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Publications

Research work and relevant papers by our team

MΒ²LInES research publications

If you are interested in understanding how MΒ²LInES 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 MΒ²LInES, 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 https://github.com/m2lines/L96_demo.

2023

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
ArXiv 2023. DOI: 10.48550/arXiv.2307.11902

Fabrizio Falasca, Pavel Perezhogin, Laure Zanna
Causal inference in spatiotemporal climate fields through linear response theory
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 +. This JupyterBook describes the key research themes in MΒ²LInES, 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 https://github.com/m2lines/L96_demo.

2024

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

2023

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

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

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


2022

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

Lorenzo Zampieri, Gabriele Arduini, Marika Holland, Sarah Keeley, Kristian S. Mogensen,
Matthew D. Shupe, Steffen Tietsche

A machine learning correction model of the clear-sky bias over the Arctic sea ice in atmospheric reanalyses
J Earth and Space Science Open Archive. 2022 (preprint) DOI: 10.1002/essoar.10511269.1

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

\ No newline at end of file diff --git a/research/talks/index.html b/research/talks/index.html index d13094d7..32ffe135 100644 --- a/research/talks/index.html +++ b/research/talks/index.html @@ -7,7 +7,7 @@

Talks

Your can find most of our past talks, and much more, on our Youtube Channel

Themes of the talks:

  • πŸ“Š Big data
  • πŸ’» Machine Learning
  • πŸŽ† Physics discovery
  • 🌎 Modeling

Recent talks


2022

Video Preview

Lorenzo Zampieri
On the design strategy of subgrid parameterizations in modern sea ice models
Arctic Model Parameterization Development - December 22nd 💻 🎆

Video Preview

Ryan Abernathey
Climate Science and AI in Big Tech
Climate Change AI Webinar Series - December 15th 📊 💻
NOTE: You can find all the talks and posters our members gave at the AGU Fall meeting in our December 2022 newsletter.

Video Preview

Pierre Gentine
Physics to Machine Learning and Machine Learning Back to Physics
Columbia Center of AI Technology - December 7th 💻 🎆

Video Preview

Laure Zanna and Steven Brunton
Machine learning in fluid dynamics and climate physics
Nature Reviews Physics + Alan Turing Institute - October 5th 💻 🎆

Video Preview

Laure Zanna
Machine Learning for Ocean and Climate Modeling: advances, challenges and outlook
LEAP Seminar - September 22nd 💻 🎆

Video Preview

Laure Zanna
AI for Climate Physics
Hammers and Nails - August 4th 💻 🎆 🌎

Video Preview

Laure Zanna
Plenary talk
SIAM conference on Mathematics for Planet Earth - July 13th 💻 🎆 🌎
NOTE: More info at this website.

Video Preview

Laure Zanna
Joint plenary talk with Galen McKinley on MΒ²LInES and LEAP
2022 CESM Workshop - June 13th 💻 🎆 🌎

Video Preview

Laure Zanna
Machine Learning for Mesoscale Closures in Ocean Models
NCAR Mesoscale & Microscale Meteorology Laboratory - June 2nd 💻 🎆 🌎

Video Preview

Pierre Gentine
Can Artificial Intelligence Help Us Better Project Climate Change?
The Artificial Intelligence for Good Group - May 4th 💻 🎆 🌎

Video Preview

Andrew Ross
Panel discussion on the future of model interpretability at the ICLR workshop on AI for Earth and Space Science - April 29th
NOTE: You can find the panel on the top main video at 7:49:25 here .

Video Preview

Laure Zanna
Online implementation of Machine Learning Eddy Parameterizations in a Hierarchy of Ocean Models
Ocean Sciences Meeting - March 4th 💻 🎆 🌎

Video Preview

Mitch Bushuk
Mechanisms of Regional Arctic Sea Ice Predictability in Dynamical Seasonal Forecast Systems
Ocean Sciences Meeting - March 3rd 🎆

Video Preview

Ryan Abernathey
OpenOceanCloud: A New Approach to Ocean Data and Computing
Ocean Sciences Meeting - March 3rd 📊

Video Preview

Lorenzo Zampieri
A machine learning correction model for the warm bias over Arctic sea ice in atmospheric reanalyses
Ocean Sciences Meeting - March 1st 💻 🎆

Video Preview

Dhruv Balwada
Tracer Ventilation, Stirring, and Variability in the Antarctic Circumpolar Current
Ocean Sciences Meeting - March 1st 🎆

Video Preview

Brandon Reichl
A potential energy analysis of ocean surface mixed layers
Ocean Sciences Meeting - February 28th

Video Preview

Andrew Ross
Evaluating machine learning parameterizations of ocean subgrid forcing
Ocean Sciences Meeting - February 28th 💻 🎆

Video Preview

Dhruv Balwada
Direct observational estimate of the dual kinetic energy cascade and its seasonality at the surface ocean from surface drifters
Ocean Sciences Meeting - February 28th 🎆

Video Preview

Ryan Abernathey
OpenOceanCloud - Transforming oceanography with a new approach to data and computing
Ocean Data Conference - February 15th 📊

Video Preview

Ryan Abernathey
Workshop AI for Earth Sciences
The National Academies Board on Atmospheric Sciences and Climate - February 10th 📊 💻 🎆

Video Preview

Pierre Gentine
Workshop AI for Earth Sciences
The National Academies Board on Atmospheric Sciences and Climate - February 7th 💻 🎆

Video Preview

Laure Zanna
Data-driven turbulence closures for ocean and climate models: advances and challenges
UW Data-driven methods in science and engineering seminar - February 4th 💻 🎆

Archive Talks

A list of talks from previous years.

2021

  • Joan Bruna - Deep Learning Barcelona: Geometric Deep Learning: Prospects and...

Subscribe to our newsletter

* indicates required
+

Talks

Your can find most of our past talks, and much more, on our Youtube Channel

Themes of the talks:

  • πŸ“Š Big data
  • πŸ’» Machine Learning
  • πŸŽ† Physics discovery
  • 🌎 Modeling

Recent talks


2024

Video Preview

Aakash Sane
Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer Using Neural Networks
CESM OMWG - February 💻 🎆

2023

Video Preview

Pavel Perezhogin
Machine-Learned parameterizations of mesoscale eddies in MOM6 ocean model: convolutional neural network and symbolic regression
28th Annual CESM Workshop - June 💻 🎆

Video Preview

Laure Zanna
Ocean and Atmosphere Dynamics
Climatematch academy - July 💻 🎆

Video Preview

Fabrizio Falasca
Exploting the nonstationarity of coastal sea level probability distributions
NOAA-CVP Webinar Series 2023 - April 💻 🎆

Video Preview

Paul O'Gorman
Improving climate models using machine learning
MIT Generative AI Week - November 💻 🎆

Video Preview

Laure Zanna
Transforming Climate Modeling with AI: hype or Reality?
UN AI for Good - March 💻 🎆

Video Preview

Pavel Perezhogin
Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model
NEMO working group on Machine-Learning - April 💻 🎆

2022

Video Preview

Janni Yuval
Neural-network parameterization of subgrid momentum transport learned from a high-resolution simulation
23rd AMS Conference on Atmospheric and Oceanic Fluid Dynamics - June 💻 🎆

Video Preview

Aakash Sane
Parameterizing Vertical Turbulent Mixing Coefficients In The Ocean Surface Boundary Layer Using Neural Networks
February 💻 🎆

Video Preview

Lorenzo Zampieri
On the design strategy of subgrid parameterizations in modern sea ice models
Arctic Model Parameterization Development - December 22nd 💻 🎆

Video Preview

Ryan Abernathey
Climate Science and AI in Big Tech
Climate Change AI Webinar Series - December 15th 📊 💻
NOTE: You can find all the talks and posters our members gave at the AGU Fall meeting in our December 2022 newsletter.

Video Preview

Pierre Gentine
Physics to Machine Learning and Machine Learning Back to Physics
Columbia Center of AI Technology - December 7th 💻 🎆

Video Preview

Laure Zanna and Steven Brunton
Machine learning in fluid dynamics and climate physics
Nature Reviews Physics + Alan Turing Institute - October 5th 💻 🎆

Video Preview

Laure Zanna
Machine Learning for Ocean and Climate Modeling: advances, challenges and outlook
LEAP Seminar - September 22nd 💻 🎆

Video Preview

Laure Zanna
AI for Climate Physics
Hammers and Nails - August 4th 💻 🎆 🌎

Video Preview

Laure Zanna
Plenary talk
SIAM conference on Mathematics for Planet Earth - July 13th 💻 🎆 🌎
NOTE: More info at this website.

Video Preview

Laure Zanna
Joint plenary talk with Galen McKinley on MΒ²LInES and LEAP
2022 CESM Workshop - June 13th 💻 🎆 🌎

Video Preview

Laure Zanna
Machine Learning for Mesoscale Closures in Ocean Models
NCAR Mesoscale & Microscale Meteorology Laboratory - June 2nd 💻 🎆 🌎

Video Preview

Pierre Gentine
Can Artificial Intelligence Help Us Better Project Climate Change?
The Artificial Intelligence for Good Group - May 4th 💻 🎆 🌎

Video Preview

Andrew Ross
Panel discussion on the future of model interpretability at the ICLR workshop on AI for Earth and Space Science - April 29th
NOTE: You can find the panel on the top main video at 7:49:25 here .

Video Preview

Laure Zanna
Online implementation of Machine Learning Eddy Parameterizations in a Hierarchy of Ocean Models
Ocean Sciences Meeting - March 4th 💻 🎆 🌎

Video Preview

Mitch Bushuk
Mechanisms of Regional Arctic Sea Ice Predictability in Dynamical Seasonal Forecast Systems
Ocean Sciences Meeting - March 3rd 🎆

Video Preview

Ryan Abernathey
OpenOceanCloud: A New Approach to Ocean Data and Computing
Ocean Sciences Meeting - March 3rd 📊

Video Preview

Lorenzo Zampieri
A machine learning correction model for the warm bias over Arctic sea ice in atmospheric reanalyses
Ocean Sciences Meeting - March 1st 💻 🎆

Video Preview

Dhruv Balwada
Tracer Ventilation, Stirring, and Variability in the Antarctic Circumpolar Current
Ocean Sciences Meeting - March 1st 🎆

Video Preview

Brandon Reichl
A potential energy analysis of ocean surface mixed layers
Ocean Sciences Meeting - February 28th

Video Preview

Andrew Ross
Evaluating machine learning parameterizations of ocean subgrid forcing
Ocean Sciences Meeting - February 28th 💻 🎆

Video Preview

Dhruv Balwada
Direct observational estimate of the dual kinetic energy cascade and its seasonality at the surface ocean from surface drifters
Ocean Sciences Meeting - February 28th 🎆

Video Preview

Ryan Abernathey
OpenOceanCloud - Transforming oceanography with a new approach to data and computing
Ocean Data Conference - February 15th 📊

Video Preview

Ryan Abernathey
Workshop AI for Earth Sciences
The National Academies Board on Atmospheric Sciences and Climate - February 10th 📊 💻 🎆

Video Preview

Pierre Gentine
Workshop AI for Earth Sciences
The National Academies Board on Atmospheric Sciences and Climate - February 7th 💻 🎆

Video Preview

Laure Zanna
Data-driven turbulence closures for ocean and climate models: advances and challenges
UW Data-driven methods in science and engineering seminar - February 4th 💻 🎆

Archive Talks

A list of talks from previous years.

2021

  • Joan Bruna - Deep Learning Barcelona: Geometric Deep Learning: Prospects and...

Subscribe to our newsletter

* indicates required