Abstract
Traditional detrending methods assign equal mean value to all tree-ring series for chronology developments, despite that the mean annual growth changes in different time periods. We find that the strength of a tree-ring model can be improved by giving more weights to tree-ring series that have a stronger climate signal and less weight to series that have a weaker signal. We thus present an ensemble weighting method to mitigate these potential biases and to more accurately extract the climate signals in dendroclimatology studies. This new method has been used to develop the first annual precipitation reconstruction (previous August to current July) at the Songmingyan Mountain and to recalculate the tree-ring chronology from Shenge site in Dulan area in northeastern Tibetan Plateau (TP), a marginal area of Asian summer monsoon. The ensemble weighting method explains 31.7% of instrumental variance for the reconstructions at Songmingyan Mountain and 57.3% of the instrumental variance in the Dulan area, which are higher than those developed using traditional methods. We focus on the newly introduced reconstruction at Songmingyan Mountain, which showsextremely dry (wet) epochs from 1862–1874, 1914–1933 and 1991–1999 (1882–1905). These dry/wet epochs were also found in the marginal areas of summer monsoon and the Indian subcontinent, indicating the linkages between regional hydroclimate changes and the Indian summer monsoon.
Highlights
Global warming has brought long-term climate data inferred from proxies into focus for both the scientific and public communities
We truncated the chronology at Songmingyan Mountain from 1773–2010 and Shenge chronology from 1041 to 1993 as there are sufficient replications indicated by an expressed population signal (EPS) over 0.90 [26]
We introduced an ensemble weighting method to alleviate two potential biases in traditional methods of chronology development
Summary
Global warming has brought long-term climate data inferred from proxies into focus for both the scientific and public communities. The medium-frequency (e.g. decadal/multi-decadal scales) variations can bias the final chronology [6] This is referred to as the ‘‘trend distortion’’ problem, which can be mitigated by the ‘‘signal-free’’ method [6]. Potential bias in the traditional methods can arise when setting the mean value of the tree-ring indices to 1 for different tree-ring indices covering different time intervals (Figure 1). In order to mitigate the three potential biases, we propose a method, termed the ‘‘ensemble weighting method’’, to iteratively weight individual tree-ring series according to their mean climate values and by the sensitivity of each series to climate
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