Methodological choices influencing uncertainties andinformation loss in research on climate adaptation of buildings

Adapting buildings to a changing climate has become an increasingly popular topic of research. A
critical tool for evaluating the proposed adaptation measures, is simulation models of future climatic
strains. Using future climate scenarios introduces uncertainties into the study conclusions, as they
are based on complex socio-economic and meteorological models and predict consequences far into
the future. Accounting for, and treatment of, these uncertainties are paramount for the quality of
the results. We have mapped the methodology used for calculating the climate strains in building
simulation models in 132 studies within the topic of building adaptation measures from the past 7
years. In particular, the treatment of climate-model induced uncertainties and presentation of their
influence on the final results have been mapped.

In order to quantify the uncertainties introduced by climate-modelling in some way, the calculated
climatic strains need to be based on more than one strain of climate-models, to root out the
variations, biases, and deviations in the chosen model. The studies presenting the most
comprehensive evaluation of climate related uncertainties produced means and standard deviations
for all used-GCM-RCM chains and evaluated the variations. The methods for downscaling and bias-
correcting were well-documented in these studies, providing a transparent presentation of the
underlying uncertainties. Producing such results is work-demanding and probabilistic results may be
more challenging to present in a clear way. However, presenting an analysis of the uncertainties
greatly increases the quality and credibility of the results, and even more importantly it reminds the
reader that assessments of complex systems far into the future are not deterministic in nature.

The different kinds of uncertainties introduced from climate-modelling found in the study selection
have been characterized and structured by level of uncertainty and source of uncertainty in this
paper, as well as how they are dealt with in principle. Further, a flow chart of how information loss
increases progressively through methodological choices along the way from emission scenario to
result presentation have been mapped and structured, as a tool for addressing the climate-
modelling-specific uncertainties in such studies. A frequent conclusion in studies calculating and
analyzing the uncertainties was that this should not be omitted, as it was found to have a great
impact on the results. By careful description of the methods, by calculating and carrying quantified
uncertainties from the climate-model to the results, and by evaluating the validity of the results, the
reader of such studies will certainly be reminded of the uncertain nature of the future.