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dc.contributor.authorTitus, Matthew Lee
dc.date.accessioned2010-08-25T17:34:21Z
dc.date.available2010-08-25T17:34:21Z
dc.date.issued2010-08-25
dc.identifier.urihttp://hdl.handle.net/10222/13019
dc.description.abstractCredible 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.language.isoenen_US
dc.subjectStatistical Downscalingen_US
dc.titleImproving Statistical Downscaling of General Circulation Modelsen_US
dc.date.defence2010-08-04
dc.contributor.departmentDepartment of Physics & Atmospheric Scienceen_US
dc.contributor.degreeMaster of Scienceen_US
dc.contributor.external-examinerHarold Ritchieen_US
dc.contributor.graduate-coordinatorTed Moncheskyen_US
dc.contributor.thesis-readerJinyu Shengen_US
dc.contributor.thesis-supervisorIan Folkins, Richard Greatbatchen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.copyright-releaseNot Applicableen_US
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