The role of nonlinear forcing singular vector tendency error in causing the “spring predictability barrier” for ENSO

Duan, Wansuo; Zhao, Peng; Hu, Junya; Xu, Hui

doi:10.1007/s13351-016-6011-4

Cited by 17 publications

(5 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally, the skill is derived from information about subsurface heat anomalies of approximately the same intensity, and the different cycles capture similar oscillation phases. Previous studies (Chen et al 1995(Chen et al , 2004Chen and Cane 2008;Stockdale et al 2011;Duan et al 2016;Lee et al 2018) have already concluded that the spring predictability barrier may not be an intrinsic barrier to the system itself, but it could rather depend on model skill, observational data availability, especially in the subsurface western tropical Pacific (Lee Here we add evidence to such claims, as we also find that the drop in forecast skill is slow and gradual for longer-lead predictions than a couple of seasons (Figure 3).…”

Section: Resultssupporting

confidence: 66%

Multiyear Statistical Prediction of ENSO Enhanced by the Tropical Pacific Observing System

et al. 2020

View full text Add to dashboard Cite

The theoretical predictability limit of El Niño–Southern Oscillation has been shown to be on the order of years, but long-lead predictions of El Niño (EN) and La Niña (LN) are still lacking. State-of-the-art forecasting schemes traditionally do not predict beyond the spring barrier. Recent efforts have been dedicated to the improvement of dynamical models, while statistical schemes still need to take full advantage of the availability of ocean subsurface variables, provided regularly for the last few decades as a result of the Tropical Ocean–Global Atmosphere Program (TOGA). Here we use a number of predictor variables, including temperature at different depths and regions of the equatorial ocean, in a flexible statistical dynamic components model to make skillful long-lead retrospective predictions (hindcasts) of the Niño-3.4 index in the period 1970–2016. The model hindcasts the major EN episodes up to 2.5 years in advance, including the recent extreme 2015/16 EN. The analysis demonstrates that events are predicted more accurately after the completion of the observational array in the tropical Pacific in 1994, as a result of the improved data quality and coverage achieved by TOGA. Therefore, there is potential to issue long-lead predictions of this climatic phenomenon at a low computational cost.

show abstract

Section: Resultssupporting

confidence: 66%

Multiyear Statistical Prediction of ENSO Enhanced by the Tropical Pacific Observing System

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The other topic for the predictability problem is related to model errors [26][27][28]. Given that the prediction model serves as an approximation to the Earth system, significant uncertainties exist in model parameters (MPs).…”

Section: Introductionmentioning

confidence: 99%

Maximum Impacts of the Initial and Model Parametric Errors on El Niño Predictions

Tao

2024

JMSE

View full text Add to dashboard Cite

With an El Niño prediction model, an advanced approach of conditional nonlinear optimal perturbation (CNOP) is used to reveal the maximum impacts of the errors occurring in initial conditions (ICs) and model parameters (MPs) on the El Niño predictions. The optimally growing initial errors CNOP-I and parameter errors CNOP-P are obtained, as well as their optimally combined mode (denoted by CNOPs). The comparisons among CNOP-I, -P, and CNOPs show that the El Niño predictions are more sensitive to the uncertainties in the MPs than in the ICs. The CNOP-I mainly affects the short-term prediction (less than 3 months), whereas the CNOP-P tends to induce much larger error over a longer prediction time. Both CNOP-I and CNOP-P can induce larger error growth during spring than during other seasons; that is to say, both of them cause the “spring predictability barrier” (SPB) phenomenon. The spring error growth caused by CNOP-I is mainly attributed to the uncertainties of the ocean advection processes, while that caused by the CNOP-P is controlled by thermodynamics. When the errors in ICs and MPs are simultaneously included in predictions, the resultant CNOPs produce much larger error growth and cause much more significant SPB; furthermore, the corresponding mechanism is dominated by the nonlinear advection processes. This certainly indicates that strong nonlinear interactions between the errors in ICs and MPs enhance the SPB, thus deepening our understanding of El Niño predictability. It is obvious that initial and model errors should be simultaneously given great attention to improve the El Niño prediction level.

show abstract

“…And it can more effectively describe the non‐linear growth of model error (Duan et al ., 2014), suggesting that it may have great potential for reflecting the impact of non‐linearity in CAEPSs. The NFSV method has been widely applied to study the predictability of the climate and weather events in terms of model uncertainties (Duan and Zhou, 2013; Duan et al ., 2014; Duan et al ., 2016; Tao and Duan, 2019, Tao et al ., 2020; Yao et al ., 2021). For instance, Duan and Zhou (2013) explored the constant non‐linear tendency error that has the largest effect on prediction uncertainties for El Niño events.…”

Section: Introductionmentioning

confidence: 99%

A stochastic and non‐linear representation of model uncertainty in a convective‐scale ensemble prediction system

Chen

et al. 2022

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

Accurately addressing model uncertainties with a consideration of the enhanced effect of non-linearities in a high-resolution convective-scale system is a crucial issue for performing convection-allowing ensemble prediction systems (CAEPSs). In this study, a conditional non-linear-stochastic perturbation method is developed to simultaneously consider both a stochastic and a non-linear representation of model uncertainties associated with physics parameterization in the Global and Regional Assimilation and Prediction Enhanced System (GRAPES)-CAEPS with a horizontal resolution of 3 km. The non-linear forcing singular vector (NFSV) for a non-linear representation of model uncertainties and the Stochastically Perturbed Parameterization Tendencies (SPPT) scheme for a stochastic representation of model uncertainties, are applied. Two experiments were carried out over South China for a month (May 1-30, 2020), one with a SPPT scheme and the other with a non-linear-stochastic perturbation using a combination of SPPT and NFSV schemes. The combination of SPPT and NFSV schemes is compared with the SPPT scheme alone to investigate whether the conditional non-linear-stochastic perturbation method that combines non-linear and stochastic schemes can represent model uncertainty better than the traditional stochastic SPPT approach. The results show that combining the NFSV and SPPT schemes improves the overall probabilistic skill and has an advantage over the SPPT scheme, which may imply that adding additional state-independent non-linear noise contributes to a more comprehensive characterization of model error for representing model uncertainties in CAEPSs.This discovery sheds light on the design and development of model perturbation strategies for convective-scale ensembles in the future.

show abstract

The role of nonlinear forcing singular vector tendency error in causing the “spring predictability barrier” for ENSO

Cited by 17 publications

References 49 publications

Multiyear Statistical Prediction of ENSO Enhanced by the Tropical Pacific Observing System

Multiyear Statistical Prediction of ENSO Enhanced by the Tropical Pacific Observing System

Maximum Impacts of the Initial and Model Parametric Errors on El Niño Predictions

A stochastic and non‐linear representation of model uncertainty in a convective‐scale ensemble prediction system

Contact Info

Product

Resources

About