Paula Harder, Fraunhofer ITWM, Kaiserslautern

Title: Emulating Aerosol Microphysics with Machine Learning

Abstract:

Aerosol particles play an important role in the climate system by absorbing and scattering radiation and by influencing cloud properties. They are also one of the biggest sources of uncertainty for climate predictions. Traditional climate models only use the aerosol mass, in order to achieve higher accuracy aerosol microphysics properties have to be resolved. This is done for example in the ECHAM-HAM global climate aerosol model by using the M7 microphysics model. But the microphysics model is computational expensive, which makes it impossible to run at a higher resolution or for a longer time. We use the original microphysics model to generate data of input-output pairs to train a machine learning model on it. We investigate different approaches, deep learning methods as well as ensemble models like random forest and gradient boosting, with the aim of achieving reasonable accuracy and being faster than the original. One challenge is the importance of mass conservation and how those physical constraints can be encoded in a model.

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