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Estimation of temperature and precipitation uncertainties using quantile neural networks
Extreme events pose significant risks and are challenging to predict. Assessing climate hazards requires placing quantitative …
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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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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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