Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model
<|reference_start|>Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model: Seasonal climate forecasts are socioeconomically important for managing the impacts of extreme weather events and for planning in sectors like agriculture and energy. Climate predictability on seasonal timescales is tied to boundary effects of the ocean on the atmosphere and coupled interactions in the ocean-atmosphere system. We present the Ocean-linked-atmosphere (Ola) model, a high-resolution (0.25{\deg}) Artificial Intelligence/ Machine Learning (AI/ML) coupled earth-system model which separately models the ocean and atmosphere dynamics using an autoregressive Spherical Fourier Neural Operator architecture, with a view towards enabling fast, accurate, large ensemble forecasts on the seasonal timescale. We find that Ola exhibits learned characteristics of ocean-atmosphere coupled dynamics including tropical oceanic waves with appropriate phase speeds, and an internally generated El Ni\~no/Southern Oscillation (ENSO) having realistic amplitude, geographic structure, and vertical structure within the ocean mixed layer. We present initial evidence of skill in forecasting the ENSO which compares favorably to the SPEAR model of the Geophysical Fluid Dynamics Laboratory.<|reference_end|>
@article{wang2024coupled,
    title={Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System
  Model},
    author={Chenggong Wang, Michael S. Pritchard, Noah Brenowitz, Yair Cohen,
  Boris Bonev, Thorsten Kurth, Dale Durran, Jaideep Pathak},
    journal={arXiv preprint arXiv:2406.08632},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08632},
    primaryClass={physics.ao-ph cs.LG}
}