Estimating Submicron Aerosol Mixing State at the Global Scale With Machine Learning and Earth System Modeling

Zheng, Zhonghua; Curtis, Jeffrey H.; Yao, Yu; Gasparik, Jessica T.; Anantharaj, Valentine G.; Zhao, Lei; West, Matthew; Riemer, Nicole

doi:10.1029/2020EA001500

Title: Estimating Submicron Aerosol Mixing State at the Global Scale With Machine Learning and Earth System Modeling

Journal Article · Thu Feb 04 00:00:00 EST 2021 · Earth and Space Science

DOI:https://doi.org/10.1029/2020EA001500· OSTI ID:1767365

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Department of Civil and Environmental Engineering University of Illinois at Urbana‐Champaign Urbana IL USA
Department of Atmospheric Sciences University of Illinois at Urbana‐Champaign Urbana IL USA, Department of Mechanical Science and Engineering University of Illinois at Urbana‐Champaign Urbana IL USA
Department of Atmospheric Sciences University of Illinois at Urbana‐Champaign Urbana IL USA
National Center for Computational Sciences Oak Ridge National Laboratory Oak Ridge TN USA
Department of Civil and Environmental Engineering University of Illinois at Urbana‐Champaign Urbana IL USA, National Center for Supercomputing Applications University of Illinois at Urbana‐Champaign Urbana IL USA
Department of Mechanical Science and Engineering University of Illinois at Urbana‐Champaign Urbana IL USA

This study integrates machine learning and particle-resolved aerosol simulations to develop emulators that predict submicron aerosol mixing state indices from the Earth system model (ESM) simulations. The emulators predict aerosol mixing state using only quantities that are predicted by the ESM, including bulk aerosol species concentrations, which do not by themselves carry mixing state information. We used PartMC-MOSAIC as the particle-resolved model and NCAR's CESM as the ESM. We trained emulators for three different mixing state indices for submicron aerosol in terms of chemical species abundance (χa), the mixing of optically absorbing and nonabsorbing species (χo), and the mixing of hygroscopic and nonhygroscopic species (χh). Our global mixing state maps show considerable spatial and seasonal variability unique to each mixing state index. Seasonal averages varied spatially between 13% and 94% for χa, between 38% and 94% for χo, and between 20% and 87% for χh with global annual averages of 67%, 68%, and 56%, respectively. High values in one index can be consistent with low values in another index depending on the grouping of species and their relative abundance, meaning that each mixing state index captures different aspects of the population mixing state. Although a direct validation with observational data has not been possible yet, our results are consistent with mixing state index values derived from ambient observations. This work is a prototypical example of using machine learning emulators to add information to ESM simulations.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM), Data Center and Oak Ridge Leadership Computing Facility (OLCF); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF); State of Illinois; National Geospatial- Intelligence Agency; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Contributing Organization:: Pacific Northwest National Laboratory (PNNL); Brookhaven National Laboratory (BNL); Argonne National Laboratory (ANL)

Grant/Contract Number:: DE‐SC0019192; DE‐AC05‐00OR22725; SC0019192; AC05-00OR22725; AGS-1254428; OCI-0725070; ACI-1238993

OSTI ID:: 1767365

Alternate ID(s):: OSTI ID: 1773469; OSTI ID: 1783013; OSTI ID: 1786651

Journal Information:: Earth and Space Science, Journal Name: Earth and Space Science Vol. 8 Journal Issue: 2; ISSN 2333-5084

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

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