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<label class=drop-icon for=News>▾</label>
</a><input type=checkbox id=News><ul class=submenu__list><li class=menu__item><a class=menu__link href=../../news/><span class=menu__text>Our news</span></a></li><li class=menu__item><a class=menu__link href=../../news/newsletters/><span class=menu__text>Newsletters</span></a></li></ul></li></ul></nav><button id=toggle-main-menu-mobile class="hamburger hamburger--slider" type=button>
<span class=hamburger-box><span class=hamburger-inner></span></span></button></div></div><div id=hero class="hero-image hero-image-setheight" style=background-image:url(/images/susan-q-yin-2JIvboGLeho-unsplash.jpg)><div class=container><div class=hero-text><span class=hero-section></span><h1>Publications Archive</h1><p></p></div></div></div><div class="container pt-6 pt-md-10"><div class=row><div class=col-12><div class=page-intro><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> M²LInES funded research</p><h3 id=2021-->2021 -</h3><ul><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> Guillaumin, A. P., & Zanna, L. <strong><a href=https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002534 target=_blank>Stochastic-Deep Learning Parameterization of Ocean Momentum Forcing</a>
</strong>JAMES 2021</p></li><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> Guillaumin A, Zanna L. <strong><a href=https://doi.org/10.1029/2021MS002534 target=_blank>Stochastic Deep Learning parameterization of Ocean Momentum Forcing.</a>
</strong><em>Journal of Advances in Modeling Earth Systems</em> 2021.</p></li><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> Zanna L, Bolton T. <strong><a href=https://doi.org/10.1002/9781119646181.ch20 target=_blank>Deep Learning of Unresolved Turbulent Ocean Processes in Climate Models.</a>
</strong>In <em>Deep learning for the Earth Sciences</em> 2021 (eds G. Camps-Valls, D. Tuia, X.X. Zhu and M. Reichstein). (author <a href=https://laurezanna.github.io/files/Zanna-Bolton-2021.pdf target=_blank>link</a>
)</p></li><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> Gentine P, Eyring V, Beucler T. <strong><a href=https://doi.org/10.1002/9781119646181.ch21 target=_blank>Deep Learning for the Parametrization of Subgrid Processes in Climate Models</a>
</strong>In <em>Deep learning for the Earth Sciences</em> 2021 (eds G. Camps-Valls, D. Tuia, X.X. Zhu and M. Reichstein).</p></li><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> Mooers G, Pritchard M, Beucler T, Ott J, Yacalis G, Baldi P, Gentine P. <strong><a href=https://doi.org/10.1029/2020MS002385 target=_blank>Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models with Real-Geography Boundary Conditions.</a>
</strong><em>Journal of Advances in Modeling Earth Systems</em> 2021</p></li><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> Beucler T, Pritchard M, Rasp S, Ott J, Baldi P, Gentine P. <strong><a href=https://doi.org/10.1103/PhysRevLett.126.098302 target=_blank>Enforcing analytic constraints in neural networks emulating physical systems.</a>
</strong>JAMES 2021</p></li><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> Zanna L, Bolton T. <strong><a href=https://doi.org/10.1002/9781119646181.ch20 target=_blank>Deep Learning of Unresolved Turbulent Ocean Processes in Climate Models.</a>
</strong>In <em>Deep learning for the Earth Sciences</em> 2021 (eds G. Camps-Valls, D. Tuia, X.X. Zhu and M. Reichstein). (author <a href=https://zanna-research.github.io/files/Zanna-Bolton-2021.pdf target=_blank>link</a>
)</p></li><li><p>Gentine P, Eyring V, Beucler T. <strong><a href=https://doi.org/10.1002/9781119646181.ch21 target=_blank>Deep Learning for the Parametrization of Subgrid Processes in Climate Models</a>
</strong>In <em>Deep learning for the Earth Sciences</em> 2021 (eds G. Camps-Valls, D. Tuia, X.X. Zhu and M. Reichstein).</p></li><li><p>Mooers G, Pritchard M, Beucler T, Ott J, Yacalis G, Baldi P, Gentine P. <strong><a href=https://doi.org/10.1029/2020MS002385 target=_blank>Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models with Real-Geography Boundary Conditions.</a>
</strong><em>Journal of Advances in Modeling Earth Systems</em> 2021</p></li><li><p>Beucler T, Pritchard M, Rasp S, Ott J, Baldi P, Gentine P. <strong><a href=https://doi.org/10.1103/PhysRevLett.126.098302 target=_blank>Enforcing analytic constraints in neural networks emulating physical systems.</a>
</strong><em>Physical Review Letters</em> 2021. (author <a href=https://gentinelab.eee.columbia.edu/sites/default/files/content/PhysRevLett.126.098302.pdf target=_blank>link</a>
)</p></li><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> Frezat H, Balarac G, Le Sommer J, Fablet R, Lguensat R. <strong><a href=https://doi.org/10.1103/PhysRevFluids.6.024607 target=_blank>Physical invariance in neural networks for subgrid-scale scalar flux modeling.</a>
)</p></li><li><p>Frezat H, Balarac G, Le Sommer J, Fablet R, Lguensat R. <strong><a href=https://doi.org/10.1103/PhysRevFluids.6.024607 target=_blank>Physical invariance in neural networks for subgrid-scale scalar flux modeling.</a>
</strong><em>Physical Review Fluids</em> 2021. (author <a href=https://hal.science/hal-03084215/file/2010.04663.pdf target=_blank>link</a>
)</p></li><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> O’Gorman PA, Li Z, Boos WR, Yuval J. <strong><a href=https://doi.org/10.1098/rsta.2019.0543 target=_blank>Response of extreme precipitation to uniform surface warming in quasi-global aquaplanet simulations at high resolution.</a>
</strong><em>Philosophical Transactions of the Royal Society A</em> 2021. (author <a href=https://halo.mit.edu/src/ogorman_quasi_global_hires_precip_extremes_2021.pdf target=_blank>link</a>
)</p></li><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> Yuval J, Hill CN, O&rsquo;Gorman PA. <strong><a href=https://doi.org/10.1029/2020GL091363 target=_blank>Use of neural networks for stable, accurate and physically consistent parameterization of subgrid atmospheric processes with good performance at reduced precision.</a>
</strong><em>Geophysical Research Letter</em> 2021.</p></li></ul><h3 id=2020>2020</h3><ul><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> Yuval J, O’Gorman PA. <strong><a href=https://doi.org/10.1038/s41467-020-17142-3 target=_blank>Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions.</a>
</strong><em>Nature communications</em> 2020.</p></li><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> Zanna L, Bolton T. <strong><a href=https://doi.org/10.1029/2020GL088376 target=_blank>Data‐Driven Equation Discovery of Ocean Mesoscale Closures.</a>
</strong><em>Geophysical Research Letters</em> 2020. (author <a href=https://laurezanna.github.io/files/Zanna-Bolton-2020.pdf target=_blank>link</a>
)</p></li><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> Mohan S, Kadkhodaie Z, Simoncelli EP, Fernandez-Granda C. <strong><a href=https://doi.org/10.48550/arXiv.1906.05478 target=_blank>Robust and interpretable blind image denoising via bias-free convolutional neural networks</a>
</strong><em>ICLR</em> 2020.</p></li></ul><h3 id=2019>2019</h3><ul><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> Bolton T, Zanna L. <strong><a href=https://doi.org/10.1029/2018MS001472 target=_blank>Applications of Deep Learning to Ocean Data Inference and Subgrid Parameterization.</a>
</strong><em>J Adv Model Earth Syst</em> 2019.</p></li><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> Held IM, Guo H, Adcroft A, Dunne JP, Horowitz LW, Krasting J, et al. <strong><a href=https://doi.org/10.1029/2019MS001829 target=_blank>Structure and Performance of GFDL’s CM4.0 Climate Model.</a>
</strong><em>J Adv Model Earth Syst</em> 2019.</p></li><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> Niall H. Robinson, Joe Hamman, Ryan Abernathey. <strong><a href=https://doi.org/10.48550/arXiv.1908.03356 target=_blank>Seven Principles for Effective Scientific Big-DataSystems.</a>
</strong><em>ArXiv190803356 Cs</em> 2019.</p></li><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> Izacard G, Mohan S, Fernandez-Granda C. <strong><a href=https://doi.org/10.48550/arXiv.1906.00823 target=_blank>Data-driven Estimation of Sinusoid Frequencies.</a>
</strong><em>Advances in Neural Information Processing Systems</em> 2019.</p></li><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> Zhao WL, Gentine P, Reichstein M, Zhang Y, Zhou S, Wen Y, et al. <strong><a href=https://doi.org/10.1029/2019GL085291 target=_blank>Physics-constrained machine learning of evapotranspiration.</a>
)</p></li><li><p>O’Gorman PA, Li Z, Boos WR, Yuval J. <strong><a href=https://doi.org/10.1098/rsta.2019.0543 target=_blank>Response of extreme precipitation to uniform surface warming in quasi-global aquaplanet simulations at high resolution.</a>
</strong><em>Philosophical Transactions of the Royal Society A</em> 2021. (author <a href=https://pog.mit.edu/src/ogorman_quasi_global_hires_precip_extremes_2021.pdf target=_blank>link</a>
)</p></li><li><p>Yuval J, Hill CN, O&rsquo;Gorman PA. <strong><a href=https://doi.org/10.1029/2020GL091363 target=_blank>Use of neural networks for stable, accurate and physically consistent parameterization of subgrid atmospheric processes with good performance at reduced precision.</a>
</strong><em>Geophysical Research Letter</em> 2021.</p></li></ul><h3 id=2020>2020</h3><ul><li><p>Yuval J, O’Gorman PA. <strong><a href=https://doi.org/10.1038/s41467-020-17142-3 target=_blank>Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions.</a>
</strong><em>Nature communications</em> 2020.</p></li><li><p>Zanna L, Bolton T. <strong><a href=https://doi.org/10.1029/2020GL088376 target=_blank>Data‐Driven Equation Discovery of Ocean Mesoscale Closures.</a>
</strong><em>Geophysical Research Letters</em> 2020. (author <a href=https://zanna-research.github.io/files/Zanna-Bolton-2020.pdf target=_blank>link</a>
)</p></li><li><p>Mohan S, Kadkhodaie Z, Simoncelli EP, Fernandez-Granda C. <strong><a href=https://doi.org/10.48550/arXiv.1906.05478 target=_blank>Robust and interpretable blind image denoising via bias-free convolutional neural networks</a>
</strong><em>ICLR</em> 2020.</p></li></ul><h3 id=2019>2019</h3><ul><li><p>Bolton T, Zanna L. <strong><a href=https://doi.org/10.1029/2018MS001472 target=_blank>Applications of Deep Learning to Ocean Data Inference and Subgrid Parameterization.</a>
</strong><em>J Adv Model Earth Syst</em> 2019.</p></li><li><p>Held IM, Guo H, Adcroft A, Dunne JP, Horowitz LW, Krasting J, et al. <strong><a href=https://doi.org/10.1029/2019MS001829 target=_blank>Structure and Performance of GFDL’s CM4.0 Climate Model.</a>
</strong><em>J Adv Model Earth Syst</em> 2019.</p></li><li><p>Niall H. Robinson, Joe Hamman, Ryan Abernathey. <strong><a href=https://doi.org/10.48550/arXiv.1908.03356 target=_blank>Seven Principles for Effective Scientific Big-DataSystems.</a>
</strong><em>ArXiv190803356 Cs</em> 2019.</p></li><li><p>Izacard G, Mohan S, Fernandez-Granda C. <strong><a href=https://doi.org/10.48550/arXiv.1906.00823 target=_blank>Data-driven Estimation of Sinusoid Frequencies.</a>
</strong><em>Advances in Neural Information Processing Systems</em> 2019.</p></li><li><p>Zhao WL, Gentine P, Reichstein M, Zhang Y, Zhou S, Wen Y, et al. <strong><a href=https://doi.org/10.1029/2019GL085291 target=_blank>Physics-constrained machine learning of evapotranspiration.</a>
</strong><em>Geophysical Research Letter</em> 2019. (ResearchGate <a href=https://www.researchgate.net/publication/337868554_Physics-Constrained_Machine_Learning_of_Evapotranspiration target=_blank>link</a>
)</p></li><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> Yang T, Sun F, Gentine P, Liu W, Wang H, Yin J, et al. <strong><a href=https://doi.org/10.1088/1748-9326/ab4d5e target=_blank>Evaluation and machine learning improvement of global hydrological model-based flood simulations.</a>
</strong><em>Environ Res Lett</em> 2019.</p></li></ul><h3 id=2018>2018</h3><ul><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> O’Gorman PA, Dwyer JG. <strong><a href=https://doi.org/10.1029/2018MS001351 target=_blank>Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events.</a>
</strong><em>J Adv Model Earth Syst</em> 2018.</p></li><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> Gentine P, Pritchard M, Rasp S, Reinaudi G, Yacalis G. <strong><a href=https://doi.org/10.1029/2018GL078202 target=_blank>Could Machine Learning Break the Convection Parameterization Deadlock?</a>
</strong><em>Geophys Res Lett</em> 2018.</p></li><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> Rasp S, Pritchard MS, Gentine P. <strong><a href=https://doi.org/10.1073/pnas.1810286115 target=_blank>Deep learning to represent subgrid processes in climate models.</a>
</strong><em>Proc Natl Acad Sci</em> 2018.</p></li><li><p><img src=../../images/newlogo.png style=width:1.5vw;height:1.5hw;vertical-align:middle alt="DOI icon"> Zanna L, Brankart JM, Huber M, Leroux S, Penduff T, Williams PD. <strong><a href=https://doi.org/10.1002/qj.3397 target=_blank>Uncertainty and scale interactions in ocean ensembles: From seasonal forecasts to multidecadal climate predictions.</a>
)</p></li><li><p>Yang T, Sun F, Gentine P, Liu W, Wang H, Yin J, et al. <strong><a href=https://doi.org/10.1088/1748-9326/ab4d5e target=_blank>Evaluation and machine learning improvement of global hydrological model-based flood simulations.</a>
</strong><em>Environ Res Lett</em> 2019.</p></li></ul><h3 id=2018>2018</h3><ul><li><p>O’Gorman PA, Dwyer JG. <strong><a href=https://doi.org/10.1029/2018MS001351 target=_blank>Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events.</a>
</strong><em>J Adv Model Earth Syst</em> 2018.</p></li><li><p>Gentine P, Pritchard M, Rasp S, Reinaudi G, Yacalis G. <strong><a href=https://doi.org/10.1029/2018GL078202 target=_blank>Could Machine Learning Break the Convection Parameterization Deadlock?</a>
</strong><em>Geophys Res Lett</em> 2018.</p></li><li><p>Rasp S, Pritchard MS, Gentine P. <strong><a href=https://doi.org/10.1073/pnas.1810286115 target=_blank>Deep learning to represent subgrid processes in climate models.</a>
</strong><em>Proc Natl Acad Sci</em> 2018.</p></li><li><p>Zanna L, Brankart JM, Huber M, Leroux S, Penduff T, Williams PD. <strong><a href=https://doi.org/10.1002/qj.3397 target=_blank>Uncertainty and scale interactions in ocean ensembles: From seasonal forecasts to multidecadal climate predictions.</a>
</strong><em>Q J R Meteorol Soc</em> 2018.</p></li></ul></div></div></div></div><div class="container pb-6 pt-6 pb-md-10 pt-md-10"><div class=row></div></div></div><style>.center-list{display:flex;justify-content:center;align-items:stretch;margin:0;text-align:center}.image-wrapper{max-width:200px;width:100%}</style><div class=footer><div class=container><div class=row><div class=col-12><div class=footer-inner><ul class=center-list><li><a href=https://schmidtfutures.com/ target=_blank class=image-wrapper><img src=../../images/schmidtsciences_primary_color.png style=width:100%;height:auto></a></li><li class=copyright>This project is supported by Schmidt Sciences, LLC.</li></ul></div></div></div></div></div><div class=sub-footer><div class=container><div class=row><div class=col-12><div class=sub-footer-inner><div class=social><a href=mailto:m2lines@nyu.edu target=blank><img src=../../images/PICOL_icon_Mailw.png title=Email alt=Email></a>
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