update news, publications, team

content/news/2601Berner.md

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+---
+date: 2026-01-03T09:29:16+10:00
+title: "Quantifying sources of subseasonal prediction skill in CESM2"
+heroHeading: ''
+heroSubHeading: 'Quantifying sources of subseasonal prediction skill in CESM2 within a perfect modelling framework'
+heroBackground: ''
+thumbnail:  'images/news/2601Berner.jpg'
+images: ['images/news/2601Berner.jpg']
+link: 'https://doi.org/10.22541/essoar.176365939.97870596/v1'
+---
+
+This [study](https://doi.org/10.22541/essoar.176365939.97870596/v1), led by Judith Berner, examines the fundamental limits of subseasonal-to-seasonal weather predictability and the role of land and ocean initial conditions. Using a climate model in a perfect-model framework, the authors show that beyond four weeks, **land surface initialization - particularly soil moisture and snow - dominates predictability over land**, with ocean conditions playing a secondary role. The results point to significant opportunities for **improving extended-range forecasts through better land initialization and land–atmosphere coupling in prediction systems**.
+

content/news/2601Falga.md

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+---
+date: 2026-01-03T09:29:16+10:00
+title: "Towards a Unified Data-Driven Boundary Layer Momentum Flux Parameterization for Ocean and Atmosphere"
+heroHeading: ''
+heroSubHeading: 'Towards a Unified Data-Driven Boundary Layer Momentum Flux Parameterization for Ocean and Atmosphere'
+heroBackground: ''
+thumbnail:  'images/news/2601Falga.png'
+images: ['images/news/2601Falga.png']
+link: 'https://doi.org/10.48550/arXiv.2511.01766'
+---
+
+Falga et al. present a [new machine-learning–based parameterization](https://doi.org/10.48550/arXiv.2511.01766) for **turbulent momentum fluxes that works consistently across both oceanic and atmospheric boundary layers.** Trained on large-eddy simulations, the neural network captures key turbulent features missed by traditional schemes and **significantly improves boundary-layer wind predictions in climate models**, reducing errors by a factor of 2–3 under convective conditions. The approach is robust to surface flux biases and generalizes well beyond the training data, **highlighting the promise of unified, data-driven turbulence closures for next-generation climate models.**

content/news/2601Wu.md

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+---
+date: 2026-01-03T09:29:16+10:00
+title: "Data-Driven Probabilistic Air-Sea Flux Parameterization"
+heroHeading: ''
+heroSubHeading: 'Data-Driven Probabilistic Air-Sea Flux Parameterization'
+heroBackground: ''
+thumbnail:  'images/news/2601Wu.png'
+images: ['images/news/2601Wu.png']
+link: 'https://doi.org/10.48550/arXiv.2503.03990'
+---
+
+Air–sea fluxes — the exchanges of heat, moisture, and gases between the ocean and atmosphere —play a key role in shaping weather and climate. Traditional models often treat these fluxes in a fixed, “one-size-fits-all” way, missing their natural variability. This **[LEAP study](https://doi.org/10.48550/arXiv.2503.03990)**, led by Jiarong Wu, introduces a new probabilistic framework that uses neural networks and observational data to **better capture both the average behavior and the uncertainty of these fluxes**. The results show that accounting for **this variability can influence ocean temperature and mixing**, especially during spring, offering a promising step toward more realistic climate and weather simulations.

content/news/2602Brettin.md

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+---
+date: 2026-02-02T09:29:16+10:00
+title: "Estimation of temperature and precipitation uncertainties using quantile neural networks"
+heroHeading: ''
+heroSubHeading: 'Estimation of temperature and precipitation uncertainties using quantile neural networks'
+heroBackground: ''
+thumbnail:  'images/news/2602Brettin.png'
+images: ['images/news/2602Brettin.png']
+link: 'https://doi.org/10.48550/arXiv.2601.17243'
+---
+
+This new [preprint](https://doi.org/10.48550/arXiv.2601.17243) led by **Andrew Brettin** introduces a machine-learning framework to **better quantify uncertainty in extreme climate events.** The proposed ReLU-bias loss quantile neural network (RBLQNN) improves the **accuracy and stability of predicted uncertainty ranges**, especially for nonlinear and non-Gaussian processes. Tested on synthetic data, temperature extremes from over 1,500 NOAA weather stations, and satellite-observed precipitation, the **method outperforms standard approaches** and captures complex dependencies that simpler models miss. This work highlights **RBLQNN as a powerful, flexible tool for assessing climate hazards and extremes.**

content/news/2602Levine.md

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+---
+date: 2026-02-02T09:29:16+10:00
+title: "Arctic temperature and precipitation extremes"
+heroHeading: ''
+heroSubHeading: 'Arctic temperature and precipitation extremes in present-day and future storyline-based variable resolution Community Earth System Model simulations'
+heroBackground: ''
+thumbnail:  'images/news/2602Levine.png'
+images: ['images/news/2602Levine.png']
+link: 'https://doi.org/10.5194/wcd-6-1241-2025'
+---
+
+A new modeling [study](https://doi.org/10.5194/wcd-6-1241-2025) co-authored by **Xavier Levine** examines how climate extremes in the Arctic may evolve as the region continues to warm faster than the global average. Using the variable-resolution CESM2.2 model, the team compares standard global simulations with high-resolution grids refined over the Arctic to better capture heat waves and heavy precipitation. They find that higher resolution improves the simulation of precipitation extremes, while temperature extremes are better represented in coarser global runs. Projections for the end of the century show stronger and longer-lasting heat extremes, fewer cold extremes, and more intense and frequent heavy precipitation, particularly in regions affected by sea-ice loss and ocean warming.

content/news/2602Nasser.md

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+---
+date: 2026-02-02T09:29:16+10:00
+title: "Porous Cavities and Smooth Boundaries"
+heroHeading: ''
+heroSubHeading: 'Porous Cavities and Smooth Boundaries: Brinkman Volume Penalization for Ice-Shelf-Ocean Interactions in Earth System Models'
+heroBackground: ''
+thumbnail:  'images/news/2602Nasser.jpg'
+images: ['images/news/2602Nasser.jpg']
+link: 'https://doi.org/10.22541/essoar.176677323.32970185/v1'
+---
+
+A recent [study](https://doi.org/10.22541/essoar.176677323.32970185/v1) led by **Antoine Nasser** investigates new ways to **improve the simulation of ice–shelf–ocean interactions** in numerical ocean models. Using an idealized ISOMIP+ configuration, the team shows the **benefit of the Brinkman Volume Penalization** (BVP) approach in mitigating spurious noise in basal melt rates and excessive mixing, which are recurring issues when ice-shelf cavities are represented in z-coordinate models. The BVP method treats the ice–ocean interface as a porous region, where smoothing acts as an effective increase in vertical resolution, producing melt patterns and circulation comparable to terrain-following models, while yielding colder, fresher ice-shelf waters and a stronger overturning circulation. These results highlight the **potential of the BVP approach to improve the representation of ice-shelves in ocean models**, paving the way toward a better understanding of Antarctic ice-sheet melting.

content/news/Newsletters/_index.md

@@ -10,6 +10,12 @@ tags:
 
  Links to our past newsletters are below.
 
+### 2026
+
+* 02/02/2026 - [M²LInES newsletter - February 2026](https://mailchi.mp/8bf7a300bfad/m2lines-feb2026)
+
+* 01/05/2026 - [M²LInES newsletter - January 2026](https://mailchi.mp/be4f07420e28/m2lines-jan2026)
+
 ### 2025
 
 * 12/02/2025 - [M²LInES newsletter - December 2025](https://mailchi.mp/14605e5ed14c/m2lines-dec2025)

content/publications/_index.md

@@ -12,6 +12,32 @@ You can also check all our publications on our **[Google Scholar profile](https:
 
 
 <img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon"> M²LInES funded research
+
+### 2026
+<div style="display: flex; align-items: center;">
+  <div style="width: 100px; height: 100px; overflow: hidden; margin-right: 10px;">
+    <img src="/images/news/2602Brettin.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">
+    <strong>Andrew Brettin, Laure Zanna</strong><br>
+    <a href="https://doi.org/10.48550/arXiv.2601.17243" target="_blank"><strong>Estimation of temperature and precipitation uncertainties using quantile neural networks</strong></a><br>
+    <i>Arxiv</i> <strong>DOI</strong>: 10.48550/arXiv.2601.17243
+  </p>
+</div>
+
+<div style="display: flex; align-items: center;">
+  <div style="width: 100px; height: 100px; overflow: hidden; margin-right: 10px;">
+    <img src="/images/news/2601Gregory.gif" 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">
+    <strong>William Gregory, Mitchell Bushuk, Yong-Fei Zhang, Alistair Adcroft, Laure Zanna, Colleen McHugh, Liwei Jia</strong><br>
+<a href="https://doi.org/10.1126/sciadv.ady8957" target="_blank"><strong>Advancing global sea ice prediction capabilities using a fully coupled climate model with integrated machine learning</strong></a><br>
+<i>Science Advances.</i> <strong>DOI</strong>: 10.1126/sciadv.ady8957
+  </p>
+</div>
+
 ### 2025
 <div style="display: flex; align-items: center;">
   <div style="width: 100px; height: 100px; overflow: hidden; margin-right: 10px;">
@@ -25,6 +51,31 @@ You can also check all our publications on our **[Google Scholar profile](https:
   </p>
 </div>
 
+<div style="display: flex; align-items: center;">
+  <div style="width: 100px; height: 100px; overflow: hidden; margin-right: 10px;">
+    <img src="/images/news/2601Shamekh.jpg" 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">
+    <strong>Katharina Hafner, Fernando Iglesias‐Suarez, Sara Shamekh, Pierre Gentine, Marco A Giorgetta, Robert Pincus, Veronika Eyring</strong><br>
+    <a href="https://doi.org/10.1029/2024JH000501" target="_blank"><strong>Interpretable machine learning‐based radiation emulation for icon</strong></a><br>
+    <i>Journal of Geophysical Research</i> <strong>DOI</strong>: 10.1029/2024JH000501
+  </p>
+</div>
+
+<div style="display: flex; align-items: center;">
+  <div style="width: 100px; height: 100px; overflow: hidden; margin-right: 10px;">
+    <img src="/images/news/2601Falga.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">
+    <strong>Renaud Falga, Sara Shamekh, Laure Zanna</strong><br>
+    <a href="https://doi.org/10.48550/arXiv.2511.01766" target="_blank"><strong>Towards a Unified Data-Driven Boundary Layer Momentum Flux Parameterization for Ocean and Atmosphere</strong></a><br>
+    <i>Authorea Preprint</i> <strong>DOI</strong>: 10.48550/arXiv.2511.01766
+  </p>
+</div>
+
+
 <div style="display: flex; align-items: center;">
   <div style="width: 100px; height: 100px; overflow: hidden; margin-right: 10px;">
     <img src="/images/publications/2510Shamekh.png" style="width: 100px; height: 100px;">

content/team/SidArora.md

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+---
+title: "Siddhant Arora"
+draft: false
+image: "/images/team/SidArora.jpg"
+jobtitle: "Affiliate, Graduate Student"
+promoted: true
+weight: 27
+Website: 
+Position: 
+tags: [Machine Learning, Data Assimilation]
+---
+
+
+NYU

content/team/YakelynJauregui.md

@@ -0,0 +1,14 @@
+---
+title: "Yakelyn R. Jauregui"
+draft: false
+image: "/images/team/Yakelyn.jpg"
+jobtitle: "Research Scientist"
+promoted: true
+weight: 15
+Website: https://yakelynrj.github.io/
+Position: Coupled Physics
+tags: [Ocean, Atmosphere, Machine Learning]
+---
+
+
+NCAR