Intraseasonal SST-precipitation coupling during the Indian Summer Monsoon, and its modulation by the Indian Ocean Dipole
Intraseasonal SST-precipitation coupling during the Indian Summer Monsoon, and its modulation by the Indian Ocean Dipole
Abstract:
The Indian Summer Monsoon (ISM) plays a crucial role in shaping the large proportion of the total precipitation over the Indian subcontinent each year. The ISM rainfall exhibits a particularly strong intraseasonal variability, that has profound socioeconomic consequences, such as agricultural planning and flood preparation. However, our understanding of the variability on this time scale is still limited due to sparse data availability in the past. In this study, we used a combination of state-of-the-art high-resolution satellite estimate of rainfall, objectively analyzed surface flux, as well as atmospheric reanalysis product to investigate the nature of the ISM intraseasonal rainfall variability and how it varies year to year. The emphasis is placed on the Bay of Bengal (BoB) where the intraseasonal ocean-atmosphere coupling is most prominent. Results show that the maximum warming of SST leads the onset of heavy precipitation event by 3-5 days, and that surface heat flux and surface wind speed are weak prior to the rain but amplifies and peaks after the rain reaches its maximum. Furthermore, the Indian Ocean Dipole (IOD) significantly affects the observed intraseasonal SST-precipitation relationship. The pre-convection SST warming is stronger and more pronounced during the negative phase of the IOD, while the signal is weaker and less organized in the positive phase. This is explained by the column-integrated moisture budget analysis which reveals that, during the ISM heavy rainfall in the BoB, there is more moisture interchange in the form of enhanced vertical advection from the ocean to atmosphere in negative IOD years as compared to positive IOD years. Knowing the distinction of ISM variabilities during opposite phases of the IOD will help contribute to a more reliable prediction of ISM activities.