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Reconstruction of late Holocene climate based on tree growth and mechanistic hierarchical models

Environmetrics

By:
, ORCID iD , , , and
https://doi.org/10.1002/env.2368

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Abstract

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 of annual growth that represents a multi-dimensional set of climatic and biotic influences. We develop a Bayesian hierarchical framework using a nonlinear, 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 autoregressive process on an annual timescale coupled with a multivariate conditional autoregressive process that accounts for temporal correlation and cross-correlation between temperature and precipitation on a monthly scale. 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 data.

Additional publication details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Reconstruction of late Holocene climate based on tree growth and mechanistic hierarchical models
Series title:
Environmetrics
DOI:
10.1002/env.2368
Volume:
27
Issue:
1
Year Published:
2016
Language:
English
Publisher:
International Environmetrics Society
Contributing office(s):
Coop Res Unit Seattle
Description:
13 p.
First page:
42
Last page:
54
Online Only (Y/N):
N
Additional Online Files (Y/N):
N