Using Artificial Intelligence for Improved Climate Tipping Point Discovery

Abstract

We describe a new multidisciplinary effort to understand how Artificial Intelligence (AI) could be used to improve climate tipping point discovery using the collapse of the Atlantic Meridional Overturning Circulation (AMOC) as a case study. Our methodology includes an AI simulated environment where a Generative Adversarial Network (GAN) is used to play an adversarial game between two deep networks. One network, the generator, tries to invoke an AMOC collapse tipping point. The other network, the discriminator, tries to predict which model configurations lead to an AMOC collapse, and which do not. The discriminator uses a climate surrogate model to run model configurations suggested by the generator network. In this study we have explored using a well-researched reduced oceanography four-box model (Gnanadesikan et al. 2018) as the surrogate. In addition, we introduce a new type of surrogate model that is used for expanding to larger models, which is based on modeling stochastic differential equations to estimate bifurcation escape times, enabling a deeper understanding of the unstable areas and how to escape them. We describe how we apply this method to larger Global Circulation Models (GCMs) by means of a CESM2-calibrated dataset. Included in this methodology is a neuro-symbolic representation of the model configurations that translates into natural language questions enabling climate modelers to ask questions of the learned adversarial space and to provide explainability. We describe the results of applying this method to study the AMOC, and evaluate our method by comparing how well it learns to predict AMOC collapse, how well it learns the area of uncertainty in state space, and how effective the neuro-symbolic language is in providing a natural language interface into the adversarial game.