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

Advances in Atmospheric Sciences 2023年第11期40卷 1998-2012,I0002,I0003页

作者： Lin WANG Hong-Li REN Fang ZHOU Nick DUNSTONE Xiangde XU State Key Laboratory of Severe Weather and Institute of Tibetan Plateau MeteorologyChinese Academy of Meteorological SciencesBeijing 100081China Department of Atmospheric Science School of Environmental StudiesChina University of GeosciencesWuhan 430074China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment ChangeNanjing University of Information Science and TechnologyNanjing 210044China Met Office Hadley Centre FitzRoy RoadExeterDevonEX13PBUnited Kingdom

The dynamical prediction of the Asian-Australian monsoon(AAM)has been an important and long-standing issue in climate *** 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 *** 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 *** first AAM mode,from various seasonal sequences,coincides with the El Niño phase transition in the eastern-central *** 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 *** 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 ***,the persistence barrier of ENSO in spring leads to poor forecasting skills of spatial *** 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 *** BCC_CSM1.1M shows promising skill in predicting the two AAM indices associated with two leading AAM *** predictability demonstrated in this study is potentially useful for AAM prediction in operational and climate services.

关键词： Asian-Australian monsoon(AAM) leading interannual variability modes El Niño seasonal forecasting models multimodel ensemble(MME)

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