24WXMOD Estimation of Ship Track Spreading Rates using a LES-informed Stochastic Particle Representation

Knowledge of the rate at which aerosols spread from a point source injection, such as a ship track, is critical for accurately representing subgrid plume spreading in a climate model. Such climate model results will guide future decisions regarding the feasibility and application of large-scale intentional Marine Cloud Brightening (MCB) via aerosol injection. Prior modeling studies have shown that the rate at which ship plumes spread may be strongly dependent on meteorological conditions, such as precipitating versus non-precipitating boundary layers.

We apply a Lagrangian particle model, governed by a Langevin stochastic differential equation, to create a simplified framework for predicting the rate of horizontal spreading from a ship-injected aerosol plume in various large-scale wind environments. The velocity and position of each stochastic particle is predicted with the acceleration of each particle being driven by the turbulent kinetic energy, dissipation rate, horizontal momentum variance, and mean wind. These inputs to the stochastic particle-velocity equation are derived from high-fidelity large-eddy simulations (LES) equipped with a prognostic aerosol-cloud microphysics scheme to simulate an aerosol injection from a ship into a cloud-topped marine boundary layer. The resulting spreading rate from the reduced-order stochastic model is then compared to the spreading rate seen in the LES. The stochastic particle-velocity representation is shown to reasonably reproduce spreading rates estimated from the LES, especially if the stochastic model input parameters are conditionally sampled from within the ship track region.