Publications Archive

M²LInES funded research

2021 -

• Guillaumin, A. P., & Zanna, L. Stochastic-Deep Learning Parameterization of Ocean Momentum Forcing JAMES 2021

• Zanna L, Bolton T. Deep Learning of Unresolved Turbulent Ocean Processes in Climate Models. In Deep learning for the Earth Sciences 2021 (eds G. Camps-Valls, D. Tuia, X.X. Zhu and M. Reichstein). (author link )

• Gentine P, Eyring V, Beucler T. Deep Learning for the Parametrization of Subgrid Processes in Climate Models 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. 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. Physical Review Letters 2021. (author link )

• Frezat H, Balarac G, Le Sommer J, Fablet R, Lguensat R. Physical invariance in neural networks for subgrid-scale scalar flux modeling. Physical Review Fluids 2021. (author link )

• 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. Philosophical Transactions of the Royal Society A 2021. (author link )

• 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. 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. Nature communications 2020.

• Zanna L, Bolton T. Data‐Driven Equation Discovery of Ocean Mesoscale Closures. Geophysical Research Letters 2020. (author link )

• Mohan S, Kadkhodaie Z, Simoncelli EP, Fernandez-Granda C. Robust and interpretable blind image denoising via bias-free convolutional neural networks ICLR 2020.

2019

• Bolton T, Zanna L. Applications of Deep Learning to Ocean Data Inference and Subgrid Parameterization. J Adv Model Earth Syst 2019.

• Held IM, Guo H, Adcroft A, Dunne JP, Horowitz LW, Krasting J, et al. Structure and Performance of GFDL’s CM4.0 Climate Model. J Adv Model Earth Syst 2019.

• Niall H. Robinson, Joe Hamman, Ryan Abernathey. Seven Principles for Effective Scientific Big-DataSystems. ArXiv190803356 Cs 2019.

• Izacard G, Mohan S, Fernandez-Granda C. Data-driven Estimation of Sinusoid Frequencies. 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. Geophysical Research Letter 2019. (ResearchGate link )

• 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. 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. J Adv Model Earth Syst 2018.

• Gentine P, Pritchard M, Rasp S, Reinaudi G, Yacalis G. Could Machine Learning Break the Convection Parameterization Deadlock? Geophys Res Lett 2018.

• Rasp S, Pritchard MS, Gentine P. Deep learning to represent subgrid processes in climate models. 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. Q J R Meteorol Soc 2018.