diff --git a/content/news/2507CREDIT.md b/content/news/2507CREDIT.md new file mode 100644 index 00000000..e738694c --- /dev/null +++ b/content/news/2507CREDIT.md @@ -0,0 +1,12 @@ +--- +date: 2025-07-02T09:29:16+10:00 +title: "Community Research Earth Digital Intelligence Twin" +heroHeading: '' +heroSubHeading: 'Community Research Earth Digital Intelligence Twin' +heroBackground: '' +thumbnail: 'images/news/2507CREDIT.png' +images: ['images/news/2507CREDIT.png'] +link: 'https://doi.org/10.1038/s41612-025-01125-6' +--- + +A new framework from NCAR, called CREDIT (Community Research Earth Digital Intelligence Twin), is making it easier for researchers to **develop and test AI-based weather prediction models**. Introduced in this [paper](https://doi.org/10.1038/s41612-025-01125-6), CREDIT supports flexible model design and training, helping **address key challenges in AI-based forecasting**. Using this platform, researchers introduced WXFormer, a novel model that outperforms traditional forecasting systems like ECMWF’s IFS on 10-day forecasts, while being much more computationally efficient. CREDIT aims to accelerate innovation and collaboration in AI-driven weather prediction. **William Chapman** and **Judith Berner** contributed to this research. \ No newline at end of file diff --git a/content/news/2507Carlos.md b/content/news/2507Carlos.md new file mode 100644 index 00000000..513e3963 --- /dev/null +++ b/content/news/2507Carlos.md @@ -0,0 +1,13 @@ +--- +date: 2025-07-02T09:29:16+10:00 +title: "Carlos Fernandez-Granda new book" +heroHeading: '' +heroSubHeading: 'Carlos Fernandez-Granda new book: Probability and Statistics for Data Science' +heroBackground: '' +thumbnail: 'images/news/2507Carlos.png' +images: ['images/news/2507Carlos.png'] +link: 'https://www.cambridge.org/core/books/probability-and-statistics-for-data-science/CC7DC7E53ED92074008803C96A67620B' +--- + +**Carlos Fernandez-Granda** new book is out! Published by [Cambridge University Press](https://www.cambridge.org/core/books/probability-and-statistics-for-data-science/CC7DC7E53ED92074008803C96A67620B), the book is a self-contained **guide to the two pillars of data science, probability theory, and statistics**. The materials, which include 200 exercises with solutions, 102 Python notebooks using 23 real-world datasets, 115 YouTube videos with slides, and a free preprint, can be found at this **[website](https://www.ps4ds.net/)**. + diff --git a/content/news/2507Gregory.md b/content/news/2507Gregory.md new file mode 100644 index 00000000..3ca64a9b --- /dev/null +++ b/content/news/2507Gregory.md @@ -0,0 +1,12 @@ +--- +date: 2025-07-02T09:29:16+10:00 +title: "Advancing global sea ice prediction capabilities" +heroHeading: '' +heroSubHeading: 'Advancing global sea ice prediction capabilities' +heroBackground: '' +thumbnail: 'images/news/2507Gregory.png' +images: ['images/news/2507Gregory.png'] +link: 'https://doi.org/10.48550/arXiv.2505.18328' +--- + +**Gregory** et al. present in this [preprint](https://doi.org/10.48550/arXiv.2505.18328), a hybrid modeling approach that **integrates machine learning (ML) into the GFDL SPEAR climate model to correct sea ice biases in real time**. Two versions are tested: one that includes coupled feedbacks (HybridCPL) and one that does not (HybridIO). HybridCPL **significantly improves Arctic and Antarctic sea ice forecasts on seasonal and subseasonal timescales**. In contrast, HybridIO performs poorly due to unanticipated feedbacks. These results highlight the **importance of training ML models within coupled climate systems** for reliable predictions. \ No newline at end of file diff --git a/content/news/2507Perezhogin.md b/content/news/2507Perezhogin.md new file mode 100644 index 00000000..be4c9315 --- /dev/null +++ b/content/news/2507Perezhogin.md @@ -0,0 +1,12 @@ +--- +date: 2025-07-02T09:29:16+10:00 +title: "Parameterization of mesoscale eddies" +heroHeading: '' +heroSubHeading: 'Parameterization of mesoscale eddies' +heroBackground: '' +thumbnail: 'images/news/2507Perezhogin.png' +images: ['images/news/2507Perezhogin.png'] +link: 'https://doi.org/10.48550/arXiv.2505.08900' +--- + +**Perezhogin** et al. propose in this [preprint](https://doi.org/10.48550/arXiv.2505.08900), a **physics-informed neural network to improve the generalization of data-driven mesoscale eddy parameterizations** in ocean models. By applying local input-output scaling based on dimensional analysis, **their method adapts to different grid resolutions and depths**. This approach enhances energy representation and affects biases in both idealized and global ocean simulations. The scaling framework is broadly applicable and robust across configurations. Results show **competitive performance compared to traditional parameterizations**. \ No newline at end of file diff --git a/content/news/2507Samudra.md b/content/news/2507Samudra.md new file mode 100644 index 00000000..e78b3495 --- /dev/null +++ b/content/news/2507Samudra.md @@ -0,0 +1,12 @@ +--- +date: 2025-07-02T09:29:16+10:00 +title: "Samudra and Laure Zanna featured in APS news article!" +heroHeading: '' +heroSubHeading: 'Samudra and Laure Zanna featured in APS news article' +heroBackground: '' +thumbnail: 'images/news/Samudra.gif' +images: ['images/news/Samudra.gif'] +link: 'https://www.aps.org/apsnews/2025/06/ai-could-shape-climate-science?utm_source=jun-amn&utm_medium=email&utm_campaign=jun-amn' +--- + +This **[APS news article](https://www.aps.org/apsnews/2025/06/ai-could-shape-climate-science?utm_source=jun-amn&utm_medium=email&utm_campaign=jun-amn)** highlights **how AI is reshaping climate science**, offering faster, smarter ways to model Earth’s complex systems. It features highlights from the Global Physics Summit, where researchers presented how machine learning is accelerating simulations, uncovering new physics, and helping build more precise climate models, all while keeping physics at the core. In particular, it spotlights interview exerts with Laure Zanna presenting **[Samudra](https://doi.org/10.1029/2024GL114318), the M²LInES created AI emulator**. \ No newline at end of file diff --git a/content/news/2508Ai2Samudra.md b/content/news/2508Ai2Samudra.md new file mode 100644 index 00000000..e9c8e012 --- /dev/null +++ b/content/news/2508Ai2Samudra.md @@ -0,0 +1,12 @@ +--- +date: 2025-08-02T09:29:16+10:00 +title: "M²LInES AI Ocean Emulator, Samudra, is now included in the Ai2 ACE codebase" +heroHeading: '' +heroSubHeading: 'M²LInES AI Ocean Emulator, Samudra, is now included in the Ai2 ACE codebase' +heroBackground: '' +thumbnail: 'images/news/Samudra.gif' +images: ['images/news/Samudra.gif'] +link: 'https://github.com/ai2cm/ace/releases/tag/2025.7.0' +--- + +The **[Ai2 Climate Modeling](https://allenai.org/climate-modeling)** team has released a new version of their climate emulator. In this **[latest release](https://github.com/ai2cm/ace/releases/tag/2025.7.0), Samudra**, the AI global ocean emulator developed by M²LInES, is now **integrated into Ai2's full model framework**. We encourage you to check out the **[Ai2 codebase](https://github.com/ai2cm/ace)** and Samudra's **[original code](https://github.com/m2lines/Samudra)** to create your own ocean simulations. \ No newline at end of file diff --git a/content/news/2508Balwada.md b/content/news/2508Balwada.md new file mode 100644 index 00000000..3fb7e72c --- /dev/null +++ b/content/news/2508Balwada.md @@ -0,0 +1,12 @@ +--- +date: 2025-08-02T09:29:16+10:00 +title: "The Impact of Sub-Grid Heterogeneity on Air-Sea Turbulent Heat Flux in Coupled Climate Models" +heroHeading: '' +heroSubHeading: 'The Impact of Sub-Grid Heterogeneity on Air-Sea Turbulent Heat Flux in Coupled Climate Models' +heroBackground: '' +thumbnail: 'images/news/2508Balwada.jpg' +images: ['images/news/2508Balwada.jpg'] +link: 'https://doi.org/10.1029/2025GL114951' +--- + +A new [paper](https://doi.org/10.1029/2025GL114951), led by **Julius Busecke** and **Dhruv Balwada**, highlights how small-scale air-sea interactions, often unresolved in climate models, can **significantly influence large-scale heat exchange between the ocean and atmosphere**. Using high-resolution coupled simulations, researchers found that this small-scale variability leads to a **systematic global ocean cooling of about 4 W/m², with regional effects up to 100 W/m²**. These findings underscore the critical role of atmospheric wind and ocean temperature heterogeneity, offering new insights for improving climate model accuracy. \ No newline at end of file diff --git a/content/news/2508Falasca.md b/content/news/2508Falasca.md new file mode 100644 index 00000000..9b2af9f5 --- /dev/null +++ b/content/news/2508Falasca.md @@ -0,0 +1,12 @@ +--- +date: 2025-08-02T09:29:16+10:00 +title: "Neural models of multiscale systems" +heroHeading: '' +heroSubHeading: 'Neural models of multiscale systems' +heroBackground: '' +thumbnail: 'images/news/2508Falasca.png' +images: ['images/news/2508Falasca.png'] +link: 'https://doi.org/10.48550/arXiv.2506.22552' +--- + +This [study](https://doi.org/10.48550/arXiv.2506.22552) examines fundamental challenges in using data-driven models, especially **neural networks, for simulating complex climate dynamics**. While these models can often reproduce average climate behavior, they struggle to capture responses to external changes. The author, **Fabrizio Falasca**, shows that this limitation becomes especially pronounced when only partial observations are available, a common scenario in real-world climate systems. His **findings highlight the importance of incorporating physically informed methods**, like coarse-graining and stochastic parameterizations, **to improve the accuracy and interpretability of neural climate emulators**. \ No newline at end of file diff --git a/content/news/2509Balwada.md b/content/news/2509Balwada.md new file mode 100644 index 00000000..6af431fa --- /dev/null +++ b/content/news/2509Balwada.md @@ -0,0 +1,12 @@ +--- +date: 2025-09-02T09:29:16+10:00 +title: "Data-driven parameterization for mesoscale thickness fluxes" +heroHeading: '' +heroSubHeading: 'Data-driven parameterization for mesoscale thickness fluxes' +heroBackground: '' +thumbnail: 'images/news/2509Balwada.png' +images: ['images/news/2509Balwada.png'] +link: 'https://doi.org/10.22541/essoar.174835313.30541637/v1' +--- + +This [study](https://doi.org/10.22541/essoar.174835313.30541637/v1), led by Dhruv Balwada, introduces a **new data-driven parameterization to better represent how mesoscale eddies remove potential energy from the ocean in climate models**. Unlike the widely used Gent-McWilliams (GM) scheme, which can hinder resolved eddies and lacks a robust basis for tuning, this approach is both **flow-aware and scale-aware, minimizing negative impacts on resolved dynamics**. Built with a lightweight neural network, the method is efficient, easy to implement, and successfully tested in NOAA’s MOM6 model. The results highlight a **promising path to reduce structural errors and improve the realism of climate simulations**. \ No newline at end of file diff --git a/content/news/2509Yongquan.md b/content/news/2509Yongquan.md new file mode 100644 index 00000000..14ac0a82 --- /dev/null +++ b/content/news/2509Yongquan.md @@ -0,0 +1,12 @@ +--- +date: 2025-09-02T09:29:16+10:00 +title: "Plug-and-Play Data Assimilation" +heroHeading: '' +heroSubHeading: 'Plug-and-Play Data Assimilation' +heroBackground: '' +thumbnail: 'images/news/2509Yongquan.png' +images: ['images/news/2509Yongquan.png'] +link: 'https://doi.org/10.48550/arXiv.2508.00325' +--- + +Led by **Yongquan Qu**, this [study](https://doi.org/10.48550/arXiv.2508.00325) presents **PnP-DA** (Plug-and-Play Data Assimilation), a new method to improve forecasts in Earth system models. As part of the **LEAP project**, the team combines lightweight analysis updates with a pretrained generative prior to overcome the limitations of traditional approaches that assume overly simple error statistics. Tests on chaotic systems show that it consistently reduces forecast errors across a range of conditions, outperforming classical data assimilation methods. This approach points toward **more reliable and efficient forecasting tools for complex Earth system dynamics**. \ No newline at end of file diff --git a/content/news/Newsletters/_index.md b/content/news/Newsletters/_index.md index 117dbefe..514c2834 100644 --- a/content/news/Newsletters/_index.md +++ b/content/news/Newsletters/_index.md @@ -10,6 +10,13 @@ tags: Links to our past newsletters are below. ### 2025 + +* 09/02/2025 - [M²LInES newsletter - September 2025](https://mailchi.mp/68907e6e11ec/m2lines-sep2025) + +* 08/01/2025 - [M²LInES newsletter - August 2025](https://mailchi.mp/24335d82d580/m2lines-aug2025) + +* 07/01/2025 - [M²LInES newsletter - July 2025](https://mailchi.mp/8877fdc291c9/m2lines-july2025) + * 06/03/2025 - [M²LInES newsletter - June 2025](https://mailchi.mp/105744018d4b/m2lines-june2025) * 05/01/2025 - [M²LInES newsletter - May 2025](https://mailchi.mp/778fbbb17f80/m2lines-may2025) diff --git a/content/publications/_index.md b/content/publications/_index.md index a6e4c9dd..d9e0b5f3 100644 --- a/content/publications/_index.md +++ b/content/publications/_index.md @@ -15,7 +15,7 @@ You can also check all our publications on our **[Google Scholar profile](https: ### 2025
@@ -39,7 +39,7 @@ You can also check all our publications on our **[Google Scholar profile](https:
@@ -49,6 +49,18 @@ You can also check all our publications on our **[Google Scholar profile](https:
+
+ Niek Kusters, Dhruv Balwada, Sjoerd Groeskamp
+ Global Observational Estimates of Mesoscale Eddy-Driven Quasi-Stokes Velocity and Buoyancy Diffusivity
+ GRL 2025 DOI:10.1029/2025GL115802
+