Analysis of cloud fraction adjustment to aerosols and its dependence on meteorological controls using explainable machine learning

Aerosol-cloud interactions (ACI) have a pronounced influence on the Earth’s radiation budget but continue to pose one of the most substantial uncertainties in the climate system. Marine boundary-layer clouds (MBLCs) are particularly important since they cover a large portion of the Earth’s surface....

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Main Authors: Jia, Yichen, Andersen, Hendrik, Cermak, Jan
Format: Article in Journal/Newspaper
Language:English
Published: Copernicus Publications 2023
Subjects:
Online Access:https://doi.org/10.5194/egusphere-2023-1667
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spelling ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00068061 2023-09-05T13:23:31+02:00 Analysis of cloud fraction adjustment to aerosols and its dependence on meteorological controls using explainable machine learning Jia, Yichen Andersen, Hendrik Cermak, Jan 2023-08 electronic https://doi.org/10.5194/egusphere-2023-1667 https://noa.gwlb.de/receive/cop_mods_00068061 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00066496/egusphere-2023-1667.pdf https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1667/egusphere-2023-1667.pdf eng eng Copernicus Publications https://doi.org/10.5194/egusphere-2023-1667 https://noa.gwlb.de/receive/cop_mods_00068061 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00066496/egusphere-2023-1667.pdf https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1667/egusphere-2023-1667.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess article Verlagsveröffentlichung article Text doc-type:article 2023 ftnonlinearchiv https://doi.org/10.5194/egusphere-2023-1667 2023-08-13T23:19:56Z Aerosol-cloud interactions (ACI) have a pronounced influence on the Earth’s radiation budget but continue to pose one of the most substantial uncertainties in the climate system. Marine boundary-layer clouds (MBLCs) are particularly important since they cover a large portion of the Earth’s surface. One of the biggest challenges in quantifying ACI from observations lies in isolating adjustments of cloud fraction (CLF) to aerosol perturbations from the covariability and influence of the local meteorological conditions. In this study, this isolation is attempted using nine years (2011–2019) of near-global daily satellite cloud products in combination with reanalysis data of meteorological parameters. With cloud-droplet number concentration (Nd) as a proxy for aerosol, MBLC CLF is predicted by region-specific gradient boosting machine learning models. By means of SHapley Additive exPlanation (SHAP) regression values, CLF sensitivity to Nd and meteorological factors as well as meteorological influences on the Nd–CLF sensitivity are quantified. The regional ML models are able to capture on average 45 % of the CLF variability. Global patterns of CLF sensitivity show that CLF is positively associated with Nd, in particular in the stratocumulus-to-cumulus transition regions and in the Southern Ocean. CLF sensitivity to estimated inversion strength (EIS) is ubiquitously positive and strongest in tropical and subtropical regions topped by stratocumulus and within the midlatitudes. Globally, increased sea surface temperature (SST) reduces CLF, particularly in stratocumulus regions. The spatial patterns of CLF sensitivity to horizontal wind components in the free troposphere point to the impact of synoptic-scale weather systems and vertical wind shear on MBLCs. The Nd–CLF relationship is found to depend more on the selected thermodynamical variables than dynamical variables, and in particular on EIS and SST. In the midlatitudes, a stronger inversion is found to amplify the Nd–CLF relationship, while this is not observed in ... Article in Journal/Newspaper Southern Ocean Niedersächsisches Online-Archiv NOA Southern Ocean
institution Open Polar
collection Niedersächsisches Online-Archiv NOA
op_collection_id ftnonlinearchiv
language English
topic article
Verlagsveröffentlichung
spellingShingle article
Verlagsveröffentlichung
Jia, Yichen
Andersen, Hendrik
Cermak, Jan
Analysis of cloud fraction adjustment to aerosols and its dependence on meteorological controls using explainable machine learning
topic_facet article
Verlagsveröffentlichung
description Aerosol-cloud interactions (ACI) have a pronounced influence on the Earth’s radiation budget but continue to pose one of the most substantial uncertainties in the climate system. Marine boundary-layer clouds (MBLCs) are particularly important since they cover a large portion of the Earth’s surface. One of the biggest challenges in quantifying ACI from observations lies in isolating adjustments of cloud fraction (CLF) to aerosol perturbations from the covariability and influence of the local meteorological conditions. In this study, this isolation is attempted using nine years (2011–2019) of near-global daily satellite cloud products in combination with reanalysis data of meteorological parameters. With cloud-droplet number concentration (Nd) as a proxy for aerosol, MBLC CLF is predicted by region-specific gradient boosting machine learning models. By means of SHapley Additive exPlanation (SHAP) regression values, CLF sensitivity to Nd and meteorological factors as well as meteorological influences on the Nd–CLF sensitivity are quantified. The regional ML models are able to capture on average 45 % of the CLF variability. Global patterns of CLF sensitivity show that CLF is positively associated with Nd, in particular in the stratocumulus-to-cumulus transition regions and in the Southern Ocean. CLF sensitivity to estimated inversion strength (EIS) is ubiquitously positive and strongest in tropical and subtropical regions topped by stratocumulus and within the midlatitudes. Globally, increased sea surface temperature (SST) reduces CLF, particularly in stratocumulus regions. The spatial patterns of CLF sensitivity to horizontal wind components in the free troposphere point to the impact of synoptic-scale weather systems and vertical wind shear on MBLCs. The Nd–CLF relationship is found to depend more on the selected thermodynamical variables than dynamical variables, and in particular on EIS and SST. In the midlatitudes, a stronger inversion is found to amplify the Nd–CLF relationship, while this is not observed in ...
format Article in Journal/Newspaper
author Jia, Yichen
Andersen, Hendrik
Cermak, Jan
author_facet Jia, Yichen
Andersen, Hendrik
Cermak, Jan
author_sort Jia, Yichen
title Analysis of cloud fraction adjustment to aerosols and its dependence on meteorological controls using explainable machine learning
title_short Analysis of cloud fraction adjustment to aerosols and its dependence on meteorological controls using explainable machine learning
title_full Analysis of cloud fraction adjustment to aerosols and its dependence on meteorological controls using explainable machine learning
title_fullStr Analysis of cloud fraction adjustment to aerosols and its dependence on meteorological controls using explainable machine learning
title_full_unstemmed Analysis of cloud fraction adjustment to aerosols and its dependence on meteorological controls using explainable machine learning
title_sort analysis of cloud fraction adjustment to aerosols and its dependence on meteorological controls using explainable machine learning
publisher Copernicus Publications
publishDate 2023
url https://doi.org/10.5194/egusphere-2023-1667
https://noa.gwlb.de/receive/cop_mods_00068061
https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00066496/egusphere-2023-1667.pdf
https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1667/egusphere-2023-1667.pdf
geographic Southern Ocean
geographic_facet Southern Ocean
genre Southern Ocean
genre_facet Southern Ocean
op_relation https://doi.org/10.5194/egusphere-2023-1667
https://noa.gwlb.de/receive/cop_mods_00068061
https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00066496/egusphere-2023-1667.pdf
https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1667/egusphere-2023-1667.pdf
op_rights https://creativecommons.org/licenses/by/4.0/
uneingeschränkt
info:eu-repo/semantics/openAccess
op_doi https://doi.org/10.5194/egusphere-2023-1667
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