AI in climate science faces systemic limitations due to data and model constraints
Original framing: “The weather and climate science AI revolution isn’t revolutionary” — Ars Technica
Scientific evaluation of AI in climate science must address its limitations in uncertainty quantification, model interpretability, and integration with physics-based models. While AI can process large datasets, it often lacks the explanatory power of traditional climate models, which are grounded in well-established physical laws.
The integration of AI into climate science is not a simple technological shift but a complex socio-technical transformation shaped by historical patterns, power dynamics, and epistemic diversity.
While AI offers powerful tools for data analysis, its limitations in capturing uncertainty, integrating diverse knowledge systems, and addressing ethical concerns must be acknowledged. By combining AI with physics-based models, indigenous knowledge, and open-source collaboration, we can move toward a more inclusive and effective climate science paradigm. This requires not only technical innovation but also a rethinking of how knowledge is produced, shared, and applied in the face of global climate challenges.