With the increase in data volume of Earth Science data over the last decade and the projected increase over the next several years, efficient, automated algorithms are required for the identification of phenomena.  The use of artificial intelligence to address this issue is currently in early development and requires further collaboration between physical, data, and computer scientists. Using AI, specifically deep learning methods, Earth Science phenomena can be identified in data and imagery to develop dynamic event databases for detailed scientific process studies, and analyze climatological trends in climate model output.  Such databases would serve to provide pathways to improve our current understanding of the physical atmosphere and how it may change in the future. The purpose of this session is to provide an interdisciplinary forum to showcase existing methods and recent advancements for automated detection of Earth Science phenomena using AI techniques. This session seeks submissions from projects that leverage AI techniques for phenomena detection across Earth Science Big Data repositories, promote the development of phenomenon-based climatologies or databases for further scientific investigation, extract/detect rare phenomena in large datasets or imagery, and overcome challenges related to computational limitation or the data representation of  various phenomena for specific datasets.