Bayesian parameter estimation and interpretation for an intermediate model of tree-ring width

We present a Bayesian model for estimating the parameters of the VS-Lite forward model of tree-ring width. The scheme also provides information about the uncertainty of the parameter estimates, as well as the uncertainty of VS-Lite itself. By inferring VS-Lite's parameters for synthetically-generated ring-width series at several hundred sites across the United States, we show that the Bayesian algorithm is skillful and robust to climatic nonstationarity over the interval tested. We also infer optimal parameter values for modeling observed ring-width data at the same network of sites. The estimated parameter values cluster in physical space, and their locations in multidimensional parameter space provide insight into the dominant climatic controls on modeled tree-ring growth at each site.