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Challenging terrestrial biosphere models with data from the long-term multifactor Prairie Heating and CO2 Enrichment experiment

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Zaehle,  Sönke
Terrestrial Biosphere Modelling, Dr. Sönke Zähle, Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;
Terrestrial Biosphere Modelling, Dr. Sönke Zähle, Department Biogeochemical Integration, Prof. Dr. Martin Heimann, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Luus,  Kristina
Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Citation

Kauwe, M. G. D., Medlyn, B. E., Walker, A. P., Zaehle, S., Asao, S., Guenet, B., et al. (2017). Challenging terrestrial biosphere models with data from the long-term multifactor Prairie Heating and CO2 Enrichment experiment. Global Change Biology, 23(9), 3623-3645. doi:10.1111/gcb.13643.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002C-586C-4
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 current 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 observations from single-factors experiments 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.