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Next-generation climate models could learn, improve on the fly

21 March 2018

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Scientists propose development of new models that use machine learning techniques to reduce uncertainties in climate predictions.

Mathematical models that simulate Earth's climate are essential for predicting future climate change. However, even today's most sophisticated Earth system models suffer from uncertainties that stem from the difficulty of simulating small-scale or complex processes, such as raindrop formation and carbon uptake by plants.

Novel computational tools may hold the potential to address these uncertainties. In a new paper, Schneider et al. outline a blueprint for a next-generation climate model that would employ advancements in data assimilation and machine learning techniques to learn continuously from real-world observations and high-resolution simulations.

Source: Eos - American Geophysical Union

Image credit: Djclimber, CC BY-SA 3.0 - Today's climate models are unable to simulate individual stratocumulus clouds, such as these seen in Jackson, Wyo. A novel machine learning approach could improve model design and reduce uncertainty in climate predictions.