Monday, November 5, 2007 - 4:15 PM
34-7
Coping with the Uncertainty of Using a Probabilistic Regional Circulation Model Forecast Ensemble in Predicting Simulated Crop Yields.
Global/Regional Circulation Models (GCM/RCM) better predict the interannual climate variability rather than the absolute values of meteorological variables. Statistical bias-correction methods increase the quality of daily model predictions of incoming solar radiation, maximum and minimum temperatures and rainfall frequency and amount. However, when bias-corrected forecasts/hindcasts are used by dynamic crop models, timing of dry-spell occurrences generate the largest uncertainty during the linking process. In this study, we used twenty ensemble members of an 18-year period provided by the Florida State University/Center for Ocean-Atmospheric Prediction Studies (FSU/COAPS) regional spectral model coupled to the National Center for Atmospheric Research Community Land Model (CLM2). The daily seasonal-climate hindcast was bias-corrected and used as input to the CERES-Maize model, thus producing twenty crop yield ensemble members. Using the observed weather for the same period, a time series of simulated crop yields was produced. Finally, Principal Component (PC) regression analysis was used to predict this time series using as predictors of the crop yield ensemble. Between 17.2 and 28.8% of the simulated corn yield interannual variability was explained using only one principal component (p<0.05), and estimated yields were in the correct tercile by margins of 8.3 to 38.2% beyond chance. Predictability of simulated corn yields using principal components was improved relative to the use of bias-corrected daily hindcasts produced by RCMs directly used by the CERES-Maize model. Bias-correcting all meteorological variables used by the crop model increased the predictability skills compared with other combinations among raw hindcasts, individual bias-correction of rainfall, and climatological values.
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