资源环境科技发展态势分析平台

Local correlation between sea surface temperature (SST) and rainfall is weak or even negative in summer over the Indo-western Pacific warm pool, a fact often taken as indicative of weak ocean feedback on the atmosphere. An Atmospheric Model Intercomparison Project (AMIP) simulation forced by monthly varying SSTs derived from a parallel coupled general circulation model (CGCM) run is used to evaluate AMIP skills in simulating interannual variability of rainfall. Local correlation of rainfall variability between AMIP and CGCMsimulations is used as a direct metric of AMIP skill. This "perfect model'' approach sidesteps the issue of model biases that complicates the traditional skill metric based on the correlation between AMIP and observations. Despite weak local SST-rainfall correlation, the AMIP-CGCM rainfall correlation exceeds a 95% significance level over most of the Indo-western Pacific warm pool, indicating the importance of remote (e.g., El Nino in the equatorial Pacific) rather than local SST forcing. Indeed, the AMIP successfully reproduces large-scale modes of rainfall variability over the Indo-western Pacific warm pool. Compared to the northwest Pacific east of the Philippines, the AMIP-CGCMrainfall correlation is low from the Bay of Bengal through the South China Sea, limited by internal variability of the atmosphere that is damped in CGCM by negative feedback from the ocean. Implications for evaluating AMIP skill in simulating observations are discussed.