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Find similar grantsSamudra: An AI Global Ocean Emulator for Climate is sponsored by National Oceanic and Atmospheric Administration (NOAA). The Samudra project aims to develop an AI-based global ocean emulator to enhance climate predictions.
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Samudra: An AI Global Ocean Emulator for Climate Coral Reef Conservation Program (CRCP) Deepwater Horizon Oil Spill and Restoration (DWH) Integrated Ecosystem Assessment (IEA) National Environmental Policy Act (NEPA) National Environmental Satellite and Data Information Service (NESDIS) National Marine Fisheries Service (NMFS) National Ocean Service (NOS) National Weather Service (NWS) NOAA Cooperative Institutes NOAA Cooperative Science Centers NOAA International Agreements Ocean Exploration Program Office of Marine and Aviation Operations (OMAO) Office of Oceanic and Atmospheric Research (OAR) Weather Research and Forecasting Innovation Act ("The Weather Act") Samudra: An AI Global Ocean Emulator for Climate Title : Samudra: An AI Global Ocean Emulator for Climate Personal Author(s) : Dheeshjith, Surya;Subel, Adam;Adcroft, Alistair;Busecke, Julius;Fernandez‐Granda, Carlos;Gupta, Shubham;Zanna, Laure; Source : Geophysical Research Letters, 52(10) DOI : https://doi.
org/10. 1029/2024GL114318 Journal Title : Geophysical Research Letters Dheeshjith, Surya et al. (2025).
Samudra: An AI Global Ocean Emulator for Climate. 52(10). https://doi.
org/10. 1029/2024GL114318 Dheeshjith, Surya et al. "Samudra: An AI Global Ocean Emulator for Climate" 52, no. 10 (2025), https://doi.
org/10. 1029/2024GL114318 Dheeshjith, Surya et al. "Samudra: An AI Global Ocean Emulator for Climate" vol.
52, no. 10, 2025, https://doi. org/10. 1029/2024GL114318 Export RIS Citation Information.
AU - Fernandez‐Granda, Carlos AB - 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. DO - https://doi.
org/10. 1029/2024GL114318 J2 - Geophysical Research Letters PB - American Geophysical Union (AGU) T2 - Geophysical Research Letters T2 - Geophysical Research Letters, 52(10) TI - Samudra: An AI Global Ocean Emulator for Climate UR - https://repository. library.
noaa. gov/view/noaa/71083 Geophysical Research Letters OAR (Oceanic and Atmospheric Research) GFDL (Geophysical Fluid Dynamics Laboratory) 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.
Geophysical Research Letters, 52(10) https://doi. org/10. 1029/2024GL114318 American Geophysical Union (AGU) https://creativecommons.
org/licenses/by/4. 0/ urn:sha-512:d48f4325e92e9d6a90740fb1615208dd97e67c5097d04db68d5573e8f3f7735a28bed10e7b2386531e6c545e9b77735fa74300eef6ba0c8e01748fe9a00a443b https://repository. library.
noaa. gov/view/noaa/71083/noaa_71083_DS1. pdf Title : Samudra: An AI Global Ocean Emulator for Climate Personal Author(s) : Dheeshjith, Surya;Subel, Adam;Adcroft, Alistair;Busecke, Julius;Fernandez‐Granda, Carlos;Gupta, Shubham;Zanna, Laure; Source : Geophysical Research Letters, 52(10) DOI : https://doi.
org/10. 1029/2024GL114318 Journal Title : Geophysical Research Letters Dheeshjith, Surya et al. (2025).
Samudra: An AI Global Ocean Emulator for Climate. 52(10). https://doi.
org/10. 1029/2024GL114318 Dheeshjith, Surya et al. "Samudra: An AI Global Ocean Emulator for Climate" 52, no. 10 (2025), https://doi.
org/10. 1029/2024GL114318 Dheeshjith, Surya et al. "Samudra: An AI Global Ocean Emulator for Climate" vol.
52, no. 10, 2025, https://doi. org/10. 1029/2024GL114318 Export RIS Citation Information.
AU - Fernandez‐Granda, Carlos AB - 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. DO - https://doi.
org/10. 1029/2024GL114318 J2 - Geophysical Research Letters PB - American Geophysical Union (AGU) T2 - Geophysical Research Letters T2 - Geophysical Research Letters, 52(10) TI - Samudra: An AI Global Ocean Emulator for Climate UR - https://repository. library.
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Samudra: An AI Global Ocean Emulator for Climate is funded by National Oceanic and Atmospheric Administration (NOAA). Verify program details on the funder's official page before applying.
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