A physically based parameterization of gravity drainage for sea‐ice modeling

Jones, David Rees; Worster, M. Grae

doi:10.1002/2013jc009296

Cited by 25 publications

(56 citation statements)

References 58 publications

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“…However, inspecting our parameter space we find low sensitivity to this parameter, and similar results could have been achieved using the original parameter (see supplementary information). We have a much lower

R a_{normalc}

when compared to those presented in Rees Jones and Worster (). Using an

R a_{normalc}

of 20 or 40 our simulated desalination was too weak.…”

Section: Methodscontrasting

confidence: 54%

“…Using an

R a_{normalc}

of 20 or 40 our simulated desalination was too weak. Rees Jones and Worster () constrained their tuning parameters partially by enforcing a delay in the onset of gravity drainage, as observed in sea ice grown from a cold plate (Wettlaufer et al, ) but not in the field (Notz & Worster, ). Rees Jones and Worster () note that without this constraint, as in this study, much lower values of

R a_{normalc}

are potentially reasonable.…”

Section: Methodsmentioning

confidence: 99%

“…

S

is then recovered for each measurement depth using

S = ϕ S_{br} false(T false)

. To calculate brine salinity, we assume thermodynamic equilibrium and use the liquidus curve derived by Rees Jones and Worster () from the data of Weast () for NaCl,

S_{br} false/ false({g kg}^{- 1} false) = - 17.6 ()(, \frac{T}{^{\circ} normalC}) - 0.389 {()(, \frac{T}{^{\circ} normalC})}^{2} - 0.00362 {()(, \frac{T}{^{\circ} normalC})}^{3} .

…”

Section: Methodsmentioning

confidence: 99%

“…Convective schemes derive from ideas developed in Wells et al () with a downward brine channel flow from each layer directly connecting the sea ice interior to the ocean, and a compensating upward return flow (Figure ). These schemes essentially solve a one‐dimensional salt advection equation of the form

\frac{\partial S}{\partial t} = - w \frac{\partial S_{br}}{\partial z},

where

w

is the upwelling Darcy velocity of the return flow (positive downward; Rees Jones & Worster, ),

S_{br}

is the brine salinity, and

z

and

t

are depth and time, respectively. Both

w

and the brine salinity gradient are negative for growing sea ice, giving a net desalination.…”

Section: Modeling Gravity Drainage As a 1‐d Vertical Processmentioning

confidence: 99%

“…Notz and Worster () documented the evolution of sea ice salinity during the early stages of natural Arctic sea ice growth using nondestructive salinity measurements (Notz et al, ). Their analysis suggests that gravity drainage intensity relates to a depth‐dependent Rayleigh number describing the competition between buoyant instability and dissipation (Rees Jones & Worster, ). High‐resolution two‐dimensional numerical simulations of growing sea ice based on mushy‐layer equations (Wells et al, ) indicate a linear relationship between the salt flux from growing sea ice due to gravity drainage and the Rayleigh number.…”

Section: Introductionmentioning

confidence: 99%

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Tracer Measurements in Growing Sea Ice Support Convective Gravity Drainage Parameterizations

et al. 2020

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Gravity drainage is the dominant process redistributing solutes in growing sea ice. Modeling gravity drainage is therefore necessary to predict physical and biogeochemical variables in sea ice. We evaluate seven gravity drainage parameterizations, spanning the range of approaches in the literature, using tracer measurements in a sea ice growth experiment. Artificial sea ice is grown to around 17 cm thickness in a new experimental facility, the Roland von Glasow air‐sea‐ice chamber. We use NaCl (present in the water initially) and rhodamine (injected into the water after 10 cm of sea ice growth) as independent tracers of brine dynamics. We measure vertical profiles of bulk salinity in situ, as well as bulk salinity and rhodamine in discrete samples taken at the end of the experiment. Convective parameterizations that diagnose gravity drainage using Rayleigh numbers outperform a simpler convective parameterization and diffusive parameterizations when compared to observations. This study is the first to numerically model solutes decoupled from salinity using convective gravity drainage parameterizations. Our results show that (1) convective, Rayleigh number‐based parameterizations are our most accurate and precise tool for predicting sea ice bulk salinity; and (2) these parameterizations can be generalized to brine dynamics parameterizations, and hence can predict the dynamics of any solute in growing sea ice.

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R a_{normalc}

when compared to those presented in Rees Jones and Worster (). Using an

R a_{normalc}

of 20 or 40 our simulated desalination was too weak.…”

Section: Methodscontrasting

confidence: 54%

“…Using an

R a_{normalc}

R a_{normalc}

are potentially reasonable.…”

Section: Methodsmentioning

confidence: 99%

“…

S

is then recovered for each measurement depth using

S = ϕ S_{br} false(T false)

. To calculate brine salinity, we assume thermodynamic equilibrium and use the liquidus curve derived by Rees Jones and Worster () from the data of Weast () for NaCl,

S_{br} false/ false({g kg}^{- 1} false) = - 17.6 ()(, \frac{T}{^{\circ} normalC}) - 0.389 {()(, \frac{T}{^{\circ} normalC})}^{2} - 0.00362 {()(, \frac{T}{^{\circ} normalC})}^{3} .

…”

Section: Methodsmentioning

confidence: 99%

\frac{\partial S}{\partial t} = - w \frac{\partial S_{br}}{\partial z},

where

w

is the upwelling Darcy velocity of the return flow (positive downward; Rees Jones & Worster, ),

S_{br}

is the brine salinity, and

z

and

t

are depth and time, respectively. Both

w

and the brine salinity gradient are negative for growing sea ice, giving a net desalination.…”

Section: Modeling Gravity Drainage As a 1‐d Vertical Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Tracer Measurements in Growing Sea Ice Support Convective Gravity Drainage Parameterizations

et al. 2020

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Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model

et al. 2016

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Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual model parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance-based approach in which Sobol' sequences are used to efficiently sample the full 39-dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. It is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model.

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Brine Convection, Temperature Fluctuations, and Permeability in Winter Antarctic Land‐Fast Sea Ice

et al. 2018

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Vertical temperature strings are used in sea ice research to study heat flow, ice growth rate, and ocean‐ice‐atmosphere interaction. We demonstrate the feasibility of using temperature fluctuations as a proxy for fluid movement, a key process for supplying nutrients to Antarctic sea ice algal communities. Four strings were deployed in growing, land‐fast sea ice in McMurdo Sound, Antarctica. By smoothing temperature data with the robust LOESS method, we obtain temperature fluctuations that cannot be explained by insolation or atmospheric heat loss. Statistical distributions of these temperature fluctuations are investigated with sensitivities to the distance from the ice‐ocean interface, average ice temperature, and sea ice structure. Fluctuations are greatest close to the base (<50 mm) at temperatures >−3°C, and are discrete events with an average active period of 43% compared to 11% when the ice is colder (–3°C to −5°C). Assuming fluctuations occur when the Rayleigh number, derived from mushy layer theory, exceeds a critical value of 10 we approximate the harmonic mean permeability of this thick (>1 m) sea ice in terms of distance from the ice‐ocean interface. Near the base, we obtain values in the same range as those measured by others in Arctic spring and summer. The permeability between the ice‐ocean interface and 0.05 ± 0.04 m above it is of order 10−9 m2. Columnar and incorporated platelet ice permeability distributions in the bottom 0.1 m of winter Antarctic sea ice are statistically significantly different although their arithmetic means are indistinguishable.

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A physically based parameterization of gravity drainage for sea‐ice modeling

Cited by 25 publications

References 58 publications

Tracer Measurements in Growing Sea Ice Support Convective Gravity Drainage Parameterizations

Tracer Measurements in Growing Sea Ice Support Convective Gravity Drainage Parameterizations

Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model

Brine Convection, Temperature Fluctuations, and Permeability in Winter Antarctic Land‐Fast Sea Ice

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