Machine learning uncovers aerosol size information from chemistry and meteorology to quantify potential cloud-forming particles

Nair, Arshad Arjunan; Yu, Fangqun; Campuzano‐Jost, Pedro; DeMott, Paul J.; Levin, Ezra J. T.; Jimenez, Jose L.; Peischl, Jeff; Pollack, Ilana B.; Fredrickson, Carley D.; Beyersdorf, Andreas J.; Nault, Benjamin A.; Park, Minsu; Yum, Seong Soo; Palm, Brett B.; Xu, Lu; Bourgeois, Ilann; Anderson, Bruce E.; Nenes, Athanasios; Ziemba, Luke D.; Moore, Richard H.; Lee, Taehyoung; Park, Taehyun; Thompson, Chelsea R.; Flocke, Frank; Huey, Lewis Gregory; Kim, Michelle J.; Peng, Qiaoyun

doi:10.1029/2021GL094133

i

Machine learning uncovers aerosol size information from chemistry and meteorology to quantify potential cloud-forming particles

2021
By: Nair, Arshad Arjunan ; Yu, Fangqun ; Campuzano‐Jost, Pedro
Source: Geophysical Research Letters, 48, e2021GL094133

Details

Journal Title:

Geophysical Research Letters
Personal Author:

Nair, Arshad Arjunan ; Yu, Fangqun ; Campuzano‐Jost, Pedro ; DeMott, Paul J. ; Levin, Ezra J. T. ; Jimenez, Jose L. ; Peischl, Jeff ; Pollack, Ilana B. ; Fredrickson, Carley D. ; Beyersdorf, Andreas J. ; Nault, Benjamin A. ; Park, Minsu ; Yum, Seong Soo ; Palm, Brett B. ; Xu, Lu ; Bourgeois, Ilann ; Anderson, Bruce E. ; Nenes, Athanasios ; Ziemba, Luke D. ; Moore, Richard H. ; Lee, Taehyoung ; Park, Taehyun ; Thompson, Chelsea R. ; Flocke, Frank ; Huey, Lewis Gregory ; Kim, Michelle J. ; Peng, Qiaoyun
NOAA Program & Office:

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

Cloud condensation nuclei (CCN) are mediators of aerosol-cloud interactions, which contribute to the largest uncertainty in climate change prediction. Here, we present a machine learning (ML)/artificial intelligence (AI) model that quantifies CCN from model-simulated aerosol composition, atmospheric trace gas, and meteorological variables. Comprehensive multi-campaign airborne measurements, covering varied physicochemical regimes in the troposphere, confirm the validity of and help probe the inner workings of this ML model: revealing for the first time that different ranges of atmospheric aerosol composition and mass correspond to distinct aerosol number size distributions. ML extracts this information, important for accurate quantification of CCN, additionally from both chemistry and meteorology. This can provide a physicochemically explainable, computationally efficient, robust ML pathway in global climate models that only resolve aerosol composition; potentially mitigating the uncertainty of effective radiative forcing due to aerosol-cloud interactions (ERFaci) and improving confidence in assessment of anthropogenic contributions and climate change projections.
Source:

Geophysical Research Letters, 48, e2021GL094133
DOI:

https://doi.org/10.1029/2021GL094133
Document Type:

Journal Article
Funding:

Grant no. NA17OAR4310012 ; Grant no. NA17OAR4310010
Rights Information:

Other
Compliance:

Submitted
Download URL:

https://repository.library.noaa.gov/view/noaa/40172/noaa_40172_DS1.pdf
File Type:

[PDF - 2.10 MB]
Collection(s):

Office of Oceanic and Atmospheric Research (OAR) ; NOAA Cooperative Institutes
Main Document Checksum:

urn:sha256:b03adff33e1ed4393c9924f53c2aeba2bd6c7a07b302bf0cdb18e8ed1ec5c8b7

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