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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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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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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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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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The GFDL-CM4X climate model hierarchy, Part I: model description and thermal properties
We present the GFDL-CM4X (Geophysical Fluid Dynamics Laboratory Climate Model version 4X) coupled climate model hierarchy. The primary …
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The GFDL-CM4X climate model hierarchy, Part II: case studies
This paper is Part II of a two-part paper that documents the CM4X (Climate Model version 4X) hierarchy of coupled climate models …
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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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