Dynamical Predictability of Leading Interannual Variability Modes of the Asian-Australian Monsoon in Climate Models

Abstract

The dynamical prediction of the Asian-Australian monsoon (AAM) has been an important and long-standing issue in climate science. In this study, the predictability of the first two leading modes of the AAM is studied using retrospective prediction datasets from the seasonal forecasting models in four operational centers worldwide. Results show that the model predictability of the leading AAM modes is sensitive to how they are defined in different seasonal sequences, especially for the second mode. The first AAM mode, from various seasonal sequences, coincides with the El Niño phase transition in the eastern-central Pacific. The second mode, initialized from boreal summer and autumn, leads El Niño by about one year but can exist during the decay phase of El Niño when initialized from boreal winter and spring. Our findings hint that ENSO, as an early signal, is conducive to better performance of model predictions in capturing the spatiotemporal variations of the leading AAM modes. Still, the persistence barrier of ENSO in spring leads to poor forecasting skills of spatial features. The multimodel ensemble (MME) mean shows some advantage in capturing the spatiotemporal variations of the AAM modes but does not provide a significant improvement in predicting its temporal features compared to the best individual models in predicting its temporal features. The BCC_CSM1.1M shows promising skill in predicting the two AAM indices associated with two leading AAM modes. The predictability demonstrated in this study is potentially useful for AAM prediction in operational and climate services.

摘要

亚澳季风(AAM)的动力预测一直是气候学研究中的重要课题. 本研究基于四个国际业务预报中心开发的季节预报模式回算数据, 对AAM年际变率前两个主模态的可预报性进行了系统性地分析. 结果表明, 模式对AAM主模态的可预测性与定义它们的季节顺序有关, 尤其是对于第二模态. 无论是从何季节开始, AAM第一模态都与厄尔尼诺在中-东太**洋的位相转变一致. 而AAM第二模态与厄尔尼诺的关系有差异: 从夏季与秋季起始的AAM第二模态超前于厄尔尼诺一年; 从春季与冬季起始的AAM第二模态则存在于厄尔尼诺衰退阶段. 研究结果进一步表明, 厄尔尼诺和南方涛动(ENSO)作为早期信号, 有助于动力模式更好地捕捉AAM主模态的时空变化特征. 然而, ENSO的春季持续障碍会影响动力模式对其空间特征的预测能力. 多模式集合在预测AAM主模态的时空变化特征方面具有一定的优势, 但与最好单模式相比, 在预测AAM主模态时间变化方面它并没有提供显著改进. 另外, BCC_CSM1.1M模式在预测AAM主模态指数时有较高的预报技巧. 整体上, 本研究的分析结果对于AAM的业务预测和气候服务具有着重要意义.

Electronic Supplementary Material to

Dynamical Predictability of Leading Interannual Variability Modes of the Asian-Australian Monsoon in Climate Models