Climate Dynamics

, Volume 51, Issue 9–10, pp 3653–3672 | Cite as

Does shortwave absorption by methane influence its effectiveness?

  • Angshuman ModakEmail author
  • Govindasamy Bala
  • Ken Caldeira
  • Long Cao


In this study, using idealized step-forcing simulations, we examine the effective radiative forcing of CH4 relative to that of CO2 and compare the effects of CH4 and CO2 forcing on the climate system. A tenfold increase in CH4 concentration in the NCAR CAM5 climate model produces similar long term global mean surface warming (~ 1.7 K) as a one-third increase in CO2 concentration. However, the radiative forcing estimated for CO2 using the prescribed-SST method is ~ 81% that of CH4, indicating that the efficacy of CH4 forcing is ~ 0.81. This estimate is nearly unchanged when the CO2 physiological effect is included in our simulations. Further, for the same long-term global mean surface warming, we simulate a smaller precipitation increase in the CH4 case compared to the CO2 case. This is because of the fast adjustment processes—precipitation reduction in the CH4 case is larger than that of the CO2 case. This is associated with a relatively more stable atmosphere and larger atmospheric radiative forcing in the CH4 case which occurs because of near-infrared absorption by CH4 in the upper troposphere and lower stratosphere. Within a month after an increase in CH4, this shortwave heating results in a temperature increase of ~ 0.8 K in the lower stratosphere and upper troposphere. In contrast, within a month after a CO2 increase, longwave cooling results in a temperature decrease of ~ 3 K in the stratosphere and a small change in the upper troposphere. These fast adjustments in the lower stratospheric and upper tropospheric temperature, along with the adjustments in clouds in the troposphere, influence the effective radiative forcing and the fast precipitation response. These differences in fast climate adjustments also produce differences in the climate states from which the slow response begins to evolve and hence they are likely associated with differing feedbacks. We also find that the tropics and subtropics are relatively warmer in the CH4 case for the same global mean surface warming because of a larger longwave clear-sky and shortwave cloud forcing over these regions in the CH4 case. Further investigation using a multi-model intercomparison framework would permit an assessment of the robustness of our results.


Methane Radiative forcing Efficacy Climate feedback Fast cloud adjustments Hydrological cycle 



The first author acknowledges the scholarship provided by the Indian Institute of Science. The model (CAM5) simulations were performed out at Centre for Atmospheric and Oceanic Sciences High Performance Computing facility funded by Fund for Improvement of S & T Infrastructure (FIST), Department of Science and Technology (DST). We thank Lei Duan and the anonymous reviewers whose comments and suggestions helped to improve the original manuscript substantially.

Supplementary material

382_2018_4102_MOESM1_ESM.docx (2.3 mb)
Supplementary material 1 (DOCX 2363 KB)


  1. Andrews T, Webb MJ (2018) The dependence of global cloud and lapse rate feedbacks on the spatial structure of tropical pacific warming. J Clim. CrossRefGoogle Scholar
  2. Andrews T, Forster PM, Gregory JM (2009) A surface energy perspective on climate change. J Clim 22:2557–2570CrossRefGoogle Scholar
  3. Andrews T, Forster PM, Boucher O, Bellouin N, Jones A (2010) Precipitation, radiative forcing and global temperature change. Geophys Res Lett 37:L14701. CrossRefGoogle Scholar
  4. Andrews T, Doutriaux-Boucher M, Boucher O, Forster PM (2011) A regional and global analysis of carbon dioxide physiological forcing and its impact on climate. Clim Dyn 36:783–792. CrossRefGoogle Scholar
  5. Andrews T, Gregory JM, Webb MJ (2015) The dependence of radiative forcing and feedback on evolving patterns of surface temperature change in climate models. J Clim 28:1630–1648. CrossRefGoogle Scholar
  6. Bala G, Calderia K, Nemani R (2010) Fast versus slow response in climate change: implications for the global hydrological cycle. Clim Dyn 35:423–434CrossRefGoogle Scholar
  7. Ban-Weiss GA, Cao L, Bala G, Caldeira K (2012) Dependence of climate forcing and response on the altitude of black carbon aerosols. Clim Dyn 38:897. CrossRefGoogle Scholar
  8. Betts RA et al (1997) Contrasting physiological and structural vegetation feedbacks in climate change simulations. Nature 387:796–799CrossRefGoogle Scholar
  9. Betts RA et al (2007) Future runoff changes due to climate and plant responses to increasing carbon dioxide. Nature 448:1037–1042. CrossRefGoogle Scholar
  10. Cao L, Bala G, Caldeira K, Nemani R, Ban-Weiss G (2010) Importance of carbon dioxide physiological forcing to future climate change. Proc Natl Acad Sci 107:9513–9518. CrossRefGoogle Scholar
  11. Cao L, Bala G, Caldeira K (2011) Why is there a short-term increase in global precipitation in response to diminished CO2 forcing? Geophys Res Lett 38:L06703. CrossRefGoogle Scholar
  12. Cao L, Bala G, Caldeira K (2012) Climate response to changes in atmospheric carbon dioxide and solar irradiance on the time scale of days to weeks. Environ Res Lett 7:034015CrossRefGoogle Scholar
  13. Cao L, Bala G, Zheng M, Caldeira K (2015) Fast and slow climate responses to CO2 and solar forcing: A linear multivariate regression model characterizing transient climate change. J Geophys Res Atmos. CrossRefGoogle Scholar
  14. Ciais P et al (2013) Carbon and other biogeochemical cycles. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  15. Collins WD et al (2006) Radiative forcing by well-mixed greenhouse gases: estimates from climate models in the intergovernmental panel on climate change (IPCC) fourth assessment report (AR4). J GeophysRes 111:D14317. CrossRefGoogle Scholar
  16. Collins M et al (2013) Long-term climate change: projections, commitments and irreversibility. In: Stocker TF et al (ed) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 1029–1136Google Scholar
  17. Danabasoglu G, Gent PR (2009) Equilibrium climate sensitivity: is it accurate to use a slab ocean model? J Clim 22:2494–2499CrossRefGoogle Scholar
  18. Dong BW, Gregory JM, Sutton RT (2009) Understanding land–sea warming contrast in response to increasing greenhouse gases. Part I: transient adjustment. J Clim 22:3079–3097CrossRefGoogle Scholar
  19. Doutriaux-Boucher M, Webb MJ, Gregory JM, Boucher O (2009) Carbon dioxide induced stomatal closure increases radiative forcing via a rapid reduction in low cloud. Geophys Res Lett 36:L02703CrossRefGoogle Scholar
  20. Etminan M, Myhre G, Highwood EJ, Shine KP (2016), Radiative forcing of carbon dioxide, methane, and nitrous oxide: a significant revision of the methane radiative forcing. Geophys Res Lett. CrossRefGoogle Scholar
  21. Forster PMF, Blackburn M, Glover R, Shine KP (2000) An examination of climate sensitivity for idealised climate change experiments in an intermediate general circulation model. Clim Dyn 16:833–849CrossRefGoogle Scholar
  22. Forster PM, Richardson T, Maycock AC, Smith CJ, Samset BH, Myhre G, Andrews T, Pincus R, Schulz M (2016), Recommendations for diagnosing effective radiative forcing from climate models for CMIP6. J Geophys Res Atmos 121:12460–12475. CrossRefGoogle Scholar
  23. Govindasamy B et al (2001) Limitations of the equivalent CO2 approximation in climate change simulations. J Geophys Res 106(D19):22593–22603. CrossRefGoogle Scholar
  24. Gregory JM et al (2004) A new method for diagnosing radiative forcing and climate sensitivity. Geophys Res Lett 310:L03205. CrossRefGoogle Scholar
  25. Hansen J, Sato M, Ruedy R (1997) Radiative forcing and climate response. J Geophys Res Atmos 102:6831–6864CrossRefGoogle Scholar
  26. Hansen J et al (2005) Efficacy of climate forcings. J Geophys Res Atmos 110:D18104CrossRefGoogle Scholar
  27. Kiehl JT, Bonan GB, Boville BA, Briegleb BP, Williamson DL, Rasch PJ (1996) Description of the NCAR community climate model (CCM3). NCAR Tech. Note, NCAR/TN-4201STR, p 152Google Scholar
  28. Kvalevag MM, Samset BH, Myhre G (2013) Hydrological sensitivity to greenhouse gases and aerosols in a global climate model. Geophys Res Lett 40:1432–1438. CrossRefGoogle Scholar
  29. Marvel K, Schmidt GA, Miller RL, Nazarenko LS (2016) Implications for climate sensitivity from the response to individual forcings. Nat Clim Change 6:386–389. CrossRefGoogle Scholar
  30. Mitchell JFB, Wilson CA, Cunnington WM (1987) On CO2 climate sensitivity and model dependence of results. QJR Meteorol Soc 113:293–322. CrossRefGoogle Scholar
  31. Mlawer EJ, Clough SA (1998) Shortwave and longwave enhancements in the rapid radiative transfer model. In: Proceedings of the 7th Atmospheric Radiation Measurement (ARM) Science Team Meeting, US Department of energy, CONF-970365Google Scholar
  32. Modak A, Bala G, Cao L, Caldeira K (2016) Why must a solar forcing be larger than a CO2 forcing to cause the same global mean surface temperature change? Environ Res Lett 11:044013CrossRefGoogle Scholar
  33. Myhre G et al (2013) Anthropogenic and natural radiative forcing. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  34. Neale et al (2012) Description of the NCAR community atmosphere model (CAM5.0). NCAR Tech. Note NCAR/ TN-4861STR, p 268Google Scholar
  35. O’Gorman PA, Allan RP, Byrne MP, Previdi M (2012) Energetic constraints on precipitation under climate change. Surv Geophys 33(3–4):585–608. CrossRefGoogle Scholar
  36. Randall DA et al (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
  37. Samset BH et al (2016) Fast and slow precipitation responses to individual climate forcers: a PDRMIP multimodel study. Geophys Res Lett 43:2782–2791. CrossRefGoogle Scholar
  38. Schmidt et al (2005) Present-day atmospheric simulations using Giss modelE: comparison to in situ, satellite, and reanalysis data. J Clim. CrossRefGoogle Scholar
  39. Shindell DT (2014) Inhomogeneous forcing and transient climate sensitivity. Nat Clim Change 4:274–277. CrossRefGoogle Scholar
  40. Shine KP, Allan RP, Collins WJ, Fuglestvedt JS (2015) Metrics for linking emissions of gases and aerosols to global precipitation changes. Earth Syst Dyn 6(2):525–540. (ISSN 2190–4987) CrossRefGoogle Scholar
  41. Wang WC, Dudek MP, Liang X, Kiehl JT (1991) Inadequacy of effective CO2 as a proxy in simulating the greenhouse effect of other radiatively active gases. Nature 350:573–577. CrossRefGoogle Scholar
  42. Wang WC, Dudek M, Pand Liang X (1992) Inadequacy of effective CO2 as a proxy in assessing the regional climate change due to other radiatively active gases. Geophys Res Lett. CrossRefGoogle Scholar
  43. Zwiers F, von Storch H (1995) Taking serial correlation into account in tests of the mean. J Clim 8:336–351CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Center for Atmospheric and Oceanic SciencesIndian Institute of ScienceBangaloreIndia
  2. 2.Department of Global EcologyCarnegie Institution for ScienceStanfordUSA
  3. 3.School of Earth SciencesZhejiang UniversityHangzhouChina

Personalised recommendations