# NeuralGCM

As Earth-system AI models approach kilometre scales, halving grid spacing quadruples the grid-point count and the end-to-end training cost. One2N splits resolution refinement into successive increments: at each level the complete parent predictor is frozen and only a cross-resolution residual correction module is trained, so the parent's activations and optimizer states are never retained. One refinement level on the NeuralGCM-0.7° baseline yields One2N-0.07°, trained for about four weeks on a single 80 GB A800 GPU; against HRES-0.07° analyses it keeps the parent's tropospheric skill while reducing upper-stratospheric errors and resolving finer structure.

As Earth-system AI models approach kilometre scales, halving grid spacing quadruples the grid-point count and the end-to-end training cost. One2N splits resolution refinement into successive increments: at each level the complete parent predictor is frozen and only a cross-resolution residual correction module is trained, so the parent's activations and optimizer states are never retained. One refinement level on the NeuralGCM-0.7° baseline yields One2N-0.07°, trained for about four weeks on a single 80 GB A800 GPU; against HRES-0.07° analyses it keeps the parent's tropospheric skill while reducing upper-stratospheric errors and resolving finer structure.

With multimillion-dollar, three-year funding from Google.org and technical support from Google Research, Ai2 is launching a climate modeling initiative to develop NeuralGCM+, an open-source model. It keeps NeuralGCM's hybrid design, using physical equations for large-scale dynamics and AI for small-scale processes such as cloud formation and rainfall, and couples the atmosphere with the ocean and sea ice to target stable simulations spanning decades. Google Research's earlier NeuralGCM simulated a year of global atmospheric conditions in eight minutes on a single TPU in its atmosphere-only version.

With multimillion-dollar, three-year funding from Google.org and technical support from Google Research, Ai2 is launching a climate modeling initiative to develop NeuralGCM+, an open-source model. It keeps NeuralGCM's hybrid design, using physical equations for large-scale dynamics and AI for small-scale processes such as cloud formation and rainfall, and couples the atmosphere with the ocean and sea ice to target stable simulations spanning decades. Google Research's earlier NeuralGCM simulated a year of global atmospheric conditions in eight minutes on a single TPU in its atmosphere-only version.