HR: 0800h
AN: A51A-0752    [Abstracts]
TI: Synoptic Weather Typing and its Application to the Assessment of Climate Change Impacts on Human Mortality in South Central Canada
AU: * Cheng, C S
EM: Shouquan.Cheng@ec.gc.ca
AF: Meteorological Service of Canada - Ontario Region, Environment Canada, 4905 Dufferin Street, Downsview, ON M3H5T4 Canada
AU: Auld, H
EM: Heather.Auld@ec.gc.ca
AF: Meteorological Service of Canada, Environment Canada, 4905 Dufferin Street, Downsview, ON M3H5T4 Canada
AU: Li, Q
EM: Qian.Li@ec.gc.ca
AF: Meteorological Service of Canada - Ontario Region, Environment Canada, 4905 Dufferin Street, Downsview, ON M3H5T4 Canada
AU: Li, G
EM: Guilong.Li@ec.gc.ca
AF: Meteorological Service of Canada - Ontario Region, Environment Canada, 4905 Dufferin Street, Downsview, ON M3H5T4 Canada
AB: A synoptic climatological classification approach, using a combination of principal components analysis, an average linkage clustering procedure (hierarchical clustering), and discriminant function analysis (nonhierarchical reclassification), was used to automatically classify distinctive synoptic categories for the period 1953-2001 at each of four selected cities (Montreal, Ottawa, Toronto, and Windsor). Elevated mortality within the identified synoptic weather types were shown to be associated with temperature extremes (heat and cold) and air pollution. Statistical downscaling methods were used to downscale GCM scenarios for the Canadian GCMs (CGCM1 IPCC IS92a & CGCM2 IPCC SRES A2/B2) and the U.S. GCMs (GFDL R30 Coupled Climate Model IPCC SRES A2/B2). The discriminant function analysis was then used to project the future weather types. The projected air pollution concentrations were estimated using the historical regression models from weather typing applied to the downscaled climate change scenarios. This model can also incorporate various air pollution emission scenarios. Two independent approaches were used to assess climate change impacts on elevated mortality for two-time windows (2040-59, 2070-89): 1) comparison of future and historical within-weather-type frequencies and 2) using elevated mortality prediction algorithms (e.g., heat/air pollution-health prediction models). The latter has accounted for not only changes in frequency of future weather types but also changes in future air pollution concentrations. The expected mortality rates due to modeled global warming and pollutant effects for both warm and cold seasons were then estimated. Preliminary results show that under climate change, the future total seasonal elevated mortality rates caused by extreme weather would be increased in summer and decreased in winter due to the expected rise in temperatures.
DE: 1699 General or miscellaneous
SC: Atmospheric Sciences [A]
MN: 2004 AGU Fall Meeting