Towards an Improved Mechanistic Understanding of Dangerous Heat Extremes Affecting US Cities in the Historical Records and Future Climate Projections

Heat extremes are happening more often and negatively affect natural environments, communities, and public health. This project studies these heat extremes, particularly those causing increased mortality in urban areas of the United States, with the goal of enhancing our understanding of these severe heat events and their anticipated changes in the coming decades. Initially, the project evaluates different metrics to identify climate variables that most robustly represent heat risks. Notably, it goes beyond temperature, including factors like humidity, solar and thermal radiation, and wind speed at the surface. It then connects major historical heat extremes to patterns in weather and long-term climate drivers. The results from analyzed data will be compared to output from global climate models using advanced statistical techniques. Lastly, the project uses high-resolution climate predictions to understand how lethal heat events may change in the future. This work will extend the community’s understanding of extreme heat and mortality, but the importance of this research extends beyond scientific understanding. Communities vulnerable to climate change, especially those with limited means to adapt, face substantial risks from heat extremes. Despite recognizing the social implications of these heat extremes, our ability to predict them on a seasonal to decadal scale is insufficient. This project addresses this issue by using a data-driven approach to associate lethal heat extremes with large-scale weather patterns. The findings from this research will be communicated to public health and urban planning partners to aid in their response to these threats. The physical insights generated through these analyses can help guide interpretation of climate model-based projection of future occurrence and severity of these dangerous heat extremes. Additionally, this project offers valuable training for graduate students in statistical modeling, data visualization, climate modeling, and climate communication. Thus, it contributes to the broader scientific community and society, not only through its results but also by training the next generation of climate scientists. This project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences. 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.