Annual Report 2008.pdf - SAMSI

Presentations:
1. State-Space Modeling of Soil Moisture. Transition Workshop for the SAMSI Program on
Development, Assessment and Utilization of Complex Computer Models, Durham, NC,
2007.
Research Area - Plans: state-space modeling of soil moisture over time in space, in response to
climatic forcing
Working Group IV Climate and Weather (Random Matrices program)
Special Tasks: working group leader, webmaster
Presentations: none
Research Area - Plans: statistical methods for climate change assessment (this was a reading
group with several outside speakers)
Working Group V Regularization and Covariance (Random Matrices program)
Special Tasks: none
Presentations: Covariance tapering for likelihood based estimation in large spatial datasets,
Oct. 20, 2006
Research Area - Plans: covariance estimation (group participant; non-active research)
Progress Report
Working Group I Climate and Weather
Functional ANOVA Modeling of Regional Climate Model Experiments
Collaborator(s) & Mentor(s): Stephan Sain, Hayley Fowler, Linda Mearns
Discussion of Key Results Obtained:
Regional climate models (RCMs) address smaller spatial regions than do global climate models
(GCMs), but their higher resolution better captures the impact of local features such as lakes and
mountains. GCM output is often used to provide boundary conditions for RCMs, and it is an
open scientific question how much variability in the RCM output is attributable to the RCM
itself, and how much is due simply to large-scale forcing from the GCM. I analyzed data from
the Prudence Project, in which RCMS were crossed with GCM forcings in a designed
experiment. Using this dataset as a motivating example, I developed a general framework for
Bayesian functional ANOVA modeling using Gaussian process prior distributions. In this
framework, inference can be carried out either in a summary fashion, by examining the joint
posterior distribution of the covariance parameters in the corresponding Gaussian processes, or
locally, by studying functional and fully Bayesian versions of the usual ANOVA
decompositions. I have submitted a paper describing the general methodology and illustrating it
on a subset of the Prudence data, and I am currently working on a second paper that applies the
methodology to the full dataset.
Publications, Submissions, Work in Preparation
1. Kaufman, C. and Sain, S., Bayesian Functional ANOVA Modeling using Gaussian
Process Prior Distributions, submitted to Journal of Computational and Graphical
Statistics.
2. Kaufman, C., Sain, S., and Fowler, H. Attributing Sources of Uncertainty in Regional
Climate Projections for Europe. In preparation.
Presentations outside SAMSI
Functional ANOVA Modeling of Regional Climate Model Experiments.