Correlating CCM upper atmosphere parameters to surface observations for regional climate change predictions
- Tulane Univ., New Orleans, LA (United States)
This paper explores the use of statistical downscaling of General Circulation Model (GCM) results for the purpose of regional climate change analysis. The strong correlation between surface observations and GCM upper air predictions is used in an approach very similar to the Model Output Statistics approach used in numerical weather prediction. The primary assumption in this analysis is that the statistical relationships remain unchanged under conditions of climatic change. These relations are applied to GCM upper atmosphere predictions for future (2*CO{sub 2}) climate predictions. The result is a set of regional climate change predictions conceptually valid at the scale of cities. The downscaling for specific cities within a GCM grid cell reveals some of the anticipated variability within the grid cell. In addition, multiple linear regression analysis may indicate warming that is significantly higher or lower for a particular region than the raw data from the GCM runs. 3 refs., 3 figs., 2 tabs.
- OSTI ID:
- 535516
- Report Number(s):
- CONF-970207-; TRN: 97:005076-0048
- Resource Relation:
- Conference: 77. annual meeting of the American Meteorological Society, Long Beach, CA (United States), 2-7 Feb 1997; Other Information: PBD: 1997; Related Information: Is Part Of Eighth symposium on global change studies; PB: 402 p.
- Country of Publication:
- United States
- Language:
- English
Similar Records
A semiempirical downscaling approach for predicting regional temperature impacts associated with climatic change
THE APPLICATION OF A STATISTICAL DOWNSCALING PROCESS TO DERIVE 21{sup ST} CENTURY RIVER FLOW PREDICTIONS USING A GLOBAL CLIMATE SIMULATION