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

• Main Authors: Chen, Y, Haywood, J, Wang, Y, Malavelle, F, Jordan, G, Partridge, D, Fieldsend, J, De Leeuw, J, Schmidt, A, Cho, N, Oreopoulos, L, Platnick, S, Grosvenor, D, Field, P, Lohmann, U
• Format: Article in Journal/Newspaper
• Language: English
• Published: Nature Research 2022
• Subjects: Iceland
• Online Access: http://hdl.handle.net/10871/130263; https://doi.org/10.1038/s41561-022-00991-6; https://doi.org/10.1038/s41561-022-01027-9

This is the author accepted manuscript. The final version is available from Nature Research via the DOI in this record Data availability: The MODIS cloud and aerosol products from Aqua (MYD08_L3) and Terra (MOD08_L3) used in this study are available from the Atmosphere Archive and Distribution System Distributed Active Archive Center of National Aeronautics and Space Administration (LAADS-DAAC, NASA), https://ladsweb.modaps.eosdis.nasa.gov. ERA5 datasets are available from the European Centre for Medium-range Weather Forecast (ECMWF) archive, https://cds.climate.copernicus.eu. The full datasets shown in the figures are provided in source data files. A correction to this article was published on 17 August 2022 at https://doi.org/10.1038/s41561-022-01027-9: "Correction to: Nature Geoscience https://doi.org/10.1038/s41561-022-00991-6, published online 1 August 2022. In the version of this article originally published, extraneous “(%)” symbols appeared in three x-axis labels in Fig. 2c. They have now been removed from the HTML and PDF versions of the figure". Aerosol-cloud interactions have a potentially large impact on climate, but are poorly quantified and thus contribute a significant 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 are frequently hampered by the considerable noise associated with meteorological co-variability. The Iceland-Holuhraun effusive eruption in 2014 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 ...