Characterizing the Uncertainty of Climate Change Projections Using Hierarchical Models

We present a suite of Bayesian hierarchical models that synthesize ensembles of climate model simulations, with the aim of reconciling different future projections of climate change, while characterizing their uncertainty in a rigorous fashion. Posterior distributions of future temperature and/or precipitation changes at regional scales are obtained, accounting for many peculiar data characteristics, like systematic biases, model-specific precisions, region-specific effects, changes in trend with increasing rates of greenhouse gas emissions. We expand on many important issues characterizing model experiments and their collection into multi-model ensembles. Also, we address the need of impact research, by proposing posterior predictive distributions as a representation of probabilistic projections. In addition, the calculation of the posterior predictive distribution for a new set of model data allows a rigorous cross-validation approach to confirm the reasonableness of our Bayesian model assumptions. ∗Climate Central, Princeton NJ 08542-3718 †Dept. of Statistics and Operations Research, University of North Carolina, Chapel Hill NC 27599-3620