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)

• Main Authors: Y. Kim, P. R. Moorcroft, I. Aleinov, M. J. Puma, N. Y. Kiang
• Format: Article
• Language: English
• Published: Copernicus Publications 2015-12-01
• Series: Geoscientific Model Development
• Online Access: http://www.geosci-model-dev.net/8/3837/2015/gmd-8-3837-2015.pdf

The Ent Terrestrial Biosphere Model (Ent TBM) is a mixed-canopy dynamic global vegetation model developed specifically for coupling with land surface hydrology and general circulation models (GCMs). This study describes the leaf phenology submodel implemented in the Ent TBM version 1.0.1.0.0 coupled to the carbon allocation scheme of the Ecosystem Demography (ED) model. The phenology submodel adopts a combination of responses to temperature (growing degree days and frost hardening), soil moisture (linearity of stress with relative saturation) and radiation (light length). Growth of leaves, sapwood, fine roots, stem wood and coarse roots is updated on a daily basis. 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, with both observed and prognostic hydrology, and observed and prognostic seasonal leaf area index. The phenology submodel is able to capture the timing and magnitude of leaf-out and senescence for temperate broadleaf deciduous forest (Harvard Forest and Morgan–Monroe State Forest, US), C3 annual grassland (Vaira Ranch, US) and California oak savanna (Tonzi Ranch, US). For evergreen needleleaf forest (Hyytiäla, Finland), the phenology submodel captures the effect of frost hardening of photosynthetic capacity on seasonal fluxes and leaf area. We address the importance of customizing parameter sets of vegetation soil moisture stress response to the particular land surface hydrology scheme. We identify model deficiencies that reveal important dynamics and parameter needs.