Environment-Sustainability Lunch: Paul O'Gorman

Paul O'Gorman, EAPS

Associate Professor O'Gorman will present research to date on his ESI-funded project: Improved Climate Modeling through Machine-learning and Data-driven Approaches.

Climate models are a key tool for future projections of climate change and the associated impacts on society and ecosystems. However, climate models exhibit regional biases and uncertain feedback processes that limit the accuracy of climate projections. An important contributor to these biases and uncertainties is the representation of unresolved processes in the atmosphere, ocean and land surface through semi-empirical subgrid models known as parameterizations. New high-resolution modeling and observational datasets provide an unprecedented opportunity to greatly improve the parameterizations used in climate models. Here we propose to develop a new class of parameterizations for the atmosphere and ocean by combining machine learning algorithms with high-resolution simulations, and we propose to better constrain climate models over land using observational data. The overall goal of the proposed research is to investigate whether machine-learning and data-driven approaches may be used to improve climate models so that they are more useful for projections of climate change and for scientific investigations of the climate system.

Professor O'Gorman's research interests are in atmospheric dynamics and the hydrological cycle. He is particularly interested in the behavior of precipitation and the general circulation in different climates.

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This monthly lunch seminar engages MIT faculty, post-docs, and graduate students in lively conversations about current environmental research and education at MIT.

Registration is required.