Neural Network Emulation of the Formation of Organic Aerosols Based on the Explicit GECKO‐A Chemistry Model

Schreck, John S.; Becker, Charles; Gagne, David John; Lawrence, Keely; Wang, Siyuan; Mouchel‐Vallon, Camille; Choi, Jinkyul; Hodzic, Alma

doi:10.1029/2021ms002974

i

Neural Network Emulation of the Formation of Organic Aerosols Based on the Explicit GECKO‐A Chemistry Model

Supporting Files

2022
By Schreck, John S. ; Becker, Charles ; Gagne, David John ; ...

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Journal Title:

Journal of Advances in Modeling Earth Systems
Personal Author:

Schreck, John S. ; Becker, Charles ; Gagne, David John ; Lawrence, Keely ; Wang, Siyuan ; Mouchel‐Vallon, Camille ; Choi, Jinkyul ; Hodzic, Alma
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; CIRES (Cooperative Institute for Research in Environmental Sciences) ; CSL (Chemical Sciences Laboratory)
Description:

Secondary organic aerosols (SOA) are formed from oxidation of hundreds of volatile organic compounds (VOCs) emitted from anthropogenic and natural sources. Accurate predictions of this chemistry are key for air quality and climate studies due to the large contribution of organic aerosols to submicron aerosol mass. Currently, only explicit models, such as the Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A), can fully represent the chemical processing of thousands of organic species. However, their extreme computational cost prohibits their use in current chemistry-climate models, which rely on simplified empirical parameterizations to predict SOA concentrations. This study demonstrates that
Keywords:

Environmental Chemistry General Earth And Planetary Sciences Global And Planetary Change General Earth And Planetary Sciences Global And Planetary Change General Earth And Planetary Sciences Global And Planetary Change
Source:

Journal of Advances in Modeling Earth Systems, 14(10)
DOI:

https://doi.org/10.1029/2021ms002974
ISSN:

1942-2466 ; 1942-2466
Format:

PDF
Publisher:

American Geophysical Union (AGU)
Document Type:

Journal Article
License:

https://creativecommons.org/licenses/by-nc-nd/4.0/
Rights Information:

CC BY-NC-ND
Compliance:

Library
Main Document Checksum:

urn:sha256:6f7a3c15b41c62042d3fbc515d6c58a6472f3f872bb5329c55fee93be4edbd22
Download URL:

https://repository.library.noaa.gov/view/noaa/54596/noaa_54596_DS1.pdf
File Type:

[PDF - 5.91 MB ]

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