Climate Dynamics

, Volume 34, Issue 7–8, pp 919–933 | Cite as

Atmospheric radiative feedbacks associated with transient climate change and climate variability

  • Robert A. ColmanEmail author
  • Scott B. Power


This study examines in detail the ‘atmospheric’ radiative feedbacks operating in a coupled General Circulation Model (GCM). These feedbacks (defined as the change in top of atmosphere radiation per degree of global surface temperature change) are due to responses in water vapour, lapse rate, clouds and surface albedo. Two types of radiative feedback in particular are considered: those arising from century scale ‘transient’ warming (from a 1% per annum compounded CO2 increase), and those operating under the model’s own unforced ‘natural’ variability. The time evolution of the transient (or ‘secular’) feedbacks is first examined. It is found that both the global strength and the latitudinal distributions of these feedbacks are established within the first two or three decades of warming, and thereafter change relatively little out to 100 years. They also closely approximate those found under equilibrium warming from a ‘mixed layer’ ocean version of the same model forced by a doubling of CO2. These secular feedbacks are then compared with those operating under unforced (interannual) variability. For water vapour, the interannual feedback is only around two-thirds the strength of the secular feedback. The pattern reveals widespread regions of negative feedback in the interannual case, in turn resulting from patterns of circulation change and regions of decreasing as well as increasing surface temperature. Considering the vertical structure of the two, it is found that although positive net mid to upper tropospheric contributions dominate both, they are weaker (and occur lower) under interannual variability than under secular change and are more narrowly confined to the tropics. Lapse rate feedback from variability shows weak negative feedback over low latitudes combined with strong positive feedback in mid-to-high latitudes resulting in no net global feedback—in contrast to the dominant negative low to mid-latitude response seen under secular climate change. Surface albedo feedback is, however, slightly stronger under interannual variability—partly due to regions of extremely weak, or even negative, feedback over Antarctic sea ice in the transient experiment. Both long and shortwave global cloud feedbacks are essentially zero on interannual timescales, with the shortwave term also being very weak under climate change, although cloud fraction and optical property components show correlation with global temperature both under interannual variability and transient climate change. The results of this modelling study, although for a single model only, suggest that the analogues provided by interannual variability may provide some useful pointers to some aspects of climate change feedback strength, particularly for water vapour and surface albedo, but that structural differences will need to be heeded in such an analysis.


Climate change Climate feedbacks Climate variability 



This work was partially supported by the Australian Climate Change Science Programme, administered by the Department of Climate Change. The authors wish to thank Bertrand Timbal, Ming Cai and an anonymous reviewer for their detailed and constructive comments.


  1. AchutaRao K, Sperber KR (2002) Simulation of the El Niño Southern Oscillation: results from the coupled model intercomparison project. Clim Dyn 19:191–209. doi: 10.1007/s00382-001-0221-9 CrossRefGoogle Scholar
  2. Allan RP, Ringer MA, Slingo A (2003) Evaluation of moisture in the Hadley Centre Climate Model using simulations of HIRS water vapour channel radiances. Q J R Meteorol Soc 129:3371–3389. doi: 10.1256/qj.02.217 CrossRefGoogle Scholar
  3. Bates JR (2007) Some considerations of the concept of climate feedback. Q J R Meteorol Soc 133:545–560. doi: 10.1002/qj.62 CrossRefGoogle Scholar
  4. Bauer M, Del Genio AD, Lanzante JR (2002) Observed and simulated temperature humidity relationships: sensitivity to sampling and analysis. J Clim 15:203–215. doi: 10.1175/1520-0442(2002)015<0203:OASTHR>2.0.CO;2 CrossRefGoogle Scholar
  5. Bettio L, Power S, Walsh K (2003) The dynamics of ENSO in a coupled GCM. Abstracts, International Conference on Earth System Modelling. Max Planck Institute for Meteorology, Hamburg, p 138Google Scholar
  6. Boer GJ, Yu B (2003) Climate sensitivity and response. Clim Dyn 20:415–429Google Scholar
  7. Bony S, Colman R, Kattsov V, Allan RP, Bretherton CS, Dufresne J-L, Hall A, Hallegatte S, Holland MM, Ingram W, Randall DA, Soden BJ, Tselioudis G, Webb MJ (2006) How well do we understand and evaluate climate change feedback processes? J Clim 19:3445–3482. doi: 10.1175/JCLI3819.1 CrossRefGoogle Scholar
  8. Cai M, Lu J (2007) Dynamical greenhouse-plus feedback and polar warming amplification. Part II: meridional and vertical asymmetries of the global warming. Clim Dyn 29:375–391. doi: 10.1007/s00382-007-0238-9 CrossRefGoogle Scholar
  9. Colman RA (2001) On the vertical extent of atmospheric feedbacks. Clim Dyn 17:391–405. doi: 10.1007/s003820000111 CrossRefGoogle Scholar
  10. Colman RA (2003) Seasonal contributions to climate feedbacks. Clim Dyn 20:825–841Google Scholar
  11. Colman RA, Power SB, McAvaney BJ (1997) Non-linear climate feedback analysis in an atmospheric GCM. Clim Dyn 13:717–731. doi: 10.1007/s003820050193 CrossRefGoogle Scholar
  12. Colman RA, Fraser JR, Rotstayn L (2001) Climate feedbacks in a general circulation model incorporating prognostic clouds. Clim Dyn 18:103–122. doi: 10.1007/s003820100162 CrossRefGoogle Scholar
  13. Davey M et al (2002) STOIC: A study of coupled model climatology and variability in tropical ocean regions. Clim Dyn 18:403–420. doi: 10.1007/s00382-001-0188-6 CrossRefGoogle Scholar
  14. Forster PF, Gregory JM (2006) The climate sensitivity and its components diagnosed from Earth Radiation Budget data. J Clim 19:39–52. doi: 10.1175/JCLI3611.1 CrossRefGoogle Scholar
  15. Gregory JM, Webb MJ (2008) Troposphere adjustment induces a cloud component in CO2 forcing. J Clim 21:58–71. doi: 10.1175/2007JCLI1834.1 CrossRefGoogle Scholar
  16. Hall A (2004) The role of surface albedo feedback in climate. J Clim 17:1550–1568. doi: 10.1175/1520-0442(2004)017<1550:TROSAF>2.0.CO;2 CrossRefGoogle Scholar
  17. Hall A, Manabe S (1999) The role of water vapour feedback in unperturbed climate variability and global warming. J Clim 12:2327–2346. doi: 10.1175/1520-0442(1999)012<2327:TROWVF>2.0.CO;2 CrossRefGoogle Scholar
  18. 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
  19. Held IM, Soden BJ (2000) Water vapour feedback and global warming. Annu Rev Energy Environ 25:441–475. doi: 10.1146/ CrossRefGoogle Scholar
  20. IPCC (2001) Climate Change 2001. In: Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Dai X, Maskell K, Johnson CA (eds) The scientific basis. Cambridge University Press, Cambridge, p 881Google Scholar
  21. IPCC (2007) Climate Change 2007: the Physical Science Basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Contribution of working group I to the fourth assessment report of the IPCC. Cambridge University Press, CambridgeGoogle Scholar
  22. Lahallec A, Hallegate S, Grandpeix J-Y, Dumas P, Blanco S (2008) Feedback characteristics of nonlinear dynamical systems. Europhys Lett 81. doi: 10.1029/0295-5075/81/60001
  23. Latif M et al (2001) ENSIP: The El Niño Simulation Intercomparison Project. Clim Dyn 18:255–276. doi: 10.1007/s003820100174 CrossRefGoogle Scholar
  24. McCarthy MP, Toumi R (2004) Observed interannual variability of tropical troposphere relative humidity. J Clim 17:3181–3191. doi: 10.1175/1520-0442(2004)017<3181:OIVOTT>2.0.CO;2 CrossRefGoogle Scholar
  25. Meehl GA, Washington WM, Arblaster JM, Hu A (2004) Factors affecting climate sensitivity in global coupled models. J Clim 17:1584–1596. doi: 10.1175/1520-0442(2004)017<1584:FACSIG>2.0.CO;2 CrossRefGoogle Scholar
  26. Minschwaner K, Dessler AE (2004) Water vapor feedback in the tropical upper troposphere: model results and observations. J Clim 17:1272–1282. doi: 10.1175/1520-0442(2004)017<1272:WVFITT>2.0.CO;2 CrossRefGoogle Scholar
  27. Minschwaner K, Dessler AE, Parnchai S (2006) Multi-model analysis of the water vapour feedback in the tropical upper troposphere. J Clim 19:5455–5464. doi: 10.1175/JCLI3882.1 CrossRefGoogle Scholar
  28. Pacanowski RC, Dixon K, Rosati A (1991) The GFDL Modular Ocean Model user’s guide, version 1.0. GFDL Ocean Group Tech Rep 2:376Google Scholar
  29. Power SB (1995) Climate drift in a global ocean general circulation model. J Phys Oceanogr 25:1025–1036. doi: 10.1175/1520-0485(1995)025<1025:CDIAGO>2.0.CO;2 CrossRefGoogle Scholar
  30. Power SB, Colman RA (2006) Multi-decadal predictability in a coupled GCM. Clim Dyn 26:247–272. doi: 10.1007/s00382-005-0055-y CrossRefGoogle Scholar
  31. Power SB, Kleeman R, Tseitkin F, Smith N (1995) A global version of the GFDL modular ocean model for ENSO studies. BMRC technical report 18 ppGoogle Scholar
  32. Power SB, Haylock MH, Colman RA, Wang X (2006) The predictability of inter-decadal changes in ENSO activity and ENSO teleconnections. J Clim 19:4755–4771. doi: 10.1175/JCLI3868.1 CrossRefGoogle Scholar
  33. Randall DA, Wood RA, Bony S, Colman R, Fichefet T, Fyfe J, Kattsov V, Pitman A, Shukla J, Srinivasan J, Stouffer RJ, Sumi A, Taylor KE (2007) Climate Models and Their Evaluation. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  34. Senior CA, Mitchell JFB (2000) The time-dependence of climate sensitivity. Geophys Res Lett 27:2685–2688. doi: 10.1029/2000GL011373 CrossRefGoogle Scholar
  35. Soden BJ, Held IM (2006) An assessment of climate feedbacks in coupled ocean-atmosphere models. J Clim 19:3354–3360. doi: 10.1175/JCLI3799.1 CrossRefGoogle Scholar
  36. Trenberth KE, Caron JM, Stepaniak DP, Worley S (2002) Evolution of El Nino-Southern Oscillation and global atmospheric surface temperatures. J Geophys Res 107. D8 4065 doi: 10.1029/2000JD000298
  37. Tsushima Y, Manabe S (2001) Influence of cloud feedback on annual variation of global mean surface temperature. J Geophys Res 106(22):635–646. doi: 10.1029/2000JD000235 Google Scholar
  38. Tsushima Y, Abe-Ouchi A, Manabe S (2005) Radiative damping of annual variation in global mean surface temperature: comparison between observed and simulated feedback. Clim Dyn 24:591–597. doi: 10.1007/s00382-005-0002-y CrossRefGoogle Scholar
  39. Watterson IG (2003) Effects of a dynamic ocean on simulated climate sensitivity to greenhouse gases. Clim Dyn 21:197–209. doi: 10.1007/s00382-003-0326-4 CrossRefGoogle Scholar
  40. Williams KD, Ingram WJ, Gregory JM (2008) Time variation of effective climate sensitivity in GCMs. J Clim 21:5076–5090. doi: 10.1175/2008JCLI2371.1 CrossRefGoogle Scholar
  41. Wu Z-J, Colman R, Power S, Wang X, McAvaney B (2002) The El Niño Southern Oscillation Response in the BMRC Coupled GCM. BMRC Research Rep. 91: 18 pp. Available at
  42. Zhu P, Hack JJ, Kiehl JT (2007) Diagnosing cloud feedbacks in general circulation models. J Clim 20:2602–2622. doi: 10.1175/JCLI4140.1 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Centre for Australian Weather and Climate ResearchBureau of MeteorologyMelbourneAustralia

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