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
Abstract
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. Finally, these results indicate that the synthetic scenarios have greater potential to reduce the uncertainty of future climate projections and capture themore »
- Authors:
-
- APEC Climate Center (APCC), Busan (Republic of Korea)
- Univ. of Quebec, Montreal (Canada). Etude et Simulation du Climat a l'Echelle Regionale (ESCER)
- Publication Date:
- Research Org.:
- APEC Climate Center (APCC), Busan (Republic of Korea)
- Sponsoring Org.:
- USDOE Office of Science (SC); National Science Foundation (NSF)
- OSTI Identifier:
- 1376064
- Grant/Contract Number:
- 14AWMP-B082564-01
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Advances in Meteorology
- Additional Journal Information:
- Journal Volume: 2016; Journal ID: ISSN 1687-9309
- Publisher:
- Hindawi
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES
Citation Formats
Eum, Hyung-Il, Gachon, Philippe, and Laprise, René. 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. United States: N. p., 2016.
Web. doi:10.1155/2016/1478514.
Eum, Hyung-Il, Gachon, Philippe, & Laprise, René. 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. United States. https://doi.org/10.1155/2016/1478514
Eum, Hyung-Il, Gachon, Philippe, and Laprise, René. Fri .
"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". United States. https://doi.org/10.1155/2016/1478514. https://www.osti.gov/servlets/purl/1376064.
@article{osti_1376064,
title = {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},
author = {Eum, Hyung-Il and Gachon, Philippe and Laprise, René},
abstractNote = {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. Finally, 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.},
doi = {10.1155/2016/1478514},
journal = {Advances in Meteorology},
number = ,
volume = 2016,
place = {United States},
year = {Fri Jan 01 00:00:00 EST 2016},
month = {Fri Jan 01 00:00:00 EST 2016}
}
Web of Science
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