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

Wang, Yunhe; Yuan, Xiaojun; Bi, Haibo; Bushuk, Mitchell; Liang, Yu; Li, Cuihua; Huang, Haijun

doi:https://doi.org/10.5194/tc-16-1141-2022

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.

https://doi.org/10.5194/tc-16-1141-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 3

Research article

|

01 Apr 2022

Research article |

| 01 Apr 2022

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

Yunhe Wang, Xiaojun Yuan, Haibo Bi, Mitchell Bushuk, Yu Liang, Cuihua Li, and Haijun Huang

Supplement

https://doi.org/10.5194/tc-16-1141-2022-supplement

Data sets

Reassessing seasonal sea ice predictability of the Pacific-Arctic sector using a Markov model: supplemental data Yunhe Wang and Xiaojun Yuan https://doi.org/10.7916/4kpg-6904

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.