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Samudra 2: Scaling Ocean Emulators across Resolutions
Ocean general circulation models (OGCMs) are essential to climate science but computationally expensive, limiting ensemble size and …
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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 …
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Impact of Data-Driven Eddy Parameterization on Climate State in an Idealized Coupled CESM Model
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Towards Infinitely Long Neural Simulations: Self-Refining Neural Surrogate Models for Dynamical Systems
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FloeNet: A mass-conserving global sea ice emulator that generalizes across climates
We introduce FloeNet, a machine-learning emulator trained on the Geophysical Fluid Dynamics Laboratory global sea ice model, SIS2. …
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Physics and causally constrained discrete-time neural models of turbulent dynamical systems
We present a framework for constructing physics and causally constrained neural models of turbulent dynamical systems from data. We …
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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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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 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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