Oak Ridge National Laboratory
24th May 2017
Challenging Terrestrial Biosphere Models with Data from the Long-Term Multi-Factor Prairie Heating and CO2 Enrichment Experiment.
Researchers challenged ten carbon-cycle models, often used to simulate ecosystem responses to environmental change, to simulate a grassland in Wyoming subjected to experimental CO2 enrichment and increased temperature.
Carbon-cycle models used for regional or global simulations are known to perform poorly when used to simulate a specific site. Researchers identified a number of areas for carbon-cycle model improvement. Model development to improve the accuracy of grassland simulations should focus on improving the realism of the controls of water availability on growth and soil nitrogen in these non-forested ecosystems.
Multi-factor experiments are often advocated as important for advancing terrestrial biosphere models, but this claim is rarely tested. As part of the DOE supported Free Air CO2 Enrichment Model Data Synthesis (FACE-MDS) project, researchers aimed to investigate how a CO2 enrichment and warming experiment can be used to identify a road map for carbon-cycle model improvement. Researchers found that the ten models tested simulated a wide spread in annual above-ground growth in current environmental conditions (i.e. not experimentally manipulated conditions). Comparison with data highlighted that the reasons for these model shortcomings were poor representation of: carbon allocation, seasonality of growth, the impact of water stress on the seasonality of growth, sensitivity to water stress, and soil nitrogen availability. In response to the experimentally manipulated conditions, models generally over-estimated the effect of warming on leaf onset and were lacking the mechanism to allow CO2-induced water savings to extend the growing season. However, when both CO2 and warming were increased, the observed effects of the experimental increase in CO2 and temperature on plant growth 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. The study outlines a series of key areas in which this and future experiments could be used to improve model predictions of grassland responses to global change.
Contacts (BER PM)
Oak Ridge National Laboratory
DOE Office of Science BER, FACE Model Data Synthesis project
De Kauwe, M. G. et al. Challenging terrestrial biosphere models with data from the long-term multifactor Prairie Heating and CO2 Enrichment experiment. Global Change Biology, awaiting page numbers (2017). doi:10.1111/gcb.13643
USDA PHACE experiment
Publication led by Martin G. De Kauwe of Macquarie University as part of the ORNL FACE Model Data Synthesis project funded by DOE Office Of Science, Biological and Environmental Research.