Climate Dynamics

, Volume 41, Issue 9–10, pp 2471–2479 | Cite as

Quantifying global climate feedbacks, responses and forcing under abrupt and gradual CO2 forcing



Experiments with abrupt CO2 forcing allow the diagnosis of the response of global mean temperature and precipitation in terms of fast temperature independent adjustments and slow, linear temperature-dependent feedbacks. Here we compare responses, feedbacks and forcings in experiments performed as part of version 5 of the coupled model inter-comparison project (CMIP5). The experiments facilitate, for the first time, a comparison of fully coupled atmosphere-ocean general circulation models (GCM’s) under both linearly increasing and abrupt radiative forcing. In the case of a 1 % per year compounded increase in CO2 concentration, we find that the non-linear evolution of surface air temperature in time, when combined with the linear evolution of the radiative balance at the top of the atmosphere, results in a feedback parameter and effective climate sensitivity having an offset compared to values computed from abrupt 4× CO2 forcing experiments. The linear evolution of the radiative balance at the top of the atmosphere also contributes to an offset between the global mean precipitation response predicted in the 1 % experiment using linear theory and that diagnosed from the experiments themselves, and a potential error between the adjusted radiative forcing and that produced using a standard linear formula. The non-linear evolution of temperature and precipitation responses are also evident in the RCP8.5 scenario and have implications for understanding, quantifying and emulating the global response of the CMIP5 climate GCMs.


CMIP5 Feedbacks Forcing Response 


  1. Andrews T, Forster P (2008) CO2 forcing induces semi-direct effects with consequences for climate feedback interpretations. Geophys Res Lett 35:L04802CrossRefGoogle Scholar
  2. Andrews T, Forster P, Gregory J (2009) A surface energy perspective on climate change. J Clim 22(10):2557–2570CrossRefGoogle Scholar
  3. Andrews T, Forster P, Boucher O, Bellouin N, Jones A (2010) Precipitation, radiative forcing and global temperature change. Geophys Res Lett 37(14):L14701CrossRefGoogle Scholar
  4. Andrews T, Gregory J, Webb M, Taylor K (2012a) Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere–ocean climate models. Geophys Res Lett 39(9):L09712CrossRefGoogle Scholar
  5. Andrews T, Gregory J, Forster P, Webb M (2012b) Cloud adjustment and its role in CO2 radiative forcing and climate sensitivity: a review. Surv Geophy 33(3):619–635CrossRefGoogle Scholar
  6. Colman R (2003) A comparison of climate feedbacks in general circulation models. Clim Dyn 20(7):865–873Google Scholar
  7. Colman R, McAvaney B (2011) On tropospheric adjustment to forcing and climate feedbacks. Clim Dyn 36(9):1649–1658CrossRefGoogle Scholar
  8. Forster P, Taylor K (2006) Climate forcings and climate sensitivities diagnosed from coupled climate model integrations. J Clim 19(23):6181–6194CrossRefGoogle Scholar
  9. Good P, Gregory J, Lowe J (2011) A step-response simple climate model to reconstruct and interpret AOGCM projections. Geophys Res Lett 38(1):L01703CrossRefGoogle Scholar
  10. Good P, Gregory J, Lowe J, Andrews T (2012) Abrupt CO2 experiments as tools for predicting and understanding CMIP5 representative concentration pathway projections. Clim Dyn 36(9):1649–1658Google Scholar
  11. Good P, Ingram W, Lambert F, Lowe J, Gregory M, Webb M, Ringer M, Wu P (2012) A step-response approach for predicting and understanding non-linear precipitation changes. Clim Dyn 39(12):2789–2803CrossRefGoogle Scholar
  12. Gregory J, Mitchell J (1997) The climate response to CO2 of the Hadley Centre coupled AOGCM with and without flux adjustment. Geophys Res Lett 24(15):1943–1946CrossRefGoogle Scholar
  13. Gregory J, Webb M (2008) Tropospheric adjustment induces a cloud component in CO2 forcing. J Clim 21(1):58–71CrossRefGoogle Scholar
  14. Gregory J, Forster P (2008) Transient climate response estimated from radiative forcing and observed temperature change. J Geophys Res Atmos 113(D23):58–71CrossRefGoogle Scholar
  15. Gregory J, Ingram W, Palmer M, Jones G, Stott P, Thorpe R, Lowe J, Johns T, Williams K (2004) A new method for diagnosing radiative forcing and climate sensitivity. Geophys Res Lett 31(3):L03205Google Scholar
  16. Harris G, Sexton D, Booth B, Collins M, Murphy J, Webb M (2006) Frequency distributions of transient regional climate change from perturbed physics ensembles of general circulation model simulations. Clim Dyn 27(4):357–375CrossRefGoogle Scholar
  17. Huntingford C, Cox P (2000) An analogue model to derive additional climate change scenarios from existing gcm simulations. Clim Dyn 16(8):575–586CrossRefGoogle Scholar
  18. Keen A, Murphy J (1997) Influence of natural variability and the cold start problem on the simulated transient response to increasing CO2. Clim Dyn 13(12):847–864CrossRefGoogle Scholar
  19. Kiehl J, Shields C, Hack J, Collins W (2006) The climate sensitivity of the community climate system model version 3 (ccsm3). J Clim 19(11):2584–2596CrossRefGoogle Scholar
  20. Lambert F, Allen M (2009) Are changes in global precipitation constrained by the tropospheric energy budget? J Clim 22(3):499–517CrossRefGoogle Scholar
  21. Lambert F, Webb M (2008) Dependency of global mean precipitation on surface temperature. Geophys Res Lett 35(16):L16706CrossRefGoogle Scholar
  22. Murphy J (1995) Transient response of the Hadley Centre coupled ocean-atmosphere model to increasing carbon dioxide. III: analysis of global-mean response using simple models. J Clim 8(3):496–514CrossRefGoogle Scholar
  23. Murphy J, Sexton D, Barnett D, Jones G, Webb M, Collins M, Stainforth D (2004) Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430(7001):768–772CrossRefGoogle Scholar
  24. Myhre G, Highwood E, Shine K, Stordal F (1998) New estimates of radiative forcing due to well mixed greenhouse gases. Geophys Res Lett 25(14):2715–2718CrossRefGoogle Scholar
  25. Raper S, Gregory J, Stouffer R (2002) The role of climate sensitivity and ocean heat uptake on aogcm transient temperature response. J Clim 15(1):124–130CrossRefGoogle Scholar
  26. Senior C, Mitchell J (2000) Time-dependence of climate sensitivity. Geophys Res Lett 27(17):2685–2688CrossRefGoogle Scholar
  27. Soden B, Held I (2006) An assessment of climate feedbacks in coupled ocean-atmosphere models. J Clim 19(14):3354–3360CrossRefGoogle Scholar
  28. Stouffer R, Manabe S (1999) Response of a coupled ocean–atmosphere model to increasing atmospheric carbon dioxide: sensitivity to the rate of increase. J Clim 12(8):2224–2237CrossRefGoogle Scholar
  29. Taylor K, Stouffer R, Meehl G (2007) A summary of the cmip5 experiment design. World 4:1–33Google Scholar
  30. Webb M, Senior C, Sexton D, Ingram W, Williams K, Ringer M, McAvaney B, Colman R, Soden B, Gudgel R et al (2006) On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles. Clim Dyn 27(1):17–38CrossRefGoogle Scholar
  31. Williams K, Ingram W, Gregory J (2008) Time variation of effective climate sensitivity in gcms. J Clim 21(19):5076–5090CrossRefGoogle Scholar
  32. Yokohata T, Emori S, Nozawa T, Ogura T, Kawamiya M, Tsushima Y, Suzuki T, Yukimoto S, Abe-Ouchi A, Hasumi H et al (2008) Comparison of equilibrium and transient responses to CO2 increase in eight state-of-the-art climate models. Tellus A 60(5):946–961CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Exeter Climate Systems, College of Engineering, Mathematics and Physical SciencesUniversity of ExeterExeterUK

Personalised recommendations