Publications

Adam Subel, Laure Zanna
Building Ocean Climate Emulators
ICLR 2024 Workshop: Tackling Climate Change with Machine Learning. DOI: 10.48550/arXiv.2402.04342

William Gregory, Mitchell Bushuk, Yongfei Zhang, Alistair Adcroft, Laure Zanna
Machine Learning for Online Sea Ice Bias Correction Within Global Ice-Ocean Simulations
Geophysical Research Letters 2024. DOI: 10.1029/2023GL106776

Pavel Perezhogin, Cheng Zhang, Alistair Adcroft, Carlos Fernandez-Granda, Laure Zanna
Implementation of a data-driven equation-discovery mesoscale parameterization into an ocean mode
James 2023. DOI: 10.48550/arXiv.2311.02517

Will Chapman and Judith Berner
Benefits of Deterministic and Stochastic Tendency Adjustments in a Climate Model
ArXiv 2023. DOI: 10.48550/arXiv.2308.15295

Christian Pedersen, Laure Zanna, Joan Bruna, Pavel Perezhogin
Reliable coarse-grained turbulent simulations through combined offline learning and neural emulation
ICML 2023 Workshop on Synergy of Scientific and Machine Learning Modeling DOI: 10.48550/arXiv.2307.13144

Emily Newsom, Laure Zanna, Jonathan Gregory
Background Pycnocline depth constrains Future Ocean Heat Uptake Efficiency
AGU Geophysical Research Letters 2023. DOI: 10.1029/2023GL105673

Fabrizio Falasca, Pavel Perezhogin, Laure Zanna
A data-driven framework for dimensionality reduction and causal inference in climate fields
ArXiv 2023. DOI: 10.48550/arXiv.2306.14433

Sara Shamekh and Pierre Gentine
Learning Atmospheric Boundary Layer Turbulence
JAMES 2023. DOI: 10.22541/essoar.168748456.60017486/v1

Aakash Sane, Brandon G. Reichl, Alistair Adcroft, Laure Zanna
Parameterizing vertical mixing coefficients in the Ocean Surface Boundary Layer using Neural Networks
JAMES 2023. DOI: 10.1029/2023MS003890

Sungduk Yu, ..., Michael S. Pritchard
ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid
multi-scale climate simulators
ArXiv 2023. DOI: 10.48550/arXiv.2306.08754

Karan Jakhar, Yifei Guan, Rambod Mojgani, Ashesh Chattopadhyay, Pedram Hassanzadeh, Laure Zanna
Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges.
ESS Open Archive. 2023. DOI: 10.22541/essoar.168677212.21341231/v1

Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad, Gherardo Varando, Georg Martius,
Emili Balaguer-Ballester, Ricardo Vinuesa, Emiliano Diaz, Laure Zanna, Jakob Runge
Discovering Causal Relations and Equations from Data.
Physics Reports 2023. DOI: 10.1016/j.physrep.2023.10.005

Rei Chemke and Janni Yuval
Human-induced weakening of the Northern Hemisphere tropical circulation
Nature. 2023. DOI: 10.1038/s41586-023-05903-1

William Gregory, Mitchell Bushuk, Alistair Adcroft, Yongfei Zhang, Laure Zanna
Deep learning of systematic sea ice model errors from data assimilation increments
JAMES 2023. DOI: 10.1029/2023MS003757

Janni Yuval and Paul A. O’Gorman
Neural-Network Parameterization of Subgrid Momentum Transport in the Atmosphere
JAMES 2023. DOI: 10.1029/2023MS003606

Karl Otness, Laure Zanna, Joan Bruna
Data-driven multiscale modeling of subgrid parameterizations in climate models
arXiv:2303.17496. Preprint submitted to ICLR Workshop on Climate Change AI. 2023. DOI: 10.48550/arXiv.2303.17496

Fabrizio Falasca, Andrew Brettin, Laure Zanna, Stephen M. Griffies, Jianjun Yin, Ming Zhao
Exploring the non-stationarity of coastal sea level probability distributions
arxiv.org:2211.04608. Preprint submitted to EDS. 2023. DOI: 10.48550/arXiv.2211.04608

Pavel Perezhogin, Laure Zanna, Carlos Fernandez-Granda
Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model
JAMES. 2023. DOI: 10.1029/2023MS003681

Pavel Perezhogin, Andrey Glazunov
Subgrid Parameterizations of Ocean Mesoscale Eddies Based on Germano Decomposition
JAMES. 2023. DOI: 10.1029/2023MS003771

Andrew Ross, Ziwei Li, Pavel Perezhogin, Carlos Fernandez-Granda, Laure Zanna
Benchmarking of machine learning ocean subgrid parameterizations in an idealized model
JAMES. 2023. DOI: 10.1029/2022MS003258

Cheng Zhang, Pavel Perezhogin, Cem Gultekin, Alistair Adcroft, Carlos Fernandez-Granda, Laure Zanna
Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization into a
Numerical Ocean Circulation Model
JAMES 2023. DOI: 10.1029/2023MS003697

Qiyu Xiao, Dhruv Balwada, C. Spencer Jones, Mario Herrero-Gonzalez, K. Shafer Smith, Ryan Abernathey
Reconstruction of Surface Kinematics from Sea Surface Height Using Neural Networks
JAMES. 2023. DOI: 10.1029/2022MS003258

Takaya Uchida, Dhruv Balwada, Quentin Jamet, William K. Dewar, Bruno Deremble,
Thierry Penduff, Julien Le Sommer
Cautionary tales from the mesoscale eddy transport tensor
ScienceDirect 2023. DOI: 10.1016/j.ocemod.2023.102172

Adam Subel, Yifei Guan, Ashesh Chattopadhyay, Pedram Hassanzadeh
Explaining the physics of transfer learning in data-driven turbulence modeling
PNAS NEXUS 2023. DOI: 10.1093/pnasnexus/pgad015

Lorenzo Zampieri, Gabriele Arduini, Marika Holland, Sarah Keeley, Kristian S. Mogensen,
Matthew D. Shupe, Steffen Tietsche
A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses
AMS Journals, Monthy Weather Review: volume151, issue6 DOI: 10.1175/MWR-D-22-0130.1

Joan Bruna, Benjamin Peherstorfer, Eric Vanden-Eijnden
Neural Galerkin Scheme with Active Learning for High-Dimensional Evolution Equations
Journal of Computational Physics DOI: 10.1016/j.jcp.2023.112588

Peidong Wang, Janni Yuval, Paul A. O'Gorman
Non-local parameterization of atmospheric subgrid processes with neural networks
JAMES 2022. DOI: 10.1029/2022MS002984

Sara Shamekh, Kara D Lamb, Yu Huang, Pierre Gentine
Implicit learning of convective organization explains precipitation stochasticity
In review. 2022. DOI: 10.1002/essoar.10512517.1

Hannah Christensen and Laure Zanna
Parametrization in Weather and Climate Models
Oxford Research Encyclopedia of Climate Science. 2022. DOI: 10.1093/acrefore/9780190228620.013.826

Sheng Liu, Aakash Kaku, Haoxiang Huang, Laure Zanna, Weicheng Zhu, Narges Razavian,
Matan Leibovich, Sreyas Mohan, Boyang Yu, Jonathan Niles-Weed, Carlos Fernandez-Granda
Deep Probability Estimation
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13746-13781, 2022.
DOI: 10.48550/arXiv.2111.10734

Nora Loose, Ryan Abernathey, Ian Grooms, Julius Busecke, Arthur Guillaumin,
Elizabeth Yankovsky, Gustavo Marques, Jacob Steinberg, Andrew Slavin Ross, Hemant Khatri,
Scott Bachman, Laure Zanna, Paige Martin
GCM-Filters: A Python Package for Diffusion-based Spatial Filtering of Gridded Data
Journal of Open Source Software 7(70), p.3947. 2022. DOI: 10.21105/joss.03947

Hugo Frezat, Julien Le Sommer, Ronan Fablet, Guillaume Balarac, Redouane Lguensat
A posteriori learning for quasi-geostrophic turbulence parametrization
JAMES. 2022. DOI: 10.1029/2022MS003124

Lei Chen and Joan Bruna
On Gradient Descent Convergence beyond the Edge of Stability
ArXiv 2022 DOI: 10.48550/arXiv.2206.04172

Mohamed Aziz Bhouri and Pierre Gentine
History-Based, Bayesian, Closure for Stochastic Parameterization: Application to Lorenz '96
ArXiv 2022. DOI: 10.48550/arXiv.2210.14488