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Diverging hydrological sensitivity among tropical basins


The three tropical basins each have unique roles in the global climate system. The main mechanism by which tropical oceans affect remote climate is the latent heating of local precipitation. Here we report major differences in hydrological sensitivity (precipitation change per unit surface warming) among tropical basins. Specifically, the Pacific hydrological sensitivity is several times as large as that of the Indian basin, while the Atlantic hydrological sensitivity is negative. This results from a thermodynamic amplification of the existing spatial unevenness in relative humidity, with the wettest basin getting wetter and the driest basin getting drier. The diverging basin hydrological sensitivity is accompanied by an interbasin repartitioning of latent heating and convective mass fluxes, with far-reaching implications on rainfall and surface temperature over tropical and mid-latitude lands. These results indicate that the previously unrecognized interbasin differences in hydrological sensitivity may contribute substantially to the geographic pattern of anthropogenic climate change.

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Fig. 1: Tropical changes from structured and uniform sea surface warming.
Fig. 2: Interbasin differences in hydrological sensitivity.
Fig. 3: Diverging basin hydrological sensitivity and its impacts on land precipitation.
Fig. 4: Factors of precipitation and precipitation change.

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Data availability

Model output and observation data can be accessed at the following websites: CMIP6,; observed SST,; GPCP,; CMAP,; TRMM,; ERA5,!/home; NCEP/DOE-II,; JRA-55,

Code availability

The CESM model code is publicly available at Scripts for the analysis and generation of figures are stored at the Zenodo online repository (ref. 41).


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We thank I. Simpson and M. Ting for helping with the CAM5 and stationary wave model simulations and J. Lynch-Stieglitz for helping with the writing of the manuscript. J.H. is supported by the National Science Foundation (NSF) grant no. AGS-2047270 and B.F. is supported by NSF grant no. AGS-2217619. 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. We acknowledge the CESM Large Ensemble Community Project and supercomputing resources provided by NSF, the National Center for Atmospheric Research (NCAR) and Yellowstone. We thank the Physical Sciences Laboratory (PSL) of the National Oceanic and Atmospheric Administration and the Goddard Earth Sciences Data and Information Services Center for providing precipitation observations. We thank the Copernicus Programme, PSL and NCAR for providing the reanalysis data.

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Authors and Affiliations



J.H. designed the study and led the writing of the manuscript. J.H. analysed the diverging basin hydrological sensitivity. K.L. conducted and analysed the diabatic heating experiments. B.F. and S.A.F. contributed to the analysis. All authors contributed to scientific interpretation and the writing of the manuscript.

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Correspondence to Jie He.

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Nature Climate Change thanks Ji Nie 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 Interbasin differences in individual models.

Basin SST change (a), HS (b–d) and percentage change in convective mass flux (e) from the ssp585 multimodel mean (MMM) and individual models.

Extended Data Fig. 2 Impacts of oceanic precipitation changes on land temperature.

Land surface temperature response in amipTRP (a), amipTRPadj (b) and the difference between amipTRP and amipTRPadj (c). Regions where responses are significantly above internal variability (Method) are stippled.

Extended Data Fig. 3 Relation between hydrological sensitivity metrics.

Intermodel relationship between interbasin discrepancies in a-b) HSmean% and basin mean HSlocal% and c-d) HSmean% and basin mean HSlocal. Intermodel correlation coefficient (R) and the two-tailed probability value based on the Student’s t-test (p) are shown in texts.

Extended Data Fig. 4 Temperature contributions to moist static energy changes.

Multimodel mean present and future MSE0rel (a) and relative low-level temperature (T0rel, b) averaged for 0.1 °C SSTrel bins for individual basins. Low-level temperature (T0) is defined as the pressure weighted air temperature averaged between 1000 hPa and 850 hPa. T0rel is defined as T0 minus by the tropical mean T0. SSTrel bins that account for less than 0.5% of the total basin area are shown in semitransparent colours.

Extended Data Fig. 5 Mechanisms for regional hydrological sensitivity.

Multimodel mean HSlocal% (a, b), MSE0rel change (c, d), RH0 change (e, f) and TRP0 change (g, h) from the amipUniform and amipAll simulations.

Extended Data Fig. 6 Thermodynamic origins of diverging basin hydrological sensitivity.

Intermodel relationship between interbasin differences in precipitation-weighted basin mean thermodynamic TRP0 change and those in basin mean precipitation change. Panel a shows the differences between the Pacific and Indian basins and panel b shows the differences between the Pacific and Atlantic basins. Intermodel correlation coefficient (R) and the two-tailed probability value based on the Student’s t-test (p) are shown in texts.

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Supplementary Information

Supplementary Figs. 1–8, Texts 1 and 2 and Table 1.

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He, J., Lu, K., Fosu, B. et al. Diverging hydrological sensitivity among tropical basins. Nat. Clim. Chang. (2024).

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