Determining climate information for assessing future crop growth, by whatever methods, requires more than monthly mean values of temperature and precipitation. For a changing or variable climate, higher-order statistics, such as variance, extremes, and persistence, may have more significant influences than changes in the mean (Takle and Mearns, 1995; Mearns et al., 1997). For example, a month having a run of several consecutive days of extreme high temperatures (higher autocorrelation of the time series) has much more impact on crops than a month with a day or two of extreme high temperatures scattered throughout the month but with the same monthly mean. Takle and Mearns (1995) reported that a 3.5-year simulation with a regional climate model produced a reduction in the standard deviation and increase in the autocorrelation, in addition to an increase in the mean, for a future scenario climate for July daily maximum temperatures in the Midwest. Although the simulated climate record was short, and the quality of the climate models was lower than currently available, these results point out the need for examining more than mean values for assessing agricultural impacts.
Timing of changes also is critical. Heat stress during the vulnerable corn-pollination period (July in the U.S. Midwest) can have a particularly significant negative impact on yields (Shaw, 1983; Carlson, 1990). Mearns et al. (1984) calculated that a 1.7°C rise in mean maximum July temperature for Des Moines, IA, increases the probability of a heat wave (5 or more consecutive days with temperature above 35°C) from 6% to 21%.
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