ICL CHARACTERIZATION OF CLIMATE FOUNDATION MODELS: WHEN CAN TRANSFORMERS LEARN WEATHER AND CLIMATE?

Climate foundation models (FMs) have achieved remarkable success in weather forecasting, yet exhibit puzzling performance gaps between tasks: deterministic field prediction rivals operational numerical weather prediction, while extreme event detection lags significantly behind. We provide a theoretical explanation through the lens of in-context learning (ICL). Extending the ICL characterization framework to spatiotemporal climate data, we prove the Climate Prediction Dichotomy Theorem: every natural climate task falls into exactly one of two complexity categories. Type A (ICL-Easy) tasks—including temperature, pressure, and wind field prediction—admit additive sufficient statistics enabling attention-based computation with sample complexity nICL = Θ(nERM). Type C (ICL-Hard) tasks—including extreme event detection, tipping point identification, and compound event localization—require combinatorial sufficient statistics that provably exceed the computational capacity of constant-depth polynomial-size transformers when the number of simultaneous events J exceeds a threshold J* = O(log log n) ≈ 3–5. We establish the predictability horizon constraint: weather forecasting is ICL-Easy for lead times τ < τL ≈ 14 days, while climate statistics remain ICL-accessible but individual trajectories are fundamentally unpredictable. Our analysis yields six testable predictions about climate FM behavior and five deployment guidelines distinguishing when ICL suffices versus when fine-tuning is required. The dichotomy provides a principled foundation for understanding why Pangu-Weather and GraphCast excel at field prediction while struggling with event detection—and guides the design of next-generation climate AI systems.