Stochastic parameterization for light absorption by internally mixed BC/dust in snow grains for application to climate models Academic Article uri icon


  • © 2014. American Geophysical Union. All Rights Reserved. A stochastic approach has been developed to model the positions of BC (black carbon)/dust internally mixed with two snow grain types: hexagonal plate/column (convex) and Koch snowflake (concave). Subsequently, light absorption and scattering analysis can be followed by means of an improved geometric-optics approach coupled with Monte Carlo photon tracing to determine BC/dust single-scattering properties. For a given shape (plate, Koch snowflake, spheroid, or sphere), the action of internal mixing absorbs substantially more light than external mixing. The snow grain shape effect on absorption is relatively small, but its effect on asymmetry factor is substantial. Due to a greater probability of intercepting photons, multiple inclusions of BC/dust exhibit a larger absorption than an equal-volume single inclusion. The spectral absorption (0.2–5 μm) for snow grains internally mixed with BC/dust is confined to wavelengths shorter than about 1.4 μm, beyond which ice absorption predominates. Based on the single-scattering properties determined from stochastic and light absorption parameterizations and using the adding/doubling method for spectral radiative transfer, we find that internal mixing reduces snow albedo substantially more than external mixing and that the snow grain shape plays a critical role in snow albedo calculations through its forward scattering strength. Also, multiple inclusion of BC/dust significantly reduces snow albedo as compared to an equal-volume single sphere. For application to land/snow models, we propose a two-layer spectral snow parameterization involving contaminated fresh snow on top of old snow for investigating and understanding the climatic impact of multiple BC/dust internal mixing associated with snow grain metamorphism, particularly over mountain/snow topography.

author list (cited authors)

  • Liou, K. N., Takano, Y., He, C., Yang, P., Leung, L. R., Gu, Y., & Lee, W. L.

citation count

  • 54

publication date

  • June 2014