气候变化领域集成服务门户(CCPortal): Canadian snow and sea ice: Assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system

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DOI	10.5194/tc-12-1137-2018
	Canadian snow and sea ice: Assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system
	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.
发表日期	2018
ISSN	19940416
卷号	12 期号:4
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.
学科领域	accuracy assessment; annual variation; climate change; climate prediction; CMIP; frozen ground; ice thickness; real time; sea ice; snow cover; surface temperature; uncertainty analysis; Canada; Canadian Arctic
语种	英语
scopus关键词	accuracy assessment; annual variation; climate change; climate prediction; CMIP; frozen ground; ice thickness; real time; sea ice; snow cover; surface temperature; uncertainty analysis; Canada; Canadian Arctic
来源期刊	Cryosphere
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/119188
作者单位	Department of Physics, University of Toronto, Toronto, M5S 1A7, Canada; Climate Research Division, Environment and Climate Change Canada, Toronto, M3H 5T4, Canada; Department of Geography, University of Guelph, Guelph, N1G 2W1, Canada; CNRS-LGGE/MEOM, Grenoble, 38041, France; Department of Environmental Science, University of Northern British Columbia, Prince George, V2N 4Z9, Canada; School of Earth and Ocean Sciences, University of Victoria, Victoria, V8W 2Y2, Canada; Department of Geography and Environmental Management, University of Waterloo, Waterloo, N2L 3G1, Canada; Department of Earth and Space Science and Engineering, York University, Toronto, M3J 1P3, Canada; Climate Sciences Division, Alfred Wegener Institute, Bremerhaven, 27570, Germany; Department of Atmospheric Sciences, University of Washington, Seattle, 98195-1640, United States; Department of Atmospheric and Oceanic Sciences, McGill University, Montréal, H3A 0B9, Canada; Pacific Climate Impacts Consortium, University of Victoria, Victoria,...
推荐引用方式 GB/T 7714	Kushner P.J.,Mudryk L.R.,Merryfield W.,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],2018,12(4).
APA	Kushner P.J..,Mudryk L.R..,Merryfield W..,Ambadan J.T..,Berg A..,...&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.Cryosphere,12(4).
MLA	Kushner P.J.,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".Cryosphere 12.4(2018).