De Kauwe, Martin and Medlyn, Belinda and Walker, Anthony and Zaehle, Sonke and Asao, Shinichi and Guenet, Bertrand and Harper, Anna and Hickler, Thomas and Jain, Atul and Luo, Yiqi and Lu, Chris and Luus, Kristina and Parton, William and Shu, Shijie and Wang, Ying-Ping and Werner, Christian and Xia, Jianyang and Pendall, Elise and Morgan, Jack and Ryan, Edmund and Carrillo, Yolima and Dijkstra, Feike and Norby, Richard (2017) Challenging terrestrial biosphere models with data from the long-term multifactor Prairie Heating and CO2 Enrichment experiment. Global Change Biology, 23 (9). pp. 3623-3645. ISSN 1354-1013
DeKauweGCB_submittedversion_Nov2016.pdf - Accepted Version
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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 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.