Climate Dynamics

, Volume 32, Issue 6, pp 873–885 | Cite as

A new framework for isolating individual feedback processes in coupled general circulation climate models. Part I: formulation

  • Jianhua Lu
  • Ming Cai


This paper proposes a coupled atmosphere–surface climate feedback–response analysis method (CFRAM) as a new framework for estimating climate feedbacks in coupled general circulation models with a full set of physical parameterization packages. The formulation of the CFRAM is based on the energy balance in an atmosphere–surface column. In the CFRAM, the isolation of partial temperature changes due to an external forcing or an individual feedback is achieved by solving the linearized infrared radiation transfer model subject to individual energy flux perturbations (external or due to feedbacks). The partial temperature changes are addable and their sum is equal to the (total) temperature change (in the linear sense). The decomposition of feedbacks is based on the thermodynamic and dynamical processes that directly affect individual energy flux terms. Therefore, not only those feedbacks that directly affect the TOA radiative fluxes, such as water vapor, clouds, and ice-albedo feedbacks, but also those feedbacks that do not directly affect the TOA radiation, such as evaporation, convections, and convergence of horizontal sensible and latent heat fluxes, are explicitly included in the CFRAM. In the CFRAM, the feedback gain matrices measure the strength of individual feedbacks. The feedback gain matrices can be estimated from the energy flux perturbations inferred from individual parameterization packages and dynamical modules. The inter-model spread of a feedback gain matrix would help us to detect the origins of the uncertainty of future climate projections in climate model simulations.


Climate feedback Global warming Climate sensitivity 



The authors are grateful for the constructive comments from three anonymous reviewers. This work is supported by grants from the NOAA/Office of Global Programs (GC04-163 and GC06-038).


  1. Bates JR (2007) Some considerations of the concept of climate feedback. Q J R Meteorol Soc 133:545–560CrossRefGoogle Scholar
  2. Bode H (1945) Network analysis and feedback amplifier design. Van Nostrand, pp 551 Google Scholar
  3. Boer GJ, Yu B (2003) Climate sensitivity and response. Clim Dyn 20:415–429Google Scholar
  4. Bony S et al (2006) How well do we understand and evaluate climate feedback processes? J Clim 19:3445–3482CrossRefGoogle Scholar
  5. Cai M (2006) Dynamical greenhouse-plus feedback and polar warming amplification. Part I: A dry radiative–transportive climate model. Clim Dyn 26:661–675CrossRefGoogle Scholar
  6. Cai M, J-H Lu (2007) Dynamical greenhouse-plus feedback and polar warming amplification. Part II: Meridional and vertical asymmetries of the global warming. Clim Dyn 29:375–391CrossRefGoogle Scholar
  7. Cess RD et al (1990) Intercomparison and interpretation of cloud–climate feedback processes in nineteen atmospheric general circulation models. J Geophys Res 95:16601–16615CrossRefGoogle Scholar
  8. Colman R (2003) A comparison of climate feedbacks in general circulation models. Clim Dyn 20:865–873Google Scholar
  9. Fu Q, Liou KN (1993) Parameterization of the radiative properties of cirrus clouds. J Atmos Sci 50:2008–2025CrossRefGoogle Scholar
  10. Hall A, Manabe S (1999) The role of water vapour feedback in unperturbed climate variability and global warming. J Clim 12: 2327–2346CrossRefGoogle Scholar
  11. Hall A, Qu X (2006) Using the current seasonal cycle to constrain snow albedo feedback in future climate change. Geophys Res Lett 33:L03502. doi: 10.1029/2005GL025127 CrossRefGoogle Scholar
  12. Hansen J et al (1984) Climate sensitivity: analysis of feedback mechanisms. In: Climate processes and climate sensitivity. Geophysics monography, vol 29. American Geophysics Union, pp 130–163Google Scholar
  13. Held IM, Soden BJ (2000) Water vapor feedback and global warming. Annu Rev Energy Environ 25:441–475CrossRefGoogle Scholar
  14. Peixoto JP, Oort AH (1992) Physics of climate. American Institute of Physics, pp 308–364Google Scholar
  15. Ramaswamy V et al (2001) Radiative forcing of climate change. In: Houghton JT et al (eds) Climate change 2001: the scientific basis. Cambridge University Press, Cambridge, pp 349–416Google Scholar
  16. Roe GH, Baker MB (2007) Why is climate sensitivity so unpredictable? Science 318:629–632CrossRefGoogle Scholar
  17. Schneider EK, Kirtman BP, Lindzen RS (1999) Tropospheric water vapor and climate sensitivity. J Atmos Sci 56:1649–1658CrossRefGoogle Scholar
  18. Soden BJ, Held IM (2006) An assessment of climate feedbacks in coupled ocean atmosphere models. J Clim 19:3354–3360CrossRefGoogle Scholar
  19. Soden BJ, Broccoli AJ, Hemler RS (2004) On the use of cloud forcing to estimate cloud feedback. J Clim 17:3661–3665CrossRefGoogle Scholar
  20. Stephens GL (2005) Cloud feedbacks in the climate system: a critical review. J Clim 18:237–273CrossRefGoogle Scholar
  21. Wetherald R, Manabe S (1988) Cloud feedback processes in a general circulation model. J Atmos Sci 45:1397–1415CrossRefGoogle Scholar
  22. Winton M (2006) Surface albedo feedback estimates for the AR4 climate models. J Clim 19:359–365CrossRefGoogle Scholar
  23. Zhang MH, Hack JJ, Kiehland JT, and Cess CD (1994) Diagnostic study of climate feedback processes in atmospheric general circulation models. J Geophys Res 99:5525–5537CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Department of MeteorologyFlorida State UniversityTallahasseeUSA

Personalised recommendations