Challenging terrestrial biosphere models with data from the long‐term multifactor Prairie Heating and CO2 Enrichment experiment
2017
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.
Multifactor 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 multifactor Prairie Heating and CO₂ Enrichment (PHACE) experiment in Wyoming, USA. Our goals were to investigate how multifactor 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 with respect to carbon allocation, phenology, and the impact of water stress on phenology. Performance against the observations from single‐factors treatments 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 N cycle models, N availability during the experiment. Models were also unable to capture observed treatment effects on phenology: they overestimated the effect of warming on leaf onset and did not allow CO₂‐induced water savings to extend the growing season length. Observed interactive (CO₂ × warming) treatment effects were subtle and contingent on water stress, phenology, and species composition. As 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.
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