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.