The initial errors that induce a significant “spring predictability barrier” for El Niño events and their implications for target observation: results from an earth system model

Duan, Wansuo; Hu, Junya

doi:10.1007/s00382-015-2789-5

Cited by 60 publications

(57 citation statements)

References 77 publications

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“…It is therefore inferred that the sensitive areas for target observations for EP-El Niño and La Niña are similar and the observational array SA SST revealed in the present study is also useful for La Nina predictions. When mentioning to the neutral years, previous studies showed the similarity between the optimal precursors (OPRs) and the OGEs of El Niño Duan and Hu 2016). Here, the so-called OPR is the initial perturbations that are superimposed to neutral states and can trigger an El Niño event.…”

Section: Summary and Discussionmentioning

confidence: 98%

“…Quite a few studies have explored the important role of the initial errors in ENSO prediction uncertainties (Chen et al 1995(Chen et al , 2004Moore and Kleeman 1996;Mu et al 2007;Thompson 1998;Xue et al 1997a, b) or emphasized the importance of the accuracy of initial analysis fields in improving ENSO forecast skill (Keenlyside et al 2005;Zheng et al 2006Zheng et al , 2007Zhu et al 2017). Moreover, recent studies showed that the initial errors with particular spatial structures cause much larger prediction uncertainty for ENSO (Mu et al 2007;Yu et al 2009;Duan et al 2009;Duan and Hu 2016). Specifically, Duan et al (2009) pointed out that the initial errors with a dipole structure of sea surface temperature anomalies (SSTAs) along the tropical Pacific are most likely to cause a significant spring predictability barrier (SPB) phenomenon for El Niño events.…”

Section: Introductionmentioning

confidence: 99%

“…Correspondingly, Mu et al (2014) proposed an idea that the ENSO forecast skill can be greatly improved by assimilating observations in some key areas that locate in the abovementioned initial errors of special structure and are covered by large errors. Such idea is related to target observations Duan and Hu 2016;Hu and Duan 2016).…”

Section: Introductionmentioning

confidence: 99%

“…They argued that the eastern equatorial Pacific is the sensitive areas for targeting observations associated with EP-El Niño forecasting. Considering that the Zebiak-Cane model is an intermediate ENSO model and does not have enough description of the subsurface layers, Duan and Hu (2016) and Hu and Duan (2016) further adopted a complex GCM and showed that the subsurface layer of western equatorial Pacific represent another sensitive area for El Niño forecasting. However, most of these above studies only focus on EP-El Niño events because most models do not have the ability to simulate two types of El Niño events well Kug et al 2012).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: Summary and Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Self Cite

View full text Add to dashboard Cite

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“…However, ENSO forecast remains challenging (Barnston et al, 2012;Chen & Cane, 2008;Kirtman & Schopf, 1998;Latif et al, 1998;. Actually, from the perspective of error growth dynamics, it has been investigated that the SPB for ENSO is the result of the combined effect of the climatological annual cycle, the ENSO itself, and the particular pattern of initial errors (Duan & Hu, 2015;Duan & Wei, 2012;Mu, Duan, & Wang, 2007;Ren et al, 2016). The spring persistence barrier has been shown to physically correlate with the sudden reduction of ENSO forecast skills across spring in various climate models, that is, the spring predictability barrier (SPB; McPhaden, 2003;Mu, Xu, & Duan, 2007;Masuda et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Decadal Modulation of ENSO Spring Persistence Barrier by Thermal Damping Processes in the Observation

Fang

Zheng

Liu

et al. 2019

Geophysical Research Letters

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The spring persistence barrier remains a critical challenge for the prediction of El Niño–Southern Oscillation (ENSO) because it severely limits the potential ability of ENSO forecast across the boreal spring. Our observational analysis shows that the spring persistence barrier exhibited a significant decadal modulation in the last half century. Further analysis shows that this decadal modulation is caused by the decadal variation in spring ENSO stability, which, in turn, is dominated by the thermal damping feedback of the sea surface temperature (SST) through latent heat flux and shortwave radiation flux. The change of the thermal damping feedback is associated with the background mean SST, with a cooler SST corresponding to a greater ENSO instability and a severer spring persistence barrier and vice versa.

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Relationship between optimal precursory disturbances and optimally growing initial errors associated with ENSO events: Implications to target observations for ENSO prediction

Duan

2016

JGR Oceans

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By superimposing initial sea temperature disturbances in neutral years, we determine the precursory disturbances that are most likely to evolve into El Niño and La Niña events using an Earth System Model. These precursory disturbances for El Niño and La Niña events are deemed optimal precursory disturbances because they are more likely to trigger strong ENSO events. Specifically, the optimal precursory disturbance for El Niño exhibits negative sea surface temperature anomalies (SSTAs) in the central‐eastern equatorial Pacific. Additionally, the subsurface temperature component exhibits negative anomalies in the upper layers of the eastern equatorial Pacific and positive anomalies in the lower layers of the western equatorial Pacific. The optimal precursory disturbance for La Niña is almost opposite to that of El Niño. The optimal precursory disturbances show that both El Niño and La Niña originate from precursory signals in the subsurface layers of the western equatorial Pacific and in the surface layers of the eastern equatorial Pacific. We find that the optimal precursory disturbances for El Niño and La Niña are particularly similar to the optimally growing initial errors associated with El Niño prediction that have been presented in previous studies. The optimally growing initial errors show that the optimal precursor source areas represent the sensitive areas for target observations associated with ENSO prediction. Combining the optimal precursory disturbances and the optimally growing initial errors for ENSO, we infer that additional observations in these sensitive areas can reduce initial errors and be used to detect precursory signals, thereby improving ENSO predictions.

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The initial errors that induce a significant “spring predictability barrier” for El Niño events and their implications for target observation: results from an earth system model

Cited by 60 publications

References 77 publications

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

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

Decadal Modulation of ENSO Spring Persistence Barrier by Thermal Damping Processes in the Observation

Relationship between optimal precursory disturbances and optimally growing initial errors associated with ENSO events: Implications to target observations for ENSO prediction

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