Climate Dynamics

, Volume 47, Issue 12, pp 3767–3782 | Cite as

Towards a physical understanding of stratospheric cooling under global warming through a process-based decomposition method

  • Yang Yang
  • R.-C. Ren
  • Ming Cai


The stratosphere has been cooling under global warming, the causes of which are not yet well understood. This study applied a process-based decomposition method (CFRAM; Coupled Surface–Atmosphere Climate Feedback Response Analysis Method) to the simulation results of a Coupled Model Intercomparison Project, phase 5 (CMIP5) model (CCSM4; Community Climate System Model, version 4), to demonstrate the responsible radiative and non-radiative processes involved in the stratospheric cooling. By focusing on the long-term stratospheric temperature changes between the “historical run” and the 8.5 W m−2 Representative Concentration Pathway (RCP8.5) scenario, this study demonstrates that the changes of radiative radiation due to CO2, ozone and water vapor are the main divers of stratospheric cooling in both winter and summer. They contribute to the cooling changes by reducing the net radiative energy (mainly downward radiation) received by the stratospheric layer. In terms of the global average, their contributions are around −5, −1.5, and −1 K, respectively. However, the observed stratospheric cooling is much weaker than the cooling by radiative processes. It is because changes in atmospheric dynamic processes act to strongly mitigate the radiative cooling by yielding a roughly 4 K warming on the global average base. In particular, the much stronger/weaker dynamic warming in the northern/southern winter extratropics is associated with an increase of the planetary-wave activity in the northern winter, but a slight decrease in the southern winter hemisphere, under global warming. More importantly, although radiative processes dominate the stratospheric cooling, the spatial patterns are largely determined by the non-radiative effects of dynamic processes.


Stratospheric cooling Global warming Processes-based decomposition CFRAM 



This work was jointly supported by a research grant from the National Natural Science Foundation of China (41575041, 41430533, 91437105), a Chinese Academy of Sciences project (XDA11010402), and a China Meteorological Administration Special Public Welfare Research Fund (GYHY201406001).


  1. Andrews DG, Holton JR, Leovy CB (1987) Middle atmosphere dynamics. Academic Press Inc., LondonGoogle Scholar
  2. Butchart N, Scaife AA, Bourqui M, Grandpré DJ (2006) Simulations of anthropogenic change in the strength of the Brewer–Dobson circulation. Clim Dyn 27:727–741. doi: 10.1007/s00382-006-0162-4 CrossRefGoogle Scholar
  3. Cai M, Lu J-H (2009) A new framework for isolating individual feedback processes in coupled general circulation climate models. Part II: method demonstrations and comparisons. Clim Dyn 32(6):887–900. doi: 10.1007/s00382-008-0424-4 CrossRefGoogle Scholar
  4. Cai M, Ren RC (2007) Meridional and downward propagation of atmospheric circulation anomalies. Part I: Northern Hemisphere cold season variability. J Atmos Sci 64:1880–1901. doi: 10.1175/JAS3922.1 CrossRefGoogle Scholar
  5. Cai M, Tung K-K (2012) Robustness of dynamical feedbacks from radiative forcing: 2 % solar versus 2 × CO2 experiments in an idealized GCM. J Atmos Sci 69:2256–2271. doi: 10.1175/JAS-D-11-0117.1 CrossRefGoogle Scholar
  6. Cordero E, Forster P (2006) Stratospheric variability and trends in models used for the IPCC AR4. Atmos Chem Phys 6:5369–5380. doi: 10.5194/acp-6-5369-2006 CrossRefGoogle Scholar
  7. Danabasoglu G, Bates SC, Briegleb BP et al (2012) The CCSM4 ocean component. J Clim 25:1361–1389. doi: 10.1175/JCLI-D-11-00091.1 CrossRefGoogle Scholar
  8. Deng Y, Park T-W, Cai M (2012) Process-based decomposition of the global surface temperature response to El Niño in boreal winter. J Atmos Sci 69:1706–1712. doi: 10.1175/JAS-D-12-023.1 CrossRefGoogle Scholar
  9. Deng Y, Park T-W, Cai M (2013) Radiative and dynamical forcing of the surface and atmospheric temperature anomalies associated with the northern annular mode. J Clim 26:5124–5138. doi: 10.1175/JCLI-D-12-00431.1 CrossRefGoogle Scholar
  10. Eyring V, Butchart N, Waugh D et al (2006) Assessment of temperature, trace species, and ozone in chemistry-climate model simulations of the recent past. J Geophys Res 111:D22308. doi: 10.1029/2006JD007327 CrossRefGoogle Scholar
  11. Fomichev V, Jonsson A, Grandpré J et al (2007) Response of the middle atmosphere to CO2 doubling: results from the Canadian Middle Atmosphere Model. J Clim 20:1121–1144. doi: 10.1175/JCLI4030.1 CrossRefGoogle Scholar
  12. Fu Q, Liou KN (1992) On the correlated K-distribution method for radiative-transfer in nonhomogeneous atmospheres. J Atmos Sci 49(22):2139–2156CrossRefGoogle Scholar
  13. Fu Q, Liou KN (1993) Parameterization of the radiative properties of cirrus clouds. J Atmos Sci 50(13):2008–2025CrossRefGoogle Scholar
  14. Fu Q, Lin P, Solomon S et al (2015) Observational evidence of strengthening of the Brewer–Dobson circulation since 1980. J Geophys Res Atmos. doi: 10.1002/2015JD023657 Google Scholar
  15. Gent PR, Danabasoglu G, Donner LJ et al (2011) The community climate system model version 4. J Clim 24:4973–4991. doi: 10.1175/2011JCLI4083.1 CrossRefGoogle Scholar
  16. Hu Y, Tung K (2002) Interannual and decadal variations of planetary wave activity, stratospheric cooling, and Northern Hemisphere annular mode. J Clim 15:1659–1673CrossRefGoogle Scholar
  17. Hunke EC, Lipscomb WH (2010) CICE: the Los Alamos Sea Ice Model documentation and software user’s manual, version 4.1. Los Alamos National Laboratory Technical Report LA-CC-06-012Google Scholar
  18. Langematz U, Kunze M, Krüger K et al (2003) Thermal and dynamical changes of the stratosphere since 1979 and their link to ozone and CO2 changes. J Geophys Res Atmos 108:4027. doi: 10.1029/2002JD002069 CrossRefGoogle Scholar
  19. Lawrence DM, Oleson KW, Flanner MG et al (2012) The CCSM4 land simulation, 1850–2005: assessment of surface climate and new capabilities. J Clim 25:2240–2260. doi: 10.1175/JCLI-D-11-00103.1 CrossRefGoogle Scholar
  20. Liu B, Zhou T, Lu J-H (2015) Quantifying contributions of model processes to the surface temperature bias in FGOALS-g2. J Adv Model Earth Syst. doi: 10.1002/2015MS000459 Google Scholar
  21. Lu J-H, Cai M (2009) A new framework for isolating individual feedback processes in coupled general circulation climate models. Part I: formulation. Clim Dyn 32(6):873–885. doi: 10.1007/S00382-008-0425-3 CrossRefGoogle Scholar
  22. Lu J-H, Cai M (2010) Quantifying contributions to polar warming amplification in an idealized coupled general circulation model. Clim Dyn 34:669–687. doi: 10.1007/s00382-009-0673-x CrossRefGoogle Scholar
  23. Neale R, Richter J, Park S et al (2013) The mean climate of the Community Atmosphere Model (CAM4) in forced SST and fully coupled experiments. J Clim 26:5150–5168. doi: 10.1175/JCLI-D-12-00236.1 CrossRefGoogle Scholar
  24. Park T-W, Deng Y, Cai M et al (2014) A dissection of the surface temperature biases in the Community Earth System Model. Clim Dyn 43:2043–2059. doi: 10.1007/s00382-013-2029-9 CrossRefGoogle Scholar
  25. Ramaswamy V, Chanin M-L, Angell J et al (2001) Stratospheric temperature trends: observations and model simulations. Rev Geophys 39:71–122. doi: 10.1029/1999RG000065 CrossRefGoogle Scholar
  26. Randel W, Shine K, Austin J et al (2009) An update of observed stratospheric temperature trends. J Geophys Res Atmos 114:D02107. doi: 10.1029/2008JD010421 CrossRefGoogle Scholar
  27. Ren RC, Yang Y (2012) Changes in winter stratospheric circulation in CMIP5 scenarios simulated by the climate system model FGOALS-s2. Adv Atmos Sci 29:1374–1389. doi: 10.1007/s00376-012-1184-y CrossRefGoogle Scholar
  28. Ren RC, Cai M, Xiang C, Wu G (2012) Observational evidence of the delayed response of stratospheric polar vortex variability to ENSO SST anomalies. Clim Dyn 38:1345–1358. doi: 10.1007/s00382-011-1137-7 CrossRefGoogle Scholar
  29. Ren RC, Yang Y, Cai M et al (2015) Understanding the systematic air temperature biases in a coupled climate system model through a process-based decomposition method. Clim Dyn. doi: 10.1007/s00382-014-2435-7 Google Scholar
  30. Seidel D, Gillett N, Lanzante J et al (2011) Stratospheric temperature trends: our evolving understanding. WIREs Clim Change 2:592–616. doi: 10.1002/wcc.125 CrossRefGoogle Scholar
  31. Sejas S, Cai M, Hu A et al (2014) Individual feedback contributions to the seasonality of surface warming. J Clim 27:5653–5669. doi: 10.1175/JCLI-D-13-00658.1 CrossRefGoogle Scholar
  32. Shine K, Bourqui M, Forster P et al (2003) A comparison of model-simulated trends in stratospheric temperatures. QJR Meteorol Soc 129:1565–1588. doi: 10.1256/qj.02.186 CrossRefGoogle Scholar
  33. Song X, Zhang G, Cai M (2014) Quantifying contributions of climate feedbacks to tropospheric warming in the NCAR CCSM3.0. Clim Dyn 42:901–917. doi: 10.1007/s00382-013-1805-x CrossRefGoogle Scholar
  34. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experimental design. Bull Am Meteorol Soc 93:485–498. doi: 10.1175/BAMS-D-11-00094.1 CrossRefGoogle Scholar
  35. Thompson D, Seidel D, Randel W et al (2012) The mystery of recent stratospheric temperature trends. Nature 491:692–697. doi: 10.1038/nature11579 CrossRefGoogle Scholar
  36. Xu J, Powell AM Jr, Zhao L (2013) Intercomparison of temperature trends in IPCC CMIP5 simulations with observations, reanalyses and CMIP3 models. Geosci Model Dev 6:1705–1714. doi: 10.5194/gmd-6-1705-2013 CrossRefGoogle Scholar
  37. Yang Y, Ren RC (2015) Understanding the global surface-atmosphere energy balance in FGOALS-s2 through an attribution analysis of the global temperature biases. Atmos Ocean Sci Lett 8:107–112. doi: 10.3878/AOSL20150019 CrossRefGoogle Scholar
  38. Yang Y, Ren RC, Cai M et al (2015) Attributing analysis on the model bias in surface temperature in the climate system model FGOALS-s2 through a process-based decomposition method. Adv Atmos Sci 32:457–469. doi: 10.1007/s00376-014-4061-z CrossRefGoogle Scholar
  39. Zerefos CS, Tourpali K, Zanis P et al (2014) Evidence for an earlier greenhouse cooling effect in the stratosphere before 1980 over the Northern Hemisphere. Atmos Chem Phys 14(15):7705–7720. doi: 10.5194/acp-14-7705-2014 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  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.University of Chinese Academy of SciencesBeijingChina
  3. 3.Department of Earth, Ocean, and Atmospheric ScienceFlorida State UniversityTallahasseeUSA

Personalised recommendations