17 citations · 26 across the 4 of their papers we have counts for
5 papers
A Framework for Hybrid Physics-AI Coupled Ocean Models
Laure Zanna, William Gregory, Pavel Perezhogin +23
Climate simulations, at all grid resolutions, rely on approximations that encapsulate the forcing due to unresolved processes on resolved variables, known as parameterizations. Par…
SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators
James P. C. Duncan, Elynn Wu, Surya Dheeshjith +15
Traditional numerical global climate models simulate the full Earth system by exchanging boundary conditions between separate simulators of the atmosphere, ocean, sea ice, land sur…
Samudra: An AI Global Ocean Emulator for Climate
Surya Dheeshjith, Adam Subel, Alistair Adcroft +4
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 simulat…
Transfer Learning for Emulating Ocean Climate Variability across forcing
Surya Dheeshjith, Adam Subel, Shubham Gupta +4
With the success of machine learning (ML) applied to climate reaching further every day, emulators have begun to show promise not only for weather but for multi-year time scales in…
ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction
Juan Nathaniel, Yongquan Qu, Tung Nguyen +4
Accurate prediction of climate in the subseasonal-to-seasonal scale is crucial for disaster preparedness and robust decision making amidst climate change. Yet, forecasting beyond t…