Untangling El Niño‐La Niña Asymmetries Using a Nonlinear Coupled Dynamic Index

Kim, Soong-Ki; An, Soon Il

doi:10.1029/2019gl085881

Cited by 13 publications

(21 citation statements)

References 43 publications

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“…S3 – S4 ). Since the non-planar behavior of the derivatives is important theoretically in case of ENSO 4 , we believe the KDE method is suited to our particular case.…”

Section: Resultsmentioning

confidence: 99%

“…For most of the year, SST response depends primarily on the thermocline, with deeper thermocline forcing surface warming, and shallower thermocline forcing surface cooling. The linear component is one of the components of a thermocline feedback 4 , and is known as “angular frequency” in some work. Positive angular frequency was also found in Stein et al 39 .…”

Section: Resultsmentioning

confidence: 99%

“…Previously, ENSO skewness has been explained by a range of physical processes 19 , 26 , which manifest themselves as (i) the quadratic term of the nonlinear dynamical heating 4 , (ii) threshold behavior of the mean term as a function of SST 40 or (iii) the temperature-dependent noise 30 , 44 , 51 . Our work indicates that the non-planarities in the dynamics (i and ii) explain the majority of ENSO’s skewness, and suggest a relatively small role for the state-dependent noise 52 .…”

Section: Resultsmentioning

confidence: 99%

“…One commonly used simple ENSO model includes two variables—SST in the eastern equatorial Pacific, and thermocline depth in the equatorial Pacific 4 , 30 . While the model has substantial skill at reproducing many observed ENSO features, it does not capture ENSO perfectly.…”

Section: Introductionmentioning

confidence: 99%

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A novel approach for discovering stochastic models behind data applied to El Niño–Southern Oscillation

Olson

Kim

et al. 2021

Sci Rep

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Stochastic differential equations (SDEs) are ubiquitous across disciplines, and uncovering SDEs driving observed time series data is a key scientific challenge. Most previous work on this topic has relied on restrictive assumptions, undermining the generality of these approaches. We present a novel technique to uncover driving probabilistic models that is based on kernel density estimation. The approach relies on few assumptions, does not restrict underlying functional forms, and can be used even on non-Markov systems. When applied to El Niño–Southern Oscillation (ENSO), the fitted empirical model simulations can almost perfectly capture key time series properties of ENSO. This confirms that ENSO could be represented as a two-variable stochastic dynamical system. Our experiments provide insights into ENSO dynamics and suggest that state-dependent noise does not play a major role in ENSO skewness. Our method is general and can be used across disciplines for inverse and forward modeling, to shed light on structure of system dynamics and noise, to evaluate system predictability, and to generate synthetic datasets with realistic properties.

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“…S3 – S4 ). Since the non-planar behavior of the derivatives is important theoretically in case of ENSO 4 , we believe the KDE method is suited to our particular case.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A novel approach for discovering stochastic models behind data applied to El Niño–Southern Oscillation

Olson

Kim

et al. 2021

Sci Rep

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“…Derivations of Eq. (2) are presented in the "Methods" section and also found in Kim et al 38 . State-dependent (multiplicative) noise can serve as a stochastic nonlinearity, as can be easily demonstrated by introducing logarithmic variables.…”

mentioning

confidence: 85%

Fokker–Planck dynamics of the El Niño-Southern Oscillation

Kim

Timmermann

2020

Sci Rep

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The asymmetric nature of the El Niño-Southern Oscillation (ENSO) is explored by using a probabilistic model (PROM) for ENSO. Based on a Fokker–Planck Equation (FPE), PROM describes the dynamics of a nonlinear stochastic ENSO recharge oscillator model for eastern equatorial Pacific temperature anomalies and equatorial Pacific basin-averaged thermocline depth changes. Eigen analyses of PROM provide new insights into the stationary and oscillatory solutions of the stochastic dynamical system. The first probabilistic eigenmode represents a stationary mode, which exhibits the asymmetric features of ENSO, in case deterministic nonlinearities or multiplicative noises are included. The second mode is linked to the oscillatory nature of ENSO and represents a cyclic asymmetric probability distribution, which emerges from the key dynamical processes. Other eigenmodes are associated with the temporal evolution of higher order statistical moments of the ENSO system. The model solutions demonstrate that the deterministic nonlinearity plays a stronger role in establishing the observed asymmetry of ENSO as compared to the multiplicative stochastic part.

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Asymmetries in the ENSO phase space

Dommenget

Al-Ansari

2022

Clim Dyn

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El Niño Southern Oscillation (ENSO) dynamics are best described by the recharge oscillator model, in which the eastern tropical Pacific sea surface temperatures (T) and subsurface heat content (thermocline depth; h) have an out-of-phase relationship. This defines a 2-dimensional phase space diagram between T and h. In an idealized, stochastically forced damped oscillator, the mean phase space diagram should be a perfectly symmetrical circle with a clockwise propagation over time. However, the observed phase space shows strong asymmetries. In this study we illustrate how the ENSO phase space can be used to discuss the phase-dependency of ENSO dynamics. A normalized spherical coordinate system allows the definition of phase-depending ENSO growth rates and phase transition speeds. Based on these we discuss the implications of the observed asymmetries with regards to the dynamics and predictability of ENSO; with a particular focus on the variations in the growth rate and coupling of ENSO along the oscillation cycle. Using linear and non-linear recharge oscillator models we will show how dynamics and noise are driving ENSO at different phases of the ENSO cycles. The results illustrate that the ENSO cycle with positive phase transitions is present in all phases but has strong variations in its strength. Much of these variations result from presenting the ENSO phase space with estimates of h based on the iso-thermal depth, that is not ideal as it is not out-of-phase with T. Future work should address how h can be estimated better, including aspects such as the vertical temperature gradients and the meridional or zonal range. We further illustrated that a non-linear growth rate of T can explain most of the observed non-linear phase space characteristics.

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Untangling El Niño‐La Niña Asymmetries Using a Nonlinear Coupled Dynamic Index

Cited by 13 publications

References 43 publications

A novel approach for discovering stochastic models behind data applied to El Niño–Southern Oscillation

A novel approach for discovering stochastic models behind data applied to El Niño–Southern Oscillation

Fokker–Planck dynamics of the El Niño-Southern Oscillation

Asymmetries in the ENSO phase space

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