Tuesday, 9 May 2000: 9:40 AM
William M. Putman, ORNL, Oak Ridge, TN; and J. B. Drake and G. Ostrouchov
Assessment of global climate change impacts requires increasingly finer spatial and temporal resolutions from General Circulation Model (GCM) predictions. Downscaling is a valuable technique capable of achieving these scale requirements at a minimal computational cost. A statistical approach to downscaling coarse resolution GCM climate scenarios is developed and applied in the downscaling of United States regional climate. Empirical orthogonal function (EOF) analysis along with multiple linear regression (MLR) in a trend analysis and canonical correlation analysis (CCA) in an anomaly analysis determine the statistical relationships which link large scale GCM results with fine resolution regional observations. The downscaling is developed with the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM3) and regional observations from the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) and United States Geological Survey (USGS). The downscaling results in 0.5 degree resolution contiguous US temperature and precipitation, and gauge station stream flow under historical and projected climate change scenarios. Transient climate scenarios are driven by the NCAR Climate System Model (CSM) historical and 1 percent CO2 increase scenarios.
The downscaled fields include seasonal, inter-annual, and inter-decadal variability with strong correlation to large scale climate patterns. Major climate oscillations such as El Nino and the Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Pacific North American (PNA) pattern are included in the statistical transfer functions and their regional impacts are clearly evident in the downscaled results. The transient CO2 scenario produces a warming trend over the continental US of 3 to 4 degrees with the largest warming occurring over the upper plains and northern Rockies. This warming is greatest during winter months and is consistent with trends observed in the CSM global climate scenario.
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