Assessing uncertainty for decision‐making in climate adaptation and risk mitigation

Future water availability or crop yield studies, tied to statistics of river flow, precipitation, temperature or evaporation over medium to long‐term horizons, are becoming frequent in climate impact and risk analysis. During the last two decades, access to multi‐system integration of climate models has given rise to the concept of using model ensembles to issue probabilistic climatological projections. These probabilistic projections have not yet been exploited to the full extent in decision support, and are still used to mainly quantify uncertainty bands only for selected climate variables and indicators. One of the reasons of this limited use is the fact that the multi‐system ensemble dispersion is sub‐optimal and does not provide an accurate and reliable representation of the predictive probability density, which is essential for rational decision support under uncertain conditions. The aims of this paper are twofold. First, it seeks to highlight the potential benefits of using climate projections in conjunction with Bayesian paradigms towards educated decision‐making. Second, it discusses how to appropriately formulate probabilistic forecasts by coherently integrating information contained in climate projection ensembles with observations to improve the estimation of the probability density function of future climate states. The results show that the proposed Bayesian approach yields unbiased and sharper predictive distributions for temperature with respect to using the unprocessed ensemble distribution. It also yields improved predictive densities with respect to the Reliability Ensemble Averaging (REA) method.