A COMPARISON OF STATISTICAL AND MODEL-BASED DOWNSCALING TECHNIQUES FOR ESTIMATING LOCAL CLIMATE VARIATIONS

The respective merits of statistical and regional modeling techniques for downscaling GCM predictions have been evaluated over New Zealand, a small mountainous country surrounded by ocean. The boundary conditio ns were supplied from twice-daily European Centre for Medium-Range Wea ther Forecasts analyses at 2.5 degrees resolution for the period 1980- 94, which were taken as the output of a ''perfect'' climate model. Dai ly and monthly estimates of minimum and maximum temperature and precip itation From both techniques were validated against readings from a ne twork of 78 climate stations. The statistical estimates were made by a screening regression technique using the EOFs of the regional height fields at 1000 and 500 hPa, and local variables derived from these fie lds, as predictors. The model interpolations made use of the RAMS mode l developed at Colorado State University running at 50-km resolution f or 1990-94 only. The model values at the nearest grid point to each st ation were rescaled using a simple linear regression to give the best fit to the station values. The results show both methods to have compa rable skill in estimating daily and monthly station anomalies of tempe ratures and rainfall. Statistical estimates of monthly departures were better obtained directly from monthly mean forcing than from a combin ation of daily estimates; however, daily values are needed if one wish es to estimate variability. While there are good physical grounds for using the modeling technique to estimate the likely effects of climate change, the statistical technique requires considerably less computat ional effort and may be preferred for many applications.