Prediction of primary climate variability modes at the Beijing Climate Center

Ren, Hong‐Li; Jin, Fei‐Fei; Song, Lianchun; Lü, Bo; Tian, Bing; Zuo, Jinqing; Liu, Ying; Wu, Jie; Zhao, Chunmei; Nie, Yu; Zhang, Peiqun; Ba, Jin; Wu, Yurong; Wan, Jie; Yan, Yuping; Zhou, Fang

doi:10.1007/s13351-017-6097-3

Cited by 53 publications

(22 citation statements)

References 112 publications

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“…In fact, the seasonal prediction of ENSO itself is very good by BCC-CSM1.1m. Ren et al (2017) showed that the ACC skill of Niño3.4 anomaly remains higher than 0.75 even 6 months ahead. Thus, the error in BCC-CSM1.1m may come from the ENSO teleconnection.…”

Section: Sources Of Prediction Skillmentioning

confidence: 99%

Skillful seasonal predictions of winter precipitation over southern China

Lü

Scaife

Dunstone

et al. 2017

Environ. Res. Lett.

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Southern China experiences large year-to-year variability in the amount of winter precipitation, which can result in severe social and economic impacts. In this study, we demonstrate prediction skill of southern China winter precipitation by three operational seasonal prediction models: the operational Global seasonal forecasting system version 5 (GloSea5), the NCEP Climate Forecast System (CFSv2) and the Beijing Climate Center Climate System Model (BCC-CSM1.1m). The correlation scores reach 0.76 and 0.67 in GloSea5 and CFSv2, respectively; and the amplitude of the ensemble mean forecast signal is comparable to the observed variations. The skilful predictions in GloSea5 and CFSv2 mainly benefit from the successful representation of the observed ENSO teleconnection. El Niño weakens the Walker circulation and leads to the strengthening of the subtropical high over the northwestern Pacific. The anti-cyclone then induces anomalous northward flow over the South China Sea and brings water vapor to southern China, resulting in more precipitation. This teleconnection pattern is too weak in BCC-CSM1.1m, which explains its low skill (0.13). Whereas the most skilful forecast system is also able to simulate the influence of the Indian Ocean on southern China precipitation via changes in southwesterly winds over the Bay of Bengal. Finally, we examine the real-time forecast for 2015/16 winter when a strong El Niño event led to the highest rainfall over southern China in recent decades. We find that the GloSea5 system gave good advice as it produced the third wettest southern China in the hindcast, but underestimated the observed amplitude. This is likely due to the underestimation of the Siberian High strength in 2015/2016 winter, which has driven strong convergence over southern China. We conclude that some current seasonal forecast systems can give useful warning of impending extremes. However, there is still need for further model improvement to fully represent the complex dynamics of the region.

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Section: Sources Of Prediction Skillmentioning

confidence: 99%

Skillful seasonal predictions of winter precipitation over southern China

Lü

Scaife

Dunstone

et al. 2017

Environ. Res. Lett.

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“…Coupled ocean–atmosphere dynamical model prediction systems have gradually made great progress and can offer overall better performance, and have become the major tool of dynamical climate prediction in recent decades (Palmer et al ., ; Kumar et al ., ; Li et al ., ; Wang et al ., ; Kug et al ., ; National Research Council, ; Ma and Wang, ). For example, the majority of East Asian climate phenomena such as WPSH, EASM, and some SST modes like ENSO and Indian Ocean basin mode (IOBM) can be accurately captured nearly half a year in advance at the Beijing Climate Center (Ren et al ., ). In addition, multi‐model ensemble (MME) has become a routine approach in climate prediction (Krishnamurti et al ., ; Kharin and Zwiers, ; Palmer et al ., ; Min et al ., ; Weisheimer et al ., ; Kirtman et al ., ; Yang et al ., ).…”

Section: Introductionmentioning

confidence: 99%

Seasonal predictability of primary East Asian summer circulation patterns by three operational climate prediction models

Zhou

Ren

et al. 2019

Quart J Royal Meteoro Soc

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Seasonal predictability of the Primary East‐Asian Summer Circulation Patterns (PEASCPs), including the Western Pacific Subtropical High (WPSH), South Asian High (SAH), anomalous Philippine Sea AntiCyclone (PSAC) and East Asian Summer Monsoon (EASM), are investigated by using the hindcasts from the three operational climate prediction models, including BCC_CSM1.1(m), NCEP CFSv2 and ECMWF System 4. We show that prediction skills of the indices for representing these PEASCPs are sensitive to the initial calendar month of model prediction, and the ensemble mean of the three models provides relatively higher and more stable skills than forecasts from an individual model. In general, the indices of intensity and area have high prediction skills while the position indices have relatively low skills. Specifically, the skills of the WPSH intensity, area, SAH centre intensity, PSAC and EASM are higher, while the skills of the WPSH western boundary and SAH centre latitude are lower, and the skills of WPSH ridge line and SAH centre longitude are the lowest. Further analysis shows that the El Niño/Southern Oscillation (ENSO) has a large contribution to these prediction skills, and these patterns of atmospheric circulation anomalies in response to ENSO can be well captured by models, which is the major predictability source of the skills.

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“…Previous studies focusing on improving ENSO predictions have made significant progress. Overall, the predictive skill for ENSO has been high when using the Niño3.4 sea surface temperature (SST) anomaly index to represent the ENSO phenomenon (Latif et al 1998;Jin et al 2008;Luo et al 2008;Ren et al 2017); this skill, however, is always relatively lower in real-time forecasts than in hindcasts and varies during epochs with considerable uncertainties (Barnston et al 2012). The recent experiences in predicting 2014-2016 super El Niño event have challenged current capability of international ENSO prediction and given enlightenments for further researches on prediction (Mu and Ren 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies have assessed the performance of dynamical models in predicting the two ENSO types (Hendon et al 2009;Lim et al 2009;Jeong et al 2012Jeong et al , 2015Yang and Jiang 2014;Imada et al 2015;Zhu et al 2015;Ren et al 2017). These studies show that the EP type has a relatively higher prediction skill than the CP type, even though the latter tends to possess a better persistence or weaker persistence barrier than the former in terms of the Niño indices (Kim et al 2009;Ren et al 2016a).…”

Section: Introductionmentioning

confidence: 99%

Statistical predictability of Niño indices for two types of ENSO

2018

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The El Niño-Southern Oscillation (ENSO) has been shown to manifest as primarily two types, the eastern Pacific (EP) type and central Pacific (CP) type, in terms of the zonal positions of the sea surface temperature (SST) anomalies. This study focuses on examining the predictability of the two types of ENSO by developing statistical models for their corresponding Niño indices, which have their own distinct key precursors. The results show that the statistical predictability of the Niño indices representing the two types of ENSO primarily originates from the preceding variations in the equatorial Pacific upper-ocean heat content and the surface zonal wind stress, which intrinsically reflect the zonally uniform and contrasted thermocline patterns, respectively. The traditional Niño3 and Niño4 indices are more predictive than the Niño indices of the EP and CP ENSO types; however, all the indices are subject to predictability barriers with different timings and intensities, which might be weakened by introducing additional external precursors. The EP ENSO indices have overall higher skills than the CP indices, in which the statistical model has much higher skill scores than persistence forecast for the EP ones while it does less for the CP ones. We demonstrate that the precursors outside the tropical Pacific, e.g., the Indian Ocean Dipole, North Pacific oscillation, North American dipole, and Southern Hemispheric SST modes, except the northern tropical Atlantic SST, as suggested in previous studies, only make limited contributions to improving the prediction skills of the two ENSO types at specific initial months and leads compared to a benchmark model built using the equatorial Pacific heat content and zonal wind stress indices. This is primarily because these precursors have already transferred most of their signals into the variation of the two indices in the benchmark model. We further show that conditionally adding the northern tropical Atlantic SST precursor to the benchmark could provide considerable additional prediction skill scores for both types of ENSO and weaken the intensity of the ENSO predictability barriers that occur during boreal spring-summer.

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Prediction of primary climate variability modes at the Beijing Climate Center

Cited by 53 publications

References 112 publications

Skillful seasonal predictions of winter precipitation over southern China

Skillful seasonal predictions of winter precipitation over southern China

Seasonal predictability of primary East Asian summer circulation patterns by three operational climate prediction models

Statistical predictability of Niño indices for two types of ENSO

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