  Uncertainty quantification during atmospheric chemistry modeling is
computationally expensive as it typically requires a large number of
simulations using complex models. As large-scale modeling is typically
performed with simplified chemical mechanisms for computational tractability,
we describe a probabilistic surrogate modeling method using principal
components analysis (PCA) and Ensemble Sparse Identification of Nonlinear
Dynamics (E-SINDy) to both automatically simplify a gas-phase chemistry
mechanism and to quantify the uncertainty introduced when doing so. We
demonstrate the application of this method on a small photochemical box model
for ozone formation. With 100 ensemble members, the calibration $R$-squared
value is 0.96 among the three latent species on average and 0.98 for ozone,
demonstrating that predicted model uncertainty aligns well with actual model
error. In addition to uncertainty quantification, this probabilistic method
also improves accuracy as compared to an equivalent deterministic version, by
$\sim$60% for the ensemble prediction mean or $\sim$50% for deterministic
prediction by the best-performing single ensemble member. Overall, the ozone
testing root mean square error (RMSE) is 15.1% of its root mean square (RMS)
concentration. Although our probabilistic ensemble simulation ends up being
slower than the reference model it emulates, we expect that use of a more
complex reference model in future work will result in additional opportunities
for acceleration. Versions of this approach applied to full-scale chemical
mechanisms may result in improved uncertainty quantification in models of
atmospheric composition, leading to enhanced atmospheric understanding and
improved support for air quality control and regulation.
