Canadian initial-condition climate ensemble: Hygrothermal simulation on wood-stud and retrofitted historical masonry

Given the long lifespan of buildings it becomes inevitable to assess the impact of climate change when designing building envelopes or retrofitting solutions. Hygrothermal simulations would benefit from using a climate ensemble to account for the large uncertainties that come with modelled climate data. However, this has been rarely done so far, and no state-of-the-art methodology exists to implement ensemble data in hygrothermal simulations. This paper presents the application of a Canadian initial-condition ensemble CanRCM4 LE in hygrothermal (HAM) modelling. A brick-clad wood-stud wall assembly and historical solid masonry wall, before and after retrofitting, are analysed for Ottawa, CA. Variations in the HAM model are studied to evaluate whether the ensemble can be represented by one smaller reduced ensemble for different studies. And, the potential of climate-based indices to predict this reduced ensemble is studied. Further, the uncertainty of the ensemble is analysed, as well as the climate change signal of the damage functions. It is found that the application of a climate ensemble is highly valuable for HAM modelling, as it is able to account for the high uncertainty of climate change data. To maintain the level of information, it is recommended to perform HAM simulations using the entire ensemble. However, there is potential to select a reduced ensemble to represent spread of the climate change signal.

Automated summary

This paper discusses the importance of assessing the impact of climate change when designing building envelopes or retrofitting solutions, and presents the application of a Canadian initial-condition ensemble in hygrothermal modeling. The value of using a climate ensemble in hygrothermal simulations and the potential application of a reduced ensemble are studied. The conclusion shows that applying a climate ensemble is highly valuable for considering the uncertainty of climate change data.