EPS Colloquium - From Microphysics to Climate: Understanding Clouds and Aerosols across Scales
Monday, Oct 26, 2026, 12:00 pm - 1:00 pm
Haller Hall, Geological Museum, Room 102, 26 Oxford St, Cambridge
Dr. Yuan Wang is an assistant professor of Earth System Science and a center fellow in the Woods Institute for the Environment at Stanford University.
Clouds and aerosols are central regulators of Earth’s energy balance, hydrological cycle, and atmospheric chemistry, yet their interactions remain among the largest sources of uncertainty in weather and climate prediction. These processes originate at the microphysical scale, but their impacts propagate across convective, regional, and global scales through complex dynamical feedbacks. Bridging this scale gap is therefore essential to developing a more complete and predictive understanding of the climate system. In this talk, I will present our recent efforts to: 1) develop an AI/ML-based cloud microphysics scheme to represent warm-rain formation in climate models; 2) improve the representation of aerosol–cloud interactions in the cloud-resolving and fire-coupled simulations of pyroCb, a wildfire-induced deep convective system. Our multi-task encoder-decoder machine learning framework based on coarse-grained large-eddy simulations reduces the marine stratocumulus mean liquid water path bias against DOE/ARM observations and uniquely maintains a physical aerosol first indirect effect while original bulk schemes weaken or reverse its trend. Our improved WRF-Chem-SFIRE model shows biomass-burning aerosols strengthen convective updrafts, weaken downdrafts, and increase convective areas, resulting in more efficient smoke injection into the upper troposphere and lower stratosphere. Taken together, our findings highlight the need for multiscale modeling tools and richer process-level atmospheric observations to advance a mechanistic understanding of clouds and aerosols in a fully coupled Earth system context.
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Clouds and aerosols are central regulators of Earth’s energy balance, hydrological cycle, and atmospheric chemistry, yet their interactions remain among the largest sources of uncertainty in weather and climate prediction. These processes originate at the microphysical scale, but their impacts propagate across convective, regional, and global scales through complex dynamical feedbacks. Bridging this scale gap is therefore essential to developing a more complete and predictive understanding of the climate system. In this talk, I will present our recent efforts to: 1) develop an AI/ML-based cloud microphysics scheme to represent warm-rain formation in climate models; 2) improve the representation of aerosol–cloud interactions in the cloud-resolving and fire-coupled simulations of pyroCb, a wildfire-induced deep convective system. Our multi-task encoder-decoder machine learning framework based on coarse-grained large-eddy simulations reduces the marine stratocumulus mean liquid water path bias against DOE/ARM observations and uniquely maintains a physical aerosol first indirect effect while original bulk schemes weaken or reverse its trend. Our improved WRF-Chem-SFIRE model shows biomass-burning aerosols strengthen convective updrafts, weaken downdrafts, and increase convective areas, resulting in more efficient smoke injection into the upper troposphere and lower stratosphere. Taken together, our findings highlight the need for multiscale modeling tools and richer process-level atmospheric observations to advance a mechanistic understanding of clouds and aerosols in a fully coupled Earth system context.
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