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

Related authors

The surface mass balance and near-surface climate of the Antarctic ice sheet in RACMO2.4p1
Christiaan T. van Dalum, Willem Jan van de Berg, Michiel R. van den Broeke, and Maurice van Tiggelen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3728,https://doi.org/10.5194/egusphere-2024-3728, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
How do extreme ENSO events affect Antarctic surface mass balance?
Jessica M. A. Macha, Andrew N. Mackintosh, Felicity S. Mccormack, Benjamin J. Henley, Helen V. McGregor, Christiaan T. van Dalum, and Ariaan Purich
EGUsphere, https://doi.org/10.5194/egusphere-2024-3425,https://doi.org/10.5194/egusphere-2024-3425, 2024
Short summary
First results of the polar regional climate model RACMO2.4
Christiaan T. van Dalum, Willem Jan van de Berg, Srinidhi N. Gadde, Maurice van Tiggelen, Tijmen van der Drift, Erik van Meijgaard, Lambertus H. van Ulft, and Michiel R. van den Broeke
The Cryosphere, 18, 4065–4088, https://doi.org/10.5194/tc-18-4065-2024,https://doi.org/10.5194/tc-18-4065-2024, 2024
Short summary
Sensitivity of Antarctic surface climate to a new spectral snow albedo and radiative transfer scheme in RACMO2.3p3
Christiaan T. van Dalum, Willem Jan van de Berg, and Michiel R. van den Broeke
The Cryosphere, 16, 1071–1089, https://doi.org/10.5194/tc-16-1071-2022,https://doi.org/10.5194/tc-16-1071-2022, 2022
Short summary
Impact of updated radiative transfer scheme in snow and ice in RACMO2.3p3 on the surface mass and energy budget of the Greenland ice sheet
Christiaan T. van Dalum, Willem Jan van de Berg, and Michiel R. van den Broeke
The Cryosphere, 15, 1823–1844, https://doi.org/10.5194/tc-15-1823-2021,https://doi.org/10.5194/tc-15-1823-2021, 2021
Short summary

Related subject area

Cryosphere
Towards deep-learning solutions for classification of automated snow height measurements (CleanSnow v1.0.2)
Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer
Geosci. Model Dev., 18, 1829–1849, https://doi.org/10.5194/gmd-18-1829-2025,https://doi.org/10.5194/gmd-18-1829-2025, 2025
Short summary
Quantitative sub-ice and marine tracing of Antarctic sediment provenance (TASP v1.0)
James W. Marschalek, Edward Gasson, Tina van de Flierdt, Claus-Dieter Hillenbrand, Martin J. Siegert, and Liam Holder
Geosci. Model Dev., 18, 1673–1708, https://doi.org/10.5194/gmd-18-1673-2025,https://doi.org/10.5194/gmd-18-1673-2025, 2025
Short summary
Tuning parameters of a sea ice model using machine learning
Anton Korosov, Yue Ying, and Einar Ólason
Geosci. Model Dev., 18, 885–904, https://doi.org/10.5194/gmd-18-885-2025,https://doi.org/10.5194/gmd-18-885-2025, 2025
Short summary
WRF-Chem simulations of snow nitrate and other physicochemical properties in northern China
Xia Wang, Tao Che, Xueyin Ruan, Shanna Yue, Jing Wang, Chun Zhao, and Lei Geng
Geosci. Model Dev., 18, 651–670, https://doi.org/10.5194/gmd-18-651-2025,https://doi.org/10.5194/gmd-18-651-2025, 2025
Short summary
Clustering simulated snow profiles to form avalanche forecast regions
Simon Horton, Florian Herla, and Pascal Haegeli
Geosci. Model Dev., 18, 193–209, https://doi.org/10.5194/gmd-18-193-2025,https://doi.org/10.5194/gmd-18-193-2025, 2025
Short summary

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
Download
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.
Share