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content/news/2504Holland.md

@@ -8,4 +8,4 @@ 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**.
+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

@@ -8,4 +8,4 @@ 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.
+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

@@ -9,4 +9,4 @@ 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!**
+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/publications/_index.md

@@ -103,7 +103,7 @@ You can also check all our publications on our **[Google Scholar profile](https:
     <img src="/images/publications/2504-Zanna.png" style="width: 100px; height: 100px;">
   </div>
   <p>
- <img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon"> 
+ <img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon">
     <strong>Moein Darman, Pedram Hassanzadeh, Laure Zanna, Ashesh Chattopadhyay</strong><br>
     <a href="https://doi.org/10.48550/arXiv.2504.15487" target="_blank"><strong>Fourier analysis of the physics of transfer learning for data-driven subgrid-scale models of ocean turbulence</strong></a><br>
     <i>Arxiv</i> <strong>DOI</strong>:10.48550/arXiv.2504.15487