A Closer Look at Regional Curve Standardization of Tree-Ring Records: Justification of the Need, aWarning of Some Pitfalls, and Suggested Improvements in Its Application - Dendroclimatology - page 114

of potential biasing problems in the application of RCS are illustrated by refer-
ence to several published studies. Further implications and suggested directions for
necessary further development of the RCS concept are discussed.

Keywords Dendrochronology · Regional curve standardization · Low-frequency

variance · Chronology bias · Signal-free regional curve standardization

5.1 Introduction

Among those high-resolution environmental proxies that have the potential to
express aspects of climate variability with perfect dating fidelity, at annual resolu-
tion, tree-ring records remain unique in the way in which they provide information
continuously spanning centuries to millennia over vast swathes of the world's extra-
tropical land areas. In general, this information is most accurate in its representation
of short-timescale variability; i.e., relative changes from year to year and decade to
decade. It is in this high-frequency part of the variance spectrum that chronology
confidence can be quantified most easily, and the empirical calibration of tree-ring
chronologies, routinely achieved by regression against observed climate variabil-
ity, can be more accurately facilitated and subjected to rigorous verification through
comparison with independent data (Fritts 1976 , Section 5.4 ; Fritts and Guiot 1990 ;
Briffa 1999 ) .
Tree-ring data series, extracted from radial tree-growth measurements, may con-
tain information about external growth influences on multidecadal, centennial, and
even longer timescales. The expression of this information in individual series
of measurements is, however, obscured by trends associated with changing tree
geometry over time. In localized site chronologies and in large regional average
chronologies, the expression and reliability of long-timescale variance is affected
by the techniques used to 'standardize' the measurements to mitigate non-climate
effects and by the manner in which the resulting standardized indices are incor-
porated within the final chronology. In this discussion, for convenience, we define
medium-frequency variability as that representing timescales of decades up to the
age of a tree. We define low-frequency variability as that manifested at timescales
beyond the age of a tree.
We begin this review by citing a simple example that demonstrates why, where
the intention is to recover evidence of long-timescale climate variability in chronolo-
gies, it is inappropriate to use common 'data-adaptive' standardization techniques
(Cook et al. 1995 ) . We also show how the presence of medium-frequency com-
mon tree-growth influences can create distortion in the recovered climate signal,
particularly at the ends of chronologies standardized by using flexible curve-fitting
techniques.
We provide some background to the history and simple application of what is
known today as regional curve standardization (RCS), a standardization approach
that has the potential to preserve the evidence of long-timescale forcing of tree
growth. We discuss a number of potential biases that arise in the simple application