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

Dangerous and extreme weather is already being experienced due to climate change. The combination of geopolitical factors and an extreme weather event can escalate risk of violence, migration, and food, water, energy, economic, and infrastructure insecurity in susceptible regions. Climate projections extend too far into the future to be useful for immediate preparation and adaptation measures. To effectively plan for the current impacts of climate change, an actionable timeframe spanning weeks is necessary. This allows for individuals, governments, and aid organizations to adequately prepare and adapt. Forecasts on a subseasonal timescale can bridge this gap. Developing a framework for analyzing extreme weather events and their predictability is essential to improving subeasonal predictions, 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.