Dynamically downscalled projections of climate change for the Canadian landscape and cryosphere

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Most of Canada is either seasonally or permanently covered by snow and in Northern regions, permafrost is a defining characteristic of the landscape. However, current knowledge regarding fundamental propertiesof snow and permafrost, such as how much water is stored in the snowpack and the depth of the active layer of permafrost, is rather poor. At the same time, significant changes are expected in both, snow coverand permafrost, due to global climate change, and it is critical for Canada to understand and quantify the expected changes. Unfortunately, at this point, the ability of global and regional climate models to faithfully simulate snow and in particular permafrost is extremely limited and projections in this area are not considered reliable. This is in part due to the fact that small errors in temperature can cause very large errors in snow and permafrost extent. Direct measurement and satellite observation are important tools in characterizing snow and permafrost in remote regions, but they have limited coverage. Physical computer modeling (simulation) can augment observations in a physically consistent way, and, moreover, can be used to make projections based on scenarios (which is essentially what climate projections are as well). In this project we intent to further develop our ability to simulate and predict snow and permafrost processes by combining a state-of-the-art regional climate model with a new land-surface model, which is capable of simulating such processes. This modeling framework will then be combined with the best available data from Environment and Climate Change Canada and Natural Resources Canada to simulate the historical evolution of snow and permafrost across Canada and to generate quantitative projections of changes in these variables, based on the latest set of global climate projections (CMIP6), which have just been released and upon which the current IPCC assessment report (AR6) is based. This approach is expected to yield reliable estimates of historical snow and permafrost extent in remote regions and the best projections of changes in these processes, that are currently possible.