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Title: Transient dynamics of terrestrial carbon storage: Mathematical foundation and its applications

Abstract

Terrestrial ecosystems have absorbed roughly 30% of anthropogenic CO2 emissions over the past decades, but it is unclear whether this carbon (C) sink will endure into the future. Despite extensive modeling and experimental and observational studies, what fundamentally determines transient dynamics of terrestrial C storage under global change is still not very clear. Here we develop a new framework for understanding transient dynamics of terrestrial C storage through mathematical analysis and numerical experiments. Our analysis indicates that the ultimate force driving ecosystem C storage change is the C storage capacity, which is jointly determined by ecosystem C input (e.g., net primary production, NPP) and residence time. Since both C input and residence time vary with time, the C storage capacity is time-dependent and acts as a moving attractor that actual C storage chases. The rate of change in C storage is proportional to the C storage potential, which is the difference between the current storage and the storage capacity. The C storage capacity represents instantaneous responses of the land C cycle to external forcing, whereas the C storage potential represents the internal capability of the land C cycle to influence the C change trajectory in the next time step. Themore » influence happens through redistribution of net C pool changes in a network of pools with different residence times. Moreover, this and our other studies have demonstrated that one matrix equation can replicate simulations of most land C cycle models (i.e., physical emulators). As a result, simulation outputs of those models can be placed into a three-dimensional (3-D) parameter space to measure their differences. The latter can be decomposed into traceable components to track the origins of model uncertainty. In addition, the physical emulators make data assimilation computationally feasible so that both C flux- and pool-related datasets can be used to better constrain model predictions of land C sequestration. Overall, this new mathematical framework offers new approaches to understanding, evaluating, diagnosing, and improving land C cycle models.« less

Authors:
 [1];  [2];  [3];  [4];  [2];  [2];  [2];  [5];  [2];  [6];  [7];  [8];  [9];  [10];  [11];  [12];  [13]; ORCiD logo [14];  [3]
+ Show Author Affiliations
  1. Univ. of Oklahoma, Norman, OK (United States); Tsinghua Univ., Beijing (China)
  2. Univ. of Oklahoma, Norman, OK (United States)
  3. CSIRO Oceans and Atmosphere, Aspendale, VIC (Australia)
  4. East China Normal Univ., Shanghai (China)
  5. Microsoft Research, Cambridge (United Kingdom)
  6. Stanford Univ., Stanford, CA (United States); Lund Univ., Lund (Sweden)
  7. Univ. of Texas, Arlington, TX (United States)
  8. McGill Univ., Montreal, QC (Canada)
  9. Univ. of California, Davis, CA (United States)
  10. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  11. Western Sydney Univ., Penrith, NSW (Australia)
  12. Chinese Academy of Sciences (CAS), Beijing (China)
  13. Imperial College, London (United Kingdom)
  14. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1339799
Alternate Identifier(s):
OSTI ID: 1376544
Report Number(s):
PNNL-SA-118267
Journal ID: ISSN 1726-4189
Grant/Contract Number:  
AC05-76RL01830; AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Biogeosciences (Online)
Additional Journal Information:
Journal Name: Biogeosciences (Online); Journal Volume: 14; Journal Issue: 1; Journal ID: ISSN 1726-4189
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; carbon cycle; disequilibrium; equilibrium; model intercomparison; traceability analysis

Citation Formats

Luo, Yiqi, Shi, Zheng, Lu, Xingjie, Xia, Jianyang, Liang, Junyi, Jiang, Jiang, Wang, Ying, Smith, Matthew J., Jiang, Lifen, Ahlstrom, Anders, Chen, Benito, Hararuk, Oleksandra, Hastings, Alan, Hoffman, Forrest, Medlyn, Belinda, Niu, Shuli, Rasmussen, Martin, Todd-Brown, Katherine, and Wang, Ying -Ping. Transient dynamics of terrestrial carbon storage: Mathematical foundation and its applications. United States: N. p., 2017. Web. doi:10.5194/bg-14-145-2017.
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Luo, Yiqi, Shi, Zheng, Lu, Xingjie, Xia, Jianyang, Liang, Junyi, Jiang, Jiang, Wang, Ying, Smith, Matthew J., Jiang, Lifen, Ahlstrom, Anders, Chen, Benito, Hararuk, Oleksandra, Hastings, Alan, Hoffman, Forrest, Medlyn, Belinda, Niu, Shuli, Rasmussen, Martin, Todd-Brown, Katherine, & Wang, Ying -Ping. Transient dynamics of terrestrial carbon storage: Mathematical foundation and its applications. United States. https://doi.org/10.5194/bg-14-145-2017
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Luo, Yiqi, Shi, Zheng, Lu, Xingjie, Xia, Jianyang, Liang, Junyi, Jiang, Jiang, Wang, Ying, Smith, Matthew J., Jiang, Lifen, Ahlstrom, Anders, Chen, Benito, Hararuk, Oleksandra, Hastings, Alan, Hoffman, Forrest, Medlyn, Belinda, Niu, Shuli, Rasmussen, Martin, Todd-Brown, Katherine, and Wang, Ying -Ping. Thu . "Transient dynamics of terrestrial carbon storage: Mathematical foundation and its applications". United States. https://doi.org/10.5194/bg-14-145-2017. https://www.osti.gov/servlets/purl/1339799.
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@article{osti_1339799,
title = {Transient dynamics of terrestrial carbon storage: Mathematical foundation and its applications},
author = {Luo, Yiqi and Shi, Zheng and Lu, Xingjie and Xia, Jianyang and Liang, Junyi and Jiang, Jiang and Wang, Ying and Smith, Matthew J. and Jiang, Lifen and Ahlstrom, Anders and Chen, Benito and Hararuk, Oleksandra and Hastings, Alan and Hoffman, Forrest and Medlyn, Belinda and Niu, Shuli and Rasmussen, Martin and Todd-Brown, Katherine and Wang, Ying -Ping},
abstractNote = {Terrestrial ecosystems have absorbed roughly 30% of anthropogenic CO2 emissions over the past decades, but it is unclear whether this carbon (C) sink will endure into the future. Despite extensive modeling and experimental and observational studies, what fundamentally determines transient dynamics of terrestrial C storage under global change is still not very clear. Here we develop a new framework for understanding transient dynamics of terrestrial C storage through mathematical analysis and numerical experiments. Our analysis indicates that the ultimate force driving ecosystem C storage change is the C storage capacity, which is jointly determined by ecosystem C input (e.g., net primary production, NPP) and residence time. Since both C input and residence time vary with time, the C storage capacity is time-dependent and acts as a moving attractor that actual C storage chases. The rate of change in C storage is proportional to the C storage potential, which is the difference between the current storage and the storage capacity. The C storage capacity represents instantaneous responses of the land C cycle to external forcing, whereas the C storage potential represents the internal capability of the land C cycle to influence the C change trajectory in the next time step. The influence happens through redistribution of net C pool changes in a network of pools with different residence times. Moreover, this and our other studies have demonstrated that one matrix equation can replicate simulations of most land C cycle models (i.e., physical emulators). As a result, simulation outputs of those models can be placed into a three-dimensional (3-D) parameter space to measure their differences. The latter can be decomposed into traceable components to track the origins of model uncertainty. In addition, the physical emulators make data assimilation computationally feasible so that both C flux- and pool-related datasets can be used to better constrain model predictions of land C sequestration. Overall, this new mathematical framework offers new approaches to understanding, evaluating, diagnosing, and improving land C cycle models.},
doi = {10.5194/bg-14-145-2017},
journal = {Biogeosciences (Online)},
number = 1,
volume = 14,
place = {United States},
year = {Thu Jan 12 00:00:00 EST 2017},
month = {Thu Jan 12 00:00:00 EST 2017}
}
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Publisher's Version of Record
https://doi.org/10.5194/bg-14-145-2017
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    Maximum carbon uptake rate dominates the interannual variability of global net ecosystem exchange
    journal, July 2019

    • Fu, Zheng; Stoy, Paul C.; Poulter, Benjamin
    • Global Change Biology, Vol. 25, Issue 10
    • DOI: 10.1111/gcb.14731

    Ages and transit times as important diagnostics of model performance for predicting carbon dynamics in terrestrial vegetation models
    journal, January 2018

    • Ceballos-Núñez, Verónika; Richardson, Andrew D.; Sierra, Carlos A.
    • Biogeosciences, Vol. 15, Issue 5
    • DOI: 10.5194/bg-15-1607-2018

    A Large Committed Long-Term Sink of Carbon due to Vegetation Dynamics
    text, January 2018

    Ages and transit times as important diagnostics of model performance for predicting carbon dynamics in terrestrial vegetation models
    posted_content, August 2017

    • Ceballos-Núñez, Verónika; Richardson, Andrew D.; Sierra, Carlos A.
    • Biogeosciences Discussions
    • DOI: 10.5194/bg-2017-308

    Greenhouse Gas Concentration and Volcanic Eruptions Controlled the Variability of Terrestrial Carbon Uptake Over the Last Millennium
    journal, June 2019

    • Zhang, Xuanze; Peng, Shushi; Ciais, Philippe
    • Journal of Advances in Modeling Earth Systems, Vol. 11, Issue 6
    • DOI: 10.1029/2018ms001566

    Limits to growth of forest biomass carbon sink under climate change
    journal, July 2018

    Approaching the potential of model-data comparisons of global land carbon storage
    journal, March 2019

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