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Title: Challenging terrestrial biosphere models with data from the long-term multifactor Prairie Heating and CO 2 enrichment experiment

Abstract

Multi-factor experiments are often advocated as important for advancing terrestrial biosphere models (TBMs), yet to date such models have only been tested against single-factor experiments. We applied 10 TBMs to the multi-factor Prairie Heating and CO 2 Enrichment (PHACE) experiment in Wyoming, USA. Our goals were to investigate how multi-factor experiments can be used to constrain models, and to identify a road map for model improvement. We found models performed poorly in ambient conditions; there was a wide spread in simulated above-ground net primary productivity (range: 31-390 g C m -2 yr -1). Comparison with data highlighted model failures particularly in respect to carbon allocation, phenology, and the impact of water stress on phenology. Performance against single-factors was also relatively poor. In addition, similar responses were predicted for different reasons across models: there were large differences among models in sensitivity to water stress and, among the nitrogen cycle models, nitrogen availability during the experiment. Models were also unable to capture observed treatment effects on phenology: they over-estimated the effect of warming on leaf onset and did not allow CO 2-induced water savings to extend growing season length. Observed interactive (CO 2 x warming) treatment effects were subtle and contingent onmore » water stress, phenology and species composition. Since the models did not correctly represent these processes under ambient and single-factor conditions, little extra information was gained by comparing model predictions against interactive responses. Finally, we outline a series of key areas in which this and future experiments could be used to improve model predictions of grassland responses to global change.« less

Authors:
ORCiD logo [1]; ORCiD logo [2];  [3];  [4];  [5];  [6];  [7];  [8];  [9];  [10];  [11];  [4];  [5];  [9];  [12];  [13];  [14];  [2];  [15]; ORCiD logo [16] more »;  [2];  [17];  [18];  [3] « less
  1. Macquarie Univ., North Ryde, NSW (Australia)
  2. Western Sydney Univ., Penrith, NSW (Australia)
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  4. Max Planck Institute for Biogeochemistry, Jena (Germany)
  5. Colorado State Univ., Fort Collins, CO (United States)
  6. Univ. Paris-Saclay, Gif-sur-Yvette (France)
  7. Univ. of Exeter, Exeter (United Kingdom)
  8. Senckenberg Biodiversity and Climate Research Centre (BiK-F), Frankfurt (Germany); Goethe-Univ., Frankfurt (Germany)
  9. Univ. of Illinois, Urbana, IL (United States)
  10. Univ. of Oklahoma, Norman, OK (United States)
  11. CSIRO Oceans and Atmosphere, VIC (Australia)
  12. CSIRO Oceans and Atmosphere, Aspendale, VIC (Australia)
  13. Senckenberg Biodiversity and Climate Research Centre (BiK-F), Frankfurt (Germany)
  14. East China Normal Univ., Shanghai (China)
  15. United States Dept. of Agriculture, Fort Collins, CO (United States)
  16. Lancaster Univ., Lancaster (United Kingdom)
  17. The Univ. of Sydney, Sydney, NSW (Australia)
  18. Univ. of Wyoming, Laramie, WY (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23); National Aeronautic and Space Administration (NASA); National Science Foundation (NSF)
OSTI Identifier:
1349613
Alternate Identifier(s):
OSTI ID: 1373820
Grant/Contract Number:
AC05-00OR22725; SC0016323; NNX14AD94G; 647204; AGS 12-43071
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Global Change Biology
Additional Journal Information:
Journal Volume: 23; Journal Issue: 9; Journal ID: ISSN 1354-1013
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; carbon dioxide; FACE; grassland; PHACE; temperature; models

Citation Formats

De Kauwe, Martin G., Medlyn, Belinda E., Walker, Anthony P., Zaehle, Sönke, Asao, Shinichi, Guenet, Bertrand, Harper, Anna B., Hickler, Thomas, Jain, Atul K., Luo, Yiqi, Lu, Xingjie, Luus, Kristina, Parton, William J., Shu, Shijie, Wang, Ying-Ping, Werner, Christian, Xia, Jianyang, Pendall, Elise, Morgan, Jack A., Ryan, Edmund M., Carrillo, Yolima, Dijkstra, Feike A., Zelikova, Tamara J., and Norby, Richard J. Challenging terrestrial biosphere models with data from the long-term multifactor Prairie Heating and CO2 enrichment experiment. United States: N. p., 2017. Web. doi:10.1111/gcb.13643.
De Kauwe, Martin G., Medlyn, Belinda E., Walker, Anthony P., Zaehle, Sönke, Asao, Shinichi, Guenet, Bertrand, Harper, Anna B., Hickler, Thomas, Jain, Atul K., Luo, Yiqi, Lu, Xingjie, Luus, Kristina, Parton, William J., Shu, Shijie, Wang, Ying-Ping, Werner, Christian, Xia, Jianyang, Pendall, Elise, Morgan, Jack A., Ryan, Edmund M., Carrillo, Yolima, Dijkstra, Feike A., Zelikova, Tamara J., & Norby, Richard J. Challenging terrestrial biosphere models with data from the long-term multifactor Prairie Heating and CO2 enrichment experiment. United States. doi:10.1111/gcb.13643.
De Kauwe, Martin G., Medlyn, Belinda E., Walker, Anthony P., Zaehle, Sönke, Asao, Shinichi, Guenet, Bertrand, Harper, Anna B., Hickler, Thomas, Jain, Atul K., Luo, Yiqi, Lu, Xingjie, Luus, Kristina, Parton, William J., Shu, Shijie, Wang, Ying-Ping, Werner, Christian, Xia, Jianyang, Pendall, Elise, Morgan, Jack A., Ryan, Edmund M., Carrillo, Yolima, Dijkstra, Feike A., Zelikova, Tamara J., and Norby, Richard J. Wed . "Challenging terrestrial biosphere models with data from the long-term multifactor Prairie Heating and CO2 enrichment experiment". United States. doi:10.1111/gcb.13643. https://www.osti.gov/servlets/purl/1349613.
@article{osti_1349613,
title = {Challenging terrestrial biosphere models with data from the long-term multifactor Prairie Heating and CO2 enrichment experiment},
author = {De Kauwe, Martin G. and Medlyn, Belinda E. and Walker, Anthony P. and Zaehle, Sönke and Asao, Shinichi and Guenet, Bertrand and Harper, Anna B. and Hickler, Thomas and Jain, Atul K. and Luo, Yiqi and Lu, Xingjie and Luus, Kristina and Parton, William J. and Shu, Shijie and Wang, Ying-Ping and Werner, Christian and Xia, Jianyang and Pendall, Elise and Morgan, Jack A. and Ryan, Edmund M. and Carrillo, Yolima and Dijkstra, Feike A. and Zelikova, Tamara J. and Norby, Richard J.},
abstractNote = {Multi-factor experiments are often advocated as important for advancing terrestrial biosphere models (TBMs), yet to date such models have only been tested against single-factor experiments. We applied 10 TBMs to the multi-factor Prairie Heating and CO2 Enrichment (PHACE) experiment in Wyoming, USA. Our goals were to investigate how multi-factor experiments can be used to constrain models, and to identify a road map for model improvement. We found models performed poorly in ambient conditions; there was a wide spread in simulated above-ground net primary productivity (range: 31-390 g C m-2 yr-1). Comparison with data highlighted model failures particularly in respect to carbon allocation, phenology, and the impact of water stress on phenology. Performance against single-factors was also relatively poor. In addition, similar responses were predicted for different reasons across models: there were large differences among models in sensitivity to water stress and, among the nitrogen cycle models, nitrogen availability during the experiment. Models were also unable to capture observed treatment effects on phenology: they over-estimated the effect of warming on leaf onset and did not allow CO2-induced water savings to extend growing season length. Observed interactive (CO2 x warming) treatment effects were subtle and contingent on water stress, phenology and species composition. Since the models did not correctly represent these processes under ambient and single-factor conditions, little extra information was gained by comparing model predictions against interactive responses. Finally, we outline a series of key areas in which this and future experiments could be used to improve model predictions of grassland responses to global change.},
doi = {10.1111/gcb.13643},
journal = {Global Change Biology},
number = 9,
volume = 23,
place = {United States},
year = {Wed Feb 01 00:00:00 EST 2017},
month = {Wed Feb 01 00:00:00 EST 2017}
}

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  • The seasonal-cycle amplitude (SCA) of the atmosphere–ecosystem carbon dioxide (CO 2) exchange rate is a useful metric of the responsiveness of the terrestrial biosphere to environmental variations. It is unclear, however, what underlying mechanisms are responsible for the observed increasing trend of SCA in atmospheric CO 2 concentration. Using output data from the Multi-scale Terrestrial Model Intercomparison Project (MsTMIP), we investigated how well the SCA of atmosphere–ecosystem CO 2 exchange was simulated with 15 contemporary terrestrial ecosystem models during the period 1901–2010. Also, we made attempt to evaluate the contributions of potential mechanisms such as atmospheric CO 2, climate, land-use,more » and nitrogen deposition, through factorial experiments using different combinations of forcing data. Under contemporary conditions, the simulated global-scale SCA of the cumulative net ecosystem carbon flux of most models was comparable in magnitude with the SCA of atmospheric CO 2 concentrations. Results from factorial simulation experiments showed that elevated atmospheric CO 2 exerted a strong influence on the seasonality amplification. When the model considered not only climate change but also land-use and atmospheric CO 2 changes, the majority of the models showed amplification trends of the SCAs of photosynthesis, respiration, and net ecosystem production (+0.19 % to +0.50 % yr -1). In the case of land-use change, it was difficult to separate the contribution of agricultural management to SCA because of inadequacies in both the data and models. The simulated amplification of SCA was approximately consistent with the observational evidence of the SCA in atmospheric CO 2 concentrations. Large inter-model differences remained, however, in the simulated global tendencies and spatial patterns of CO 2 exchanges. Further studies are required to identify a consistent explanation for the simulated and observed amplification trends, including their underlying mechanisms. Nevertheless, this study implied that monitoring of ecosystem seasonality would provide useful insights concerning ecosystem dynamics.« less
  • The seasonal-cycle amplitude (SCA) of the atmosphere–ecosystem carbon dioxide (CO 2) exchange rate is a useful metric of the responsiveness of the terrestrial biosphere to environmental variations. It is unclear, however, what underlying mechanisms are responsible for the observed increasing trend of SCA in atmospheric CO 2 concentration. Using output data from the Multi-scale Terrestrial Model Intercomparison Project (MsTMIP), we investigated how well the SCA of atmosphere–ecosystem CO 2 exchange was simulated with 15 contemporary terrestrial ecosystem models during the period 1901–2010. Also, we made attempt to evaluate the contributions of potential mechanisms such as atmospheric CO 2, climate, land-use,more » and nitrogen deposition, through factorial experiments using different combinations of forcing data. Under contemporary conditions, the simulated global-scale SCA of the cumulative net ecosystem carbon flux of most models was comparable in magnitude with the SCA of atmospheric CO 2 concentrations. Results from factorial simulation experiments showed that elevated atmospheric CO 2 exerted a strong influence on the seasonality amplification. When the model considered not only climate change but also land-use and atmospheric CO 2 changes, the majority of the models showed amplification trends of the SCAs of photosynthesis, respiration, and net ecosystem production (+0.19 % to +0.50 % yr –1). In the case of land-use change, it was difficult to separate the contribution of agricultural management to SCA because of inadequacies in both the data and models. The simulated amplification of SCA was approximately consistent with the observational evidence of the SCA in atmospheric CO 2 concentrations. Large inter-model differences remained, however, in the simulated global tendencies and spatial patterns of CO 2 exchanges. Further studies are required to identify a consistent explanation for the simulated and observed amplification trends, including their underlying mechanisms. Furthermore, this study implied that monitoring of ecosystem seasonality would provide useful insights concerning ecosystem dynamics.« less
  • The seasonal-cycle amplitude (SCA) of the atmosphere–ecosystem carbon dioxide (CO 2) exchange rate is a useful metric of the responsiveness of the terrestrial biosphere to environmental variations. It is unclear, however, what underlying mechanisms are responsible for the observed increasing trend of SCA in atmospheric CO 2 concentration. Using output data from the Multi-scale Terrestrial Model Intercomparison Project (MsTMIP), we investigated how well the SCA of atmosphere–ecosystem CO 2 exchange was simulated with 15 contemporary terrestrial ecosystem models during the period 1901–2010. Also, we made attempt to evaluate the contributions of potential mechanisms such as atmospheric CO 2, climate, land-use,more » and nitrogen deposition, through factorial experiments using different combinations of forcing data. Under contemporary conditions, the simulated global-scale SCA of the cumulative net ecosystem carbon flux of most models was comparable in magnitude with the SCA of atmospheric CO 2 concentrations. Results from factorial simulation experiments showed that elevated atmospheric CO 2 exerted a strong influence on the seasonality amplification. When the model considered not only climate change but also land-use and atmospheric CO2 changes, the majority of the models showed amplification trends of the SCAs of photosynthesis, respiration, and net ecosystem production (+0.19 % to +0.50 % yr –1). In the case of land-use change, it was difficult to separate the contribution of agricultural management to SCA because of inadequacies in both the data and models. The simulated amplification of SCA was approximately consistent with the observational evidence of the SCA in atmospheric CO 2 concentrations. Large inter-model differences remained, however, in the simulated global tendencies and spatial patterns of CO 2 exchanges. Further studies are required to identify a consistent explanation for the simulated and observed amplification trends, including their underlying mechanisms. Furthermore, this study implied that monitoring of ecosystem seasonality would provide useful insights concerning ecosystem dynamics.« less
  • Large uncertainty exists in model projections of the land carbon (C) sink response to increasing atmospheric CO 2. Free-Air CO 2 Enrichment (FACE) experiments lasting a decade or more have investigated ecosystem responses to a step change in atmospheric CO 2 concentration. To interpret FACE results in the context of gradual increases in atmospheric CO 2 over decades to centuries, we used a suite of seven models to simulate the Duke and Oak Ridge FACE experiments extended for 300 years of CO 2 enrichment. We also determine key modeling assumptions that drive divergent projections of terrestrial C uptake and evaluatemore » whether these assumptions can be constrained by experimental evidence. All models simulated increased terrestrial C pools resulting from CO 2 enrichment, though there was substantial variability in quasi-equilibrium C sequestration and rates of change. In two of two models that assume that plant nitrogen (N) uptake is solely a function of soil N supply, the net primary production response to elevated CO 2 became progressively N limited. In four of five models that assume that N uptake is a function of both soil N supply and plant N demand, elevated CO 2 led to reduced ecosystem N losses and thus progressively relaxed nitrogen limitation. Many allocation assumptions resulted in increased wood allocation relative to leaves and roots which reduced the vegetation turnover rate and increased C sequestration. Additionally, self-thinning assumptions had a substantial impact on C sequestration in two models. As a result, accurate representation of N process dynamics (in particular N uptake), allocation, and forest self-thinning is key to minimizing uncertainty in projections of future C sequestration in response to elevated atmospheric CO 2.« less