613 Virtual Seminar Series: Elizabeth Barnes/CSU

See TEAMS meeting information following abstract
 

Elizabeth A. Barnes
Associate Professor, Dept. of Atmospheric Science, CSU

Viewing Climate Signals through an AI Lens 

Much of climate science is viewed as a signal-to-noise problem and the field has many statistical methods for extracting the signal of interest. Here, we argue that artificial neural networks (ANNs) are an additional useful tool for the “climate toolbox”. As an example, we demonstrate their utility for extracting forced climate patterns from model simulations and observations whereby the ANN identifies patterns that are complex, non-linear combinations of signal and noise. While neural networks are often viewed as black boxes, we further demonstrate how to visualize what the network has learned using recent advances in visualization tools within the computer science community. This approach suggests that viewing climate patterns through an AI lens has the power to uncover new insights into climate variability and change. 
 

Bio:
Dr. Elizabeth (Libby) Barnes is an associate professor of Atmospheric Science at Colorado State University. She joined the CSU faculty in 2013 after obtaining dual B.S. degrees (Honors) in Physics and Mathematics from the University of Minnesota, obtaining her Ph.D. in Atmospheric Science from the University of Washington, and spending a year as a NOAA Climate & Global Change Fellow at the Lamont-Doherty Earth Observatory. Professor Barnes' research is largely focused on climate variability and change and the data analysis tools used to understand it. Topics of interest include earth system predictability, jet-stream dynamics, Arctic-midlatitude connections, subseasonal-to-decadal (S2D) prediction, and data science methods for earth system research (e.g. machine learning, causal discovery). She teaches graduate courses on fundamental atmospheric dynamics and data science and statistical analysis methods. Professor Barnes is involved in a number of research community activities. In addition to being a lead of the new US CLIVAR Working Group: Emerging Data Science Tools for Climate Variability and Predictability and a funded member of the NSF AI Institute for Research on Trustworthy AI in Weather, Climate and Coastal Oceanography (AI2ES), she recently finished being the lead of the NOAA MAPP S2S Prediction Task Force (2016-2020).

 

Dr. Barnes received the AGU Turco Lectureship for 2020, AMS Clarence Leroy Meisinger Award for 2020, and was awarded an NSF CAREER grant in 2018. She received the George T. Abell Outstanding Early-Career Faculty Award in 2016 and was recognized for her teaching and mentoring by being awarded an Honorable Mention for the CSU Graduate Advising and Mentorship Award in 2017 and being named the Outstanding Professor of the Year Award in 2016 by the graduate students of the Department of Atmospheric Science. In 2014 she was the recipient of an AGU James R. Holton Junior Scientist Award.

 

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613 Seminar Series Coordinators 

 

Reed.Espinosa@nasa.gov 
Jie.Gong@nasa.gov