Obtaining accurate estimates of uncertainty in climate scenarios often requires generating large ensembles of high-resolution climate simulations, a computationally expensive and memory intensive process. To address this challenge, we train a novel generative deep learning approach on extensive sets of climate simulations. The model consists of two components: a variational autoencoder for dimensionality reduction and a denoising diffusion probabilistic model that generates multiple ensemble members. We validate our model on the Max Planck Institute Grand Ensemble and show that it achieves good agreement with the original ensemble in terms of variability. By leveraging the latent space representation, our model can rapidly generate large ensembles on-the-fly with minimal memory requirements, which can significantly improve the efficiency of uncertainty quantification in climate simulations.

翻译：获取气候情景不确定性的精确估计通常需要生成大量高分辨率气候模拟的集合，这是一个计算成本高昂且内存密集的过程。为应对这一挑战，我们在广泛的气候模拟数据集上训练了一种新颖的生成式深度学习方法。该模型由两个组件构成：一个用于降维的变分自编码器，以及一个生成多个集合成员的去噪扩散概率模型。我们在马克斯·普朗克研究所大集合上验证了我们的模型，结果表明其在变异性方面与原始集合取得了良好的一致性。通过利用潜在空间表示，我们的模型能够以最小的内存需求快速即时生成大型集合，这可以显著提高气候模拟中不确定性量化的效率。