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TC | Volume 12, issue 4
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.
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

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. Kushner et al.
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Cited articles  
Ambadan, J. T., Berg, A., and Merryfield, W. J.: Influence of snow and soil moisture initialization on sub-seasonal predictability and forecast skill in boreal spring, Clim. Dynam., 47, 1–17, https://doi.org/10.1007/s00382-015-2821-9, 2015. 
Arora, V. K., Scinocca, J. F., Boer, G. J., Christian, J. R., Denman, K. L., Flato, G. M., Kharin, V. V., Lee, W. G., and Merryfield, W. J.: Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases, Geophys. Res. Lett., 38, L05805, https://doi.org/10.1029/2010GL046270, 2011. 
Blanchard-Wrigglesworth, E., Armour, K. C., Bitz, C. M., and DeWeaver, E.: Persistence and Inherent Predictability of Arctic Sea Ice in a GCM Ensemble and Observations, J. Climate, 24, 231–250, https://doi.org/10.1175/2010JCLI3775.1, 2011. 
Brown, R. and Derksen, C.: Is Eurasian October snow cover extent increasing?, Environ. Res. Lett., 8, 024006, https://doi.org/10.1088/1748-9326/8/2/024006, 2013. 
Brown, R., Derksen, C., and Wang, L.: A multi-data set analysis of variability and change in Arctic spring snow cover extent, 1967–2008, J. Geophys. Res., 115, D16111, https://doi.org/10.1029/2010JD013975, 2010. 
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...
Citation