Title: Physical Statistical Environmental Modeling

Speaker: Mark Berliner, Ohio State University

Abstract: Combining scientific reasoning, often reflected in numerical

models, and observational data is a crucial challenge in environmental

analysis and prediction. The hierarchical Bayesian framework provides

opportunities for merging diverse information sources in a coherent

framework, and in a fashion which manages the uncertainty. A key to

the hierarchical viewpoint is that separate statistical models are

developed for the process variables studied and the observations

conditional on those variables. Modeling process variables in this

way enables incorporation of scientific models across a spectrum of

levels of intensity ranging from qualitative use of physical reasoning

to strong reliance on numerical models. I will review the basic

notions and present two examples. The first involves understanding

controls on glacial dynamics. The second is a climate forecasting

analysis assessing impacts of different scenarios for future carbon

dioxide concentrations, incorporating model output from large-scale

climate system models.