Atmospheric moisture as a proxy for the ISMR variability and associated extreme weather events
This study explores the potential of atmospheric moisture content, its transport and its divergence over the ocean and land as proxies for the variability of Indian summer monsoon rainfall (ISMR) for the period 1950–2019. The analyses using multiple linear regression reveal that the interannual and intraseasonal variability of ISMR and the mean ISMR is largely controlled by Arabian Sea moisture flux and Ganga river basin moisture content, and these parameters exhibit statistically significant high correlations in most regions. The regression model and the parameters are statistically significant and the model could explain rainfall variability of about 12%–50% in various regions. The model shows a false alarm rate (FAR) of 0.25–0.45 and a probability of detection (POD) of 0.43–0.50 for wet years in West Central, North West and North Central India. The FAR and POD are about 0.06–0.32 and 0.60–0.70, respectively for dry years in those regions. The model reproduces flood and drought years of about 32%–50% and 55%–70% in those regions. Also, the moisture indices could clearly identify the majority of wet and dry years that occurred during the period. The ISMR variability associated with moisture indices is unaffected by El Niño Southern Oscillation. Henceforth, this study demonstrates the significance of atmospheric moisture on regional rainfall distribution and suggests that these parameters can be used in both statistical and dynamical models to better predict monsoon and global precipitation.