The ClimEx Project: A 50-Member Ensemble of Climate Change Projections at 12-km Resolution over Europe and Northeastern North America with the Canadian Regional Climate Model (CRCM5)

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.

Evaluation of CLASS 2.7 and 3.5 Simulations of Snow Properties from the Canadian Regional Climate Model (CRCM4) over Québec, Canada*

Snow cover simulations from versions 2.7 and 3.5 of the Canadian Land Surface Scheme (CLASS) coupled to the Canadian Regional Climate Model, version 4 (CRCM4), are evaluated over northern Québec and the larger Québec domain using in situ and remotely sensed datasets. Version 2.7 of CLASS has been used in the operational version of CRCM4 at Ouranos since 2006. Version 3.5 includes a number of improvements to the snow processes as well as a more realistic parameterization of snow thermal conductivity. The evaluation shows that version 3.5 provides improved simulations of snow water equivalent, density, depth, and snowpack temperature values. However, snowpack density still contains systematic biases during the snow season that need to be addressed. The snow albedo parameterization in CLASS was found to be very sensitive to an empirical snowfall rate threshold for albedo refreshment and does not keep track of the snow accumulation history in estimating the snow surface albedo. A modified albedo scheme based on snow-specific surface areas is proposed to address this problem.

Application of Satellite Data for Evaluating the Cold Climate Performance of the Canadian Regional Climate Model over Québec, Canada

This study evaluates key aspects of the snow cover, cloud cover, and radiation budget simulated by the Canadian Regional Climate Model, version 4 (CRCM4), coupled with two versions of the Canadian Land Surface Scheme (CLASS). CRCM4 coupled with CLASS version 2.7 has been used operationally at Ouranos since 2006, while, more recently, CRCM4 has been coupled experimentally with CLASS 3.5, which includes a number of improvements to the representation of snow cover processes. The simulations showed evidence of a systematic cold temperature bias. Evaluation of cloud cover and radiation fluxes with satellite data suggests this bias is related to insufficient cloud radiative forcing from a combination of underestimated cloud cover, excessive cloud albedo, and too low cloud emissivity in the model. This cold bias is reinforced by a positive snow albedo feedback manifest through earlier snow cover onset in the fall and early winter period. Snow albedo was found to be very sensitive to the treatment of albedo refresh but insignificantly influenced by the partitioning of solid precipitation in CLASS. This study demonstrates that atmospheric forcing can exert a significant impact on the simulation of snow cover and surface albedo. The results highlight the need to evaluate parameterizations in land surface models designed for climate models in fully coupled mode.