Data-driven deep learning models are on the verge of transforming global weather forecasting. It is an open question if this success can extend to climate modeling, where long inference rollouts and data complexity pose significant challenges. Here, we present the first conditional generative model able to produce global climate ensemble simulations that are accurate and physically consistent. Our model runs at 6-hourly time steps and is shown to be stable for 10-year-long simulations. Our approach beats relevant baselines and nearly reaches a gold standard for successful climate model emulation. We discuss the key design choices behind our dynamics-informed diffusion model-based approach which enables this significant step towards efficient, data-driven climate simulations that can help us better understand the Earth and adapt to a changing climate.

翻译：数据驱动的深度学习模型正处于变革全球天气预报的边缘。这种成功能否延伸至气候建模领域，仍是一个悬而未决的问题，因为长期推理推演和数据复杂性带来了重大挑战。本文提出了首个能够生成准确且物理一致的全球气候集合模拟的条件生成模型。该模型以6小时为时间步长运行，并证明在长达10年的模拟中保持稳定。我们的方法超越了相关基线，并近乎达到了成功气候模型模拟的黄金标准。我们讨论了基于动力学信息的扩散模型方法背后的关键设计选择，该方法为实现高效、数据驱动的气候模拟迈出了重要一步，有助于我们更好地理解地球并适应不断变化的气候。