Using Deep Learning for Climate Tipping Point Discovery to Understand Atlantic Meridional Overturning Circulation (AMOC) Collapse

Abstract

In 2018, the IPCC summarized in a special report the potential risks surrounding climate tipping point. In 2019, Lenton et al. highlighted climate tipping points that could contribute to irreversible changes to our world including ice melt, deforestation, and circulation slowing. In this work we describe a study of the Atlantic Meridional Overturning Circulation (AMOC), and the use of an Artificial Intelligence (AI) assisted climate model methodology capable of performing unsupervised tipping point discovery to discover conditions which could lead to AMOC collapse.

We developed a novel AI generative adversarial network (GAN), where a set of deep learning generators attempt to invoke AMOC collapse by perturbing a constrained set of parameters, while another deep learning network, the discriminator, tries to learn how to avoid AMOC collapse. A surrogate model is used to run model configurations to test this adversarial method. We show that our methodology can be used to discover areas in model space that are consistent with fold bifurcations where the system moves from an on state to an off state. We measured the performance of this method by comparing it to an AMOC four box model and experiments described in (Gnanadesikan 2018) which uses the four box model to understand overturning stability.

We have found that the deep learning method can be used to exploit the area of uncertainty that is consistent with the area that separates the two stable states in a fold bifurcation model. When we compared the results of the adversarial network to the Gnanadesikan experiments we observed that when incorporating information regarding the uncertainty in the loss function, increasing the number of AI generators caused the AI agents to become more focused on this area of uncertainty. This area of uncertainty is consistent with what is described as the separatrix. In this study, we show the benefit of using this novel unsupervised approach as part of an AI assisted climate modeling methodology.