P03 - A Novel Stochastic Parameterization for Lagrangian Modeling of Atmospheric Aerosol Transport
DescriptionIn recent years, it has become clear that the behavior of atmospheric aerosols has a non-negligible effect on radiative forcing within Earth's climate and the computational models that simulate it [Carslaw, et al., Nature, 2013]. Thus, we must obtain descriptive aerosol models that are also predictive, particularly in a time when aerosol-emitting ships may soon traverse the polar arctic ocean and there is credible talk about climate intervention strategies like stratospheric aerosol injection. This begs the question of how we may accurately describe our changing climate or dynamic weather patterns in the face of such uncertainty. We propose a novel stochastic model that employs transport parameters that operate on differing scales and vary according to their respective machine-learned probability distribution. This parameterization allows our transport variables to be functions of space, time, and relevant exogenic properties, and forcing effects may be added, subtracted, or altered as we gain more confidence in the machine learning model. To verify and validate this model, particle simulation results are compared to corresponding LES simulations, data from fog chamber experiments, and satellite imagery of ship tracks in the Pacific Ocean off the coast of California.
TimeTuesday, June 2719:30 - 21:30 CEST