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Amplified warming of extreme temperatures over tropical land

Abstract

Extreme temperatures have warmed substantially over recent decades and are projected to continue warming in response to future climate change. Warming of extreme temperatures is amplified over land, with severe implications for human health, wildfire risk and food production. Using simulations from 18 climate models, I show that hot days over tropical land warm substantially more than the average day. For example, warming of the hottest 5% of land days is a factor of 1.21 ± 0.07 larger than the time-mean warming averaged across models. The climate change response of extreme temperatures over tropical land is interpreted using a theory based on atmospheric dynamics. According to the theory, warming is amplified for hot land days because those days are dry, which is termed the ‘drier get hotter’ mechanism. Changes in near-surface relative humidity further increase tropical land warming, with decreases in land relative humidity being particularly important. The theory advances physical understanding of the tropical climate and highlights land surface dryness as a key factor determining how extreme temperatures respond to climate change.

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Fig. 1: Projected warming of the hottest 5% of days relative to the zonal-mean warming.
Fig. 2: Climate model projections and theoretical estimates of tropical land temperature responses across percentiles.
Fig. 3: Ratios of land-to-ocean changes in tropical temperature, specific humidity and moist static energy.
Fig. 4: Components of the land temperature response.

Data availability

The CMIP6 model data are provided by the World Climate Research Programme’s Working Group on Coupled Modelling and can be accessed at https://esgf-node.llnl.gov/search/cmip6/.

Code availability

The code used in this paper is available from the corresponding author on request.

References

  1. Patz, J. A., Campbell-Lendrum, D., Holloway, T. & Foley, J. A. Impact of regional climate change on human health. Nature 438, 310–317 (2005).

    Article  Google Scholar 

  2. Burke, M., Hsiang, S. M. & Miguel, E. Global non-linear effect of temperature on economic production. Nature 527, 235–239 (2015).

    Article  Google Scholar 

  3. Seneviratne, S. I., Donat, M. G., Pitman, A. J., Knutti, R. & Wilby, R. L. Allowable CO2 emissions based on regional and impact-related climate targets. Nature 529, 477–483 (2016).

    Article  Google Scholar 

  4. Sutton, R. T., Dong, B. & Gregory, J. M. Land/sea warming ratio in response to climate change: IPCC AR4 model results and comparison with observations. Geophys. Res. Lett. 34, L02701 (2007).

    Article  Google Scholar 

  5. Lambert, F. H. & Chiang, J. C. H. Control of land–ocean temperature contrast by ocean heat uptake. Geophys. Res. Lett. 34, L13704 (2007).

    Google Scholar 

  6. 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. Dyn. 30, 455–465 (2008).

    Article  Google Scholar 

  7. Byrne, M. P. & O’Gorman, P. A. Land–ocean warming contrast over a wide range of climates: convective quasi-equilibrium theory and idealized simulations. J. Clim. 26, 4000–4016 (2013).

    Article  Google Scholar 

  8. 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 

  9. Byrne, M. P. & O'Gorman, P. A. Trends in continental temperature and humidity directly linked to ocean warming. Proc. Natl Acad. Sci. USA 115, 4863–4868 (2018).

    Article  Google Scholar 

  10. Vogel, M. M. et al. Regional amplification of projected changes in extreme temperatures strongly controlled by soil moisture–temperature feedbacks. Geophys. Res. Lett. 44, 1511–1519 (2017).

    Article  Google Scholar 

  11. Schär, C. et al. The role of increasing temperature variability in European summer heatwaves. Nature 427, 332–336 (2004).

    Article  Google Scholar 

  12. Diffenbaugh, N. S. & Ashfaq, M. Intensification of hot extremes in the United States. Geophys. Res. Lett. 37, L15701 (2010).

    Article  Google Scholar 

  13. Mueller, B. & Seneviratne, S. I. Hot days induced by precipitation deficits at the global scale. Proc. Natl Acad. Sci. USA 109, 12398–12403 (2012).

    Article  Google Scholar 

  14. Seneviratne, S. I. et al. Impact of soil moisture-climate feedbacks on CMIP5 projections: first results from the GLACE-CMIP5 experiment. Geophys. Res. Lett. 40, 5212–5217 (2013).

    Article  Google Scholar 

  15. Miralles, D. G., Teuling, A. J., Van Heerwaarden, C. C. & De Arellano, J. V.-G. Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nat. Geosci. 7, 345–349 (2014).

    Article  Google Scholar 

  16. Lorenz, R. et al. Influence of land-atmosphere feedbacks on temperature and precipitation extremes in the GLACE-CMIP5 ensemble. J. Geophys. Res. Atmos. 121, 607–623 (2016).

    Article  Google Scholar 

  17. Screen, J. A. Arctic amplification decreases temperature variance in northern mid-to high-latitudes. Nat. Clim. Change 4, 577–582 (2014).

    Article  Google Scholar 

  18. Schneider, T., Bischoff, T. & Płotka, H. Physics of changes in synoptic midlatitude temperature variability. J. Clim. 28, 2312–2331 (2015).

    Article  Google Scholar 

  19. Tamarin-Brodsky, T., Hodges, K., Hoskins, B. J. & Shepherd, T. G. Changes in Northern Hemisphere temperature variability shaped by regional warming patterns. Nat. Geosci. 13, 414–421 (2020).

    Article  Google Scholar 

  20. Wehrli, K., Guillod, B. P., Hauser, M., Leclair, M. & Seneviratne, S. I. Identifying key driving processes of major recent heat waves. J. Geophys. Res. Atmos. 124, 11746–11765 (2019).

    Article  Google Scholar 

  21. Vargas Zeppetello, L. R. & Battisti, D. S. Projected increases in monthly midlatitude summertime temperature variance over land are driven by local thermodynamics. Geophys. Res. Lett. 47, e2020GL090197 (2020).

    Article  Google Scholar 

  22. McKinnon, K. A., Rhines, A., Tingley, M. P. & Huybers, P. The changing shape of Northern Hemisphere summer temperature distributions. J. Geophys. Res. Atmos. 121, 8849–8868 (2016).

    Article  Google Scholar 

  23. Linz, M., Chen, G. & Hu, Z. Large-scale atmospheric control on non-Gaussian tails of midlatitude temperature distributions. Geophys. Res. Lett. 45, 9141–9149 (2018).

    Article  Google Scholar 

  24. Holmes, C. R., Woollings, T., Hawkins, E. & De Vries, H. Robust future changes in temperature variability under greenhouse gas forcing and the relationship with thermal advection. J. Clim. 29, 2221–2236 (2016).

    Article  Google Scholar 

  25. Pfahl, S. & Wernli, H. Quantifying the relevance of atmospheric blocking for co-located temperature extremes in the Northern Hemisphere on (sub-) daily time scales. Geophys. Res. Lett. 39, L12807 (2012).

    Article  Google Scholar 

  26. Liu, Q. On the definition and persistence of blocking. Tellus A 46, 286–298 (1994).

    Article  Google Scholar 

  27. Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: a review. Earth Sci. Rev. 99, 125–161 (2010).

    Article  Google Scholar 

  28. Donat, M. G. & Alexander, L. V. The shifting probability distribution of global daytime and night-time temperatures. Geophys. Res. Lett. 39, L14707 (2012).

    Article  Google Scholar 

  29. O'Gorman, P. A. & Schneider, T. The physical basis for increases in precipitation extremes in simulations of 21st-century climate change. Proc. Natl Acad. Sci. USA 106, 14773–14777 (2009).

    Article  Google Scholar 

  30. O’Gorman, P. A. Contrasting responses of mean and extreme snowfall to climate change. Nature 512, 416–418 (2014).

    Article  Google Scholar 

  31. Pfahl, S., O’Gorman, P. A. & Fischer, E. M. Understanding the regional pattern of projected future changes in extreme precipitation. Nat. Clim. Change 7, 423–427 (2017).

    Article  Google Scholar 

  32. Perkins-Kirkpatrick, S. E. & Gibson, P. B. Changes in regional heatwave characteristics as a function of increasing global temperature. Sci. Rep. 7, 12256 (2017).

    Article  Google Scholar 

  33. Harrington, L. J. & Otto, F. E. L. Reconciling theory with the reality of African heatwaves. Nat. Clim. Change 10, 796–798 (2020).

    Article  Google Scholar 

  34. Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Article  Google Scholar 

  35. O’Neill, B. C. et al. The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).

    Article  Google Scholar 

  36. Zhang, Y. & Fueglistaler, S. How tropical convection couples high moist static energy over land and ocean. Geophys. Res. Lett. 47, e2019GL086387 (2020).

    Google Scholar 

  37. Duan, S. Q., Findell, K. L. & Wright, J. S. Three regimes of temperature distribution change over dry land, moist land and oceanic surfaces. Geophys. Res. Lett. e2020GL090997 (2020).

  38. Johnson, N. C. & Xie, S.-P. Changes in the sea surface temperature threshold for tropical convection. Nat. Geosci. 3, 842–845 (2010).

    Article  Google Scholar 

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

    Article  Google Scholar 

  40. Sobel, A. H., Nilsson, J. & Polvani, L. M. The weak temperature gradient approximation and balanced tropical moisture waves. J. Atmos. Sci. 58, 3650–3665 (2001).

    Article  Google Scholar 

  41. Byrne, M. P. & O’Gorman, P. A. Understanding decreases in land relative humidity with global warming: conceptual model and GCM simulations. J. Clim. 29, 9045–9061 (2016).

    Article  Google Scholar 

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

    Article  Google Scholar 

  43. Zhang, Y., Held, I. & Fueglistaler, S. Projections of tropical heat stress constrained by atmospheric dynamics. Nat. Geosci. 14, 133–137 (2021).

    Article  Google Scholar 

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

    Article  Google Scholar 

  45. Chadwick, R., Good, P. & Willett, K. M. A simple moisture advection model of specific humidity change over land in response to SST warming. J. Clim. 29, 7613–7632 (2016).

    Article  Google Scholar 

  46. Held, I. M. & Soden, B. J. Water vapor feedback and global warming. Annu. Rev. Energy Environ. 25, 441–475 (2000).

    Article  Google Scholar 

  47. Schneider, T., O’Gorman, P. A. & Levine, X. J. Water vapor and the dynamics of climate changes. Rev. Geophys. 48, RG3001 (2010).

    Article  Google Scholar 

  48. Fischer, E. M. & Knutti, R. Robust projections of combined humidity and temperature extremes. Nat. Clim. Change 3, 126–130 (2013).

    Article  Google Scholar 

  49. Bolton, D. The computation of equivalent potential temperature. Mon. Weather Rev. 108, 1046–1053 (1980).

    Article  Google Scholar 

  50. Boer, G. J. Climate change and the regulation of the surface moisture and energy budgets. Clim. Dyn. 8, 225–239 (1993).

    Article  Google Scholar 

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Acknowledgements

I thank E. Newsom, P. O’Gorman, L. Zanna and Y. Zhang for helpful discussions. All analyses were performed using Pangeo, a community platform for Big Data geoscience (https://pangeo.io). I acknowledge support from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 794063.

Author information

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Authors

Contributions

M.P.B. derived the theory, performed the analyses and wrote the manuscript.

Corresponding author

Correspondence to Michael P. Byrne.

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The author declares no competing interests.

Additional information

Peer review information Primary handling editor(s): Thomas Richardson. Nature Geoscience thanks Erich Fischer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Relationship between percentiles of temperature and moist static energy.

Moist static energy percentiles (y-axis) corresponding to the average moist static energy of days exceeding the given temperature percentile on the x-axis over land (red) and ocean (blue). Solid and dashed lines denote the historical and Shared Socioeconomic Pathway 4.5 (ssp245) simulations, respectively. The decrease with warming of the moist static energy percentile to which hot land days corresponds is indicated (δpx). For this and all other figures in the Extended Data, the quantities plotted are multimodel means unless otherwise stated.

Source data

Extended Data Fig. 2 Dependence of relative humidity on temperature percentile.

Pseudo relative humidity over land (red) and ocean (blue) for the historical (solid) and ssp245 simulations (dashed). Over land, pseudo relative humidity is defined as the average specific humidity for days exceeding the x-th percentile of temperature divided by the corresponding average saturation specific humidity (Methods). Over ocean, pseudo relative humidity is defined as the pxth percentile of specific humidity divided by the pxth percentile of saturation specific humidity. Note that px is the percentile of land moist static energy in the historical simulation equal to the average moist static energy of days exceeding the xth percentile of land temperature.

Source data

Extended Data Fig. 3 Hot land days projected to become relatively less energetic as climate warms.

Simulated (solid) and theory estimates (dashed) of (a) changes in the percentile of land moist static energy equal to the average moist static energy of days exceeding the xth percentile of land temperature (that is, δpx) and (b) \(\Delta h={h}_{{{{\rm{L}}}}}^{{{{\rm{ssp245}}}}}({p}^{x}+\delta {p}^{x})-{h}_{{{{\rm{L}}}}}^{{{{\rm{ssp245}}}}}({p}^{x})\), which quantifies the effect of δpx on the moist static energy of days exceeding the xth percentile of land temperature. The theory estimates in panels (a) and (b) are computed using equations (23) and (24), respectively.

Source data

Extended Data Fig. 4 Theoretical sensitivities of land temperature to changes in relative humidity and ocean temperature.

Parameters quantifying the sensitivities of land temperatures exceeding a given temperature percentile, x, to changes in (a) ocean temperature [δTO] and (b) relative humidities [δr] over ocean (blue) and land (red). The sensitivity parameters \({\gamma }^{{T}_{{{{\rm{O}}}}}}\), \({\gamma }^{{r}_{{{{\rm{O}}}}}}\) and \({\gamma }^{{r}_{{{{\rm{L}}}}}}\) are defined by equations (15), (16) and (27), respectively.

Source data

Extended Data Fig. 5 Projected warming of hot land days well captured by theory.

Simulated versus theory estimates [using equation (5)] of (a) changes in temperature [\(\delta {T}_{{{{\rm{L}}}}}^{x}\)] and (b) scaling factors [\(\delta {T}_{{{{\rm{L}}}}}^{x}/\delta \overline{{T}_{{{{\rm{L}}}}}}\)] for the hottest 5% of land days. Each dot is a different climate model and the one-to-one lines are plotted in blue. Correlation coefficients are quoted in each panel and are similar for different temperature percentiles (not shown).

Source data

Extended Data Fig. 6 Dependence of specific humidity on temperature percentile.

Specific humidity over land (red) and ocean (blue) for the historical (solid) and ssp245 simulations (dashed). Over land, the average specific humidities for days exceeding the xth percentile of temperature are shown. Over ocean, the pxth percentile of specific humidity in each simulation is plotted.

Source data

Supplementary information

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Supplementary Figs. 1–8.

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Source Data Extended Data Fig. 1

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Source Data Extended Data Fig. 2

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Source Data Extended Data Fig. 6

Numerical source data (.csv).

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Byrne, M.P. Amplified warming of extreme temperatures over tropical land. Nat. Geosci. 14, 837–841 (2021). https://doi.org/10.1038/s41561-021-00828-8

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