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From local perception to global perspective


Recent sociological studies show that over short time periods the large day-to-day, month-to-month or year-to-year variations in weather at a specific location can influence and potentially bias our perception of climate change, a more long-term and global phenomenon. By weighting local temperature anomalies with the number of people that experience them and considering longer time periods, we illustrate that the share of the world population exposed to warmer-than-normal temperatures has steadily increased during the past few decades. Therefore, warming is experienced by an increasing number of individuals, counter to what might be simply inferred from global mean temperature anomalies. This behaviour is well-captured by current climate models, offering an opportunity to increase confidence in future projections of climate change irrespective of the personal local perception of weather.

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Figure 1: An example of spatial heterogeneity of temperature anomalies based on GISS surface temperature anomalies9.
Figure 2: Time series of the fraction of population that experienced a 1σ, 2σ or 3σ exceedance with reference to 1951–1980.
Figure 3: Annual mean temperature anomaly and temperature exceedances.
Figure 4: Fraction of the world population that experiences a specific temperature σ exceedance as simulated by the CMIP5 models.


  1. Herring, S. C., Hoerling, M. P., Peterson, T. C. & Stott, P. A. Explaining extreme events of 2013 from a climate perspective. Bull. Am. Meteorol. Soc. 95, Spec. Suppl. (2014).

    Article  Google Scholar 

  2. Stott, P. A., Stone, D. A. & Allen, M. R. Human contribution to the European heatwave of 2003. Nature 432, 610–614 (2004).

    CAS  Article  Google Scholar 

  3. Trenberth, K. E., Fasullo, J. T. & Shepherd, T. G. Attribution of climate extreme events. Nature Clim. Change 5, 725–730 (2015).

    Article  Google Scholar 

  4. IPCC Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) (Cambridge Univ. Press, 2012).

  5. McCright, A. M., Dunlap, R. E. & Xiao, C. The impacts of temperature anomalies and political orientation on perceived winter warming. Nature Clim. Change 4, 1077–1081 (2014).

    Article  Google Scholar 

  6. Howe, P. D., Markowitz, E. M., Lee, T. M., Ko, C-Y. & Leiserowitz, A. Global perceptions of local temperature change. Nature Clim. Change 3, 352–356 (2013).

    Article  Google Scholar 

  7. Howe, P. D. & Leiserowitz, A. Who remembers a hot summer or a cold winter? The asymmetric effect of beliefs about global warming on perceptions of local climate conditions in the U.S. Glob. Environ. Change 23, 1488–1500 (2013).

    Article  Google Scholar 

  8. Zaval, L., Keenan, E. A., Johnson, E. J. & Weber, E. U. How warm days increase belief in global warming. Nature Clim. Change 4, 143–147 (2014).

    Article  Google Scholar 

  9. Hansen, J., Ruedy, R., Sato, M. & Lo, K. Global surface temperature change. Rev. Geophys. 48, (2010).

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

    Google Scholar 

  11. Mahlstein, I. & Knutti, R. Early onset of significant local warming in low latitude countries. Environ. Res. Lett. 6, 034009 (2011).

    Article  Google Scholar 

  12. Dunne, J. P., Stouffer, R. J. & John, J. G. Reductions in labour capacity from heat stress under climate warming. Nature Clim. Change 3, 563–566 (2013).

    CAS  Article  Google Scholar 

  13. Klein Goldewijk, K. Beusen, A. & Janssen, P. Long term dynamic modeling of global population and built-up area in a spatially explicit way, HYDE 3.1. The Holocene 20, 565–573 (2010).

    Article  Google Scholar 

  14. Hansen, J., Sato, M. & Ruedy, R. Perception of climate change. Proc. Natl Acad. Sci. USA 109, E2415–2423 (2012).

    CAS  Article  Google Scholar 

  15. Seneviratne, S. I., Donat, M. G., Mueller, B. & Alexander, L. V. No pause in the increase of hot temperature extremes. Nature Clim. Change 4, 161–163 (2014).

    Article  Google Scholar 

  16. Coumou, D. & Robinson, A. Historic and future increase in the global land area affected by monthly heat extremes. Environ. Res. Lett. 8, 034018 (2013).

    Article  Google Scholar 

  17. Cowtan, K. & Way, R. G. Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. Q. J. R. Meteorol. Soc. 140, 1935–1944 (2014).

    Article  Google Scholar 

  18. van Vuuren, D. P. et al. A proposal for a new scenario framework to support research and assessment in different climate research communities. Glob. Environ. Change 22, 21–35 (2012).

    Article  Google Scholar 

  19. IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. et al.) (Cambridge Univ. Press, 2014).

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We are grateful to K. Klein Goldewijk for providing the population data and thank C. C. Raible, A. Pendergrass, K. Trenberth, G. Turi, L. Terray and C. Deser for discussion and constructive comments. The National Center for Atmospheric Research is sponsored by the National Science Foundation. F.L. is supported by an Early Postdoc Mobility fellowship from the Swiss National Science Foundation. T.F.S. is grateful for funding by the Swiss National Science Foundation. 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.

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F.L. and T.F.S. designed the study and wrote the article. F.L. analysed the data.

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Correspondence to Flavio Lehner.

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Lehner, F., Stocker, T. From local perception to global perspective. Nature Clim Change 5, 731–734 (2015).

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