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UID:submissions.pasc-conference.org_PASC24_sess159_msa207@linklings.com
SUMMARY:Estimating Submicron Aerosol Mixing State at the Global Scale with
  Machine Learning and Earth System Modeling
DESCRIPTION:Minisymposium\n\nMatthew West (University of Illinois Urbana-C
 hampaign)\n\nAerosol mixing state refers to the way that different chemica
 l components are distributed amongst the particles in an aerosol populatio
 n. It is an important emergent property of the atmospheric aerosol that af
 fects aerosol radiative forcing and aerosol–cloud interactions. However, c
 urrent aerosol models used in global Earth system models are limited in th
 eir capability to represent aerosol mixing state, thereby suffering from l
 argely unquantified structural uncertainty. In contrast, particle-resolved
  aerosol simulations are able to capture aerosol mixing state faithfully, 
 however, they are too computationally expensive to be directly implemented
  into Earth system models.\n\nThis study integrates machine learning and p
 article-resolved aerosol simulations to develop emulators that predict sub
 micron aerosol mixing state indices from the Earth system model simulation
 s. The emulators predict aerosol mixing state using only quantities that a
 re predicted by the Earth system model, including bulk aerosol species con
 centrations, which do not by themselves carry mixing state information. Th
 is work is a prototypical example of using machine learning emulators to a
 dd information to Earth system model simulations and to quantify structura
 l uncertainty in Earth system models.\n\nDomain: Climate, Weather, and Ear
 th Sciences, Computational Methods and Applied Mathematics\n\nSession Chai
 rs: Lekha Patel (Sandia National Laboratories), Nicole Riemer (University 
 of Illinois Urbana-Champaign), and Matthew West (University of Illinois Ur
 bana-Champaign)
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