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: Text
Language:English
Published: 2023
Subjects:
Online Access:https://doi.org/10.5194/egusphere-2023-1667
https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1667/
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spelling ftcopernicus:oai:publications.copernicus.org:egusphere113384 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 2023-08-08 application/pdf https://doi.org/10.5194/egusphere-2023-1667 https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1667/ eng eng doi:10.5194/egusphere-2023-1667 https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1667/ eISSN: Text 2023 ftcopernicus https://doi.org/10.5194/egusphere-2023-1667 2023-08-14T16:24:22Z 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 ( N d ) 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 N d and meteorological factors as well as meteorological influences on the N d –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 N d , 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 N d –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 N d –CLF relationship, while this is not ... Text Southern Ocean Copernicus Publications: E-Journals Southern Ocean
institution Open Polar
collection Copernicus Publications: E-Journals
op_collection_id ftcopernicus
language English
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 ( N d ) 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 N d and meteorological factors as well as meteorological influences on the N d –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 N d , 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 N d –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 N d –CLF relationship, while this is not ...
format Text
author Jia, Yichen
Andersen, Hendrik
Cermak, Jan
spellingShingle Jia, Yichen
Andersen, Hendrik
Cermak, Jan
Analysis of cloud fraction adjustment to aerosols and its dependence on meteorological controls using explainable machine learning
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
publishDate 2023
url https://doi.org/10.5194/egusphere-2023-1667
https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1667/
geographic Southern Ocean
geographic_facet Southern Ocean
genre Southern Ocean
genre_facet Southern Ocean
op_source eISSN:
op_relation doi:10.5194/egusphere-2023-1667
https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1667/
op_doi https://doi.org/10.5194/egusphere-2023-1667
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