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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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<imgsrc="/images/newlogo.png"style="width: 1.5vw; height: 1.5hw; vertical-align: middle;"alt="DOI icon"> M²LInES funded research
<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>
<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>
<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>
<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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