Latent-Constrained Conditional VAEs for Augmenting Large-Scale Climate Ensembles

2026-01-01

arXiv.org

AI Summary

This study addresses the high computational cost of large climate model ensembles and the frequent need for additional statistically consistent spatiotemporal climate realizations in downstream analyses. To this end, the authors propose a latent-space-constrained conditional variational autoencoder (LC-CVAE), which aligns representations from multiple simulations in the latent space by sharing geospatial anchor points, thereby effectively mitigating the latent space fragmentation commonly observed in standard CVAEs. The framework further integrates multi-output Gaussian process regression to enable interpolation in the latent space, followed by decoding to generate full spatiotemporal fields. Experimental results demonstrate that high-quality synthetic climate fields can be achieved using only around five climate simulations, while also revealing a trade-off between spatial coverage density and reconstruction accuracy.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Large climate-model ensembles are computationally expensive; yet many downstream analyses would benefit from additional, statistically consistent realizations of spatiotemporal climate variables. We study a generative modeling approach for producing new realizations from a limited set of available runs by transferring structure learned across an ensemble. Using monthly near-surface temperature time series from ten independent reanalysis realizations (ERA5), we find that a vanilla conditional variational autoencoder (CVAE) trained jointly across realizations yields a fragmented latent space that fails to generalize to unseen ensemble members. To address this, we introduce a latent-constrained CVAE (LC-CVAE) that enforces cross-realization homogeneity of latent embeddings at a small set of shared geographic'anchor'locations. We then use multi-output Gaussian process regression in the latent space to predict latent coordinates at unsampled locations in a new realization, followed by decoding to generate full time series fields. Experiments and ablations demonstrate (i) instability when training on a single realization, (ii) diminishing returns after incorporating roughly five realizations, and (iii) a trade-off between spatial coverage and reconstruction quality that is closely linked to the average neighbor distance in latent space.

Problem

• climate ensembles
• generative modeling
• spatiotemporal data
• statistical consistency
• data augmentation

Innovation

• latent-constrained CVAE
• climate ensemble augmentation
• Gaussian process regression
• spatiotemporal generation
• anchor-based latent homogeneity