85th AMS Annual Meeting

Monday, 10 January 2005
An assessment of four model bulk aerodynamic algorithms used over sea ice
Michael A. Brunke, University of Arizona, Tucson, AZ; and M. Zhou, X. Zeng, and E. L. Andreas

Recently, the importance of the Arctic in the global climate system has been recognized. A key component of the Arctic climate system is sea ice, the presence of which fundamentally changes the energy and momentum exchange between the ocean and the atmosphere. Thus, it is essential for there to be an accurate determination of surface turbulent fluxes in climate models. At the Conference we will present an intercomparison and evaluation of bulk aerodynamic algorithms that calculate surface turbulent fluxes in four climate and numerical weather prediction models that was undertaken using data from the Surface Heat Budget of the Arctic Ocean (SHEBA) field experiment which occurred on an ice floe north of Alaska from October 1997 to October 1998. Comparison of the model-calculated fluxes with those observed during the experiment shows that some model algorithms produce substantially different sensible heat fluxes, latent heat fluxes, and wind stress for some stability and wind speed regimes. Also, all algorithms produce significantly lower sensible heat fluxes and higher latent heat fluxes in summer. Large differences are also found by comparing the observed fluxes at several heights on a 20-m tower at the experiment site with algorithm-calculated fluxes.

The observed roughness lengths for momentum and heat are additionally compared to the constant values used in most of the model algorithms. No trend can be deduced from the observed roughness length for heat consistent with Andreas et al. (2004), and most likely it can be considered a constant at the value used in GFS (0.0001 m). The roughness length for momentum is exponential in nature as a function of the friction velocity approaching a higher value for temperatures warmer than about -4°C. Furthermore, some improvements will be suggested at the Conference based upon the weaknesses in these four model algorithms.

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