Data Science & AI Lecture Series
Navigating Advances and Inflections in Machine Learning for Atmospheric Chemistry Modeling
Makoto Kelp
Navigating Advances and Inflections in Machine Learning for Atmospheric Chemistry Modeling
| When | Friday, February 6, 2026, 1:30 PM – 2:30 PM (MT) |
|---|---|
| Where | WEB L112 |
Abstract
Global climate and Earth system models rarely include comprehensive atmospheric chemistry because of its high computational cost. A bottleneck is the chemical solver that integrates the large-dimensional coupled systems of kinetic equations describing the chemical mechanism. In recent years, machine learning (ML) methods have been proposed as a potentially transformative approach to reducing this cost by replacing traditional solvers with fast emulators. However, early efforts showed that ML-based chemical solvers often suffer from rapid error growth and instability. In this talk, I will review the evolving landscape of ML for atmospheric chemistry modeling over the past decade and how its trajectory mirrors broader developments in climate and weather AI. I begin with detailing how to achieve stable emulation in 0-D box models and then show how these principles translate to complex global atmospheric models. I conclude by discussing the current state of ML for modeling atmospheric chemistry and outlining how mechanistic interpretability in geospatial foundation models may offer a promising future line of inquiry.
Speaker
Makoto Kelp
Makoto Kelp is an Assistant Professor in the Department of Atmospheric Sciences and a Fellow in the Wilkes Center for Climate Science & Policy at the University of Utah. His research focuses on the intersections of atmospheric chemistry, fires, and human-environmental systems, with an emphasis on using data-driven methods and machine learning techniques. He earned his PhD from Harvard University and conducted his Post Doc at Stanford University as a NOAA Climate & Global Change Fellow.
Tags: climate & environment machine learning optimization
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