  Computer models are used to model complex processes in various disciplines.
Often, a key source of uncertainty in the behavior of complex computer models
is uncertainty due to unknown model input parameters. Statistical computer
model calibration is the process of inferring model parameter values, along
with associated uncertainties, from observations of the physical process and
from model outputs at various parameter settings. Observations and model
outputs are often in the form of high-dimensional spatial fields, especially in
the environmental sciences. Sound statistical inference may be computationally
challenging in such situations. Here we introduce a composite likelihood-based
approach to perform computer model calibration with high-dimensional spatial
data. While composite likelihood has been studied extensively in the context of
spatial statistics, computer model calibration using composite likelihood poses
several new challenges. We propose a computationally efficient approach for
Bayesian computer model calibration using composite likelihood. We also develop
a methodology based on asymptotic theory for adjusting the composite likelihood
posterior distribution so that it accurately represents posterior
uncertainties. We study the application of our new approach in the context of
calibration for a climate model.
