Abstract
Tree growth trends can affect the interpretation of the response of tree-ring proxies (especially tree-ring width) to climate in the low-frequency band, which in turn may limit quantitative understanding of centennial-scale climate variability. As such, it is difficult to determine if long-term trends in tree-ring measurements are caused by age-dependent growth effects or climate. Here, a trend similarity ranking method is proposed to define the range of tree growth effects on tree-ring width chronologies. This method quantifies the inner and outer boundaries of the tree growth effect following two extreme standardization methods: curve fitting standardization and regional curve standardization. The trend similarity ranking method classifies and detrends tree-ring measurements according to the ranking similarity between the regional growth curve and their long-term trends through curve fitting. This standardization process mainly affects the secular trend in tree-ring chronologies, and has no effect on their inter-annual to multi-decadal variations. Applications of this technique to the Yamal and Torneträsk tree-ring width datasets and the maximum latewood density dataset from northern Scandinavia reveals that multi-centennial and millennial-scale temperature variations in the three regions provide substantial positive contributions to the linear warming trends in the instrumental period, and that the summer warming rate during the 20th century is not unprecedented over the past two millennia in any of the three regions.
Highlights
Tree-ring sequences from various proxies are widely used to reconstruct climate variability over recent millennia, because tree-rings can be1 3 Vol.:(0123456789)accurately dated and such data can be obtained from widely distributed geographical regions
We propose a trend similarity ranking (TSR) method to quantitatively estimate the uncertainty ranges in the tree-ring chronologies associated with age-trend effect using the two standardization methods
We provide six examples to illustrate the results of using the curve fitting standardization (CFS) and regional curve standardization (RCS) methods on the detrended measurements (Fig. 2)
Summary
Tree-ring sequences from various proxies (including treering width, TRW) are widely used to reconstruct climate variability over recent millennia, because tree-rings can be. The regional curve standardization (RCS) method is designed to retain as much long-term climate variability information as possible from tree-ring data, placing it on the opposite side of the standardization spectrum from CFS This method assumes that the environmental factors limiting growth in trees are randomly distributed and expected to cancel out when the age-aligned data are averaged to form a common growth curve (Fritts 1976; Briffa et al 1992; Briffa and Melvin 2011). We introduce a novel trend similarity ranking (TSR) method, and evaluate it on three tree-ring datasets, all covering the Common Era: two TRW chronologies, namely the Yamal (Briffa et al 2013) and Torneträsk (Melvin et al 2013), and one maximum latewood density (MXD) chronology from Northern Scandinavia (Esper et al 2012) We hypothesize that his new method enhances our ability to obtain low-frequency climate variability recorded by tree-ring archives
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