Developing a Framework for Examining Sources of Predictability for Extreme Events on Subseasonal Timescales in Southeast Asia & Latin America

Rising temperatures due to climate change are leading to an increase in extreme weather events, posing significant risks to vulnerable regions worldwide. These areas are already experiencing heightened incidences including extreme precipitation, droughts, and floods, which threaten climate security and endanger lives and property. Immediate preparation and adaptation efforts are crucial to mitigate these risks. Subseasonal prediction can provide more timely information for effective climate adaptation strategies. Developing a framework for analyzing these events and their predictability is essential to improving subeasonal predictions of extreme weather, thereby reducing risks to impacted regions.

In this work, recent extreme weather events are analyzed, including the extreme precipitation that caused extensive flooding in Pakistan during the summer of 2022. Using ECMWF Reanalysis v5 (ERA5) and observational datasets, such as Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data, potential drivers and mechanisms behind these events are examined. NCAR Community Earth System Model (CESM) skill is used to understand how far in advance useful information could have been provided to impacted countries. Further, a unique set of initialized prediction experiments from NCAR CESM with climatological initial conditions for different combinations of earth system components (e.g., land, ocean, and atmosphere) are used to identify which components contribute most to the occurrence and prediction of these events. Lastly, analysis of the causes of these events will be presented. This methodology is utilized on recent extreme weather events in Southeast Asia and Latin America to assess its use in different geographic locations. This process and analysis will lead to understanding sources of predictability for extreme weather in these countries.