<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>

COPY SCRIPT

For further information contact us at helpdesk@openaire.eu

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

descriptionPublicationkeyboard_double_arrow_right Article 01 Aug 2022Embargo end date: 01 Jan 2022 Switzerland, United Kingdom, Germany Publisher:Springer Science and Business Media LLCJournal:Nature Geoscience, volume 15, pages 609-614 (issn: 1752-0894, eissn: 1752-0908,

Authors: Chen, Ying; Haywood, Jim; Wang, Yu; Malavelle, Florent; Jordan, George; Partridge, Daniel; Fieldsend, Jonathan; +8 Authors

doi: 10.1038/s41561-022-00991-6 , 10.3929/ethz-b-000561341

handle: 10871/130263

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

- Summary
- Subjects
- Metrics

Abstract

Nature Geoscience, 15

ISSN:1752-0908

ISSN:1752-0894

Countries

Switzerland, United Kingdom, Germany

Related Organizations

University of Leeds
United Kingdom
University of Exeter
United Kingdom
German Aerospace Center
Germany
Met Office
United Kingdom
University of Cambridge
United Kingdom

View all View all

Keywords

Holuhraun 2014-2015, 550, 551, Aerosol-cloud interactions, Aerosol's fingerprint on clouds, Aerosol-cloud, Aerosol-cloud interactions; Climate change; Machine learning; Aerosol's fingerprint on clouds, Machine learning, Climate change, volcanic aerosol

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	49
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 1%