资源环境科技发展态势分析平台

This study investigates the impact of different specification of the underlying sea surface temperature (SST) on the prediction of intraseasonal rainfall variation associated with strong Monsoon Intraseasonal Oscillation ( MISO) events in the northern Indian Ocean. A series of forecast experiments forced with observed hourly, daily, or seasonal SSTs are performed for three selected strong MISO events using the National Centers for Environmental Predictions (NCEP) atmospheric Global Forecast System (GFS). The comparison between these GFS forecasts shows that the intraseasonal SST variability is more important than its diurnal variability in the MISO prediction. The GFS experiments forced with daily SST which includes intraseasonal variability has higher prediction skill and faster speed in the northward propagation of the MISO intraseasonal rainfall anomalies than those forced with seasonal SST that do not include intraseasonal variability. No significant difference is found in the MISO prediction when GFS was forced by SST with or without SST diurnal cycle. The GFS runs forced with warmer and colder seasonal SSTs which mimic possible biases in SST prediction have comparable skill in the MISO prediction. A modified version of the NCEP Climate Forecast System coupled model (CFSm5) with 1- and 10-m vertical resolutions in the upper ocean is then used to examine their performance in the MISO prediction when all aspects of SST are actively included. The CFSm5 with 1-m vertical resolution in the upper ocean (CFSm501) shows larger amplitude of intraseasonal SST anomaly, with higher prediction skill in both intraseasonal SST and rainfall than the CFSm5 with the typical 10-m vertical resolution in the upper ocean (CFSm510) does. Compared with the uncoupled GFS, both CFSm501 and CFSm510, despite errors in predicted SSTs, have better prediction skill and more reasonable rainfall variability, which is attributed to the inclusion of active air-sea interaction. These results suggest the importance of intraseasonal variability of SST and air-sea interaction in improving the intraseasonal rainfall prediction associated with the MISO.