• Alekseev, V. A., E. M. Volodin, V. Y. Galin, V. P. Dymnikov, and V. N. Lykossov, 1998: Modelling of the present-day climate by the atmospheric model of INM RAS “DNM GCM.” INM Tech. Rep. N2086-B98, Institute of Numerical Mathematics, Russian Academy of Sciences, 208 pp.

  • Budyko, M. I., 1969: The effect of solar radiation variations on the climate of the earth. Tellus, 21 , 611619.

  • Cess, R. D., and G. L. Potter, 1988: A methodology for understanding and intercomparing atmospheric climate feedback processes in general circulation models. J. Geophys. Res., 93 , D7. 83058314.

    • Search Google Scholar
    • Export Citation
  • Cess, R. D., and Coauthors, 1991: Interpretation of snow-climate feedback as produced by 17 general circulation models. Science, 253 , 888892.

    • Search Google Scholar
    • Export Citation
  • Chalita, S., and H. Le Treut, 1994: The albedo of temperate and boreal forest and the Northern Hemisphere climate: A sensitivity experiment using the LMD GCM. Climate Dyn., 10 , 231240.

    • Search Google Scholar
    • Export Citation
  • Cox, P. M., R. A. Betts, C. B. Bunton, R. L. H. Essery, P. R. Rowntree, and J. Smith, 1999: The impact of new land surface physics on the GCM simulation of climate and climate sensitivity. Climate Dyn., 15 , 183203.

    • Search Google Scholar
    • Export Citation
  • Douville, H., J-F. Roye, and J-F. Mahfouf, 1995: A new snow parameterization for the Météo-France climate model. Climate Dyn., 12 , 2135.

    • Search Google Scholar
    • Export Citation
  • Essery, R., M. Best, and P. Cox, 2001: MOSES 2.2 technical documentation. Hadley Centre Tech. Note 30, 31 pp.

  • Frei, A., J. A. Miller, and D. A. Robinson, 2003: Improved simulations of snow extent in the second phase of the Atmospheric Model Intercomparison Project (AMIP-2). J. Geophys. Res., 108 .4369, doi:10.1029/2002JD003030.

    • Search Google Scholar
    • Export Citation
  • Gordon, H. B., and Coauthors, 2002: The CSIRO Mk3 Climate System Model. CSIRO Atmospheric Research Tech. Paper 60, 134 pp.

  • Hall, A., 2004: The role of surface albedo feedback in climate. J. Climate, 17 , 15501568.

  • Hall, A., and X. Qu, 2006: Using the current seasonal cycle to constrain snow albedo feedback in future climate change. Geophys. Res. Lett., 33 .L03502, doi:10.1029/2005GL025127.

    • Search Google Scholar
    • Export Citation
  • Hansen, J., G. Russell, D. Rind, P. Stone, A. Lacis, S. Lebedeef, R. Ruedy, and L. Travis, 1983: Efficient three-dimensional global models for climate studies: Models I and II. Mon. Wea. Rev., 111 , 609662.

    • Search Google Scholar
    • Export Citation
  • Milly, P. C. D., and A. B. Shmakin, 2002: Global modeling of land water and energy balances. Part I: The Land Dynamics (LaD) model. J. Hydrometeor., 3 , 283299.

    • Search Google Scholar
    • Export Citation
  • Oleson, K. W., and Coauthors, 2004: Techical description of the Community Land Model (CLM). NCAR Tech. Note NCAR/TN-464+STR, 186 pp.

  • Qu, X., and A. Hall, 2006: Assessing snow albedo feedback in simulated climate change. J. Climate, 19 , 26172630.

  • Randall, D. A., and Coauthors, 1994: Analysis of snow feedbacks in 14 general circulation models. J. Geophys. Res., 99 , D10. 2075720772.

    • Search Google Scholar
    • Export Citation
  • Robock, A., 1980: The seasonal cycle of snow cover, sea ice and surface albedo. Mon. Wea. Rev., 108 , 267285.

  • Robock, A., 1983: Ice and snow feedbacks and the latitudinal and seasonal distribution of climate sensitivity. J. Atmos. Sci., 40 , 986997.

    • Search Google Scholar
    • Export Citation
  • Robock, A., 1985: An updated climate feedback diagram. Bull. Amer. Meteor. Soc., 66 , 786787.

  • Roeckner, E., and Coauthors, 1996: The atmospheric general circulation model ECHAM4: Model description and simulation of present-day climate. Max Planck Institute for Meteorology Rep. 218, 90 pp.

  • Roeckner, E., and Coauthors, 2003: The atmospheric general circulation model ECHAM5: Part I: Model description. Max Planck Institute for Meteorology Rep. 349, 140 pp.

  • Schneider, S. H., and R. E. Dickinson, 1974: Climate modeling. Rev. Geophys. Space Phys., 12 , 447493.

  • Sellers, W. D., 1969: A global climatic model based on the energy balance of the earth-atmosphere system. J. Appl. Meteor., 8 , 392400.

    • Search Google Scholar
    • Export Citation
  • Takata, K., S. Emoria, and T. Watanabe, 2003: Development of the minimal advanced treatments of surface interaction and runoff. Global Planet. Change, 38 , 209222.

    • Search Google Scholar
    • Export Citation
  • Verseghy, D. L., N. A. McFarlane, and M. Lazare, 1993: CLASS—A Canadian land surface scheme for GCMs, II. Vegetation model and coupled runs. Int. J. Climatol., 13 , 347370.

    • Search Google Scholar
    • Export Citation
  • Wiscombe, W. J., and S. G. Warren, 1980: A model for the spectral albedo of snow. I: Pure snow. J. Atmos. Sci., 37 , 27122733.

  • Yu, Y., X. Zhang, and Y. Guo, 2004: Global coupled ocean-atmosphere general circulation models in LASG/IAR. Adv. Atmos. Sci., 21 , 444455.

    • Search Google Scholar
    • Export Citation
  • Yukimoto, S., and Coauthors, 2006: Present-day climate and climate sensitivity in the Meteorological Research Institute Coupled GCM version 2.3 (MRI-CGCM2.3). J. Meteor. Soc. Japan, 84 , 333363.

    • Search Google Scholar
    • Export Citation
  • Zhang, Y-C., W. B. Rossow, and P. W. Stackhouse Jr., 2006: Comparison of different global information sources used in surface radiative flux calculation: Radiative properties of the near-surface atmosphere. J. Geophys. Res., 111 .D13106, doi:10.1029/2005JD006873.

    • Search Google Scholar
    • Export Citation
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What Controls the Strength of Snow-Albedo Feedback?

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  • 1 Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California
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Abstract

The strength of snow-albedo feedback (SAF) in transient climate change simulations of the Fourth Assessment of the Intergovernmental Panel on Climate Change is generally determined by the surface-albedo decrease associated with a loss of snow cover rather than the reduction in snow albedo due to snow metamorphosis in a warming climate. The large intermodel spread in SAF strength is likewise attributable mostly to the snow cover component. The spread in the strength of this component is in turn mostly attributable to a correspondingly large spread in mean effective snow albedo. Models with large effective snow albedos have a large surface-albedo contrast between snow-covered and snow-free regions and exhibit a correspondingly large surface-albedo decrease when snow cover decreases. Models without explicit treatment of the vegetation canopy in their surface-albedo calculations typically have high effective snow albedos and strong SAF, often stronger than observed. In models with explicit canopy treatment, completely snow-covered surfaces typically have lower albedos and the simulations have weaker SAF, generally weaker than observed. The authors speculate that in these models either snow albedos or canopy albedos when snow is present are too low, or vegetation shields snow-covered surfaces excessively. Detailed observations of surface albedo in a representative sampling of snow-covered surfaces would therefore be extremely useful in constraining these parameterizations and reducing SAF spread in the next generation of models.

Corresponding author address: Xin Qu, Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, P.O. Box 951565, Los Angeles, CA 90095-1565. Email: xinqu@atmos.ucla.edu

Abstract

The strength of snow-albedo feedback (SAF) in transient climate change simulations of the Fourth Assessment of the Intergovernmental Panel on Climate Change is generally determined by the surface-albedo decrease associated with a loss of snow cover rather than the reduction in snow albedo due to snow metamorphosis in a warming climate. The large intermodel spread in SAF strength is likewise attributable mostly to the snow cover component. The spread in the strength of this component is in turn mostly attributable to a correspondingly large spread in mean effective snow albedo. Models with large effective snow albedos have a large surface-albedo contrast between snow-covered and snow-free regions and exhibit a correspondingly large surface-albedo decrease when snow cover decreases. Models without explicit treatment of the vegetation canopy in their surface-albedo calculations typically have high effective snow albedos and strong SAF, often stronger than observed. In models with explicit canopy treatment, completely snow-covered surfaces typically have lower albedos and the simulations have weaker SAF, generally weaker than observed. The authors speculate that in these models either snow albedos or canopy albedos when snow is present are too low, or vegetation shields snow-covered surfaces excessively. Detailed observations of surface albedo in a representative sampling of snow-covered surfaces would therefore be extremely useful in constraining these parameterizations and reducing SAF spread in the next generation of models.

Corresponding author address: Xin Qu, Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, P.O. Box 951565, Los Angeles, CA 90095-1565. Email: xinqu@atmos.ucla.edu

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