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UID:submissions.pasc-conference.org_PASC24_sess159_msa178@linklings.com
SUMMARY:Bringing the Complexity of Organic Chemistry to Climate Models wit
 h Machine Learning Techniques
DESCRIPTION:Minisymposium\n\nCamille Mouchel-Vallon (Barcelona Supercomput
 ing Center) and Alma Hodzic, John Schreck, and Charlie Becker (National Ce
 nter of Atmospheric Research)\n\nPredicting secondary organic aerosol (SOA
 ) mass is of crucial importance as it is a significant contributor to the 
 atmospheric particulate load. SOA therefore has an impact on aerosol optic
 al, hygroscopic and toxic properties. There is a still a large gap between
  the complexity of processes involved in SOA formation and the simplicity 
 of their representation in air quality and climate models. GECKO-A is a to
 ol used for generating explicit organic chemistry chemical mechanisms, aim
 ing at reproducing the complexity of SOA formation processes. These mechan
 isms cannot be applied in 3D models due to their unpractical sizes. Machin
 e learning has been used to accelerate chemistry solvers in 3D models by e
 mulating their behavior at a fraction of their computational cost. Here we
  present a similar approach to emulate the behavior of complex GECKO-A mec
 hanisms to predict SOA formation. Different methods, including neural netw
 orks and random forests, are used and we quantify their performances and w
 eaknesses. This specific problem requires the construction of ad-hoc datas
 ets, and we illustrate the issues associated with this.\n\nDomain: Climate
 , Weather, and Earth Sciences, Computational Methods and Applied Mathemati
 cs\n\nSession Chairs: Lekha Patel (Sandia National Laboratories), Nicole R
 iemer (University of Illinois Urbana-Champaign), and Matthew West (Univers
 ity of Illinois Urbana-Champaign)
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