Current Status of Intraseasonal–seasonal-to-Interannual Prediction of the Indo-Pacific Climate

Luo, Jing–Jia; Yuan, Chaoxia; Sasaki, Wataru; Behera, Swadhin K.; Masumoto, Yukio; Yamagata, Toshio; Lee, June‐Yi; Masson, Sébastien

doi:10.1142/9789814696623_0003

Cited by 59 publications

(30 citation statements)

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“…In contrast to boreal fall and summer, very little predictive skill is evident for the boreal winter target seasons, for which the ACC quickly decreases below 0.4 for a three‐month lead time forecast (Figures a–h). This is consistent with the well‐known winter prediction barrier for the IOD (Feng et al, ; Luo et al, ; Wajsowicz, ; Zhao & Hendon, ), which might be related to the annual reversal of the monsoon (Luo et al, , ) and a very small signal to predict for that time of the year (Figure S2b). The prediction barrier for the IOD lasts through the spring and early summer in the persistence and CFSv2 forecasts (Figures a and b).…”

Section: Resultssupporting

confidence: 89%

“…Moreover, the SDM experiments with observed ENSO forcing (SDM‐P and SDM‐P‐F) demonstrate a common feature that the ACC recovers slightly in boreal late winter/early spring (February‐March‐April) and then decreases again in late spring with minimum ACC values in April‐May‐June showing a small local peak (Figures e and f). This rebound is not evident in the SDM without ENSO forcing (Figures c and d), and only weakly represented in those with CFSv2 forecast ENSO forcing (Figures g and h), indicating that the IOD spring predictability barrier is related to ENSO (Luo et al, ; Wang et al, ) and that improved ENSO predictions could also potentially lead to increased IOD predictive skill for a target season in February‐March‐April.…”

Section: Resultsmentioning

confidence: 96%

“…Many severe climate events with pronounced societal and economic impacts can be attributed to the IOD. These include for instance haze problems in Indonesia associated with forest fires, droughts in Australia, and floods in East Africa (Cai et al, ; Luo et al, ). Therefore, skillful IOD predictions with a long enough lead time would potentially allow for the implementation of mitigation measures and thereby could provide pronounced societal benefits.…”

Section: Introductionmentioning

confidence: 99%

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Improved Predictability of the Indian Ocean Dipole Using Seasonally Modulated ENSO Forcing Forecasts

Zhao

Jin

Stuecker

2019

Geophysical Research Letters

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Despite recent progress in seasonal forecast systems, the predictive skill for the Indian Ocean Dipole (IOD) remains typically limited to a lead time of one season or less in both dynamical and empirical models. Here we develop a simple stochastic‐dynamical model (SDM) to predict the IOD using seasonally modulated El Niño–Southern Oscillation (ENSO) forcing together with a seasonally modulated Indian Ocean coupled ocean‐atmosphere feedback. The SDM, with either observed or forecasted ENSO forcing, exhibits generally higher skill and longer lead times for predicting IOD events than the operational Climate Forecast System version 2 and the Scale Interaction Experiment–Frontier system. The improvements mainly originate from better prediction of ENSO‐dependent IOD events and from reducing false alarms. These results affirm our hypothesis that operational IOD predictability beyond persistence is largely controlled by ENSO predictability and the signal‐to‐noise ratio of the system. Therefore, potential future ENSO improvements in models should translate to more skillful IOD predictions.

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Section: Resultssupporting

confidence: 89%

Section: Resultsmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved Predictability of the Indian Ocean Dipole Using Seasonally Modulated ENSO Forcing Forecasts

Zhao

Jin

Stuecker

2019

Geophysical Research Letters

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“…Recently, Duan et al (2017) showed that the nonlinearity presents more influences in EP-El Niño than in CP-El Niño, which may cause more irregularities in EP-El Niño and then limit the predictability of EP-El Niño much more. There also exist a few studies that performed seasonal prediction experiments and argued that the CP-El Niño present much lower forecast skill than the EP-El Niño (Jeong et al 2012;Imada et al 2015;Luo et al 2016;Zheng and Yu 2017). In fact, these studies did not separate the impact of initial errors and model errors and are hardly regarded as evidence that CP-El Niño is less predictable than EP-El Niño.…”

Section: Summary and Discussionmentioning

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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“…The long-lasting La Niña events following big El Niño events in 1982Niño events in /1983Niño events in , 1997Niño events in /1998Niño events in , and 2015Niño events in /2016 as well as the 2010-2012 La Niña can be predicted up to 2 years ahead (DiNezio et al, 2017;Luo et al, 2008Luo et al, , 2016Luo et al, , 2017. Nevertheless, the second La Niña is more difficult to be predicted than the first La Niña.…”

Section: Introductionmentioning

confidence: 97%

Impacts of Tropical Indian and Atlantic Ocean Warming on the Occurrence of the 2017/2018 La Niña

Zhang

Luo

2019

Geophysical Research Letters

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The occurrence of the 2017/2018 La Niña, following a weak‐to‐neutral La Niña in boreal winter 2016/2017, was surprising. Based on observational records and multiple linear regression analysis for the Pacific zonal wind tendency (dU/dt), this study investigates possible reasons why the La Niña condition suddenly happened in late 2017. Similar to previous four double‐peaked La Niña events (1983–1985, 1998–2000, 2007–2009, and 2010–2012), we find that the multiyearly persistent easterly anomaly in the central equatorial Pacific is a key condition to the development of the second La Niña. The occurrence of the 2017/2018 La Niña results from large warm sea surface temperature (SST) anomalies in the tropical Indian and Atlantic Oceans that act to force the persistent easterly anomaly in the Pacific via modifying the Walker Circulations. About 24% of the variance of the Pacific dU/dt can be statistically explained by the tropical Indian Ocean and Atlantic SST anomalies.

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Current Status of Intraseasonal–seasonal-to-Interannual Prediction of the Indo-Pacific Climate

Cited by 59 publications

References 0 publications

Improved Predictability of the Indian Ocean Dipole Using Seasonally Modulated ENSO Forcing Forecasts

Improved Predictability of the Indian Ocean Dipole Using Seasonally Modulated ENSO Forcing Forecasts

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

Impacts of Tropical Indian and Atlantic Ocean Warming on the Occurrence of the 2017/2018 La Niña

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