Improving Statistical Downscaling of General Circulation Models
Credible projections of future local climate change are in demand. One way to accomplish this is to statistically downscale General Circulation Models (GCM’s). A new method for statistical downscaling is proposed in which the seasonal cycle is first removed, a physically based predictor selection pr...
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| Language: | en |
| Published: |
2010
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| Online Access: | http://hdl.handle.net/10222/13019 |
| Summary: | Credible projections of future local climate change are in demand. One way to accomplish
this is to statistically downscale General Circulation Models (GCM’s). A new method for
statistical downscaling is proposed in which the seasonal cycle is first removed, a physically
based predictor selection process is employed and principal component regression
is then used to train the regression. A regression model between daily maximum and minimum
temperature at Shearwater, NS, and NCEP principal components in the 1961-2000
period is developed and validated and output from the CGCM3 is then used to make future
projections. Projections suggest Shearwater’s mean temperature will be five degrees
warmer by 2100. |
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