AMS2007-12: Joint Projections of Temperature and Precipitation Change from Multiple Climate Models: A Hierarchical Bayes Approach

Claudia Tebaldi and Bruno Sansó
12/31/2007 09:00 AM
Applied Mathematics & Statistics
Posterior distributions for the joint projections of future temperature and precipitation trends and changes are derived by applying a Bayesian hierachical model to a rich dataset of simulated climate from Global Circulations Models. The simulations here analyzed constitute the most reliable future projections on which the Intergovernmental Panel of Climate Change based its recent summary report on the future of our planet's climate, albeit without any sophisticated statistical handling of the data. Here we quantify the uncertainty represented by the variable results of the different models and their limited ability to represent the observed climate both at global and regional scales. We do so in a Bayesian framework, by estimating posterior distributions of the climate change signals in terms of trends or differences between future and current periods, while we fully characterize the uncertain nature of a suite of other parameters, like biases, correlation terms and model-specific precisions. Besides presenting our results in terms of posterior distributions of the climate signals, we offer as an alternative representation of the uncertainties in climate change projections the use of the posterior-predictive distribution of a new model's projections. The results from our analysis can find straightforward applications in impact studies, which necessitate not only best guesses but a full representation of the uncertainty in climate change projections. For water resource and crop models, for example, it is vital to use joint projections of temperature and precipitation in order to best represent the characteristics of future climate, and our statistical analysis delivers just that.

AMS2007-12