update news April

Author: johannag126

content/news/2504Holland.md

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+---
+date: 2025-04-02T09:29:16+10:00
+title: "Regional multi-year predictability of Antarctic sea ice in CESM2 and its implications for marine ecosystems"
+heroHeading: ''
+heroSubHeading: 'Regional multi-year predictability of Antarctic sea ice in CESM2 and its implications for marine ecosystems'
+heroBackground: ''
+thumbnail:  'images/news/2504Holland.png'
+images: ['images/news/2504Holland.png']
+link: 'https://doi.org/10.1175/JCLI-D-24-0258.1'
+---
+This new [article](https://doi.org/10.1175/JCLI-D-24-0258.1) explores the predictability of Antarctic sea ice, which varies by region and is influenced by ocean and atmospheric conditions. Using climate models, Marika Holland and co-authors, confirm that **sea ice changes can be predicted months in advance, with cycles of predictability linked to ice growth and retreat**: it remains predictable during growth, loses predictability when melting, and regains it as it reforms. Ocean temperature patterns near the ice edge play a key role, with variations across different regions. These ice changes also influence marine ecosystems by affecting light availability. Understanding these patterns can **improve climate predictions and support the management of the Southern Ocean’s biodiversity**.

content/news/2504Pedersen.md

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+---
+date: 2025-04-02T09:29:16+10:00
+title: "Thermalizer: Stable autoregressive neural emulation of spatiotemporal chaos"
+heroHeading: ''
+heroSubHeading: 'Thermalizer: Stable autoregressive neural emulation of spatiotemporal chaos'
+heroBackground: ''
+thumbnail:  'images/news/2504Pedersen.png'
+images: ['images/news/2504Pedersen.png']
+link: 'https://doi.org/10.48550/arXiv.2503.18731'
+---
+Autoregressive surrogate models (emulators) make fast predictions for dynamical systems but become unstable over time due to error buildup. This [study](https://doi.org/10.48550/arXiv.2503.18731), led by Chris Pedersen, introduces thermalization, a technique leveraging diffusion models to adaptively correct errors during inference. **By stabilizing predictions, it extends emulated rollouts of chaotic and turbulent systems by several orders of magnitude**. This breakthrough application of diffusion models enhances the utility of emulators, and can be applied to autoregressive models across science and engineering.

content/news/2504Zanna.md

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+---
+date: 2025-04-02T09:29:16+10:00
+title: "The futures of climate modeling"
+heroHeading: ''
+heroSubHeading: 'The futures of climate modeling'
+heroBackground: ''
+thumbnail:  'images/news/2504Zanna.jpeg'
+images: ['images/news/2504Zanna.jpeg']
+link: 'https://doi.org/10.1038/s41612-025-00955-8'
+---
+
+New tools like kilometer-scale modeling, parameter tuning, and AI/machine learning are reshaping the climate modeling field, sparking debate on the best path forward. Five internationally renowned female climate scientists, including Laure Zanna, sign this [perspective piece](https://doi.org/10.1038/s41612-025-00955-8) in Nature climate and atmospheric science. Their analysis draws lessons from past research to guide the future of climate modeling. The conclusion: **the future of climate modeling depends on embracing diverse tools and methodologies to drive meaningful progress!**

content/news/Newsletters/_index.md

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  Links to our past newsletters are below.
 ### 2025
+* 04/02/2025 - [M²LInES newsletter - April 2025](https://mailchi.mp/efd79c6eae08/m2lines-apr2025)
+
 * 03/03/2025 - [M²LInES newsletter - March 2025](https://mailchi.mp/14656511dff3/m2lines-mar2025)
 
 * 02/04/2025 - [M²LInES newsletter - February 2025](https://mailchi.mp/382387c99943/m2lines-feb2025-2)