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Climate Data to Support the Adaptation of Buildings to Climate Change in Canada

Author

Listed:
  • Abhishek Gaur

    (Construction Research Center, National Research Council Canada, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada)

  • Michael Lacasse

    (Construction Research Center, National Research Council Canada, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada)

Abstract

Climate change will continue to bring about unprecedented climate extremes in the future, and buildings and infrastructure will be exposed to such conditions. To ensure that new and existing buildings deliver satisfactory performance over their design lives, their performance under current and future projected climates needs to be assessed by undertaking building simulations. This study prepares climate data needed for building simulations for 564 locations by bias-correcting the Canadian Regional Climate Model version 4 (CanRCM4) large ensemble (LE) simulations with reference to observations. Technical validation results show that bias-correction effectively reduces the bias associated with CanRCM4-LE simulations in terms of their marginal distributions and the inter-relationship between climate variables. To ensure that the range of projected climate change impacts are encompassed within these data sets, and to furthermore provide building moisture and energy reference years, the reference year files were prepared from bias-corrected CanRCM4-LE simulations and are comprised of a typical meteorological year for building energy applications, a typical and extreme moisture reference year, a typical downscaled year, an extreme warm year, and an extreme cold year.

Suggested Citation

  • Abhishek Gaur & Michael Lacasse, 2022. "Climate Data to Support the Adaptation of Buildings to Climate Change in Canada," Data, MDPI, vol. 7(4), pages 1-22, April.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:4:p:42-:d:788158
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    References listed on IDEAS

    as
    1. Jentsch, Mark F. & James, Patrick A.B. & Bourikas, Leonidas & Bahaj, AbuBakr S., 2013. "Transforming existing weather data for worldwide locations to enable energy and building performance simulation under future climates," Renewable Energy, Elsevier, vol. 55(C), pages 514-524.
    2. Abhishek Gaur & Michael Lacasse & Marianne Armstrong, 2019. "Climate Data to Undertake Hygrothermal and Whole Building Simulations Under Projected Climate Change Influences for 11 Canadian Cities," Data, MDPI, vol. 4(2), pages 1-17, May.
    3. Moazami, Amin & Nik, Vahid M. & Carlucci, Salvatore & Geving, Stig, 2019. "Impacts of future weather data typology on building energy performance – Investigating long-term patterns of climate change and extreme weather conditions," Applied Energy, Elsevier, vol. 238(C), pages 696-720.
    4. Nik, Vahid M., 2016. "Making energy simulation easier for future climate – Synthesizing typical and extreme weather data sets out of regional climate models (RCMs)," Applied Energy, Elsevier, vol. 177(C), pages 204-226.
    5. A. T. D. Perera & Vahid M. Nik & Deliang Chen & Jean-Louis Scartezzini & Tianzhen Hong, 2020. "Quantifying the impacts of climate change and extreme climate events on energy systems," Nature Energy, Nature, vol. 5(2), pages 150-159, February.
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    Cited by:

    1. Juan Botero-Valencia & Adrian Martinez-Perez & Ruber Hernández-García & Luis Castano-Londono, 2023. "Exploring Spatial Patterns in Sensor Data for Humidity, Temperature, and RSSI Measurements," Data, MDPI, vol. 8(5), pages 1-13, April.

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