AGS-PRF: Understanding Compound Heat Wave-Wildfire Events in the Western United States: Large-Scale Meteorology, Remote Climate Influences, and Climate Model Simulations

Heat waves when coupled with wildfires are some of the most severe weather-related disasters, affecting individual life and property, resource management, agriculture, and regional and national economies, and such impacts are only likely to worsen as the climate continues to warm. Given the likely increase in the frequency, intensity, and duration of heat waves, and the associated increased risk of the occurrence of wildfires, due to continued climate change, the goal of this work is to gain a better understanding of compound heat wave-wildfire events in the Western United States and their response to climate change.

The expected outcomes of this study will contribute to the growing knowledge base that underpins physical models and information systems used by decision makers and the public to adapt and mitigate the impacts of these events. The study funds a postdoctoral researcher with Prof. Alex Hall and Prof.

Colin Zarzycki as mentors and the University of California Los Angeles and Penn State University as host institutions. As a Postdoctoral Research Program project, this investment also launches a new career in atmospheric science, growing the science and technology workforce of the next generation.

The goal of this work is to examine the large-scale climate dynamics associated with compound heat wave and wildfire events and the evolution of these conditions as the events unfold under different climate regimes. The expected outcome is a unified framework that can be used to study these co-occurring events across the Western United States. The research tasks will first assess the large-scale meteorology surrounding co-occurring events using machine learning techniques including self-organizing maps and k-means clustering, followed by an assessment of the role of remote climate influences on these events using reanalyses.

The study will additionally examine CMIP6 and high-resolution, multi-model ensembles to assess how well these models capture the present day and future regional and remote signals associated with co-occurring heat wave and wildfire events. Finally, the Model for Prediction Across Scales (MPAS) will be used to produce pseudo global warming scenarios to examine how the dynamic conditions associated with events might change in the future.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.