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Regional aerosol warming enhanced by the diurnal cycle of low cloud

Nature Geoscience volume 18, pages 702–708 (2025)Cite this article

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Abstract

Atmospheric aerosols are an important component of the Earth’s climate system and contribute substantial uncertainties to predictions of future climate change. In the southeast Atlantic, where expansive light-absorbing smoke aerosol plumes overlie semi-permanent stratocumulus clouds, the direct aerosol radiative effect (DARE) induces warming, but the magnitude of this effect varies widely among climate models. Thus, it is essential to improve estimates based on observations to help constrain model uncertainties. However, the impact of the observed cloud diurnal cycle on DARE remains unclear. Here we quantify DARE using radiative transfer modelling based on hourly satellite observations of clouds focusing on the region 20° S–0° and 10° W–15° E. We find that accounting for the observed cloud diurnal cycle over the southeast Atlantic, rather than assuming a constant early-afternoon cloud field throughout the entire day, results in a more than twofold increase (+1.7 ± 0.4 W m−2) in the regional mean aerosol radiative warming. The increase in DARE results from morning hours when cloud fractions and optical depths are higher. Neglect of the cloud diurnal cycle adds to the underestimated radiative warming in the southeast Atlantic associated with underestimated aerosol absorption among climate models. Future observations-based estimates of aerosol climatic effects need to account for the cloud diurnal cycle.

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Fig. 1: Biomass burning aerosols above low-level cloud over the southeast Atlantic.
Fig. 2: Monthly mean daytime cycles of the COD and CF.
Fig. 3: Monthly mean diurnal cycles of DARETOA.
Fig. 4: Equivalent DARE uncertainties when the cloud diurnal cycle is neglected during 2016–2018.
Fig. 5: Monthly mean differences in DARETOA between varying and fixed cloud properties.

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Data availability

The VIIRS satellite images were adapted from NASA Worldview (https://worldview.earthdata.nasa.gov/). The SEVIRI cloud data are available from NASA SatCORPS at https://satcorps.larc.nasa.gov/. The MERRA-2 data are available from NASA GES DISC at https://disc.gsfc.nasa.gov/datasets/M2I3NVAER_5.12.4/summary. The processed data used to present the results in this Article are available via Zenodo at https://doi.org/10.5281/zenodo.15540263 (ref. 60).

Code availability

The codes used to generate figures in this publication are available via Zenodo at https://doi.org/10.5281/zenodo.15540263 (ref. 60).

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Acknowledgements

This research has been supported by the University of North Carolina at Charlotte start-up package (grant no. 134033) and the University of Oklahoma (OU) start-up package (grant no. 122007900). Part of the computation in this Article was performed at the Supercomputing Center for Education & Research (OSCER) at the University of Oklahoma (OU). D.P. and W.S. acknowledge support from NASA’s Atmospheric Composition Campaign Data Analysis and Modeling and Clouds and the Earth’s Radiant Energy System (CERES) programmes. P.Z. acknowledges support from NASA grant no. 80NSSC21K1344 and DOE ASR grant no. DE-SC002125. The contribution of S.J.D. to this publication is partially funded by the Cooperative Institute for Climate, Ocean, & Ecosystem Studies (CICOES) under NOAA Cooperative Agreement NA20OAR4320271, Contribution No. 2025-1458.

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Authors and Affiliations

  1. Department of Earth, Environmental, and Geographical Sciences, University of North Carolina at Charlotte, Charlotte, NC, USA

    Ian Chang

  2. School of Meteorology, University of Oklahoma, Norman, OK, USA

    Ian Chang, Lan Gao, Emily D. Lenhardt, Abdulamid A. Fakoya, Connor J. Flynn & Jens Redemann

  3. Department of Life and Environmental Sciences, University of California, Merced, Merced, CA, USA

    Adeyemi A. Adebiyi

  4. Cooperative Institute for Climate, Ocean and Ecosystem Studies, University of Washington, Seattle, WA, USA

    Sarah J. Doherty

  5. Department of Atmospheric and Climate Science, University of Washington, Seattle, WA, USA

    Sarah J. Doherty & Robert Wood

  6. NASA Langley Research Center, Hampton, VA, USA

    David Painemal & William L. Smith Jr

  7. Goddard Earth Sciences Technology and Research II, University of Maryland, Baltimore County, Baltimore, MD, USA

    Jianyu Zheng & Zhibo Zhang

  8. NASA Goddard Space Flight Center, Greenbelt, MD, USA

    Jianyu Zheng, Patricia Castellanos & Arlindo M. da Silva

  9. Graduate School, Howard University, Washington, DC, USA

    Zhifeng Yang

  10. Department of Physics, University of Maryland, Baltimore County, Baltimore, MD, USA

    Zhibo Zhang

  11. Rosenstiel School of Marine, Atmospheric, and Earth Science, University of Miami, Miami, FL, USA

    Paquita Zuidema

  12. Department of Atmospheric and Earth Science, University of Alabama in Huntsville, Huntsville, AL, USA

    Sundar A. Christopher

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Contributions

I.C. and J.R. conceived the study. I.C., D.P. and Z.Z. developed the methodology. I.C. and L.G. performed the radiative transfer calculations. I.C., L.G., D.P., W.L.S. Jr, E.D.L., J.Z. and Z.Y. analysed the SEVIRI cloud data. A.A.F., P.C. and A.M.d.S. analysed the MERRA-2 data. All authors reviewed and edited the Article.

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Correspondence to Ian Chang.

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Nature Geoscience thanks the anonymous reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Thomas Richardson, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Table 1 Monthly mean DARE (in W m−2) at the surface and within the atmosphere
Full size table
Extended Data Table 2 The representative monthly SSA values at 500 nm for the 20 grid boxes
Full size table

Extended Data Fig. 1 Monthly mean diurnal cycles of DARESurface.

a–c. DARESurface for hourly varying cloud properties with those based on diurnally fixed cloud properties from 1300 UTC throughout the day during 2016–2018 for August (a), September (b), and October (c). The filled circles represent DARE based on hourly varying COD and CF and three-hourly variations of the aerosol optical depth. The dashed lines denote DARE using diurnally fixed cloud and aerosol properties. The monthly mean DARE values are summarized in Extended Data Table 1.

Extended Data Fig. 2 Monthly mean diurnal cycles of DAREATM.

a–c. DAREATM for hourly varying cloud properties with those based on diurnally fixed cloud properties from 1300 UTC throughout the day during 2016–2018 for August (a), September (b), and October (c). The filled circles represent DARE based on hourly varying COD and CF and three-hourly variations of the aerosol optical depth. The dashed lines denote DARE using diurnally fixed cloud and aerosol properties. The monthly mean DARE values are summarized in Extended Data Table 1.

Extended Data Fig. 3 The twenty monthly mean aerosol models over the southeast Atlantic.

Each of the twenty numbered grids spans over 10° × 10° with a unique set of aerosol SSA and asymmetry parameter for COD corrections and flux calculations.

Extended Data Fig. 4 Monthly mean differences in DARESurface between hourly varying and diurnally fixed cloud properties.

Same as Fig. 5, but for the surface.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2.

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Chang, I., Gao, L., Adebiyi, A.A. et al. Regional aerosol warming enhanced by the diurnal cycle of low cloud. Nat. Geosci. 18, 702–708 (2025). https://doi.org/10.1038/s41561-025-01740-1

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