A new downscaling method leverages machine learning to speed up climate model simulations at finer resolutions, making them usable on local levels.


Copyright: news.mit.edu – “Making Climate Models Relevant For Local Decision-Makers With Machine Learning”


Climate models are a key technology in predicting the impacts of climate change. By running simulations of the Earth’s climate, scientists and policymakers can estimate conditions like sea level rise, flooding, and rising temperatures, and make decisions about how to appropriately respond. But current climate models struggle to provide this information quickly or affordably enough to be useful on smaller scales, such as the size of a city.

Now, authors of a new open-access paper published in the Journal of Advances in Modeling Earth Systems have found a method to leverage machine learning to utilize the benefits of current climate models, while reducing the computational costs needed to run them.

“It turns the traditional wisdom on its head,” says Sai Ravela, a principal research scientist in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS) who wrote the paper with EAPS postdoc Anamitra Saha.

Traditional wisdom

In climate modeling, downscaling is the process of using a global climate model with coarse resolution to generate finer details over smaller regions. Imagine a digital picture: A global model is a large picture of the world with a low number of pixels. To downscale, you zoom in on just the section of the photo you want to look at — for example, Boston. But because the original picture was low resolution, the new version is blurry; it doesn’t give enough detail to be particularly useful.

“If you go from coarse resolution to fine resolution, you have to add information somehow,” explains Saha. Downscaling attempts to add that information back in by filling in the missing pixels. “That addition of information can happen two ways: Either it can come from theory, or it can come from data.”[…]

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