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Merge pull request #111 from johannag126/master
update publications
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content/publications/_index.md

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## M²LInES research and other relevant publications
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M²LInES educational [Jupyter Book](https://m2lines.github.io/L96_demo) is out. Learn about about the interface between machine learning & climate modeling whether you are an ML expert or a climate scientist.
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You can also check all our publications on our **[Google Scholar profile](https://scholar.google.com/citations?hl=en&user=iY8RO4QAAAAJ)**
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<img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon"> M²LInES funded research
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### 2025
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<img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon">
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<strong>Tarun Verma, Feiyu Lu, Alistair Adcroft, Laure Zanna, Anand Gnanadesikan</strong><br>
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<a href="https://doi.org/10.22541/essoar.174547920.05119265/v1" target="_blank"><strong>Deep Learning of Systematic Ocean Model Errors in a Coupled GCM from Data Assimilation Increments</strong></a><br>
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<i>Essoar</i> <strong>DOI</strong>:10.22541/essoar.174547920.05119265/v1
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<!-- <img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon"> -->
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<strong>Moein Darman, Pedram Hassanzadeh, Laure Zanna, Ashesh Chattopadhyay</strong><br>
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<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>
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<i>Arxiv</i> <strong>DOI</strong>:10.48550/arXiv.2504.15487
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<img src="/images/news/2504-Chapman.png" style="width: 100px; height: 100px;">
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<img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon">
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<strong>William Chapman, John Schreck, Yingkai Sha, David John Gagne II, Dhamma Kimpara, Laure Zanna, Kirsten Mayer, Judith Berner</strong><br>
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<a href="https://doi.org/10.48550/arXiv.2504.06007" target="_blank"><strong>CAMulator: Fast Emulation of the Community Atmosphere Model</strong></a><br>
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<i>Arxiv</i> <strong>DOI</strong>:10.48550/arXiv.2504.06007
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<!-- <img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon"> -->
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<strong>Marika M Holland, Kristen Krumhardt, Alice DuVivier, Laura Landrum </strong><br>
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<a href="https://doi.org/10.1175/JCLI-D-24-0258.1" target="_blank"><strong>Regional Multiyear Predictability of Antarctic Sea Ice in CESM2 and Its Implications for Marine Ecosystems</strong></a><br>
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<i>Journal of Climate</i> <strong>DOI</strong>:10.1175/JCLI-D-24-0258.1
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<img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon">
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<strong>William Chapman and Judith Berner </strong><br>
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<a href="https://doi.org/10.1029/2024GL114106" target="_blank"><strong>Improving Climate Bias and Variability via CNN-Based State-Dependent Model-Error Corrections</strong></a><br>
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<i>GRL</i> <strong>DOI</strong>:10.1029/2024GL114106
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<!-- <img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon"> -->
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<strong>Andrew Brettin, Laure Zanna, Elizabeth Barnes</strong><br>
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<a href="https://doi.org/10.1029/2024GL112835" target="_blank"><strong>Learning Propagators for Sea Surface Height Forecasts Using Koopman Autoencoders</strong></a><br>
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<i>GRL</i> <strong>DOI</strong>:10.1029/2024GL112835
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<strong>William E. Chapman and Judith Berner </strong><br>
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<a href="https://doi.org/10.22541/essoar.172526800.05354621/v2" target="_blank"><strong>A State-Dependent Model-Error Representation for Online Climate Model Bias Correction</strong></a><br>
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<i>Preprint in ESS</i> <strong>DOI</strong>:10.22541/essoar.172526800.05354621/v2
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