Reassessing seasonal sea ice predictability of the Pacific-Arctic sector using a Markov model

doi:10.5194/tc-16-1141-2022

IOCAS-IR > 海洋地质与环境重点实验室

	Reassessing seasonal sea ice predictability of the Pacific-Arctic sector using a Markov model
	Wang, Yunhe 1,4; Yuan, Xiaojun 2; Bi, Haibo 1,3,4; Bushuk, Mitchell 5; Liang, Yu 1,6; Li, Cuihua 2; Huang, Haijun 1,3,4,6
	2022-04-01
发表期刊	CRYOSPHERE
ISSN	1994-0416
卷号	16 期号:3 页码:1141-1156
通讯作者	Yuan, Xiaojun(xyuan@ldeo.columbia.edu)
摘要	In this study, a regional linear Markov model is developed to assess seasonal sea ice predictability in the Pacific-Arctic sector. Unlike an earlier pan-Arctic Markov model that was developed with one set of variables for all seasons, the regional model consists of four seasonal modules with different sets of predictor variables, accommodating seasonally varying driving processes. A series of sensitivity tests are performed to evaluate the predictive skill in cross-validated experiments and to determine the best model configuration for each season. The prediction skill, as measured by the sea ice concentration (SIC) anomaly correlation coefficient (ACC) between predictions and observations, increased by 32 % in the Bering Sea and 18 % in the Sea of Okhotsk relative to the pan-Arctic model. The regional Markov model's skill is also superior to the skill of an anomaly persistence forecast. SIC trends significantly contribute to the model skill. However, the model retains skill for detrended sea ice extent predictions for up to 7-month lead times in the Bering Sea and the Sea of Okhotsk. We find that subsurface ocean heat content (OHC) provides a crucial source of prediction skill in all seasons, especially in the cold season, and adding sea ice thickness (SIT) to the regional Markov model has a substantial contribution to the prediction skill in the warm season but a negative contribution in the cold season. The regional model can also capture the seasonal reemergence of predictability, which is missing in the pan-Arctic model.
DOI	10.5194/tc-16-1141-2022
收录类别	SCI
语种	英语
资助项目	Lamont Endowment ; National Natural Science Foundation of China[42106223] ; National Natural Science Foundation of China[42076185] ; Natural Science Foundation of Shandong Province, China[ZR2021QD059] ; China Postdoctoral Science Foundation[2020TQ0322] ; Open Funds for the Key Laboratory of Marine Geology and Environment, Institute of Oceanology, Chinese Academy of Sciences[MGE2021KG15] ; Open Funds for the Key Laboratory of Marine Geology and Environment, Institute of Oceanology, Chinese Academy of Sciences[MGE2020KG04]
WOS研究方向	Physical Geography ; Geology
WOS类目	Geography, Physical ; Geosciences, Multidisciplinary
WOS记录号	WOS:000776573900001
出版者	COPERNICUS GESELLSCHAFT MBH
引用统计	被引频次：6[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/337002/178610
专题	海洋地质与环境重点实验室
通讯作者	Yuan, Xiaojun
作者单位	1.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Marine Geol & Environm, Qingdao, Peoples R China 2.Columbia Univ, Lamont Doherty Earth Observ, Palisades, NY 10964 USA 3.Qingdao Natl Lab Marine Sci & Technol, Lab Marine Geol, Qingdao, Peoples R China 4.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao, Peoples R China 5.NOAA, Geophys Fluid Dynam Lab, Princeton, NJ USA 6.Univ Chinese Acad Sci, Beijing, Peoples R China
第一作者单位	中国科学院海洋研究所; 中国科学院海洋大科学研究中心
推荐引用方式 GB/T 7714	Wang, Yunhe,Yuan, Xiaojun,Bi, Haibo,et al. Reassessing seasonal sea ice predictability of the Pacific-Arctic sector using a Markov model[J]. CRYOSPHERE,2022,16(3):1141-1156.
APA	Wang, Yunhe.,Yuan, Xiaojun.,Bi, Haibo.,Bushuk, Mitchell.,Liang, Yu.,...&Huang, Haijun.(2022).Reassessing seasonal sea ice predictability of the Pacific-Arctic sector using a Markov model.CRYOSPHERE,16(3),1141-1156.
MLA	Wang, Yunhe,et al."Reassessing seasonal sea ice predictability of the Pacific-Arctic sector using a Markov model".CRYOSPHERE 16.3(2022):1141-1156.

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