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. The Canadian Sea Ice and SnowEvolution (CanSISE) Network is a climate research network focused ondeveloping and applying state-of-the-art observational data to advancedynamical prediction, projections, and understanding of seasonal snow coverand sea ice in Canada and the circumpolar Arctic. This study presents anassessment from the CanSISE Network of the ability of the second-generation CanadianEarth System Model (CanESM2) and the Canadian Seasonal to InterannualPrediction System (CanSIPS) to simulate and predict snow and sea ice fromseasonal to multi-decadal timescales, with a focus on the Canadian sector. Toaccount for observational uncertainty, model structural uncertainty, andinternal climate variability, the analysis uses multi-source observations,multiple Earth system models (ESMs) in Phase 5 of the Coupled ModelIntercomparison Project (CMIP5), and large initial-condition ensembles ofCanESM2 and other models. It is found that the ability of the CanESM2simulation to capture snow-related climate parameters, such as cold-regionsurface temperature and precipitation, lies within the range of currentlyavailable international models. Accounting for the considerable disagreementamong satellite-era observational datasets on the distribution of snow waterequivalent, CanESM2 has too much springtime snow mass over Canada,reflecting a broader northern hemispheric positive bias. Biases in seasonalsnow cover extent are generally less pronounced. CanESM2 also exhibitsretreat of springtime snow generally greater than observational estimates,after accounting for observational uncertainty and internal variability. Seaice is biased low in the Canadian Arctic, which makes it difficult to assessthe realism of long-term sea ice trends there. The strengths and weaknessesof the modelling system need to be understood as a practical tradeoff: theCanadian models are relatively inexpensive computationally because of theirmoderate resolution, thus enabling their use in operational seasonalprediction and for generating large ensembles of multidecadal simulations.Improvements in climate-prediction systems like CanSIPS rely not just onsimulation quality but also on using novel observational constraints and theready transfer of research to an operational setting. Improvements inseasonal forecasting practice arising from recent research include accurateinitialization of snow and frozen soil, accounting for observationaluncertainty in forecast verification, and sea ice thickness initializationusing statistical predictors available in real time.

Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system