12B.2 Subseasonal-Seasonal Predictability and Prediction of Asian Monsoon Rainfalls

Thursday, 26 January 2017: 8:45 AM
609 (Washington State Convention Center )
Song Yang, Naval Research Laboratory, Guangzhou, China; and T. Zhang

The predictability and prediction skills of Asian monsoon rainfalls have been studied using various hindcast products by the NCEP Climate Prediction System and other climate models. The most predictable modes of monsoon rainfalls on both seasonal and subseasonal time scales have been depicted by a maximized signal-to-noise empirical orthogonal function (MSN EOF) analysis. For large-scale rainfall patterns, the most predictable pattern (the dominant MSN EOF mode) in spring is related to the effect of ENSO and can be predicted several months in advance. The most predictable pattern in summer (the summer monsoon mode) and that in autumn, which is related to the effect of the tropical Indian Ocean Dipole mode, have skills shorter than a few months.

On subseasonal time scale, the predictability and prediction skills of rainfalls over tropical oceans (the Arabian Sea, the Bay of Bengal and the South China Sea) and land portions (India, the Indo-China Peninsula, southern China and the Maritime Continent) have been investigated. The relative roles of ocean-atmosphere interaction and land-atmosphere interaction in different ocean and land domains are discussed, with an emphasis on the importance of ocean-atmosphere interaction over the Arabian Sea and land-atmosphere interaction over India. The effect of realistically-predicting the rainfall over the Maritime Continent has an impact on the skill of predicting the Indian monsoon rainfall.

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