  Quantifying long-term historical climate is fundamental to understanding
recent climate change. Most instrumentally recorded climate data are only
available for the past 200 years, so proxy observations from natural archives
are often considered. We describe a model-based approach to reconstructing
climate defined in terms of raw tree-ring measurement data that simultaneously
accounts for non-climatic and climatic variability. In this approach we specify
a joint model for the tree-ring data and climate variable that we fit using
Bayesian inference. We consider a range of prior densities and compare the
modeling approach to current methodology using an example case of Scots pine
from Tornetrask, Sweden to reconstruct growing season temperature. We describe
how current approaches translate into particular model assumptions. We explore
how changes to various components in the model-based approach affect the
resulting reconstruction. We show that minor changes in model specification can
have little effect on model fit but lead to large changes in the predictions.
In particular, the periods of relatively warmer and cooler temperatures are
robust between models, but the magnitude of the resulting temperatures are
highly model dependent. Such sensitivity may not be apparent with traditional
approaches because the underlying statistical model is often hidden or poorly
described.
