A machine learning-enabled framework to quantify the structural uncertainty induced by aerosol representation in Earth system models

The use of Artificial Intelligence in Earth and environmental sciences is on the rise, however, there is a desire to limit the amount of training data while training a decent data-driven model. Aerosols, on the other hand, contribute to the large uncertainty in estimates of climate radiative forcing, but they are difficult to model. Here we are interested in developing an "economical and automated” machine learning framework for aerosol modelling, and investigating the effects of aerosol representation in Earth system models. Specifically, we will 1) develop a machine learning-enabled framework that integrates particle-resolved model simulations and data-driven emulation in a way that minimizes the effort required to collect training data and optimizes the learning process; and 2) utilize the framework to quantify the structural uncertainty induced by aerosol representations in Earth system models over the globe for multi-year simulations. The results will provide insight into potential improvements to model process representation of aerosols towards better radiative forcing estimations. The machine learning framework can be used for various applications in environmental engineering and science.

To make an application please visit - https://www.ees.manchester.ac.uk/study/postgraduate-research/how-to-apply/

Please search and select PhD Environmental Science (academic programme) and PhD Atmospheric Science(academic plan)

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