Multi-fidelity climate model parameterization for better generalization and extrapolation

• URL: http://arxiv.org/abs/2309.10231v1
• Date: Tue, 19 Sep 2023 01:03:39 GMT
• Title: Multi-fidelity climate model parameterization for better generalization and extrapolation
• Authors: Mohamed Aziz Bhouri, Liran Peng, Michael S. Pritchard, Pierre Gentine
• Abstract summary: We show that a multi-fidelity approach, which integrates datasets of different accuracy and abundance, can provide the best of both worlds. In an application to climate modeling, the multi-fidelity framework yields more accurate climate projections without requiring major increase in computational resources.
• Score: 0.3860305383611933
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine-learning-based parameterizations (i.e. representation of sub-grid processes) of global climate models or turbulent simulations 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 training data, which is however critical to projecting climate change or unobserved regimes of turbulence. Here we show that a multi-fidelity approach, which integrates datasets of different accuracy and abundance, can provide the best of both worlds: the capacity to extrapolate leveraging the physically-based parameterization and a higher accuracy using the machine-learning-based parameterizations. In an application to climate modeling, the multi-fidelity framework yields more accurate climate projections without requiring major increase in computational resources. Our multi-fidelity randomized prior networks (MF-RPNs) combine physical parameterization data as low-fidelity and storm-resolving historical run's data as high-fidelity. To extrapolate beyond the training data, the MF-RPNs are tested on high-fidelity warming scenarios, $+4K$, data. We show the MF-RPN's capacity to return much more skillful predictions compared to either low- or high-fidelity (historical data) simulations trained only on one regime while providing trustworthy uncertainty quantification across a wide range of scenarios. 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 climate change.

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