Modeling the anisotropic brine microstructure in first‐year Arctic sea ice

Abstract

.[1] Cross-borehole DC resistivity tomography has recently been used to monitor the temporal variation of the anisotropic bulk electrical resistivity of first-year Arctic sea ice during the period of spring warming. These measurements cannot be explained by standard models of sea ice microstructure which treat the brine phase as isolated ellipsoidal pores. A simple structural model which does satisfy the observed electrical data shows that the brine phase must be connected both vertically and horizontally. Calculation of the temporal and thermal evolution of the microstructure suggests that although vertical connectivity is through pore tubes and sheets with widths of $100 mm, horizontal connectivity is through much thinner connections which are interpreted as inter-and intragranular brine layers. As the temperature increases the width of vertical channels increases smoothly. In contrast, at temperatures above about À2°C there is a rapid increase in the thickness of horizontal connections which we interpret as a change from conduction through intergranular brine layers to the development of horizontal pores. The electrical data also broadly exhibit a percolation transition predicted by mathematical models. However, the critical brine volume fraction for vertical electrical connection is very small, while that for horizontal electrical connection is derived to be about 0.5%. The difference between these and the critical threshold of 5% for fluid permeability is presumed to arise because of the strong dependence of the latter on brine channel width.

In situ field measurements of the temporal evolution of low-frequency sea-ice dielectric properties in relation to temperature, salinity, and microstructure

“…The positive relationship found between ε and brine volume fraction is therefore in agreement with the relationship between ε and temperature. Similarly the strong positive correlation at all frequencies between ε and brine volume fraction is in agreement with the increase in bulk DC conductivity of sea ice which occurs as brine volume fraction increases -previously observed and modeled by Jones et al (2010Jones et al ( , 2012. How temperature and brine volume fraction may separately influence measurements of ε is displayed in Fig.…”

“…Findings from the PCA indicate that the loadings of mean pore volume and bulk brine volume fraction are near equal for the first principal component responsible for 52 % of the variance in measurements of ε and 56 % of the variance in measurements of ε . Similar to the above analysis examining the impact of brine volume fraction on ε , the correlation between ε and pore volume (0.836) is expected given the relationship between sea-ice conductivity and pore connectivity (Jones et al, 2012). In processing of tomographic images, brine pores, layers, and channels were not differentiated; therefore, a large "pore" may be composed of a highly connected brine channel.…”

“…A significant correlation is found between the imaginary part of the permittivity and temperature at all frequencies. Jones et al (2012) established that ε is closely related to the DC conductivity of the sea ice, which depends on the connectivity of pore spaces. An increase in temperature leads to an increase in the magnitude of ε for both ice and brine.…”

“…This important transition triggers the draining of surface melt ponds, the initiation of sea-water convection throughout the ice volume, and a change in the thermal regime of the ice. Jones et al (2012) subsequently created a theoretical structural model of sea ice to link measurements of resistivity to the connectivity of the brine pores. They were successful in modeling temporal changes in the relative size of pores and their connectivity.…”

Fractal structures in freezing brine

“…Alyaev, E. Keilegavlen, J. M. Nordbotten and I. S. Pop through laboratory experiments and numerical simulations Eide & Martin (1975), Allison et al (1985), Worster (1992), Eicken (2003), Vancoppenolle, Fichefet & Bitz (2006), Golden et al (2007), Peppin et al (2007), Notz & Worster (2008), Hunke et al (2011), Wells, Wettlaufer & Orszag (2011), Jones, Ingham & Eicken (2012). These experimental studies provide the basis for the heuristic relationships required when developing computational models, which in their turn are needed for understanding large-scale dynamics.…”