• Andrews, T., , J. M. Gregory, , M. J. Webb, , and K. E. Taylor, 2012: Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere–ocean climate models. Geophys. Res. Lett., 39, L09712, doi:10.1029/2012GL051607.

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

    • Search Google Scholar
    • Export Citation
  • Boer, G. J., , and B. Yu, 2003a: Climate sensitivity and climate state. Climate Dyn., 21, 167176.

  • Boer, G. J., , and B. Yu, 2003b: Climate sensitivity and response. Climate Dyn., 20, 415429.

  • Boer, G. J., , and B. Yu, 2003c: Dynamical aspects of climate sensitivity. Geophys. Res. Lett., 30, 1135, doi:10.1029/2002GL016549.

  • 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, 83058314.

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

  • Colman, R., 2004: On the structure of water vapour feedbacks in climate models. Geophys. Res. Lett., 31, L21109, doi:10.1029/2004GL020708.

    • Search Google Scholar
    • Export Citation
  • Danabasoglu, G., , and P. R. Gent, 2009: Equilibrium climate sensitivity: Is it accurate to use a slab ocean model? J. Climate, 22, 24942499.

    • Search Google Scholar
    • Export Citation
  • Dessler, A. E., , and S. Wong, 2009: Estimates of the water vapor climate feedback during El Niño–Southern Oscillation. J. Climate, 22, 64046412.

    • Search Google Scholar
    • Export Citation
  • Forster, P., , and K. E. Taylor, 2006: Climate forcings and climate sensitivities diagnosed from coupled climate model integrations. J. Climate, 19, 61816194.

    • Search Google Scholar
    • Export Citation
  • 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. M., 2000: Vertical heat transports in the ocean and their effect on time-dependent climate change. Climate Dyn., 16, 501515.

    • Search Google Scholar
    • Export Citation
  • Gregory, J. M., , and J. F. B. Mitchell, 1997: The climate response to CO2 of the Hadley Centre coupled AOGCM with and without flux adjustment. Geophys. Res. Lett., 24, 19431946.

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

  • Gregory, J. M., and Coauthors, 2004: A new method for diagnosing radiative forcing and climate sensitivity. Geophys. Res. Lett., 31, L03205, doi:10.1029/2003GL018747.

    • Search Google Scholar
    • Export Citation
  • Held, I. M., , and B. J. Soden, 2006: Robust responses of the hydrological cycle to global warming. J. Climate, 19, 56865699.

  • Held, I. M., , and K. M. Shell, 2012: Using relative humidity as a state variable in climate feedback analysis. J. Climate, 25, 25782582.

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

  • Hwang, Y.-T., , and D. M. W. Frierson, 2010: Increasing atmospheric poleward energy transport with global warming. Geophys. Res. Lett., 37, L24807, doi:10.1029/2010GL045440.

    • Search Google Scholar
    • Export Citation
  • Jonko, A. K., , 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
  • Jonko, A. K., , K. M. Shell, , B. M. Sanderson, , and G. Danabasoglu, 2013: Climate feedbacks in CCSM3 under changing CO2 forcing. Part II: Variation of climate feedbacks and sensitivity with forcing. J. Climate, 26, 2784–2795.

    • Search Google Scholar
    • Export Citation
  • Manabe, S., , R. Stouffer, , M. Spelman, , and K. Bryan, 1991: Transient responses of a coupled ocean–atmosphere model to gradual changes of atmospheric CO2. Part I: Annual mean response. J. Climate, 4, 785818.

    • Search Google Scholar
    • Export Citation
  • Meehl, G., and Coauthors, 2007: Global climate projections. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 747–845.

  • Murphy, J. M., 1995: Transient response of the Hadley Centre coupled ocean–atmosphere model to increasing carbon dioxide. Part III: Analysis of global-mean response using simple models. J. Climate, 8, 496514.

    • Search Google Scholar
    • Export Citation
  • NRC, 2003: Understanding Climate Change Feedbacks. National Academies Press, 152 pp.

  • Randall, D., and Coauthors, 2007: Climate models and their evaluation. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 589–662.

  • Raper, S. C. B., , J. M. Gregory, , and T. J. Osborn, 2001: Use of an upwelling-diffusion energy balance climate model to simulate and diagnose A/OGCM results. Climate Dyn., 17, 601613.

    • Search Google Scholar
    • Export Citation
  • Raper, S. C. B., , J. M. Gregory, , and R. J. Stouffer, 2002: The role of climate sensitivity and ocean heat uptake on AOGCM transient temperature response. J. Climate, 15, 124130.

    • Search Google Scholar
    • Export Citation
  • Senior, C. A., , and J. F. B. Mitchell, 2000: The time-dependence of climate sensitivity. Geophys. Res. Lett., 27, 26852688.

  • 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. J. Broccoli, , and R. S. Hemler, 2004: On the use of cloud forcing to estimate cloud feedback. J. Climate, 17, 36613665.

    • Search Google Scholar
    • Export Citation
  • 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, 35043520.

    • Search Google Scholar
    • Export Citation
  • Stouffer, R. J., , and S. Manabe, 1999: Response of a coupled ocean–atmosphere model to increasing atmospheric carbon dioxide: Sensitivity to the rate of increase. J. Climate, 12, 22242237.

    • 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
  • Watterson, I. G., 2000: Interpretation of simulated global warming using a simple model. J. Climate, 13, 202215.

  • Watterson, I. G., 2003: Effects of a dynamic ocean on simulated climate sensitivity to greenhouse gases. Climate Dyn., 21, 197209, doi:10.1007/s00382-003-0326-4.

    • Search Google Scholar
    • Export Citation
  • Williams, K., , W. Ingram, , and J. Gregory, 2008: Time variation of effective climate sensitivity in GCMs. J. Climate, 21, 50765090.

  • Yokohata, T., and Coauthors, 2008: Comparison of equilibrium and transient responses to CO2 increase in eight state-of-the-art climate models. Tellus, 60, 946961.

    • Search Google Scholar
    • Export Citation
  • Yoshimori, M., , J. Hargreaves, , J. Annan, , T. Yokohata, , and A. Abe-Ouchi, 2011: Dependency of feedbacks on forcing and climate state in physics parameter ensembles. J. Climate, 24, 64406455.

    • Search Google Scholar
    • Export Citation
  • Zelinka, M., , and D. Hartmann, 2012: Climate feedbacks and their implications for poleward energy flux changes in a warming climate. J. Climate, 25, 608624.

    • Search Google Scholar
    • Export Citation
  • Zhang, M. H., , 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, 55255537.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 78 79 12
PDF Downloads 64 64 11

Consistent Differences in Climate Feedbacks between Atmosphere–Ocean GCMs and Atmospheric GCMs with Slab-Ocean Models

View More View Less
  • 1 College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, Oregon
© Get Permissions
Restricted access

Abstract

Climate sensitivity is generally studied using two types of models. Atmosphere–ocean general circulation models (AOGCMs) include interactive ocean dynamics and detailed heat uptake. Atmospheric GCMs (AGCMs) with slab ocean models (SOMs) cannot fully simulate the ocean’s response to and influence on climate. However, AGCMs are computationally cheaper and thus are often used to quantify and understand climate feedbacks and sensitivity. Here, physical climate feedbacks are compared between AOGCMs and SOM-AGCMs from the Coupled Model Intercomparison Project phase 3 (CMIP3) using the radiative kernel technique. Both the global-average (positive) water vapor and (negative) lapse-rate feedbacks are consistently stronger in AOGCMs. Water vapor feedback differences result from an essentially constant relative humidity and peak in the tropics, where temperature changes are larger for AOGCMs. Differences in lapse-rate feedbacks extend to midlatitudes and correspond to a larger ratio of tropical- to global-average temperature changes. Global-average surface albedo feedbacks are similar between models types because of a near cancellation of Arctic and Antarctic differences. In AOGCMs, the northern high latitudes warm faster than the southern latitudes, resulting in interhemispheric differences in albedo, water vapor, and lapse-rate feedbacks lacking in the SOM-AGCMs. Meridional heat transport changes also depend on the model type, although there is a large intermodel spread. However, there are no consistent global or zonal differences in cloud feedbacks. Effects of the forcing scenario [Special Report on Emissions Scenarios A1B (SRESa1b) or the 1% CO2 increase per year to doubling (1%to2x) experiments] on feedbacks are model dependent and generally of lesser importance than the model type. Care should be taken when using SOM-AGCMs to understand AOGCM feedback behavior.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-12-00519.s1.

Corresponding author address: Karen M. Shell, College of Earth, Ocean, and Atmospheric Sciences, 104 CEOAS Admin. Bldg., Corvallis, OR 97331-5503. E-mail: kshell@coas.oregonstate.edu

Abstract

Climate sensitivity is generally studied using two types of models. Atmosphere–ocean general circulation models (AOGCMs) include interactive ocean dynamics and detailed heat uptake. Atmospheric GCMs (AGCMs) with slab ocean models (SOMs) cannot fully simulate the ocean’s response to and influence on climate. However, AGCMs are computationally cheaper and thus are often used to quantify and understand climate feedbacks and sensitivity. Here, physical climate feedbacks are compared between AOGCMs and SOM-AGCMs from the Coupled Model Intercomparison Project phase 3 (CMIP3) using the radiative kernel technique. Both the global-average (positive) water vapor and (negative) lapse-rate feedbacks are consistently stronger in AOGCMs. Water vapor feedback differences result from an essentially constant relative humidity and peak in the tropics, where temperature changes are larger for AOGCMs. Differences in lapse-rate feedbacks extend to midlatitudes and correspond to a larger ratio of tropical- to global-average temperature changes. Global-average surface albedo feedbacks are similar between models types because of a near cancellation of Arctic and Antarctic differences. In AOGCMs, the northern high latitudes warm faster than the southern latitudes, resulting in interhemispheric differences in albedo, water vapor, and lapse-rate feedbacks lacking in the SOM-AGCMs. Meridional heat transport changes also depend on the model type, although there is a large intermodel spread. However, there are no consistent global or zonal differences in cloud feedbacks. Effects of the forcing scenario [Special Report on Emissions Scenarios A1B (SRESa1b) or the 1% CO2 increase per year to doubling (1%to2x) experiments] on feedbacks are model dependent and generally of lesser importance than the model type. Care should be taken when using SOM-AGCMs to understand AOGCM feedback behavior.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-12-00519.s1.

Corresponding author address: Karen M. Shell, College of Earth, Ocean, and Atmospheric Sciences, 104 CEOAS Admin. Bldg., Corvallis, OR 97331-5503. E-mail: kshell@coas.oregonstate.edu

Supplementary Materials

    • Supplemental Materials (PDF 688.09 KB)
Save