Fast Dimension-Reduced Climate Model Calibration

• Activity Number: 602
• Type: Contributed
• Date/Time: Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Won Chang*+ and Murali Haran and Roman Olson and Klaus Keller
• Companies: The Pennsylvania State University and The Pennsylvania State U. and The Pennsylvania State University and The Pennsylvania State University
• Keywords: Computer Model Calibration ; High-Dimensional Data ; AMOC ; Climate Model ; Data Aggregation ; Spatial Data

Abstract

We consider the problem of making projections of the North Atlantic meridional overturning circulation (AMOC). Uncertainties about climate model parameters play a key role in uncertainties in AMOC projections. When the observational data and the climate model output are high-dimensional spatial data sets, the data are typically aggregated due to computational constraints. The effects of aggregation are unclear because statistically rigorous approaches for model parameter inference have been infeasible for high-resolution data. Here we develop a flexible and computationally efficient approach using principal components and basis expansions to study the effect of spatial data aggregation on parametric and projection uncertainties. Our Bayesian reduced-dimensional calibration approach allows us to study the effect of complicated error structures and data-model discrepancies on our ability to learn about climate model parameters from high-dimensional data. Considering high-dimensional spatial observations reduces the effect of deep uncertainty associated with different priors. I will also briefly describe a composite likelihood-based climate model calibration approach.