Structure, Propagation of the 10–25-Day and 30–60-Day Intraseasonal Oscillations and Their Role in Triggering the South China Sea Summer Monsoon Onset

Abstract

Abstract The onset of the South China Sea (SCS) summer monsoon (SCSSM) is a complex process that involves multiscale variabilities, including the 10–25-day high-frequency intraseasonal oscillation (HISO) and the 30–60-day low-frequency intraseasonal oscillation (LISO) along with the seasonal variation. Using the reanalysis and satellite data during the period of 1979–2020, this study comprehensively investigates how the HISO and LISO trigger the SCSSM onset, which improves the understanding of the abrupt SCSSM onset from the point of intraseasonal view. It is clearly documented that in most years, the abrupt SCSSM onsets are triggered by the first branch of HISO and/or LISO propagating into the northern SCS along their distinctive pathways. The HISOs (LISO) originating from the northwestern Pacific Ocean (tropical eastern Indian Ocean) propagates westward (northeastward) toward central-to-northern SCS. The diagnosis based on the moist static energy (MSE) budget reveals the different governing mechanisms of HISO and LISO propagations. HISO’s westward propagation is favored by the zonal advection of the anomalous MSE by the mean easterly wind. In contrast, the surface turbulent heating by sea surface temperature (SST) is the dominant term for LISO’s northward propagation, followed by the meridional advection of the anomalous MSE by the mean southerly wind. Significance Statement This study provides the comprehensive intraseasonal view of the SCSSM onset, where the respective roles of 10–20-day HISO and 30–60-day LISO in triggering the SCSSM onset are identified and their propagating mechanisms are revealed. The prevailing easterlies in the northwestern Pacific and SST-induced turbulent heat flux in SCS are confirmed as the respective dominant processes governing their distinct propagations. These results improve the understanding of the SCSSM onset and hence have significant implication to the monitoring, simulation, and prediction of SCSSM.