Samudra: An AI Global Ocean Emulator for Climate
Abstract 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...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-05-01
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| Series: | Geophysical Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024GL114318 |
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| _version_ | 1849725257402810368 |
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| author | Surya Dheeshjith Adam Subel Alistair Adcroft Julius Busecke Carlos Fernandez‐Granda Shubham Gupta Laure Zanna |
| author_facet | Surya Dheeshjith Adam Subel Alistair Adcroft Julius Busecke Carlos Fernandez‐Granda Shubham Gupta Laure Zanna |
| author_sort | Surya Dheeshjith |
| collection | DOAJ |
| description | Abstract 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 | Article |
| id | doaj-art-2dac4fa24e404ab98e4e1dc5d8deb7e5 |
| institution | DOAJ |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-2dac4fa24e404ab98e4e1dc5d8deb7e52025-08-20T03:10:31ZengWileyGeophysical Research Letters0094-82761944-80072025-05-015210n/an/a10.1029/2024GL114318Samudra: An AI Global Ocean Emulator for ClimateSurya Dheeshjith0Adam Subel1Alistair Adcroft2Julius Busecke3Carlos Fernandez‐Granda4Shubham Gupta5Laure Zanna6Courant Institute of Mathematical Sciences New York University New York NY USACourant Institute of Mathematical Sciences New York University New York NY USAProgram in Atmospheric and Oceanic Sciences Princeton University Princeton NJ USALamont Doherty Earth Observatory Columbia University Palisades NY USACourant Institute of Mathematical Sciences New York University New York NY USACourant Institute of Mathematical Sciences New York University New York NY USACourant Institute of Mathematical Sciences New York University New York NY USAAbstract 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.https://doi.org/10.1029/2024GL114318oceansclimate emulatormachine learning |
| spellingShingle | Surya Dheeshjith Adam Subel Alistair Adcroft Julius Busecke Carlos Fernandez‐Granda Shubham Gupta Laure Zanna Samudra: An AI Global Ocean Emulator for Climate Geophysical Research Letters oceans climate emulator machine learning |
| title | Samudra: An AI Global Ocean Emulator for Climate |
| title_full | Samudra: An AI Global Ocean Emulator for Climate |
| title_fullStr | Samudra: An AI Global Ocean Emulator for Climate |
| title_full_unstemmed | Samudra: An AI Global Ocean Emulator for Climate |
| title_short | Samudra: An AI Global Ocean Emulator for Climate |
| title_sort | samudra an ai global ocean emulator for climate |
| topic | oceans climate emulator machine learning |
| url | https://doi.org/10.1029/2024GL114318 |
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