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Assimilating summer sea-ice thickness observations improves Arctic sea-ice forecast
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  • Ruizhe Song,
  • Longjiang Mu,
  • Svetlana Loza,
  • Frank Kauker,
  • Xianyao Chen
Ruizhe Song
Ocean University of China
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Longjiang Mu
Laoshan Laboratory

Corresponding Author:[email protected]

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Svetlana Loza
Alfred Wener Institute for Polar and Marine Research
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Frank Kauker
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Xianyao Chen
Ocean University of China
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Proper Arctic sea-ice forecasting for the melt season is still a major challenge because of the recent lack of reliable pan-Arctic summer sea-ice thickness (SIT) data. A new summer CryoSat-2 SIT observation data set based on an artificial intelligence algorithm may alleviate this situation. We assess the impact of this new data set on the initialization of sea-ice forecasts in the melt seasons of 2015 and 2016 in a coupled sea ice-ocean model with data assimilation. We find that the assimilation of the summer CryoSat-2 SIT observations can reduce the summer ice-edge forecast error. Further, adding SIT observations to an established forecast system with sea-ice concentration assimilation leads to more realistic short-term summer ice-edge forecasts in the Arctic Pacific sector. The long-term Arctic-wide SIT prediction is also improved. In spite of remaining uncertainties, summer CryoSat-2 SIT observations have the potential to improve Arctic sea-ice forecast on multiple time scales.
01 Feb 2024Submitted to ESS Open Archive
01 Feb 2024Published in ESS Open Archive