Using Artificial Intelligence to aid Scientific Discovery of Climate Tipping Points

• URL: http://arxiv.org/abs/2302.06852v1
• Date: Tue, 14 Feb 2023 06:00:39 GMT
• Title: Using Artificial Intelligence to aid Scientific Discovery of Climate Tipping Points
• Authors: Jennifer Sleeman, David Chung, Chace Ashcraft, Jay Brett, Anand Gnanadesikan, Yannis Kevrekidis, Marisa Hughes, Thomas Haine, Marie-Aude Pradal, Renske Gelderloos, Caroline Tang, Anshu Saksena, Larry White
• Abstract summary: We propose a hybrid Artificial Intelligence (AI) climate modeling approach that enables climate modelers in scientific discovery. We describe how this methodology can be applied to the discovery of climate tipping points and, in particular, the collapse of the Atlantic Meridional Overturning Circulation (AMOC) We show preliminary results of neuro-symbolic method performance when translating between natural language questions and symbolically learned representations.
• Score: 1.521140899164062
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a hybrid Artificial Intelligence (AI) climate modeling approach that enables climate modelers in scientific discovery using a climate-targeted simulation methodology based on a novel combination of deep neural networks and mathematical methods for modeling dynamical systems. The simulations are grounded by a neuro-symbolic language that both enables question answering of what is learned by the AI methods and provides a means of explainability. We describe how this methodology can be applied to the discovery of climate tipping points and, in particular, the collapse of the Atlantic Meridional Overturning Circulation (AMOC). We show how this methodology is able to predict AMOC collapse with a high degree of accuracy using a surrogate climate model for ocean interaction. We also show preliminary results of neuro-symbolic method performance when translating between natural language questions and symbolically learned representations. Our AI methodology shows promising early results, potentially enabling faster climate tipping point related research that would otherwise be computationally infeasible.

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