Math Modeling Seminar: Learning and Uncertainty in Sea-Level Rise and Coastal Risk

Math Modeling Seminar
Learning and Uncertainty in Sea-Level Rise and Coastal Risk

Zoom Link here

Dr. Tony Wong
Associate Professor
Rochester Institute of Technology

Abstract:
   Sea-level rise is a major driver of coastal risk, yet estimates of future sea levels and coastal damages depend on a complex modeling chain with substantial uncertainty. In this talk, I present mathematical and statistical approaches for representing, learning about, and attributing uncertainty in sea-level rise. I describe a Bayesian framework for updating beliefs about future sea levels as new information becomes available. This approach is used with a coastal adaptation model to formalize how improved sea-level knowledge can inform coastal adaptation decisions. Random forests are used to quantify the influence of climate model parameters and processes on uncertainty in future sea levels, clarifying which uncertainties dominate at different time horizons. Despite the many inherent uncertainties in climate and sea level modeling, we find that coastal damages from climate change are much worse beyond 2 degrees of global warming, with losses felt inequitably by the Global South.

Speaker Bio:
   Tony Wong is an Assistant Professor of Applied Mathematics at RIT. He earned a BS in Mathematics and Astrophysics in 2010 and a PhD in Applied Mathematics in 2016, followed by a postdoc position in Earth Science at Penn State and serving as an Assistant Teaching Professor in Computer Science at the University of Colorado Boulder. His research lies at the intersection of applied mathematics, statistics, and climate science, focusing on uncertainty quantification, model-data fusion, and coastal risk from climate change, as well as on STEM education research on computational literacy and student persistence. His work has contributed to sea-level rise and climate impact assessments cited by the Intergovernmental Panel on Climate Change and adopted by environmental policy-making agencies in the U.S. and beyond.

Intended Audience:
Beginners, undergraduates, graduates. Those with interest in the topic.

Interpreters have been requested.