资源环境科技发展态势分析平台

We use seasonal forecasts from the Climate Historical Forecast Project (CHFP) to study the skill of multiple climate models in predicting Indian summer monsoon precipitation. The multi-model average of seasonal forecasts from eight prediction systems shows statistically significant skill for predicting Indian monsoon precipitation at seasonal lead times. Rapid convergence of tropical rainfall skill with ensemble size suggests that the skill of seasonal monsoon rainfall forecasts improves only marginally when using multi-model ensemble (MME) means as compared to the single most skillful system. There is also a large range in the skill of individual models. Some individual models show correlation skill as high as 0.6, which is similar to the MME mean, while others show low skill. We also investigate the effect of spatial averaging on the skill of predicting monsoon rainfall and show that the predictions averaged over a larger area than the verifying observations can yield higher skill due to the extended spatial coherence of monsoon rainfall variability. We also document current errors in seasonal prediction systems and show that these are more strongly related to the errors in El-Nino Southern Oscillation (ENSO) teleconnections than they are to mean rainfall biases. Finally, we examine the ENSO-monsoon relationship and confirm that this relationship is likely to be stationary, despite fluctuations in the observed relationship, which can simply be explained as sampling variability on an underlying stationary teleconnection between ENSO and the Indian monsoon.