Articles | Volume 13, issue 1
The Cryosphere, 13, 125–139, 2019
https://doi.org/10.5194/tc-13-125-2019
The Cryosphere, 13, 125–139, 2019
https://doi.org/10.5194/tc-13-125-2019

Research article 14 Jan 2019

Research article | 14 Jan 2019

New insight from CryoSat-2 sea ice thickness for sea ice modelling

David Schröder et al.

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Cited articles

Allard, R. A., Farrell, S. L., Hebert, D. A., Johnston, W. F., Li, L., Kurtz, N. T., Phelps, M. W., Posey, P. G., Tilling, R., Ridout, A., and Wallcraft, A. J.: Utilizing CryoSat-2 sea ice thickness to initialize a coupled ice-ocean modeling system, Adv. Space Res., 62, 1265–1280, https://doi.org/10.1016/j.asr.2017.12.030, 2018. 
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Blockley, E. W. and Peterson, K. A.: Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness, The Cryosphere, 12, 3419–3438, https://doi.org/10.5194/tc-12-3419-2018, 2018. 
Briegleb, B. P. and Light, B.: A delta-Eddington multiple scattering parameterization for solar radiation in the sea ice component of the Com- munity Climate System Model, Tech. Note 472, Natl. Cent. for Atmos. Res., Boulder, CO, 2007. 
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This paper uses sea ice thickness data (CryoSat-2) to identify and correct shortcomings in simulating winter ice growth in the widely used sea ice model CICE. Adding a model of snow drift and using a different scheme for calculating the ice conductivity improve model results. Sensitivity studies demonstrate that atmospheric winter conditions have little impact on winter ice growth, and the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season.