Machine learning for climate physics and simulations
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arXiv
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| Format: | Preprint |
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2024
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| author | Lai, Ching-Yao Hassanzadeh, Pedram Sheshadri, Aditi Sonnewald, Maike Ferrari, Raffaele Balaji, Venkatramani |
| author_facet | Lai, Ching-Yao Hassanzadeh, Pedram Sheshadri, Aditi Sonnewald, Maike Ferrari, Raffaele Balaji, Venkatramani |
| contents | We discuss the emerging advances and opportunities at the intersection of machine learning (ML) and climate physics, highlighting the use of ML techniques, including supervised, unsupervised, and equation discovery, to accelerate climate knowledge discoveries and simulations. We delineate two distinct yet complementary aspects: (1) ML for climate physics and (2) ML for climate simulations. While physics-free ML-based models, such as ML-based weather forecasting, have demonstrated success when data is abundant and stationary, the physics knowledge and interpretability of ML models become crucial in the small-data/non-stationary regime to ensure generalizability. Given the absence of observations, the long-term future climate falls into the small-data regime. Therefore, ML for climate physics holds a critical role in addressing the challenges of ML for climate simulations. We emphasize the need for collaboration among climate physics, ML theory, and numerical analysis to achieve reliable ML-based models for climate applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_13227 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine learning for climate physics and simulations Lai, Ching-Yao Hassanzadeh, Pedram Sheshadri, Aditi Sonnewald, Maike Ferrari, Raffaele Balaji, Venkatramani Atmospheric and Oceanic Physics Chaotic Dynamics Computational Physics Fluid Dynamics Geophysics We discuss the emerging advances and opportunities at the intersection of machine learning (ML) and climate physics, highlighting the use of ML techniques, including supervised, unsupervised, and equation discovery, to accelerate climate knowledge discoveries and simulations. We delineate two distinct yet complementary aspects: (1) ML for climate physics and (2) ML for climate simulations. While physics-free ML-based models, such as ML-based weather forecasting, have demonstrated success when data is abundant and stationary, the physics knowledge and interpretability of ML models become crucial in the small-data/non-stationary regime to ensure generalizability. Given the absence of observations, the long-term future climate falls into the small-data regime. Therefore, ML for climate physics holds a critical role in addressing the challenges of ML for climate simulations. We emphasize the need for collaboration among climate physics, ML theory, and numerical analysis to achieve reliable ML-based models for climate applications. |
| title | Machine learning for climate physics and simulations |
| topic | Atmospheric and Oceanic Physics Chaotic Dynamics Computational Physics Fluid Dynamics Geophysics |
| url | https://arxiv.org/abs/2404.13227 |