DOI | 10.1038/s41561-022-00991-6
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| 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
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发表日期 | 2022
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ISSN | 1752-0894
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EISSN | 1752-0908
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起始页码 | 609
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结束页码 | +
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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
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语种 | 英语
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WOS研究方向 | Geology
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WOS记录号 | WOS:000834732800002
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来源期刊 | NATURE GEOSCIENCE
(IF:14.48[JCR-2018],15.781[5-Year]) |
文献类型 | 期刊论文
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条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/272501
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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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推荐引用方式 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.
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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.
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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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