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

Simple item page
dc.contributor.authorKushner, Paul
dc.contributor.authorMudryk, Lawrence R.
dc.contributor.authorMerryfield, William J.
dc.contributor.authorAmbadan, Jaison T.
dc.contributor.authorBerg, Aaron
dc.contributor.authorBichet, Adéline
dc.contributor.authorBrown, Ross
dc.contributor.authorDerksen, Chris
dc.contributor.authorDéry, Stephen J.
dc.contributor.authorDirkson, Arlan
dc.contributor.authorFlato, Greg
dc.contributor.authorFletcher, Christopher G.
dc.contributor.authorFyfe, John C.
dc.contributor.authorGillett, Nathan P.
dc.contributor.authorHaas, Christian
dc.contributor.authorHowell, Stephen E. L.
dc.contributor.authorLaliberté, Frédéric
dc.contributor.authorMcCusker, Kelly
dc.contributor.authorSigmond, Michael
dc.contributor.authorSospedra-Alfonso, Reinel
dc.contributor.authorTandon, Neil F.
dc.contributor.authorThackeray, Chad
dc.contributor.authorTremblay, Bruno
dc.contributor.authorZwiers, Francis W.
dc.date.accessioned2025-04-10T20:27:24Z
dc.date.available2025-04-10T20:27:24Z
dc.date.issued2018
dc.description.abstractThe 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.
dc.description.reviewstatusReviewed
dc.description.scholarlevelFaculty
dc.description.sponsorshipThis work represents “Deliverable 1” of the CanSISE Network. CanSISE was funded under the auspices of the Natural Science and Engineering Research Council of Canada’s Climate Change and Atmospheric Research Program, with additional support provided by Environment and Climate Change Canada, the Pacific Climate Impacts Consortium, and the University of Toronto.
dc.identifier.citationKushner, P., Mudryk, L. R., Merryfield, W. J., 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. P., Haas, C., Howell, S. E. L., Laliberté, F., McCusker, K., Sigmond, M., … Zwiers, F. W. (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. The Cryosphere, 12(4), 1137–1156. https://doi.org/10.5194/tc-12-1137-2018
dc.identifier.urihttps://doi.org/10.5194/tc-12-1137-2018
dc.identifier.urihttps://hdl.handle.net/1828/21766
dc.language.isoen
dc.publisherThe Cryosphere
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectPacific Climate Impacts Consortium (PCIC)
dc.subject.departmentSchool of Earth and Ocean Sciences
dc.titleCanadian snow and sea ice: Assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system
dc.typeArticle

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
kushner_paulj_cryosphere_2017.pdf
Size:
4.46 MB
Format:
Adobe Portable Document Format
Download

Collections

Faculty and Staff Publications
PCIC Publications
 
University of Victoria Libraries

We acknowledge and respect the Lək̓ʷəŋən (Songhees and Esquimalt) Peoples on whose territory the university stands, and the Lək̓ʷəŋən and W̱SÁNEĆ Peoples whose historical relationships with the land continue to this day.