Samudra: An AI Global Ocean Emulator for Climate
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866908387229302784 |
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| author | Dheeshjith, Surya Subel, Adam Adcroft, Alistair Busecke, Julius Fernandez-Granda, Carlos Gupta, Shubham Zanna, Laure |
| author_facet | Dheeshjith, Surya Subel, Adam Adcroft, Alistair Busecke, Julius Fernandez-Granda, Carlos Gupta, Shubham Zanna, Laure |
| contents | AI emulators for forecasting have emerged as powerful tools that can outperform conventional numerical predictions. The next frontier is to build emulators for long climate simulations with skill across a range of spatiotemporal scales, a particularly important goal for the ocean. Our work builds a skillful global emulator of the ocean component of a state-of-the-art climate model. We emulate key ocean variables, sea surface height, horizontal velocities, temperature, and salinity, across their full depth. We use a modified ConvNeXt UNet architecture trained on multi-depth levels of ocean data. We show that the ocean emulator - Samudra - which exhibits no drift relative to the truth, can reproduce the depth structure of ocean variables and their interannual variability. Samudra is stable for centuries and 150 times faster than the original ocean model. Samudra struggles to capture the correct magnitude of the forcing trends and simultaneously remain stable, requiring further work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03795 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Samudra: An AI Global Ocean Emulator for Climate Dheeshjith, Surya Subel, Adam Adcroft, Alistair Busecke, Julius Fernandez-Granda, Carlos Gupta, Shubham Zanna, Laure Atmospheric and Oceanic Physics Machine Learning AI emulators for forecasting have emerged as powerful tools that can outperform conventional numerical predictions. The next frontier is to build emulators for long climate simulations with skill across a range of spatiotemporal scales, a particularly important goal for the ocean. Our work builds a skillful global emulator of the ocean component of a state-of-the-art climate model. We emulate key ocean variables, sea surface height, horizontal velocities, temperature, and salinity, across their full depth. We use a modified ConvNeXt UNet architecture trained on multi-depth levels of ocean data. We show that the ocean emulator - Samudra - which exhibits no drift relative to the truth, can reproduce the depth structure of ocean variables and their interannual variability. Samudra is stable for centuries and 150 times faster than the original ocean model. Samudra struggles to capture the correct magnitude of the forcing trends and simultaneously remain stable, requiring further work. |
| title | Samudra: An AI Global Ocean Emulator for Climate |
| topic | Atmospheric and Oceanic Physics Machine Learning |
| url | https://arxiv.org/abs/2412.03795 |