2

FloeNet: A mass-conserving global sea ice emulator that generalizes across climates
We introduce FloeNet, a graph neural network trained to emulate the Geophysical Fluid Dynamics Laboratory global sea ice model, SIS2. …
PDF Cite DOI
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 …
PDF Cite DOI
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 …
PDF Cite DOI
Machine learned equations for vertical mixing coefficients in the ocean surface boundary layer
Neural networks offer novel ways to parameterize unresolved ocean mixing but are challenging to interpret. Here, we derive compact …
PDF Cite DOI
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 …
PDF Cite DOI
Parameterizing isopycnal mixing via kinetic energy backscatter in an eddy-permitting ocean model
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.
PDF Cite DOI
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 …
PDF Cite DOI
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 …
PDF Cite DOI
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 …
PDF Cite DOI
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 …
PDF Cite DOI