Model-Specific Radiative Kernels for Calculating Cloud and Noncloud Climate Feedbacks

Benjamin M. Sanderson National Center for Atmospheric Research,* Boulder, Colorado

Search for other papers by Benjamin M. Sanderson in
Current site
Google Scholar
PubMed
Close
and
Karen M. Shell College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, Oregon

Search for other papers by Karen M. Shell in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Radiative kernels have become a common tool for evaluating and comparing radiative feedbacks to climate change in different general circulation models. However, kernel feedback calculations are inaccurate for simulations where the atmosphere is significantly perturbed from its base state, such as for very large forcing or perturbed physics simulations. In addition, past analyses have not produced kernels relating to prognostic cloud variables because of strong nonlinearities in their relationship to radiative forcing. A new methodology is presented that allows for fast statistical optimizing of existing kernels such that accuracy is increased for significantly altered climatologies. International Satellite Cloud Climatology Project (ISCCP) simulator output is used to relate changes in cloud-type histograms to radiative fluxes. With minimal additional computation, an individual set of kernels is created for each climate experiment such that climate feedbacks can be reliably estimated even in significantly perturbed climates.

This methodology is applied to successive generations of the Community Atmosphere Model (CAM). Increased climate sensitivity in CAM5 is shown to be due to reduced negative stratus and stratocumulus feedbacks in the tropics and midlatitudes, strong positive stratus feedbacks in the southern oceans, and a strengthened positive longwave cirrus feedback. Results also suggest that CAM5 exhibits a stronger surface albedo feedback than its predecessors, a feature not apparent when using a single kernel. Optimized kernels for CAM5 suggest weaker global-mean shortwave cloud feedback than one would infer from using the original kernels and an adjusted cloud radiative forcing methodology.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Benjamin Sanderson, NCAR, 1850 Table Mesa Dr., Boulder, CO 80305. E-mail: bsander@ucar.edu

Abstract

Radiative kernels have become a common tool for evaluating and comparing radiative feedbacks to climate change in different general circulation models. However, kernel feedback calculations are inaccurate for simulations where the atmosphere is significantly perturbed from its base state, such as for very large forcing or perturbed physics simulations. In addition, past analyses have not produced kernels relating to prognostic cloud variables because of strong nonlinearities in their relationship to radiative forcing. A new methodology is presented that allows for fast statistical optimizing of existing kernels such that accuracy is increased for significantly altered climatologies. International Satellite Cloud Climatology Project (ISCCP) simulator output is used to relate changes in cloud-type histograms to radiative fluxes. With minimal additional computation, an individual set of kernels is created for each climate experiment such that climate feedbacks can be reliably estimated even in significantly perturbed climates.

This methodology is applied to successive generations of the Community Atmosphere Model (CAM). Increased climate sensitivity in CAM5 is shown to be due to reduced negative stratus and stratocumulus feedbacks in the tropics and midlatitudes, strong positive stratus feedbacks in the southern oceans, and a strengthened positive longwave cirrus feedback. Results also suggest that CAM5 exhibits a stronger surface albedo feedback than its predecessors, a feature not apparent when using a single kernel. Optimized kernels for CAM5 suggest weaker global-mean shortwave cloud feedback than one would infer from using the original kernels and an adjusted cloud radiative forcing methodology.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Benjamin Sanderson, NCAR, 1850 Table Mesa Dr., Boulder, CO 80305. E-mail: bsander@ucar.edu
Save
  • Andrews, T., and P. M. Forster, 2008: CO2 forcing induces semi-direct effects with consequences for climate feedback interpretations. Geophys. Res. Lett., 35, L04802, doi:10.1029/2007GL032273.

    • Search Google Scholar
    • Export Citation
  • Bitz, C. M., K. M. Shell, P. R. Gent, D. A. Bailey, G. Danabasoglu, K. C. Armour, M. M. Holland, and J. T. Kiehl, 2012: Climate sensitivity in the Community Climate System Model, version 4. J. Climate, 25, 30533070.

    • Search Google Scholar
    • Export Citation
  • Bony, S., and Coauthors, 2006: How well do we understand and evaluate climate change feedback processes? J. Climate, 19, 34453482.

  • 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
  • Charney, J. G., and Coauthors, 1979: Carbon dioxide and climate: A scientific assessment. National Research Council Rep., 33 pp.

  • Collins, W., and Coauthors, 2004: Description of the NCAR Community Atmosphere Model (CAM 3.0). NCAR Tech. Note NCAR/TN-464+STR, 226 pp.

  • Colman, R., 2003: A comparison of climate feedbacks in general circulation models. Climate Dyn., 20, 865873.

  • Colman, R., S. B. Power, and B. J. McAvaney, 1997: Non-linear climate feedback analysis in an atmospheric general circulation model. Climate Dyn., 13, 717731.

    • Search Google Scholar
    • Export Citation
  • Dessler, A., 2010: A determination of the cloud feedback from climate variations over the past decade. Science, 330, 1523–1527.

  • Fletcher, R., and M. Powell, 1963: A rapidly convergent descent method for minimization. Comput. J., 6, 163–168.

  • Gettelman, A., J. E. Kay, and K. M. Shell, 2012: The evolution of climate sensitivity and climate feedbacks in the Community Atmosphere Model. J. Climate, 25, 14531469.

    • Search Google Scholar
    • Export Citation
  • Gregory, J., and M. Webb, 2008: Tropospheric adjustment induces a cloud component in CO2 forcing. J. Climate, 21, 5871.

  • Hartmann, D., M. Ockert-Bell, and M. Michelsen, 1992: The effect of cloud type on Earth’s energy balance: Global analysis. J. Climate, 5, 12811304.

    • Search Google Scholar
    • Export Citation
  • Jonko, A., K. M. Shell, B. M. Sanderson, and G. Danabasoglu, 2012: Climate feedbacks in CCSM3 under changing CO2 forcing. Part I: Adapting the linear radiative kernel technique to feedback calculations for a broad range of forcings. J. Climate, 25, 52605272.

    • Search Google Scholar
    • Export Citation
  • Kay, J. E., and Coauthors, 2012: Exposing global cloud biases in the Community Atmosphere Model (CAM) using satellite observations and their corresponding instrument simulators. J. Climate, 25, 51905207.

    • Search Google Scholar
    • Export Citation
  • Klein, S., and C. Jakob, 1999: Validation and sensitivities of frontal clouds simulated by the ECMWF model. Mon. Wea. Rev., 127, 25142531.

    • Search Google Scholar
    • Export Citation
  • Knutti, R., and G. Hegerl, 2008: The equilibrium sensitivity of the Earth’s temperature to radiation changes. Nat. Geosci., 1, 735743.

    • Search Google Scholar
    • Export Citation
  • Lin, S., 2004: A “vertically Lagrangian” finite-volume dynamical core for global models. Mon. Wea. Rev., 132, 22932307.

  • Lin, W., and M. Zhang, 2004: Evaluation of clouds and their radiative effects simulated by the NCAR Community Atmospheric Model against satellite observations. J. Climate, 17, 33023318.

    • Search Google Scholar
    • Export Citation
  • Ockert-Bell, M., and D. Hartmann, 1992: The effect of cloud type on Earth’s energy balance: Results for selected regions. J. Climate, 5, 11571171.

    • Search Google Scholar
    • Export Citation
  • Pincus, R., R. Hemler, and S. Klein, 2006: Using stochastically generated subcolumns to represent cloud structure in a large-scale model. Mon. Wea. Rev., 134, 36443656.

    • Search Google Scholar
    • Export Citation
  • Raymond, D., and A. Blyth, 1992: Extension of the stochastic mixing model to cumulonimbus clouds. J. Atmos. Sci., 49, 19681983.

  • Richter, J., and P. Rasch, 2008: Effects of convective momentum transport on the atmospheric circulation in the Community Atmosphere Model, version 3. J. Climate, 21, 14871499.

    • Search Google Scholar
    • Export Citation
  • Rossow, W., and R. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor. Soc., 80, 22612288.

  • Sanderson, B., K. Shell, and W. Ingram, 2010: Climate feedbacks determined using radiative kernels in a multi-thousand member ensemble of AOGCMs. Climate Dyn., 35, 12191236.

    • Search Google Scholar
    • Export Citation
  • Schiffer, R. A., and W. B. Rossow, 1983: The International Satellite Cloud Climatology Project (ISCCP): The first project of the World Climate Research Programme. Bull. Amer. Meteor. Soc., 64, 779–784.

    • Search Google Scholar
    • Export Citation
  • Shell, K. M., J. T. Kiehl, and C. A. Shields, 2008: Using the radiative kernel technique to calculate climate feedbacks in NCAR’s Community Atmospheric Model. J. Climate, 21, 22692282.

    • Search Google Scholar
    • Export Citation
  • Soden, B. J., and I. M. Held, 2006: An assessment of climate feedbacks in coupled ocean–atmosphere models. J. Climate, 19, 33543360.

    • Search Google Scholar
    • Export Citation
  • Soden, B. J., A. Broccoli, and R. Hemler, 2004: On the use of cloud forcing to estimate cloud feedback. J. Climate, 17, 36613665.

  • Soden, B. J., I. M. Held, R. Colman, K. M. Shell, J. T. Kiehl, and C. A. Shields, 2008: Quantifying climate feedbacks using radiative kernels. J. Climate, 21, 3504–3520.

    • Search Google Scholar
    • Export Citation
  • Taylor, K., M. Crucifix, P. Braconnot, C. Hewitt, C. Doutriaux, A. Broccoli, J. Mitchell, and M. Webb, 2007: Estimating shortwave radiative forcing and response in climate models. J. Climate, 20, 25302543.

    • Search Google Scholar
    • Export Citation
  • Webb, M., C. Senior, S. Bony, and J. Morcrette, 2001: Combining ERBE and ISCCP data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate models. Climate Dyn., 17, 905922.

    • Search Google Scholar
    • Export Citation
  • Webb, M., and Coauthors, 2006: On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles. Climate Dyn., 27, 1738.

    • Search Google Scholar
    • Export Citation
  • Wetherald, R., and S. Manabe, 1988: Cloud feedback processes in a general circulation model. J. Atmos. Sci., 45, 13971416.

  • Winton, M., 2006: Surface albedo feedback estimates for the AR4 climate models. J. Climate, 19, 359365.

  • Yokohata, T., S. Emori, T. Nozawa, Y. Tsushima, T. Ogura, and M. Kimoto, 2005: A simple scheme for climate feedback analysis. Geophys. Res. Lett., 32, L19703, doi:10.1029/2005GL023673.

    • Search Google Scholar
    • Export Citation
  • Zelinka, M. D., S. A. Klein, and D. L. Hartmann, 2012: Computing and partitioning cloud feedbacks using cloud property histograms. Part I: Cloud radiative kernel. J. Climate, 25, 37153735.

    • Search Google Scholar
    • Export Citation
  • Zhang, M., J. J. Hack, J. T. Kiehl, and R. D. Cess, 1994: Diagnostic study of climate feedback processes in atmospheric general circulation models. J. Geophys. Res., 99 (D3), 55255537.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 871 366 137
PDF Downloads 386 79 16