Sea ice growth rates from tide‐driven visible banding

Turner, Kate E.; Smith, Inga I.J.; Tison, Jean‐Louis; Verbeke, Véronique; McGuinness, Mark; Ingham, M.; Vennell, Ross; Trodahl, Joe

doi:10.1002/2016jc012524

Cited by 3 publications

(7 citation statements)

References 22 publications

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“…With no snow cover the dynamic simulation overestimates the banding-inferred growth rates, and the steady-state model presented in [104] (which also made no allowance for a snow cover) fits the early growth rates more closely than our transient model. This may be due to the different freeze in dates selected to tune each model.…”

Section: Further Application: Snow Covermentioning

confidence: 59%

“…We compare our model simulated results to sea ice growth rates measured via banding analysis performed on an ice core from 1999 in McMurdo Sound, Antarctica, and a previously used steady state model as described in [104]. The banding analysis uses dark bands sometimes seen in young sea ice to infer sea ice growth rate as a function of thickness and can be used to measure growth rates for thin sea ice when a thermistor string cannot safely be deployed.…”

Section: Further Application: Snow Covermentioning

confidence: 99%

“…The banding analysis uses dark bands sometimes seen in young sea ice to infer sea ice growth rate as a function of thickness and can be used to measure growth rates for thin sea ice when a thermistor string cannot safely be deployed. An ice thickness of 110±5 cm was measured using data from a nearby thermistor string [94,104] on 21 June. Congelation ice formation started at 15 cm thickness.…”

Section: Further Application: Snow Covermentioning

confidence: 99%

“…The models were tuned to match the sudden rise in air temperatures causing a drop in the growth rates near a thickness of 60 cm and to match the final ice thickness. The model presented in [104] was tuned with a freeze in time (15 cm thickness of ice) of the early hours of 14 May. Our simulation with no snow had a freeze in time of the early hours of 15 May and with a 15 mm snow cover a freeze in time of midday on 11 May.…”

Section: Further Application: Snow Covermentioning

confidence: 99%

“…Figure 2.8:A comparison of ice growth rates computed using a steady state model as described in[104] (solid red curve) with growth rates computed using the new fully transient model presented in this chapter (dashed blue curve). Data values are computed from a banding analysis of ice cores (black symbols)[104]. Air temperatures used are those given in[104] Left: No snow layer is present in the transient simulations (dashed blue curve).…”

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Thermal Oscillations in Sea Ice and Soils

Ben-Shachar¹

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We present mathematical analysis of temperature oscillations in depth-dependent media by investigating the thermodynamics of sea ice and of soils. Time-series temperature measurements from thermistor strings are common in both sea ice and soils and are used to study their properties, evolution, seepage flux and a host of interactions with their environment. We use numerical tools and perturbation theory to study the propagation of high frequency, small amplitude temperature oscillations through the in-homogeneous media using one dimensional models. Analytical tools for studying such thermal waves are derived. In sea ice the absorption of solar radiation and oscillating air temperatures result in two distinct thermal wave propagation behaviours. At depths, stationary waves associated with in place solar heating are observed, whereas near the surface, travelling thermal waves are present due to the quick decay in the absorbed solar radiation and the oscillatory air temperatures. These are observed in thermistor string data taken in McMurdo Sound, Antarctica between 1996-2003. Using a variety of mathematical tools, the leading order behaviour of the diurnal temperature oscillation is approximated in terms of elementary functions and is compared with results from numerical simulations. The thermodynamics of soils differ from sea ice in that all the solar radiation is absorbed at the upper boundary and water movement within the soil carries heat. Macroscale in-homogeneity in the advection-diffusion equation is considered and the thermal wave propagation characteristics are studied using a WKB approximation. The leading order behaviour is shown to reduce exactly to the Stallman equations, being the solution to the thermal wave propagation in a homogeneous soil with constant, uniform water flow. We use the leading order WKB expansion to estimate errors in the homogeneous soil assumption commonly made to estimate the seepage velocity and soil diffusivity. It is shown that the diffusivity estimations are relatively stable and provide reasonably accurate results, but the seepage velocity estimations incur significant errors that should be considered. A frequency dependence in the error leads us to suggest multi-frequency analysis for detection and further studies of the effects of in-homogeneous soil thermodynamics.

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Section: Further Application: Snow Covermentioning

confidence: 59%

Section: Further Application: Snow Covermentioning

confidence: 99%

Section: Further Application: Snow Covermentioning

confidence: 99%

Section: Further Application: Snow Covermentioning

confidence: 99%

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confidence: 99%

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