Wildfires modify land surface characteristics, such as vegetation composition, soil carbon, surface albedo, with significant consequences for regional carbon, water, and energy cycles. Wildfires globally emit 1~2 PgC yr-1 and dust and aerosols that can alter regional climate and air quality. While greenhouse gas emissions contribute to climate change, other toxic species and airborne particulate matter from wildfires lead to substantial health hazards, including elevated premature mortality. However, predictive modeling of wildfires activities at large scale is challenging due to limited understanding of human, climate, and ecosystem controls on fire number, fire size, and burned area. Dr. Qing Zhu will share recent progress on wildfire spatial-temporal modeling and risk assessment under both present-day conditions and under future projected climate scenarios.

Qing Zhu

Qing Zhu is a Research Scientist at Lawrence Berkeley National Lab, working on earth system model development and analysis. He is one of the major contributors to the E3SM wildfire module that integrates process representation of ignition, spread, extinction, duration of wildfire as well as the state-of-the-art machine learning and artificial intelligence models. He also works on assessing and mitigating the environmental impacts of wildfire emitted greenhouse gases and aerosols.