Progress in ENSO prediction and predictability study

Tang, Youmin; Zhang, Ronghua; Liu, Ting; Duan, Wansuo; Yang, Dong; Zheng, Fei; Ren, Hong‐Li; Lian, Tao; Gao, Chuan; Chen, Dake; Mu, Mu

doi:10.1093/nsr/nwy105

Cited by 191 publications

(104 citation statements)

References 90 publications

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“…4. Unit: °C/month event from 2014 to 2016 poses a severe challenge to the classic ENSO theory-based forecasting models (Tang et al 2018). Almost all models failed and missed the strongest warming event when predictions were initialized in early 2015.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

A study of the effects of westerly wind bursts on ENSO based on CESM

et al. 2019

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Numerous works have indicated that westerly wind bursts (WWBs) have a significant contribution to the development of El Niño events. However, the simulation of WWBs commonly suffers from large biases in the current generation of coupled general circulation models (CGCMs), limiting our ability to predict El Niño events. In this study, we introduce a WWBs parameterization scheme into the global coupled Community Earth System Model (CESM) to improve the representation of WWBs and to study the impacts of WWBs on El Niño-Southern Oscillation (ENSO) characteristics. It is found that CESM with the WWBs parameterization scheme can generate more realistic characteristics of WWBs, in particular their location and seasonal variation of occurrence. With the parameterized WWBs, the skewness of the Niño 3 index is increased, in better agreement with observation. Eastern Pacific El Niño and central Pacific El Niño events could be successfully reproduced in the model run with WWBs parameterization. Further diagnoses show that the enhanced horizontal advection in the central Pacific and vertical advection in the eastern Pacific, both of which are triggered by WWBs, are crucial factors responsible for the improvements in ENSO simulation. Clearly, WWBs have important effects on ENSO asymmetry and ENSO diversity.

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Section: Conclusion and Discussionmentioning

confidence: 99%

A study of the effects of westerly wind bursts on ENSO based on CESM

et al. 2019

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“…Seasonal predictability of El Nino Southern Oscillation (ENSO) has been well established for many years (Cane et al, 1986) and the high predictability of ENSO is now a cornerstone of current seasonal prediction capability (Smith et al, 2012). Nevertheless, outstanding questions remain (Tang et al, 2018), for example regarding the predictability of different ENSO types (Imada et al, 2015;Ren et al, 2019), or increases in skill as models improve (e.g., Luo et al, 2008). Although comprehensive models now appear to have the edge over simpler prediction models, there is also variation in prediction skill over time (Barnston et al, 2012).…”

Section: Prediction Skill Of Ensomentioning

confidence: 99%

Does increased atmospheric resolution improve seasonal climate predictions?

Scaife

Camp

Comer

et al. 2019

Atmospheric Science Letters

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We assess the impact of atmospheric horizontal resolution on the prediction skill and fidelity of seasonal forecasts. We show the response to an increase of atmospheric resolution from 0.8 to 0.3° horizontal grid spacing in parallel ensembles of forecasts. Changes in the prediction skill of major modes of tropical El Nino Southern Oscillation (ENSO) and extratropical North Atlantic Oscillation (NAO) variability are small and not detected and there is no discernible impact on the weak signal‐to‐noise ratio in seasonal predictions of the winter NAO at this range of resolutions. Although studies have shown improvements in the simulation of tropical cyclones as model resolution is increased, we find little impact on seasonal prediction skill of either their numbers or intensity. Over this range of resolutions it appears that the benefit of increasing atmospheric resolution to seasonal climate predictions is minimal. However, at yet finer scales there appears to be increased eddy feedback which could strengthen weak signals in predictions of the NAO. Until prediction systems can be run operationally at these scales, it may be better to use additional computing resources for other enhancements such as increased ensemble size, for which there is a clear benefit in extratropical seasonal prediction skill.

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“…There have been many reviews on El Niño predictability (Barnston et al, 2019(Barnston et al, , 2012Chen & Cane, 2008;Latif, 1998;Tang et al, 2018;Timmermann et al, 2018), indicating that both statistical models (those capturing behavior of past statistics) and dynamical models (i.e., those based on underlying physical conservation laws) are used for El Niño prediction. Multimodel ensemble results are given at the International Research Institute for Climate and Society (https://iri.columbia.edu/our-expertise/climate/ forecasts/#ENSO&urluscore;Forecasts).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Forecasting of El Niño Using Neural Network Models

Petersik

Dijkstra

2020

Geophysical Research Letters

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We apply Gaussian density neural network and quantile regression neural network ensembles to predict the El Niño–Southern Oscillation. Both models are able to assess the predictive uncertainty of the forecast by predicting a Gaussian distribution and the quantiles of the forecasts, respectively. This direct estimation of the predictive uncertainty for each given forecast is a novel feature in the prediction of the El Niño–Southern Oscillation by statistical models. The predicted mean and median, respectively, show a high‐correlation skill for long lead times (r=0.5, 12 months) for the 1963–2017 evaluation period. For the 1982–2017 evaluation period, the probabilistic forecasts by the Gaussian density neural network can better estimate the predictive uncertainty than a standard method to assess the predictive uncertainty of statistical models.

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Progress in ENSO prediction and predictability study

Cited by 191 publications

References 90 publications

A study of the effects of westerly wind bursts on ENSO based on CESM

A study of the effects of westerly wind bursts on ENSO based on CESM

Does increased atmospheric resolution improve seasonal climate predictions?

Probabilistic Forecasting of El Niño Using Neural Network Models

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