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

• Published in: The Cryosphere
• Main Authors: Kushner, P.J., Mudryk, L.R., Merryfield, W., Ambadan, J.T., Berg, A., Bichet, A., Brown, R., Derksen, C., Dery, S.J., Dirkson, A., Flato, G., Fletcher, C.G., Fyfe, J.C., Gillet, N., Haas, C., Howell, S., Laliberte, F., McCusker, K., Sigmond, M., Sospedra-Alfonso, R., Tandon, N.F., Thackeray, C., Tremblay, B., Zwiers, F.W.
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: 2018
• Subjects: Sea ice; The Cryosphere
• Online Access: https://epic.awi.de/id/eprint/48384/; https://epic.awi.de/id/eprint/48384/1/tc-12-1137-2018.pdf; https://www.the-cryosphere.net/12/1137/2018/; https://hdl.handle.net/10013/epic.8fa53711-4a0e-4245-8819-9999dddaae29

The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state-of-the-art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. This study presents an assessment from the CanSISE Network of the ability of the second-generation Canadian Earth System Model (CanESM2) and the Canadian Seasonal to Interannual Prediction System (CanSIPS) to simulate and predict snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth system models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and large initial-condition ensembles of CanESM2 and other models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as cold-region surface temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow mass over Canada, reflecting a broader northern hemispheric positive bias. Biases in seasonal snow cover extent are generally less pronounced. CanESM2 also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea ice trends there. The strengths and weaknesses of the modelling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their use in operational seasonal prediction and for ...