Articles | Volume 12, issue 12
https://doi.org/10.5194/gmd-12-5157-2019
https://doi.org/10.5194/gmd-12-5157-2019
Model description paper
 | 
11 Dec 2019
Model description paper |  | 11 Dec 2019

A module to convert spectral to narrowband snow albedo for use in climate models: SNOWBAL v1.2

Christiaan T. van Dalum, Willem Jan van de Berg, Quentin Libois, Ghislain Picard, and Michiel R. van den Broeke

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Cited articles

Abbot, C. G.: The solar constant of radiation, P. Am. Philos. Soc., 50, 235–245, 1911. a
Ackermann, M., Ahrens, J., Bai, X. et al.: Optical properties of deep glacial ice at the South Pole, J. Geophys. Res.-Atmos., 111, d13203, https://doi.org/10.1029/2005JD006687, 2006. a, b
Anderson, G. P., Clough, S. A., Kneizys, F., Chetwynd, J. H., and Shettle, E. P.: AFGL atmospheric constituent profiles (0.120 km), available at: https://apps.dtic.mil/dtic/tr/fulltext/u2/a175173.pdf (last access: 4 December 2019), 1986. a, b
Aoki, T., Kuchiki, K., Niwano, M., Kodama, Y., Hosaka, M., and Tanaka, T.: Physically based snow albedo model for calculating broadband albedos and the solar heating profile in snowpack for general circulation models, J. Geophys. Res.-Atmos., 116, D11114, https://doi.org/10.1029/2010JD015507, 2011. a
Bory, A. J.-M., Bory, Biscaye, P. E., Svensson, A., and Grousset, F. E.: Seasonal variability in the origin of recent atmospheric mineral dust at NorthGRIP, Greenland, Earth Planet. Sci. Lett., 196, 123–134, https://doi.org/10.1016/S0012-821X(01)00609-4, 2002. a
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Short summary
Climate models are often limited to relatively simple snow albedo schemes. Therefore, we have developed the SNOWBAL module to couple a climate model with a physically based wavelength dependent snow albedo model. Using SNOWBAL v1.2 to couple the snow albedo model TARTES with the regional climate model RACMO2 indicates a potential performance gain for the Greenland ice sheet.
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