Sensitivity of seasonal precipitation extremes to model configuration of the Canadian Regional Climate Model over eastern Canada using historical simulations |
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| Authors: | Philippe Roy Philippe Gachon René Laprise |
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| Institution: | 1. étude et Simulation du Climat à l’échelle Régionale (ESCER) Centre, University of Québec at Montréal, Montreal, QC, H3C 3P8, Canada
2. Canadian Centre for Climate Modelling and Analysis, Climate Research Division, Environment Canada, Montréal, QC, H5A 1L9, Canada
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| Abstract: | This study analyzes the uncertainty of seasonal (winter and summer) precipitation extremes as simulated by a recent version of the Canadian Regional Climate Model (CRCM) using 16 simulations (1961–1990), considering four sources of uncertainty from: (a) the domain size, (b) the driving Atmosphere–Ocean Global Climate Models (AOGCM), (c) the ensemble member for a given AOGCM and (d) the internal variability of the CRCM. These 16 simulations are driven by 2 AOGCMs (i.e. CGCM3, members 4 and 5, and ECHAM5, members 1 and 2), and one set of re-analysis products (i.e. ERA40), using two domain sizes (AMNO, covering all North America and QC, a smaller domain centred over the Province of Québec). In addition to the mean seasonal precipitation, three seasonal indices are used to characterize different types of variability and extremes of precipitation: the number of wet days, the maximum number of consecutive dry days, and the 95th percentile of daily precipitation. Results show that largest source of uncertainty in summer comes from the AOGCM selection and the choice of domain size, followed by the choice of the member for a given AOGCM. In winter, the choice of the member becomes more important than the choice of the domain size. Simulated variance sensitivity is greater in winter than in summer, highlighting the importance of the large-scale circulation from the boundary conditions. The study confirms a higher uncertainty in the simulated heavy rainfall than the one in the mean precipitation, with some regions along the Great Lakes—St-Lawrence Valley exhibiting a systematic higher uncertainty value. |
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