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DOI10.1038/s41561-022-00991-6
Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover
Chen, Ying; Haywood, Jim; Wang, Yu; Malavelle, Florent; Jordan, George; Partridge, Daniel; Fieldsend, Jonathan; De Leeuw, Johannes; Schmidt, Anja; Cho, Nayeong; Oreopoulos, Lazaros; Platnick, Steven; Grosvenor, Daniel; Field, Paul; Lohmann, Ulrike
发表日期2022
ISSN1752-0894
EISSN1752-0908
起始页码609
结束页码+
卷号15期号:8页码:18
英文摘要Satellite-based machine-learning analysis of a diffusive volcanic eruption suggests that aerosol climate forcing is dominated by changes in cloud cover, rather than changes in cloud brightness. 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.
学科领域Geosciences, Multidisciplinary
语种英语
WOS研究方向Geology
WOS记录号WOS:000834732800002
来源期刊NATURE GEOSCIENCE (IF:14.48[JCR-2018],15.781[5-Year])
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/272501
作者单位University of Exeter; Met Office - UK; Hadley Centre; Swiss Federal Institutes of Technology Domain; ETH Zurich; Met Office - UK; University of Cambridge; University of Cambridge; University of Munich; National Aeronautics & Space Administration (NASA); NASA Goddard Space Flight Center; UK Research & Innovation (UKRI); Natural Environment Research Council (NERC); NERC National Centre for Atmospheric Science; University of Leeds; University of Leeds; Swiss Federal Institutes of Technology Domain; Paul Scherrer Institute; Helmholtz Association; German Aerospace Centre (DLR)
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GB/T 7714
Chen, Ying,Haywood, Jim,Wang, Yu,et al. Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover[J],2022,15(8):18.
APA Chen, Ying.,Haywood, Jim.,Wang, Yu.,Malavelle, Florent.,Jordan, George.,...&Lohmann, Ulrike.(2022).Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover.NATURE GEOSCIENCE,15(8),18.
MLA Chen, Ying,et al."Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover".NATURE GEOSCIENCE 15.8(2022):18.
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