6B.6 Mean State and Interannual Variability of the Indian Summer Monsoon Simulation by NCEP CFSv2

Tuesday, 12 January 2016: 4:45 PM
La Nouvelle A ( New Orleans Ernest N. Morial Convention Center)
Ravi Shukla, COLA, Fairfax, VA; and B. Huang

The capability of the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) in simulating the Indian summer monsoon (ISM) is evaluated in the context of the global monsoon in the Indo-Pacific domain and its variability. Quantitatively, the CFSv2 demonstrates a severe dry bias over the Indian subcontinent. South of the equator, the model easterly winds over the central Indian Ocean also turn too early equatorward along its route to the Somali Jet, which generates excessive convergence over the central and eastern equatorial Indian Ocean and reduces the moisture transport toward the Indian subcontinent.

The leading mode of the June-September averaged CFSv2 rainfall anomalies covering the ISM and its adjacent oceanic regions is qualitatively similar to that of the observations, characterized by a spatial pattern of strong anomalies over either side of the Indian peninsula as well as center of opposite sign over Myanmar. However, the model fails to reproduce the northward expansion of rainfall anomalies from Myanmar, leading to opposite anomalies over northeast India and Himalayas region. The active regional influences in the observations may contribute to its baroclinic vertical structure of the geopotential height anomalies in the ISM region, compared with the predominantly barotropic one in CFSv2. During and before the monsoon season, ENSO is overly acting in CFSv2 simulation and persist for a longer time period in comparison to observation.

Using observation-based analyses, our study identifies the leading interannual pattern of the Indian summer monsoon rainfall (ISMR) independent of ENSO and examines the potential mechanisms of its formation. This study suggests that the premonsoon Arabian Sea condition may be an important contributor to the ISM predictability from monthly to seasonal scales.

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