Many climate modeling studies have demon-strated the importance of two-way interactions betweenozone and atmospheric dynamics. However, atmosphericchemistry models needed for calculating changes in ozoneare computationally expensive. Nowack et al. [1] high-lighted the potential of machine learning-based ozoneparameterizations in constant climate forcing simulations,with ozone being predicted as a function of the atmo-spheric temperature state. Here we investigate the roleof additional time-lagged temperature information underpreindustrial forcing conditions. In particular, we testif the use of Long Short-Term Memory (LSTM) neuralnetworks can significantly improve the predictive skill ofthe parameterization. We then introduce a novel workflowto transfer the regression model to the new UK EarthSystem Model (UKESM). For this, we show for the firsttime how machine learning parameterizations could betransferred between climate models, a pivotal step tomaking any such parameterization widely applicable inclimate science. Our results imply that ozone parame-terizations could have much-extended scope as they arenot bound to individual climate models but, once trained,could be used in a number of different models. We hope tostimulate similar transferability tests regarding machinelearning parameterizations developed for other Earthsystem model components such as ocean eddy modeling,convection, clouds, or carbon cycle schemes.