The biosphere plays an important role controlling the abundance and variability of trace gases and aerosol in the atmosphere.

Our work explores topics like the emissions and deposition of reactive trace gases to vegetation, the impact of land use change on atmospheric composition, and the role of urban trees on air quality..

Data science and machine learning techniques are revolutionizing the way we do climate science.

In our group, we use these tools to make climate models faster and more accurate, and develop new methods for understanding the climate system. Recent work includes exploring the use of graph theory to study atmospheric chemistry and applying new-to-the-field deep learning methods for air quality and climate research.

Large computational models of the atmosphere are critical tools for improving our scientific understanding and exploring solutions to the largest problems facing society.

We use and develop these models extensively to make predictions and interpret observations. Recent work includes investigating the drivers of cloud formation in the atmosphere and exploring impacts of wildfire on the urban and regional-scale atmosphere.