Dynamically downscaling predictions for deciduous tree leaf emergence in California under current and future climate

Models that predict the timing of deciduous tree leaf emergence are typically very sensitive to temperature. However, many temperature data products, including those from climate models, have been developed at a very coarse spatial resolution. Such coarse-resolution temperature products can lead to...

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Bibliographic Details
Published in:International journal of biometeorology 2016-07, Vol.60 (7), p.935-944
Main Authors: Medvigy, David, Seung Hee Kim, Jinwon Kim, Menas C. Kafatos
Format: Article
Language:English
Subjects:
Aesculus - growth & development
Animal Physiology
bioclimatology
Biological and Medical Physics
Biophysics
California
Climate
Climate models
Deciduous trees
Dynamic programming
Earth and Environmental Science
Environment
Environmental Health
Flowers & plants
General Circulation Models
leaf emergence
Leaves
Meteorology
Models, Theoretical
Original Paper
Plant Leaves - growth & development
Plant Physiology
prediction
Quercus - growth & development
Temperature
Trees
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Summary:Models that predict the timing of deciduous tree leaf emergence are typically very sensitive to temperature. However, many temperature data products, including those from climate models, have been developed at a very coarse spatial resolution. Such coarse-resolution temperature products can lead to highly biased predictions of leaf emergence. This study investigates how dynamical downscaling of climate models impacts simulations of deciduous tree leaf emergence in California. Models for leaf emergence are forced with temperatures simulated by a general circulation model (GCM) at
ISSN:0020-7128
1432-1254
DOI:10.1007/s00484-015-1086-7