9 papers
Samudra 2: Scaling Ocean Emulators across Resolutions
Yuan Yuan, Jesse Rusak, Alexander Merose +5
Ocean general circulation models (OGCMs) are essential to climate science but computationally expensive, limiting ensemble size and forcing scenarios. Neural emulators promise orde…
Calibration of a neural network ocean closure for improved mean state and variability
Pavel Perezhogin, Alistair Adcroft, Laure Zanna
Global ocean models exhibit biases in the mean state and variability, particularly at coarse resolution, where mesoscale eddies are unresolved. To address these biases, parameteriz…
Impact of Data-Driven Eddy Parameterization on Climate State in an Idealized Coupled CESM Model
Jia-Rui Shi, Pavel Perezhogin, Laure Zanna +1
Mesoscale eddies remain poorly represented in most climate models, motivating the use of parameterizations to account for their dynamical effects on the coupled system. In this stu…
Data-Driven Probabilistic Air-Sea Flux Parameterization
Jiarong Wu, Pavel Perezhogin, David John Gagne +4
Accurately quantifying air-sea fluxes is important for understanding air-sea interactions and improving coupled weather and climate systems. This study introduces a probabilistic f…
A Framework for Hybrid Physics-AI Coupled Ocean Models
Laure Zanna, William Gregory, Pavel Perezhogin +23
Climate simulations, at all grid resolutions, rely on approximations that encapsulate the forcing due to unresolved processes on resolved variables, known as parameterizations. Par…
Generalizable neural-network parameterization of mesoscale eddies in idealized and global ocean models
Pavel Perezhogin, Alistair Adcroft, Laure Zanna
Data-driven methods have become popular to parameterize the effects of mesoscale eddies in ocean models. However, they perform poorly in generalization tasks and may require retuni…