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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Cited
11 citations as recorded by crossref.
- Reducing the Spring Barrier in Predicting Summer Arctic Sea Ice Concentration J. Zeng et al. 10.1029/2022GL102115
- Decadal transformations of antarctic sea ice modes B. Guo et al. 10.3389/fmars.2024.1506715
- Seasonal forecasting of Pan-Arctic sea ice with state space model W. Wang et al. 10.1038/s41612-025-01058-0
- Analysis of the Atmosphere and the Ocean Upper Layer State Predictability for up to 5 Years Ahead Using the INMCM5 Climate Model Hindcasts V. Vorobeva et al. 10.3103/S106837392307004X
- SICNetseason V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by incorporating sea ice thickness data Y. Ren et al. 10.5194/gmd-18-2665-2025
- Predicting the Daily Sea Ice Concentration on a Subseasonal Scale of the Pan-Arctic During the Melting Season by a Deep Learning Model Y. Ren & X. Li 10.1109/TGRS.2023.3279089
- ENSO’s impact on linear and nonlinear predictability of Antarctic sea ice Y. Wang et al. 10.1038/s41612-025-00962-9
- An effective deep-learning prediction of Arctic sea-ice concentration based on the U-Net model Y. Xie et al. 10.1016/j.aosl.2025.100691
- Subseasonal-to-seasonal prediction of arctic sea ice Using a Fully Coupled dynamical ensemble forecast system A. Liu et al. 10.1016/j.atmosres.2023.107014
- Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model Y. Wang et al. 10.1029/2023GL104347
- Reassessing seasonal sea ice predictability of the Pacific-Arctic sector using a Markov model Y. Wang et al. 10.5194/tc-16-1141-2022
10 citations as recorded by crossref.
- Reducing the Spring Barrier in Predicting Summer Arctic Sea Ice Concentration J. Zeng et al. 10.1029/2022GL102115
- Decadal transformations of antarctic sea ice modes B. Guo et al. 10.3389/fmars.2024.1506715
- Seasonal forecasting of Pan-Arctic sea ice with state space model W. Wang et al. 10.1038/s41612-025-01058-0
- Analysis of the Atmosphere and the Ocean Upper Layer State Predictability for up to 5 Years Ahead Using the INMCM5 Climate Model Hindcasts V. Vorobeva et al. 10.3103/S106837392307004X
- SICNetseason V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by incorporating sea ice thickness data Y. Ren et al. 10.5194/gmd-18-2665-2025
- Predicting the Daily Sea Ice Concentration on a Subseasonal Scale of the Pan-Arctic During the Melting Season by a Deep Learning Model Y. Ren & X. Li 10.1109/TGRS.2023.3279089
- ENSO’s impact on linear and nonlinear predictability of Antarctic sea ice Y. Wang et al. 10.1038/s41612-025-00962-9
- An effective deep-learning prediction of Arctic sea-ice concentration based on the U-Net model Y. Xie et al. 10.1016/j.aosl.2025.100691
- Subseasonal-to-seasonal prediction of arctic sea ice Using a Fully Coupled dynamical ensemble forecast system A. Liu et al. 10.1016/j.atmosres.2023.107014
- Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model Y. Wang et al. 10.1029/2023GL104347
1 citations as recorded by crossref.
Latest update: 15 Sep 2025
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...
