19 Dec 2022
19 Dec 2022
Status: this preprint is open for discussion.

Seasonal Sea Ice Prediction with the CICE Model and Positive Impact of CryoSat-2 Ice Thickness Initialization

Shan Sun1 and Amy Solomon2 Shan Sun and Amy Solomon
  • 1NOAA/Global Systems Laboratory, USA
  • 2University of Colorado Boulder, Cooperative Institute for Research in Environmental Sciences and NOAA/Physical Sciences Laboratory, USA

Abstract. The Los Alamos sea ice model (CICE) is being tested in standalone mode to identify biases that limit its suitability for seasonal prediction. The prescribed atmospheric forcings to drive CICE are from the NCEP Climate Forecast System Reanalysis (CFSR). A built-in mixed layer ocean model in CICE is used. Initial conditions for the sea ice and the mixed layer ocean are also from CFSR in the control experiments. The simulated sea ice extent is generally in good agreement with observations in the warm season at all lead times up to 12 months both in the Arctic and Antarctic, suggesting that CICE is able to provide useful sea ice edge information for seasonal prediction. However, the Arctic sea ice thickness forecast has a positive bias stemming from the initial conditions, and this bias often persists for more than a season, limiting the model’s seasonal forecast skill. When this bias is reduced by initializing ice thickness using the CryoSat-2 satellite observations while keeping all other initial fields unchanged in the CS2_IC experiments, both simulated ice edge and thickness improve. This confirms the important roles of sea ice thickness initialization in sea ice seasonal prediction seen in many studies.

Shan Sun and Amy Solomon

Status: open (until 23 Feb 2023)

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Shan Sun and Amy Solomon

Shan Sun and Amy Solomon


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Short summary
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.