NSF Award
2308680
Climate profoundly influences the severity and frequency of extreme phenomena like large wildfires, heatwaves, floods, and droughts. Resilience to the most dramatic effects of climate change requires an understanding of extreme events under future climate conditions. Climate models are invaluable tools for interrogating the dynamics of the Earth system, but they have shortcomings with respect to extreme event analysis. First, the behavior of extremes of many meteorological variables shows a profound mismatch compared to real-life observations. Second, they live in gridded spaces that must be reconciled with the continuous world in which observations are made and for which risk analysis is performed. To resolve these two difficulties, we will model weather phenomena in their native real-world domain, leveraging information from representations of large-scale patterns from climate models, in a way that preserves realistic properties of extreme events. The project also provides research training opportunities for graduate students. <br/><br/>PI will focus on two main research aims, which develop and apply analytical tools that turn climate model output into a realistic analysis of extreme events. First, this project will develop models, and associated model-fitting software, that leverage dynamically-derived large-scale features from climate model output to inform stochastic process descriptions of local extreme meteorological phenomena in continuous space. This will require two interconnected modeling components: 1) a stochastic analogue model to link climate model output in gridded space to extreme spatial events in continuous space, and 2) a stochastic process model, conditional on the analogue model, that realistically represents spatial tail dependence. Second, this project will generate model-based projections of extreme events for use in impact analysis. Random draws from the model developed in the first research aim, conditional on climate model projections of large-scale features, functionally constitute an extreme weather generator. PI will use these random draws as inputs to one of the two impact models: precipitation draws feed into a hydrology model to project pluvial flood risks, and wind, temperature, and precipitation draw feed into a fire spread model to project wildfire risk. The software implementation of their model will produce stochastic draws of potential future climate variables that have realistic tail behavior, which can be used downstream as inputs to other numerical models that directly aid in risk assessment.<br/><br/>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.
