Towards the Predictability of Compound Dry and Hot Extremes through Complexity Science

Abstract

Compound Dry and Hot Extremes (CDHE) have an adverse impact on socioeconomic factors during the Indian summer monsoon, and a future exacerbation is anticipated. The occurrence of CDHE is influenced by teleconnections, which play a crucial role in determining its likelihood on a seasonal scale. Despite the importance, there is a lack of studies unravelling the teleconnections of CDHE in India. Previous investigations specifically focused on teleconnections between precipitation, temperature, and climate indices. Hence, there is a need to unravel the teleconnections of CDHE. This study presents a framework combining event coincidence analysis (ECA) with complexity science. ECA evaluates the synchronization between CDHE and climate indices. Subsequently, complexity science is utilized to construct a driver-CDHE network to identify the critical drivers of CDHE. A logistic regression model is employed to evaluate the proposed drivers' effectiveness. The occurrence of CDHE exhibits distinct patterns from July to September when considering intra-seasonal variability. Our findings contribute to the identification of drivers associated with CDHE. The primary driver for Eastern, Western India and Central India is the indices in the Pacific Ocean and Atlantic Ocean, respectively, followed by the indices in the Indian Ocean. These identified drivers outperform the traditional Niño 3.4-based predictions. Overall, our results demonstrate the effectiveness of integrating ECA and complexity science to enhance the prediction of CDHE occurrences.