Research work and relevant papers by our team
If you are interested in understanding how M²LInES is using machine learning to improve climate models, we have developed an educational JupyterBook Learning Machine Learning for Climate modeling with Lorenz 96. This JupyterBook describes the key research themes in M²LInES, through the use of a simple climate model and machine learning algorithms. You can run the notebooks yourself, contribute to the development of the JupyterBook or let us know what you think on GitHub https://github.com/m2lines/L96_demo.
📙 Chemke R, Yuval J. Human-induced weakening of the Northern Hemisphere tropical circulation Nature. 2023
📘 Otness K, Zanna L, Bruna J. Data-driven multiscale modeling of subgrid parameterizations in climate models. arXiv:2303.17496. Preprint submitted to ICLR Workshop on Climate Change AI. 2023
📄 Falasca F, Brettin A, Zanna L, Griffies SM, Yin J, Zhao M. Exploring the non-stationarity of coastal sea level probability distributions. arxiv.org:2211.04608. Preprint submitted to EDS. 2023
📄 Perezhogin P, Fernandez-Granda C, Zanna L. Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model. Preprint submitted to JAMES. 2023
📙 Ross AS, Li Z, Perezhogin P, Fernandez-Granda C, Zanna L. Benchmarking of machine learning ocean subgrid parameterizations in an idealized model . doi.org/10.1029/2022MS003258 JAMES. 2023
📄 Shamekh S, Lamb K.D, Huang Y, Gentine P. Implicit learning of convective organization explains precipitation stochasticity In review. 2022
📙 Christensen H, Zanna L. Parametrization in Weather and Climate Models. Oxford Research Encyclopedia of Climate Science. 2022
📘 Liu S, Kaku A, Zhu W, Leibovich M, Mohan S, Yu B, Huang H, Zanna L, Razavian N, Niles-Weed J, Fernandez-Granda C. Deep Probability Estimation, Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13746-13781, 2022.
📙 Loose N, Abernathey R, Grooms I, Busecke J, Guillaumin A, Yankovsky E, Marques G, Steinberg J, Ross AS, Khatri H, Bachman S, Zanna L, Martin P. GCM-Filters: A Python Package for Diffusion-based Spatial Filtering of Gridded Data., Journal of Open Source Software 7(70), p.3947. 2022
📙 Frezat H, Le Sommer J, Fablet R, Balarac G, Lguensat R. A posteriori learning for quasi-geostrophic turbulence parametrization JAMES. 2022
📙 Zampieri L, Arduini G, Holland M, Keeley S, Mogensen KS, Tietsche S. A machine learning correction model of the clear-sky bias over the Arctic sea ice in atmospheric reanalyses J Earth and Space Science Open Archive. 2022 (preprint)
📄 Chen L, Bruna J. On Gradient Descent Convergence beyond the Edge of Stability. arXiv preprint arXiv:2206.04172, 2022 (preprint)
📄 Bhouri MA, Gentine P. History-Based, Bayesian, Closure for Stochastic Parameterization: Application to Lorenz ‘96 arXiv preprint arXiv:2210.14488 (preprint)
📄 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 arXiv preprint arXiv:2112.08440. 2021 (preprint)
📄 Wang P, Yuval J, O’Gorman PA. Non-local parameterization of atmospheric subgrid processes with neural networks arXiv preprint arXiv:2201.00417. 2022 (preprint)
📄 Yuval J, O’Gorman PA. Neural-network parameterization of subgrid momentum transport in the atmosphere. J Earth and Space Science Open Archive (preprint)
📙 Guillaumin A, Zanna L. Stochastic Deep Learning parameterization of Ocean Momentum Forcing. Journal of Advances in Modeling Earth Systems 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.
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)
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.
Robinson NH, Hamman J, Abernathey R. Science needs to rethink how it interacts with big data: Five principles for effective scientific big data systems. ArXiv190803356 Cs 2019.
Beucler T, Rasp S, Pritchard M, Gentine P. Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling. ArXiv190606622 Phys 2019.
Izacard G, Mohan S, Fernandez-Granda C. Data-driven Estimation of Sinusoid Frequencies. Advances in Neural Information Processing Systems 2019.
Mohan S, Kadkhodaie Z, Simoncelli EP, Fernandez-Granda C. Robust and interpretable blind image denoising via bias-free convolutional neural networks 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.
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.