Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover
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 Syste...
Main Authors: | , , , , , , , , , , , , , , |
---|---|
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Nature Research
2022
|
Subjects: | |
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 |
id |
ftunivexeter:oai:ore.exeter.ac.uk:10871/130263 |
---|---|
record_format |
openpolar |
spelling |
ftunivexeter:oai:ore.exeter.ac.uk:10871/130263 2024-09-15T18:14:25+00:00 Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover 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 2022 http://hdl.handle.net/10871/130263 https://doi.org/10.1038/s41561-022-00991-6 https://doi.org/10.1038/s41561-022-01027-9 en eng Nature Research https://ladsweb.modaps.eosdis.nasa.gov https://cds.climate.copernicus.eu orcid:0000-0002-2143-6634 (Haywood, James) Vol. 15, pp. 609–614 doi:10.1038/s41561-022-00991-6 https://doi.org/10.1038/s41561-022-01027-9 NE/T006897/1 820829 GA01101 2021-HS-332 NE/S00436X/1 http://hdl.handle.net/10871/130263 1752-0894 Nature Geoscience © The Author(s), under exclusive licence to Springer Nature Limited 2022 2023-02-01 Under embargo until 1 February 2023 in compliance with publisher policy http://www.rioxx.net/licenses/all-rights-reserved Article 2022 ftunivexeter https://doi.org/10.1038/s41561-022-00991-610.1038/s41561-022-01027-9 2024-07-29T03:24:14Z 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 ... Article in Journal/Newspaper Iceland University of Exeter: Open Research Exeter (ORE) |
institution |
Open Polar |
collection |
University of Exeter: Open Research Exeter (ORE) |
op_collection_id |
ftunivexeter |
language |
English |
description |
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 ... |
format |
Article in Journal/Newspaper |
author |
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 |
spellingShingle |
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 Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover |
author_facet |
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 |
author_sort |
Chen, Y |
title |
Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover |
title_short |
Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover |
title_full |
Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover |
title_fullStr |
Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover |
title_full_unstemmed |
Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover |
title_sort |
machine learning reveals climate forcing from aerosols is dominated by increased cloud cover |
publisher |
Nature Research |
publishDate |
2022 |
url |
http://hdl.handle.net/10871/130263 https://doi.org/10.1038/s41561-022-00991-6 https://doi.org/10.1038/s41561-022-01027-9 |
genre |
Iceland |
genre_facet |
Iceland |
op_relation |
https://ladsweb.modaps.eosdis.nasa.gov https://cds.climate.copernicus.eu orcid:0000-0002-2143-6634 (Haywood, James) Vol. 15, pp. 609–614 doi:10.1038/s41561-022-00991-6 https://doi.org/10.1038/s41561-022-01027-9 NE/T006897/1 820829 GA01101 2021-HS-332 NE/S00436X/1 http://hdl.handle.net/10871/130263 1752-0894 Nature Geoscience |
op_rights |
© The Author(s), under exclusive licence to Springer Nature Limited 2022 2023-02-01 Under embargo until 1 February 2023 in compliance with publisher policy http://www.rioxx.net/licenses/all-rights-reserved |
op_doi |
https://doi.org/10.1038/s41561-022-00991-610.1038/s41561-022-01027-9 |
_version_ |
1810452182984556544 |