Physics-informed learning of aerosol microphysics

Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of uncertainty for climate modeling. Many climate models do not include aerosols in sufficient detail due to computational constraints. To represent key processes, aerosol microphysical properties and processes have to be accounted for. This is done in the ECHAM-HAM (European Center for Medium-Range Weather Forecast-Hamburg-Hamburg) global climate aerosol model using the M7 microphysics, but high computational costs make it very expensive to run with finer resolution or for a longer time. We aim to use machine learning to emulate the microphysics model at sufficient accuracy and reduce the computational cost by being fast at inference time. The original M7 model is used to generate data of input-output pairs to train a neural network (NN) on it. We are able to learn the variables' tendencies achieving an average R-2 score of 77.1%. We further explore methods to inform and constrain the NN with physical knowledge to reduce mass violation and enforce mass positivity. On a Graphics processing unit (GPU), we achieve a speed-up of up to over 64 times faster when compared to the original model. Impact Statement To achieve better climate predictions, we need to model aerosols with reduced computational effort. We accomplish this by using a neural network that accurately learns the input-output mapping from a traditional aerosol microphysics model, while being significantly faster. Physical constraints are added to make the emulator feasible for a stable long-term global climate model run.