Patrick Obin Sturm, Astrid Manders, Ruud Janssen, Arjo Segers , Anthony S. Wexler, Hai Xiang Lin

The 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 machine learning (ML) method based on matrix factorization to find latent patterns in the VBS tracers that correspond to a lower-dimension 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 ML 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 to 1.8. Concentrations remain numerically stable over model simulation times of two weeks, including simulations at higher spatial resolutions than the ML models were trained on. Results from this case study show that this method can be used to enable detailed, process-based secondary organic aerosol representation in air quality operational forecasts in a computationally efficient manner. Beyond this case study, the physically consistent ML 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.