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March 8, 2024

Toward a Deeper Understanding of Our Climate System Through Data Science | Emily Gordon

About This Video

Emily Gordon, Data Science Postdoctoral Fellow, Stanford University presents a Technical Talk at the 2024 WiDS Worldwide, Stanford conference.

This talk highlights the challenges in understanding climate change due to limitations in satellite observations and the complex effects of greenhouse gas emissions. To address these issues, scientists rely on climate models to simulate Earth’s system under various warming scenarios, providing crucial insights into historical variability and potential future changes. The talk also explores the increasing importance of data science and machine learning in analyzing observational data, refining climate models, and predicting the impacts of climate change.

In This Video
Data Science Postdoctoral Fellow, Stanford University

Talk Title: Toward a Deeper Understanding of Our Climate System Through Data Science

Abstract: Climate change is in a crisis phase, and understanding of the effects of this change is hampered by our relatively short record of satellite observations. Compounding this issue, greenhouse gas emissions, which are responsible for global warming, also affect internal processes in the system, contaminating our already limited observational record. To overcome these problems, scientists turn to climate models, which allow us to generate computer simulations of our Earth system under various scenarios of warming, and interrogate underlying mechanisms. These models are fundamental in helping understand our changing climate system and can generate large amounts of data. As such, data science plays a major role in understanding historical climate variability, and potential scenarios for future change. In my talk, I discuss how climate models provide both a fundamental understanding of our climate system and allow us to probe the potential consequences of climate change. I further discuss the growing role of machine learning in climate science, both in analyzing observational and model output, and for improving the models themselves.

Bio: Emily Gordon is a Stanford Data Science Postdoctoral Fellow with research interests at the intersection of climate variability and predictability, and novel data science methods. Her research is focused on developing machine learning approaches to understand the predictability of the near term impacts of climate change. This research involves investigating predictability across Earth system models and observations, and developing methods to best utilize the information available from troves of climate data. Prior to joining Stanford, Emily received her PhD from Colorado State University as a Fulbright scholar, and her BSc and MSc from the University of Otago in New Zealand.