  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.
