Statistical downscaling of hourly and daily climate scenarios for various meteorological variables in South-central Canada

被引:35
作者
Cheng, C. S. [1 ]
Li, G. [1 ]
Li, Q. [1 ]
Auld, H. [2 ]
机构
[1] Environm Canada, Meteorol Serv Canada Branch Ontario, Atmospher Sci & Applicat Unit, Toronto, ON M3H 5T4, Canada
[2] Environm Canada, MSC Branch, Adaptat & Impacts Res Div, Toronto, ON, Canada
关键词
D O I
10.1007/s00704-007-0302-8
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
A regression-based methodology was used to downscale hourly and daily station-scale meteorological variables from outputs of large-scale general circulation models (GCMs). Meteorological variables include air temperature, dew point, and west-east and south-north wind velocities at the surface and three upper atmospheric levels (925, 850, and 500 hPa), as well as mean sea-level air pressure and total cloud cover. Different regression methods were used to construct downscaling transfer functions for different weather variables. Multiple stepwise regression analysis was used for all weather variables, except total cloud cover. Cumulative logit regression was employed for analysis of cloud cover, since cloud cover is an ordered categorical data format. For both regression procedures, to avoid multicollinearity between explanatory variables, principal components analysis was used to convert inter-correlated weather variables into uncorrelated principal components that were used as predictors. The results demonstrated that the downscaling method was able to capture the relationship between the premises and the response; for example, most hourly downscaling transfer functions could explain over 95% of the total variance for several variables (e.g. surface air temperature, dew point, and air pressure). Downscaling transfer functions were validated using a cross-validation scheme, and it was concluded that the functions for all weather variables used in the study are reliable. Performance of the downscaling method was also evaluated by comparing data distributions and extreme weather characteristics of downscaled GCM historical runs and observations during the period 1961-2000. The results showed that data distributions of downscaled GCM historical runs for all weather variables are significantly similar to those of observations. In addition, extreme characteristics of the downscaled meteorological variables (e.g. temperature, dew point, air pressure, and total cloud cover) were examined.
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页码:129 / 147
页数:19
相关论文
共 60 条
[1]  
Allison PD, 2000, TECHNOMETRICS, V42, P323
[2]  
[Anonymous], 2011, Workshop Report of the Intergovernmental Panel on Climate Change Workshop on Impacts of Ocean Acidification on Marine Biology and Ecosystems
[3]  
Bardossy A, 1994, CLIMATE CHANGE, UNCERTAINTY, AND DECISION-MAKING, P33
[4]   Downscaling procedures as a tool for integration of multiple air issues [J].
Bass, B ;
Brook, JR .
ENVIRONMENTAL MONITORING AND ASSESSMENT, 1997, 46 (1-2) :151-174
[5]   Empirically downscaled temperature scenarios for northern Europe based on a multi-model ensemble [J].
Benestad, RE .
CLIMATE RESEARCH, 2002, 21 (02) :105-125
[6]   Expanded downscaling for generating local weather scenarios [J].
Burger, G .
CLIMATE RESEARCH, 1996, 7 (02) :111-128
[7]  
*CAN I CLIM STUD, 2005, CAN CLIM IMP SCEN
[8]   Translating monthly temperature from regional to local scale in the southeastern United States [J].
Carbone, GJ ;
Bramante, PD .
CLIMATE RESEARCH, 1995, 5 (03) :229-242
[9]  
CHENG CS, 2006, WATER AIR SOIL POLLU
[10]  
Cheng CS, 2005, DIFFERENTIAL COMBINE, DOI DOI 10.3390/ijerph120809768