Predictable Mode Analysis of Asian Summer Monsoon Rainfall Predictability

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Thursday, 8 January 2015: 1:45 PM
125AB (Phoenix Convention Center - West and North Buildings)
Bin Wang, Univ. of Hawaii, Honolulu, HI; and B. Xiang and J. Y. Lee

To what extent the Asian summer monsoon (ASM) rainfall is predictable has been an important and long-standing issue in climate science. Here we introduce a predictable mode analysis (PMA) method to estimate the predictability of the ASM rainfall. The PMA is an integrated approach combining empirical analysis of most important modes, understanding of physical processes governing these modes, establishing physics-based empirical prediction models and assessing dynamical models' hindcast to identify the “predictable modes”, and estimating potential predictability using the predictable modes. This approach also provides a bias correction of spatial patterns of predictable modes to improve prediction skills. For the ASM rainfall variability, four major modes are identified by analysis of the 1979-2010 observations: (1) a forced mode by developing El Niño and Southern Oscillation (ENSO), (2) an monsoon-ocean coupled mode sustained by a positive thermodynamic feedback with the aid of background mean circulations, (3) the Indian Ocean dipole (IOD) mode, and (4) a trend mode. We show that these four modes can be predicted reasonably well by a physical-empirical prediction model as well as the atmosphere-ocean coupled models' multi-model ensemble (MME), thus they are regarded as “predictable” modes. The PMA provides a useful approach for assessing the seasonal predictability and improve prediction skill. The predictability of the Indian summer monsoon rainfall is also explored with both P-E model and dynamical models.