Neural General Circulation Models for Weather and Climate
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866910539379113984 |
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| author | Kochkov, Dmitrii Yuval, Janni Langmore, Ian Norgaard, Peter Smith, Jamie Mooers, Griffin Klöwer, Milan Lottes, James Rasp, Stephan Düben, Peter Hatfield, Sam Battaglia, Peter Sanchez-Gonzalez, Alvaro Willson, Matthew Brenner, Michael P. Hoyer, Stephan |
| author_facet | Kochkov, Dmitrii Yuval, Janni Langmore, Ian Norgaard, Peter Smith, Jamie Mooers, Griffin Klöwer, Milan Lottes, James Rasp, Stephan Düben, Peter Hatfield, Sam Battaglia, Peter Sanchez-Gonzalez, Alvaro Willson, Matthew Brenner, Michael P. Hoyer, Stephan |
| contents | General circulation models (GCMs) are the foundation of weather and climate prediction. GCMs are physics-based simulators which combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such as cloud formation. Recently, machine learning (ML) models trained on reanalysis data achieved comparable or better skill than GCMs for deterministic weather forecasting. However, these models have not demonstrated improved ensemble forecasts, or shown sufficient stability for long-term weather and climate simulations. Here we present the first GCM that combines a differentiable solver for atmospheric dynamics with ML components, and show that it can generate forecasts of deterministic weather, ensemble weather and climate on par with the best ML and physics-based methods. NeuralGCM is competitive with ML models for 1-10 day forecasts, and with the European Centre for Medium-Range Weather Forecasts ensemble prediction for 1-15 day forecasts. With prescribed sea surface temperature, NeuralGCM can accurately track climate metrics such as global mean temperature for multiple decades, and climate forecasts with 140 km resolution exhibit emergent phenomena such as realistic frequency and trajectories of tropical cyclones. For both weather and climate, our approach offers orders of magnitude computational savings over conventional GCMs. Our results show that end-to-end deep learning is compatible with tasks performed by conventional GCMs, and can enhance the large-scale physical simulations that are essential for understanding and predicting the Earth system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_07222 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Neural General Circulation Models for Weather and Climate Kochkov, Dmitrii Yuval, Janni Langmore, Ian Norgaard, Peter Smith, Jamie Mooers, Griffin Klöwer, Milan Lottes, James Rasp, Stephan Düben, Peter Hatfield, Sam Battaglia, Peter Sanchez-Gonzalez, Alvaro Willson, Matthew Brenner, Michael P. Hoyer, Stephan Atmospheric and Oceanic Physics Machine Learning Computational Physics General circulation models (GCMs) are the foundation of weather and climate prediction. GCMs are physics-based simulators which combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such as cloud formation. Recently, machine learning (ML) models trained on reanalysis data achieved comparable or better skill than GCMs for deterministic weather forecasting. However, these models have not demonstrated improved ensemble forecasts, or shown sufficient stability for long-term weather and climate simulations. Here we present the first GCM that combines a differentiable solver for atmospheric dynamics with ML components, and show that it can generate forecasts of deterministic weather, ensemble weather and climate on par with the best ML and physics-based methods. NeuralGCM is competitive with ML models for 1-10 day forecasts, and with the European Centre for Medium-Range Weather Forecasts ensemble prediction for 1-15 day forecasts. With prescribed sea surface temperature, NeuralGCM can accurately track climate metrics such as global mean temperature for multiple decades, and climate forecasts with 140 km resolution exhibit emergent phenomena such as realistic frequency and trajectories of tropical cyclones. For both weather and climate, our approach offers orders of magnitude computational savings over conventional GCMs. Our results show that end-to-end deep learning is compatible with tasks performed by conventional GCMs, and can enhance the large-scale physical simulations that are essential for understanding and predicting the Earth system. |
| title | Neural General Circulation Models for Weather and Climate |
| topic | Atmospheric and Oceanic Physics Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2311.07222 |