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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References
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).
Dai, A., Trenberth, K. E. & Karl, T. R. Global variations in droughts and wet spells: 1990–1995. Geophys. Res. Lett. 25, 3367–3370 (1998).
Barnard, P. L. et al. Coastal vulnerability across the Pacific dominated by El Niño/Southern Oscillation. Nature Geosci. 8, 801–807 (2015).
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).
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).
Dommenget, D. The slab ocean El Niño. Geophys. Res. Lett. 37, L20701 (2010).
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).
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).
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).
Chen, D. et al. Strong influence of westerly wind bursts on El Niño diversity. Nature Geosci. 8, 339–345 (2015).
Chen, X. & Wallace, J. M. ENSO-like variability: 1900–2013. J. Clim. http://dx.doi.org/10.1175/JCLI-D-15-0322.1 (2015).
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).
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).
Bjerknes, J. Atmospheric teleconnections from the equatorial Pacific. Mon. Weath. Rev. 97, 163–172 (1969).
Wyrtki, K. El Niño—the dynamic response of the equatorial Pacific Ocean to atmospheric forcing. J. Phys. Oceanogr. 5, 572–584 (1975).
Cane, M. & Zebiak, S. A theory for El Niño and the Southern Oscillation. Science 228, 1085–1087 (1985).
Bony, S. et al. Clouds, circulation and climate sensitivity. Nature Geosci. 8, 261–268 (2015).
Gill, A. E. Some simple solutions for heat-induced tropical circulation. Q. J. R. Meteorol. Soc. 106, 447–462 (1980).
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).
Nilsson, J. & Emanuel, K. A. Equilibrium atmospheres of a two-column radiative-convective model. Q. J. R. Meteorol. Soc. 125, 2239–2264 (1999).
Chiodi, A. & Harrison, D. Characterizing warm-ENSO variability in the equatorial Pacific: an OLR perspective. J. Clim. 23, 2428–2439 (2010).
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).
Mauritsen, T. et al. Tuning the climate of a global model. J. Adv. Model. Earth Syst. 4, M00A01 (2012).
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).
Cai, W. et al. Increasing frequency of extreme El Niño events due to greenhouse warming. Nature Clim. Change 4, 111–116 (2014).
Fedorov, A. V. et al. The pliocene paradox (mechanisms for a permanent El Niño). Science 312, 1485–1489 (2006).
Hartmann, D. & Larson, K. An important constraint on tropical cloud-climate feedback. Geophys. Res. Lett. 29, 1951 (2002).
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).
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).
Allan, R. P. et al. Changes in global net radiative imbalance 1985–2012. Geophys. Res. Lett. 41, 5588–5597 (2014).
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).
Mauritsen, T. et al. Climate feedback efficiency and synergy. Clim. Dynam. 41, 2539–2554 (2013).
Bi, D. et al. The ACCESS coupled model: description, control climate and evaluation. Aust. Meteorol. Oceanogr. J. 63, 41–64 (2013).
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).
Ji, D. et al. Description and basic evaluation of BNU-ESM version 1. Geosci. Model Dev. 7, 1601–1647 (2014).
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).
Meehl, G. A. et al. Climate system response to external forcings and climate change projections in CCSM4. J. Clim. 25, 3661–3683 (2012).
Meehl, G. A. et al. Climate change projections in CESM1(CAM5) compared to CCSM4. J. Clim. 26, 6287–6308 (2013).
Voldoire, A. et al. The CNRM-CM5.1 global climate model: description and basic evaluation. Clim. Dynam. 40, 2091–2121 (2012).
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).
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).
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).
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).
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).
Jones, C. D. et al. The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci. Model Dev. 4, 543–570 (2011).
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).
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).
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).
Watanabe, M. et al. Improved climate simulation by MIROC5: mean states, variability, and climate sensitivity. J. Clim. 23, 6312–6335 (2010).
Watanabe, S. et al. MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geosci. Model Dev. 4, 845–872 (2011).
Stevens, B. et al. Atmospheric component of the MPI-M Earth System Model: ECHAM6. J. Adv. Model. Earth Syst. 5, 146–172 (2013).
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).
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).
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.
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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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DOI: https://doi.org/10.1038/ngeo2630
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