Publications
Research work and relevant papers by our team
M²LInES research and other relevant publications
If you are interested in understanding how M²LInES is using machine learning to improve climate models, we have developed an educational JupyterBook Learning Machine Learning for Climate modeling with Lorenz 96 . This JupyterBook describes the key research themes in M²LInES, through the use of a simple climate model and machine learning algorithms. You can run the notebooks yourself, contribute to the development of the JupyterBook or let us know what you think on GitHub.
You can also check all our publications on our Google Scholar profile
M²LInES funded research
2024
William E. Chapman and Judith Berner
A State-Dependent Model-Error Representation for Online Climate Model Bias Correction
Preprint in ESS DOI:10.22541/essoar.172526800.05354621/v2
John Schreck, Yingkai Sha, William Chapman, Dhamma Kimpara, Judith Berner, Seth McGinnis, Arnold Kazadi, Negin Sobhani, Ben Kirk, David John Gagne II
Community Research Earth Digital Intelligence Twin (CREDIT)
ArXiv 2024 DOI: 10.48550/arXiv.2411.07814
Pavel Perezhogin, Cheng Zhang, Alistair Adcroft, Carlos Fernandez-Granda, Laure Zanna
A Stable Implementation of a Data-Driven Scale-Aware Mesoscale Parameterization
JAMES DOI: 10.1029/2023MS004104
Brandon G Reichl, Andrew T. Wittenberg, Stephen M Griffies, Alistair Adcroft
Improved Equatorial Upper Ocean Vertical Mixing in the NOAA/GFDL OM4 Model
Earth and Space Science DOI:10.1029/2023EA003485
Joan Bruna, Benjamin Peherstorfer, Eric Vanden-Eijnden
Neural Galerkin schemes with active learning for high-dimensional evolution equations
Journal of Computational Physics DOI:10.1016/j.jcp.2023.112588
Dmitrii Kochkov, ... Stephan Hoyer
Neural general circulation models for weather and climate
Nature 2024 DOI: 10.1038/s41586-024-07744-y
Tom Beucler, Arthur Grundner, Sara Shamekh, Peter Ukkonen, Matthew Chantry, Ryan Lagerquist
Distilling Machine Learning's Added Value: Pareto Fronts in Atmospheric Applications
ArXiv 2024 DOI: 10.48550/arXiv.2408.02161
William Gregory, Ronald MacEachern, So Takao, Isobel R. Lawrence, Carmen Nab, Marc Peter Deisenroth, Michel Tsamados
Scalable interpolation of satellite altimetry data with probabilistic machine learning
Nature Communications 2024 DOI: 10.1038/s41467-024-51900-x
Veronika Eyring, ... Laure Zanna
Pushing the frontiers in climate modelling and analysis with machine learning
Nature Climate Change 2024 DOI: 10.1038/s41558-024-02095-y
Jerry Lin, Sungduk Yu, Liran Peng, Tom Beucler, Eliot Wong-Toi, Zeyuan Hu, Pierre Gentine, Margarita Geleta, Michael S Pritchard
Sampling Hybrid Climate Simulation at Scale to Reliably Improve Machine Learning Parameterization
Preprint submitted to JAMES DOI:10.22541/essoar.172072688.86581349/v1
Surya Dheeshjith, Adam Subel, Shubham Gupta, Alistair Adcroft, Carlos Fernandez-Granda, Julius Busecke, Laure Zanna
Transfer Learning for Emulating Ocean Climate Variability across CO2 forcing
Preprint accepted at ICML 2024 ML4ESM DOI: 10.48550/arXiv.2405.18585
Justin Finkel and Paul A. O’Gorman
Bringing Statistics to Storylines: Rare Event Sampling for Sudden, Transient Extreme Events
JAMES 2024 DOI: 10.1029/2024MS004264
David Bonan, Andrew Thompson, Tapio Schneider, Laure Zanna, Kyle Armour, Shantong Sun
Constraints imply limited future weakening of Atlantic meridional overturning circulation
Preprint under review at Nature Portfolio DOI: doi.org/10.21203/rs.3.rs-4456168/v1
Lorenzo Zampieri, David Clemens-Sewall, Anne Sledd, Nils Hutter, Marika Holland
Modeling the Winter Heat Conduction Through the Sea Ice System During MOSAiC
Geophysical Research Letters 2024 DOI: 10.1029/2023GL106760
William Gregory, Ronald MacEachern, So Takao, Isobel Lawrence, Carmen Nab, Marc Deisenroth, Michel Tsamados
Scalable interpolation of satellite altimetry data with probabilistic machine learning
Nature Comms. 2024 DOI: 10.21203/rs.3.rs-4209064/v1
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
Fabrizio Falasca, Pavel Perezhogin, Laure Zanna
Data-driven framework for dimensionality reduction and causal inference in climate fields
APS Physics Review E 2024 DOI: 10.1103/PhysRevE.109.044202
Tom Beucler, ... Pierre Gentine
Climate-Invariant Machine Learning
Science Advances 2024 DOI: 10.1126/sciadv.adj7250
2023
Abigail Bodner, Dhruv Balwada, Laure Zanna
A Data-Driven Approach for Parameterizing Submesoscale Vertical Buoyancy Fluxes in the Ocean Mixed Layer
Arxiv 2023. DOI: 10.48550/arXiv.2312.06972
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
Pavel Perezhogin, Cheng Zhang, Alistair Adcroft, Carlos Fernandez-Granda, Laure Zanna
Implementation of a data-driven equation-discovery mesoscale parameterization into an ocean model
James 2023. DOI: 10.48550/arXiv.2311.02517
Will Chapman and Judith Berner
Deterministic and stochastic tendency adjustments derivedfrom data assimilation and nudging
QJRMS 2023. DOI: 10.1002/qj.4652
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
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
NeurIPS 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
Preprint accepted at 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
EDS. volume 2 2023. DOI: 10.1017/eds.2023.10
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
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
Lei Chen and Joan Bruna
On Gradient Descent Convergence beyond the Edge of Stability
ICLR 2023 DOI: 10.5555/3618408.3618580
2022
Andrew Ross, Ziwei Li, Pavel Perezhogin, Carlos Fernandez-Granda, Laure Zanna
Benchmarking of machine learning ocean subgrid parameterizations in an idealized model
JAMES. 2022. DOI: 10.1029/2022MS003258
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
Publications Archive
M²LInES funded research
2021 -
- Guillaumin, A. P., & Zanna, L. Stochastic-Deep Learning Parameterization of...
