This research develops a machine-learning and data-assimilation framework that combines idealized and operational Earth systems models into a high-resolution, physically realistic “bridging model.” Applied to the El Niño–Southern Oscillation, the approach improves climate simulation accuracy while enabling exploration of alternative climate regimes and physically consistent what-if scenarios.

This research improves weather and climate forecasting by studying how dry air mixes into thunderstorm clouds, a process called entrainment. Using satellite observations, radar data, and interpretable machine learning, the work refines outdated cloud physics models, helping scientists better predict severe weather, hurricanes, and long-term climate behavior.

This research investigates atmospheric trace elements as indicators of pollution sources, focusing on toxic metal emissions from urban firestorms during the Eaton Canyon and Palisades fires. Elevated airborne lead concentrations prompted the creation of Phoenix, a community-based post-fire air monitoring network designed to track hazardous dust resuspension during debris cleanup.

This research investigates methane emissions from restored marshes as a climate solution. While marshes sequester CO₂, their methane output varies widely. By measuring emissions and environmental factors, the study examines how interactions influence outcomes, highlighting that restoration can aid climate mitigation but requires deeper understanding to ensure effectiveness.

This research compares Earth’s energy balance to a personal budget and examines how aerosols—especially black carbon—disturb that balance. By simulating how black carbon interacts with cloud droplets and light, the study helps improve understanding of climate impacts. The goal is better climate modeling and reducing harmful atmospheric pollution.

This research examines how atmospheric aerosols influence cloud formation and rainfall, particularly under turbulent conditions. Using a laboratory cloud chamber and computer modeling, the study investigates how particle size and concentration affect droplet growth. The findings aim to improve climate models and weather forecasting in both polluted and clean environments.