Journal cover Journal topic
The Cryosphere An interactive open-access journal of the European Geosciences Union
The Cryosphere, 12, 1137-1156, 2018
https://doi.org/10.5194/tc-12-1137-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
04 Apr 2018
Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system
Paul J. Kushner1, Lawrence R. Mudryk2, William Merryfield2, Jaison T. Ambadan3, Aaron Berg3, Adéline Bichet4, Ross Brown2, Chris Derksen2, Stephen J. Déry5, Arlan Dirkson6, Greg Flato2, Christopher G. Fletcher7, John C. Fyfe2, Nathan Gillett2, Christian Haas8,9, Stephen Howell2, Frédéric Laliberté2, Kelly McCusker10, Michael Sigmond2, Reinel Sospedra-Alfonso2, Neil F. Tandon2, Chad Thackeray7, Bruno Tremblay11, and Francis W. Zwiers12 1Department of Physics, University of Toronto, Toronto, M5S 1A7, Canada
2Climate Research Division, Environment and Climate Change Canada, Toronto, M3H 5T4, Canada
3Department of Geography, University of Guelph, Guelph, N1G 2W1, Canada
4CNRS-LGGE/MEOM, 38041 Grenoble, France
5Department of Environmental Science, University of Northern British Columbia, Prince George, V2N 4Z9, Canada
6School of Earth and Ocean Sciences, University of Victoria, Victoria, V8W 2Y2, Canada
7Department of Geography and Environmental Management, University of Waterloo, Waterloo, N2L 3G1, Canada
8Department of Earth and Space Science and Engineering, York University, Toronto, M3J 1P3, Canada
9Climate Sciences Division, Alfred Wegener Institute, 27570 Bremerhaven, Germany
10Department of Atmospheric Sciences, University of Washington, Seattle, 98195-1640, USA
11Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, H3A 0B9, Canada
12Pacific Climate Impacts Consortium, University of Victoria, Victoria, V8P 5C2, Canada
Abstract. 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 generating large ensembles of multidecadal simulations. Improvements in climate-prediction systems like CanSIPS rely not just on simulation quality but also on using novel observational constraints and the ready transfer of research to an operational setting. Improvements in seasonal forecasting practice arising from recent research include accurate initialization of snow and frozen soil, accounting for observational uncertainty in forecast verification, and sea ice thickness initialization using statistical predictors available in real time.
Citation: Kushner, P. J., Mudryk, L. R., Merryfield, W., Ambadan, J. T., Berg, A., Bichet, A., Brown, R., Derksen, C., Déry, S. J., Dirkson, A., Flato, G., Fletcher, C. G., Fyfe, J. C., Gillett, N., Haas, C., Howell, S., Laliberté, F., McCusker, K., Sigmond, M., Sospedra-Alfonso, R., Tandon, N. F., Thackeray, C., Tremblay, B., and Zwiers, F. W.: Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system, The Cryosphere, 12, 1137-1156, https://doi.org/10.5194/tc-12-1137-2018, 2018.
Publications Copernicus
Short summary
Here, the Canadian research network CanSISE uses state-of-the-art observations of snow and sea ice to assess how Canada's climate model and climate prediction systems capture variability in snow, sea ice, and related climate parameters. We find that the system performs well, accounting for observational uncertainty (especially for snow), model uncertainty, and chaotic climate variability. Even for variables like sea ice, where improvement is needed, useful prediction tools can be developed.
Here, the Canadian research network CanSISE uses state-of-the-art observations of snow and sea...
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