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Advances in Meteorology
Volume 2016 (2016), Article ID 1478514, 17 pages
Research Article

Impacts of Model Bias on the Climate Change Signal and Effects of Weighted Ensembles of Regional Climate Model Simulations: A Case Study over Southern Québec, Canada

1APEC Climate Center (APCC), 12 Centum 7-ro, Haeundae-gu, Busan 612-020, Republic of Korea
2Étude et Simulation du Climat à l’Échelle Régionale (ESCER), University of Québec at Montreal, 201 Président Kennedy Avenue, Montréal, QC, Canada H2X 3Y7

Received 19 August 2015; Revised 27 October 2015; Accepted 17 November 2015

Academic Editor: Xiaofeng Li

Copyright © 2016 Hyung-Il Eum et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This study examined the impact of model biases on climate change signals for daily precipitation and for minimum and maximum temperatures. Through the use of multiple climate scenarios from 12 regional climate model simulations, the ensemble mean, and three synthetic simulations generated by a weighting procedure, we investigated intermodel seasonal climate change signals between current and future periods, for both median and extreme precipitation/temperature values. A significant dependence of seasonal climate change signals on the model biases over southern Québec in Canada was detected for temperatures, but not for precipitation. This suggests that the regional temperature change signal is affected by local processes. Seasonally, model bias affects future mean and extreme values in winter and summer. In addition, potentially large increases in future extremes of temperature and precipitation values were projected. For three synthetic scenarios, systematically less bias and a narrow range of mean change for all variables were projected compared to those of climate model simulations. In addition, synthetic scenarios were found to better capture the spatial variability of extreme cold temperatures than the ensemble mean scenario. These results indicate that the synthetic scenarios have greater potential to reduce the uncertainty of future climate projections and capture the spatial variability of extreme climate events.