Improving Statistical Downscaling of General Circulation Models

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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Bibliographic Details
Main Author: Titus, Matthew Lee
Language:en
Published: 2010
Subjects:
Statistical Downscaling
Online Access:http://hdl.handle.net/10222/13019
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spelling ndltd-LACETR-oai-collectionscanada.gc.ca-NSHD.ca#10222-130192013-10-04T04:12:51ZImproving Statistical Downscaling of General Circulation ModelsTitus, Matthew LeeStatistical DownscalingCredible 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 2100.2010-08-25T17:34:21Z2010-08-25T17:34:21Z2010-08-252010-08-04http://hdl.handle.net/10222/13019en
collection NDLTD
language en
sources NDLTD
topic Statistical Downscaling
spellingShingle Statistical Downscaling
Titus, Matthew Lee
Improving Statistical Downscaling of General Circulation Models
description 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.
author Titus, Matthew Lee
author_facet Titus, Matthew Lee
author_sort Titus, Matthew Lee
title Improving Statistical Downscaling of General Circulation Models
title_short Improving Statistical Downscaling of General Circulation Models
title_full Improving Statistical Downscaling of General Circulation Models
title_fullStr Improving Statistical Downscaling of General Circulation Models
title_full_unstemmed Improving Statistical Downscaling of General Circulation Models
title_sort improving statistical downscaling of general circulation models
publishDate 2010
url http://hdl.handle.net/10222/13019
work_keys_str_mv AT titusmatthewlee improvingstatisticaldownscalingofgeneralcirculationmodels
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