While both the mean and variance of daily temperature are forecasted to increase in future climate scenarios, studies of spring frost risk to vegetation have relied on changes in mean temperature to understand frost risk in these scenarios. We present a probabilistic model of spring frost risk based on the stochastic-crossing properties of a coupled temperature-phenology model in which the mean, variance, and autocorrelation structure of spring temperature may be controlled through independent parameters. The model results show that frost risk to vegetation is as sensitive to increases in daily temperature variance (which increases frost risk) as to increases in the mean temperature (which decreases frost risk.
All Science Journal Classification (ASJC) codes
- Earth and Planetary Sciences(all)