NOAA scientists harness machine learning to advance climate models

Machine learning gives climate models a new edge. Credit: NOAA GFDL

When you hear the term “machine learning,” you might think of controversial chatbots or the algorithms that govern your social media feeds. But NOAA GFDL scientists are investigating how to use machine learning in another way: to improve climate, weather and other earth system models.

Unlike traditional climate models, which make predictions by simulating land, ocean and atmospheric processes, machine learning allows systems to “learn” from results of those simulations.

“In contrast to models that follow a set of explicit and pre-defined rules, machine learning aims towards building systems that can learn and infer such rules based on patterns in data,” NOAA GFDL scientist Maike Sonnewald and co-authors Christopher Irrgang, Niklas Boers, and Jan Saynisch-Wagner write in Carbon Brief. “As a result, a new line of climate research is emerging that aims to complement and extend the use of observations and climate models. The overall goal is to tackle persistent challenges of climate research and to improve projections for the future.”

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