Ensemble‐mean dynamics of the ENSO recharge oscillator under state‐dependent stochastic forcing

Jin, Fei‐Fei; Lin, Li‐Ching; Timmermann, Axel; Zhao, Jinping

doi:10.1029/2006gl027372

Cited by 133 publications

(171 citation statements)

References 34 publications

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“…However, since version 3.5, CCSM represents ENSO as a series of events, owing to improved simulation of the meridional extent of the wind anomalies and their associated ocean response (Neale et al 2008). In the real world, warm events also appear to arise from a stable basic state excited by stochastic forcing (Penland and Sardeshmukh 1995;Thompson and Battisti 2001;Kessler 2002 Jin et al 2007); however, our choice of white Gaussian noise with a 1-month decorrelation time scale produces an adequate simulation of the CCSM4 ENSO, given the simplicity of the model. Therefore, nonlinear and seasonally dependent feedbacks excited with Gaussian white noise appear to be the most parsimonious model of CCSM4's La Niña.…”

Section: B Effect Of External Noisementioning

confidence: 99%

“…A1a,b, green line). These differences arise because the zonal current anomaly u 0 comprises a wind-driven component-a direct response to the wind anomalies-and a Kelvin wave componentassociated with the delayed thermocline response (Jin et al 2006). The Q 0 u variability correlated with SST anomalies contributes to the positive Bjerknes feedback (in the same way than Q 0 w ), while the variability in quadrature contributes to the delayed thermocline feedback.…”

Section: Composite Heat Budget Analysismentioning

confidence: 99%

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Nonlinear Controls on the Persistence of La Niña*

2014

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A large fraction (35%-50%) of observed La Niña events last two years or longer, in contrast to the great majority of El Niño events, which last one year. Here, the authors explore the nonlinear processes responsible for the multiyear persistence of La Niña in the Community Climate System Model, version 4 (CCSM4), a coupled climate model that simulates the asymmetric duration of La Niña and El Niño events realistically. The authors develop a nonlinear delayed-oscillator (NDO) model of the El Niño-Southern Oscillation (ENSO) to explore the mechanisms governing the duration of La Niña. The NDO includes nonlinear and seasonally dependent feedbacks derived from the CCSM4 heat budget, which allow it to simulate key ENSO features in quantitative agreement with CCSM4.Sensitivity experiments with the NDO show that the nonlinearity in the delayed thermocline feedback is the sole process controlling the duration of La Niña events. The authors' results show that, as La Niña events become stronger, the delayed thermocline response does not increase proportionally. This nonlinearity arises from two processes: 1) the response of winds to sea surface temperature anomalies and 2) the ability of thermocline depth anomalies to influence temperatures at the base of the mixed layer. Thus, strong La Niña events require that the thermocline remains deeper for longer than 1 yr for sea surface temperatures to return to neutral. Ocean reanalysis data show evidence for this thermocline nonlinearity, suggesting that this process could be at work in nature.

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Section: B Effect Of External Noisementioning

confidence: 99%

Section: Composite Heat Budget Analysismentioning

confidence: 99%

Nonlinear Controls on the Persistence of La Niña*

2014

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“…Tsyrulnikov (2005) utilized this idea to characterize the combined effect of the sub-grid parameterization, the unrecognized physical processes, and the atmospheric noise by adopting the Markov chain models; Jin et al (2007) used state-dependent stochastic noise to represent combined model errors and study the predictability and dynamics of the ENSO recharge oscillator. Zheng and Zhu (2016) added random terms to the tendency of an ENSO model and explicitly defined them as model errors.…”

Section: Introductionmentioning

confidence: 99%

Towards optimal observational array for dealing with challenges of El Niño-Southern Oscillation predictions due to diversities of El Niño

Duan

Tian

2018

Clim Dyn

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This paper investigates the optimal observational array for improving the initialization of El Niño-Southern Oscillation predictions by exploring the sensitive areas for target observations of two types of El Niño predictions. The sensitive areas are identified by calculating the optimally growing errors (OGEs) of the Zebiak-Cane model, as corrected by the optimal forcing vector that is determined by assimilating the observed sea surface temperature anomalies (SSTAs). It is found that although the OGEs have similar structures for different start months of predictions, the regions covered by much large errors for the SSTA component tend to locate at different zonal positions and depends on the start months. Furthermore, these regions are also in difference between two types of El Niño events. The regions covered by large errors of OGEs represent the sensitive areas for target observations. Considering the dependence of the sensitive areas on related El Niño types and the start months of predictions, the present study propose a quantitative frequency method to determine the sensitive areas for target observations associated with two types of El Niño predictions, which is expected to be applicable for both types of El Niño predictions with different start months. As a result, the sensitive areas that describe the array of target observations are presented with a reversal triangle-like shape locating in the eastern Pacific, specifically the area of 120°W-85°W, 0°S-11°S, and an extension to the west along the equator and then gathering at the 180° longitude and the western boundary. "Hindcast" experiments demonstrated that such observational array is very useful in distinguishing two types of El Niño and superior to the TAO/TRITON array. It is therefore suggested that the observational array provided in the present study is towards the optimal one and the original TAO/TRITON array should be further optimized when applied to predictions of the diversities of El Niño events.

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“…Palmer (2001) suggested that subgrid processes could be represented by simplified nonlinear stochastic-dynamic models as an alternative to the deterministic bulk-formula approach. Shutts (2005) and Shutts and Palmer (2007) showed that a cellular automaton scheme to introduce stochastic perturbations in the physical tendencies had a beneficial impact in a mediumrange global forecast model, while Jin et al (2007) employed a state-dependent stochastic multiplicative forcing to improve El Niño-Southern Oscillation (ENSO) simulations in a simplified model.…”

Section: Introductionmentioning

confidence: 99%

Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts

Doblas‐Reyes

Weisheimer

Déqué

et al. 2009

Quart J Royal Meteoro Soc

123

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ABSTRACT:The relative merits of three forecast systems addressing the impact of model uncertainty on seasonal/annual forecasts are described. One system consists of a multi-model, whereas two other systems sample uncertainties by perturbing the parametrization of reference models through perturbed parameter and stochastic physics techniques. Ensemble reforecasts over 1991 to 2001 were performed with coupled climate models started from realistic initial conditions. Forecast quality varies due to the different strategies for sampling uncertainties, but also to differences in initialisation methods and in the reference forecast system. Both the stochastic-physics and perturbed-parameter ensembles improve the reliability with respect to their reference forecast systems, but not the discrimination ability. Although the multi-model experiment has an ensemble size larger than the other two experiments, most of the assessment was done using equally-sized ensembles. The three ensembles show similar levels of skill: significant differences in performance typically range between 5 and 20%. However, a nine-member multi-model shows better results for seasonal predictions with lead times shorter than five months, followed by the stochastic-physics and perturbed-parameter ensembles. Conversely, for seasonal predictions with lead times longer than four months, the perturbed-parameter ensemble gives more often better results. All systems suggest that spread cannot be considered a useful predictor of skill. Annual-mean predictions showed lower forecast quality than seasonal predictions. Only small differences between the systems were found. The full multi-model ensemble has improved quality with respect to all other systems, mainly from the larger ensemble size for lead times longer than four months and annual predictions.

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Ensemble‐mean dynamics of the ENSO recharge oscillator under state‐dependent stochastic forcing

Cited by 133 publications

References 34 publications

Nonlinear Controls on the Persistence of La Niña*

Nonlinear Controls on the Persistence of La Niña*

Towards optimal observational array for dealing with challenges of El Niño-Southern Oscillation predictions due to diversities of El Niño

Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts

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