Projections of nonstationary climate risks can vary considerably from
one source to another, posing considerable communication and
decision-analytical challenges. One such challenge is how to present
trade-offs under deep uncertainty in a salient and interpretable manner.
Some common approaches include analyzing a small subset of projections
or treating all considered projections as equally likely. These
approaches can underestimate risks, hide deep uncertainties, and are
mostly silent on which assumptions drive decision-relevant outcomes.
Here we introduce and demonstrate a transparent Bayesian framework for
synthesizing deep uncertainties to inform climate risk management. The
first step of this workflow is to generate an ensemble of simulations
representing possible futures and analyze them through standard
exploratory modeling techniques. Next, a small set of probability
distributions representing subjective beliefs about the likelihood of
possible futures is used to weight the scenarios. Finally, these weights
are used to compute and characterize trade-offs, conduct robustness
checks, and reveal implicit assumptions. We demonstrate the framework
through a didactic case study analyzing how high to elevate a house to
manage coastal flood risks.