Understanding Arctic Sea Ice Thickness Predictability by a Markov Model

doi:10.1175/JCLI-D-22-0525.1

IOCAS-IR > 海洋环流与波动重点实验室

	Understanding Arctic Sea Ice Thickness Predictability by a Markov Model
	Wang, Yunhe1,4 ; Yuan, Xiaojun 2; Bi, Haibo 3,4; Ren, Yibin 1,4; Liang, Yu 3,5; Li, Cuihua 2; Li, Xiaofeng 1,4
	2023-08-01
发表期刊	JOURNAL OF CLIMATE
ISSN	0894-8755
卷号	36 期号:15 页码:4879-4897
通讯作者	Yuan, Xiaojun(xyuan@ldeo.columbia.edu) ; Li, Xiaofeng(lixf@qdio.ac.cn)
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.
关键词	Arctic Sea ice Climate prediction Ice thickness
DOI	10.1175/JCLI-D-22-0525.1
收录类别	SCI
语种	英语
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研究方向	Meteorology & Atmospheric Sciences
WOS类目	Meteorology & Atmospheric Sciences
WOS记录号	WOS:001024031100001
出版者	AMER METEOROLOGICAL SOC
WOS关键词	ATMOSPHERIC RESPONSE ; INITIAL CONDITIONS ; BEAUFORT GYRE ; COLD WINTERS ; PREDICTION ; SATELLITE ; OCEAN ; CRYOSAT-2 ; FORECAST ; AMPLIFICATION
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/182464
专题	海洋环流与波动重点实验室海洋地质与环境重点实验室
通讯作者	Yuan, Xiaojun; Li, Xiaofeng
作者单位	1.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China 2.Columbia Univ, Lamont Doherty Earth Observ, Palisades, NY 10964 USA 3.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Marine Geol & Environm, Qingdao, Peoples R China 4.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao, Peoples R China 5.Univ Chinese Acad Sci, Beijing, Peoples R China
第一作者单位	中国科学院海洋研究所
通讯作者单位	中国科学院海洋研究所
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.