diff --git a/content/news/2512AGU.md b/content/news/2512AGU.md
index f3c4a15f..6205fabb 100644
--- a/content/news/2512AGU.md
+++ b/content/news/2512AGU.md
@@ -1,5 +1,5 @@
---
-date: 2025-11-25T09:29:16+10:00
+date: 2025-12-02T09:29:16+10:00
title: "M²LInES at AGU"
heroHeading: ''
heroSubHeading: 'AGU 2025 – M²LInES team members and affiliates Schedule'
diff --git a/content/news/2512Otness.md b/content/news/2512Otness.md
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+---
+date: 2025-12-01T09:29:16+10:00
+title: "Data-driven multiscale modeling for correcting dynamical systems"
+heroHeading: ''
+heroSubHeading: 'Data-driven multiscale modeling for correcting dynamical systems'
+heroBackground: ''
+thumbnail: 'images/news/2512Otness.png'
+images: ['images/news/2512Otness.png']
+link: 'https://doi.org/10.48550/arXiv.2510.22676'
+---
+
+In this [article](https://doi.org/10.1088/2632-2153/ae1a36), Karl Otness and co-authors present a new multiscale machine-learning approach designed to **improve predictions in dynamical systems**. The method captures information moving both from fine to coarse scales and from coarse to fine, **boosting model accuracy and stability**, with only **minimal added computational cost** compared to standard architectures. The team evaluates the approach on an idealized fluid-dynamics closure task, where the multiscale networks learn to correct a chaotic model by representing unresolved small-scale processes. The work highlights the **potential of multiscale AI architectures to enhance the reliability of physical system modeling.**
\ No newline at end of file
diff --git a/content/news/2512Zanna.md b/content/news/2512Zanna.md
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+---
+date: 2025-12-01T09:29:16+10:00
+title: "A Framework for Hybrid Physics-AI Coupled Ocean Models"
+heroHeading: ''
+heroSubHeading: 'A Framework for Hybrid Physics-AI Coupled Ocean Models'
+heroBackground: ''
+thumbnail: 'images/news/2512framework.png'
+images: ['images/news/2512framework.png']
+link: 'https://doi.org/10.48550/arXiv.2510.22676'
+---
+
+In this [preprint](https://doi.org/10.48550/arXiv.2510.22676), M²LInES demonstrates the power of **AI driven methods in producing reliable climate simulations.** We introduce a new framework that brings physics- and scale-aware machine learning into climate models. Traditional parameterizations of physical processes often produce significant biases, but AI can now learn these processes directly from data. Our team **implements a suite of data-driven parameterizations in the ocean and sea-ice components of a state-of-the-art model**, ranging from deep learning to interpretable equation-based methods. Our results demonstrate that AI-driven parameterizations can run effectively in operational climate simulations, enabling **hybrid atmosphere–ocean–sea-ice modeling. All tools are open source and available to the community.**
\ No newline at end of file
diff --git a/content/news/Newsletters/_index.md b/content/news/Newsletters/_index.md
index 6d500977..2f85c074 100644
--- a/content/news/Newsletters/_index.md
+++ b/content/news/Newsletters/_index.md
@@ -12,6 +12,8 @@ tags:
### 2025
+* 12/02/2025 - [M²LInES newsletter - December 2025](https://mailchi.mp/14605e5ed14c/m2lines-dec2025)
+
* 11/03/2025 - [M²LInES newsletter - November 2025](https://mailchi.mp/5f5c32598bba/m2lines-nov2025)
* 10/01/2025 - [M²LInES newsletter - October 2025](https://mailchi.mp/0608f769fe88/m2lines-oct2025)
diff --git a/content/publications/_index.md b/content/publications/_index.md
index 7bef421b..3b8117c5 100644
--- a/content/publications/_index.md
+++ b/content/publications/_index.md
@@ -13,6 +13,174 @@ You can also check all our publications on our **[Google Scholar profile](https:
M²LInES funded research
### 2025
+
+
+ Karl Otness, Laure Zanna, Joan Bruna
+ Data-driven multiscale modeling for correcting dynamical systems
+ Machine Learning: Science and Technology DOI: 10.1088/2632-2153/ae1a36
+
+
+ Sara Shamekh, Pedro Angulo-Umana, Paul A O'gorman
+ Data-driven Modeling of Stratiform and Convective Rain Area
+ Authorea Preprint DOI: 10.22541/au.176185709.98292011/v1
+
+
+ Laure Zanna, William Gregory, Pavel Perezhogin, Aakash Sane, Cheng Zhang, Alistair Adcroft, Mitch Bushuk, Carlos Fernandez-Granda, Brandon Reichl, Dhruv Balwada, Julius Busecke, William Chapman, Alex Connolly, Danni Du, Kelsey Everard, Fabrizio Falasca, Renaud Falga, David Kamm, Etienne Meunier, Qi Liu, Antoine Nasser, Matthew Pudig, Andrew Shao, Julia L. Simpson, Linus Vogt, Jiarong Wu
+ A Framework for Hybrid Physics-AI Coupled Ocean Models
+ Arxiv DOI:10.48550/arXiv.2510.22676
+
+
+ Pavel Perezhogin, Alistair Adcroft, Laure Zanna
+ Generalizable neural‐network parameterization of mesoscale eddies in idealized and global ocean models
+ GRL DOI:10.1029/2025GL117046
+
+
+ Danni Du, Feiyu Lu, Alistair Adcroft.
+ Reducing Model Biases with Machine Learning Corrections Derived from Ocean Data Assimilation Increments
+ Authorea Preprints DOI:10.22541/essoar.176083747.76188196/v2
+
+
+ Yingkai Sha, John S. Schreck, William Chapman, David John Gagne II
+ Investigating the Use of Terrain-Following Coordinates in AI-Driven Precipitation Forecasts
+ GRL DOI:10.1029/2025GL118478
+
+
+ Yingkai Sha, John S. Schreck, William Chapman, David John Gagne II
+ Improving AI Weather Prediction Models Using Global Mass and Energy Conservation Schemes
+ JAMES DOI:10.1029/2025MS005138
+
+
+ James P. C. Duncan, Elynn Wu, Surya Dheeshjith, Adam Subel, Troy Arcomano, Spencer K. Clark, Brian Henn, Anna Kwa, Jeremy McGibbon, W. Andre Perkins, William Gregory, Carlos Fernandez-Granda, Julius Busecke, Oliver Watt-Meyer, William J. Hurlin, Alistair Adcroft, Laure Zanna, Christopher Bretherton
+ SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators
+ Arxiv DOI:10.48550/arXiv.2509.12490
+
+
+ Katharina Hafner, Fernando Iglesias-Suarez, Sara Shamekh, Pierre Gentine, Marco A. Giorgetta, Robert Pincus, Veronika Eyring
+ Interpretable machine learning‐based radiation emulation for icon
+ Journal of Geophysical Research: Machine Learning and Computation DOI:10.1029/2024JH000501
+
+
+ Abigail Bodner, Dhruv Balwada, Laure Zanna
+ A data‐driven approach for parameterizing ocean submesoscale buoyancy fluxes
+ JAMES DOI: 10.1029/2025MS004991
+
+
+ Stephen M Griffies, Alistair Adcroft, Rebecca L Beadling, Mitchell Bushuk, Chiung‐Yin Chang, Henri F Drake, Raphael Dussin, Robert W Hallberg, William J Hurlin, Hemant Khatri, John P Krasting, Matthew Lobo, Graeme A MacGilchrist, Brandon G Reichl, Aakash Sane, Olga Sergienko, Maike Sonnewald, Jacob M Steinberg, Jan‐Erik Tesdal, Matthew Thomas, Katherine E Turner, Marshall L Ward, Michael Winton, Niki Zadeh, Laure Zanna, Rong Zhang, Wenda Zhang, Ming Zhao
+ The GFDL‐CM4X climate model hierarchy, Part I: Model description and thermal properties
+ JAMES DOI:10.1029/2024MS004861
+
+
+ Shuchang Liu, Paul A. O'Gorman
+ CERA: A Framework for Improved Generalization of Machine Learning Models to Changed Climates
+ Arxiv DOI:10.48550/arXiv.2509.00010
+
+
+ William E. Chapman, Francine Schevenhoven, Judith Berner, Noel Keenlyside, Ingo Bethke, Ping-Gin Chiu, Alok Gupta, Jesse Nusbaumer
+ Implementation and validation of a supermodeling framework into Community Earth System Model version 2.1.5
+ Geosci. Model Dev. DOI:10.5194/gmd-18-5451-2025
+
+
+ Jia-Rui Shi, Laure Zanna, Alistair Adcroft
+ The Impact of Natural External Forcing on Ocean Heat Uptake Efficiency Since the 1980s
+ GRL DOI:10.1029/2025GL116305
+