I study climate change and decision making under uncertainty. My research interests encompass a broad range of questions related to climate prediction, uncertainty, learning, and economic policy analysis. What is our uncertainty about the future climate, how can we reduce this uncertainty, and what are its policy implications, accounting for our ability to learn over time? What are our practical limitations in how accurately and quickly we can learn about the future?
My work involves many aspects of the Earth system, including climate system feedbacks, nonlinearities, and thresholds ("tipping points"); ice sheet dynamics and sea level rise; shallow and deep time paleoclimate; biogeochemistry and the terrestrial and ocean carbon cycles; and interactions between these systems (e.g., cryosphere-ocean-carbon cycle interactions).
My main methodological approach is Bayesian data-model calibration, which assigns probabilities to climate simulation scenarios in proportion to how well they agree with observed data. I am interested in how to statistically combine diverse lines of evidence and models in order to most effectively use the information contained therein, accounting for inevitable limitations in the quality of both the data and the models.
I received B.S. degrees in Physics, Computer Science, and Mathematics from Virginia Tech in 1997, and a Ph.D. and M.Ed. in Physics from Penn State in 2006.
I am working with Michael Oppenheimer at STEP on projects related to learning about climate system feedbacks and ice sheet stability.