Abstract: The role of aerosol in the Earth system remains a major source of uncertainty due to insufficient data, understanding, and computational power. In this talk, I will introduce the effort of developing faster and better representation of aerosol and aerosol-cloud interactions for a next-generation Earth system model by improving physics, increasing resolution, and utilizing machine learning techniques. In this effort, we have generated hundreds of terabytes of data that consists of process model, large-eddy, convection-permitting, and climate-scale perturbed physics simulations to train ML models. These ML models are used as new parameterizations in an Earth system model representing aerosol radiative properties, droplet nucleation, warm rain processes, and cloud adjustments, and as new diagnostic tools to reveal causal links between aerosol and the intricate Earth system. I will discuss our progress as well as challenges we encountered when integrating the ML models in the Earth system model.
Bio: Po-Lun Ma is an Earth scientist in Atmospheric Sciences & Global Change Division at Pacific Northwest National Laboratory (PNNL). He studies aerosol and clouds as well as their roles in the climate system. He co-leads the Directorate Objective of Transforming Earth and Environmental Systems Modeling with AI/ML in Earth and Biological Sciences Directorate at PNNL. He is the Principal Investigator of DOE’s “Enabling Aerosol-cloud interactions at GLobal convection-permitting scalES (EAGLES)” project to improve the representation of aerosol and aerosol-cloud interactions in DOE’s Earth system model with new modeling techniques that are scientifically robust and computationally efficient.