Role of SST feedback in the prediction of the boreal summer monsoon intraseasonal oscillation

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