Climate Dynamics

, Volume 25, Issue 4, pp 351–362 | Cite as

Intercomparison and validation of snow albedo parameterization schemes in climate models

  • Christina A. PedersenEmail author
  • Jan-Gunnar Winther


Snow albedo is known to be crucial for heat exchange at high latitudes and high altitudes, and is also an important parameter in General Circulation Models (GCMs) because of its strong positive feedback properties. In this study, seven GCM snow albedo schemes and a multiple linear regression model were intercompared and validated against 59 years of in situ data from Svalbard, the French Alps and six stations in the former Soviet Union. For each site, the significant meteorological parameters for modeling the snow albedo were identified by constructing the 95% confidence intervals. The significant parameters were found to be: temperature, snow depth, positive degree day and a dummy of snow depth, and the multiple linear regression model was constructed to include these. Overall, the intercomparison showed that the modeled snow albedo varied more than the observed albedo for all models, and that the albedo was often underestimated. In addition, for several of the models, the snow albedo decreased at a faster rate or by a greater magnitude during the winter snow metamorphosis than the observed albedo. Both the temperature dependent schemes and the prognostic schemes showed shortcomings.


Root Mean Square Error Snow Depth Multiple Linear Regression Model Surface Albedo Former Soviet Union 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The work is supported by the Research Council of Norway, the Norwegian Polar Institute and the University of Tromsø. We thank J.-B. Ørbæk for providing data from Ny-Ålesund, P. Etchevers for providing data from Col de Porte, and A. Robock and N. Speranskaya for valuable information regarding the data from the former Soviet stations. Further, we acknowledge R. Essery, J. Hansen, E. Roeckner, D. Verseghy and Z.-L. Yang for answering questions regarding their respective GCM models and albedo parameterization, and G. Elvebakk for valuable discussions regarding the multiple linear regression model. We also would like to thank F. Godtliebsen, D. K. Hall, B. Ivanov, M. Køltzow, A. Ohmura, E. Roeckner and A. C. Roesch for valuable comments during the early stage of the work, and R. Hall and P. Lewis are acknowledged for their careful review of the manuscript.


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Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  1. 1.Norwegian Polar InstituteTromsøNorway

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