Chasing Clouds: AI Rewrites the Rules of Climate Modeling

Clouds remain the biggest uncertainty in climate projections, pushing researchers to blend physics with AI. Projects like CLIMA and ACE2 train neural networks on real atmospheric data and high‑resolution cloud simulations to emulate cloud effects more accurately and, in some cases, to forecast with far less computational cost than traditional Navier‑Stokes–based models. CLIMA refines cloud parameters through large-eddy simulations to double model accuracy, while ACE2 uses data-driven forecasts to capture cloud-influenced dynamics and speed up predictions, sparking debate over long-term reliability and the best balance between data and physics.