EGU24-17601, updated on 11 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Machine learning aerosol impacts on regional climate change.

Maura Dewey1, Annica Ekman1, Duncan Watson-Parris2, and Hans Christen Hansson1
Maura Dewey et al.
  • 1Stockholm University, Stockholm, Sweden
  • 2University of California San Diego, USA

Here we develop a machine learning emulator based on the Norwegian Earth System Model (NorESM) to predict regional climate responses to aerosol emissions and use it to study the sensitivity of surface temperature to anthropogenic emission changes in key policy regions. Aerosol emissions have both an immediate local effect on air quality, and regional effects on climate in terms of changes to temperature and precipitation distributions via direct radiative impacts and indirect cloud-aerosol interactions. Regional climate change depends on a balance between aerosol and greenhouse gas forcing, and in particular extreme events are very sensitive to changes in aerosol emissions. Our goal is to provide a tool which can be used to test the impacts of policy-driven emission changes efficiently and accurately, while retaining the spatio-temporal complexity of the larger physics-based Earth System Model.

How to cite: Dewey, M., Ekman, A., Watson-Parris, D., and Hansson, H. C.: Machine learning aerosol impacts on regional climate change., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17601,, 2024.