The Cryosphere
The Cryosphere
TC
Articles | Volume 15, issue 3
Articles | Volume 15, issue 3
https://doi.org/10.5194/tc-15-1277-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-15-1277-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
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

Viewed

Total article views: 2,828 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,796 891 141 2,828 423 173 226
  • HTML: 1,796
  • PDF: 891
  • XML: 141
  • Total: 2,828
  • Supplement: 423
  • BibTeX: 173
  • EndNote: 226
Views and downloads (calculated since 13 May 2020)
Cumulative views and downloads (calculated since 13 May 2020)

Viewed (geographical distribution)

Total article views: 2,828 (including HTML, PDF, and XML) Thereof 2,688 with geography defined and 140 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 02 May 2026
Download
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.
Read more
Share