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

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 but degrades in the central Arctic compared to requiring the parameter to be uniform everywhere, suggesting that spatially varying parameters will likely improve PE performance at local scales and should be considered with caution. We compare experiments with both PE and state estimation to experiments with only the latter and have found that the benefits of PE mostly occur after the data assimilation 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.

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Author Zhang, Y.
Bitz, C. M.
Anderson, Jeffrey L.
Collins, Nancy S.
Hoar, Timothy J.
Raeder, Kevin D.
Blanchard-Wrigglesworth, E.
Publisher UCAR/NCAR - Library
Publication Date 2021-03-11T00:00:00
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Topic Category geoscientificInformation
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Metadata Date 2025-07-11T16:17:18.163909
Metadata Record Identifier edu.ucar.opensky::articles:24309
Metadata Language eng; USA
Suggested Citation Zhang, Y., Bitz, C. M., Anderson, Jeffrey L., Collins, Nancy S., Hoar, Timothy J., Raeder, Kevin D., Blanchard-Wrigglesworth, E.. (2021). Estimating parameters in a sea ice model using an ensemble Kalman filter. UCAR/NCAR - Library. https://n2t.org/ark:/85065/d74171fm. Accessed 04 August 2025.

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