The focus of this project is the development of a climate and climate-impact prediction framework by applying Bayesian reasoning. Bayesian analysis is a theoretically grounded technique for combining information, learning from data and then forming predictions while representing uncertainties in these steps. Modern hierarchical Bayesian modeling has proved very effective in complicated settings. The strategy involves the specification of probability distributions, known as data models, for a variety of observational data sources. In addition, probability distributions, known as prior process models, are formulated for unknown processes interacting with each other on various space-time scales, as well as unknown parameters. The prior distributions for the processes are combined with the data models via Bayes' Theorem to produce a posterior probability distribution for the unknown quantities. This distribution serves as the basis for prediction. The reliance on probability means that uncertainty management is built-in to the approach. The key to our approach is to incorporate climate model output, at both global and regional scales, as if they are observations. These data are then combined with relatively simple, though physically based, prior models. The corresponding computations needed for prediction and uncertainty quantification are orders of magnitude easier than those associated with large-scale climate system models. Probability theory provides techniques for using the analyses to produce predictions and associated uncertainties of impacts. These results then serve as the basis for decision support.
A critical challenge in current climate research is the prediction of regional climate behavior on decadal time scales. Reliable predictions of climate on decadal scales are the cornerstone of decision support for policy makers in their selection of adaptation strategies to address the impacts of climate change. Substantial information regarding climate is produced by climate system models. However, model results are subject to a variety of uncertainties. Further, these models are very demanding computationally and produce massive datasets, so there are severe limitations on our ability to quantify the uncertainties associated with model-based predictions. To respond to the challenge, we develop Bayesian techniques for predictive analyses that use climate model output in an efficient fashion and also deal with uncertainty. This combined use of large-scale climate system models, regional climate models, and simple climate models creates a synergistic environment in climate science. Next, because our strategy relies on comparatively simple calculations, we provide techniques for producing impact-specific and local or regional-specific predictive information in response to the needs of a variety of decision makers having a variety of responsibilities.
