# Emulating Aerosol Microphysics with Machine Learning

arXiv: https://arxiv.org/abs/2109.10593 · HTML: https://mlsanity.com/paper/2109.10593/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 22 Sep 2021.

## Builds on (most cited first, 2 of 2)

- [Deep learning to represent sub-grid processes in climate models](https://mlsanity.com/paper/1806.04731/) (2018)
- [Towards Physically-consistent, Data-driven Models of Convection](https://mlsanity.com/paper/2002.08525/) (2020)

## Built on by (most cited first, 3 of 3)

- [Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science](https://mlsanity.com/paper/2303.00652/) (2023)
- [Physics-Informed Learning of Aerosol Microphysics](https://mlsanity.com/paper/2207.11786/) (2022)
- [Assessing Emulator Design and Training for Modal Aerosol Microphysics Parameterizations in E3SMv2](https://mlsanity.com/paper/2604.21233/) (2026)

## Abstract (arXiv)

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. In order to achieve higher accuracy, aerosol microphysical properties and processes have to be accounted for. This is done in the ECHAM-HAM global climate aerosol model using the M7 microphysics model, but increased computational costs make it very expensive to run at higher resolutions or for a longer time. We aim to use machine learning to approximate 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 on it. By using a special logarithmic transform we are able to learn the variables tendencies achieving an average R2 score of 89%. On a GPU we achieve a speed-up of 120 compared to the original model.