What Controls ENSO‐Amplitude Diversity in Climate Models?

Wengel, Christian; Dommenget, Dietmar; Latif, Mojib; Bayr, Tobias; Vijayeta, Asha

doi:10.1002/2017gl076849

Cited by 20 publications

(18 citation statements)

References 30 publications

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“…It can enhance the strength of SPB to some extent. Furthermore, Wengel, Dommenget, et al (2018) find that only a comprehensive consideration of stochastic forcing and growth rate can explain most of ENSO amplitude variance in CMIP5 simulations. How to separate these effects on ENSO SPB strength still needs further investigation.…”

Section: Summary and Discussionmentioning

confidence: 99%

Controls of Spring Persistence Barrier Strength in Different ENSO Regimes and Implications for 21st Century Changes

Jin

Liu

2020

Geophysical Research Letters

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This paper investigates potential factors that control the El Niño–Southern Oscillation (ENSO) Spring Persistence Barrier (SPB) strength in two different ENSO regimes and apply it to explain the ENSO SPB strength modulation after the 21st century. In a damped, noise‐driven model, the theoretical solution of SPB strength illustrates that a weaker ENSO growth rate strengthens SPB. In the self‐sustained regime, as in the Cane‐Zebiak model (chaotic system), the strengthened thermodynamic damping and weakened thermocline positive feedback lead to a more negative ENSO growth rate and, in turn, a stronger SPB. Therefore, in both ENSO regimes, a weaker ENSO growth rate intensifies the SPB. The application of the theory to the real world suggests that a more negative ENSO growth rate, corresponding to a more damped feedback system, is responsible for the stronger SPB in recent decades than in 1980–2000.

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Section: Summary and Discussionmentioning

confidence: 99%

Controls of Spring Persistence Barrier Strength in Different ENSO Regimes and Implications for 21st Century Changes

Jin

Liu

2020

Geophysical Research Letters

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“…In historical simulations with estimates of observed external forcing, ENSO amplitude greatly differs among the CMIP5 (Bellenger et al 2014) and CMIP6 models (Brown et al 2020;Planton et al 2020). Differences in the wind-SST feedback (Vijayeta and Dommenget 2018) and stochastic forcing explain part of the spread (Wengel et al 2018). Further, model bias is still an issue.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty of ENSO-amplitude projections in CMIP5 and CMIP6 models

et al. 2021

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There is a long-standing debate on how the El Niño/Southern Oscillation (ENSO) amplitude may change during the twenty-first century in response to global warming. Here we identify the sources of uncertainty in the ENSO amplitude projections in models participating in the Coupled Model Intercomparison Phase 5 (CMIP5) and Phase 6 (CMIP6), and quantify scenario uncertainty, model uncertainty and uncertainty due to internal variability. The model projections exhibit a large spread, ranging from increasing standard deviation of up to 0.6 °C to diminishing standard deviation of up to − 0.4 °C by the end of the twenty-first century. The ensemble-mean ENSO amplitude change is close to zero. Internal variability is the main contributor to the uncertainty during the first three decades; model uncertainty dominates thereafter, while scenario uncertainty is relatively small throughout the twenty-first century. The total uncertainty increases from CMIP5 to CMIP6: while model uncertainty is reduced, scenario uncertainty is considerably increased. The models with “realistic” ENSO dynamics have been analyzed separately and categorized into models with too small, moderate and too large ENSO amplitude in comparison to instrumental observations. The smallest uncertainties are observed in the sub-ensemble exhibiting realistic ENSO dynamics and moderate ENSO amplitude. However, the global warming signal in ENSO-amplitude change is undetectable in all sub-ensembles. The zonal wind-SST feedback is identified as an important factor determining ENSO amplitude change: global warming signal in ENSO amplitude and zonal wind-SST feedback strength are highly correlated across the CMIP5 and CMIP6 models.

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“…The noise has a random part and a state-dependent part, and is a Heaviside step function, which has a value of one if the argument positive and zero otherwise. From the observed time series of and , we estimated ROM coefficients ( , , , ) via a least-square fitting of and tendencies against and 39 . Although NDH provides a physical meaning on the quadrature terms in ( 3a ), we do not advocate it as the only nonlinear process in ENSO system.…”

Section: Resultsmentioning

confidence: 99%

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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What Controls ENSO‐Amplitude Diversity in Climate Models?

Cited by 20 publications

References 30 publications

Controls of Spring Persistence Barrier Strength in Different ENSO Regimes and Implications for 21st Century Changes

Controls of Spring Persistence Barrier Strength in Different ENSO Regimes and Implications for 21st Century Changes

Uncertainty of ENSO-amplitude projections in CMIP5 and CMIP6 models

Fokker–Planck dynamics of the El Niño-Southern Oscillation

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