Brief communication: Impacts of ocean-wave-induced breakup of Antarctic sea ice via thermodynamics in a stand-alone  version of the CICE sea-ice model

Bennetts, Luke G.; O’Farrell, Siobhan; Uotila, Petteri

doi:10.5194/tc-11-1035-2017

Cited by 64 publications

(106 citation statements)

References 18 publications

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“…We are also currently implementing the more conservative lateral melting model of Steele (1992) in our model to include this effect to some extent. With simulations using a WIM coupled to a standalone version of CICE-E, which contains the model of Steele (1992), Bennetts et al (2017) found that the concentration in the vicinity of the Antarctic ice edge could drop by a modest amount (of the order of 10 %) in the summer. However, this could also change with coupling to an ocean model, as well as if a different parameterisation that reflects the increased lateral melting of larger floes was used.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

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Wave–ice interactions in the neXtSIM sea-ice model

2017

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Abstract. In this paper we describe a waves-in-ice model (WIM), which calculates ice breakage and the wave radiation stress (WRS). This WIM is then coupled to the new sea-ice model neXtSIM, which is based on the elasto-brittle (EB) rheology. We highlight some numerical issues involved in the coupling and investigate the impact of the WRS, and of modifying the EB rheology to lower the stiffness of the ice in the area where the ice has broken up (the marginal ice zone or MIZ). In experiments in the absence of wind, we find that wind waves can produce noticeable movement of the ice edge in loose ice (concentration around 70 %) -up to 36 km, depending on the material parameters of the ice that are used and the dynamical model used for the broken ice. The ice edge position is unaffected by the WRS if the initial concentration is higher ( 0.9). Swell waves (monochromatic waves with low frequency) do not affect the ice edge location (even for loose ice), as they are attenuated much less than the higher-frequency components of a wind wave spectrum, and so consequently produce a much lower WRS (by about an order of magnitude at least).In the presence of wind, we find that the wind stress dominates the WRS, which, while large near the ice edge, decays exponentially away from it. This is in contrast to the wind stress, which is applied over a much larger ice area. In this case (when wind is present) the dynamical model for the MIZ has more impact than the WRS, although that effect too is relatively modest. When the stiffness in the MIZ is lowered due to ice breakage, we find that on-ice winds produce more compression in the MIZ than in the pack, while off-ice winds can cause the MIZ to be separated from the pack ice.

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Section: Conclusion and Discussionmentioning

confidence: 99%

“…In the latter model, each thickness category can have its own FSD. More parametric approaches have also been used (Dumont et al, 2011;Williams et al, 2013a;Bennetts et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Wave–ice interactions in the neXtSIM sea-ice model

2017

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“…At present, we do not know the dominant mechanism that removes wave energy as it propagates in the miz , which is a major shortcoming in models. This lack of knowledge means that these models are speculative and require validation against field data (Bennetts et al, ).…”

Section: Introductionmentioning

confidence: 99%

Dispersion Relations, Power Laws, and Energy Loss for Waves in the Marginal Ice Zone

et al. 2018

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Analysis of field measurements of ocean surface wave activity in the marginal ice zone, from campaigns in the Arctic and Antarctic and over a range of different ice conditions, shows the wave attenuation rate with respect to distance has a power law dependence on the frequency with order between two and four. With this backdrop, the attenuation‐frequency power law dependencies given by three dispersion relation models are obtained under the assumptions of weak attenuation, negligible deviation of the wave number from the open water wave number, and thin ice. It is found that two of the models (both implemented in WAVEWATCH III®), predict attenuation rates that are far more sensitive to frequency than indicated by the measurements. An alternative method is proposed to derive dispersion relation models, based on energy loss mechanisms. The method is used to generate example models that predict power law dependencies that are comparable with the field measurements.

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“…This is the transition from loosely joined pancakes to cemented pancakes mentioned above, which is a freezing‐together of floes, rather than a dynamic process, impacting only the floe size distribution and not sea ice concentration. Zhang et al () notes that the coalescence of sea ice floes is “difficult to determine” because of “lack of knowledge about the welding processes.” As there are no empirical observations justifying the appropriate rate of welding, Bennetts et al () represent welding processes by doubling their representative floe diameter (up to a maximum diameter) each time step in grid cells where the ocean temperature is below freezing. Similarly, Zhang et al () move all floes into the largest floe size category when the ice growth rate in a certain grid cell exceeds a certain threshold, a number which is chosen as part of their model tuning.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Growth of Pancake Sea Ice Floes Using Images From Drifting Buoys

2018

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New sea ice in the polar regions often begins as small pancake floes in autumn and winter that grow laterally and weld together into larger floes. However, conditions in polar oceans during freezeup are harsh, rendering in situ observations of small‐scale sea ice growth processes difficult and infrequent. Here we apply image processing techniques to images obtained by drifting wave buoys (SWIFTs) deployed in the autumn Arctic Ocean to quantify these processes in situ for the first time. Small pancake ice floes were observed to form and grow gradually in freezing, low‐wave conditions. We find that pancake floe diameters are limited by the wave field, such that floe diameter is proportional to wavelength and amplitude over time. Floe welding correlates well with sea ice concentration, and the observations can be used to estimate a key model parameter for floe size evolution. There is some agreement between observed lateral growth rates and those predicted using a theoretical model based on heat flux balance, but the model lateral growth rates are too conservative in these conditions. These results will be used to inform description of lateral floe growth and floe welding in new models that evolve sea ice floe size distribution.

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Brief communication: Impacts of ocean-wave-induced breakup of Antarctic sea ice via thermodynamics in a stand-alone version of the CICE sea-ice model

Cited by 64 publications

References 18 publications

Wave–ice interactions in the neXtSIM sea-ice model

Wave–ice interactions in the neXtSIM sea-ice model

Dispersion Relations, Power Laws, and Energy Loss for Waves in the Marginal Ice Zone

Quantifying Growth of Pancake Sea Ice Floes Using Images From Drifting Buoys

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