Global aerosol mixtures and their multiyear and seasonal characteristics
The optical and microphysical characteristics of distinct aerosol types in the atmosphere are not yet specified at the level of detail required for climate forcing studies. What is even less well known are the characteristics of mixtures of aerosol and, in particular, their precise global spatial di...
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ftuniveastangl:oai:ueaeprints.uea.ac.uk:76338 2023-05-15T13:06:47+02:00 Global aerosol mixtures and their multiyear and seasonal characteristics Taylor, M. Kazadzis, S. Amiridis, V. Kahn, R. A. 2015-09 https://ueaeprints.uea.ac.uk/id/eprint/76338/ https://www.sciencedirect.com/science/article/pii/S1352231015301709 https://doi.org/10.1016/j.atmosenv.2015.06.029 unknown Taylor, M., Kazadzis, S., Amiridis, V. and Kahn, R. A. (2015) Global aerosol mixtures and their multiyear and seasonal characteristics. Atmospheric Environment, 116. pp. 112-129. ISSN 1352-2310 doi:10.1016/j.atmosenv.2015.06.029 Article PeerReviewed 2015 ftuniveastangl https://doi.org/10.1016/j.atmosenv.2015.06.029 2023-03-23T23:32:40Z The optical and microphysical characteristics of distinct aerosol types in the atmosphere are not yet specified at the level of detail required for climate forcing studies. What is even less well known are the characteristics of mixtures of aerosol and, in particular, their precise global spatial distribution. Here, cluster analysis is applied to seven years of 3-hourly, gridded 2.5° × 2° aerosol optical depth data from the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model, one of the most-studied global simulations of aerosol type currently available, to construct a spatial partition of the globe into a finite number of aerosol mixtures. The optimal number of aerosol mixtures is obtained with a k-means algorithm with smart seeding in conjunction with a stopping condition based on applying the ‘law of diminishing returns’ to the norm of the Euclidean distance to provide upper and lower bounds on the number of clusters. Each cluster has a distinct composition in terms of the proportion of biomass burning, sulfate, dust and marine (sea salt) aerosol and this leads rather naturally to a taxonomy for labeling aerosol mixtures. In addition, the assignment of primary colors to constituent aerosol types enables true color-mixing and the production of easy-to-interpret maps of their distribution. The mean multiyear global partition as well as partitions deduced on the seasonal timescale are used to extract aerosol robotic network (AERONET) Level 2.0 Version 2 inversion products in each cluster for estimating the values of key optical and microphysical parameters to help characterize aerosol mixtures. On the multiyear timescale, the globe can be spatially partitioned into 10 distinct aerosol mixtures, with only marginally more variability on the seasonal timescale. In the context of the observational constraints and uncertainties associated with AERONET retrievals, bivariate analysis suggests that mixtures dominated by dust and marine aerosol can be detected with reference to their single scattering albedo ... Article in Journal/Newspaper Aerosol Robotic Network University of East Anglia: UEA Digital Repository Atmospheric Environment 116 112 129 |
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University of East Anglia: UEA Digital Repository |
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ftuniveastangl |
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description |
The optical and microphysical characteristics of distinct aerosol types in the atmosphere are not yet specified at the level of detail required for climate forcing studies. What is even less well known are the characteristics of mixtures of aerosol and, in particular, their precise global spatial distribution. Here, cluster analysis is applied to seven years of 3-hourly, gridded 2.5° × 2° aerosol optical depth data from the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model, one of the most-studied global simulations of aerosol type currently available, to construct a spatial partition of the globe into a finite number of aerosol mixtures. The optimal number of aerosol mixtures is obtained with a k-means algorithm with smart seeding in conjunction with a stopping condition based on applying the ‘law of diminishing returns’ to the norm of the Euclidean distance to provide upper and lower bounds on the number of clusters. Each cluster has a distinct composition in terms of the proportion of biomass burning, sulfate, dust and marine (sea salt) aerosol and this leads rather naturally to a taxonomy for labeling aerosol mixtures. In addition, the assignment of primary colors to constituent aerosol types enables true color-mixing and the production of easy-to-interpret maps of their distribution. The mean multiyear global partition as well as partitions deduced on the seasonal timescale are used to extract aerosol robotic network (AERONET) Level 2.0 Version 2 inversion products in each cluster for estimating the values of key optical and microphysical parameters to help characterize aerosol mixtures. On the multiyear timescale, the globe can be spatially partitioned into 10 distinct aerosol mixtures, with only marginally more variability on the seasonal timescale. In the context of the observational constraints and uncertainties associated with AERONET retrievals, bivariate analysis suggests that mixtures dominated by dust and marine aerosol can be detected with reference to their single scattering albedo ... |
format |
Article in Journal/Newspaper |
author |
Taylor, M. Kazadzis, S. Amiridis, V. Kahn, R. A. |
spellingShingle |
Taylor, M. Kazadzis, S. Amiridis, V. Kahn, R. A. Global aerosol mixtures and their multiyear and seasonal characteristics |
author_facet |
Taylor, M. Kazadzis, S. Amiridis, V. Kahn, R. A. |
author_sort |
Taylor, M. |
title |
Global aerosol mixtures and their multiyear and seasonal characteristics |
title_short |
Global aerosol mixtures and their multiyear and seasonal characteristics |
title_full |
Global aerosol mixtures and their multiyear and seasonal characteristics |
title_fullStr |
Global aerosol mixtures and their multiyear and seasonal characteristics |
title_full_unstemmed |
Global aerosol mixtures and their multiyear and seasonal characteristics |
title_sort |
global aerosol mixtures and their multiyear and seasonal characteristics |
publishDate |
2015 |
url |
https://ueaeprints.uea.ac.uk/id/eprint/76338/ https://www.sciencedirect.com/science/article/pii/S1352231015301709 https://doi.org/10.1016/j.atmosenv.2015.06.029 |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_relation |
Taylor, M., Kazadzis, S., Amiridis, V. and Kahn, R. A. (2015) Global aerosol mixtures and their multiyear and seasonal characteristics. Atmospheric Environment, 116. pp. 112-129. ISSN 1352-2310 doi:10.1016/j.atmosenv.2015.06.029 |
op_doi |
https://doi.org/10.1016/j.atmosenv.2015.06.029 |
container_title |
Atmospheric Environment |
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116 |
container_start_page |
112 |
op_container_end_page |
129 |
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1766021031029374976 |