MIT researchers develop training technique for faster, more trustworthy climate emulators

MIT researchers have developed a training technique that boosts the performance and adaptability of AI climate emulators while using only a fraction of the data typically required. The method generates optimized synthetic training scenarios with rapid emissions variations, letting an emulator trained on far less data accurately predict unseen scenarios where traditional emulators failed. The work appears in the Proceedings of the National Academy of Sciences.