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 117

and it is because the means of index series from different trees can vary through
time, that the chronology constructed from them can exhibit long-timescale variance
at periods up to the length of the chronology or beyond (Briffa et al. 1992 ; Cook
et al. 1995 ; Briffaetal. 1996 ) .
The use of the curve formed by calculating mean ring width of radial measure-
ments ordered by cambial age has a long history in forestry and dendroclimatic
studies, and an earlier awareness of some of the problems associated with it can
be recognized. In seeking to study past climate changes in California, Huntington
( 1914 ) used a curve of growth rate plotted against ring age, which included a correc-
tion for longevity because he recognized that older trees tended to growmore slowly,
even when young, compared to others. In a study of the relationship between tree
growth and climate in Sweden, Erlandsson ( 1936 ) calculated growth rate curves for
specific age classes of trees at various locations and applied a correction factor to
enable comparison of different age classes. Mitchell ( 1967 ) showed that the shape of
the mean curve by ring age varied between species and for the same species in dif-
ferent geographical locations. Becker ( 1989 ) used trees from generally even-aged,
living stands but selected a large number of stands with as wide a range of stand
ages as possible in order to eliminate the effect of 'trends according to calendar
years.' Dupouey et al. ( 1992 ) developed a mean growth by age curve to model and
remove the age trend while retaining long-timescale variance. Briffa et al. ( 1992 ,
1996 ) introduced the term 'regional curve standardization' to describe the method
in the specific context of attempting to recover long-timescale climate trends but
used large numbers of subfossil trees, hoping to eliminate the problem of modern
climate biasing the parameters of the RCS curve.
Nicolussi et al. ( 1995 ) examined how tree-growth rates change when they are
quantified for a specific ring age class through time and discussed problems asso-
ciated with the interpretation of these changes. Badeau et al. ( 1996 ) examined
potential sources of bias in the use of regionally based age curves. Esper et al. ( 2002 )
used two different RCS curves to standardize tree measurements from a wide range
of sites being analyzed together, and Esper et al. ( 2003 ) also examined other aspects
of RCS implementation. Helama et al. ( 2005a ) examined the effect of forest density
on the shape of the RCS curve.
Since its recent reintroduction for dendroclimatic studies, there has been a resur-
gence in the application of RCS, and it has been adopted and sometimes adapted
in dendroclimatic studies intended to capture long-timescale climate variance (e.g.,
Rathgeber et al. 1999a ; Cook et al. 2000 ; Grudd et al. 2002 ; Helama et al. 2002 ;
Melvin 2004 ; Naurzbaev et al. 2004 ; Büntgen et al. 2005 ; D'Arrigo et al. 2005 ;
Linderholm and Gunnarson 2005 ; Luckman and Wilson 2005 ; Wilson et al. 2005 )
5.4 Potential Biases in RCS
Previous discussions of the recent application of RCS make it clear that the
advantage offered by this approach, in terms of its potential to represent long-
timescale variability in chronologies, must be weighed against the likelihood of
large uncertainty associated with this information. Fritts ( 1976 , p. 280) pointed out