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Accelerating scientific discovery with the common task framework
Machine learning (ML) and artificial intelligence (AI) algorithms are transforming and empowering the characterization and control of …
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Towards a Unified Data-Driven Boundary Layer Momentum Flux Parameterization for Ocean and Atmosphere
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A framework for hybrid physics-AI coupled ocean models
Climate simulations, at all grid resolutions, rely on approximations that encapsulate the forcing due to unresolved processes on …
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Controls on the ocean response to idealized Antarctic meltwater input
Antarctic meltwater is expected to increase throughout the coming centuries and impact sea level, ocean circulation, and the coupled …
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Parameterizing isopycnal mixing via kinetic energy backscatter in an eddy-permitting ocean model
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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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Design and implementation of a data-driven parameterization for mesoscale thickness fluxes
Mesoscale eddies are a major sink of available potential energy (APE) in the ocean. When these eddies are not resolved or only …
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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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CAMulator: Fast Emulation of the Community Atmosphere Model
We introduce CAMulator version 1, an auto-regressive machine-learned (ML) emulator of the Community Atmosphere Model version 6 (CAM6) …
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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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