Rights statement: This is the peer reviewed version of the following article: De Kauwe, M. G., Medlyn, B. E., Walker, A. P., Zaehle, S., Asao, S., Guenet, B., Harper, A. B., Hickler, T., Jain, A. K., Luo, Y., Lu, X., Luus, K., Parton, W. J., Shu, S., Wang, Y.-P., Werner, C., Xia, J., Pendall, E., Morgan, J. A., Ryan, E. M., Carrillo, Y., Dijkstra, F. A., Zelikova, T. J. and Norby, R. J. (2017), Challenging terrestrial biosphere models with data from the long-term multifactor Prairie Heating and CO2 Enrichment experiment. Glob Change Biol, 23: 3623–3645. doi:10.1111/gcb.13643 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/GCB.13643/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Challenging terrestrial biosphere models with data from the long-term multifactor Prairie Heating and CO2 Enrichment experiment
AU - De Kauwe, Martin
AU - Medlyn, Belinda
AU - Walker, Anthony
AU - Zaehle, Sonke
AU - Asao, Shinichi
AU - Guenet, Bertrand
AU - Harper, Anna
AU - Hickler, Thomas
AU - Jain, Atul
AU - Luo, Yiqi
AU - Lu, Chris
AU - Luus, Kristina
AU - Parton, William
AU - Shu, Shijie
AU - Wang, Ying-Ping
AU - Werner, Christian
AU - Xia, Jianyang
AU - Pendall, Elise
AU - Morgan, Jack
AU - Ryan, Edmund
AU - Carrillo, Yolima
AU - Dijkstra, Feike
AU - Norby, Richard
N1 - This is the peer reviewed version of the following article: De Kauwe, M. G., Medlyn, B. E., Walker, A. P., Zaehle, S., Asao, S., Guenet, B., Harper, A. B., Hickler, T., Jain, A. K., Luo, Y., Lu, X., Luus, K., Parton, W. J., Shu, S., Wang, Y.-P., Werner, C., Xia, J., Pendall, E., Morgan, J. A., Ryan, E. M., Carrillo, Y., Dijkstra, F. A., Zelikova, T. J. and Norby, R. J. (2017), Challenging terrestrial biosphere models with data from the long-term multifactor Prairie Heating and CO2 Enrichment experiment. Glob Change Biol, 23: 3623–3645. doi:10.1111/gcb.13643 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/GCB.13643/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
PY - 2017/9
Y1 - 2017/9
N2 - 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. 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.
AB - 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. 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.
KW - allocation
KW - carbon dioxide
KW - FACE
KW - grassland
KW - models
KW - PHACE
KW - phenology
KW - soil moisture
KW - temperature
U2 - 10.1111/gcb.13643
DO - 10.1111/gcb.13643
M3 - Journal article
VL - 23
SP - 3623
EP - 3645
JO - Global Change Biology
JF - Global Change Biology
SN - 1354-1013
IS - 9
ER -