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Change in the magnitude and mechanisms of global temperature variability with warming

Nature Climate Change volume 7, pages 743–748 (2017)Cite this article

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Abstract

Natural unforced variability in global mean surface air temperature (GMST) can mask or exaggerate human-caused global warming, and thus a complete understanding of this variability is highly desirable. Significant progress has been made in elucidating the magnitude and physical origins of present-day unforced GMST variability, but it has remained unclear how such variability may change as the climate warms. Here we present modelling evidence that indicates that the magnitude of low-frequency GMST variability is likely to decline in a warmer climate and that its generating mechanisms may be fundamentally altered. In particular, a warmer climate results in lower albedo at high latitudes, which yields a weaker albedo feedback on unforced GMST variability. These results imply that unforced GMST variability is dependent on the background climatological conditions, and thus climate model control simulations run under perpetual pre-industrial conditions may have only limited relevance for understanding the unforced GMST variability of the future.

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Figure 1: Magnitude and geographic origin of GMST variability in the GFDL CM3 pre-industrial control and 2 × CO2 runs.
Figure 2: Relationship between GMST and the TOA and surface energy budgets in the GFDL CM3 pre-industrial control and 2 × CO2 runs.
Figure 3: Change in the TOA energy budget contribution to GMST variability between the GFDL CM3 pre-industrial control and 2 × CO2 runs.
Figure 4: Change in local temperature variability.

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References

  1. Bindoff, N. L. et al. In Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 10 (IPCC, Cambridge Univ. Press, 2013).

    Google Scholar 

  2. Hawkins, E. & Sutton, R. The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soc. 90, 1095–1107 (2009).

    Article  Google Scholar 

  3. Meehl, G. A. et al. Decadal prediction. Bull. Am. Meteorol. Soc. 90, 1467–1485 (2009).

    Article  Google Scholar 

  4. Hasselmann, K. Stochastic climate models. Part I. Theory. Tellus 28, 473–485 (1976).

    Article  Google Scholar 

  5. Brown, P. T., Li, W. & Xie, S.-P. Regions of significant influence on unforced global mean surface air temperature variability in climate models. J. Geophys. Res. 120, 480–494 (2015).

    Article  Google Scholar 

  6. Brown, P. T., Li, W., Li, L. & Ming, Y. Top-of-atmosphere radiative contribution to unforced decadal global temperature variability in climate models. Geophys. Res. Lett. 41, 5175–5183 (2014).

    Article  Google Scholar 

  7. Knutson, T. R., Zhang, R. & Horowitz, L. W. Prospects for a prolonged slowdown in global warming in the early 21st century. Nat. Commun. 7, 13676 (2016).

    Article  CAS  Google Scholar 

  8. Huber, M. & Knutti, R. Natural variability, radiative forcing and climate response in the recent hiatus reconciled. Nat. Geosci. 7, 651–656 (2014).

    Article  CAS  Google Scholar 

  9. Roberts, C. D., Palmer, M. D., McNeall, D. & Collins, M. Quantifying the likelihood of a continued hiatus in global warming. Nat. Clim. Change 5, 337–342 (2015).

    Article  Google Scholar 

  10. Middlemas, E. & Clement, A. M. Y. Spatial patterns and frequency of unforced decadal-scale changes in global mean surface temperature in climate models. J. Clim. 29, 6245–6257 (2016).

    Article  Google Scholar 

  11. Brown, P. T., Li, W., Cordero, E. C. & Mauget, S. A. Comparing the model-simulated global warming signal to observations using empirical estimates of unforced noise. Sci. Rep. 5, 9957 (2015).

    Article  CAS  Google Scholar 

  12. Schurer, A. P., Hegerl, G. C., Mann, M. E., Tett, S. F. B. & Phipps, S. J. Separating forced from chaotic climate variability over the past millennium. J. Clim. 26, 6954–6973 (2013).

    Article  Google Scholar 

  13. Crowley, T. J., Obrochta, S. P. & Liu, J. Recent global temperature ‘plateau’ in the context of a new proxy reconstruction. Earth’s Future 2, 281–294 (2014).

    Article  Google Scholar 

  14. Jones, G. S., Stott, P. A. & Christidis, N. Attribution of observed historical near-surface temperature variations to anthropogenic and natural causes using CMIP5 simulations. J. Geophys. Res. 118, 4001–4024 (2013).

    Google Scholar 

  15. Crowley, T. J. Causes of climate change over the past 1,000 years. Science 289, 270–277 (2000).

    Article  CAS  Google Scholar 

  16. Mann, M. E., Steinman, B. A. & Miller, S. K. On forced temperature changes, internal variability and the AMO. Geophys. Res. Lett. 41, 3211–3219 (2014).

    Article  Google Scholar 

  17. Muller, R. A. et al. Decadal variations in the global atmospheric land temperatures. J. Geophys. Res. 118, 5280–5286 (2013).

    Article  Google Scholar 

  18. Meehl, G. A., Teng, H. & Arblaster, J. M. Climate model simulations of the observed early-2000s hiatus of global warming. Nat. Clim. Change 4, 898–902 (2014).

    Article  Google Scholar 

  19. Santer, B. D. et al. Separating signal and noise in atmospheric temperature changes: the importance of timescale. J. Geophys. Res. 116, D22105 (2011).

    Article  Google Scholar 

  20. Risbey, J. S. Well-estimated global surface warming in climate projections selected for ENSO phase. Nat. Clim. Change 4, 835–840 (2014).

    Article  Google Scholar 

  21. Maher, N., Gupta, A. S. & England, M. H. Drivers of decadal hiatus periods in the 20th and 21st centuries. Geophys. Res. Lett. 41, 5978–5986 (2014).

    Article  Google Scholar 

  22. Collins, M. et al. In Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 12 (IPCC, Cambridge Univ. Press, 2013).

    Google Scholar 

  23. de Lavergne, C., Palter, J. B., Galbraith, E. D., Bernardello, R. & Marinov, I. Cessation of deep convection in the open Southern Ocean under anthropogenic climate change. Nat. Clim. Change 4, 278–282 (2014).

    Article  Google Scholar 

  24. Joshi, M. M., Gregory, J. M., Webb, M. J., Sexton, D. M. H. & Johns, T. C. Mechanisms for the land/sea warming contrast exhibited by simulations of climate change. Clim. Dynam. 30, 455–465 (2007).

    Article  Google Scholar 

  25. Huntingford, C., Jones, P. D., Livina, V. N., Lenton, T. M. & Cox, P. M. No increase in global temperature variability despite changing regional patterns. Nature 500, 327–330 (2013).

    Article  CAS  Google Scholar 

  26. Räisänen, J. CO2-induced changes in interannual temperature and precipitation variability in 19 CMIP2 experiments. J. Clim. 15, 2395–2411 (2002).

    Article  Google Scholar 

  27. Boer, J. G., Flato, G. & Ramsden, D. A transient climate change simulation with greenhouse gas and aerosol forcing: projected climate to the twenty-first century. Clim. Dynam. 16, 427–450 (2000).

    Article  Google Scholar 

  28. Meehl, G. A., Wheeler, M. & Washington, W. M. Low-frequency variability and CO2 transient climate change. Part 3. Intermonthly and interannual variability. Clim. Dynam. 10, 277–303 (1994).

    Article  Google Scholar 

  29. Rind, D., Goldberg, R. & Ruedy, R. Change in climate variability in the 21st century. Climatic Change 14, 5–37 (1989).

    Article  CAS  Google Scholar 

  30. Sakai, D., Itoh, H. & Yukimoto, S. Changes in the interannual surface air temperature variability in the Northern Hemisphere in response to global warming. J. Meteorol. Soc. Jpn 87, 721–737 (2009).

    Article  Google Scholar 

  31. Boer, G. J. Changes in interannual variability and decadal potential predictability under global warming. J. Clim. 22, 3098–3109 (2009).

    Article  Google Scholar 

  32. Stouffer, R. J. & Wetherald, R. T. Changes of variability in response to increasing greenhouse gases. Part I: temperature. J. Clim. 20, 5455–5467 (2007).

    Article  Google Scholar 

  33. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2011).

    Article  Google Scholar 

  34. Cleveland, W. S. Robust locally weighted regression and smoothing scatterplots. J. Am. Stat. Assoc. 74, 829–836 (1979).

    Article  Google Scholar 

  35. England, M. H. et al. Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nat. Clim. Change 4, 222–227 (2014).

    Article  Google Scholar 

  36. Chen, X. & Tung, K.-K. Varying planetary heat sink led to global-warming slowdown and acceleration. Science 345, 897–903 (2014).

    Article  CAS  Google Scholar 

  37. Drijfhout, S. S. et al. Surface warming hiatus caused by increased heat uptake across multiple ocean basins. Geophys. Res. Lett. 41, 7868–7874 (2014).

    Article  Google Scholar 

  38. Meehl, G. A., Hu, A., Arblaster, J. M., Fasullo, J. & Trenberth, K. E. Externally forced and internally generated decadal climate variability associated with the interdecadal Pacific oscillation. J. Clim. 26, 7298–7310 (2013).

    Article  Google Scholar 

  39. Xie, S.-P., Kosaka, Y. & Okumura, Y. M. Distinct energy budgets for anthropogenic and natural changes during global warming hiatus. Nat. Geosci. 9, 29–33 (2016).

    Article  CAS  Google Scholar 

  40. Hedemann, C., Mauritsen, T., Jungclaus, J. & Marotzke, J. The subtle origins of surface-warming hiatuses. Nat. Clim. Change 7, 336–339 (2017).

    Article  Google Scholar 

  41. Pedro, J. B. et al. Southern Ocean deep convection as a driver of Antarctic warming events. Geophys. Res. Lett. 43, 2192–2199 (2016).

    Article  Google Scholar 

  42. Behrens, E. et al. Southern Ocean deep convection in global climate models: a driver for variability of subpolar gyres and Drake Passage transport on decadal timescales. J. Geophys. Res. 121, 3905–3925 (2016).

    Article  Google Scholar 

  43. Martin, T., Park, W. & Latif, M. Multi-centennial variability controlled by Southern Ocean convection in the Kiel Climate Model. Clim. Dynam. 40, 2005–2022 (2013).

    Article  Google Scholar 

  44. Brown, P. T., Lozier, M. S., Zhang, R. & Li, W. The necessity of cloud feedback for a basin-scale Atlantic Multidecadal Oscillation. Geophys. Res. Lett. 43, 3955–3963 (2016).

    Article  Google Scholar 

  45. Brown, P. T., Li, W., Jiang, J. H. & Su, H. Spread in the magnitude of climate model interdecadal global temperature variability traced to disagreements over high-latitude oceans. Geophys. Res. Lett. 43, 12543–12549 (2016).

    Article  Google Scholar 

  46. Sherwood, S. & Fu, Q. A drier future? Science 343, 737–739 (2014).

    Article  CAS  Google Scholar 

  47. Byrne, M. P. & O’Gorman, P. A. Link between land-ocean warming contrast and surface relative humidities in simulations with coupled climate models. Geophys. Res. Lett. 40, 5223–5227 (2013).

    Article  Google Scholar 

  48. Berg, A. et al. Land–atmosphere feedbacks amplify aridity increase over land under global warming. Nat. Clim. Change 6, 869–874 (2016).

    Article  Google Scholar 

  49. Flato, G. J. et al. In Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 9 (IPCC, Cambridge Univ. Press, 2013).

    Google Scholar 

  50. Huang, N. E. et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. A 454, 903–995 (1998).

    Article  Google Scholar 

  51. Wu, Z., Huang, N. E., Long, S. R. & Peng, C.-K. On the trend, detrending, and variability of nonlinear and nonstationary time series. Proc. Natl Acad. Sci. USA 104, 14889–14894 (2007).

    Article  CAS  Google Scholar 

  52. Griffies, S. M. & Greatbatch, R. J. Physical processes that impact the evolution of global mean sea level in ocean climate models. Ocean Model. 51, 37–72 (2012).

    Article  Google Scholar 

  53. Milly, P. C. D. & Shmakin, A. B. Global modeling of land water and energy balances. Part I: the land dynamics (LaD) model. J. Hydrometeorol. 3, 283–299 (2002).

    Article  Google Scholar 

  54. Shevliakova, E. et al. Carbon cycling under 300 years of land use change: importance of the secondary vegetation sink. Glob. Biogeochem. Cycles 23, 3520–3544 (2009).

    Article  Google Scholar 

  55. Griffies, S. M. et al. The GFDL CM3 coupled climate model: characteristics of the ocean and sea ice simulations. J. Clim. 24, 3520–3544 (2011).

    Article  Google Scholar 

  56. 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 

  57. Delworth, T. L. et al. GFDL’s CM2 global coupled climate models. Part I: formulation and simulation characteristics. J. Clim. 19, 643–674 (2006).

    Article  Google Scholar 

  58. Levy, H. et al. The roles of aerosol direct and indirect effects in past and future climate change. J. Geophys. Res. 118, 4521–4532 (2013).

    Google Scholar 

  59. Austin, J. & Wilson, R. J. Ensemble simulations of the decline and recovery of stratospheric ozone. J. Geophys. Res. 111, 5145–5153 (2006).

    Article  Google Scholar 

  60. Hurrell, J. W., Hack, J. J., Shea, D., Caron, J. M. & Rosinski, J. A new sea surface temperature and sea ice boundary dataset for the community atmosphere model. J. Clim. 21, 5145–5153 (2008).

    Article  Google Scholar 

  61. Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change 109, 213–241 (2011).

    Article  CAS  Google Scholar 

  62. Ramanathan, V. et al. Cloud-radiative forcing and climate: results from the Earth radiation budget experiment. Science 243, 57–63 (1989).

    Article  CAS  Google Scholar 

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Acknowledgements

We would like to acknowledge M. Winton and T. Knutson for internal reviews and discussion of this work. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. This research was partially conducted at the NOAA Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey and it was partially supported by NIH-1R21AG044294-01A1. S.A.H. was supported by a Department of Defense National Defense Science and Engineering Graduate Fellowship and a National Science Foundation Atmospheric and Geospace Sciences Postdoctoral Research Fellowship.

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Authors and Affiliations

  1. Department of Global Ecology, Carnegie Institution for Science, Stanford, California, 94305, USA

    Patrick T. Brown

  2. Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, New Jersey, 08540, USA

    Yi Ming

  3. Earth and Ocean Sciences, Nicholas School of the Environment, Duke University, Durham, North Carolina, 27708, USA

    Wenhong Li

  4. Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, California, 90095, USA

    Spencer A. Hill

  5. Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California, 91125, USA

    Spencer A. Hill

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Contributions

Y.M. conceived of the study. S.A.H. proposed and conducted the fixed SST model runs. P.T.B. performed the data analysis and wrote the initial draft of the manuscript. All authors contributed to interpreting results and refinement of the manuscript.

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Correspondence to Patrick T. Brown.

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Brown, P., Ming, Y., Li, W. et al. Change in the magnitude and mechanisms of global temperature variability with warming. Nature Clim Change 7, 743–748 (2017). https://doi.org/10.1038/nclimate3381

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