Explore the vast range of titles available.

Extensive degradation of near-surface permafrost is projected during the twenty-first century1, which will have detrimental effects on northern communities, ecosystems and engineering systems. This degradation is predicted to have consequences for many processes, which previous modelling studies have suggested would occur gradually. Here we project that soil moisture will decrease abruptly (within a few months) in response to permafrost degradation over large areas of the present-day permafrost region, based on analysis of transient climate change simulations performed using a state-of-the-art regional climate model. This regime shift is reflected in abrupt increases in summer near-surface temperature and convective precipitation, and decreases in relative humidity and surface runoff. Of particular relevance to northern systems are changes to the bearing capacity of the soil due to increased drainage, increases in the potential for intense rainfall events and increases in lightning frequency. Combined with increases in forest fuel combustibility, these are projected to abruptly and substantially increase the severity of wildfires, which constitute one of the greatest risks to northern ecosystems, communities and infrastructures. The fact that these changes are projected to occur abruptly further increases the challenges associated with climate change adaptation and potential retrofitting measures.

Infrastructure and transportation systems on which northern communities rely are exposed to a variety of climatic hazards over a broad range of scales. Efforts to adapt these systems to the rapidly warming Arctic climate require high-quality climate projections. Here, a state-of-the-art regional climate model is used to perform simulations at 4-km resolution over the eastern and central Canadian Arctic. These include, for the first time over this region, high-resolution climate projections extending to the year 2040. Validation shows that the model adequately simulates base climate variables, as well as variables hazardous to northern engineering and transportation systems, such as degrading permafrost, extreme rainfall, and extreme wind gust. Added value is found over coarser resolution simulations. A novel approach integrating climate model output and machine learning is used for deriving fog—an important, but complex hazard. Hotspots of change to climatic hazards over the next two decades (2021–2040) are identified. These include increases to short-duration rainfall intensity extremes exceeding 50%, suggesting Super–Clausius–Clapeyron scaling. Increases to extreme wind gust pressure are projected to reach 25% over some regions, while widespread increases in active layer thickness and ground temperature are expected. Overall fog frequency is projected to increase by around 10% over most of the study region by 2040, due to increasing frequency of high humidity conditions. Given that these changes are projected to be already underway, urgent action is required to successfully adapt northern transportation and engineering systems located in regions where the magnitude of hazards is projected to increase.

Lakes affect regional climate by modulating surface albedo, surface energy, and moisture budgets. This is especially important for regions such as Northeast Canada with approximately 10 % of the landmass covered by lakes, wetlands and rivers. From the regional hydrology perspective, interactions between lakes and rivers are important as streamflow patterns can be significantly modified by lake storage, and similarly lake levels can be modified by streamflows. In this study, using a suite of experiments performed with the fifth generation Canadian Regional Climate Model (CRCM5) driven by the European Centre for Medium range Weather Forecasting ERA40 reanalysis data at the lateral boundaries for the 1979–2010 period, lake–river–atmosphere interactions and their impact on the regional climate/hydrology of north-east Canada are assessed. In these CRCM5 simulations, a one-dimensional lake model represents lakes, while the rivers are modeled using a distributed routing scheme, and one of the simulations includes interflow, i.e. lateral flow of water in the soil layers. Comparison of CRCM5 simulations with and without lakes suggests significant differences in winter/summer precipitation and winter temperature for the study region. CRCM5 simulations performed with and without lake–river interactions suggest improved representation of streamflows when lake storage and routing are taken into account. Adding the interflow process leads to increased streamflows during summer and fall seasons for the majority of the rivers, causing modest changes to land–atmosphere interactions via modified soil moisture. The impact of interflow on streamflow, obtained in this study, is comparable to the impact of lake–atmosphere interactions on streamflows. This study clearly demonstrates the need for realistic representation of lake–river interactions in regional climate models for realistic simulation of regional hydrology, particularly streamflows.

The impact of snow-atmosphere coupling on climate variability and extremes over North America is investigated using modeling experiments with the fifth generation Canadian Regional Climate Model (CRCM5). To this end, two CRCM5 simulations driven by ERA-Interim reanalysis for the 1981–2010 period are performed, where snow cover and depth are prescribed (uncoupled) in one simulation while they evolve interactively (coupled) during model integration in the second one. Results indicate systematic influence of snow cover and snow depth variability on the inter-annual variability of soil and air temperatures during winter and spring seasons. Inter-annual variability of air temperature is larger in the coupled simulation, with snow cover and depth variability accounting for 40–60% of winter temperature variability over the Mid-west, Northern Great Plains and over the Canadian Prairies. The contribution of snow variability reaches even more than 70% during spring and the regions of high snow-temperature coupling extend north of the boreal forests. The dominant process contributing to the snow-atmosphere coupling is the albedo effect in winter, while the hydrological effect controls the coupling in spring. Snow cover/depth variability at different locations is also found to affect extremes. For instance, variability of cold-spell characteristics is sensitive to snow cover/depth variation over the Mid-west and Northern Great Plains, whereas, warm-spell variability is sensitive to snow variation primarily in regions with climatologically extensive snow cover such as northeast Canada and the Rockies. Furthermore, snow-atmosphere interactions appear to have contributed to enhancing the number of cold spell days during the 2002 spring, which is the coldest recorded during the study period, by over 50%, over western North America. Additional results also provide useful information on the importance of the interactions of snow with large-scale mode of variability in modulating temperature extreme characteristics.

The performance of reanalysis-driven Canadian Regional Climate Model, version 5 (CRCM5) in reproducing the present climate over the North American COordinated Regional climate Downscaling EXperiment domain for the 1989–2008 period has been assessed in comparison with several observation-based datasets. The model reproduces satisfactorily the near-surface temperature and precipitation characteristics over most part of North America. Coastal and mountainous zones remain problematic: a cold bias (2–6 °C) prevails over Rocky Mountains in summertime and all year-round over Mexico; winter precipitation in mountainous coastal regions is overestimated. The precipitation patterns related to the North American Monsoon are well reproduced, except on its northern limit. The spatial and temporal structure of the Great Plains Low-Level Jet is well reproduced by the model; however, the night-time precipitation maximum in the jet area is underestimated. The performance of CRCM5 was assessed against earlier CRCM versions and other RCMs. CRCM5 is shown to have been substantially improved compared to CRCM3 and CRCM4 in terms of seasonal mean statistics, and to be comparable to other modern RCMs.

Lakes influence the regional climate and hydrology in a number of ways and therefore they should be represented in climate models in a realistic manner. Lack of representation of lakes in models can lead to errors in simulated energy and water fluxes, for lake-rich regions. This study focuses on the assessment of the impact of climate change on lakes and hydrology as well as on the influence of lakes on projected changes to regional climate and surface hydrology, particularly streamflows, for Northeast Canada. To this end, transient climate change simulations spanning the 1950–2100 period are performed, with and without lakes, with the fifth generation of the Canadian Regional Climate Model (CRCM5), driven by the Canadian Earth System Model (CanESM2) at the lateral boundaries for Representative Concentration Pathway 8.5. An additional CRCM5 simulation, driven by European Centre for Medium-Range Weather Forecasts Re-Analysis Interim (ERA-Interim) for the 1980–2010 period, is performed in order to assess performance and boundary forcing errors. Performance errors are assessed by comparing the ERA-Interim-driven simulation with available observation datasets, for the 1980–2010 period, for selected variables: 2-m air temperature, total precipitation, snow water equivalent and streamflow. The validation results indicate reasonable model performance over the study region. Boundary forcing errors are studied by comparing ERA-Interim-driven simulation with the one driven by CanESM2 for the current 1980–2010 period, to identify regions and seasons for which projected changes should be interpreted with extra caution. Comparison of projected changes from the CRCM5 simulations with and without lakes suggest that the presence of lakes results in a dampening of projected increases to 2-m air temperature for all seasons almost everywhere in the study domain, with maximum dampening of the order of 2 °C occurring during winter, mostly in the vicinity of the lakes. As for streamflows, projected increases to spring streamflows, based on the simulation with lakes, are found to be smaller than that without lakes and this is due to the storage effect of lakes. Similarly, lower decreases in summer streamflows in future climate are noted in the simulation with lakes due to the gradual release of snowmelt water stored in lakes. An additional CRCM5 transient climate change simulation with lakes and interflow, i.e. lateral flow in the soil layers, is compared with the simulation with lakes, but without interflow, to assess the impact of interflow on projected changes to regional climate and hydrology. Maximum interflow is projected to shift earlier in spring and the maximum interflow rate is expected to decrease by around 25 % in future. Results suggest that the impact of interflow on projected changes to precipitation, soil moisture and humidity are modest, even though the interflow intensity is changing noticeably in future climate. The impact of the interflow on projected changes to streamflows is in the range of ±50 m 3 /s. This study thus for the first time demonstrates the impact of lakes and interflow on projected changes to the regional climate and hydrology for the study region using a single regional modelling system.

Soil moisture–atmosphere interactions play a key role in modulating climate variability and extremes. This study investigates how soil moisture–atmosphere coupling may affect future extreme events, particularly the role of projected soil moisture in modulating the frequency and maximum duration of hot spells over North America, using the fifth-generation Canadian Regional Climate Model (CRCM5). With this objective, CRCM5 simulations, driven by two coupled general circulation models (MPI-ESM and CanESM2), are performed with and without soil moisture–atmosphere interactions for current (1981–2010) and future (2071–2100) climates over North America, for representative concentration pathways (RCPs) 4.5 and 8.5. Analysis indicates that, in future climate, the soil moisture–temperature coupling regions, located over the Great Plains in the current climate, will expand farther north, including large parts of central Canada. Results also indicate that soil moisture–atmosphere interactions will play an important role in modulating temperature extremes in the future by contributing more than 50% to the projected increase in hot-spell days over the southern Great Plains and parts of central Canada, especially for the RCP4.5 scenario. This higher contribution of soil moisture–atmosphere interactions to the future increases in hot-spell days for RCP4.5 is related to the fact that the projected decrease in soil moisture caused the soil to remain in a transitional regime between wet and dry state that is conducive to soil moisture–atmosphere coupling. For the RCP8.5 scenario, on the other hand, the future projected soil state over the southern United States and northern Mexico is too dry to have an impact on evapotranspiration and therefore on temperature.

This study investigates the sensitivity of the Canadian Regional Climate Model (CRCM5) simulated near surface permafrost and its climate interactions to soil and snow formulations. In particular, sensitivities to the depth of the soil column, inclusion of organic soils and modified snow conductivity formulation are investigated. The impact of these modifications are first assessed in offline simulations performed with the Canadian Land Surface Scheme (CLASS), which is the land surface scheme used in CRCM5, when driven by ERA-40/ERA-Interim for the 1957–2008 period. Analysis of CLASS simulations shows major improvements in the simulated permafrost extent, particularly with a deeper soil column. Inclusion of organic soil decreased the summer ground heat flux and therefore the summer soil temperatures, leading to improvements in the simulated active layer thickness (ALT). The impact of the new snow thermal conductivity formulation is moderate compared to the effect of organic soils, but reduces the cold biases in winter soil temperatures. CRCM5 experiments revealed similar sensitivities to soil depth, organic soil and snow conductivity changes as with the offline simulations. Significant changes are noted in the land–atmosphere interactions, through modified energy and moisture partitioning at the surface resulting from the inclusion of the organic soils. The inter-annual variability of the ALT shows larger sensitivities to summer temperatures for mineral soil while experiments including organic soils show increased sensitivities to annual temperatures. The ALT trends in the CRCM5 are similar to the observed values, despite the overestimation of ALT associated with a warm bias in the CRCM5 climate.

Physical modeling of precipitation at fine (sub-kilometer) spatial scales is computationally very expensive. This study develops a highly efficient framework for this task by coupling deep learning (DL) and physical modeling. This framework is developed and tested using regional climate simulations performed over a domain covering Montreal and adjoining regions, for the summers of 2015–2020, at 2.5 km and 250 m resolutions. The DL framework uses a recurrent approach and considers atmospheric physical processes, such as advection, to generate high-resolution information from low-resolution data, which enables it to recreate fine details and produce temporally consistent fields. The DL framework generates realistic high-resolution precipitation estimates, including intense short-duration precipitation events, which allows it to be applied in engineering problems, such as evaluating the climate resiliency of urban storm drainage systems. The results portray the value of the proposed DL framework, which can be extended to other resolutions, periods, and regions.