Low-dimensional nonlinearity of ENSO and its impact on predictability

Tang, Youmin; Deng, Ziwang

doi:10.1016/j.physd.2009.11.006

Cited by 7 publications

(5 citation statements)

References 43 publications

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“…While the large perturbation growth occurs prior to the decay phase of El Nino (bins 11-13) and during the transition period from a cold to a warm state (bins 3-5). These results are generally consistent with former SV studies (e.g., Chen et al 1997;Xue et al 1997a) and breeding vector results (e.g., Cai et al 2003;Tang and Deng 2009) and further confirm the sensitivity of perturbation growth on ENSO phase. In the next section, we will identify and investigate the possible physical processes controlling the perturbation (error) growth in the ZC model.…”

Section: Variations Of the Singular Valuesupporting

confidence: 91%

ENSO ensemble prediction and predictability for the past 148 years from 1856--2003.

Cheng¹

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Section: Variations Of the Singular Valuesupporting

confidence: 91%

ENSO ensemble prediction and predictability for the past 148 years from 1856--2003.

Cheng¹

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“…This increase in the volatility and second-order moment may lead to an increasing frequency of extreme events such as extreme precipitation and temperature (Arblaster & Alexander, 2012;Higgins, Kousky, & Xie, 2011) or extreme drought conditions (Baling & Goodrich, 2007;Rajagopalan, Cook, Lall, & Ray, 2000) in relation to extreme ENSO phases. These changes may also lead to an increase in ENSO-driven climate uncertainty and variability and may make future climate prediction extremely difficult at both regional and global levels (Tang & Deng, 2010).…”

Section: Discussionmentioning

confidence: 99%

Testing and Modelling the Volatility Change in ENSO

Modarres¹,

Ouarda²

2013

Atmosphere-Ocean

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The El Niño-Southern Oscillation (ENSO) is by far the most energetic climate signal. Any change in ENSO characteristics will have serious consequences for the global climate system. This work suggests a different view at the change in ENSO volatility in addition to change in its descriptive statistics. The volatility or the conditional variance of ENSO is tested and modelled using both the Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroscedasticity (ARMA-GARCH) error model and the GARCH model, to investigate the change in the short-run and long-run persistency of the second-order moment of ENSO before and after a change point detected by a Bayesian change point analysis. Nonparametric tests revealed a significant change in descriptive statistical characteristics such as the mean, the (unconditional) variance, and the probability distribution of ENSO after a change point in 1975. An Engle's test did not show heteroscedasticity in the random process (residuals) of the Southern Oscillation Index (SOI) time series before 1975 although heteroscedasticity increased and appeared after 1975. The GARCH model indicates an increasing short-run persistency after 1975 and decreasing long-run persistency. A seasonal shift in extreme heteroscedasticity is observed from summer to winter. In addition, the non-linearity and nonstationarity of the SOI volatility have increased in recent decades. This may be caused by an increase in frequency and magnitude of extreme volatilities after 1975. The results of this study indicate that ENSO has become more dynamic and uncertain in recent decades. The increase in the frequency of extreme events together with extreme conditional variance after 1975 may increase the prediction uncertainty of ENSO-driven climate phenomena.RÉSUMÉ [Traduit par la rédaction] Le El Niño-oscillation australe (ENSO) est de loin le signal climatique le plus énergétique. Tout changement dans les caractéristiques de l'ENSO aura de sérieuses conséquences sur le système climatique planétaire. Le présent travail propose un regard différent sur le changement dans la volatilité de l'ENSO en plus du changement dans ses statistiques descriptives. Nous testons et modélisons la volatilité ou variance conditionnelle de l'ENSO en nous servant à la fois du modèle d'erreur ARMA-GARCH (moyenne mobile autorégressive -hétéroscédasticité conditionnelle autorégressive généralisée) et du modèle GARCH pour étudier le changement à court terme et à long terme de la persistance du moment de second ordre de l'ENSO avant et après un point de changement détecté par une analyse bayésienne de point de changement. Des tests non paramétriques ont révélé un changement significatif dans les caractéristiques statistiques descriptives comme la moyenne, la variance (inconditionnelle) et la distribution de probabilités de l'ENSO après un point de changement en 1975. Un test d'Engle n'a pas montré d'hétéroscédasticité dans le processus aléatoire (résidus) de la série chronologique de l'indice d'oscillation australe ...

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“…The theory that ENSO time series is a stochastic system rather than a chaotic one has led to the development of stochastic approach for ENSO (Ubilava and Helmers 2013;Hall et al 2001). Several studies have identify chaos in the dynamics of ENSO using tools such as bred vector (Tang and Deng 2010), dynamical models (Vallis 1986;Chang et al 1996;Tziperman et al 1994), false nearest neighbour and correlation dimension (Chang et al 1996;Tsonis 2009), Lyapunov Exponent (Tsonis 2009) and nonlinear prediction error (Elsner and Tsonis 1993). The dimension of ENSO reported from dynamical models include 3.5 (Tziperman et al 1994), 2.4 & 2.6 (Chang et al 1996, < 2 (Jin et al 2005), 3 and 4 (Tsonis 2009) and 8 (Aijain 1998).…”

Section: Introductionmentioning

confidence: 99%

Chaotic signature of climate extremes

Fuwape

Oluyamo

Rabiu

et al. 2019

Theor Appl Climatol

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Understanding the dynamics of climate extreme is important in its prediction and modeling. In this study, linear trends in percentile, threshold, absolute, and duration based temperature and precipitation extremes indicator were obtained for the period 1979 -2012 using the ETCCDI data set. The pattern of trend was compared with nonlinear measures (Entropy, Hurst Exponent, Recurrence Quantification Analysis) of temperature and precipitation. Regions which show positive trends in temperature based extremes were found to be areas with low entropy and chaotic. Complexity measures also revealed that the dynamics of the southern hemisphere differs from that of the northern hemisphere.

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Low-dimensional nonlinearity of ENSO and its impact on predictability

Cited by 7 publications

References 43 publications

ENSO ensemble prediction and predictability for the past 148 years from 1856--2003.

ENSO ensemble prediction and predictability for the past 148 years from 1856--2003.

Testing and Modelling the Volatility Change in ENSO

Chaotic signature of climate extremes

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