Beyond the Forecast: Hybrid Intelligence as a Force Multiplier for Earth Science

AI-driven weather and climate prediction has become highly visible in recent years, and most Earth scientists are now familiar with learned forecast models. Far fewer are aware that the same advances in artificial intelligence are producing general-purpose systems that can autonomously review literature, write and debug code, design experiments, and carry out extended research tasks with minimal supervision. These capabilities may ultimately have a greater impact on everyday scientific practice than any single prediction model.

AI-based forecasting represents only a narrow entry point into a broader transformation driven by hybrid intelligence, in which domain-specific Earth system models are combined with general AI systems such as large language and multimodal models and autonomous agents. In practice, this hybrid intelligence already spans simulation, data assimilation, downscaling, and analysis, while general AI systems increasingly handle coding, synthesis, and workflow orchestration. Together, these systems function less as isolated tools and more as adaptive research partners. Drawing on examples from NVIDIA’s Earth-2 research program and related international efforts, this talk examines how this shift reconfigures the human role toward problem formulation, validation, interpretation, and ethical governance, and highlights practical AI-assisted workflows already reshaping research productivity. Framing AI for environmental prediction within this wider context invites a broader discussion of how hybrid intelligence should be integrated thoughtfully into future Earth system science.