Articles | Volume 15, issue 3
Research article
11 Mar 2021
Research article |  | 11 Mar 2021

Estimating parameters in a sea ice model using an ensemble Kalman filter

Yong-Fei Zhang, Cecilia M. Bitz, Jeffrey L. Anderson, Nancy S. Collins, Timothy J. Hoar, Kevin D. Raeder, and Edward Blanchard-Wrigglesworth


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (12 Oct 2020) by Petra Heil
AR by Yongfei Zhang on behalf of the Authors (23 Nov 2020)  Author's response
ED: Publish subject to technical corrections (15 Jan 2021) by Petra Heil
AR by Yongfei Zhang on behalf of the Authors (01 Feb 2021)  Author's response    Manuscript
Short summary
Sea ice models suffer from large uncertainties arising 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.