Intercomparison and validation of snow albedo parameterization schemes in climate models

Intercomparison and validation of snow albedo parameterization schemes in climate models 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Climate Dynamics Springer Journals

Intercomparison and validation of snow albedo parameterization schemes in climate models

Climate Dynamics, Volume 25 (4) – Jul 15, 2005

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Publisher
Springer Journals
Copyright
Copyright © 2005 by Springer-Verlag
Subject
Earth Sciences; Geophysics/Geodesy; Climatology; Oceanography
ISSN
0930-7575
eISSN
1432-0894
D.O.I.
10.1007/s00382-005-0037-0
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Climate DynamicsSpringer Journals

Published: Jul 15, 2005

References

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