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Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES)

Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES)

Authors :
E. C. Massoud
C. Xu
R. A. Fisher
R. G. Knox
A. P. Walker
S. P. Serbin
B. O. Christoffersen
J. A. Holm
L. M. Kueppers
D. M. Ricciuto
L. Wei
D. J. Johnson
J. Q. Chambers
C. D. Koven
N. G. McDowell
J. A. Vrugt
Source :
Geoscientific Model Development, Vol 12, Pp 4133-4164 (2019)
Publication Year :
2019
Publisher :
Copernicus Publications, 2019.

Abstract

Vegetation plays an important role in regulating global carbon cycles and is a key component of the Earth system models (ESMs) that aim to project Earth's future climate. In the last decade, the vegetation component within ESMs has witnessed great progress from simple “big-leaf” approaches to demographically structured approaches, which have a better representation of plant size, canopy structure, and disturbances. These demographically structured vegetation models typically have a large number of input parameters, and sensitivity analysis is needed to quantify the impact of each parameter on the model outputs for a better understanding of model behavior. In this study, we conducted a comprehensive sensitivity analysis to diagnose the Community Land Model coupled to the Functionally Assembled Terrestrial Simulator, or CLM4.5(FATES). Specifically, we quantified the first- and second-order sensitivities of the model parameters to outputs that represent simulated growth and mortality as well as carbon fluxes and stocks for a tropical site with an extent of 1×1∘. While the photosynthetic capacity parameter (Vc,max25) is found to be important for simulated carbon stocks and fluxes, we also show the importance of carbon storage and allometry parameters, which determine survival and growth strategies within the model. The parameter sensitivity changes with different sizes of trees and climate conditions. The results of this study highlight the importance of understanding the dynamics of the next generation of demographically enabled vegetation models within ESMs to improve model parameterization and structure for better model fidelity.

Subjects

Subjects :
Geology
QE1-996.5

Details

Language :
English
ISSN :
1991959X and 19919603
Volume :
12
Database :
Directory of Open Access Journals
Journal :
Geoscientific Model Development
Publication Type :
Academic Journal
Accession number :
edsdoj.24c349e669e34f4d8255356607cfbcca
Document Type :
article
Full Text :
https://doi.org/10.5194/gmd-12-4133-2019