Simple ENSO Models

Jin, Fei‐Fei; Chen, Han‐Ching; Zhao, Sen; Hayashi, Minoru; Karamperidou, C.; Stuecker, Malte F.; Xie, Rui; Geng, Licheng

doi:10.1002/9781119548164.ch6

Cited by 34 publications

(57 citation statements)

References 106 publications

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“…3 B), aside from the lower overall variability. Previous work has identified that seasonally-varying Bjerknes feedback ( ) is responsible for ENSO seasonal locking behavior 30 , 39 , 42 . Moreover, even using a seasonally-resolved 1D model can lead to a good simulation of seasonal cycle 41 .…”

Section: Resultsmentioning

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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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%

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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“…where E  denotes the slow adjustment rate from energy leakage at the western boundary and mixing. 2 E F represents the discharge-recharge efficiency of the WWV w (Jin, 1997b;Jin et al, 2020…”

Section: Analytic Solution Of Lead Times Of Wwv and Wwv W To Enso Sst In The Ro Frameworkmentioning

confidence: 99%

“…Figure S2 shows the analytical approximated WWVs using the observed Niño-3 index in place of e ). In the observations and fully coupled simulations, the dominant ENSO frequency can be well approximated by the Wyrtki frequency index (Jin et al, 2020;Lu et al, 2018),…”

Section: Analytic Solution Of Lead Times Of Wwv and Wwv W To Enso Sst In The Ro Frameworkmentioning

confidence: 99%

Understanding Lead Times of Warm Water Volumes to ENSO Sea Surface Temperature Anomalies

Zhao

Jin

Stuecker

2021

Geophysical Research Letters

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The El Niño-Southern Oscillation (ENSO) is a climate phenomenon that recurs about every 2-7 yr between its warm (El Niño) and cold (La Niña) phases across the equatorial Pacific, strongly affecting global climate and environment (e.g., McPhaden et al., 2006;Timmermann et al., 2018). It is commonly viewed as a low-frequency coupled ocean-atmosphere mode energized by stochastic wind forcing, with its growth and phase transition originating from the combination of the positive ocean-atmosphere instability and the delayed negative oceanic feedbacks (Bjerknes, 1969;Cane & Zebiak, 1985;Neelin et al., 1998;Wyrtki, 1985). Jin's (1997aJin's ( , 1997b recharge oscillator (RO), the simplest possible paradigm for ENSO, incorporates these feedback processes involving sea surface temperature (SST), zonal wind stress, and thermocline depth (20°C isotherm depth, D20) and depicts ENSO phase transition as a result of the slow recharge/discharge of equatorial upper-ocean heat content (also known as warm water volume above D20; WWV). This paradigm has been substantiated in the observations, showing that basinwide WWV anomalies lead ENSO SST anomalies by about one to three seasons (Meinen & McPhaden, 2000), making WWV a good ENSO predictor (Clarke & Van Gorder, 2003;McPhaden, 2003). The anomalous western equatorial Pacific WWV (WWV w ), which was highlighted in pioneering studies (Jin, 1997a;Wyrtki, 1985), can precede ENSO SST anomalies up to five to seven seasons (Ballester et al., 2016;Izumo et al., 2019;Meinen & McPhaden, 2000), hence it has been argued recently as a better ENSO predictor beyond 1-yr lead times (Izumo et al., 2019;Planton et al., 2018). The slowly evolving heat content governed by the deterministic wind-driven ocean dynamics (Sverdrup transport) is presumably responsible for long-lead predictability of ENSO in climate models (Chen et al., 2004;Latif et al., 1998).

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“…Our schemes may be used in the CZ‐type mechanistic ENSO models that played a key role in advancing the ENSO theory for the past three decades (Jin et al, 2020). Further investigations applying the schemes developed in this work to a hierarchical ENSO modeling may lead to better understanding of the fundamental dynamics of ENSO and more accurate simulations and predictions of ENSO as well.…”

Section: Summary and Discussionmentioning

confidence: 99%

A Concise and Effective Expression Relating Subsurface Temperature to the Thermocline in the Equatorial Pacific

Yuan

Jin

Zhang

2020

Geophysical Research Letters

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The temperature of the subsurface water entrained into the surface mixed layer plays a key role in controlling the sea surface temperature (SST) and its interannual variability in the equatorial Pacific. In this paper, we combine a hyperbolic tangent function bounded by the warm pool SST and centered at the thermocline depth with a variable sharpness parameter to describe the time‐space evolutions of the subsurface temperature. Under simple approximations of the sharpness parameter, this concise expression becomes remarkably efficient in capturing the observed and climate‐model simulated subsurface temperature variability in terms of anomalies of the thermocline depth and SST of the El Niño‐Southern Oscillation (ENSO) phenomenon. The formulations for the subsurface temperature and thermocline sharpness developed in this work should be useful tools for evaluating and understanding the role of the thermocline feedback in ENSO behaviors in both theoretical and comprehensive climate models.

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Simple ENSO Models

Cited by 34 publications

References 106 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

Understanding Lead Times of Warm Water Volumes to ENSO Sea Surface Temperature Anomalies

A Concise and Effective Expression Relating Subsurface Temperature to the Thermocline in the Equatorial Pacific

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