Solar Geoengineering Research Program Lunch Talk Ben Kravitz of Indiana University
ฝัง
- เผยแพร่เมื่อ 10 ธ.ค. 2024
- Speaker: Ben Kravitz, Associate Professor, Department of Earth and Atmospheric Sciences, Indiana University
Abstract: Climate engineering is increasingly being discussed as a potential, temporary means of alleviating some of the worst effects of climate change. If society is going to pursue it someday, we need to have confidence that it will do what it is intended to do. So how do we know? Here I will discuss some of the things we know about the natural climate effects of climate engineering, how we know them, and what some of the largest uncertainties are. I will point out major gaps and offer a discussion of how the natural science research community might be able to close those gaps. There will ultimately be uncertainties that cannot be reduced, but some can potentially be managed, and some are likely irreducible. These discussions will form the beginnings of a roadmap toward responsible decision making around climate engineering.
Bio: Dr. Ben Kravitz is an associate professor in the Department of Earth and Atmospheric Sciences at Indiana University. He holds a B.A. in mathematics from Northwestern University, an M.S. in mathematics from Purdue University, and an M.S. and Ph.D. in atmospheric science from Rutgers University. He completed a postdoctoral research position at the Carnegie Institution for Science and another postdoctoral research position at Pacific Northwest National Laboratory, where he became a staff scientist in 2015. He joined the faculty at Indiana University in 2019, maintaining a joint appointment at Pacific Northwest National Laboratory. Dr. Kravitz is an international expert in climate model simulations of climate engineering. His current activities also include using engineering and mathematical techniques in climate models to better understand climate feedbacks, studying teleconnections in high latitude climate, and developing climate model emulators for use in Integrated Assessment Models.