ORILAM-SOA: A computationally efficient model for predicting secondary organic aerosols in three-dimensional atmospheric models

Abstract

This paper describes the aerosol model Organic Inorganic Lognormal Aerosol Model including Secondary Organic Aerosol (ORILAM-SOA) which is an extension of the lognormal aerosol dynamics model ORILAM. ORILAM-SOA consists of the original aerosol dynamics routines, a photochemical scheme able to predict SOA precursors, and an equilibrium scheme able to predict partitioning of the precursors between the gas and aerosol phases. We show that ORILAM-SOA is computationally efficient enough to be run in three-dimensional (3-D) atmospheric models. ORILAM-SOA is based on existing models. We use a numerical reduction technique to reduce the Caltech Atmospheric Chemistry Mechanism (CACM) and a new, fast, convergent iteration technique to increase the speed of the Model to Predict the Multiphase Partitioning of Organics (MPMPO). We compare the ORILAM-SOA to its parent models in terms of gas concentrations, aerosol concentrations, and CPU time spent during the computations. For illustrative purposes we include a 3-D simulation of SOA over southern France. Copyright 2006 by the American Geophysical Union.