A STATISTICAL-MODEL TO DOWNSCALE LOCAL DAILY TEMPERATURE EXTREMES FROM SYNOPTIC-SCALE ATMOSPHERIC CIRCULATION PATTERNS IN THE AUSTRALIAN REGION

The study seeks to describe one method of deriving information about l ocal daily temperature extremes from larger scale atmospheric flow pat terns using statistical tools. This is considered to be one step towar ds downscaling coarsely gridded climate data from global climate model s (GCMs) to finer spatial scales. Downscaling is necessary in order to bridge the spatial mismatch between GCMs and climate impact models wh ich need information on spatial scales that the GCMs cannot provide. T he method of statistical downscaling is based on physical interaction between atmospheric processes with different spatial scales, in this c ase between synoptic scale mean sea level pressure (MSLP) fields and l ocal temperature extremes at several stations in southeast Australia. In this study it was found that most of the day-to-day spatial variabi lity of the synoptic circulation over the Australian region can be cap tured by six principal components. Using the scores of these PCs as mu ltivariate indicators of the circulation a substantial part of the loc al daily temperature variability could be explained. The inclusion of temperature persistence noticeably improved the performance of the sta tistical model. The model established and tested with observations is thought to be finally applied to GCM-simulated pressure fields in orde r to estimate pressure-related changes in local temperature extremes u nder altered CO2 conditions.