DATE:

Friday, November 4, 2022

TIME:

3:30 PM

LOCATION:

Virtual Zoom Seminar

SPEAKER:

Dr. Nathan Urban, Applied Mathematics, Brookhaven National Laboratory

ABSTRACT:

As carbon dioxide emissions to the atmosphere continue from fossil fuel consumption, the Earth’s climate will change considerably over the course of this century due to the resulting greenhouse effect. Societies and ecosystems will feel these global changes in the form of extreme weather, recurrent flooding and droughts, and other regional impacts. But how certain are we in projections about the distant future? Every prediction of a physical theory comes with error bars arising from limitations in data and approximations that are made for the sake of computational tractability. The scientific question is not whether the climate will change, but how likely it is to change by a given amount. This talk will discuss at a conceptual level the general mathematical formalism of statistical uncertainty quantification, and how it is being applied within the U.S. Department of Energy and other research institutions to estimate the plausible range of possible climate futures. The mathematical methods discussed may include Bayesian parameter estimation for nonlinear regression, Monte Carlo sampling, sensitivity analysis, model averaging, and surrogate modeling or emulation of expensive computer simulations. Applications may include probabilistic predictions of global warming and climate feedbacks, sea level rise from Antarctic ice sheet disintegration, and coastal flooding, among others. As time permits, I will also discuss educational training and career paths into this research field at the forefront of interdisciplinary science.

Bio: Dr. Nathan Urban leads the Applied Mathematics group in the Computational Science Initiative at Brookhaven National Laboratory, on Long Island, New York. He received undergraduate degrees in physics, computer science, and mathematics from Virginia Tech, and a Ph.D. and M.Ed. in physics (computational statistical mechanics) from Penn State. After graduating, he moved into climate uncertainty quantification with postdoctoral appointments at Penn State and Princeton, and a staff position at Los Alamos National Laboratory. He received a DOE Office of Science Early Career Research award for multi-model climate uncertainties, and has led major projects involving coastal resilience planning under uncertainty and “in-situ” methods for embedding scalable statistical inference algorithms within exascale simulations. At Brookhaven he develops methodologies for uncertainty quantification, decision making under uncertainty and optimal experimental design, model reduction, scientific machine learning, and integrated computational frameworks for decision support, applied to problems in climate science, biomedicine, materials science, and others.

HOST:

Ignacio Sepulveda (Engineering) and the Sustainable Horizons Institute CRLC Virtual Seminar Series

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