Preprints
https://doi.org/10.5194/tc-2021-284
https://doi.org/10.5194/tc-2021-284

  21 Sep 2021

21 Sep 2021

Review status: this preprint is currently under review for the journal TC.

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

Yunhe Wang1,4, Xiaojun Yuan2, Haibo Bi1,3,4, Mitchell Bushuk5, Yu Liang1,6, Cuihua Li2, and Haijun Huang1,3,4,6 Yunhe Wang et al.
  • 1CAS Key Laboratory of Marine Geology and Environment, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
  • 2Lamont-Doherty Earth Observatory of Columbia University, New York, USA
  • 3Laboratory for Marine Geology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
  • 4Center for Ocean Mega‐Science, Chinese Academy of Sciences, Qingdao, China
  • 5National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA
  • 6University of Chinese Academy of Sciences, Beijing, China

Abstract. In this study, a regional linear Markov model is developed to assess seasonal sea ice predictability in the Arctic Pacific 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 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 pan-Arctic model. The regional Markov model's skill is also superior to the skill of an anomaly persistence forecast. Sea ice concentration (SIC) trends significantly contribute to the model skill. However, the model retains skill for detrended sea ice extent predictions up to 6 month lead times in the Bering Sea and the Sea of Okhotsk. We find that surface radiative fluxes contribute to predictability in the cold season and geopotential height and winds play an indispensable role in the warm-season forecast, contrasting to the thermodynamic processes dominating the pan-Arctic predictability. The regional model can also capture the seasonal reemergence of predictability, which is missing in the pan-Arctic model.

Yunhe Wang et al.

Status: open (until 16 Nov 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-284', Anonymous Referee #1, 17 Oct 2021 reply
  • RC2: 'Comment on tc-2021-284', Anonymous Referee #2, 20 Oct 2021 reply

Yunhe Wang et al.

Yunhe Wang et al.

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Short summary
We develop a regional linear Markov model consisting of four modules with seasonal dependent variables in the Pacific sector. The regional model retains skill for detrended sea ice extent predictions up to 6 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.