Machine Learning-Based Parameterisation of Photolysis in GEOS-Chem

Photolysis schemes are an integral part of chemical transport models. However, they are computationally intensive. The chemical transport model GEOS-Chem uses the photolysis scheme Fast-JX, which takes a significant amount of time to run. Through the developments of machine learning, parameterised approaches for physical processes have become a common way of speeding up calculations. This study looks to develop a proof-of-concept approach for a machine learning-based parameterisation of the Fast-JX photolysis calculations using a collection of XGBoost models. The machine learning models were integrated into the GEOS-Chem Fortran code base and were quantitatively evaluated against the standard Fast-JX scheme. This work additionally determines the wider impact the photolysis predictions had on the GEOS-Chem simulation in regards to the calculated concentration of key components such as O3 and NO2 .
Results show high accuracy for most species, with 103 out of 105 unique photolysis rates maintaining an R2 greater than 0.95 throughout a six month simulation period. While the current implementation is minimally optimised, and hence computationally slower than Fast-JX, it successfully demonstrates that a machine learning parameterisation of photolysis rates is feasible in Fortran based chemical transport models and provides a foundation for future optimisations.