This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: The writer Malcolm Gladwell popularized the term "tipping points" to refer to the moment when a trend or social behavior crosses a threshold and rapidly spreads everywhere. In physics, tipping points occur when systems—say, Earth's climate—reach an unstable point and topple over into a sea of change that is sometimes irreversible.
Examples include global warming or the melting of polar ice caps. While scientists have previously used AI models to predict smaller changes in weather, such as day-to-day variations, forecasting tipping points comes with more uncertainty and has thus proven more challenging. Now, a new Caltech-led paper demonstrates that an AI program—specifically a type of neural network called a recurrent neural operator (RNO)—can be used to predict when tipping points occur in simple models, including models of a climate-change system and simulations of turbulent airflow over a plane's wing.
The paper was published in the Proceedings of the National Academy of Sciences (PNAS). "A tipping point is where you don't have that point of return anymore because it's nonstationary," says Anima Anandkumar, Caltech's Bren professor of computing and mathematical sciences and the principal author of the study. "Parts of a system are changing in a way so that you cannot bring the system back to its original state.
That's why it is seen as something that could be catastrophic." The new research, cited in a recent report from the United Nations on the importance of tipping points in climate science, was performed by Anandkumar and her colleagues at Caltech, the University of Colorado Boulder (CU Boulder) and the tech company NVIDIA. Because tipping points are so universal, the AI tool has potential applications in the fields of climate science, ecology, epidemiology, aeronautical design and more. "This work lays the foundations for characterizing tipping points and predicting when they might occur in the future," Anandkumar says.
"We demonstrated it on a simplified climate model and a model of air turbulence over a wing—systems that weren't tractable with earlier methods." In one part of the study, the researchers looked at a specific climate scenario in which low clouds over the oceans rapidly disappear due to rising carbon dioxide levels—a situation that has been predicted to occur in the future but has not yet been observed. This "point-of-no-return" loss of low cloud cover would further add to global warming. The researchers tested their AI program on simulated data to see if it could forecast when the clouds would disappear.
The RNO was able to look more than two years ahead of the expected tipping point in the simulated data and predict its timing with an error of about 11 days. "In the past, we used AI to learn mostly stationary systems that assume the weather from before will be the same as weather in the future," Anandkumar says. "Our group has trained such AI-based weather models successfully in the past and can even predict extreme events like hurricanes with good accuracy.
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