The Cryosphere
The Cryosphere
TC
Articles | Volume 18, issue 7
Articles | Volume 18, issue 7
https://doi.org/10.5194/tc-18-3033-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-18-3033-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 7
Research article
 | 
03 Jul 2024
Research article |  | 03 Jul 2024

Suitability of the CICE sea ice model for seasonal prediction and positive impact of CryoSat-2 ice thickness initialization

Shan Sun and Amy Solomon

Data sets

ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis C. Amante and B. W. Eakins https://www.ncei.noaa.gov/products/etopo-global-relief-model/

NCEP Climate Forecast System Version 2 (CFSv2) 6-hourly Products, Research Data Archive at the National Center for Atmospheric Research S. Saha et al. https://doi.org/10.5065/D61C1TXF

Sea Ice Index F. Fetterer et al. https://doi.org/10.7265/N5K072F8

CICE: the Los Alamos sea ice model documentation and software user's manual version 5.1 (https://github.com/CICE-Consortium/CICE) E. C. Hunke et al. https://csdms.colorado.edu/w/images/CICE_documentation_and_software_user's_manual.pdf

Suitability of CICE sea ice model for seasonal prediction and positive impact of CryoSat-2 ice thickness initialization S. Sun and A. Solomon https://doi.org/10.5281/zenodo.12176714

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Short summary
The study brings to light the suitability of CICE for seasonal prediction being contingent on several factors, such as initial conditions like sea ice coverage and thickness, as well as atmospheric and oceanic conditions including oceanic currents and sea surface temperature. We show there is potential to improve seasonal forecasting by using a more reliable sea ice thickness initialization. Thus, data assimilation of sea ice thickness is highly relevant for advancing seasonal prediction skills.
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