Articles | Volume 8, issue 12
https://doi.org/10.5194/gmd-8-3837-2015
https://doi.org/10.5194/gmd-8-3837-2015
Development and technical paper
 | 
08 Dec 2015
Development and technical paper |  | 08 Dec 2015

Variability of phenology and fluxes of water and carbon with observed and simulated soil moisture in the Ent Terrestrial Biosphere Model (Ent TBM version 1.0.1.0.0)

Y. Kim, P. R. Moorcroft, I. Aleinov, M. J. Puma, and N. Y. Kiang

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Cited articles

Abramopoulos, F., Rosenzweig, C., and Choudhury, B. J.: Improved ground hydrology calculations for global climate models (GCMs): soil water movement and evapotranspiration, J. Climate, 1, 921–941, 1988.
Amthor, J. S.: The McCree–de Wit–Penning de Vries–Thornley respiration paradigms: 30 years later, Ann. Bot.-London, 86, 1–20, 2000.
Aranibar, J. N., Berry, J. A., Riley, W. J., Pataki, D. E., Law, B. E., and Ehleringer, J. R.: Combining meteorology, eddy fluxes, isotope measurements, and modeling to understand environmental controls of carbon isotope discrimination at the canopy scale, Glob. Change Biol., 12, 710–730, 2006.
Archetti, M., Richardson, A. D., O'Keefe, J., and Delpierre, N.: Predicting Climate Change Impacts on the Amount and Duration of Autumn Colors in a New England Forest, PLoS ONE, 8, e57373, https://doi.org/10.1371/journal.pone.0057373, 2013.
Arora, V. K. and Boer, G. J.: A parameterization of leaf phenology for the terrestrial ecosystem component of climate models, Glob. Change Biol., 11, 39–59, 2005.
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Short summary
The Ent Terrestrial Biosphere Model is a mixed-canopy dynamic global vegetation model developed specifically for coupling with land surface hydrology and general circulation models. This study describes the leaf phenology submodel implemented in the Ent TBM. We evaluate the performance in reproducing observed leaf seasonal growth as well as water and carbon fluxes for four plant functional types at five Fluxnet sites.
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