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Arctic Ocean Sea Ice Snow Depth Simulation Impacts Community Climate System Model
Published: December 13, 2013
Posted: February 07, 2014

Sea-ice cover in the Arctic Ocean continues to be a focus area as the amount of summer ice has declined significantly in recent years. Sea-ice loss is expected to accelerate warming and further loss due to the exposure of significantly more open Arctic water with lower albedo. Thus, it is critical that climate models accurately simulate sea-ice features and processes. A recent study team, including a Department of Energy (DOE)-funded researcher at Los Alamos National Laboratory, investigated the importance of snow overlying sea ice in the Arctic Ocean. Snow depth errors or biases in the Community Climate System Model (CCSM), using the DOE-sponsored sea-ice model CICE, were shown to impact not only the sea ice properties, but also the overall Arctic climate. Following the identification of these seasonal snow biases, the thermodynamic transfer through the snow-ice column was perturbed to determine model sensitivity to these biases. The study concluded that perturbations on the order of the observed biases result in modification of the annual mean conductive energy flux through the snow-ice column and suggested that the ice has a complex response to snow characteristics, with ice of different thicknesses producing distinct reactions. The results indicate the importance of an accurate simulation of snow on the Arctic sea ice, and simple “tuning” of an overly simplistic scheme will not capture the nonlinearities in processes. Consequently, future work investigating the impact of current precipitation biases and missing snow processes, such as blowing snow, densification, and seasonal changes, is warranted.

Reference:Blazey, B. A., M. M. Holland, and E. C. Hunke. 2013. “Arctic Ocean Sea Ice Snow Depth Evaluation and Bias Sensitivity in CCSM,” The Cryosphere 7, 1887-1900. DOI:10.5194/tc-7-1887-2013. (Reference link)

Contact: Dorothy Koch, SC-23.1, (301) 903-0105
Topic Areas:

  • Research Area: Earth and Environmental Systems Modeling

Division: SC-33.1 Earth and Environmental Sciences Division, BER

 

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