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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 May 2020

13 May 2020

Review status
A revised version of this preprint is currently under review for the journal TC.

Estimating Parameters in a Sea Ice Model using an Ensemble Kalman Filter

Yong-Fei Zhang1,a, Cecilia M. Bitz1, Jeffrey L. Anderson2, Nancy S. Collins2, Timothy J. Hoar2, Kevin D. Raeder2, and Edward Blanchard-Wrigglesworth1 Yong-Fei Zhang et al.
  • 1Department of Atmospheric Sciences, University of Washington, Seattle, Washington, USA
  • 2IMAGe, CISL, National Center for Atmospheric Research, Boulder, Colorado, USA
  • anow at: Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, New Jersey, USA

Abstract. Uncertain or inaccurate parameters in sea ice models influence seasonal predictions and climate change projections in terms of both mean and trend. We explore the feasibility and benefits of applying an Ensemble Kalman filter (EnKF) to estimate parameters in the Los Alamos sea ice model (CICE). Parameter estimation (PE) is applied to the highly influential dry snow grain radius and combined with state estimation in a series of perfect model observing system simulation experiments (OSSEs). Allowing the parameter to vary in space improves performance along the sea ice edge compared to requiring the parameter to be uniform everywhere. We compare experiments with both PE and state estimation to experiments with only the latter and found that the benefits of PE mostly occur after the DA period, when no observations are available to assimilate (i.e., the forecast period), which suggests PE’s relevance for improving seasonal predictions of Arctic sea ice.

Yong-Fei Zhang et al.

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Yong-Fei Zhang et al.

Yong-Fei Zhang et al.


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Latest update: 03 Dec 2020
Publications Copernicus
Short summary
Sea ice models suffer from large uncertainties arisen from multiple sources, among which parametric uncertainty is highly under-investigated. We select a key ice-albedo parameter and update it by assimilating either sea ice concentration or thickness observations. We found that the sea ice albedo parameter is improved by data assimilation, especially by assimilating sea ice thickness observations. The improved parameter can further benefit the forecast of sea ice after data assimilation stops.
Sea ice models suffer from large uncertainties arisen from multiple sources, among which...