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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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.
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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.
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M.P.B. derived the theory, performed the analyses and wrote the manuscript.
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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.
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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.
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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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DOI: https://doi.org/10.1038/s41561-021-00828-8
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