17 citations · 30 across the 14 of their papers we have counts for
15 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…
Design principles for stable and generalizable data-driven discretizations for solving linear hyperbolic conservation laws
Antoine-Alexis Nasser, Alistair Adcroft
We investigate data-driven finite-volume discretizations of the linear advection equation in one dimension. Neural networks for use as numerical advection schemes are constructed a…
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…
Universal effect of ammonia pressure on synthesis of colloidal metal nitrides in molten salts
Ruiming Lin, Vikash Khokhar, Ningxin Jiang +11
Metal nitrides represent a large class of materials with extensive applications in optoelectronics, energy, and healthcare technologies. For example, GaN and related nitride semico…
FloeNet: A mass-conserving global sea ice emulator that generalizes across climates
William Gregory, Mitchell Bushuk, James Duncan +8
We introduce FloeNet, a machine-learning emulator trained on the Geophysical Fluid Dynamics Laboratory global sea ice model, SIS2. FloeNet is a mass-conserving model, emulating 6-h…
Harmonic Extension for Multiscale Analysis and Modeling Near Boundaries, with an Ocean Application
Benjamin A. Storer, Mehrnoush Kharghani, Alistair Adcroft +1
Treatment of fields near domain boundaries is a long-standing problem in signal processing that has come into renewed focus following recent efforts in convolution-based multiscale…
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…
SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators
James P. C. Duncan, Elynn Wu, Surya Dheeshjith +15
Traditional numerical global climate models simulate the full Earth system by exchanging boundary conditions between separate simulators of the atmosphere, ocean, sea ice, land sur…
Advancing global sea ice prediction capabilities using a fully-coupled climate model with integrated machine learning
William Gregory, Mitchell Bushuk, Yong-Fei Zhang +4
We showcase a hybrid modeling framework which embeds machine learning (ML) inference into the GFDL SPEAR climate model, for online sea ice bias correction during a set of global fu…
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…
The Impact of Natural External Forcing on Ocean Heat Uptake Efficiency Since the 1980s
Jia-Rui Shi, Laure Zanna, Alistair Adcroft
We investigate the temporal evolution of ocean heat uptake efficiency (OHUE) using observations and large ensemble model simulations. OHUE, defined as the ratio of the rate in ocea…
Samudra: An AI Global Ocean Emulator for Climate
Surya Dheeshjith, Adam Subel, Alistair Adcroft +4
AI emulators for forecasting have emerged as powerful tools that can outperform conventional numerical predictions. The next frontier is to build emulators for long climate simulat…
An Analysis of Deep Learning Parameterizations for Ocean Subgrid Eddy Forcing
Cem Gultekin, Adam Subel, Cheng Zhang +5
Due to computational constraints, climate simulations cannot resolve a range of small-scale physical processes, which have a significant impact on the large-scale evolution of the…
Addressing out-of-sample issues in multi-layer convolutional neural-network parameterization of mesoscale eddies applied near coastlines
Cheng Zhang, Pavel Perezhogin, Alistair Adcroft +1
This study addresses the boundary artifacts in machine-learned (ML) parameterizations for ocean subgrid mesoscale momentum forcing, as identified in the online ML implementation fr…
Transfer Learning for Emulating Ocean Climate Variability across forcing
Surya Dheeshjith, Adam Subel, Shubham Gupta +4
With the success of machine learning (ML) applied to climate reaching further every day, emulators have begun to show promise not only for weather but for multi-year time scales in…