Claim: Machine learning may be a game-changer for climate prediction

From the COLUMBIA UNIVERSITY SCHOOL OF ENGINEERING AND APPLIED SCIENCE and the "learn garbage in, get garbage out" department. New York, NY–June 19, 2018–A major challenge in current climate prediction models is how to accurately represent clouds and their atmospheric heating and moistening. This challenge is behind the wide spread in climate prediction. Yet accurate predictions of global warming in response to increased greenhouse gas concentrations are essential for policy-makers (e.g. the Paris climate agreement). In a paper recently published online in Geophysical Research Letters (May 23), researchers led by Pierre Gentine, associate professor of earth and environmental engineering at Columbia Engineering, demonstrate that machine learning techniques can be used to tackle this issue and better represent clouds in coarse resolution ( 100km) climate models, with the potential to narrow the range of prediction.