Nonstationary temporal response of mountain hemlock growth to climatic variability in the North Cascade Range, Washington, USA

A stationary response of tree radial growth to climatic variables is assumed as a basis for climatic reconstructions and future growth projections in response to climate change. Mountain hemlock (Tsuga mertensiana (Bong.) Carrière) trees on the western slopes of the North Cascade Range (Washington,...

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Bibliographic Details
Published inCanadian journal of forest research Vol. 45; no. 6; pp. 676 - 688
Main Authors Marcinkowski, Kailey, David L. Peterson, Gregory J. Ettl
Format Journal Article
LanguageEnglish
Published Ottawa NRC Research Press 01.06.2015
Canadian Science Publishing NRC Research Press
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Summary:A stationary response of tree radial growth to climatic variables is assumed as a basis for climatic reconstructions and future growth projections in response to climate change. Mountain hemlock (Tsuga mertensiana (Bong.) Carrière) trees on the western slopes of the North Cascade Range (Washington, USA) were examined for stability in growth response to climatic influences at a small spatial scale. Moving correlation functions demonstrate that climate–growth interactions are nonstationary over time, alternating between periods of significant and nonsignificant responses. Correlations between growth and winter precipitation have weakened, becoming statistically insignificant in the last decade, but correlations with spring temperature and previous-year summer temperature have strengthened, becoming statistically significant. The Pacific Decadal Oscillation influences patterns in climate–growth correlations but does not seem to account for the most recent changes in correlation strength. At an interannual scale, growth differs between El Niño Southern Oscillation phases, specifically between El Niño and La Niña years and between La Niña and neutral phase years. The variability in growth response to climate at interannual and interdecadal time frames, especially with the climate changes emerging in recent decades, will challenge the reliability and accuracy of reconstruction and predictive models.
Bibliography:http://dx.doi.org/10.1139/cjfr-2014-0231
ISSN:1208-6037
0045-5067
1208-6037
DOI:10.1139/cjfr-2014-0231