气候变化领域集成服务门户(CCPortal): Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean-sea ice modelling system

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DOI	10.5194/tc-13-491-2019
	Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean-sea ice modelling system
	Fritzner S.; Graversen R.; Christensen K.; Rostosky P.; Wang K.
发表日期	2019
ISSN	19940416
EISSN	13
起始页码	491
结束页码	509
卷号	13 期号:2
英文摘要	The accuracy of the initial state is very important for the quality of a forecast, and data assimilation is crucial for obtaining the best-possible initial state. For many years, sea-ice concentration was the only parameter used for assimilation into numerical sea-ice models. Sea-ice concentration can easily be observed by satellites, and satellite observations provide a full Arctic coverage. During the last decade, an increasing number of sea-ice related variables have become available, which include sea-ice thickness and snow depth, which are both important parameters in the numerical sea-ice models. In the present study, a coupled ocean-sea-ice model is used to assess the assimilation impact of sea-ice thickness and snow depth on the model. The model system with the assimilation of these parameters is verified by comparison with a system assimilating only ice concentration and a system having no assimilation. The observations assimilated are sea ice concentration from the Ocean and Sea Ice Satellite Application Facility, thin sea ice from the European Space Agency's (ESA) Soil Moisture and Ocean Salinity mission, thick sea ice from ESA's CryoSat-2 satellite, and a new snow-depth product derived from the National Space Agency's Advanced Microwave Scanning Radiometer (AMSR-E/AMSR-2) satellites. The model results are verified by comparing assimilated observations and independent observations of ice concentration from AMSR-E/AMSR-2, and ice thickness and snow depth from the IceBridge campaign. It is found that the assimilation of ice thickness strongly improves ice concentration, ice thickness and snow depth, while the snow observations have a smaller but still positive short-term effect on snow depth and sea-ice concentration. In our study, the seasonal forecast showed that assimilating snow depth led to a less accurate long-term estimation of sea-ice extent compared to the other assimilation systems. The other three gave similar results. The improvements due to assimilation were found to last for at least 3-4 months, but possibly even longer. © 2019. This work is distributed under the Creative Commons Attribution 4.0 License.
学科领域	AMSR-E; concentration (composition); CryoSat; data assimilation; depth; ice thickness; ice-ocean interaction; satellite data; sea ice; SMOS; snow; Arctic
语种	英语
scopus关键词	AMSR-E; concentration (composition); CryoSat; data assimilation; depth; ice thickness; ice-ocean interaction; satellite data; sea ice; SMOS; snow; Arctic
来源期刊	The Cryosphere
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/118931
作者单位	UiT the Arctic University of Norway, Troms, Norway; Norwegian Meteorological Institute, Oslo, Norway; Institute of Environmental Physics, University of Bremen, Bremen, Germany; Norwegian Meteorological Institute, Troms, Norway
推荐引用方式 GB/T 7714	Fritzner S.,Graversen R.,Christensen K.,et al. Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean-sea ice modelling system[J],2019,13(2).
APA	Fritzner S.,Graversen R.,Christensen K.,Rostosky P.,&Wang K..(2019).Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean-sea ice modelling system.The Cryosphere,13(2).
MLA	Fritzner S.,et al."Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean-sea ice modelling system".The Cryosphere 13.2(2019).