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

Zhang, Y., Bitz, C. M., Anderson, J. L., Collins, N. S., Hoar, T. J., et al. (2021). Estimating parameters in a sea ice model using an ensemble Kalman filter. The Cryosphere, doi:10.5194/tc-15-1277-2021

Title Estimating parameters in a sea ice model using an ensemble Kalman filter
Author(s) Yong-Fei Zhang, Cecilia M. Bitz, Jeffrey L. Anderson, Nancy S. Collins, Timothy J. Hoar, Kevin D. Raeder, Edward Blanchard-Wrigglesworth
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 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.
Publication Title The Cryosphere
Publication Date Mar 11, 2021
Publisher's Version of Record https://dx.doi.org/10.5194/tc-15-1277-2021
OpenSky Citable URL https://n2t.net/ark:/85065/d74171fm
OpenSky Listing View on OpenSky
CISL Affiliations TDD, DARES

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