Learning from climate change to build a better future : heavy precipitation-associated extreme weathers in East Asia

Abstract

Extreme weather events represent substantial threats to human safety and property. Among these, tropical cyclones, mesoscale convective systems, and atmospheric rivers are particularly associated with intense precipitation. Under the ongoing influence of global warming, the behavior and characteristics of these weather systems have likely already undergone significant changes and are projected to evolve further in the future. This thesis focuses on examining the observed historical transformations and forecasting future shifts in various weather systems across the East Asia region. It begins by examining the northward shift in tropical cyclone stalling behavior and its association with increased flood risks in East Asia. Tropical cyclones could also become less frequent but more intense under the influence of stronger cloud radiative effects under global warming. For atmospheric rivers, this thesis comprehensively assesses the performance of them in Phase 6 of the Coupled Model Intercomparison Project models on both seasonal and interannual timescales within the historical period and investigates the future projection of ARs under different emission scenarios on a global scale. Finally, this thesis harnesses the powerful capabilities of deep learning models to generate precipitation patterns for various weather systems, with the goal of achieving more accurate precipitation simulations and predictions in the future study. This series of works could effectively contribute to addressing the need for future adaptations to extreme weather events across the East Asian region.

Date of Award	2024
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology
Supervisor	Mengqian LU (Supervisor) & Jianping GAN (Supervisor)