Machine Learning Workflows in Climate Modeling: Design Patterns and Insights from Case Studies
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915532523962368 |
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| author | Zheng, Tian Venkatasubramanian, Subashree Li, Shuolin Braverman, Amy Ke, Xinyi Hou, Zhewen Jin, Peter Agrawal, Samarth Sanjay |
| author_facet | Zheng, Tian Venkatasubramanian, Subashree Li, Shuolin Braverman, Amy Ke, Xinyi Hou, Zhewen Jin, Peter Agrawal, Samarth Sanjay |
| contents | Machine learning has been increasingly applied in climate modeling on system emulation acceleration, data-driven parameter inference, forecasting, and knowledge discovery, addressing challenges such as physical consistency, multi-scale coupling, data sparsity, robust generalization, and integration with scientific workflows. This paper analyzes a series of case studies from applied machine learning research in climate modeling, with a focus on design choices and workflow structure. Rather than reviewing technical details, we aim to synthesize workflow design patterns across diverse projects in ML-enabled climate modeling: from surrogate modeling, ML parameterization, probabilistic programming, to simulation-based inference, and physics-informed transfer learning. We unpack how these workflows are grounded in physical knowledge, informed by simulation data, and designed to integrate observations. We aim to offer a framework for ensuring rigor in scientific machine learning through more transparent model development, critical evaluation, informed adaptation, and reproducibility, and to contribute to lowering the barrier for interdisciplinary collaboration at the interface of data science and climate modeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03305 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning Workflows in Climate Modeling: Design Patterns and Insights from Case Studies Zheng, Tian Venkatasubramanian, Subashree Li, Shuolin Braverman, Amy Ke, Xinyi Hou, Zhewen Jin, Peter Agrawal, Samarth Sanjay Machine Learning Atmospheric and Oceanic Physics Applications 62P12 62p12 Machine learning has been increasingly applied in climate modeling on system emulation acceleration, data-driven parameter inference, forecasting, and knowledge discovery, addressing challenges such as physical consistency, multi-scale coupling, data sparsity, robust generalization, and integration with scientific workflows. This paper analyzes a series of case studies from applied machine learning research in climate modeling, with a focus on design choices and workflow structure. Rather than reviewing technical details, we aim to synthesize workflow design patterns across diverse projects in ML-enabled climate modeling: from surrogate modeling, ML parameterization, probabilistic programming, to simulation-based inference, and physics-informed transfer learning. We unpack how these workflows are grounded in physical knowledge, informed by simulation data, and designed to integrate observations. We aim to offer a framework for ensuring rigor in scientific machine learning through more transparent model development, critical evaluation, informed adaptation, and reproducibility, and to contribute to lowering the barrier for interdisciplinary collaboration at the interface of data science and climate modeling. |
| title | Machine Learning Workflows in Climate Modeling: Design Patterns and Insights from Case Studies |
| topic | Machine Learning Atmospheric and Oceanic Physics Applications 62P12 62p12 |
| url | https://arxiv.org/abs/2510.03305 |