HR: 08:30h
AN: GC51B-03    [Abstracts]
TI: Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods
AU: * Maurer, E
EM: emaurer@engr.scu.edu
AF: Santa Clara University Civil Engineering Department, 500 El Camino Real, Santa Clara, CA 95053-0563, United States
AU: Hidalgo, H
EM: hhidalgo@ucsd.edu
AF: Scripps Institution of Oceanography, Climate Research Division, Nierenberg Hall, 8810 Shellback Way, La Jolla, CA 92037, United States
AB: Downscaling of climate model data is essential to most impact analysis. We compare two methods of statistical downscaling to produce continuous, gridded time series of precipitation and surface air temperature at a 1/8- degree (approximately 140km2 per grid cell) resolution over the western U.S. We use NCEP/NCAR Reanalysis data from 1950-1999 as a surrogate General Circulation Model (GCM). The two methods included are constructed analogues (CA) and a bias correction and spatial downscaling (BCSD), both of which have been shown to be skillful in different settings, and BCSD has been used extensively in hydrologic impact analysis. Both methods use the coarse scale Reanalysis fields of precipitation and temperature as predictors of the corresponding fine scale fields. CA downscales daily large-scale data directly and BCSD downscales monthly data, with a random resampling technique to generate daily values. The methods produce comparable skill in producing downscaled, gridded fields of precipitation and temperatures at a monthly and seasonal level. For daily precipitation, both methods exhibit some skill in reproducing both observed wet and dry extremes and the difference between the methods is not significant, reflecting the general low skill in daily precipitation variability in the reanalysis data. For low temperature extremes, the CA method produces greater downscaling skill than BCSD for fall and winter seasons. For high temperature extremes, CA demonstrates higher skill than BCSD in summer. We find that the choice of most appropriate downscaling technique depends on the variables, seasons, and regions of interest, on the availability of daily data, and whether the day to day correspondence of weather from the GCM needs to be reproduced for some applications. The ability to produce skillful downscaled daily data depends primarily on the ability of the climate model to show daily skill.
DE: 1616 Climate variability (1635, 3305, 3309, 4215, 4513)
DE: 1637 Regional climate change
DE: 1807 Climate impacts
DE: 1833 Hydroclimatology
DE: 1854 Precipitation (3354)
SC: Global Environmental Change [GC]
MN: 2007 Fall Meeting