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Missing iris effect as a possible cause of muted hydrological change and high climate sensitivity in models

Nature Geoscience volume 8, pages 346–351 (2015)Cite this article

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A Corrigendum to this article was published on 31 March 2016

This article has been updated

Abstract

Equilibrium climate sensitivity to a doubling of CO2 falls between 2.0 and 4.6 K in current climate models, and they suggest a weak increase in global mean precipitation. Inferences from the observational record, however, place climate sensitivity near the lower end of this range and indicate that models underestimate some of the changes in the hydrological cycle. These discrepancies raise the possibility that important feedbacks are missing from the models. A controversial hypothesis suggests that the dry and clear regions of the tropical atmosphere expand in a warming climate and thereby allow more infrared radiation to escape to space. This so-called iris effect could constitute a negative feedback that is not included in climate models. We find that inclusion of such an effect in a climate model moves the simulated responses of both temperature and the hydrological cycle to rising atmospheric greenhouse gas concentrations closer to observations. Alternative suggestions for shortcomings of models — such as aerosol cooling, volcanic eruptions or insufficient ocean heat uptake — may explain a slow observed transient warming relative to models, but not the observed enhancement of the hydrological cycle. We propose that, if precipitating convective clouds are more likely to cluster into larger clouds as temperatures rise, this process could constitute a plausible physical mechanism for an iris effect.

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Figure 1
Figure 2: Regression lines calculated from anomalies of top of atmosphere radiation versus surface temperature in the tropics (20° S to 20° N).
Figure 3: Decomposition of feedback into individual mechanisms that control variations in model equilibrium climate sensitivity.
Figure 4: Analysis of hydrological sensitivity and a separation into the contributions from individual mechanisms.

Change history

  • 25 February 2016

    In the version of the Perspective originally published, there were errors in the Supplementary Information. After correcting these, the reported correlations between actual climate change feedback and tropical regression in the sentence beginning 'In the analysis of the CMIP5 ensemble presented here...' are +0.38 and +0.32 for the AMIP and historical experiments, respectively. In addition, the subsequent statement now reads: 'Of the eleven models that match CERES net regression in either experiment, four have ECS above 3 K and seven below. When run with a prescribed evolution of sea surface temperatures (AMIP) only the two versions of the Beijing Climate Center (BCC) model match observations in the slope of the regression between net, longwave and shortwave radiation with temperature. If run in coupled mode (historical) only one version of the Goddard Institute for Space Studies (GISS-E2-H) model matches CERES data.' The authors acknowledge David Coppin for pointing out these errors. These errors have been corrected in the online versions of the Perspective.

References

  1. Lindzen, R. S., Chou, M-D. & Hou, A. U. Does the Earth have an adaptive infrared iris? Bull. Am. Meteorol. Soc. 82, 417–432 (2001).

    Article  Google Scholar 

  2. Hartmann, D. L. & Michelsen, M. L. No evidence for iris. Bull. Am. Meteorol. Soc. 83, 249–254 (2002).

    Article  Google Scholar 

  3. Lau, K. M. & Wu, H. T. Warm rain processes over tropical oceans and climate implications. Geophys. Res. Lett. 30, 1944–8007 (2003).

    Article  Google Scholar 

  4. Rapp, A. D., Kummerow, C., Berg, W. & Griffith, B. An evaluation of the proposed mechanism of the adaptive infrared iris hypothesis using TRMM VIRS and PR measurements. J. Clim. 18, 4185–4194 (2005).

    Article  Google Scholar 

  5. Rondanelli, R. & Lindzen, R. S. Observed variations in convective precipitation fraction and stratiform area with sea surface temperature. J. Geophys. Res. 113, D16119 (2008).

    Article  Google Scholar 

  6. Lindzen, R. S. & Choi, Y-S. On the determination of climate feedbacks from ERBE data. Geophys. Res. Lett. 36, L16705 (2009).

    Article  Google Scholar 

  7. Trenberth, K. E., Fasullo, J. T., O'Dell, C. & Wong, T. Relationships between tropical sea surface temperature and top-of-atmosphere radiation. Geophys. Res. Lett. 37, L03702 (2010).

    Article  Google Scholar 

  8. Lindzen, R. S. & Choi, Y-S. On the observational determination of climate sensitivity and its implications. Asia-Pacif. J. Atmos. Sci. 47, 377–390 (2011).

    Article  Google Scholar 

  9. Su, H. et al. Variations of tropical upper tropospheric clouds with sea surface temperature and implications for radiative effects. J. Geophys. Res. 113, D10211 (2008).

    Article  Google Scholar 

  10. Rondanelli, R. & Lindzen, R. S. Comment on 'Variations of tropical upper tropospheric clouds with sea surface temperature and implications for radiative effects by H. Su et al.' J. Geophys. Res. 115, D06202 (2009).

    Google Scholar 

  11. Collins, M et al. in Climate Change 2013: The Physical Science Basis. (eds Stocker, T. F. et al.) 1029–1136 (IPCC, Cambridge Univ. Press, 2013).

    Google Scholar 

  12. Fu, Q., Baker, M. & Hartmann, D. L. Tropical cirrus and water vapor: An effective Earth infrared iris feedback? Atmos. Chem. Phys. 2, 31–37 (2002).

    Article  Google Scholar 

  13. Lin, B., Wielicki, B. A., Chambers, L. H., Hu, Y. & Xu, K-M. The iris hypothesis: A negative or positive cloud feedback? J. Clim. 15, 3–7 (2002).

    Article  Google Scholar 

  14. Dessler, A. E. Observations of climate feedbacks over 2000–10 and comparison to climate models. J. Clim. 26, 333–342 (2013).

    Article  Google Scholar 

  15. Forster, P. M. & Gregory, J. M. The climate sensitivity and its components diagnosed from earth radiation budget data. J. Clim. 19, 39–52 (2006).

    Article  Google Scholar 

  16. Block, K. & Mauritsen, T. Forcing and feedback in the MPI-ESMLR coupled model under abruptly quadrupled CO2 . J. Adv. Model. Earth Syst. 5, 1–16 (2013).

    Article  Google Scholar 

  17. Clement, A. C. & Soden, B. The sensitivity of the tropical-mean radiation budget. J. Clim. 18, 3189–3203 (2005).

    Article  Google Scholar 

  18. Nilsson, J. & Emanuel, K. Equilibrium atmospheres of a two-column radiative-convective model. Q. J. R. Meteorol. Soc. 125, 2239–2264 (1999).

    Article  Google Scholar 

  19. Emanuel, K., Wing, A. A. & Vincent, E. M. Radiative-convective instability. J. Adv. Model. Earth Syst. http://dx.doi.org/10.1002/2013MS000270 (2014).

  20. Trenberth, K. E. Atmospheric moisture residence times and cycling: Implications for rainfall rates and climate change. Clim. Change 39, 667–694 (1998).

    Article  Google Scholar 

  21. Bretherton, C. S., Blossey, P. N. & Khairoutdinov, M. An energy balance analysis of deep convective self-aggregation above uniform SST. J. Atmos. Sci. 62, 4273–4292 (2005).

    Article  Google Scholar 

  22. Tobin, I., Bony, S. & Roca, R. Observational evidence for relationship between the degree of aggregation of deep convection, water vapor, surface fluxes and radiation. J. Clim. 25, 6885–6904 (2012).

    Article  Google Scholar 

  23. Muller, C. J. & Held, I. M. Detailed investigation of the self-aggregation of convection in cloud-resolving simulations. J. Clim. 69, 2551–2565 (2012).

    Google Scholar 

  24. Satoh, M., Iga, S-I., Tomita, H., Tsushima, Y. & Noda, A. T. Response of upper clouds in global warming experiments obtained using a global nonhydrostatic model with explicit cloud processes. J. Clim. 25, 2178–2191 (2012).

    Article  Google Scholar 

  25. Tsushima, Y. et al. High cloud increase in a perturbed SST experiment with a global nonhydrostatic model including explicit convective processes. J. Adv. Model. Earth Syst. http://dx.doi.org/10.1002/2013MS000301 (2014).

  26. Klocke, D., Pincus, R. & Quaas, J. On constraining estimates of climate sensitivity with present-day observations through model weighting. J. Clim. 24, 6092–6099 (2011).

    Article  Google Scholar 

  27. Stevens, B. & Bony, S. Water in the atmosphere. Phys. Today 66 (6), 29–34 (2013).

    Article  Google Scholar 

  28. Vial, J., Dufresne, J-L. & Bony, S. On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates. Clim. Dyn. 41, 3339–3362 (2013).

    Article  Google Scholar 

  29. Clement, A., Burgman, R. & Norris, J. Observational and model evidence for positive low-level cloud feedback. Science 325, 460–464 (2009).

    Article  Google Scholar 

  30. Rieck, M., Nuijens, L. & Stevens, B. Marine boundary layer cloud feedbacks in a constant relative humidity atmosphere. J. Atmos. Sci. 69, 2538–2550 (2012).

    Article  Google Scholar 

  31. Fasullo, J. T. & Trenberth, K. E. A less cloudy future: The role of subtropical subsidence in climate sensitivity. Science 338, 792–794 (2012).

    Article  Google Scholar 

  32. Sherwood, S. C., Bony, S. & Dufresne, J-L. Spread in model climate sensitivity traced to atmospheric convective mixing. Nature 505, 37–42 (2014).

    Article  Google Scholar 

  33. Su, H. et al. Weakening and strengthening structures in the Hadley Circulation change under global warming and implications for cloud response and climate sensitivity. J. Geophys. Res. Atmos. 119, 5787–5805 (2014).

    Article  Google Scholar 

  34. Otto, A. et al. Energy budget constraints on climate response. Nature Geosci. 6, 415–416 (2013).

    Article  Google Scholar 

  35. Skeie, R. B., Berntsen, T., Aldrin, M., Holden, M. & Myhre, G. A lower and more constrained estimate of climate sensitivity using updated observations and detailed radiative forcing time series. Earth Syst. Dynam. 5, 139–175 (2014).

    Article  Google Scholar 

  36. Lewis, N. & Curry, J. A. The implications for climate sensitivity of AR5 forcing and heat uptake estimates. Clim. Dynam. http://doi.org/3hn (2014).

  37. Haimberger, L., Tavolato, C. & Sperka, S. Homogenization of the global radiosonde temperature dataset through combined comparison with reanalysis background series and neighboring stations. J. Clim. 25, 8108–8131 (2012).

    Article  Google Scholar 

  38. Po-Chedley, S. & Fu, Q. Discrepancies in tropical upper tropospheric warming between atmospheric circulation models and satellites. Environ. Res. Lett. 7, 044018 (2012).

    Article  Google Scholar 

  39. Newell, R. E., Herman, G. F., Gould-Stewart, S. & Tanaka, M. Decreased global rainfall during the past ice age. Nature 253, 33–34 (1975).

    Article  Google Scholar 

  40. Held, I. M. & Soden, B. J. Robust responses of the hydrological cycle to global warming. J. Clim. 19, 5686–5699 (2006).

    Article  Google Scholar 

  41. Zhang, X. et al. Detection of human influence on twentieth-century precipitation trends. Nature 448, 461–466 (2007).

    Article  Google Scholar 

  42. Lambert, F. H., Stine, A. R., Krakauer, N. Y. & Chiang, J. C. H. How much will precipitation increase with global warming? EOS 89, 193200 (2008).

    Article  Google Scholar 

  43. Durack, P. J., Wijffels, S. E. & Matear, R. J. Ocean salinities reveal strong global water cycle intensification during 1950 to 2000. Science 336, 455–458 (2012).

    Article  Google Scholar 

  44. Ren, L., Arkin, P., Smith, T. M. & Shen, S. S. P. Global precipitation trends in 1900–2005 from a reconstruction and coupled model simulations. J. Geophys. Res. Atmos. 118, 1679–1689 (2013).

    Article  Google Scholar 

  45. Johanson, C. M. & Fu, Q. Hadley cell widening: Model simulations versus observations. J. Clim. 22, 2713–2725 (2009).

    Article  Google Scholar 

  46. Shindell, D. T. Inhomogeneous forcing and transient climate sensitivity. Nature Clim. Change 4, 274–277 (2014).

    Article  Google Scholar 

  47. Santer, B. D. et al. Volcanic contribution to decadal changes in tropospheric temperature. Nature Geosci. 7, 185–189 (2014).

    Article  Google Scholar 

  48. Trenberth, K. E. & Fasullo, J. T. Tracking Earth's energy. Science 328, 316–317 (2010).

    Article  Google Scholar 

  49. Ramanathan, V., Crutzen, P. J., Kiehl, J. T. & Rosenfeld, D. Aerosols, climate, and the hydrological cycle. Science 294, 2119–2124 (2001).

    Article  Google Scholar 

  50. Llovel, W., Willis, J. K., Landerer, F. W. & Fukumori, I. Deep-ocean contribution to sea level and energy budget not detectable over past decade. Nature Clim. Change 4, 1031–1035 (2014).

    Article  Google Scholar 

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Acknowledgements

Contributions from S. Bony, P. Forster, Quiang Fu, A. Gettelman, J. Gregory, I. Held, S. Klein, R. Lindzen, R. Pierrehumbert, S. Po-Chedley, D. Popke, F. Rauser, S. Sherwood and M. Zelinka were valuable in advancing this study. CERES data were obtained from the NASA Langley Research Center, HadCRUT4 data are provided by the Met Office Hadley Centre and the Climatic Research Unit at the University of East Anglia, and CMIP5 data from the coupled modelling groups (Supplementary Table 3) coordinated by the World Climate Research Programme's Working Group on Coupled Modelling. This work was supported by the Max-Planck-Gesellschaft (MPG) and by funding through the EUCLIPSE project from the European Union, Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 244067. Computational resources were made available by Deutsches Klimarechenzentrum (DKRZ) through support from Bundesministerium für Bildung und Forschung (BMBF).

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  1. Max Planck Institute for Meteorology, Bundesstrasse 53, Hamburg, 20146, Germany

    Thorsten Mauritsen & Bjorn Stevens

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Correspondence to Thorsten Mauritsen.

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Mauritsen, T., Stevens, B. Missing iris effect as a possible cause of muted hydrological change and high climate sensitivity in models. Nature Geosci 8, 346–351 (2015). https://doi.org/10.1038/ngeo2414

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