Improved ensemble-mean forecasting of ENSO events by a zero-mean stochastic error model of an intermediate coupled model

Zheng, Fei; Zhu, Jiang

doi:10.1007/s00382-016-3048-0

Cited by 39 publications

(12 citation statements)

References 98 publications

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“…To objectively evaluate the skill of the dynamical model, persistence predictions were conducted. For example, for the effective way to improve the skill and reduce the uncertainty of ENSO predictions (Zheng and Zhu 2016). For the EMI, the skill scores of the anomaly hindcasts initiated from any month are higher than their counterparts in the full-field hindcasts (Figure 3(e)-(h)).…”

Section: Predictive Skill For Temporal Evolution Of Niño34 and El Nimentioning

confidence: 98%

ENSO hindcast skill of the IAP-DecPreS near-term climate prediction system: comparison of full-field and anomaly initialization

Qian

Zhou

et al. 2017

Atmospheric and Oceanic Science Letters

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Model initialization is a key process of climate predictions using dynamical models. In this study, the authors evaluated the performances of two distinct initialization approaches-anomaly and full-field initializations-in ENSO predictions conducted using the IAP-DecPreS near-term climate prediction system developed by the Institute of Atmospheric Physics (IAP). IAP-DecPreS is composed of the FGOALS-s2 coupled general circulation model and a newly developed ocean data assimilation scheme called 'ensemble optimal interpolation-incremental analysis update' (EnOI-IAU). It was found that, for IAP-DecPreS, the hindcast runs using the anomaly initialization have higher predictive skills for both conventional ENSO and El Niño Modoki, as compared to using the full-field initialization. The anomaly hindcasts can predict super El Niño/La Nina 10 months in advance and have good skill for most moderate and weak ENSO events about 4-7 months in advance. The predictive skill of the anomaly hindcasts for El Niño Modoki is close to that for conventional ENSO. On the other hand, the anomaly hindcasts at 1-and 4-month lead time can reproduce the major features of large-scale patterns of sea surface temperature, precipitation and atmospheric circulation anomalies during conventional ENSO and El Niño Modoki winter. 摘要本文使用中国科学院大气物理研究所近期气候预测系统IAP-DecPreS，评估了全场初始化和异常场初始化对ENSO的预测技巧。结果表明：采用异常场初始化方法对典型ENSO和El Niño Modoki的预报技巧均优于采用全场初始化方法的预报技巧。采用异常场初始化方法的回报结果能超前10个月回报强ENSO事件，超前4-7个月回报相对较弱的ENSO事件。采用异常场初始化方法对El Niño Modoki和典型ENSO的预报技巧相当。此外，采用异常场初始化方法的回报结果能超前1-4个月模拟出典型ENSO和El Niño Modoki的冬季海表面温度、降水以及大气环流异常的主要空间分布特征。

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Section: Predictive Skill For Temporal Evolution Of Niño34 and El Nimentioning

confidence: 98%

ENSO hindcast skill of the IAP-DecPreS near-term climate prediction system: comparison of full-field and anomaly initialization

Qian

Zhou

et al. 2017

Atmospheric and Oceanic Science Letters

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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. In fact, Duan et al (2014) also proposed an approach of optimal forcing vector (OFV) to depict the combined effect of kinds of model errors and used it to correct the Zebiak-Cane model, finally reproducing successfully the observed two types of El Niño events.…”

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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“…5). The performance of the prediction system is documented in Zheng and Zhu (2016), in which a 20-year retrospective forecast comparison shows that good forecast skill of the EPS with a prediction lead time of up to one year is possible. The most recent ensemble forecast suggests that there is at least a 95% chance that La Niña will persist through the 2020/21 winter, with a potential transition to ENSO-Neutral during spring 2021 (~50% chance).…”

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confidence: 99%

The 2020/21 Extremely Cold Winter in China Influenced by the Synergistic Effect of La Niña and Warm Arctic

et al. 2021

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In the first half of winter 2020/21, China has experienced an extremely cold period across both northern and southern regions, with record-breaking low temperatures set in many stations of China. Meanwhile, a moderate La Niña event which exceeded both oceanic and atmospheric thresholds began in August 2020 and in a few months developed into its mature phase, just prior to the 2020/21 winter. In this report, the mid−high-latitude large-scale atmospheric circulation anomalies in the Northern Hemisphere, which were forced by the negative phase of Arctic Oscillation, a strengthened Siberian High, an intensified Ural High and a deepened East Asian Trough, are considered to be the direct reason for the frequent cold surges in winter 2020/21. At the same time, the synergistic effect of the warm Arctic and the cold tropical Pacific (La Niña) provided an indispensable background, at a hemispheric scale, to intensify the atmospheric circulation anomalies in middleto-high latitudes. In the end, a most recent La Niña prediction is provided and the oncoming evolution of climate is discussed for the remaining part of the 2020/21 winter for the purpose of future decision-making and early warning.

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Improved ensemble-mean forecasting of ENSO events by a zero-mean stochastic error model of an intermediate coupled model

Cited by 39 publications

References 98 publications

ENSO hindcast skill of the IAP-DecPreS near-term climate prediction system: comparison of full-field and anomaly initialization

ENSO hindcast skill of the IAP-DecPreS near-term climate prediction system: comparison of full-field and anomaly initialization

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

The 2020/21 Extremely Cold Winter in China Influenced by the Synergistic Effect of La Niña and Warm Arctic

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