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Advancing global sea ice prediction capabilities using a fully coupled climate model with integrated machine learning
We showcase a hybrid modeling framework that embeds machine learning (ML) inference into the Geophysical Fluid Dynamics Laboratory …
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Data-driven multiscale modeling for correcting dynamical systems
We propose a multiscale approach for predicting quantities in dynamical systems which is explicitly structured to extract information …
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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 Data-Driven Approach for Parameterizing Ocean Submesoscale Buoyancy Fluxes
Parameterizations of O(1-10)km submesoscale mixed layer instabilities in General Circulation Models (GCMs) represent the effects of …
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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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Generalizable neural-network parameterization of mesoscale eddies in idealized and global ocean models
Data-driven methods have become popular to parameterize the effects of mesoscale eddies in ocean models. However, they perform poorly …
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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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The Impact of Natural External Forcing on Ocean Heat Uptake Efficiency Since the 1980s
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Parameterizing isopycnal mixing via kinetic energy backscatter in an eddy-permitting ocean model
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