Abstract
Extreme temperatures—both heat waves and cold snaps—pose a great hazard to human health, property, and infrastructure. Furthermore, projected changes in extreme temperature event frequency and intensity complicates adaptation and mitigation planning for local governments providing cooling/warming centers, entities depending on outdoor labor, and utilities responding to changing energy demand. Flexible tools evaluating the current and future risk of extreme temperature events could help to optimize hazard response. Here, we present an expanded framework for temperature intensity-duration-frequency (TIDF) curve analysis, including an objective fitting algorithm and uncertainty quantification, for the observational record. Prior to this study, the application of TIDF curves has been focused on heat waves. We expand that application to consider the utility of TIDF curves for extreme cold events as well as “near-extreme” events for a more robust quantification of the risk of extreme temperature events. Using a probabilistic approach to extremes, we also calculate confidence intervals, providing additional context for the severity and (where applicable) unprecedented nature of historically extreme events. Further analysis of confidence interval width offers insight to characterize differences in the uncertainty associated with hot and cold extremes. Finally, the flexibility of this new TIDF framework is demonstrated by analyzing two recent extreme temperature events—the 2021 Pacific Northwest heat wave and 2021 Texas cold snap—showcasing the broad utility and potential cross-sector application of a TIDF approach.