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Deep Learning of Systematic Ocean Model Errors in a Coupled GCM from Data Assimilation Increments
We present a novel, data-driven approach to predict systematic model errors in the ocean component of a coupled general circulation …
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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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CAMulator: Fast Emulation of the Community Atmosphere Model
We introduce CAMulator version 1, an auto-regressive machine-learned (ML) emulator of the Community Atmosphere Model version 6 (CAM6) …
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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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Uncertainty-permitting machine learning reveals sources of dynamic sea level predictability across daily-to-seasonal timescales
Reliable dynamic sea level forecasts are hindered by numerous sources of uncertainty on daily-to-seasonal timescales (1-180 days) due …
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Learning Propagators for Sea Surface Height Forecasts Using Koopman Autoencoders
Due to the wide range of processes impacting the sea surface height (SSH) on daily-to-interannual timescales, SSH forecasts are …
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Learning Machine Learning with Lorenz-96
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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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An Analysis of Deep Learning Parameterizations for Ocean Subgrid Eddy Forcing
Due to computational constraints, climate simulations cannot resolve a range of small-scale physical processes, which have a …
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A Monte Carlo Framework for Calibrated Uncertainty Estimation in Sequence Prediction
Probabilistic prediction of sequences from images and other high-dimensional data is a key challenge, particularly in risk-sensitive …
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