Late Holocene Tree Ring Widths, Hudson Valley: Level 1

Reconstruction of pre-instrumental, late Holocene climate is important for understanding how climate has changed in the past and how climate might change in the future. Statistical prediction of paleoclimate from tree ring widths is challenging because tree ring widths are a one dimensional summary that represents 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 to describe the response of a 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 reasonable climate scenarios given tree ring width. This material is based upon work supported by the National Science Foundation under Grants #DEB-1241874, 1241868, 1241870, 1241851, 1241891, 1241846, 1241856, 1241930.