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AMS 2024 session on AI for Actionable Insights


Katie Dagon
New Member
Dear colleagues,

If you are currently working within the broad field of using AI techniques for actionable insights and applications in climate science, we invite you to consider submitting an abstract to our AMS session: Artificial Intelligence for Actionable Insights and Applications in Climate Science (a joint session between the 23rd Conference on Artificial Intelligence for Environmental Science and the 37th Conference on Climate Variability and Change). The 104th AMS Meeting will be taking place January 28th- February 1st, 2024 in Baltimore, MD. The deadline to submit an abstract is August 24th 2023.

Topic Description: Artificial intelligence (AI) applied to the earth sciences has recently been a rapidly expanding field in both academic and industry spaces, due in part to its ability to extract nonlinear relationships from noisy data. The use of AI, particularly machine learning, can lead to identification of predictable signals from purely data-driven methods. Further, explainable AI techniques allow for opening of the AI “black box” to understand the model’s decision-making strategy. AI-driven advancements in climate predictability on subseasonal through multidecadal timescales, today and in a changing climate, allows for increased predictive skill and lead time, which can improve preparation. The use of AI in climate data analysis has cultivated actionable insights for the purpose of both scientific discovery and for managing climate risk.

We invite abstracts that discuss the use of AI for actionable insights, including high resolution climate forecasting and informing of adaptation and mitigation strategies, as well as using AI to isolate at-risk areas exposed to various climate impacts. This session also welcomes AI approaches applied to climate models and observational data that can be used by decision-makers and stakeholders for planning purposes. Equal consideration will be given to reproducible novel AI techniques, explainable and interpretable AI methods for exploring the climate system, sources of predictability on subseasonal-to-multidecadal timescales, and forecasts of opportunity.

Topic Keywords: Climate, Machine Learning, Predictability, artificial intelligence, climate risk

Session Conveners:
Marybeth Arcodia (Colorado State University)
Eleanor Middlemas (PricewaterhouseCoopers)
Zane Martin (PricewaterhouseCoopers)
Katie Dagon (NCAR)

Please feel free to reach out with any questions ( We hope to see you in Baltimore!