Preprints
https://doi.org/10.5194/egusphere-2026-691
https://doi.org/10.5194/egusphere-2026-691
06 May 2026
 | 06 May 2026
Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

MSR v1.0: A High-Resolution Ocean Parameterization Approach via Multiphysics Super-Resolution

Fuhua Zhu, Zhan ao Huang, Pengfei Pan, Wenhao Huo, Fengtao Zuo, Xian Zhang, Xiaojie Li, Yongqiang Yu, and Xi Wu

Abstract. High-resolution reconstruction of ocean dynamics is challenging because spectral bias and the neglect of cross-variable couplings in existing super-resolution (SR) methods often lead to over-smoothed, physically inconsistent outputs, limiting their utility for eddy parameterizations. To overcome these limitations, we present a Multiphysics Super-Resolution version 1.0 (MSR v1.0) framework that jointly reconstructs eight closure-relevant diagnostics—vorticity, deformation measures, stress tensor components, and subgrid momentum forcing terms—directly from low-resolution (LR) velocity fields, consistency is maintained under a filtering scale that aligns with the definition of subgrid tendencies. Our approach integrates three key components: (1) a Dynamic Enhancement Feature (DEF) module to prioritize dynamically active regions; (2) a High-Frequency Enhancement (HFE) module that fuses spatial and spectral operators via learned gating to restore suppressed fine-scale structures such as fronts and eddy rims; and (3) a Physical Consistency Loss that aligns derivative-level structures and algebraic couplings across diagnostics. Experiments on an eddy-resolving simulation dataset across multiple ocean basins and downscaling factors show that MSR consistently outperforms strong SR baselines, yielding sharper reconstructions with improved high-wavenumber spectra and cross-variable consistency. The MSR-reconstructed diagnostics are closure-ready for low-resolution ocean models and can inform or constrain eddy parameterizations, providing a practical, spectrally selective, scale-aware surrogate for high-fidelity multiphysics diagnostics.

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Fuhua Zhu, Zhan ao Huang, Pengfei Pan, Wenhao Huo, Fengtao Zuo, Xian Zhang, Xiaojie Li, Yongqiang Yu, and Xi Wu

Status: open (until 01 Jul 2026)

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Fuhua Zhu, Zhan ao Huang, Pengfei Pan, Wenhao Huo, Fengtao Zuo, Xian Zhang, Xiaojie Li, Yongqiang Yu, and Xi Wu
Fuhua Zhu, Zhan ao Huang, Pengfei Pan, Wenhao Huo, Fengtao Zuo, Xian Zhang, Xiaojie Li, Yongqiang Yu, and Xi Wu
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Latest update: 06 May 2026
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Short summary
To better capture ocean swirls that coarse simulations miss, we trained a learning system on high-detail ocean model data to turn low-detail currents into eight related measures of motion and forces, while keeping them mutually consistent. It improved accuracy versus strong baselines: average root mean square error fell from 0.126 to 0.113 and the coefficient of determination reached 0.947. This can help tune and test climate models.
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