Inferring Likelihoods and Climate System Characteristics from Climate Models and Spatio-Temporal Tracer Data

• Activity Number: 461
• Type: Topic Contributed
• Date/Time: Wednesday, August 4, 2010 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): K. Sham Bhat*+ and Murali Haran and Klaus Keller and Roman Tonkonojenkov
• Companies: Penn State and Penn State and Penn State and Penn State
• Keywords: spatiotemporal data ; hierarchical Bayes ; Gaussian process ; computer experiments ; multivariate spatial data ; climate change

Abstract

Computer model calibration involves combining information from a complex computer model with physical observations of the process. Computer model output is in the form of multiple spatial fields, particularly in climate science. We study an effective approach for computer model calibration with multivariate spatial data, applied to climate science, accounting for dependence and uncertainty, while ensuring tractability. We obtain sharper posterior inference by combining information from multiple spatial fields than from a single spatial field. In addition, we investigate the effects of including a model discrepancy term and find that including that term usually results in more accurate and sharper inference of the calibration parameter. We also consider more flexible approaches allowing for non-linear relationships among spatial fields and using non-separable covariance functions.