Machine Learning to Predict the Global Distribution of Aerosol Mixing State Metrics
Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Department of Atmospheric Sciences, Colorado State University, Fort Collins, CO 80523, USA
Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Author to whom correspondence should be addressed.
Received: 20 November 2017 / Revised: 4 January 2018 / Accepted: 5 January 2018 / Published: 9 January 2018
Atmospheric aerosols are evolving mixtures of chemical species. In global climate models (GCMs), this “aerosol mixing state” is represented in a highly simplified manner. This can introduce errors in the estimates of climate-relevant aerosol properties, such as the concentration of cloud condensation nuclei. The goal for this study is to determine a global spatial distribution of aerosol mixing state with respect to hygroscopicity, as quantified by the mixing state metric
. In this way, areas can be identified where the external or internal mixture assumption is more appropriate. We used the output of a large ensemble of particle-resolved box model simulations in conjunction with machine learning techniques to train a model of the mixing state metric
. This lower-order model for
uses as inputs only variables known to GCMs, enabling us to create a global map of
based on GCM data. We found that
varied between 20% and nearly 100%, and we quantified how this depended on particle diameter, location, and time of the year. This framework demonstrates how machine learning can be applied to bridge the gap between detailed process modeling and a large-scale climate model.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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MDPI and ACS Style
Hughes, M.; Kodros, J.K.; Pierce, J.R.; West, M.; Riemer, N. Machine Learning to Predict the Global Distribution of Aerosol Mixing State Metrics. Atmosphere 2018, 9, 15.
Hughes M, Kodros JK, Pierce JR, West M, Riemer N. Machine Learning to Predict the Global Distribution of Aerosol Mixing State Metrics. Atmosphere. 2018; 9(1):15.
Hughes, Michael; Kodros, John K.; Pierce, Jeffrey R.; West, Matthew; Riemer, Nicole. 2018. "Machine Learning to Predict the Global Distribution of Aerosol Mixing State Metrics." Atmosphere 9, no. 1: 15.
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