globalchange  > 影响、适应和脆弱性
DOI: 10.5194/tc-12-1137-2018
Scopus记录号: 2-s2.0-85045011270
论文题名:
Canadian snow and sea ice: Assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system
作者: 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; , Zwiers F; W
刊名: Cryosphere
ISSN: 19940416
出版年: 2018
卷: 12, 期:4
起始页码: 1137
结束页码: 1156
语种: 英语
英文摘要: 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. © Author(s) 2018.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/75388
Appears in Collections:影响、适应和脆弱性
气候变化与战略

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作者单位: Department of Physics, University of Toronto, Toronto, Canada; Climate Research Division, Environment and Climate Change Canada, Toronto, Canada; Department of Geography, University of Guelph, Guelph, Canada; CNRS-LGGE/MEOM, Grenoble, France; Department of Environmental Science, University of Northern British Columbia, Prince George, Canada; School of Earth and Ocean Sciences, University of Victoria, Victoria, Canada; Department of Geography and Environmental Management, University of Waterloo, Waterloo, Canada; Department of Earth and Space Science and Engineering, York University, Toronto, Canada; Climate Sciences Division, Alfred Wegener Institute, Bremerhaven, Germany; Department of Atmospheric Sciences, University of Washington, Seattle, United States; Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, Canada; Pacific Climate Impacts Consortium, University of Victoria, Victoria, Canada

Recommended Citation:
Kushner P,J,, Mudryk L,et al. Canadian snow and sea ice: Assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system[J]. Cryosphere,2018-01-01,12(4)
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