The quasi climatological intraseasonal oscillation over the South China Sea and its relationship with summer monsoon onset and MJO
摘要
East Asian monsoon shows strong annual cycle with the wet season in summer and dry in winter. In addition to annual cycle, some monsoon regions exhibit distinct climatological sub-seasonal variations such as fast annual cycle, climatological intraseasonal oscillations (CISOs) and climate singularities. In this paper a significant cyclic-type outgoing longwave radiation (OLR) singularity during the spring to summer transition period over the South China Sea (SCS) is identified based on the daily OLR data from 1979 to 2023. The quasi-CISO in each individual year is invented as an analogue to CISO. Its relationship with Madden–Julian Oscillation (MJO) and the SCS summer monsoon onset (SCSSM) is analyzed. The quasi-CISO shows large interannual variability. A group of years with the quasi-CISO amplitudes larger than the medium of all sample years are selected to form the quasi-CISO mode. Among them 72% of the quasi-CISO cyclic valley coincides with the SCSSM onset time represented by the persistent intensification of the monsoonal westerlies, which are the SCS-CISO years. The MJO activity during the sixteen SCS-CISO years shows three different levels. Composite results of eight SCS-CISO years with strong MJO show clear synchronized planetary- and regional-scale convection and circulation progressions where strong MJOs over the equatorial Indian Ocean appeared two weeks before SCSSM onset. Strong convection over the SCS associated with the SCSSM onset process can be triggered by the enhanced low-level southerly flow by MJO’s subsidence leg over the equatorial maritime continent through increasing the moisture transport. This study provides observational-based evidence that supports earlier findings about CISO, CMJO and SCSSM onset. The findings can be applied to evaluate the Sub-seasonal to Seasonal forecast model products for better understanding of the extended-range weather and climate predictability.