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An aerosol classification scheme for global simulations using the K-means machine learning method

Li, Jingmin and Hendricks, Johannes and Righi, Mattia and Beer, Christof Gerhard (2022) An aerosol classification scheme for global simulations using the K-means machine learning method. Geoscientific Model Development, 15 (2), pp. 509-533. Copernicus Publications. doi: 10.5194/gmd-15-509-2022. ISSN 1991-959X.

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Official URL: http://dx.doi.org/10.5194/gmd-15-509-2022

Abstract

The K-means machine learning algorithm is applied to climatological data of seven aerosol properties from a global aerosol simulation using EMAC-MADE3. The aim is to partition the aerosol properties across the global atmosphere in specific aerosol regimes; this is done mainly for evaluation purposes. K-means is an unsupervised machine learning method with the advantage that an a priori definition of the aerosol classes is not required. Using K-means, we are able to quantitatively define global aerosol regimes, so-called aerosol clusters, and explain their internal properties and their location and extension. This analysis shows that aerosol regimes in the lower troposphere are strongly influenced by emissions. Key drivers of the clusters' internal properties and spatial distribution are, for instance, pollutants from biomass burning and biogenic sources, mineral dust, anthropogenic pollution, and corresponding mixtures. Several continental clusters propagate into oceanic regions as a result of long-range transport of air masses. The identified oceanic regimes show a higher degree of pollution in the Northern Hemisphere than over the southern oceans. With increasing altitude, the aerosol regimes propagate from emission-induced clusters in the lower troposphere to roughly zonally distributed regimes in the middle troposphere and in the tropopause region. Notably, three polluted clusters identified over Africa, India, and eastern China cover the whole atmospheric column from the lower troposphere to the tropopause region. The results of this analysis need to be interpreted taking the limitations and strengths of global aerosol models into consideration. On the one hand, global aerosol simulations cannot estimate small-scale and localized processes due to the coarse resolution. On the other hand, they capture the spatial pattern of aerosol properties on the global scale, implying that the clustering results could provide useful insights for aerosol research. To estimate the uncertainties inherent in the applied clustering method, two sensitivity tests have been conducted (i) to investigate how various data scaling procedures could affect the K-means classification and (ii) to compare K-means with another unsupervised classification algorithm (HAC, i.e. hierarchical agglomerative clustering). The results show that the standardization based on sample mean and standard deviation is the most appropriate standardization method for this study, as it keeps the underlying distribution of the raw data set and retains the information of outliers. The two clustering algorithms provide similar classification results, supporting the robustness of our conclusions. The classification procedures presented in this study have a markedly wide application potential for future model-based aerosol studies.

Item URL in elib:https://elib.dlr.de/148498/
Document Type:Article
Additional Information:This research has been supported by the Bundesministerium für Wirtschaft und Klimaschutz (BMWK) (grant no. 20X1701B)
Title:An aerosol classification scheme for global simulations using the K-means machine learning method
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Li, JingminDLR, IPAhttps://orcid.org/0000-0002-4434-0029
Hendricks, JohannesDLR, IPAUNSPECIFIED
Righi, MattiaDLR, IPAhttps://orcid.org/0000-0003-3827-5950
Beer, Christof GerhardDLR, IPAhttps://orcid.org/0000-0003-3815-0007
Date:25 January 2022
Journal or Publication Title:Geoscientific Model Development
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:15
DOI:10.5194/gmd-15-509-2022
Page Range:pp. 509-533
Publisher:Copernicus Publications
ISSN:1991-959X
Status:Published
Keywords:global aerosol simulation, aerosol regime, K-means, machine-learning
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Transport System
DLR - Research area:Transport
DLR - Program:V VS - Verkehrssystem
DLR - Research theme (Project):V - Transport und Klima, R - Project MABAK
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Atmospheric Physics > Earth System Modelling
Deposited By: Li, Jingmin
Deposited On:27 Jan 2022 08:38
Last Modified:24 May 2022 23:48

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