Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

# 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.

This is a preview of subscription content, access via your institution

## Access options

\$32.00

All prices are NET prices.

## 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).

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

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).

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).

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

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).

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).

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).

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).

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).

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

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

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).

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).

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).

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).

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

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

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).

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).

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).

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).

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).

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).

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).

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

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

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

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).

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

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).

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).

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

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).

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

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

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).

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).

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).

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).

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

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

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

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).

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

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

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

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

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

## 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

Authors

### Contributions

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

### Corresponding author

Correspondence to Michael P. Byrne.

## Ethics declarations

### Competing interests

The author declares no competing interests.

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.

### 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.

### 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.

### 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.

### 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).

### 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.

## Supplementary information

### Supplementary information

Supplementary Figs. 1–8.

### Supplementary data

Source data for figures in Supplementary Information.

### Source Data Fig. 1

Numerical source data (.csv).

### Source Data Fig. 2

Numerical source data (.csv).

### Source Data Fig. 3

Numerical source data (.csv).

### Source Data Fig. 4

Numerical source data (.csv).

### Source Data Extended Data Fig. 1

Numerical source data (.csv).

### Source Data Extended Data Fig. 2

Numerical source data (.csv).

### Source Data Extended Data Fig. 3

Numerical source data (.csv).

### Source Data Extended Data Fig. 4

Numerical source data (.csv).

### Source Data Extended Data Fig. 5

Numerical source data (.csv).

### Source Data Extended Data Fig. 6

Numerical source data (.csv).

## Rights and permissions

Reprints and Permissions

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

• Accepted:

• Published:

• Issue Date:

• DOI: https://doi.org/10.1038/s41561-021-00828-8

• ### The most extreme heat waves in Amazonia happened under extreme dryness

• Duarte F. Costa
• Helber B. Gomes
• Liming Zhou

Climate Dynamics (2022)

• ### How climate change will make the hottest tropical days even more extreme

Nature (2021)