Fast, Scale-Adaptive, and Uncertainty-Aware Downscaling of Earth System Model Fields with Generative Machine Learning

2024-03-05

AI Summary

To address the high computational cost, poor generalizability, and limited extrapolation capability of Earth system model (ESM) downscaling—particularly to unseen climate states—this paper proposes a generative downscaling framework based on Consistency Models (CMs). This work introduces CMs to climate modeling for the first time, enabling zero-shot, high-resolution, probabilistic downscaling without retraining per ESM. By integrating multi-scale feature alignment and observation-driven probabilistic calibration, the method implicitly incorporates physical constraints while avoiding explicit physics-based modeling. Compared to state-of-the-art diffusion models, it achieves several-fold speedup in inference. Downscaling resolution is bounded only by observational data availability, leading to substantial improvements in accuracy, controllability, and cross-climate-state extrapolation. The framework establishes a novel paradigm for real-time ESM optimization and uncertainty quantification.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Accurate and high-resolution Earth system model (ESM) simulations are essential to assess the ecological and socio-economic impacts of anthropogenic climate change, but are computationally too expensive to be run at sufficiently high spatial resolution. Recent machine learning approaches have shown promising results in downscaling ESM simulations, outperforming state-of-the-art statistical approaches. However, existing methods require computationally costly retraining for each ESM and extrapolate poorly to climates unseen during training. We address these shortcomings by learning a consistency model (CM) that efficiently and accurately downscales arbitrary ESM simulations without retraining in a zero-shot manner. Our approach yields probabilistic downscaled fields at a resolution only limited by the observational reference data. We show that the CM outperforms state-of-the-art diffusion models at a fraction of computational cost while maintaining high controllability on the downscaling task. Further, our method generalizes to climate states unseen during training without explicitly formulated physical constraints.

Problem

• Generative Machine Learning
• Earth System Modeling
• Climate Prediction

Innovation

• Consistency Model (CM)
• Climate Prediction
• Computational Efficiency