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

Elias C. Massoud, Chonggang Xu, Rosie A. Fisher, Ryan G. Knox, Anthony P. Walker, Shawn P. Serbin, Bradley O. Christoffersen, Jennifer A. Holm, Lara M. Kueppers, Daniel M. Ricciuto, Liang Wei, Daniel J. Johnson, Jeffrey Q. Chambers, Charlie D. Koven, Nate G. McDowell, Jasper A. Vrugt

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

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.

Original languageEnglish
Pages (from-to)4133-4164
Number of pages32
JournalGeoscientific Model Development
Volume12
Issue number9
DOIs
StatePublished - Sep 23 2019

Funding

Financial support. This work was supported by the United States Department of Energy (US DOE) Office of Science Next Generation Ecosystem Experiment at Tropics (NGEE-T) project, the DOE Graduate Student Researcher (SCGSR) Fellowship, and the UC-Lab Fees Research Program (grant nos. 237285 and LFR-18-542511). Shawn P. Serbin was also partially supported by the United States Department of Energy contract no. DE-SC0012704 to Brookhaven National Laboratory. A portion of Elias C. Mas-soud’s contribution to this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration, Copyright 2019. This work was supported by the United States Department of Energy (US DOE) Office of Science Next Generation Ecosystem Experiment at Tropics (NGEE-T) project, the DOE Graduate Student Researcher (SCGSR) Fellowship, and the UC-Lab Fees Research Program (grant nos. 237285 and LFR-18-542511). Shawn P. Serbin was also partially supported by the United States Department of Energy contract no. DE-SC0012704 to Brookhaven National Laboratory. A portion of Elias C. Massoud's contribution to this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration, Copyright 2019.

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