Understanding the controlling factors behind the chemical composition of the Earth’s atmosphere is a critical step toward addressing the modern environmental challenges of air pollution and climate change. Traditional methods interrogating theoretical predictions with observations have been highly successful in addressing these challenges, particularly in light of the recent immense growth of data availability in the Earth system. However, there are still gaps in our understanding due to limitations in modern scientific techniques (e.g., theoretical frameworks, observational systems, and computational power). Data-driven methods from the data science and artificial intelligence literature, when informed and guided by scientific understanding, present a valuable tool in addressing these knowledge gaps.

In this seminar, I will present results from recent work using a variety of data science and A.I. methods to better constrain modern understanding of atmospheric composition and the climate system. Specifically, I will discuss recent results on the application of deep learning techniques to develop improved representations of aerosol-cloud interactions in climate models, and current work using graph theoretical methods to investigate atmospheric chemical reaction networks.