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

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 x 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 withmore » 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.« less

Authors:
 [1]; ORCiD logo [2];  [3];  [4]; ORCiD logo [5];  [6];  [7];  [4];  [4]; ORCiD logo [5]; ORCiD logo [2]; ORCiD logo [8];  [4]; ORCiD logo [4];  [9];  [10]
  1. California Inst. of Technology (CalTech),Pasadena, CA (United States). Jet Propulsion Lab.; Univ. of California, Irvine, CA (United States). Dept. of Civil and Environmental Engineering
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Earth and Environmental Sciences Div.
  3. Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique (CERFACS), Toulouse (France)
  4. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Climate and Ecosystem Sciences Div.
  5. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Environmental Sciences Div.
  6. Brookhaven National Lab. (BNL), Upton, NY (United States). Biological, Environmental & Climate Sciences Dept.
  7. Univ. of Texas Rio Grande Valley, Edinburg, TX (United States)
  8. Univ. of Florida, Gainesville, FL (United States). School of Forest Resources and Conservation
  9. Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Earth Systems Analysis and Modeling Div.
  10. Univ. of California, Irvine, CA (United States). Dept. of Civil and Environmental Engineering; Univ. of California, Irvine, CA (United States). Dept. of Earth System Science
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Biological and Environmental Research (BER). Climate and Environmental Sciences Division; Office of Science (SC), Biological and Environmental Research (BER). Earth and Environmental Systems Science Division
OSTI Identifier:
1562475
Alternate Identifier(s):
OSTI ID: 1571627; OSTI ID: 1580929; OSTI ID: 1649090
Report Number(s):
BNL-212083-2019-JAAM; LA-UR-19-29632
Journal ID: ISSN 1991-9603
Grant/Contract Number:  
SC0012704; AC02-05CH11231; 89233218CNA000001; AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Geoscientific Model Development (Online)
Additional Journal Information:
Journal Name: Geoscientific Model Development (Online); Journal Volume: 12; Journal Issue: 9; Related Information: Office of Science Next Generation Ecosystem Experiment at Tropics (NGEE-T) project; Journal ID: ISSN 1991-9603
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Earth Sciences

Citation Formats

Massoud, Elias C., Xu, Chonggang, Fisher, Rosie A., Knox, Ryan G., Walker, Anthony P., Serbin, Shawn P., Christoffersen, Bradley O., Holm, Jennifer A., Kueppers, Lara M., Ricciuto, Daniel M., Wei, Liang, Johnson, Daniel J., Chambers, Jeffrey Q., Koven, Charlie D., McDowell, Nate G., and Vrugt, Jasper A. Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES). United States: N. p., 2019. Web. doi:10.5194/gmd-12-4133-2019.
Massoud, Elias C., Xu, Chonggang, Fisher, Rosie A., Knox, Ryan G., Walker, Anthony P., Serbin, Shawn P., Christoffersen, Bradley O., Holm, Jennifer A., Kueppers, Lara M., Ricciuto, Daniel M., Wei, Liang, Johnson, Daniel J., Chambers, Jeffrey Q., Koven, Charlie D., McDowell, Nate G., & Vrugt, Jasper A. Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES). United States. doi:10.5194/gmd-12-4133-2019.
Massoud, Elias C., Xu, Chonggang, Fisher, Rosie A., Knox, Ryan G., Walker, Anthony P., Serbin, Shawn P., Christoffersen, Bradley O., Holm, Jennifer A., Kueppers, Lara M., Ricciuto, Daniel M., Wei, Liang, Johnson, Daniel J., Chambers, Jeffrey Q., Koven, Charlie D., McDowell, Nate G., and Vrugt, Jasper A. Mon . "Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES)". United States. doi:10.5194/gmd-12-4133-2019. https://www.osti.gov/servlets/purl/1562475.
@article{osti_1562475,
title = {Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES)},
author = {Massoud, Elias C. and Xu, Chonggang and Fisher, Rosie A. and Knox, Ryan G. and Walker, Anthony P. and Serbin, Shawn P. and Christoffersen, Bradley O. and Holm, Jennifer A. and Kueppers, Lara M. and Ricciuto, Daniel M. and Wei, Liang and Johnson, Daniel J. and Chambers, Jeffrey Q. and Koven, Charlie D. and McDowell, Nate G. and Vrugt, Jasper A.},
abstractNote = {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 x 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.},
doi = {10.5194/gmd-12-4133-2019},
journal = {Geoscientific Model Development (Online)},
number = 9,
volume = 12,
place = {United States},
year = {2019},
month = {9}
}

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Figures / Tables:

Figure 1. Figure 1.: Recycled climate drivers for the study area including annual mean precipitation, relative humidity, and air temperature for the years 1948–1972. The annual radiation and air pressure are not plotted as they are quite stable across years.

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