Assessing Snow Albedo Feedback in Simulated Climate Change

Xin Qu Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California

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Alex Hall Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California

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Abstract

In this paper, the two factors controlling Northern Hemisphere springtime snow albedo feedback in transient climate change are isolated and quantified based on scenario runs of 17 climate models used in the Intergovernmental Panel on Climate Change Fourth Assessment Report. The first factor is the dependence of planetary albedo on surface albedo, representing the atmosphere's attenuation effect on surface albedo anomalies. It is potentially a major source of divergence in simulations of snow albedo feedback because of large differences in simulated cloud fields in Northern Hemisphere land areas. To calculate the dependence, an analytical model governing planetary albedo was developed. Detailed validations of the analytical model for two of the simulations are shown, version 3 of the Community Climate System Model (CCSM3) and the Geophysical Fluid Dynamics Laboratory global coupled Climate Model 2.0 (CM2.0), demonstrating that it facilitates a highly accurate calculation of the dependence of planetary albedo on surface albedo given readily available simulation output. In all simulations it is found that surface albedo anomalies are attenuated by approximately half in Northern Hemisphere land areas as they are transformed into planetary albedo anomalies. The intermodel standard deviation in the dependence of planetary albedo on surface albedo is surprisingly small, less than 10% of the mean. Moreover, when an observational estimate of this factor is calculated by applying the same method to the satellite-based International Satellite Cloud Climatology Project (ISCCP) data, it is found that most simulations agree with ISCCP values to within about 10%, despite further disagreements between observed and simulated cloud fields. This suggests that even large relative errors in simulated cloud fields do not result in significant error in this factor, enhancing confidence in climate models. The second factor, related exclusively to surface processes, is the change in surface albedo associated with an anthropogenically induced temperature change in Northern Hemisphere land areas. It exhibits much more intermodel variability. The standard deviation is about ⅓ of the mean, with the largest value being approximately 3 times larger than the smallest. Therefore this factor is unquestionably the main source of the large divergence in simulations of snow albedo feedback. To reduce the divergence, attention should be focused on differing parameterizations of snow processes, rather than intermodel variations in the attenuation effect of the atmosphere on surface albedo anomalies.

Corresponding author address: Xin Qu, Dept. of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA 90095. Email: xinqu@atmos.ucla.edu

Abstract

In this paper, the two factors controlling Northern Hemisphere springtime snow albedo feedback in transient climate change are isolated and quantified based on scenario runs of 17 climate models used in the Intergovernmental Panel on Climate Change Fourth Assessment Report. The first factor is the dependence of planetary albedo on surface albedo, representing the atmosphere's attenuation effect on surface albedo anomalies. It is potentially a major source of divergence in simulations of snow albedo feedback because of large differences in simulated cloud fields in Northern Hemisphere land areas. To calculate the dependence, an analytical model governing planetary albedo was developed. Detailed validations of the analytical model for two of the simulations are shown, version 3 of the Community Climate System Model (CCSM3) and the Geophysical Fluid Dynamics Laboratory global coupled Climate Model 2.0 (CM2.0), demonstrating that it facilitates a highly accurate calculation of the dependence of planetary albedo on surface albedo given readily available simulation output. In all simulations it is found that surface albedo anomalies are attenuated by approximately half in Northern Hemisphere land areas as they are transformed into planetary albedo anomalies. The intermodel standard deviation in the dependence of planetary albedo on surface albedo is surprisingly small, less than 10% of the mean. Moreover, when an observational estimate of this factor is calculated by applying the same method to the satellite-based International Satellite Cloud Climatology Project (ISCCP) data, it is found that most simulations agree with ISCCP values to within about 10%, despite further disagreements between observed and simulated cloud fields. This suggests that even large relative errors in simulated cloud fields do not result in significant error in this factor, enhancing confidence in climate models. The second factor, related exclusively to surface processes, is the change in surface albedo associated with an anthropogenically induced temperature change in Northern Hemisphere land areas. It exhibits much more intermodel variability. The standard deviation is about ⅓ of the mean, with the largest value being approximately 3 times larger than the smallest. Therefore this factor is unquestionably the main source of the large divergence in simulations of snow albedo feedback. To reduce the divergence, attention should be focused on differing parameterizations of snow processes, rather than intermodel variations in the attenuation effect of the atmosphere on surface albedo anomalies.

Corresponding author address: Xin Qu, Dept. of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA 90095. Email: xinqu@atmos.ucla.edu

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  • Briegleb, B. P., C. M. Bitz, E. C. Hunke, W. H. Lipscomb, M. M. Holland, J. L. Schramm, and R. E. Moritz, 2004: Scientific description of the sea ice component in the Community Climate System Model, version three. Tech. Note NCAR/TN-463+STR, 70 pp.

  • 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
  • Collins, W. D., and Coauthors, 2004: Description of the NCAR Community Atmosphere Model (CAM3.0). Tech. Note NCAR/TN-464+STR, 214 pp.

  • Cubasch, U., and Coauthors, 2001: Projections of future climate change. Climate Change 2001: The Scientific Basis, J. W. Kim and J. Stone, Eds. Cambridge University Press, 525–582.

    • Search Google Scholar
    • Export Citation
  • GFDL Global Atmosphere Model Development Team (GAMDT), 2004: The new GFDL global atmosphere and land model AM2/CM2.0: Evaluation with prescribed SST simulations. J. Climate, 17 , 46414673.

    • Search Google Scholar
    • Export Citation
  • Groisman, P. Y., T. R. Karl, and R. W. Knight, 1994a: Observed impact of snow cover on the heat balance and the rise of continental spring temperatures. Science, 263 , 198200.

    • Search Google Scholar
    • Export Citation
  • Groisman, P. Y., T. R. Karl, and R. W. Knight, 1994b: Changes of snow cover, temperatures and radiative heat balance over the Northern Hemisphere. J. Climate, 7 , 16331656.

    • Search Google Scholar
    • Export Citation
  • Hall, A., 2004: The role of surface albedo feedback in climate. J. Climate, 17 , 15501568.

  • Holland, M. M., and C. M. Bitz, 2003: Polar amplification of climate change in coupled models. Climate Dyn, 21 , 221232.

  • 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: Technical description of the Community Land Model (CLM). Tech. Note NCAR/TN-464+STR, 174 pp.

  • Prentice, I. C., and Coauthors, 2001: The carbon cycle and atmospheric carbon dioxide. Climate Change 2001: The Scientific Basis, L. Pitelka and A. Ramirez Rojas, Eds. Cambridge University Press, 183–237.

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

  • Rossow, W. B., and R. A. Schiffer, 1991: ISCCP cloud data products. Bull. Amer. Meteor. Soc, 72 , 220.

  • Rossow, W. B., and L. C. Garder, 1993a: Cloud detection using satellite measurements of infrared and visible radiances for ISCCP. J. Climate, 6 , 23412369.

    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., and L. C. Garder, 1993b: Validation of ISCCP cloud detections. J. Climate, 6 , 23702393.

  • Rossow, W. B., and R. A. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor. Soc, 80 , 22612287.

  • Rossow, W. B., A. W. Walker, and L. C. Garder, 1993: Comparison of ISCCP and other cloud amounts. J. Climate, 6 , 23942418.

  • Rossow, W. B., A. W. Walker, D. Beuschel, and M. Roiter, 1996: International Satellite Cloud Climatological Project (ISCCP) description of new cloud datasets. Tech. Doc. WMO/TD 737, World Climate Research Programme (ICSU AND WMO), 113 pp.

  • Smith, R., and P. Gent, 2004: Reference Manual for the Parallel Ocean Program POP: Ocean System Model (CCSM2.0 and 3.0). National Center for Atmospheric Research, 75 pp.

  • Zhang, Y-C., W. B. Rossow, A. A. Lacis, V. Oinas, and M. M. Mishchenko, 2004: Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global datasets: Refinements of the radiative transfer model and the input data. J. Geophys. Res, 109 .D19105, doi:10.1029/2003JD004457.

    • Search Google Scholar
    • Export Citation
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