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.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:NSHD.ca#10222/13019 |
| Date | 04 August 2010 |
| Creators | Titus, Matthew Lee |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
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