HR: 0800h
AN: C41C-0207    [Abstracts]
TI: Thermal dependence of brine salinity in the surface layer of snow-covered sea ice under the variable conditions
AU: * Kojima, S
EM: floe1999@pop12.odn.ne.jp
AF: Kitami Institute of Technology Department of Civil Engineering, 165, Koen-cho, Kitami, Hokkaido, 090-8507 Japan
AU: Saito, Y
EM: mcv03010@std.kitami-it.ac.jp
AF: Kitami Institute of Technology Department of Civil Engineering, 165, Koen-cho, Kitami, Hokkaido, 090-8507 Japan
AU: Enomoto, H
EM: enomoto@mail.kitami-it.ac.jp
AF: Kitami Institute of Technology Department of Civil Engineering, 165, Koen-cho, Kitami, Hokkaido, 090-8507 Japan
AB: We observed snow covered sea ice in Barrow, Alaska in February 2004 to investigate thermal dependence of brine salinity in the surface layer. The observed data was compared with simulated data, which was estimated by existing thermal models (Nakawo and Sinha, 1981; Ono, 1968). The brine salinity of the surface layer is very important to analyze microwave images, because its dielectric properties determine emissivity and penetration depth. In combination with the observation, we took some sea ice cores, measuring their length, temperature and salinity. After the observation, we obtained vertical temperature and salinity profiles. We also measured air temperature, snow surface temperature, snow/ice interface temperature, water temperature and snow depth and density. These data were used for the simulations. Regarding the simulation, at first, we estimated snow/ice interface temperature using the equation of Nakawo and Sinha (1981). This equation is formed by 6 parameters, which are the thermal conductivity of snow and sea ice, snow depth, sea ice thickness, air temperature and melting point of sea ice. And then, we estimated brine salinity using the results of Nakawo and Sinha and the equation of Ono (1968). This equation for obtaining brine salinity is calculated as a function of sea ice temperature. When we simulate brine salinity, only four data points are needed, because melting point and thermal conductivity of sea ice can be assumed as -1.8$\deg$C and 2 W m$^{-1}$ K$^{-1}$, respectively. As for the thermal conductivity of snow, we can obtain from the equation of Devaux (1933). This equation requires information on snow density. Namely, we need air temperature, snow depth, snow density and thickness of sea ice when we simulate brine salinity. This means that field work and calculation relevant to simulation becomes more easily. Comparisons between observation and simulation have indicated good correlation. This result suggests the applicability of the simple simulation method. Based on these positive results, we calculated the relation between snow/ice interface temperature and snow depth as a function of air temperature. Specifically, we varied air temperature between -10$\deg$C, -20$\deg$C and -30$\deg$C. For a sea ice thickness of 1.25m and 2m, and an air temperature of -10$\deg$C, snow/ice interface temperature is almost same. On the other hand, when sea ice thickness was 30cm, snow/ice interface temperature became almost same at each air temperature if snow depth was over 50cm. And also, mean snow/ice interface temperature was higher than for the other two cases. We also calculated brine salinity in the surface layer of snow covered sea ice as a function of air temperature using the simulated results of snow/ice interface temperature. The variable range of air temperature is same as case of simulation for the snow/ice interface temperature. There was proportionality relation between sea ice thickness and brine salinity in the surface of sea ice, but relation between snow/ice interface temperature and brine salinity indicated inverse proportion. As indicated above, we compared observed data with simulated results to investigate the thermal dependence of brine salinity in the surface layer of snow covered sea ice. As a result, observed data and simulated results have indicated good correlation. We can estimate the dielectric constant of snow covered sea ice in the surface layer by simulating brine salinity under the various conditions. Hereby, we can expect an improvement of the accuracy of observation by satellite microwave remote sensing.
DE: 1863 Snow and ice (1827)
SC: Cryosphere [C]
MN: 2004 AGU Fall Meeting