Engineering x Climate Projections

By Grant Currin


Engineers tend to speak different languages than scientists—or even people in different parts of engineering.

Pierre Gentine

Developing AI models to forecast climate change “isn’t so different from building ChatGPT,” says Pierre Gentine, the Maurice Ewing and J. Lamar Worzel Professor of Geophysics in the Departments of Earth and Environmental Engineering and professor of Earth and Environmental Sciences at Columbia Engineering. “Instead of trying to predict text, we’re trying to predict the climate.”

As director of Learning the Earth with AI and Physics (LEAP), an NSF Science and Technology Center, Gentine leads a team that includes engineers, climatologists, statisticians, earth scientists, education researchers, and social workers.

“We use different terms to mean the same thing, and that’s been a real challenge,” he says. “Engineers tend to speak different languages than scientists — or even people in different parts of engineering.”

The team overcomes this challenge through “internal knowledge transfer” sessions, where everyone makes a point of being explicit about what each term they use means to their community. “We try to keep an open mind, stay humble, and listen closely to make sure everyone understands what everyone else is doing,” he says. “Ensuring that junior scientists have a voice has also proven to be very important.”

That spirit of collaboration is central to LEAP’s effort to jump-start the development of climate data science and transform a messy tangle of data streams into a practical resource for the world’s leaders.

“It’s not just science and equations — we’re trying to translate data and knowledge into something useful to the world,” he says. “The communications piece is crucial.”

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