An aerosol classification scheme for global simulations using the K-means machine learning method
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Published:2022-01-25
Issue:2
Volume:15
Page:509-533
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ISSN:1991-9603
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Container-title:Geoscientific Model Development
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language:en
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Short-container-title:Geosci. Model Dev.
Author:
Li Jingmin, Hendricks Johannes, Righi MattiaORCID, Beer Christof G.ORCID
Abstract
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.
Funder
Bundesministerium für Wirtschaft und Energie Deutsches Zentrum für Luft- und Raumfahrt
Publisher
Copernicus GmbH
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