Articles | Volume 16, issue 3
https://doi.org/10.5194/tc-16-1141-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-1141-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Apr 2022
Research article |
| 01 Apr 2022
| 01 Apr 2022
Reassessing seasonal sea ice predictability of the Pacific-Arctic sector using a Markov model
Yunhe Wang
CAS Key Laboratory of Marine Geology and Environment, Institute of
Oceanology, Chinese Academy of Sciences, Qingdao, China
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao,
China
Lamont-Doherty Earth Observatory of Columbia University, Palisades, New York,
USA
Haibo Bi
CAS Key Laboratory of Marine Geology and Environment, Institute of
Oceanology, Chinese Academy of Sciences, Qingdao, China
Laboratory for Marine Geology, Qingdao National Laboratory for Marine
Science and Technology, Qingdao, China
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao,
China
Mitchell Bushuk
National Oceanic and Atmospheric Administration/Geophysical Fluid
Dynamics Laboratory, Princeton, New Jersey, USA
Yu Liang
CAS Key Laboratory of Marine Geology and Environment, Institute of
Oceanology, Chinese Academy of Sciences, Qingdao, China
University of Chinese Academy of Sciences, Beijing, China
Cuihua Li
Lamont-Doherty Earth Observatory of Columbia University, Palisades, New York,
USA
Haijun Huang
CAS Key Laboratory of Marine Geology and Environment, Institute of
Oceanology, Chinese Academy of Sciences, Qingdao, China
Laboratory for Marine Geology, Qingdao National Laboratory for Marine
Science and Technology, Qingdao, China
Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao,
China
University of Chinese Academy of Sciences, Beijing, China
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Short summary
We develop a regional linear Markov model consisting of four modules with seasonally dependent variables in the Pacific sector. 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. The prediction skill, as measured by the percentage of grid points with significant correlations (PGS), increased by 75 % in the Bering Sea and 16 % in the Sea of Okhotsk relative to the earlier pan-Arctic model.
We develop a regional linear Markov model consisting of four modules with seasonally dependent...
