Climate Physicists Face the Ghosts in Their Machines: Clouds
Source: Slashdot
Climate scientists trying to predict how much hotter the planet will get have long grappled with a surprisingly stubborn problem—clouds, which both reflect sunlight and trap heat, account for more than half the variation between climate predictions and are the main reason warming projections for the next 50 years range from 2 to 6 °C.
Two research groups are now racing to close that gap using AI, though they disagree sharply on method. Tapio Schneider at Caltech built CLIMA, a model that uses machine learning to optimize cloud parameters within traditional physics equations; it will be unveiled at a conference in Japan in March. Chris Bretherton at the Allen Institute for AI took a different path—his ACE2 neural network, released in 2024, learns from 50 years of atmospheric data and largely bypasses physics equations altogether.