Role of mean, variability and teleconnection of clouds behind Indian summer monsoon rainfall

• Published in: International Journal of Climatology
• Main Authors: Hazra, Anupam, Dutta, Ushnanshu, Chaudhari, Hemantkumar S., Pokhrel, Samir, Konwar, Mahen
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2023
• Subjects: Indian; Pacific; North Atlantic; North Atlantic oscillation
• Online Access: http://dx.doi.org/10.1002/joc.8076; https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.8076

Abstract Skilful prediction of the seasonal Indian summer monsoon (ISM) rainfall (ISMR) at least one season in advance has great socio‐economic value. The ISM is a lifeline for about a sixth of the world's population. The ISMR prediction remained a challenging problem with the subcritical skills of the dynamical models due to a limited understanding of the interaction among clouds, convection and circulation. In this study, we have analysed the seasonal mean of high cloud fraction, ice mixing ratio and ice cloud fraction from satellite and reanalysis and demonstrated their importance for ISM. The variability of the mixing ratio of cloud ice in different time scales (3–7 days, 10–20 days and 30–60 days bands) is also examined from reanalysis during ISM. Here, we have shown the teleconnection of different cloud variables over the ISM region with global sea surface temperature. We found that they are tied with slowly varying forcing (e.g., El Niño and Southern Oscillation). Besides, the correlation of cloud ice with different indices (Niño, Pacific Decadal Oscillation, North Atlantic Oscillation and Extratropics) may enhance the potential predictability of ISMR. The representation of deep convective clouds, which involve the ice‐phase processes in a coupled climate model, strongly modulates ISMR variability in association with global predictors. The results from the two sensitivity simulations using coupled global climate model (CGCM) demonstrate the importance of the cloud ice on ISM rainfall predictability. Therefore, this study provides a scientific basis for improving the simulation of the seasonal ISMR by developing the physical processes of the cloud on a subseasonal time scale and motivating further research in this direction.