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Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically …
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SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators
Traditional numerical global climate models simulate the full Earth system by exchanging boundary conditions between separate …
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Machine learned equations for vertical mixing coefficients in the ocean surface boundary layer
Neural networks offer novel ways to parameterize unresolved ocean mixing but are challenging to interpret. Here, we derive compact …
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Samudra 2: Scaling Ocean Emulators across Resolutions
Ocean general circulation models (OGCMs) are essential to climate science but computationally expensive, limiting ensemble size and …
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Fourier analysis of the physics of transfer learning for data-driven subgrid-scale models of ocean turbulence
Transfer learning (TL) is a powerful tool for enhancing the performance of neural networks (NNs) in applications such as weather and …
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Calibration of a neural network ocean closure for improved mean state and variability
Global ocean models exhibit biases in the mean state and variability, particularly at coarse resolution, where mesoscale eddies are …
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Impact of Data-Driven Eddy Parameterization on Climate State in an Idealized Coupled CESM Model
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Parameterizing isopycnal mixing via kinetic energy backscatter in an eddy-permitting ocean model
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Data-Driven Probabilistic Air-Sea Flux Parameterization
Accurately quantifying air-sea fluxes is important for understanding air-sea interactions and improving coupled weather and climate …
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Towards Infinitely Long Neural Simulations: Self-Refining Neural Surrogate Models for Dynamical Systems
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