Simple stochastic dynamical models capturing the statistical diversity of El Niño Southern Oscillation

Chen, Nan; Majda, Andrew J.

doi:10.1073/pnas.1620766114

Cited by 22 publications

(40 citation statements)

References 63 publications

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“…Multiplicative atmospheric forcing, such as the westerly wind bursts (WWBs) and nonlinear advection, have been suggested to be important for the two main El Niño types (Chen et al 2015;Majda 2016, 2017;Hayashi and Watanabe 2017). In addition, Capotondi and Sardeshmukh (2015) reported that different initial subsurface thermocline conditions may lead to the occurrences of two types of El Niño.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Two Leading ENSO Modes and El Niño Types in the Zebiak–Cane Model

Xie

Jin

2018

J. Climate

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Modern instrumental records reveal that El Niño events differ in their spatial patterns and temporal evolutions. Attempts have been made to categorize them roughly into two main types: eastern Pacific (EP; or cold tongue) and central Pacific (CP; or warm pool) El Niño events. In this study, a modified version of the Zebiak-Cane (MZC) coupled model is used to examine the dynamics of these two types of El Niño events. Linear eigenanalysis of the model is conducted to show that there are two leading El Niño-Southern Oscillation (ENSO) modes with their SST patterns resembling those of two types of El Niño. Thus, they are referred to as the EP and CP ENSO modes. These two modes are sensitive to changes in the mean states. The heat budget analyses demonstrate that the EP (CP) mode is dominated by thermocline (zonal advective) feedback. Therefore, the weak (strong) mean wind stress and deep (shallow) mean thermocline prefer the EP (CP) ENSO mode because of the relative dominance of thermocline (zonal advective) feedback under such a mean state. Consistent with the linear stability analysis, the occurrence ratio of CP/EP El Niño events in the nonlinear simulations generally increases toward the regime where the linear CP ENSO mode has relatively higher growth rate. These analyses suggest that the coexistence of two leading ENSO modes is responsible for two types of El Niño simulated in the MZC model. This model result may provide a plausible scenario for the observed ENSO diversity.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Two Leading ENSO Modes and El Niño Types in the Zebiak–Cane Model

Xie

Jin

2018

J. Climate

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“…Levine et al (2016) show that the ENSO state modifies the fetch and the wind speed of WWBs, but also the number of WWBs increases following a strong WWB. However, a series of strong EWBs, leading the WWBs and event peak, can contribute to the buildup of the heat content in the western Pacific (Chen & Majda, 2017;Hu & Fedorov, 2016).…”

Section: Introductionmentioning

confidence: 99%

ENSO Bimodality and Extremes

Rodrigues

Subramanian

Zanna

et al. 2019

Geophysical Research Letters

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Tropical sea surface temperature (SST) and winds vary on a wide range of timescales and have a substantial impact on weather and climate across the globe. Here we study the variability of SST and zonal wind during El Niño‐Southern Oscillation (ENSO) between 1982 and 2014. We focus on changes in extreme statistics using higher‐order moments of SST and zonal winds. We find that ENSO characteristics exhibit bimodal distributions and fat tails with extreme warm and cold temperatures in 1982–1999, but not during 2000–2014. The changes in the distributions coincide with changes in the intensity of ENSO events and the phase of the Interdecadal Pacific Oscillation. We also find that the strongest Easterly Wind Bursts occur during extreme El Niños and not during La Niñas. Maps of SST kurtosis can serve as a diagnostic for the thermocline feedback mechanism responsible for the differences in ENSO diversity between the two periods.

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“…In [52], a simple modeling framework has been developed that automatically captures the statistical diversity of ENSO. The starting model is a deterministic, linear and stable model that includes the coupled atmosphere, ocean and SST [175].…”

Section: A Coupled El Niño Model Capturing Observed El Niño Diversitymentioning

confidence: 99%

“…Secondly, a simple nonlinear zonal advection with no ad hoc parameterization of the background SST gradient and a mean easterly trade wind anomaly representing the multidecadal acceleration of the trade wind are both incorporated into the coupled model that enable anomalous warm SST in the central Pacific [176]. Then, a three-state stochastic Markov jump process is utilized to drive the wind burst activity that depends on the strength of the western Pacific warm pool in a simple and effective fashion [52]. It allows the coupled model to simulate the quasi-regular moderate traditional eastern Pacific El Niño, the super El Niño, the CP El Niño as well as the La Niña with realistic features [177].…”

Section: A Coupled El Niño Model Capturing Observed El Niño Diversitymentioning

confidence: 99%

“…In addition to the data-driven physics-constrained nonlinear stochastic models, many large-scale dynamical models in turbulence, fluids and geophysical flows also have conditional Gaussian structures. These stochastic partial differential equation (SPDE) models include the stochastic skeleton model for the MJO [51] and a coupled El Niño dynamical model capturing the El Niño diversity [52], where nonlinearity interacts with state-dependent (multiplicative) noise and many salient non-Gaussian features such as intermittency and fat-tailed PDFs are observed in these systems. The stochastically forced Boussinesq equation [11] for the Rayleigh-Bénard convection [53,54] and strongly stratified flows in geophysics [55] as well as its generalized version, namely the Darcy-Brinkman-Oberbeck-Boussinesq system [56] for the convection phenomena in porous media, also belong to the conditional Gaussian framework.…”

Section: Introductionmentioning

confidence: 99%

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Conditional Gaussian Systems for Multiscale Nonlinear Stochastic Systems: Prediction, State Estimation and Uncertainty Quantification

Chen

Majda

2018

Entropy

Self Cite

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A conditional Gaussian framework for understanding and predicting complex multiscale nonlinear stochastic systems is developed. Despite the conditional Gaussianity, such systems are nevertheless highly nonlinear and are able to capture the non-Gaussian features of nature. The special structure of the system allows closed analytical formulae for solving the conditional statistics and is thus computationally efficient. A rich gallery of examples of conditional Gaussian systems are illustrated here, which includes data-driven physics-constrained nonlinear stochastic models, stochastically coupled reaction–diffusion models in neuroscience and ecology, and large-scale dynamical models in turbulence, fluids and geophysical flows. Making use of the conditional Gaussian structure, efficient statistically accurate algorithms involving a novel hybrid strategy for different subspaces, a judicious block decomposition and statistical symmetry are developed for solving the Fokker–Planck equation in large dimensions. The conditional Gaussian framework is also applied to develop extremely cheap multiscale data assimilation schemes, such as the stochastic superparameterization, which use particle filters to capture the non-Gaussian statistics on the large-scale part whose dimension is small whereas the statistics of the small-scale part are conditional Gaussian given the large-scale part. Other topics of the conditional Gaussian systems studied here include designing new parameter estimation schemes and understanding model errors.

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Simple stochastic dynamical models capturing the statistical diversity of El Niño Southern Oscillation

Cited by 22 publications

References 63 publications

Two Leading ENSO Modes and El Niño Types in the Zebiak–Cane Model

Two Leading ENSO Modes and El Niño Types in the Zebiak–Cane Model

ENSO Bimodality and Extremes

Conditional Gaussian Systems for Multiscale Nonlinear Stochastic Systems: Prediction, State Estimation and Uncertainty Quantification

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