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  • Author or Editor: James S. O'Brien x
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D. W. Shin, J. G. Bellow, T. E. LaRow, S. Cocke, and James J. O'Brien

Abstract

An advanced land model [the National Center for Atmospheric Research (NCAR) Community Land Model, version 2 (CLM2)] is coupled to the Florida State University (FSU) regional spectral model to improve seasonal surface climate outlooks at very high spatial and temporal resolution and to examine its potential for crop yield estimation. The regional model domain is over the southeast United States and is run at 20-km resolution, roughly resolving the county level. Warm-season (March–September) simulations from the regional model coupled to the CLM2 are compared with those from the model with a simple land surface scheme (i.e., the original FSU model). In this comparison, two convective schemes are also used to evaluate their roles in simulating seasonal climate, primarily for rainfall. It is shown that the inclusion of the CLM2 produces consistently better seasonal climate scenarios of surface maximum and minimum temperatures, precipitation, and shortwave radiation, and hence provides superior inputs to a site-based crop model to simulate crop yields. The FSU regional model with the CLM2 exhibits some capability in the simulation of peanut (Arachis hypogaea L.) yields, depending upon the convective scheme employed and the site selected.

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D. W. Shin, G. A. Baigorria, Y-K. Lim, S. Cocke, T. E. LaRow, James J. O’Brien, and James W. Jones

Abstract

A comprehensive evaluation of crop yield simulations with various seasonal climate data is performed to improve the current practice of crop yield projections. The El Niño–Southern Oscillation (ENSO)-based historical data are commonly used to predict the upcoming season crop yields over the southeastern United States. In this study, eight different seasonal climate datasets are generated using the combinations of two global models, a regional model, and a statistical downscaling technique. One of the global models and the regional model are run with two different convective schemes. These datasets are linked to maize and peanut dynamic models to assess their impacts on crop yield simulations and are then compared with the ENSO-based approach. Improvement of crop yield simulations with the climate model data is varying, depending on the model configuration and the crop type. Although using the global climate model data directly provides no improvement, the dynamically and statistically downscaled data show increased skill in the crop yield simulations. A statistically downscaled operational seasonal climate model forecast shows statistically significant (at the 5% level) interannual predictability in the peanut yield simulation. Since the yield amount simulated by the dynamical crop model is highly sensitive to wet/dry spell sequences (water stress) during the growing season, fidelity in simulating the precipitation variability is essential.

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