Researchers have devised a new machine learning method to improve large-scale climate model projections and demonstrated that the new tool makes the models more accurate at both the global and regional level.
Researchers have devised a new machine learning method to improve large-scale climate model projections and demonstrated that the new tool makes the models more accurate at both the global and regional level. This advance should provide policymakers with improved climate projections that can be used to inform policy and planning decisions.
“Global climate models are essential for policy planning, but these models often struggle with ‘compound extreme events,’ which is when extreme events happen in short succession – such as when extreme rainfall is followed immediately by a period of extreme heat,” says Shiqi Fang, first author of a paper on the work and a research associate at North Carolina State University.
“Specifically, these models struggle to accurately capture observed patterns regarding compound events in the data used to train the models,” Fang says. “This leads to two additional problems: difficulty in providing accurate projections of compound events on a global scale; and difficulty in providing accurate projections of compound events on a local scale. The work we’ve done here addresses all three of those challenges.”
Read more at North Carolina State University
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