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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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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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FloeNet: A mass-conserving global sea ice emulator that generalizes across climates
We introduce FloeNet, a machine-learning emulator trained on the Geophysical Fluid Dynamics Laboratory global sea ice model, SIS2. …
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Physics and causally constrained discrete-time neural models of turbulent dynamical systems
We present a framework for constructing physics and causally constrained neural models of turbulent dynamical systems from data. We …
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