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A non-intrusive machine learning framework for debiasing long-time coarse resolution climate simulations and quantifying rare events statistics
To overcome this challenge, we introduce a dynamical systems approach where the correction operator is trained using reference data and a coarse model simulation nudged towards that reference.
Learning bias corrections for climate models using deep neural operators
In this study, we replace the bias correction process with a surrogate model based on the Deep Operator Network (DeepONet).
Quantifying and attributing time step sensitivities in present-day climate simulations conducted with EAMv1
This study assesses the relative importance of time integration error in present-day climate simulations conducted with the atmosphere component of the Energy Exascale Earth System Model version 1 (EAMv1) at 1-degree horizontal resolution.
Atmospheric and Oceanic Physics
