Assessment of future overheating conditions in Canadian cities using a reference year selection method. (15th June 2022)

• Record Type: Journal Article
• Authors: Zou, Jiwei; Gaur, Abhishek; Wang, Liangzhu (Leon); Laouadi, Abdelaziz; Lacasse, Michael
• Is Part Of: Building and environment. Volume 218(2022)
• Publication Date: 2022-06-15
• Subjects: Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2022.109102
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: Climate change has led to prolonged, more frequent, intense, and severe extreme weather events, such as summertime heatwaves, creating many challenges on the economy and society and human health and energy resources. For example, the 2010 and 2018 heatwave in Quebec, Canada, resulted in about 280 and 93 heat-related deaths, and there were around 500 fatalities due to overheated indoor environments in 2021 around entire Canada. Therefore, it is imperative to understand and evaluate the overheating conditions in buildings, for which selecting suitable future reference weather data under climate change is one of the first critical steps. This study evaluated a reference year selection method in terms of typical and extreme reference years based on future climate datasets to assess both outdoor and indoor overheating in the future. The future climate data were collected from the Coordinated Regional Downscaling Experiment (CORDEX) program. Three Canadian cities (Montreal, Toronto, Vancouver) were selected for the overheating evaluation during three selected periods (2001–2020, 2041–2060, 2081–2100). The CORDEX climate projections were first bias-corrected by the multivariate quantile mapping correction method with the observational data. Then, the typical and extreme reference year data were generated as well as climate data from the design summer year for comparison. The performance of the reference year selection method was evaluated by comparing the maximum, minimum, and average overheating hours for the 20-years data of each period. This study demonstrates that the multivariate quantile mapping bias correction method can improve the reliability of future climate data making it one of the most important steps for any future weather projection study. Besides, the reference year selection method could efficiently capture maximum and minimum monthly overheating hours providing the upper and lower boundary of possible outdoor and indoor overheating conditions.. In contrast, neither the severest nor the typical monthly outdoor and indoor overheating conditions could be predicted by the design summer year method. Finally, owing to the effects of climate change, average monthly overheating hours normally increase by around one time (from 50% to 150%) until the mid-term future (2041–2060) and by around two to three times (even up to nine times for some scenarios) during the long-term future (2081–2100).