Researchers are developing artificial intelligence that could assess climate change tipping points. The deep learning algorithm could act as an early warning system against runaway climate change.
Chris Bauch, a professor of applied mathematics at the University of Waterloo, is co-author of a recent research paper reporting results on the new deep-learning algorithm. The research looks at thresholds beyond which rapid or irreversible change happens in a system, Bauch said.
"We found that the new algorithm was able to not only predict the tipping points more accurately than existing approaches but also provide information about what type of state lies beyond the tipping point," Bauch said. "Many of these tipping points are undesirable, and we'd like to prevent them if we can."Some tipping points that are often associated with run-away climate change include melting Arctic permafrost, which could release mass amounts of methane and spur further rapid heating; breakdown of oceanic current systems, which could lead to almost immediate changes in weather patterns; or ice sheet disintegration, which could lead to rapid sea-level change.
The innovative approach with this AI, according to the researchers, is that it was programmed to learn not just about one type of tipping point but the characteristics of tipping points generally.
