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Identification of key parameters controlling demographicallystructured vegetation dynamics in a Land Surface Model [CLM4.5(ED)].

Authors :
Massoud, Elias C.
Chonggang Xu
Fisher, Rosie
Knox, Ryan
Walker, Anthony
Serbin, Shawn
Christoffersen, Bradley
Holm, Jennifer
Kueppers, Lara
Ricciuto, Daniel M.
Liang Wei
Johnson, Daniel
Chambers, Jeff
Koven, Charlie
McDowell, Nate
Vrugt, Jasper
Source :
Geoscientific Model Development Discussions; 2019, p1-44, 44p
Publication Year :
2019

Abstract

Vegetation plays a key role in regulating global carbon cycles and is a key component of the Earth System Models (ESMs) aimed to project Earth's future climates. In the last decade, the vegetation component within ESMs has witnessed great progresses from simple 'big-leaf' approaches to demographically-structured approaches, which has a better representation of plant size, canopy structure, and disturbances. The demographically-structured vegetation models are typically controlled by a large number of parameters, and sensitivity analysis is generally needed to quantify the impact of each parameter on the model outputs for a better understanding of model behaviors. In this study, we use the Fourier Amplitude Sensitivity Test (FAST) to diagnose the Community Land Model coupled to the Ecosystem Demography Model, or CLM4.5(ED). We investigate 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. While the photosynthetic capacity parameter Vcmax25 is found to be important for simulated carbon stocks and fluxes, we also show the importance of carbon storage and allometry parameters, which are shown here to determine vegetation demography and carbon stocks through their impacts on survival and growth strategies. The results of this study highlights the importance of understanding the dynamics of the next generation of demographically-enabled vegetation models within ESMs toward improved model parameterization and model structure for better model fidelity. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19919611
Database :
Complementary Index
Journal :
Geoscientific Model Development Discussions
Publication Type :
Academic Journal
Accession number :
136233358
Full Text :
https://doi.org/10.5194/gmd-2019-6