Keeping global warming within 1.5 °C constrains emergence of aridification


Aridity—the ratio of atmospheric water supply (precipitation; P) to demand (potential evapotranspiration; PET)—is projected to decrease (that is, areas will become drier) as a consequence of anthropogenic climate change, exacerbating land degradation and desertification1,2,3,4,5,6. However, the timing of significant aridification relative to natural variability—defined here as the time of emergence for aridification (ToEA)—is unknown, despite its importance in designing and implementing mitigation policies7,8,9,10. Here we estimate ToEA from projections of 27 global climate models (GCMs) under representative concentration pathways (RCPs) RCP4.5 and RCP8.5, and in doing so, identify where emergence occurs before global mean warming reaches 1.5 °C and 2 °C above the pre-industrial level. On the basis of the ensemble median ToEA for each grid cell, aridification emerges over 32% (RCP4.5) and 24% (RCP8.5) of the total land surface before the ensemble median of global mean temperature change reaches 2 °C in each scenario. Moreover, ToEA is avoided in about two-thirds of the above regions if the maximum global warming level is limited to 1.5 °C. Early action for accomplishing the 1.5 °C temperature goal can therefore markedly reduce the likelihood that large regions will face substantial aridification and related impacts.

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Fig. 1: Spatial distributions of the aridity index and related climate regime in present climate, and regime changes if the aridity index decreases by 0.5N.
Fig. 2: Spatial distributions of multi-model ensemble median year of ToEA and the 16–84% range.
Fig. 3: Spatial distributions of regions with ToEA ≤ t 1.5 and t 1.5 < ToEA ≤ t 2, and proportions of GCM simulations that show t 1.5, t 2 and ToEA in each particular year.
Fig. 4: Total area and present-day population count over regions with ToEA ≤ t 1.5 and ToEA ≤ t 2 under the RCP4.5 and RCP8.5 scenarios.


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S.-J.J. and C.-E.P. were supported by the startup funding of the Southern University of Science and Technology (SUSTECH). J.L. was supported by the National Science Fund for Distinguished Youth Scholars (41625001). J.L. was supported by part of the research funding provided by the Southern University of Science and Technology (grant no. G01296001). T.O. was supported by the Belmont Forum/JPI-Climate project INTEGRATE (NERC NE/P006809/1). M.J. was supported by the UK Natural Environment Research Council Grant Robust Spatial Projections (NE/N018397/1). C.-H.H. and H.P. were funded by the Korea Ministry of Environment as part of the ‘Climate Change Correspondence Program’. B.-M.K. was supported by Korea Polar Research Institute Project (PE17130). We acknowledge the World Climate Research Program’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Supplementary Table 1 of this paper) 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.

Author information

C.-E.P. and S.-J.J. conceived and designed the study, analysed data and wrote the paper. M.J. and T.J.O. improved the study, provided data and wrote the paper. C.-H.H., S.P., D.C., J.L., H.Y., H.P., B.-M.K. wrote the paper. S.F. provided data and wrote the paper.

Correspondence to Su-Jong Jeong.

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Supplementary Information

Model Validation, Supplementary References, Supplementary Table 1 and Supplementary Figures 1–9

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