Quantifying Variability of a Stochastic Particle-Resolved Aerosol Model
Aerosols are a key player for climate and atmospheric chemistry. A variety of compounds such as sulfates, black carbon, organic carbon, sea salt, and mineral dust contribute to the composition of individual particles: the so-called mixing state. To assess the impact of aerosol particles in the climate system, the mixing state plays an important role and accurate aerosol models are needed to predict it. To expand knowledge about aerosols, a new, higher-detailed aerosol modeling component, called PartMC has been developed to predict number-, the surface- and the mass-size distributions, as well as the composition, fractal dimension, and the mixing state. This is achieved by applying a stochastic Monte Carlo algorithm. Inherent to such models is a certain degree of randomness in the results, which to this end has not yet been quantified. The aim of this project is to quantify the variability of PartMC for the number and mass size distributions, and how this depends on input parameters such as number of Monte Carlo particles and the ensemble size of Monte Carlo runs.