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Recent southwestern US drought exacerbated by anthropogenic aerosols and tropical ocean warming

Nature Geoscience volume 18, pages 578–585 (2025)Cite this article

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A Publisher Correction to this article was published on 21 October 2025

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Abstract

The southwestern United States is currently in a multi-decade drought that has developed since a precipitation maximum in the 1980s. While anthropogenic warming has made the drought more severe, it is the decline in winter–spring precipitation that has had a more profound effect on water resources and ecosystems. This precipitation decline is not well understood beyond its attribution to the post-1980 La Niña-like cooling trend in tropical sea surface temperatures, which caused a North Pacific anti-cyclonic atmospheric circulation trend conducive to declining precipitation in the southwestern United States. Using a hierarchy of model simulations, we show that, even under El Niño-like sea surface temperature trends, there is a tendency towards a North Pacific anti-cyclonic circulation trend and declining precipitation in the southwestern United States, counter to the canonical El Niño teleconnection. This unintuitive yet robust circulation change arises from non-additive responses to tropical mean sea surface temperature warming and radiative effects from anthropogenic aerosols. The post-1980 period exhibits the fastest southwestern US soil moisture drying among past and future periods of similar length due to the combination of this forced precipitation decline and anthropogenic warming. While the precipitation trend might reverse due to future projected El Niño-like warming and aerosol emissions reduction, it is unlikely to substantially alleviate the currently projected future drought risk.

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Fig. 1: Observed and simulated 1980–2014 DJFMAM trends in tropical sea surface temperature, North Pacific sea-level pressure and precipitation.
Fig. 2: Impacts of tropical Pacific sea surface temperature trends on DJFMAM North Pacific sea-level pressure and precipitation trends in different climate mean states.
Fig. 3: DJFMAM tropical–North Pacific atmospheric teleconnection shifts after 1980 in CESM2 (CAM6).
Fig. 4: Decomposing drivers of the DJFMAM North Pacific atmospheric teleconnection shift with CESM2 (CAM6).
Fig. 5: The relationship between SWUS trends in JJA air temperature, DJFMAM precipitation and JJA soil moisture for 1980–2014.
Fig. 6: The time series and the running trends of SWUS JJA air temperature, DJFMAM precipitation and JJA soil moisture for 1950–2050.

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Data availability

The information to access CESM2-LE and single-forcing simulations can be found at https://www.cesm.ucar.edu/community-projects/lens2/data-sets; Research Data Archive (RDA) at NCAR provides access to ERSSTv5 (ref. 50; https://www.ncei.noaa.gov/) and ERA5 (ref. 52; https://www.ecmwf.int/en/forecasts/datasets); GPCC is accessed through ref. 51.

Code availability

Figures are generated with Python, and maps are generated with the library Cartopy68, including the basemap from Natural Earth69. Codes to generate the results in this study are available via Zenodo at https://doi.org/10.5281/zenodo.14990892 (ref. 70).

Change history

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Acknowledgements

We thank W. Yang, A. Hoell and H. Kim for helpful discussions and comments. Y.-N.K. and F.L. were supported by NOAA MAPP award NA21OAR4310349. F.L. and J.M.A. acknowledge support from the US Department of Energy, Office of Science, Office of Biological & Environmental Research (BER), Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program under award number DE-SC0022070. F.L. also acknowledges support from National Science Foundation (NSF) IA 1947282. I.R.S., C.D. and A.S.P. acknowledge funding from the NSF National Center for Atmospheric Research (NCAR), which is a major facility sponsored by the National Science Foundation under cooperative agreement no. 1852977. I.R.S. also acknowledges support from NOAA MAPP awards NA20OAR4310413 and NA23OAR4310634. M.N. and S.-I.S. acknowledge support from NOAA Cooperative Agreement NA22OAR4320151. S.W. and J.M.A. were supported by the Australian Research Council Centre of Excellence for Climate Extremes (grant CE170100023). The ACCESS simulations were undertaken with the assistance of resources and services from the National Computational Infrastructure, which is supported by the Australian Government. We also acknowledge the CESM Climate Variability and Change working group for making available the regular CESM TOGA, AAER and xAAER simulations used in this work. Simulations were conducted on UCAR’s supercomputers Cheyenne (https://doi.org/10.5065/D6RX99HX) and Derecho (https://doi.org/10.5065/qx9a-pg09), operated by NCAR’s Computational and Information Systems Laboratory.

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Authors and Affiliations

  1. Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY, USA

    Yan-Ning Kuo & Flavio Lehner

  2. Climate and Global Dynamics Laboratory, NSF National Center for Atmospheric Research, Boulder, CO, USA

    Flavio Lehner, Isla R. Simpson, Clara Deser, Adam S. Phillips & Julie M. Arblaster

  3. Polar Bears International, Bozeman, MT, USA

    Flavio Lehner

  4. Physical Sciences Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, USA

    Matthew Newman & Sang-Ik Shin

  5. CIRES, University of Colorado, Boulder, CO, USA

    Sang-Ik Shin

  6. School of Earth Atmosphere and Environment, Monash University, Melbourne, Victoria, Australia

    Spencer Wong & Julie M. Arblaster

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Contributions

Y.-N.K., F.L., C.D., M.N. and I.R.S. conceptualized this study and the experimental set-ups. M.N. and S.-I.S. generated the synthetic sea surface temperature (SST) with the linear inverse model. Y.-N.K., A.S.P. and S.W. conducted the prescribed SST experiments (LIM-TOGA and F2000climo sensitivity experiments). Y.-N.K. performed the data analyses and visualizations. Y.-N.K., F.L., C.D., I.R.S., S.-I.S. and J.M.A. wrote and edited the initial versions of the manuscript. All the authors participated in discussions on interpreting the results and contributed to the paper.

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Correspondence to Yan-Ning Kuo.

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Extended data

Extended Data Fig. 1 DJFMAM sea level pressure (psl) and precipitation (pr) trends over 1980-2014.

Trends from a, global ocean global atmosphere (GOGA; that is, standard AMIP experiment) simulation (20; including both CAM6/CLM5 and UM7.3/CABLE), b, the difference between GOGA (20) and TOGA (20), c, GOGA from CAM6/CLM5 (10), d, GOGA from UM7.3/CABLE (10), e, TOGA from CAM6/CLM5 (10), f, TOGA from UM7.3/CABLE (10). The Hatching/Stippling in (a, c, d, e, f) indicates 67% of the ensemble members agree with the sign of ensemble mean trend. The Hatching/Stippling in b indicates the difference between GOGA and TOGA is significant at 95% confidence level (t test).

Extended Data Fig. 2 Same as Figs. 2c-2h but from ACCESS-ESM1.5.

a,c,e, El Niño-like SST trends and associated psl and pr responses in ACCESS-ESM1.5 from PiCtrl (22) (a), Hist (4) (c) and LIM-TOGA (10) (e). b,d,f, Same as a,c,e, but for La Niña-like SST trends. Hatching/stippling indicates psl/pr trends where 67% of the ensemble members agree with the sign of the ensemble mean. Contours represent sea-level pressure trends starting from ±0.1 hPa decade–1 (red solid lines for positive values; blue dashed lines for negative values), with contour spacing 0.3 hPa decade–1.

Extended Data Fig. 3 The residual DJFMAM psl, pr trends in fully coupled large ensemble and single forcing simulations and the sea surface temperature trends in single forcing simulations.

a, The residual trends of psl and pr from the Anthropogenic Aerosols (AAER) and everything-but-anthropogenic aerosols (xAAER) forcing simulations. The residual trends are calculated as the difference between the all forcing large ensemble (Fig. 1g) and the sum of the AAER (Fig. 4b) and xAAER (Fig. 4c). Hatching/stippling indicate psl/pr trends with 67% of the ensemble members agreeing with the sign of the ensemble mean for model simulations. Contours represent sea-level pressure trends starting from ±0.1 hPa decade–1 (red solid lines for positive values; blue dashed lines for negative values), with contour spacing 0.3 hPa decade–1. The tropical SST trends from b, AAER, and c, xAAER simulations.

Extended Data Fig. 4 DJFMAM trends of 200 hPa zonal wind (U200), 200 hPa geopotential height (Z200), and sea level pressure (psl) for 1980–2014.

(a, b) anthropogenic aerosol (AAER) simulation, (c, d) radiative forcing only (RF-only) simulation, (e, f) ElNiño-like-only simulation, and (g, h) TOGA: El Niño-like simulation. Black contours in (a, c, e, g) are the DJFMAM climatological U200 from the F2000climo control run, starting from 20 m/s with a contour spacing 10 m/s, and hatching indicates 67% of the ensemble members agree on the change in U200. Red/Blue contours in (b, d, f, h) are psl trends shown in the Main text, and hatching indicates 67% of the ensemble members agree on the change in Z200.

Extended Data Fig. 5 Continued DJFMAM trends of 200 hPa zonal wind (U200), 200 hPa geopotential height (Z200), and sea level pressure (psl) for 1980–2014.

(a, b) residual from ElNiño-like-only and RF-only, (c, d) residual from AAER and xAAER, (e, f) xAAER, and (g, h) ALL. Black contours in (a, c, e, g) are the DJFMAM climatological U200 from the F2000climo control run, starting from 20 m/s with a contour spacing 10 m/s, and hatching indicates 67% of the ensemble members agree on the change in U200. Red/Blue contours in (b, d, f, h) are psl trends shown in the Main text, and hatching indicates 67% of the ensemble members agree on the change in Z200.

Extended Data Fig. 6 DJFMAM lower tropospheric static stability change under 2 K uniform tropical warming.

The lower tropospheric static stability is defined as the difference of the potential temperatures at 700 hPa and at the surface. Stippling indicates 67% of the ensemble members agree with the sign of ensemble mean change.

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Kuo, YN., Lehner, F., Simpson, I.R. et al. Recent southwestern US drought exacerbated by anthropogenic aerosols and tropical ocean warming. Nat. Geosci. 18, 578–585 (2025). https://doi.org/10.1038/s41561-025-01728-x

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