Estimation of local precipitation statistics reflecting climate change

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Abstract
A stochastic approach is developed to estimate the probability of wet days, and the mean and standard deviation of local daily nonzero precipitation reflecting global climate change scenarios. The approach is based on the analysis of daily atmospheric circulation patterns (CPs) and the linkage between types of CPs and daily precipitation. Three CP data sets are used for the 500‐hPa pressure field: 40‐year historical, 10‐year 1×CO2, and 10‐year 2×CO2scenarios obtained from the atmospheric general circulation model of the Canadian Climate Centre. CP types obtained by clustering techniques and their frequency distribution are similar for the three data sets. The linkage between CP types and precipitation considers an additional variable, the spatial average pressure height within each CP type. The difference in pressure heights among the three CP data sets makes it possible to estimate the effect of global change on local precipitation statistics. Under the dry continental climate of eastern Nebraska the effect of 2×CO2scenario on local precipitation regime is spatially variable and significant: the number of wet days slightly decreases but both the mean and variance of daily precipitation increase resulting in a more variable precipitation regime.