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AI experts at APL and oceanographers from the Johns Hopkins University Department of Earth and Planetary Sciences to integrate AI with traditional climate modeling methods to enable scientific researchers to better understand climate tipping points.

The project — the Physics-informed AI Climate Model Agent Neuro-symbolic Simulator (PACMANS) for Tipping Point Discovery — is supported by the Defense Advanced Research Projects Agency (DARPA) as part of its (ACTM) program, which aims to leverage AI and machine learning to better model the complex processes that affect Earth’s climate.

To understand how climate tipping points can trigger cascading responses, APL researchers are developing an AI scientific discovery assistant named Causal-Cascading AI Tipping-Point Neuro-symbolic Intervention and Prediction (CATNIP).

CATNIP includes a generative adversarial network () that learns to predict what could tip the Atlantic Meridional Overturning Circulation (AMOC), an important climate system of ocean currents that circulates warm water from the South Atlantic to the colder North Atlantic. If the AMOC were to slow or collapse, there could be consequences for food security, sea levels, delicate ecosystems, and the Arctic.

The program was the first demonstration that AI can be used as a climate modeling assistant, where researchers can run experiments by asking the AI climate modeling assistant natural language questions, similar to using a large language model. CATNIP extends this capability to examine the tipping points of coral reefs, sea-ice melt, and sea-level rise so it can study how tipping points are interconnected and interventions that could mitigate individual and cascading tips.