Sensitivity of ceres-maize yields to statistical structure of daily weather series

To study impacts of climate variations on crop production, the growth model s are used to simulate yields in present vs. changed climate conditions. Me t&Roll is a four-variate (precipitation amount, solar radiation, minimum an d maximum temperatures) stochastic weather generator used to supply synthet ic daily weather series for the crop growth model CERES-Maize. Three groups of experiments were conducted in this study: (1) Validation of Met&Roll re veals some discrepancies in the statistical structure of synthetic weather series, e.g., (i) the frequency of occurrence of long dry spells, extreme v alues of daily precipitation amount and variability of monthly means are un derestimated by the generator; (ii) correlations and lag-1 correlations amo ng weather characteristics exhibit a significant annual cycle not assumed b y the model. On the whole, the best fit of the observed and synthetic weath er series is experienced in summer months. (2) The Wilcoxon test was employ ed to compare distributions of maize yields simulated with use of observed vs. synthetic weather series. As no statistically significant differences w ere detected, it is assumed that the generator imperfections in reproducing the statistical structure of weather series negligibly affect the model yi elds. (3) The sensitivity of model yields to selected characteristics of th e daily weather series was examined. Emphasis was placed on the characteris tics not addressed by typical GCM-based climate change scenarios: daily amp litude of temperature, persistence of the weather series, shape of the dist ribution of daily precipitation amount, and frequency of occurrence of wet days. The results indicate that some of these characteristics may significa ntly affect crop yields and should therefore be considered in the developme nt of climate change scenarios.