Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover

Ying Chen, Jim Haywood, Yu Wang, Florent Malavelle, George Jordan, Daniel Partridge, Jonathan Fieldsend, Johannes De Leeuw, Anja Schmidt, Nayeong Cho, Lazaros Oreopoulos, Steven Platnick, Daniel Grosvenor, Paul Field, Ulrike Lohmann

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Aerosol–cloud interactions have a potentially large impact on climate but are poorly quantified and thus contribute a substantial and long-standing uncertainty in climate projections. The impacts derived from climate models are poorly constrained by observations because retrieving robust large-scale signals of aerosol–cloud interactions is frequently hampered by the considerable noise associated with meteorological co-variability. The 2014 Holuhraun effusive eruption in Iceland resulted in a massive aerosol plume in an otherwise near-pristine environment and thus provided an ideal natural experiment to quantify cloud responses to aerosol perturbations. Here we disentangle significant signals from the noise of meteorological co-variability using a satellite-based machine-learning approach. Our analysis shows that aerosols from the eruption increased cloud cover by approximately 10%, and this appears to be the leading cause of climate forcing, rather than cloud brightening as previously thought. We find that volcanic aerosols do brighten clouds by reducing droplet size, but this has a notably smaller radiative impact than changes in cloud fraction. These results add substantial observational constraints on the cooling impact of aerosols. Such constraints are critical for improving climate models, which still inadequately represent the complex macro-physical and microphysical impacts of aerosol–cloud interactions.
Original languageUndefined/Unknown
Pages (from-to)609-614
JournalNature Geoscience
Volume15
DOIs
Publication statusPublished - 1 Aug 2022

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