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  Comprehensive ecosystem model-data synthesis using multiple data sets at two temperate forest free-air CO2 enrichment experiments: Model performance at ambient CO2 concentration

Walker, A. P., Hanson, P. J., Kauwe, M. G. D., Medlyn, B. E., Zaehle, S., Asao, S., et al. (2014). Comprehensive ecosystem model-data synthesis using multiple data sets at two temperate forest free-air CO2 enrichment experiments: Model performance at ambient CO2 concentration. Journal of Geophysical Research: Biogeosciences, 119(5), 937-964. doi:10.1002/2013JG002553.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0019-D261-8 Version Permalink: https://hdl.handle.net/21.11116/0000-000A-4396-1
Genre: Journal Article

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http://onlinelibrary.wiley.com/wol1/doi/10.1002/2013JG002553/abstract (Publisher version)
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 Creators:
Walker, Anthony P., Author
Hanson, Paul J., Author
Kauwe, Martin G. De, Author
Medlyn, Belinda E., Author
Zaehle, Sönke1, 2, Author           
Asao, Shinichi, Author
Dietze, Michael, Author
Hickler, Thomas, Author
Huntingford, Chris, Author
Iversen, Colleen M., Author
Jain, Atul, Author
Lomas, Mark, Author
Luo, Yiqi, Author
McCarthy, Heather, Author
Parton, William J., Author
Prentice, I. Colin, Author
Thornton, Peter E., Author
Wang, Shusen, Author
Wang, Ying-Ping, Author
Warlind, David, Author
Weng, Ensheng, AuthorWarren, Jeffrey M., AuthorWoodward, F. Ian, AuthorOren, Ram, AuthorNorby, Richard J., Author more..
Affiliations:
1Terrestrial Biosphere Modelling , Dr. Sönke Zähle, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society, ou_1938309              
2Terrestrial Biosphere Modelling , Dr. Sönke Zähle, Department Biogeochemical Integration, Prof. Dr. Martin Heimann, Max Planck Institute for Biogeochemistry, Max Planck Society, ou_1497787              

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 Abstract: Free-air CO2 enrichment (FACE) experiments provide a remarkable wealth of data which can be
used to evaluate and improve terrestrial ecosystem models (TEMs). In the FACE model-data synthesis project,
11 TEMs were applied to two decadelong FACE experiments in temperate forests of the southeastern U.S.—the
evergreen Duke Forest and the deciduous Oak Ridge Forest. In this baseline paper, we demonstrate our
approach to model-data synthesis by evaluating the models’ ability to reproduce observed net primary
productivity (NPP), transpiration, and leaf area index (LAI) in ambient CO2 treatments. Model outputs were
compared against observations using a range of goodness-of-fit statistics. Many models simulated annual NPP
and transpiration within observed uncertainty. We demonstrate, however, that high goodness-of-fit values do not
necessarily indicate a successful model, because simulation accuracy may be achieved through compensating
biases in component variables. For example, transpiration accuracy was sometimes achieved with compensating
biases in leaf area index and transpiration per unit leaf area. Our approach to model-data synthesis therefore
goes beyond goodness-of-fit to investigate the success of alternative representations of component processes.
Here we demonstrate this approach by comparing competing model hypotheses determining peak LAI. Of
three alternative hypotheses—(1) optimization to maximize carbon export, (2) increasing specific leaf area with
canopy depth, and (3) the pipe model—the pipe model produced peak LAI closest to the observations. This
example illustrates how data sets from intensive field experiments such as FACE can be used to reduce model uncertainty despite compensating biases by evaluating individual model assumptions.

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 Dates: 2014-04-252014-05-27
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: Other: BGC2061
DOI: 10.1002/2013JG002553
 Degree: -

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Title: Journal of Geophysical Research: Biogeosciences
  Other : J. Geophys. Res. - E
  Abbreviation : JGR-E
Source Genre: Journal
 Creator(s):
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Publ. Info: [Washington, DC] : American Geophysical Union
Pages: - Volume / Issue: 119 (5) Sequence Number: - Start / End Page: 937 - 964 Identifier: ISSN: 2169-8961
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000326920