Hybrid physics-ML & parameterization
Probabilistic multi-fidelity climate model parameterization for better generalization
In one sentence. Multi-fidelity ML parameterization trained on warmer-climate low-fidelity data generalises to warmer climates, with uncertainty that tracks prediction error.
Abstract
Machine-learning-based parameterizations (ie., representation of sub-grid processes) of global climate models have recently been proposed as a powerful alternative to physical, but empirical, representations, offering a lower computational cost and higher accuracy. Yet, those approaches still suffer from a lack of generalization and extrapolation beyond the high-fidelity training data, which is however critical to projecting climate change. Here we show that a multi-fidelity approach can provide the best of both worlds: it generalizes to a warmer climate for which high fidelity data are not available, guided by abundant low-fidelity data from an even warmer climate, while retaining the accuracy of high-fidelity data, by learning a nonlinear mapping from low- to high-fidelity. In an application to climate modeling, the multi-fidelity framework yields more accurate climate projections without requiring major increase in computational resources, while providing uncertainty estimates that increase coherently with prediction error. Even though the multi-fidelity model was only tested offline, our approach paves the way for the use of machine-learning based methods that can optimally leverage historical observations or high-fidelity simulations and extrapolate to unseen regimes such as warmer climates.
Labels
- Topic
- Hybrid physics-ML & parameterization, Climate emulation
- Type
- New model or method
- Method
- Other
- Time scale
- Climate projections / centennial
- Domain
- Global
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1088/3049-4753/aea999 OpenAlex:W7213597378
- Added
- 2026-10-22
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