Submitted to: International Journal of Climatology
Publication Type: Peer reviewed journal
Publication Acceptance Date: 12/3/2011
Publication Date: 2/1/2013
Citation: Zhang, X.J. 2013. Verifying a temporal disaggregation method for generating daily precipitation of potentially non-stationary climate change for site-specific impact assessment. International Journal of Climatology. 33(2):326-342. Interpretive Summary: Climate change projected by global climate models (GCM in short) needs to be spatially and temporally downscaled to finer scales for use in assessing the potential impacts of climate change on natural resources at particular farms. The objective of this work is to verify that a statistical (empirical) method, which downscales monthly precipitation to daily values using a computer climate generator, is fully applicable to all climate conditions. Daily precipitation records of about 100 years from five meteorological stations dispersed in Oklahoma, with mean annual precipitation ranging from 450 to 1330 mm, were split into a calibration period and a validation period. The statistical downscaling method was calibrated using the observed daily precipitation data of the calibration period, and then used to simulate daily precipitation data for the validation period. Simulated daily precipitation amounts resembled those of observed daily amounts of the validation periods reasonably well. This downscaling method preserved the statistics of the observed monthly precipitation amounts extremely well, demonstrating the validity of the method for downscaling monthly precipitation to daily precipitation values. This work provides useful information to climatologists and natural resource modelers that empirical relationships developed based on historical climate with this particular method are fully applicable to future climate change.
Technical Abstract: Statistical approaches have been widely used to downscale global climate model (GCM) projections to finer spatiotemporal resolution for impact assessment of climate change. However, a major concern of the approaches is whether a statistical relationship derived from historical climate will hold for changed climate. The objective is to verify that a statistical method, which downscales monthly precipitation to daily series using a stochastic weather generator, is fully applicable to any climate state, using historical station data that have experienced non-stationary changes. Daily precipitation records of about 100 years from five meteorological stations, with mean annual precipitation ranging from 450 to 1330 mm, were split into calibration and validation periods based on abrupt changes in mean annual precipitation. Linear relationships between transition probabilities of wet-follow-wet (Pw/w) and wet-follow-dry (Pw/d) and mean monthly precipitation amounts were established between 30 driest months and 30 wettest months of the calibration periods, and were used to interpolate Pw/w and Pw/d for the validation periods. Mean and standard deviation of daily precipitation amounts were algebraically derived using mean and standard deviation of monthly precipitation values of the validation periods as well as the interpolated Pw/w and Pw/d. Statistics of downscaled daily precipitation amounts resembled those of observed daily amounts of the validation periods reasonably well. The downscaling method preserved statistics of monthly precipitation amounts extremely well, demonstrating the validity of the method for temporal downscaling of non-stationary climate. The linear relationships that transcend the driest and wettest hypothetical climate states are suitable for interpolating Pw/w and Pw/d with mean monthly precipitation to any intermediate climate state.