这篇论文揭示了AIWP模型预测准确性的来源和缺陷，对理解AI模型在气候预测中的应用具有重要意义。
AIWP模型在预测过去方面表现出色，但准确性低于预测，违反热力学第二定律。研究指出，这是由于训练数据粗粒化导致的，减少粗粒化使预测更接近物理模型，但准确性下降。研究解释了AIWP模型的预测技能，并呼吁重新审视可预测性理论和长期气候模拟策略。
Missing the Butterfly and Predicting the Past: Features or Bugs of Accurate AI Weather Models?
AI weather prediction (AIWP) models rival physics-based models, yet the sources of their unexpected forecast accuracy and the degree of their physical fidelity remain unclear. Here, across a hierarchy spanning observation-based reanalysis, a general circulation model, and the multi-scale Lorenz system, we show that AI models can be trained to skillfully predict the past (backcast), though backcasts are systematically less accurate than forecasts. However, skillful backcasting appears to violate the second law of thermodynamics, and all these forecasting and backcasting models miss the butterfly effect. We trace the surprising forecast accuracy, missing butterfly, and skillful backcasting to a single cause: inevitable coarse-graining of training data, which removes fast, small scales and/or some variables. From the Lorenz system to official Pangu-Weather models, reducing coarse-graining makes AI predictions more physics-like (arrow of time and butterfly-like effects emerge), but forecast accuracy declines. Results offer an explanation for AIWP models' forecast skill: unlike physics-based models, they implicitly learn how fast, small scales affect large scales without inheriting their rapid error growth. Broader implications are that AI models' proliferation calls for revisiting predictability theories and long-term climate emulation strategies, and backcasting offers a useful, new lens for such analyses.