Theoretical and Applied Climatology

, Volume 110, Issue 1–2, pp 1–10 | Cite as

Climate sensitivity and cloud feedback processes imposed by two different external forcings in an aquaplanet GCM

Original Paper


The sensitivity of climate to an increase in sea surface temperature (SST) and CO2, as well as cloud feedback processes, is analyzed through a series of aquaplanet experiments listed in the Coupled Model Intercomparison Project. Rainfall is strengthened in a +4K anomaly SST experiment due to the enhanced surface evaporation; while in a quadruple CO2 experiment, precipitation and total cloud cover are appreciably weakened. In both the +4K and quadruple CO2 (4xCO2) experiments, the Hadley cell is impaired, with an increase in moderate subsidence and a decrease in the frequency of strong convective activity. Regarding cloud radiation forcing (CRF), the analysis technique of Bony et al. (Climate Dynamics, 22:71–86, 2004) is used to sort cloud variables by dynamic regimes using the 500-hPa vertical velocity in tropical areas (30°S–30°N). Results show that the tropically averaged CRF change is negative and is dominated mainly by the thermodynamic component. Within convective regimes, the behavior of longwave CRF is different in the +4K and 4xCO2 experiments, with positive and negative changes, respectively. The globally averaged CRF also reveals a negative change in both aquaplanet and Earthlike experiments, implying that clouds may play a role in decelerating global warming. The calculated climate sensitivity demonstrates that our results are close to those obtained from other models, with 0.384 and 0.584 Km2 W−1 for aquaplanet and Earthlike experiments, respectively.


Cloud Fraction Cloud Feedback Total Cloud Cover Cloud Radiation Force Cloud Water Path 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was supported by the “Strategic Priority Research Program—Climate Change: Carbon Budget and Relevant Issues” of the Chinese Academy of Sciences (XDA05110303), the National Key Basic Research Program (2012CB417203), and National Science Foundation of China (40805038 and 41023002).


  1. Bao Q, Wu G, Liu Y, Yang J, Wang Z, Zhou T (2010) An introduction to the coupled model FGOALS1.1-s and its performance in East Asia. Adv Atmos Sci 27:1131–1142CrossRefGoogle Scholar
  2. Betts AK, Ridgway W (1989) Climatic equilibrium of the atmospheric convective boundary layer over a tropical ocean. J Atmos Sci 46:2621–2641CrossRefGoogle Scholar
  3. Bipasha P, Sathiyamoorthy V, Pal P, Johsi P (2009) Effects of cloud types on cloud-radiation interaction over the Asia monsoon region. Theor Appl Climatol 97:287–295CrossRefGoogle Scholar
  4. Bony S, Dufresne JL, Le Treut H, Morcrette JJ, Senior C (2004) On dynamic and thermodynamic components of cloud changes. Clim Dyn 22:71–86CrossRefGoogle Scholar
  5. Bony S, et al. (2009) The cloud feedback model intercomparison project: summary of activities and recommendations for advancing assessments of cloud-climate feedbacks. Available online at:
  6. Cess RD et al (1990) Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. J Geophys Res 95:16601–16615CrossRefGoogle Scholar
  7. Cess RD et al (1996) Cloud feedback in atmospheric general circulation models: an update. J Geophys Res 101:12791–12794CrossRefGoogle Scholar
  8. Charlock TP, Ramanathan V (1985) The albedo field and cloud radiative forcing produced by a general circulation model with internally generated cloud optics. J Atmos Sci 42:1408–1429CrossRefGoogle Scholar
  9. Edwards J, Slingo A (1996) Studies with a flexible new radiation code. I: Choosing a configuration for a large-scale model. Q J R Meteorol Soc 122:689–719CrossRefGoogle Scholar
  10. Hansen J, Lacis A, Rind D et al (1984) Climate sensitivity: analysis of feedback mechanisms. In: Hansen JE, Takahashi T (eds) Climate processes and climate sensitivity, AGU geophysical monograph 29, Maurice Ewing, vol 5. American Geophysical Union, Washington, pp 130–163Google Scholar
  11. Holtslag A, Boville B (1993) Local versus nonlocal boundary-layer diffusion in a global climate model. J Clim 6:1825–1842CrossRefGoogle Scholar
  12. Hoskins B, Neale R, Rodwell M, Yang G-Y (1999) Aspects of the large-scale tropical atmospheric circulation. Tellus B 51:33–44CrossRefGoogle Scholar
  13. Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Dai X, Maskell K, Johnson CA (eds) (2001) Climate change 2001: the scientific basis. Cambridge University Press, Cambridge, p 944Google Scholar
  14. Knutson TR, Manabe S (1995) Time–mean response over the tropical Pacific to increased CO2 in a coupled ocean–atmosphere model. J Clim 8:2181–2199CrossRefGoogle Scholar
  15. Liu P, Wang B, Sperber KR, Li T, Meehl GA (2005) MJO in the NCAR CAM2 with the Tiedtke convective scheme. J Clim 18:3007–3020CrossRefGoogle Scholar
  16. Liu Y, Guo L, Wu G, Wang Z (2010) Sensitivity of ITCZ configuration to cumulus convective parameterizations on an aqua planet. Clim Dyn 34:223–240CrossRefGoogle Scholar
  17. Lorenz EN (1967) The nature and theory of the general circulation of the atmosphere. World Meteorological Organization, Geneva, p 161Google Scholar
  18. Manabe S, Wetherald RT (1967) Thermal equilibrium of the atmosphere with a given distribution of relative humidity. J Atmos Sci 24:241–259CrossRefGoogle Scholar
  19. Martin G, Johnson D, Spice A (1994) The measurement and parameterization of effective radius of droplets in warm stratocumulus clouds. J Atmos Sci 51:1823–1842CrossRefGoogle Scholar
  20. Medeiros B, Stevens B, Held IM, Zhao M, Williamson DL, Olson JG, Bretherton CS (2008) Aquaplanets, climate sensitivity, and low clouds. J Clim 21:4974–4991CrossRefGoogle Scholar
  21. Mitas CM, Clement A (2006) Recent behavior of the Hadley cell and tropical thermodynamics in climate models and reanalyses. Geophys Res Lett 33:1810–1813CrossRefGoogle Scholar
  22. Neale RB, Hoskins B (2000a) A standard test for AGCMs including their physical parametrizations: I: the proposal. Atmos Sci Lett 1:101–107CrossRefGoogle Scholar
  23. Neale RB, Hoskins BJ (2000b) A standard test for AGCMs including their physical parameterizations: II: results for the Met office model. Atmos Sci Lett. doi: 10.1006/asle.2000.0019
  24. Nordeng TE (1994) Extended versions of the convective parameterization schemes at ECMWF and their impact on the mean and transient activity of the model in the tropics. ECMWF Tech Memo 206:41Google Scholar
  25. Slingo JM (1980) A cloud parametrization scheme derived from GATE data for use with a numerical model. Q J R Meteorol Soc 106:747–770CrossRefGoogle Scholar
  26. Soden BJ, Broccoli AJ, Hemler RS (2004) On the use of cloud forcing to estimate cloud feedback. J Clim 17:3661–3665CrossRefGoogle Scholar
  27. Song XL (2005) The evaluation analysis of two kinds of mass flux cumulus parameterizations in climate simulation. Doctoral dissertation. Beijing: Institute of Atmospheric Physics, Chinese Academy of Sciences, 119–145 (in Chinese)Google Scholar
  28. Sun Z (2005) Parameterizations of radiation and cloud optical properties. BMRC Res Rep 107–112. Available online at:
  29. Taylor KE, Stouffer RJ, Meehl GA (2009) A summary of the CMIP5 experimental design. CLIVAR, 30 pp. Available online at:
  30. Tiedtke M (1989) A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon Weather Rev 117:1779–1800CrossRefGoogle Scholar
  31. Wang XC, Bao Q, Liu K, Wu GX, Liu YM (2011) Features of rainfall and latent heating structure simulated by two convective parameterization schemes. Sci China Earth Sci 54:1779–1788CrossRefGoogle Scholar
  32. Wu G, Liu H, Zhao Y, Li W (1996) A nine-layer atmospheric general circulation model and its performance. Adv Atmos Sci 13(1):1–18CrossRefGoogle Scholar
  33. Zhang M, Song H (2006) Evidence of deceleration of atmospheric vertical overturning circulation over the tropical Pacific. Geophys Res Lett 33:L12701. doi: 10.1029/2006GL025942 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Xiaocong Wang
    • 1
    • 2
  • Yimin Liu
    • 1
  • Qing Bao
    • 1
  • Zaizhi Wang
    • 3
  1. 1.State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.Graduate University of Chinese Academy of SciencesBeijingChina
  3. 3.National Climate CenterChina Meteorological AdministrationBeijingChina

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