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.
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.
the Creative Commons Attribution 4.0 License.
Suitability of the CICE sea ice model for seasonal prediction and positive impact of CryoSat-2 ice thickness initialization
Shan Sun
CORRESPONDING AUTHOR
NOAA Global Systems Laboratory, Boulder, Colorado, USA
Amy Solomon
Cooperative Institute for Research in Environmental Sciences and NOAA Physical Sciences Laboratory, University of Colorado Boulder, Boulder, Colorado, USA
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Preprint archived
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We evaluate sea ice prediction skill at seasonal time scales using the CICE sea ice model. It confirms the importance of the accuracy in ice thickness initialization for seasonal sea ice prediction. It suggests that there exists a potentially important source of additional skill in seasonal forecasting, namely, a more reliable sea ice thickness initialization. Hence, assimilation of sea ice thickness appears to be highly relevant for advancing seasonal prediction skill.
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Preprint withdrawn
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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.
The study brings to light the suitability of CICE for seasonal prediction being contingent on...