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UID:submissions.pasc-conference.org_PASC24_sess159@linklings.com
SUMMARY:MS5B - Data-Driven Modeling of Aerosols and Clouds for Climate Pre
 dictions
DESCRIPTION:Minisymposium\n\nAerosols and clouds have large impacts on cli
 mate, with macroscale climate impacts depending on the size and compositio
 n of individual aerosol particles and cloud droplets. Large uncertainties 
 exist in simulating aerosols and clouds and in assessing their climate imp
 acts on the global scale. The objective of this minisymposium is to bring 
 together researchers focusing on data-driven methods for the development o
 f cloud and aerosol model parameterizations. These methods have the potent
 ial to reduce structural and parametric uncertainty and to improve the con
 sistency of the representation of aerosol and cloud processes across spati
 al and temporal scales. Our speakers will present on machine learning for 
 Earth system prediction and predictability, reduced mechanisms of atmosphe
 ric chemistry, Bayesian methods to estimate aerosol process rates, and the
  learning of highly-efficient reduced order models of cloud microphysics. 
 While our minisymposium will focus on the area of climate, the methods hav
 e the potential to be applicable in other areas that have similar multi-sc
 ale structures.\n\nEstimating Aerosol Process Rates Using Bayesian Inverse
  Methods\n\nThe last decade has been a huge leap forward in atmospheric ne
 w particle formation (NPF) research. Novel instrument development has allo
 wed us to measure more and more details of the dynamics of even the smalle
 st clusters. At the same time, however, for example NPF and particle growt
 h rates have bee...\n\n\nTeemu Salminen, Aku Seppänen, Matti Niskanen, and
  Kari Lehtinen (University of Eastern Finland)\n---------------------\nEst
 imating Submicron Aerosol Mixing State at the Global Scale with Machine Le
 arning and Earth System Modeling\n\nAerosol mixing state refers to the way
  that different chemical components are distributed amongst the particles 
 in an aerosol population. It is an important emergent property of the atmo
 spheric aerosol that affects aerosol radiative forcing and aerosol–cloud i
 nteractions. However, current aero...\n\n\nMatthew West (University of Ill
 inois Urbana-Champaign)\n---------------------\nBringing the Complexity of
  Organic Chemistry to Climate Models with Machine Learning Techniques\n\nP
 redicting 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 optical, hygroscopic and toxic properti
 es. There is a still a large gap between the complexity of processes invol
 ved...\n\n\nCamille Mouchel-Vallon (Barcelona Supercomputing Center) and A
 lma Hodzic, John Schreck, and Charlie Becker (National Center of Atmospher
 ic Research)\n---------------------\nDiscussion: Scaling Data-Driven Metho
 ds for Aerosols and Clouds to Global Climate Predictions\n\nFollowing the 
 presentations, we will have a discussion on the challenges and the potenti
 als of scaling data-driven methods for aerosols and clouds to global clima
 te predictions.\n\n\nNicole Riemer (University of Illinois Urbana-Champaig
 n), Lekha Patel (Sandia National Laboratories), and Matthew West (Universi
 ty of Illinois Urbana-Champaign)\n\nDomain: Climate, Weather, and Earth Sc
 iences, Computational Methods and Applied Mathematics\n\nSession Chairs: L
 ekha Patel (Sandia National Laboratories), Nicole Riemer (University of Il
 linois Urbana-Champaign), and Matthew West (University of Illinois Urbana-
 Champaign)
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