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.

A process‐based model that incorporates hydrodynamic feedbacks between the land surface, soil, and groundwater zones is used to assess the sensitivity of the hydrological response (river discharge, aquifer recharge, and soil water storage) to future climate conditions. The analysis is based on the Intergovernmental Panel on Climate Change Special Report on Emissions Scenario A2 and the des Anglais catchment in southwestern Quebec (Canada). Application of the coupled hydrological model (CATHY) to the study basin revealed significant spatiotemporal variations in the river discharge response to climate change owing to a different partitioning between the overland runoff and base flow components of the hydrograph, with the latter alleviating the marked decrease in discharge during the summer period. A spatial analysis of recharge patterns shows that the greatest variations are expected to occur, throughout the year, in the southern portion of the catchment, where the elevations are highest. Compared to river discharge and aquifer recharge, the soil water storage volumes are less sensitive to climate changes. From a spatial analysis of soil moisture variations it was possible to observe organizational patterns that follow the topographic and pedologic characteristics of the catchment. In addition to these analyses, we also compare predictions obtained with the land surface scheme (CLASS) that is coupled to the regional climate model (CRCM) to those from the detailed catchment model for past and future climate change projections. An examination of the runoff revealed that CLASS produces higher estimates than CATHY of surface and subsurface runoff throughout the annual cycle for both past and future projections. For soil water storage, the two models are in general agreement in terms of the intra‐annual variability of moisture content at shallower soil layers, whereas a larger difference is found for the deepest layer, with CATHY predicting wetter soil conditions over the entire simulation period and moisture fluctuations of much smaller amplitude.

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The impacts of climate change on future soil temperature Ts and soil moisture Ms of northern forests are uncertain. In this study, the authors first calibrated Ts and Ms models [Forest Soil Temperature Model (ForSTeM) and Forest Hydrology Model (ForHyM), respectively] using long-term observations of Ts and Ms at different depths measured at three forest sites in eastern Canada. The two models were then used to project Ts and Ms for the period 1971–2100 using historical and future climate scenarios generated by one regional and five global climate models. Results indicate good model performance by ForSTeM and ForHyM in predicting observed Ts and Ms values at various depths for the three sites. Projected annual-mean Ts at these sites increased between 1.1° and 1.9°C and between 1.9° and 3.3°C from the present 30-yr averages (1971–2000) to the periods 2040–69 and 2070–99, respectively. Increases as high as 5.0°C were projected at the black spruce site during the growing season (June) for the period 2070–99. Changes in annual-mean Ms were relatively small; however, seasonally Ms is projected to increase in April, because of earlier snowmelt, and to decrease during the growing season, mainly because of higher evapotranspiration rates. Soil moisture in the growing season could be reduced by 20%–40% for the period 2070–99 compared to the reference period. The projected warmer and drier soil conditions in the growing season could have significant impacts on forests growth and biogeochemical cycles.