Explainable Climate Science

Zack Labe, a Post-Doctoral Researcher at Colorado State University, joins us today to discuss his work “Detecting Climate Signals using Explainable AI with Single Forcing Large Ensembles.” Works Mentioned <a class="c-link" tabindex="-1" href= "https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002464" target="_blank" rel="noopener" data-stringify-link= "https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002464" data-sk="tooltip_parent" data-remove-tab-index="true">“Detecting Climate Signals using Explainable AI with Single Forcing Large Ensembles”</a> by Zachary M. Labe, Elizabeth A. Barnes Sponsored by: <a href="https://astrato.io/dataskeptic" target="_blank" rel= "noopener">Astrato</a> and <a href="https://www.barebones.com" target="_blank" rel= "noopener">BBEdit by Bare Bones Software</a>

Guest

Zack Labe: I am a postdoctoral researcher in the Department of Atmospheric Science at Colorado State University. I received my Ph.D. from the Department of Earth System Science at the University of California, Irvine in May 2020 and a B.Sc. in Atmospheric Science from Cornell University in May 2015. Broadly, I am interested in a signal-to-noise problem. The signal is climate change. The noise is weather. My research intends to disentangle the two components to improve our understanding of climate variability and extreme events in a warming world. In addition to academic research, I am very passionate about improving science communication, accessibility, and outreach through engaging data visualizations.