|CHEN, MING - University Of Minnesota
|GRIFFIS, TIMOTHY - University Of Minnesota
|WOOD, JEFFREY - University Of Minnesota
|XIAO, KE - University Of Minnesota
Submitted to: Journal of Geophysical Research-Biogeosciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/24/2015
Publication Date: 2/26/2015
Publication URL: https://handle.nal.usda.gov/10113/60455
Citation: Chen, M., Griffis, T.J., Baker, J.M., Wood, J.D., Xiao, K. 2015. Simulating crop phenology in the Community Land Model and its impact on energy and carbon fluxes. Journal of Geophysical Research: Biogeosciences. 120(2):310-325. DOI: 10.1002/2014JG002780.
Interpretive Summary: Global climate models (GCMs) are a valuable tool to predict the impact of rising greenhouse gas concentrations on climate. Among the key processes that they must simulate are the exchange of heat, water, and carbon dioxide between the surface and the atmosphere. To this point, most land-atmosphere schemes in such models have been developed with data from natural ecosystems, so they do not perform well in agricultural regions. Recently, however, several new land surface schemes have been developed to address this deficiency. We tested two of them (CLM4-Crop and CLM-CornSoy) against measured data from agricultural fields at Rosemount, MN. The results indicated that accuracy, particularly in predicting CO2 exchange, was heavily dependent on accurate representation of phenology, i.e. the timing of planting, emergence, and harvest. The models consistently overestimated early season growth, and also overestimated net (absorbed) radiation, probably due to unrealistic assumptions about the leaf angle distribution in corn and soybeans. Finslly, one of the models, CLM4-Crop, overestimated the fraction of roots in deep soil layers, which apparently caused it to underestimate soil moisture. These results will be useful in improving these models, leading to more accurate GCM results.
Technical Abstract: A reasonable representation of crop phenology and biophysical processes in land surface models is necessary to accurately simulate energy, water and carbon budgets at the field, regional, and global scales. However, the evaluation of crop models that can be coupled to earth system models is relatively rare. Here, we evaluated two such models (CLM4-Crop and CLM-CornSoy), both implemented within the Community Land Model (CLM) framework, at two AmeriFlux corn-soybean sites to assess their ability to simulate phenology, energy, and carbon fluxes. Our results indicated that the accuracy of net ecosystem exchange and gross primary production simulations was intimately connected to the phenology simulations. The CLM4-Crop model consistently overestimated early growing season leaf area index, causing an overestimation of gross primary production, to such an extent that the model simulated a carbon sink instead of the measured carbon source for corn. The CLM-CornSoy simulated LAI, energy, and carbon fluxes showed stronger correlations with observations compared to CLM4-Crop. Net radiation was biased high in both models and was especially pronounced for soybeans. We hypothesize that the leaf optical parameter, 'L, needs to be modified towards a more horizontal leaf angle distribution to better represent crops. CLM4-Crop overestimated deep root fraction and underestimated soil water content during mid-growing season in all soil layers at the two sites, which caused unrealistic water stress, especially for soybean. Future work regarding the detailed parameterizations of leaf angle and root distributions and early growing season phenology is needed to improve crop simulations within CLM.