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Amplification of El Niño by cloud longwave coupling to atmospheric circulation

Abstract

The El Niño/Southern Oscillation (ENSO) is the dominant mode of inter-annual variability, with major impacts on social and ecological systems through its influence on extreme weather, droughts and floods1,2,3. The ability to forecast El Niño, as well as anticipate how it may change with warming, requires an understanding of the underlying physical mechanisms that drive it. Among these, the role of atmospheric processes remains poorly understood4,5,6,7,8,9,10,11. Here we present numerical experiments with an Earth system model, with and without coupling of cloud radiative effects to the circulation, suggesting that clouds enhance ENSO variability by a factor of two or more. Clouds induce heating in the mid and upper troposphere associated with enhanced high-level cloudiness12 over the El Niño region, and low-level clouds cool the lower troposphere in the surrounding regions13. Together, these effects enhance the coupling of the atmospheric circulation to El Niño surface temperature anomalies, and thus strengthen the positive Bjerknes feedback mechanism14 between west Pacific zonal wind stress and sea surface temperature gradients. Behaviour consistent with the proposed mechanism is robustly represented in other global climate models and in satellite observations. The mechanism suggests that the response of ENSO amplitude to climate change will in part be determined by a balance between increasing cloud longwave feedback and a possible reduction in the area covered by upper-level clouds.

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Figure 1: El Niño circulation anomalies and longwave radiation are closely tied.
Figure 2: Impact of cloud-circulation interactions in MPI-ESM-LR on Niño-3.4 variance.
Figure 3: Cloud radiative heating and cooling patterns enhance the coupling of El Niño to atmospheric circulation.
Figure 4: TOA longwave feedback in observations and in models (Supplementary Table 4).

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References

  1. Nicholls, N., Lavery, B., Frederiksen, C., Drosdowsky, W. & Torok, S. Recent apparent changes in relationships between the El Niño–Southern Oscillation and Australian rainfall and temperature. Geophys. Res. Lett. 23, 3357–3360 (1996).

    Article  Google Scholar 

  2. Dai, A., Trenberth, K. E. & Karl, T. R. Global variations in droughts and wet spells: 1990–1995. Geophys. Res. Lett. 25, 3367–3370 (1998).

    Article  Google Scholar 

  3. Barnard, P. L. et al. Coastal vulnerability across the Pacific dominated by El Niño/Southern Oscillation. Nature Geosci. 8, 801–807 (2015).

    Article  Google Scholar 

  4. Guilyardi, E. et al. Representing El Niño in coupled ocean-atmosphere GCMs: the dominant role of the atmosphere component. J. Clim. 17, 4623–4629 (2004).

    Article  Google Scholar 

  5. Sun, D. Z. et al. Radiative and dynamical feedbacks over the equatorial cold tongue: results from nine atmospheric GCMs. J. Clim. 19, 4059–4074 (2006).

    Article  Google Scholar 

  6. Dommenget, D. The slab ocean El Niño. Geophys. Res. Lett. 37, L20701 (2010).

    Article  Google Scholar 

  7. Lloyd, J., Guilyardi, E. & Weller, H. The role of atmospheric feedbacks during ENSO in CMIP3 models. Part III: the shortwave flux feedback. J. Clim. 25, 4275–4293 (2012).

    Article  Google Scholar 

  8. Chen, L., Yu, Y. & Sun, D.-Z. Cloud and water vapor feedbacks to the El Niño warming: are they still biased in CMIP5 models? J. Clim. 26, 4947–4961 (2013).

    Article  Google Scholar 

  9. Bellenger, H., Guilyardi, E., Leloup, J., Lengaigne, M. & Vialard, J. ENSO representation in climate models: from CMIP3 to CMIP5. Clim. Dynam. 42, 1999–2018 (2014).

    Article  Google Scholar 

  10. Chen, D. et al. Strong influence of westerly wind bursts on El Niño diversity. Nature Geosci. 8, 339–345 (2015).

    Article  Google Scholar 

  11. Chen, X. & Wallace, J. M. ENSO-like variability: 1900–2013. J. Clim. http://dx.doi.org/10.1175/JCLI-D-15-0322.1 (2015).

  12. Bretherton, C. S. & Sobel, A. H. A simple model of a convectively coupled Walker circulation using the weak temperature gradient approximation. J. Clim. 15, 2907–2920 (2002).

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Bjerknes, J. Atmospheric teleconnections from the equatorial Pacific. Mon. Weath. Rev. 97, 163–172 (1969).

    Article  Google Scholar 

  15. Wyrtki, K. El Niño—the dynamic response of the equatorial Pacific Ocean to atmospheric forcing. J. Phys. Oceanogr. 5, 572–584 (1975).

    Article  Google Scholar 

  16. Cane, M. & Zebiak, S. A theory for El Niño and the Southern Oscillation. Science 228, 1085–1087 (1985).

    Article  Google Scholar 

  17. Bony, S. et al. Clouds, circulation and climate sensitivity. Nature Geosci. 8, 261–268 (2015).

    Article  Google Scholar 

  18. Gill, A. E. Some simple solutions for heat-induced tropical circulation. Q. J. R. Meteorol. Soc. 106, 447–462 (1980).

    Article  Google Scholar 

  19. Emanuel, K. A., Neelin, J. D. & Bretherton, C. S. On large-scale circulation in convecting atmospheres. Q. J. R. Meteorol. Soc. 120, 1111–1143 (1994).

    Article  Google Scholar 

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

    Article  Google Scholar 

  21. Chiodi, A. & Harrison, D. Characterizing warm-ENSO variability in the equatorial Pacific: an OLR perspective. J. Clim. 23, 2428–2439 (2010).

    Article  Google Scholar 

  22. Giorgetta, M. et al. Climate and carbon cycle changes demo 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. J. Adv. Model. Earth Syst. 5, 572–597 (2013).

    Article  Google Scholar 

  23. Mauritsen, T. et al. Tuning the climate of a global model. J. Adv. Model. Earth Syst. 4, M00A01 (2012).

    Article  Google Scholar 

  24. Nam, C., Bony, S., Dufresne, J.-L. & Chepfer, H. The ‘too few, too bright’ tropical low-cloud problem in CMIP5 models. Geophys. Res. Lett. 39, L21801 (2012).

    Article  Google Scholar 

  25. Cai, W. et al. Increasing frequency of extreme El Niño events due to greenhouse warming. Nature Clim. Change 4, 111–116 (2014).

    Article  Google Scholar 

  26. Fedorov, A. V. et al. The pliocene paradox (mechanisms for a permanent El Niño). Science 312, 1485–1489 (2006).

    Article  Google Scholar 

  27. Hartmann, D. & Larson, K. An important constraint on tropical cloud-climate feedback. Geophys. Res. Lett. 29, 1951 (2002).

    Article  Google Scholar 

  28. 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).

    Article  Google Scholar 

  29. Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. 108, 4407 (2003).

    Article  Google Scholar 

  30. Allan, R. P. et al. Changes in global net radiative imbalance 1985–2012. Geophys. Res. Lett. 41, 5588–5597 (2014).

    Article  Google Scholar 

  31. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Article  Google Scholar 

  32. Mauritsen, T. et al. Climate feedback efficiency and synergy. Clim. Dynam. 41, 2539–2554 (2013).

    Article  Google Scholar 

  33. Bi, D. et al. The ACCESS coupled model: description, control climate and evaluation. Aust. Meteorol. Oceanogr. J. 63, 41–64 (2013).

    Article  Google Scholar 

  34. Xiao-Ge, X. et al. How well does BCC CSM1.1 reproduce the 20th century climate change over China? Atmos. Ocean. Sci. Lett. 6, 21–26 (2012).

    Article  Google Scholar 

  35. Ji, D. et al. Description and basic evaluation of BNU-ESM version 1. Geosci. Model Dev. 7, 1601–1647 (2014).

    Article  Google Scholar 

  36. von Salzen, K. et al. The Canadian fourth generation atmospheric global climate model (CanAM4). Part I: representation of physical processes. Atmos. Ocean 51, 104–125 (2013).

    Article  Google Scholar 

  37. Meehl, G. A. et al. Climate system response to external forcings and climate change projections in CCSM4. J. Clim. 25, 3661–3683 (2012).

    Article  Google Scholar 

  38. Meehl, G. A. et al. Climate change projections in CESM1(CAM5) compared to CCSM4. J. Clim. 26, 6287–6308 (2013).

    Article  Google Scholar 

  39. Voldoire, A. et al. The CNRM-CM5.1 global climate model: description and basic evaluation. Clim. Dynam. 40, 2091–2121 (2012).

    Article  Google Scholar 

  40. Rotstayn, L. D. et al. Aerosol- and greenhouse gas-induced changes in summer rainfall and circulation in the Australasian region: a study using single-forcing climate simulations. Atmos. Chem. Phys. 12, 6377–6404 (2012).

    Article  Google Scholar 

  41. Li, L. et al. The flexible global ocean-atmosphere-land system model, Grid-point Version 2: FGOALS-g2. Adv. Atmos. Sci. 30, 543–560 (2013).

    Article  Google Scholar 

  42. Donner, L. J. et al. The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3. J. Clim. 24, 3484–3519 (2011).

    Article  Google Scholar 

  43. Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon earth system models. Part I: physical formulation and baseline simulation characteristics. J. Clim. 25, 6646–6665 (2012).

    Article  Google Scholar 

  44. Schmidt, G. A. et al. Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. J. Adv. Model. Earth Syst. 6, 141–184 (2014).

    Article  Google Scholar 

  45. Jones, C. D. et al. The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci. Model Dev. 4, 543–570 (2011).

    Article  Google Scholar 

  46. Volodin, E. M., Dianskii, N. A. & Gusev, A. V. Simulating present-day climate with the INMCM4.0 coupled model of the atmospheric and oceanic general circulations. Izv. Atmos. Ocean. Phys. 46, 414–431 (2010).

    Article  Google Scholar 

  47. Dufresne, J. L. et al. Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim. Dynam. 40, 2123–2165 (2013).

    Article  Google Scholar 

  48. Hourdin, F. et al. LMDZ5B: the atmospheric component of the IPSL climate model with revisited parameterizations for clouds and convection. Clim. Dynam. 40, 2193–2222 (2013).

    Article  Google Scholar 

  49. Watanabe, M. et al. Improved climate simulation by MIROC5: mean states, variability, and climate sensitivity. J. Clim. 23, 6312–6335 (2010).

    Article  Google Scholar 

  50. Watanabe, S. et al. MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geosci. Model Dev. 4, 845–872 (2011).

    Article  Google Scholar 

  51. Stevens, B. et al. Atmospheric component of the MPI-M Earth System Model: ECHAM6. J. Adv. Model. Earth Syst. 5, 146–172 (2013).

    Article  Google Scholar 

  52. Yukimoto, S. et al. A new global climate model of the meteorological research institute: MRI-CGCM3: model description and basic performance. J. Meteorol. Soc. Jpn 90A, 23–64 (2012).

    Article  Google Scholar 

  53. Bentsen, M. et al. The Norwegian Earth System Model, NorESM1-M–Part 1: description and basic evaluation of the physical climate. Geosci. Model Dev. 6, 687–720 (2013).

    Article  Google Scholar 

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Acknowledgements

This work is supported by the Max-Planck-Gesellschaft (MPG) and funding by the Federal Ministry for Education and Research in Germany (BMBF) through the research programme MiKlip project FKZ:01LP1128B. Computational resources were made available by Deutsches Klimarechenzentrum (DKRZ) through support from BMBF and by the Swiss National Supercomputing Centre (CSCS). D.D. acknowledges support from the ARC Centre of Excellence for Climate System Science grant CE110001028 and project DP120101442. D.M. acknowledges support from BMBF through the cooperative Project RACE.

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B.S., G.R. and T.M. conceived the original idea for this study. G.R. and T.M. developed the methodology and conducted the experiments. The bulk of the analysis was done by G.R., T.M., B.S. and D.M., although all authors contributed to the interpretation of the results. G.R. and T.M. led the writing of the manuscript with contributions and input from all authors.

Corresponding author

Correspondence to Thorsten Mauritsen.

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The authors declare no competing financial interests.

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Rädel, G., Mauritsen, T., Stevens, B. et al. Amplification of El Niño by cloud longwave coupling to atmospheric circulation. Nature Geosci 9, 106–110 (2016). https://doi.org/10.1038/ngeo2630

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