add news and publications

Author: johannag126

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'

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.**

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.**

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)

content/publications/_index.md

@@ -2,7 +2,7 @@
 title: "Kelsey Everard"
 draft: false
 image: "/images/team/KelseyEverard.jpg"
-jobtitle: "Postdoc"
+jobtitle: "Affiliate"
 promoted: true
 weight: 20
 position: Mesoscale eddy parameterisation