Asian summer monsoon rainfall predictability: a predictable mode analysis

作者：Wang, Bin; Lee, June-Yi^*; Xiang, Baoqiang

来源：Climate Dynamics, 2015, 44(1-2): 61-74.

DOI：10.1007/s00382-014-2218-1

• Asian summer monsoon rainfall
• Seasonal predictability
• Predictable mode analysis
• ENSO
• Monsoon-ocean coupled mode
• Physics-based empirical prediction
• Multi-model ensemble

To what extent the Asian summer monsoon (ASM) rainfall is predictable has been an important but long-standing issue in climate science. Here we introduce a predictable mode analysis (PMA) method to estimate predictability of the ASM rainfall. The PMA is an integral approach combining empirical analysis, physical interpretation and retrospective prediction. The empirical analysis detects most important modes of variability; the interpretation establishes the physical basis of prediction of the modes; and the retrospective predictions with dynamical models and physics-based empirical (P-E) model are used to identify the "predictable" modes. Potential predictability can then be estimated by the fractional variance accounted for by the "predictable" modes. For the ASM rainfall during June-July-August, we identify four major modes of variability in the domain (20A degrees S-40A degrees N, 40A degrees E-160A degrees E) during 1979-2010: (1) El Nio-La Nina developing mode in central Pacific, (2) Indo-western Pacific monsoon-ocean coupled mode sustained by a positive thermodynamic feedback with the aid of background mean circulation, (3) Indian Ocean dipole mode, and (4) a warming trend mode. We show that these modes can be predicted reasonably well by a set of P-E prediction models as well as coupled models' multi-model ensemble. The P-E and dynamical models have comparable skills and complementary strengths in predicting ASM rainfall. Thus, the four modes may be regarded as "predictable" modes, and about half of the ASM rainfall variability may be predictable. This work not only provides a useful approach for assessing seasonal predictability but also provides P-E prediction tools and a spatial-pattern-bias correction method to improve dynamical predictions. The proposed PMA method can be applied to a broad range of climate predictability and prediction problems.

• 出版日期2015-1
• 单位南京信息工程大学