Advecting Superspecies: Efficiently Modeling Transport of Organic Aerosol With a Mass‐Conserving Dimensionality Reduction Method

Abstract

AbstractThe chemical transport model LOTOS‐EUROS uses a volatility basis set (VBS) approach to represent the formation of secondary organic aerosol (SOA) in the atmosphere. Inclusion of the VBS approximately doubles the dimensionality of LOTOS‐EUROS and slows computation of the advection operator by a factor of two. This complexity limits SOA representation in operational forecasts. We develop a mass‐conserving dimensionality reduction method based on matrix factorization to find latent patterns in the VBS tracers that correspond to a smaller set of superspecies. Tracers are reversibly compressed to superspecies before transport, and the superspecies are subsequently decompressed to tracers for process‐based SOA modeling. This physically interpretable data‐driven method conserves the total concentration and phase of the tracers throughout the process. The superspecies approach is implemented in LOTOS‐EUROS and found to accelerate the advection operator by a factor of 1.5–1.8. Concentrations remain numerically stable over model simulation times of 2 weeks, including simulations at higher spatial resolutions than the data‐driven models were trained on. The reversible compression of VBS tracers enables detailed, process‐based SOA representation in LOTOS‐EUROS operational forecasts in a computationally efficient manner. Beyond this case study, the physically consistent data‐driven approach developed in this work enforces conservation laws that are essential to other Earth system modeling applications, and generalizes to other processes where computational benefit can be gained from a two‐way mapping between detailed process variables and their representation in a reduced‐dimensional space.