skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Matrix approach to land carbon cycle modeling: A case study with the Community Land Model

Authors:
ORCiD logo [1];  [2];  [3];  [4];  [5];  [6];  [7];  [4];  [8]
  1. Department of Microbiology and Plant Biology, University of Oklahoma, Norman OK USA, Now at Laboratoire des Sciences du Climat et de l'Environnement, Gif-sur-Yvette France
  2. Department of Microbiology and Plant Biology, University of Oklahoma, Norman OK USA, Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff AZ USA
  3. Department of Microbiology and Plant Biology, University of Oklahoma, Norman OK USA
  4. Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder CO USA
  5. Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley CA USA
  6. Tiantong National Forest Ecosystem Observation and Research Station, School of Ecological and Environmental Sciences, East China Normal University, Shanghai China, Research Center for Global Change and Ecological Forecasting, East China Normal University, Shanghai China
  7. Research Center for Global Change and Ecological Forecasting, East China Normal University, Shanghai China
  8. Department of Microbiology and Plant Biology, University of Oklahoma, Norman OK USA, Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff AZ USA, Department of Earth System Science, Tsinghua University, Beijing China
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1410578
Grant/Contract Number:
SC0008270; SC00114085
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Global Change Biology
Additional Journal Information:
Journal Volume: 24; Journal Issue: 3; Related Information: CHORUS Timestamp: 2018-02-16 05:07:31; Journal ID: ISSN 1354-1013
Publisher:
Wiley-Blackwell
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Huang, Yuanyuan, Lu, Xingjie, Shi, Zheng, Lawrence, David, Koven, Charles D., Xia, Jianyang, Du, Zhenggang, Kluzek, Erik, and Luo, Yiqi. Matrix approach to land carbon cycle modeling: A case study with the Community Land Model. United Kingdom: N. p., 2017. Web. doi:10.1111/gcb.13948.
Huang, Yuanyuan, Lu, Xingjie, Shi, Zheng, Lawrence, David, Koven, Charles D., Xia, Jianyang, Du, Zhenggang, Kluzek, Erik, & Luo, Yiqi. Matrix approach to land carbon cycle modeling: A case study with the Community Land Model. United Kingdom. doi:10.1111/gcb.13948.
Huang, Yuanyuan, Lu, Xingjie, Shi, Zheng, Lawrence, David, Koven, Charles D., Xia, Jianyang, Du, Zhenggang, Kluzek, Erik, and Luo, Yiqi. 2017. "Matrix approach to land carbon cycle modeling: A case study with the Community Land Model". United Kingdom. doi:10.1111/gcb.13948.
@article{osti_1410578,
title = {Matrix approach to land carbon cycle modeling: A case study with the Community Land Model},
author = {Huang, Yuanyuan and Lu, Xingjie and Shi, Zheng and Lawrence, David and Koven, Charles D. and Xia, Jianyang and Du, Zhenggang and Kluzek, Erik and Luo, Yiqi},
abstractNote = {},
doi = {10.1111/gcb.13948},
journal = {Global Change Biology},
number = 3,
volume = 24,
place = {United Kingdom},
year = 2017,
month =
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on November 28, 2018
Publisher's Accepted Manuscript

Save / Share:
  • Land carbon sensitivity to atmospheric CO 2 concentration (β L) and climate warming (γ L) is a crucial part of carbon-climate feedbacks in the earth system. Using the Community Land Model version 4 with a coupled carbon-nitrogen cycle, we examine whether the inclusion of a dynamic global vegetation model (CNDV) significantly changes the land carbon sensitivity from that obtained with prescribed vegetation cover (CN). For decadal timescale in the late twentieth century, β L is not substantially different between the two models but γ L of CNDV is stronger (more negative) than that of CN. The main reason for themore » difference in γL is not the concurrent change in vegetation cover driving the carbon dynamics, but rather the smaller nitrogen constraint on plant growth in CNDV compared with CN, which arises from the deviation of CNDV's near-equilibrium vegetation distribution from CN’s prescribed, historical land cover. The smaller nitrogen constraint makes the enhanced nitrogen mineralization with warming less effective in stimulating plant productivity to counter moisture stress in a warmer climate, leading to a more negative γ L. This represents a new indirect pathway that has not been identified for dynamic vegetation in the coupled carbon-nitrogen cycle to affect the terrestrial carbon-climate feedbacks in the earth system.« less
  • Land carbon sensitivity to atmospheric CO 2 concentration (β L) and climate warming (γ L) is a crucial part of carbon-climate feedbacks in the earth system. Using the Community Land Model version 4 with a coupled carbon-nitrogen cycle, we examine whether the inclusion of a dynamic global vegetation model (CNDV) significantly changes the land carbon sensitivity from that obtained with prescribed vegetation cover (CN). For decadal timescale in the late twentieth century, β L is not substantially different between the two models but γ L of CNDV is stronger (more negative) than that of CN. The main reason for themore » difference in γL is not the concurrent change in vegetation cover driving the carbon dynamics, but rather the smaller nitrogen constraint on plant growth in CNDV compared with CN, which arises from the deviation of CNDV's near-equilibrium vegetation distribution from CN’s prescribed, historical land cover. The smaller nitrogen constraint makes the enhanced nitrogen mineralization with warming less effective in stimulating plant productivity to counter moisture stress in a warmer climate, leading to a more negative γ L. This represents a new indirect pathway that has not been identified for dynamic vegetation in the coupled carbon-nitrogen cycle to affect the terrestrial carbon-climate feedbacks in the earth system.« less
  • The use of full-fuel-cycle analysis as a scientific, economic, and policy tool for the evaluation of alternative sources of transportation energy has become increasingly widespread. However, consistent methods for performance of these types of analyses are only now becoming recognized and utilized. The work presented here provides a case study of full-fuel-cycle analysis methods applied to the evaluation of gasoline in the southeastern region of the United States. Results of the study demonstrate the significance of nonvehicle processes, such as fuel refining, in terms of energy expenditure and emissions production. Unique to this work is the application of the MOBILE5more » mobile emissions model in the full-fuel-cycle analysis. Estimates of direct and indirect greenhouse gas production are also presented and discussed using the full-fuel-cycle analysis method.« less