A Multi-Scale Reconstruction of Bivariate Paleoclimate from Tree Rings Using a Mechanistic Growth Model

• Activity Number: 91
• Type: Invited
• Date/Time: Sunday, August 9, 2015 : 9:30 PM to 10:15 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): John Tipton* and Mevin Hooten
• Companies: Colorado State University and Colorado State University
• Keywords: Bayesian hierarchical model ; Predictive validation ; Multi-scale time series ; Paleoclimate ; Scientific process model

Abstract

Reconstruction of pre-instrumental, late Holocene climate is important for understanding how climate changed in the past and how climate might change in the future. The statistical prediction of paleoclimate from tree ring widths is challenging because tree ring widths are a one dimensional summary representing a multi-dimensional set of climate and biotic influences. We develop a Bayesian hierarchical framework using a non-linear, biologically motivated tree ring growth model to jointly reconstruct temperature and precipitation in the Hudson Valley, New York. Using a common growth function form to describe the response of the tree to climate, we allow for species-specific parameterizations of the growth response. To enable predictive backcasts, we model the climate variables with a vector auto-regressive process on an annual time scale coupled with a multivariate conditional auto-regressive process that accounts for temporal correlation and cross-correlation between temperature and precipitation within years. Our multi-scale temporal model allows for flexibility in the climate response through time at different temporal scales and predicts climate scenarios given tree ring width.