A stochastic model of future extreme temperature events for infrastructure analysis. (May 2023)

• Record Type: Journal Article
• Authors: Villa, Daniel L.; Schostek, Tyler; Govertsen, Krissy; Macmillan, Madeline
• Is Part Of: Environmental modelling & software. Volume 163(2023)
• Publication Date: 2023-05
• Subjects: Climate change -- Extreme temperatures -- Heat waves -- Shifts in frequency -- Duration and intensity -- Infrastructure -- Building energy modeling -- Resilience -- Stochastic models; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2023.105663
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Applying extreme temperature events for future conditions is not straightforward for infrastructure resilience analyses. This work introduces a stochastic model that fills this gap. The model uses at least 50 years of daily extreme temperature records, climate normals with 10%–90% confidence intervals, and shifts/offsets for increased frequency and intensity of heat wave events. Intensity and frequency are shifted based on surface temperature anomaly from 1850–1900 for 32 models from CMIP6. A case study for Worcester, Massachusetts passed 85% of cases using the two-sided Kolmogorov–Smirnov p -value test with 95% confidence for both temperature and duration. Future shifts for several climate scenarios to 2020, 2040, 2060, and 2080 had acceptable errors between the shifted model and 10- and 50-year extreme temperature event thresholds with the largest error being 2.67°C. The model is likely to be flexible enough for other patterns of extreme weather such as extreme precipitation and hurricanes. Highlights: A stochastic model is introduced for cold and hot extreme temperature events. Fast run-times in comparison to gridded models enable use by infrastructure analyses. The model enables future shifts in frequency, duration, and intensity. Fits 85% of cases with 95% confidence for the Kolmogorov–Smirnov test for a Worcester, Massachusetts case study. The model is open-source software: https://github.com/sandialabs/MEWS .