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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Volume 14, issue 6
The Cryosphere, 14, 2029–2052, 2020
https://doi.org/10.5194/tc-14-2029-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
The Cryosphere, 14, 2029–2052, 2020
https://doi.org/10.5194/tc-14-2029-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 24 Jun 2020

Research article | 24 Jun 2020

Satellite-retrieved sea ice concentration uncertainty and its effect on modelling wave evolution in marginal ice zones

Takehiko Nose1, Takuji Waseda1, Tsubasa Kodaira1, and Jun Inoue2 Takehiko Nose et al.
  • 1Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
  • 2National Institute of Polar Research, Tachikawa, Tokyo, Japan

Abstract. Ocean surface waves are known to decay when they interact with sea ice. Wave–ice models implemented in a spectral wave model, e.g. WAVEWATCH III® (WW3), derive the attenuation coefficient based on several different model ice types, i.e. how the model treats sea ice. In the marginal ice zone (MIZ) with sea ice concentration (SIC)  <  1, the wave attenuation is moderated by SIC: we show that subgrid-scale processes relating to the SIC and sea ice type heterogeneity in the wave–ice models are missing and the accuracy of SIC plays an important role in the predictability. Satellite-retrieved SIC data (or a sea ice model that assimilates them) are often used to force wave–ice models, but these data are known to have uncertainty. To study the effect of SIC uncertainty ΔSIC on modelling MIZ waves during the 2018 R/V Mirai observational campaign in the refreezing Chukchi Sea, a WW3 hindcast experiment was conducted using six satellite-retrieved SIC products based on four algorithms applied to SSMIS and AMSR2 data. The results show that ΔSIC can cause considerable wave prediction discrepancies in ice cover. There is evidence that bivariate uncertainty data (model significant wave heights and SIC forcing) are correlated, although off-ice wave growth is more complicated due to the cumulative effect of ΔSIC along an MIZ fetch. The analysis revealed that the effect of ΔSIC can overwhelm the uncertainty arising from the choice of model ice types, i.e. wave–ice interaction parameterisations. Despite the missing subgrid-scale physics relating to the SIC and sea ice type heterogeneity in WW3 wave–ice models – which causes significant modelling uncertainty – this study found that the accuracy of satellite-retrieved SIC used as model forcing is the dominant error source of modelling MIZ waves in the refreezing ocean.

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Short summary
Accurate wave modelling in and near ice-covered ocean requires true sea ice concentration mapping of the model region. The information derived from satellite instruments has considerable uncertainty depending on retrieval algorithms and sensors. This study shows that the accuracy of satellite-retrieved sea ice concentration estimates is a major error source in wave–ice models. A similar feedback effect of sea ice concentration uncertainty may also apply to modelling lower atmospheric conditions.
Accurate wave modelling in and near ice-covered ocean requires true sea ice concentration...
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