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Title: Microdensitometric records from humid subtropical China show distinct climate signals in earlywood and latewood
Authors: Xinguang Cao
Keyan Fang
Ping Chen
Peng Zhang
Jesper Björklund
Nathsuda Pumijumnong
Zhengtang Guo
CAS Center for Excellence & Innovation in Tibetan Plateau Earth System Sciences
Faculty of Environment and Resource Studies, Mahidol University
Huanggang Normal University
University of Chinese Academy of Sciences
Chinese Academy of Sciences
Fujian Normal University
Göteborgs Universitet
Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft WSL
Keywords: Agricultural and Biological Sciences;Environmental Science
Issue Date: 1-Dec-2020
Citation: Dendrochronologia. Vol.64, (2020)
Abstract: © 2020 Elsevier GmbH Blue intensity (BI) from tree rings is a technique that has been widely explored for temperature reconstruction purposes in middle and high latitudes. However, it is still rather untested at lower latitudes and in drier climates, particularly in subtropical areas. Here, we develop the first series of BI-based tree-ring parameters (earlywood BI, EWBI; latewood BI, LWBI and ΔBI, the difference between LWBI and EWBI) in humid subtropical China from the species Pinus massiniana. Although the BI parameters have weaker inter-series correlations than do ring widths, they are generally better correlated with climate parameters. Our study shows a positive temperature response in the EWBI parameter and negative responses in the LWBI and ΔBI parameters. Interestingly, the correlation pattern is almost the opposite of that observed at high latitudes, where there is a pronounced positive sensitivity of the LWBI/ΔBI/MXBI parameters to temperature. We find the EWBI to be the most robust parameter for reconstruction purposes. The positive March–May average temperature signal of EWBI is stable across frequencies and shows consistent interdecadal variations with other temperature proxy series from the region. The compilation of new tree-ring records using the BI technique will ultimately support our understanding of climate history. For this reason, we encourage similar attempts to push the boundaries of the BI technique even further.
ISSN: 16120051
Appears in Collections:Scopus 2020

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