Examining the variability of cloud hydrometeors and its importance on the Indian summer monsoon rainfall predictability

TL;DR

This study investigates how variability in cloud hydrometeors influences the predictability of Indian summer monsoon rainfall, highlighting the role of sub-seasonal cloud processes and their connection to global climate factors.

Contribution

It reveals the significance of cloud ice variability and deep convective cloud processes in improving monsoon rainfall predictions in climate models.

Findings

Cloud hydrometeor variability is partly predictable and linked to global forcing.

Deep convective cloud representation affects ISMR variability.

Sensitivity simulations demonstrate the importance of cloud processes for prediction accuracy.

Abstract

Skilful prediction of the seasonal Indian summer monsoon (ISM) rainfall (ISMR) at least one season in advance has great socio-economic value. It represents a lifeline for about a sixth of the world's population. The ISMR prediction remained a challenging problem with the sub-critical skills of the dynamical models attributable to limited understanding of the interaction among clouds, convection, and circulation. The variability of cloud hydrometeors (cloud ice and cloud water) in different time scales (3-7 days, 10-20 days and 30-60 days bands) are examined from re-analysis data during Indian summer monsoon (ISM). Here, we also show that the 'internal' variability of cloud hydrometeors (particularly cloud ice) associated with the ISM sub-seasonal (synoptic + intra-seasonal) fluctuations is partly predictable as they are found to be tied with slowly varying forcing (e.g., El Ni\~no and Southern Oscillation). The representation of deep convective clouds, which involve 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) are provided to demonstrate the importance of the cloud hydrometeors on ISM rainfall predictability. Therefore, this study provides a scientific basis for improving the simulation of the seasonal ISMR by improving the physical processes of the cloud on a sub-seasonal time scale and motivating further research in this direction.