Improving Statistical Downscaling of General Circulation Models
| dc.contributor.author | Titus, Matthew Lee | |
| dc.contributor.copyright-release | Not Applicable | en_US |
| dc.contributor.degree | Master of Science | en_US |
| dc.contributor.department | Department of Physics & Atmospheric Science | en_US |
| dc.contributor.ethics-approval | Not Applicable | en_US |
| dc.contributor.external-examiner | Harold Ritchie | en_US |
| dc.contributor.graduate-coordinator | Ted Monchesky | en_US |
| dc.contributor.manuscripts | Not Applicable | en_US |
| dc.contributor.thesis-reader | Jinyu Sheng | en_US |
| dc.contributor.thesis-supervisor | Ian Folkins, Richard Greatbatch | en_US |
| dc.date.accessioned | 2010-08-25T17:34:21Z | |
| dc.date.available | 2010-08-25T17:34:21Z | |
| dc.date.defence | 2010-08-04 | |
| dc.date.issued | 2010-08-25 | |
| dc.description.abstract | 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. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10222/13019 | |
| dc.language.iso | en | en_US |
| dc.subject | Statistical Downscaling | en_US |
| dc.title | Improving Statistical Downscaling of General Circulation Models | en_US |