The Canadian Regional Climate Model (CRCM5) Large Ensemble (CRCM5-LE) consists of a dynamically downscaled version of the CanESM2 50-member initial-conditions ensemble (CanESM2-LE). The downscaling was performed at 12-km resolution over two domains, Europe (EU) and northeastern North America (NNA), and the simulations extend from 1950 to 2099, following the RCP8.5 scenario. In terms of validation, warm biases are found over the EU and NNA domains during summer, whereas during winter cold and warm biases appear over EU and NNA, respectively. For precipitation, simulations are generally wetter than the observations but slight dry biases also occur in summer. Climate change projections for 2080–99 (relative to 2000–19) show temperature changes reaching 8°C in summer over some parts of Europe, and exceeding 12°C in northern Québec during winter. For precipitation, central Europe will become much dryer during summer (−2 mm day−1) and wetter during winter (>1.2 mm day−1). Similar changes are observed over NNA, although summer drying is not as prominent. Projected changes in temperature interannual variability were also investigated, generally showing increasing and decreasing variability during summer and winter, respectively. Temperature variability is found to increase by more than 70% in some parts of central Europe during summer and to increase by 80% in the northernmost part of Québec during the month of May as the snow cover becomes subject to high year-to-year variability in the future. Finally, CanESM2-LE and CRCM5-LE are compared with respect to extreme precipitation, showing evidence that the higher resolution of CRCM5-LE allows a more realistic representation of local extremes, especially over coastal and mountainous regions.

An analysis of several multidecadal simulations of the present (1971–90) and future (2041–60) climate from the Canadian Regional Climate Model (CRCM) is presented. The effects on the CRCM climate of model domain size, internal variability of the general circulation model (GCM) used to provide boundary conditions, and modifications to the physical parameterizations used in the CRCM are investigated. The influence of boundary conditions is further investigated by comparing the GCM-driven simulations of the current climate with simulations performed using boundary conditions from meteorological reanalyses. The present climate of the model in these different configurations is assessed by comparing the seasonal averages and interannual variability of precipitation and surface air temperature with an observed climatology. Generally, small differences are found between the two simulations on different domains, though both domains are quite large as compared with previously reported results. Simulations driven by GCM output show a significant warm bias for wintertime surface air temperatures over northern regions. This warm bias is much reduced in the GCM-driven simulation when an updated set of physical parameterizations is used in the CRCM. The warm bias is also reduced for simulations with the standard set of physical parameterizations when the CRCM is driven with reanalysis data. However, use of the modified physics package for reanalysis-driven simulations results in surface air temperatures that are colder than the observations. Summertime precipitation in the model is much larger than observed, a bias that is present in both the GCM-driven and reanalysis-driven simulations. The bias in summertime precipitation is reduced for both types of driving data when the updated set of physical parameterizations is used. Model projections of climate change between the present and future periods are also presented and the sensitivity of these projections to many of the above-mentioned modifications is assessed. Changes in surface air temperature are predicted to be largest over northern regions in winter, with smaller changes over more southerly regions and in the summer season. Changes in seasonal average precipitation are projected to be in the range of ±10% of present-day amounts for most regions and seasons. The CRCM projections of surface air temperature changes are strongly affected by the internal variability of the driving GCM over high northern latitudes and to changes in the physical parameterizations over many regions for the summer season.

nouveau Mod ele Canadien de Climat Rà egional (MCCR). La formulation du MCCR re- IntroductionThe last quarter of the century has witnessed important progress in the performance and achievements of global forecast and climate models. This progress is based on more accurate and efþcient numerical methods, more comprehensive parametrization of the ensemble effects of unresolved physical processes upon the resolved part of the ow, more sophisticated programming techniques to speed-up execution of computer programs and þnally { but not least { increased resolution afforded by faster computers. Together with improved model initialization and data assimilation methods, this evolution has led to better weather analyses and short-term forecasts. The increasing ability of global models to reproduce complex phenomena that operate on longer timescales, such as the ENSO (El Niño { Southern Oscillation), lends conþdence to the possibility of achieving seasonal dynamical extended-range forecasts. General Circulation Models (GCMs) have evolved from the initial purely atmospheric models into Global Climate Models which include oceans, sea-ice and land-surface processes as well as detailed physical parametrizations of subgrid-scale phenomena. Serving as computerized experimentation laboratories, such models constitute useful tools to further our understanding of climate as a physical system with multiple interacting processes.Numerical Weather Prediction (NWP) and climate models differ in a number of ways, resolution being one of the most obvious. Nowadays NWP centres operate global models with computational meshes of the order of one hundred kilometres, whereas the resolution of GCMs typically used for climate simulations is coarser by about a factor of three or þve or even more. This coarser resolution is imposed by computational constraints associated with the very long simulations (on timescales of decades to centuries) required for climate studies, as opposed to days for forecasts. The computing cost of a global model increases by more than two orders of magnitude for a factor of 5 increase in resolution. Lesser resolution is hence a

No abstract

During periods of endemic meningococcal disease, serogroup B Neisseria meningitidis is responsible for a significant percentage of invasive diseases, and no particular clone or strain predominates (F. E. Ashton and D. A. Caugant, Can. J. Microbiol. 47: 293-289, 2001), However, in the winter of 2004 to 2005, a cluster of serogroup B meningococcal disease occurred in one region in the province of Québec, Canada. The N. meningitidis strain responsible for this cluster of cases was identified as sequence type ST-269 with the antigenic formula B:17:P1.19. Retrospective analysis of isolates from 2000 onwards showed that this clone first emerged in the province of Québec in 2003. The emergence of this clone of serogroup B meningococci occurred after a mass vaccination against serogroup C N. meningitidis , suggesting possible capsule replacement.