407A
Advances in Coupling Regional Circulation/Land Surface Models and Dynamic Crop Models

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Thursday, 27 January 2011
Advances in Coupling Regional Circulation/Land Surface Models and Dynamic Crop Models
Washington State Convention Center
Guillermo A. Baigorria, University of Florida, Gainesville, FL; and D. W. Shin

Regional circulation models (RCM) coupled to land surface models (LSM), have a static representation of agricultural surfaces. This type of surface, which is around 30% of the total area in the southeast USA, is just characterized by monthly tables containing a set of optic and topographic characteristics without making differences between crops and its different management (i.e. a field of carrots has the same energy and water fluxes than a field of sugar cane). Agriculture is responsible for the largest man-made inter-seasonal and inter-annual variability land use change. The regional aggregated effect of this man-made variability has an un-explored impact on the regional climate, which can be analyzed only if a dynamic approach of agricultural lands is incorporated to the RCM/LSM models. In this research we coupled the Florida State University/Center for Ocean-Atmospheric Prediction Studies (FSU/COAPS) Regional Spectral Model already coupled to the National Center for Atmospheric Research (NCAR) community Land Model (CLM2) with the dynamic crop models from the Crop System Models Decision Support System for Agrotechnology Transfer (CSM-DSSAT) family models. The objectives were (i) to better represent the effects of land use inter-annual variability in RCM/LSM models, (ii) to increase RCM performance replacing the current empirical agricultural parameterization by a new dynamical one, (iii) to analyze the bidirectional relationships between regional climate and agriculture, and (iv) to analyze scenarios of land use change. After coupling the models, a series of simulation experiments were conducted in order to measure improvements in the model retrospective forecasts' skill. The model was also used to simulate projections of land-cover changes resulting from transforming forest and agricultural lands to land development for housing in the SE-USA. Significant differences were found between the simulated regional climate patterns under different land development scenarios, differences that would potentially influence population requirements of water and energy.