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<!-- ## M²LInES research publications archive -->
<img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon"> M²LInES funded research

### 2021 -

* Guillaumin, A. P., & Zanna, L. **[Stochastic-Deep Learning Parameterization of Ocean Momentum Forcing](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002534)** JAMES 2021
* <img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon"> Guillaumin, A. P., & Zanna, L. **[Stochastic-Deep Learning Parameterization of Ocean Momentum Forcing](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021MS002534)** JAMES 2021

* <img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon"> Guillaumin A, Zanna L. **[Stochastic Deep Learning parameterization of Ocean Momentum Forcing.](https://doi.org/10.1029/2021MS002534)** _Journal of Advances in Modeling Earth Systems_ 2021.

* Beucler T, Pritchard M, Yuval J, Gupta A, Peng L, Rasp S, Ahmed F, O'Gorman PA, Neelin JD, Lutsko NJ, Gentine P. **[Climate-Invariant Machine Learning](https://doi.org/10.48550/arXiv.2112.08440)** arXiv preprint arXiv:2112.08440. 2021 _(preprint)_
* <img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon"> Zanna L, Bolton T. **[Deep Learning of Unresolved Turbulent Ocean Processes in Climate Models.](https://doi.org/10.1002/9781119646181.ch20)** In _Deep learning for the Earth Sciences_ 2021 (eds G. Camps-Valls, D. Tuia, X.X. Zhu and M. Reichstein). (author [link](https://laurezanna.github.io/files/Zanna-Bolton-2021.pdf))

* Wang P, Yuval J, O'Gorman PA. **[Non-local parameterization of atmospheric subgrid processes with neural networks](https://doi.org/10.48550/arXiv.2201.00417)** arXiv preprint arXiv:2201.00417. 2022 _(preprint)_
* <img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon"> Gentine P, Eyring V, Beucler T. **[Deep Learning for the Parametrization of Subgrid Processes in Climate Models](https://doi.org/10.1002/9781119646181.ch21)** In _Deep learning for the Earth Sciences_ 2021 (eds G. Camps-Valls, D. Tuia, X.X. Zhu and M. Reichstein).

* Yuval J, O'Gorman PA. **[Neural-network parameterization of subgrid momentum transport in the atmosphere.](https://www.essoar.org/doi/abs/10.1002/essoar.10507557.1)** _J Earth and Space Science Open Archive_ _(preprint)_

* Guillaumin A, Zanna L. **[Stochastic Deep Learning parameterization of Ocean Momentum Forcing.](https://doi.org/10.1029/2021MS002534)** _Journal of Advances in Modeling Earth Systems_ 2021.

* Zanna L, Bolton T. **[Deep Learning of Unresolved Turbulent Ocean Processes in Climate Models.](https://doi.org/10.1002/9781119646181.ch20)** In _Deep learning for the Earth Sciences_ 2021 (eds G. Camps-Valls, D. Tuia, X.X. Zhu and M. Reichstein). (author [link](https://laurezanna.github.io/files/Zanna-Bolton-2021.pdf))

* Gentine P, Eyring V, Beucler T. **[Deep Learning for the Parametrization of Subgrid Processes in Climate Models](https://doi.org/10.1002/9781119646181.ch21)** In _Deep learning for the Earth Sciences_ 2021 (eds G. Camps-Valls, D. Tuia, X.X. Zhu and M. Reichstein).

* Mooers G, Pritchard M, Beucler T, Ott J, Yacalis G, Baldi P, Gentine P. **[Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models with Real-Geography Boundary Conditions.]( https://doi.org/10.1029/2020MS002385)**
* <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. **[Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models with Real-Geography Boundary Conditions.]( https://doi.org/10.1029/2020MS002385)**
_Journal of Advances in Modeling Earth Systems_ 2021

* Beucler T, Pritchard M, Rasp S, Ott J, Baldi P, Gentine P. **[Enforcing analytic constraints in neural networks emulating physical systems.](https://doi.org/10.1103/PhysRevLett.126.098302)** _Physical Review Letters_ 2021. (author [link](https://gentinelab.eee.columbia.edu/sites/default/files/content/PhysRevLett.126.098302.pdf))
* <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. **[Enforcing analytic constraints in neural networks emulating physical systems.](https://doi.org/10.1103/PhysRevLett.126.098302)** _Physical Review Letters_ 2021. (author [link](https://gentinelab.eee.columbia.edu/sites/default/files/content/PhysRevLett.126.098302.pdf))

* Frezat H, Balarac G, Le Sommer J, Fablet R, Lguensat R. **[Physical invariance in neural networks for subgrid-scale scalar flux modeling.](https://doi.org/10.1103/PhysRevFluids.6.024607)** _Physical Review Fluids_ 2021. (author [link](https://mycore.core-cloud.net/index.php/s/lQCP7AfbolI7klN?path=%2F2021#pdfviewer))
* <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. **[Physical invariance in neural networks for subgrid-scale scalar flux modeling.](https://doi.org/10.1103/PhysRevFluids.6.024607)** _Physical Review Fluids_ 2021. (author [link](https://hal.science/hal-03084215/file/2010.04663.pdf))

* O’Gorman PA, Li Z, Boos WR, Yuval J. **[Response of extreme precipitation to uniform surface warming in quasi-global aquaplanet simulations at high resolution.](https://doi.org/10.1098/rsta.2019.0543)** _Philosophical Transactions of the Royal Society A_ 2021. (author [link](https://halo.mit.edu/src/ogorman_quasi_global_hires_precip_extremes_2021.pdf))
* <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. **[Response of extreme precipitation to uniform surface warming in quasi-global aquaplanet simulations at high resolution.](https://doi.org/10.1098/rsta.2019.0543)** _Philosophical Transactions of the Royal Society A_ 2021. (author [link](https://halo.mit.edu/src/ogorman_quasi_global_hires_precip_extremes_2021.pdf))

* Yuval J, Hill CN, O'Gorman PA. **[Use of neural networks for stable, accurate and physically consistent parameterization of subgrid atmospheric processes with good performance at reduced precision.](https://doi.org/10.1029/2020GL091363)** _Geophysical Research Letter_ 2021.
* <img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon"> Yuval J, Hill CN, O'Gorman PA. **[Use of neural networks for stable, accurate and physically consistent parameterization of subgrid atmospheric processes with good performance at reduced precision.](https://doi.org/10.1029/2020GL091363)** _Geophysical Research Letter_ 2021.

### 2020

* Yuval J, O’Gorman PA. **[Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions.](https://doi.org/10.1038/s41467-020-17142-3)** _Nature communications_ 2020.
* <img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon"> Yuval J, O’Gorman PA. **[Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions.](https://doi.org/10.1038/s41467-020-17142-3)** _Nature communications_ 2020.

* Zanna L, Bolton T. **[Data‐Driven Equation Discovery of Ocean Mesoscale Closures.](https://doi.org/10.1029/2020GL088376)** _Geophysical Research Letters_ 2020. (author [link](https://laurezanna.github.io/files/Zanna-Bolton-2020.pdf))
* <img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon"> Zanna L, Bolton T. **[Data‐Driven Equation Discovery of Ocean Mesoscale Closures.](https://doi.org/10.1029/2020GL088376)** _Geophysical Research Letters_ 2020. (author [link](https://laurezanna.github.io/files/Zanna-Bolton-2020.pdf))

### 2019

* Bolton T, Zanna L. **[Applications of Deep Learning to Ocean Data Inference and Subgrid Parameterization.](https://doi.org/10.1029/2018MS001472)** _J Adv Model Earth Syst_ 2019.
* <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. **[Robust and interpretable blind image denoising via bias-free convolutional neural networks](https://doi.org/10.48550/arXiv.1906.05478)** _ICLR_ 2020.

* Held IM, Guo H, Adcroft A, Dunne JP, Horowitz LW, Krasting J, et al. **[Structure and Performance of GFDL’s CM4.0 Climate Model.](https://doi.org/10.1029/2019MS001829)** _J Adv Model Earth Syst_ 2019.
### 2019

* Robinson NH, Hamman J, Abernathey R. **[Science needs to rethink how it interacts with big data: Five principles for effective scientific big data systems.](https://doi.org/10.48550/arXiv.1908.03356)** _ArXiv190803356 Cs_ 2019.
* <img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon"> Bolton T, Zanna L. **[Applications of Deep Learning to Ocean Data Inference and Subgrid Parameterization.](https://doi.org/10.1029/2018MS001472)** _J Adv Model Earth Syst_ 2019.

* Beucler T, Rasp S, Pritchard M, Gentine P. **[Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling.](https://doi.org/10.48550/arXiv.1906.06622)** _ArXiv190606622 Phys_ 2019.
* <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. **[Structure and Performance of GFDL’s CM4.0 Climate Model.](https://doi.org/10.1029/2019MS001829)** _J Adv Model Earth Syst_ 2019.

* Izacard G, Mohan S, Fernandez-Granda C. **[Data-driven Estimation of Sinusoid Frequencies.](https://doi.org/10.48550/arXiv.1906.00823)** _Advances in Neural Information Processing Systems_ 2019.
* <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. **[Seven Principles for Effective Scientific Big-DataSystems.](https://doi.org/10.48550/arXiv.1908.03356)** _ArXiv190803356 Cs_ 2019.

* Mohan S, Kadkhodaie Z, Simoncelli EP, Fernandez-Granda C. **[Robust and interpretable blind image denoising via bias-free convolutional neural networks](https://doi.org/10.48550/arXiv.1906.05478)** 2019.
* <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. **[Data-driven Estimation of Sinusoid Frequencies.](https://doi.org/10.48550/arXiv.1906.00823)** _Advances in Neural Information Processing Systems_ 2019.

* Zhao WL, Gentine P, Reichstein M, Zhang Y, Zhou S, Wen Y, et al. **[Physics-constrained machine learning of evapotranspiration.](https://doi.org/10.1029/2019GL085291)** _Geophysical Research Letter_ 2019. (ResearchGate [link](https://www.researchgate.net/publication/337868554_Physics-Constrained_Machine_Learning_of_Evapotranspiration))
* <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. **[Physics-constrained machine learning of evapotranspiration.](https://doi.org/10.1029/2019GL085291)** _Geophysical Research Letter_ 2019. (ResearchGate [link](https://www.researchgate.net/publication/337868554_Physics-Constrained_Machine_Learning_of_Evapotranspiration))

* Yang T, Sun F, Gentine P, Liu W, Wang H, Yin J, et al. **[Evaluation and machine learning improvement of global hydrological model-based flood simulations.](https://doi.org/10.1088/1748-9326/ab4d5e)** _Environ Res Lett_ 2019.
* <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. **[Evaluation and machine learning improvement of global hydrological model-based flood simulations.](https://doi.org/10.1088/1748-9326/ab4d5e)** _Environ Res Lett_ 2019.

### 2018

* O’Gorman PA, Dwyer JG. **[Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events.](https://doi.org/10.1029/2018MS001351)** _J Adv Model Earth Syst_ 2018.
* <img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon"> O’Gorman PA, Dwyer JG. **[Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events.](https://doi.org/10.1029/2018MS001351)** _J Adv Model Earth Syst_ 2018.

* Gentine P, Pritchard M, Rasp S, Reinaudi G, Yacalis G. **[Could Machine Learning Break the Convection Parameterization Deadlock?](https://doi.org/10.1029/2018GL078202)** _Geophys Res Lett_ 2018.
* <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. **[Could Machine Learning Break the Convection Parameterization Deadlock?](https://doi.org/10.1029/2018GL078202)** _Geophys Res Lett_ 2018.

* Rasp S, Pritchard MS, Gentine P. **[Deep learning to represent subgrid processes in climate models.](https://doi.org/10.1073/pnas.1810286115)** _Proc Natl Acad Sci_ 2018.
* <img src="/images/newlogo.png" style="width: 1.5vw; height: 1.5hw; vertical-align: middle;" alt="DOI icon"> Rasp S, Pritchard MS, Gentine P. **[Deep learning to represent subgrid processes in climate models.](https://doi.org/10.1073/pnas.1810286115)** _Proc Natl Acad Sci_ 2018.

* Zanna L, Brankart JM, Huber M, Leroux S, Penduff T, Williams PD. **[Uncertainty and scale interactions in ocean ensembles: From seasonal forecasts to multidecadal climate predictions.](https://doi.org/10.1002/qj.3397)** _Q J R Meteorol Soc_ 2018.
* <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. **[Uncertainty and scale interactions in ocean ensembles: From seasonal forecasts to multidecadal climate predictions.](https://doi.org/10.1002/qj.3397)** _Q J R Meteorol Soc_ 2018.
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---

Our goal: leverage AI for scientific discovery in climate physics, towards more reliable climate projections.
<div style="color: #244769; font-family: Arial, sans-serif;">
<center>
<h3><b>Our goal: leverage AI for scientific discovery in climate physics, towards more reliable climate projections.</b></h3>
</center>
</div>
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