIBLIOS

Tipo
Artigos em Revista

Tipo de Documento
Artigo Completo

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

Participantes na publicação
Carlos A. L. Pires (Author)
Dep. Engenharia Geográfica, Geofísica e Energia
IDL
Abdel Hannachi (Author)

Resumo
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
Taylor and Francis Online


Instituição
Instituto Dom Luiz (IDL), Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749–016, Lisboa, Portugal

Suporte
Tellus A: Dynamic Meteorology and Oceanography

Identificadores da Publicação
ISSN - 1600-0870

Editora
Informa UK Limited



Identificadores do Documento
DOI - https://doi.org/10.1080/16000870.2020.1866393
URL - http://dx.doi.org/10.1080/16000870.2020.1866393

Identificadores de Qualidade
SCIMAGO Q1 (2020) - 1.016 - Oceanography
SCIMAGO Q1 (2018) - 1.153 - Oceanography

Tags
El Niño Bispectrum Predictability Stochastic Modelling

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