Articles | Volume 15, issue 3
The Cryosphere, 15, 1277–1284, 2021
The Cryosphere, 15, 1277–1284, 2021

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 et al.

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Cited articles

Anderson, J.: An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129, 2884–2903, 2002. 
Anderson, J. L.: An adaptive covariance inflation error correction algorithm for ensemble filters, Tellus, 59, 210–224, 2007. 
Anderson, J. L., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and Arellano, A.: The Data Assimilation Research Testbed: Acommunity facility, B. Am. Meteor. Soc., 90, 1283–1296,, 2009. 
Annan, J. D., Hargreaves, J. C., Edwards, N. R., and Marsh, R.: Parameter estimation in an intermediate complexity earth system model using an ensemble Kalman filter, Ocean Modell., 8, 135–154,, 2005. 
Blanchard-Wrigglesworth, E., Armour, K. C., Bitz, C. M., and deWeaver, E.: Persistence and inherent predictability of Arctic sea ice in a GCM ensemble and observations, J. Climate, 24, 231–250,, 2011. 
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.