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UID:submissions.pasc-conference.org_PASC23_sess159_msa257@linklings.com
SUMMARY:Quantifying Errors in Modeled Aerosol Effects on Climate from Nume
 rical Approximations of Particle Distributions
DESCRIPTION:Minisymposium\n\nLaura Fierce (Pacific Northwest National Labo
 ratory); Payton Beeler (Washington University in St. Louis); and Mahantesh
  Halappanavar, Marco Minutoli, Sai Munikoti, ManishKumar Shrivastava, and 
 Alla Zelenyuk-Imre (Pacific Northwest National Laboratory)\n\nFor the past
  two decades, atmospheric aerosol has been the largest source of inter-mod
 el variability in radiative forcing among climate simulations. Climate-rel
 evant aerosol properties depend critically on the distribution in size, sh
 ape, and chemical composition of particle populations that evolve in time 
 as they are transported through Earth’s atmosphere. However, tracking such
  particle-level details is computationally impractical for large-scale, lo
 ng-running climate simulations. Instead, aerosol modules in Earth System M
 odels necessarily simplify the representation of particle characteristics,
  leading to errors in climate-relevant aerosol properties that have not be
 en well quantified. Here we present a framework for using particle-resolve
 d simulations of aerosol-cloud-chemistry interactions to quantify structur
 al errors from the numerical representation of particle populations. Parti
 cle-resolved models track per-particle composition among evolving aerosol 
 populations and are, therefore, not subject to many of the numerical error
 s that plague reduced aerosol schemes. In addition to quantifying error in
  reduced aerosol schemes, we will introduce an approach for using these de
 tailed particle-resolved simulations to train surrogate models that accoun
 t for the impact of unresolved particle characteristics on predictions of 
 climate-relevant properties and uncertainty in those predictions.\n\nDomai
 n: Climate, Weather and Earth Sciences\n\nSession Chair: Michael Schmidt (
 Sandia National Laboratories)
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