Improving the Representation of Atmospheric Aerosols in Large-scale Models through Particle-based Modeling
Environmental and Climate Sciences
Brookhaven National Laboratory
TCS Building 240
Aerosol radiative properties depend on the size and chemical composition of individual particles, but particle-level characteristics are not fully resolved in global-scale models. In my talk, I will describe two different particle-based methods for advancing aerosol representations in large-scale models.
In the first part of my talk, I will describe the application of a particle-resolved model, which is computationally too expensive for large-scale simulation, for benchmarking reduced aerosol representations and for parameterizing unresolved aerosol properties. I will demonstrate that aerosol absorption is overestimated if diversity in particle composition is neglected, but also show that the effects of composition diversity can be approximately represented by a parametric relationship derived from a series of particle-resolved simulations.
In the second part of my talk, I will introduce a new sparse-particle model based on the quadrature method of moments, which is designed for use in large-scale atmospheric models. I will demonstrate that cloud condensation nuclei activity of particle-resolved populations, which are comprised of 10,000 to 1,000,000 Monte Carlo particles, are accurately represented by an optimized set of only eight sparse particles.
This study is a first step toward a new aerosol simulation scheme that will track multivariate aerosol distributions with sufficient computational efficiency for reliable aerosol simulation in global-scale models.