用视频扩散模型生成热带气候波动，通过简化条件揭示其物理机制。
Climate Prompting: Generating the Madden-Julian Oscillation using Video Diffusion and Low-Dimensional Conditioning
- 基于大气再分析数据训练视频扩散模型，以低维指标为条件生成长序列MJO
- 生成的MJO包含对流耦合波、功率谱等关键特征，虽有偏差但整体合理
- 通过理想化条件提示，可分离季节、厄尔尼诺等驱动因素，适合气候机制研究
生成式深度学习是模拟热带马登-朱利安振荡（MJO）的强大工具，但其与传统理论框架的关系尚不明确。本文提出一种基于大气再分析数据训练的视频扩散模型，能够以关键低维指标为条件，合成长时间尺度的MJO序列。生成的MJO在复合结构、功率谱及多尺度结构（包括对流耦合波）上均表现出重要特征，尽管存在部分偏差。进一步地，通过引入有意理想化的低维条件（如永久性MJO、季节或厄尔尼诺-南方涛动的孤立调制），可实现对潜在过程的分解，并识别物理驱动因子。该方法为连接低维MJO理论与高分辨率大气复杂性提供了实用框架，有助于提升热带大气预测能力。
原文摘要 · Abstract (English)
Generative Deep Learning is a powerful tool for modeling of the Madden-Julian oscillation (MJO) in the tropics, yet its relationship to traditional theoretical frameworks remains poorly understood. Here we propose a video diffusion model, trained on atmospheric reanalysis, to synthetize long MJO sequences conditioned on key low-dimensional metrics. The generated MJOs capture key features including composites, power spectra and multiscale structures including convectively coupled waves, despite some bias. We then prompt the model to generate more tractable MJOs based on intentionally idealized low-dimensional conditionings, for example a perpetual MJO, an isolated modulation by seasons and/or the El Nino-Southern Oscillation, and so on. This enables deconstructing the underlying processes and identifying physical drivers. The present approach provides a practical framework for bridging the gap between low-dimensional MJO theory and high-resolution atmospheric complexity and will help tropical atmosphere prediction.
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