Understanding Arctic Sea Ice Thickness Predictability by a Markov Model

doi:10.1175/JCLI-D-22-0525.1

CORC > 海洋研究所 > 中国科学院海洋研究所 > 海洋环流与波动重点实验室

	Understanding Arctic Sea Ice Thickness Predictability by a Markov Model
	Wang, Yunhe 2,4; Yuan, Xiaojun 1; Bi, Haibo 4,5; Ren, Yibin 2,4; Liang, Yu 3,5; Li, Cuihua 1; Li, Xiaofeng 2,4
刊名	JOURNAL OF CLIMATE
	2023-08-01
卷号	36 期号:15 页码:4879-4897
关键词	Arctic Sea ice Climate prediction Ice thickness
ISSN号	0894-8755
DOI	10.1175/JCLI-D-22-0525.1
通讯作者	Yuan, Xiaojun(xyuan@ldeo.columbia.edu) ; Li, Xiaofeng(lixf@qdio.ac.cn)
英文摘要	The Arctic sea ice decline and associated change in maritime accessibility have created a pressing need for sea ice thickness (SIT) predictions. This study developed a linear Markov model for the seasonal prediction of model-assimilated SIT. It tested the performance of physically relevant predictors by a series of sensitivity tests. As measured by the anomaly correlation coefficient (ACC) and root-mean-square error (RMSE), the SIT prediction skill was evaluated in different Arctic regions and across all seasons. The results show that SIT prediction has better skill in the cold season than in the warm season. The model performs best in the Arctic basin up to 12 months in advance with ACCs of 0.7-0.8. Linear trend contributions to model skill increase with lead months. Although monthly SIT trends contribute largely to the model skill, the model remains skillful up to 2-month leads with ACCs of 0.6 for detrended SIT predictions in many Arctic regions. In addition, the Markov model's skill generally outperforms an anomaly persistence forecast even after all trends were removed. It also shows that, apart from SIT itself, upper-ocean heat content (OHC) generally contributes more to SIT prediction skill than other variables. Sea ice concentration (SIC) is a relatively less sensitive predictor for SIT prediction skill than OHC. Moreover, the Markov model can capture the melt-to-growth season reemergence of SIT predictability and does not show a spring predictability barrier, which has previously been observed in regional dynamical model forecasts of September sea ice area, suggesting that the Markov model is an effective tool for SIT seasonal predictions.
资助项目	Natural Science Foundation of Shandong Province, China[ZR2021QD059] ; Natural Science Foundation of Shandong Province, China[ZR2020MD100] ; National Natural Science Foundation of China[42106223] ; China Postdoctoral Science Foundation[2020TQ0322] ; Laoshan Laboratory[LSKJ202203003] ; Laoshan Laboratory[LSKJ202202303] ; Open Funds for the Key Laboratory of Marine Geology and Environment, Institute of Oceanology ; Chinese Academy of Sciences[MGE2021KG15] ; Chinese Academy of Sciences[MGE2020KG04] ; Lamont-Doherty Earth Observatory of Columbia University
WOS关键词	ATMOSPHERIC RESPONSE ; INITIAL CONDITIONS ; BEAUFORT GYRE ; COLD WINTERS ; PREDICTION ; SATELLITE ; OCEAN ; CRYOSAT-2 ; FORECAST ; AMPLIFICATION
WOS研究方向	Meteorology & Atmospheric Sciences
语种	英语
出版者	AMER METEOROLOGICAL SOC
WOS记录号	WOS:001024031100001
内容类型	期刊论文
源URL	[http://ir.qdio.ac.cn/handle/337002/182463]
专题	海洋研究所_海洋环流与波动重点实验室
通讯作者	Yuan, Xiaojun; Li, Xiaofeng
作者单位	1.Columbia Univ, Lamont Doherty Earth Observ, Palisades, NY 10964 USA 2.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao, Peoples R China 5.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Marine Geol & Environm, Qingdao, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yunhe,Yuan, Xiaojun,Bi, Haibo,et al. Understanding Arctic Sea Ice Thickness Predictability by a Markov Model[J]. JOURNAL OF CLIMATE,2023,36(15):4879-4897.
APA	Wang, Yunhe.,Yuan, Xiaojun.,Bi, Haibo.,Ren, Yibin.,Liang, Yu.,...&Li, Xiaofeng.(2023).Understanding Arctic Sea Ice Thickness Predictability by a Markov Model.JOURNAL OF CLIMATE,36(15),4879-4897.
MLA	Wang, Yunhe,et al."Understanding Arctic Sea Ice Thickness Predictability by a Markov Model".JOURNAL OF CLIMATE 36.15(2023):4879-4897.