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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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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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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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The Impact of Natural External Forcing on Ocean Heat Uptake Efficiency Since the 1980s
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A landscape of mesoscale eddy vertical structure: the influence of bathymetric slope and roughness on kinetic energy
Surface and upper ocean measurements of mesoscale eddies have revealed the central role they play in ocean transport, but their …
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A theory for how the depth of meltwater injection impacts regional sea level evolution
Mass loss from the Antarctic ice sheet is projected to continue over the coming century. The resultant sea level change will have a …
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A fluctuation-dissipation theorem perspective on radiative responses to temperature perturbations
Radiative forcing drives warming in the Earth system, leading to changes in sea surface temperatures (SSTs) and associated radiative …
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Observational constraints imply limited future Atlantic meridional overturning circulation weakening
The degree to which the Atlantic meridional overturning circulation (AMOC) weakens over the twenty-first century varies widely across …
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Addressing Out-of-Sample Issues in Multi-Layer Convolutional Neural-Network Parameterization of Mesoscale Eddies Applied Near Coastlines
This study addresses the boundary artifacts in machine-learned (ML) parameterizations for ocean subgrid mesoscale momentum forcing, as …
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