Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Geosci. Model Dev., 10, 1873-1888, 2017
https://doi.org/10.5194/gmd-10-1873-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Development and technical paper
05 May 2017
Representing winter wheat in the Community Land Model (version 4.5)
Yaqiong Lu1,2, Ian N. Williams1, Justin E. Bagley1, Margaret S. Torn1,3, and Lara M. Kueppers1,3 1Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
2Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA
3Energy and Resources Group, University of California, Berkeley, Berkeley, California, USA
Abstract. Winter wheat is a staple crop for global food security, and is the dominant vegetation cover for a significant fraction of Earth's croplands. As such, it plays an important role in carbon cycling and land–atmosphere interactions in these key regions. Accurate simulation of winter wheat growth is not only crucial for future yield prediction under a changing climate, but also for accurately predicting the energy and water cycles for winter wheat dominated regions. We modified the winter wheat model in the Community Land Model (CLM) to better simulate winter wheat leaf area index, latent heat flux, net ecosystem exchange of CO2, and grain yield. These included schemes to represent vernalization as well as frost tolerance and damage. We calibrated three key parameters (minimum planting temperature, maximum crop growth days, and initial value of leaf carbon allocation coefficient) and modified the grain carbon allocation algorithm for simulations at the US Southern Great Plains ARM site (US-ARM), and validated the model performance at eight additional sites across North America. We found that the new winter wheat model improved the prediction of monthly variation in leaf area index, reduced latent heat flux, and net ecosystem exchange root mean square error (RMSE) by 41 and 35 % during the spring growing season. The model accurately simulated the interannual variation in yield at the US-ARM site, but underestimated yield at sites and in regions (northwestern and southeastern US) with historically greater yields by 35 %.

Citation: Lu, Y., Williams, I. N., Bagley, J. E., Torn, M. S., and Kueppers, L. M.: Representing winter wheat in the Community Land Model (version 4.5), Geosci. Model Dev., 10, 1873-1888, https://doi.org/10.5194/gmd-10-1873-2017, 2017.
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Short summary
Predicting winter wheat growth in the future climate scenarios is crucial for food security. We developed a winter wheat model in the Community Land Model to better predict winter wheat growth and grain production at multiple temporal and spatial scales. We validated the model and found that the new winter wheat model improved the prediction of winter wheat growth related variables during the spring growing season but underestimated yield in regions with historically greater yields.
Predicting winter wheat growth in the future climate scenarios is crucial for food security. We...
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