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Session F4.4: Climate change and bioclimatic design
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15:00 - 15:18
Urban heat islands in future climate scenarios
University of Toronto, Canada
Aim and Approach
Climate change is a pressing global event , and the possibility of mitigating the impacts of anthropogenic effects on climate seems remote as shown by the fact that carbon emission trends are still growing  and at least until 2009 followed the worst case ‘A1’ scenario of the Intergovernmental Panel on Climate Change (IPCC). Anthropogenic changes are also evident on localized urban climates , often known as urban heat island (UHI). UHI can increase heat stress and operational energy use. Both climate change and UHI should be taken into account when designing for the future; however, typical climate files  are always based on the past. In the case of Canadian standard CWEC weather files, source weather data goes back as far as 1950. With this paper, we assess UHI under different future climate scenarios and under a variety of urban design strategies using simulations [5, 6] in order to understand the impact of future climate on design decision making to reduce UHI, the size of that effect, and how it varies over time. We simulated over 48,000 UHI scenarios under five climates and nine other urban and building design variables to build a robust predictive analysis.
Scientific Innovation and Relevance
We assess nine different urban design parameters in terms of their impact on UHI: height, green coverage ratio, green roofs, site coverage ratio, tree canopy ratio, building material selection, façade-to-site ratio, building program types, and albedo. All parameters are assessed under five climate files for Toronto, ON, Canada. Two are based on measured data—TMYx  files for 1950-2018 and 2004-2018—while three others are simulated future climates  for 2020, 2050, and 2080. Overall 48,558 simulated climate files accounting for climate change and UHI were created. Each file is assessed for typical UHI metrics: maximum UHI, mean daily UHI, maximum daytime temperature, and maximum nighttime temperature. In addition, proxies for the frequency of heat and cold stress are included—the number of hours above 30°C and the number of hours below 5°C. This study is highly relevant to the Building Simulation community, because it is a novel application of simulation technology to assess how urban design may need to change over time and how urban masterplans and projects designed today can have variable impacts in the future.
Preliminary Results and Conclusions
We found that as the Toronto climate warms, the maximum UHI decreases (TMYx 1950-2018 median 7.9°C; CCWWG 2080 median 6.1°C) as does the mean daily UHI (TMYx 1950-2018 median 2.0°C; CCWWG 2080 median 1.8°C). However, the range of possible heat stress outcomes rises dramatically under future climates, underscoring the importance of intelligent urban design to mitigate UHI (TMYx 1950-2018 range 41 hours; CCWWG 2080 range 180 hours). Using Pearson correlation analysis, we found that the influence of site coverage ratio and building height on mean UHI decreased slightly over time, but the influence of urban greenery becomes significantly more important in reducing UHI in future climate files.
1. PCC Working Group III, Climate Change 2014-Mitigation of Climate Change: Summary for Policymakers. 2014: Intergovernmental Panel on Climate Change.
2. Friedlingstein, P., et al., Global carbon budget 2019. Earth System Science Data, 2019. 11(4): p. 1783-1838.
3. Oke, T.R., City size and the urban heat island. Atmospheric Environment (1967), 1973. 7(8): p. 769-779.
4. Hall, I.J., et al., Generation of a typical meteorological year. 1978, Sandia Labs., Albuquerque, NM (USA).
5. Jentsch, M.F., et al., Transforming existing weather data for worldwide locations to enable energy and building performance simulation under future climates. Renewable Energy, 2013. 55: p. 514-524.
6. Bueno, B., et al., The urban weather generator. Journal of Building Performance Simulation, 2013. 6(4): p. 269-281.
7. Crawley, D.B. and L.K. Lawrie, Should We Be Using Just ‘Typical’ Weather Data in Building Performance Simulation?
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