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

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.