Uncertainty quantification for low-likelihood high-impact weather events using spatio-temporal statistical modeling

Oregon Convention Center 

Determining the probability and severity of a low-likelihood high-impact weather event from the historical record is difficult due to their relatively rare occurrence. Instead, we shift our focus to the drivers of the climatology surrounding weather events. Specifically, we represent the climate system as the sum of two parts, the climatological forcing and internal variability, and model the drivers of these two processes. We model the climatological forcing as changes in the system due to anthropogenic induced climate change using a set of measurable variables. The internal variability represents the variation in the system due to its natural cycle, which we model using Bayesian singular value decomposition where the basis functions in the decomposition capture the spatial and temporal modes of variability. By decomposing the climate system in terms of its climate forcing and internal variability, we can determine which combination of the drivers result high-impact weather events and the probability of these events occurring. We apply our framework to two-meter air temperature in the Pacific Northwest, providing additional insight into the 2021 heatwave.