USMILE publications

  • Gentine, P., Eyring, V. and Beucler, T. Deep Learning for the Parametrization of Subgrid Processes in Climate Models. In Deep learning for the Earth Sciences (eds G. Camps-Valls, D. Tuia, X.X. Zhu and M. Reichstein), (2021).
  • Eyring, V., Mishra, V., Griffith, G.P., Chen, L., Keenan, T., Turetsky, M.R., Brown, S., Jotzo, F., Moore, F.C. & Van der Linden, S. Reflections and projections on a decade of climate science. Nature Clim. Change. 11, 279–285, (2021).
  • Schlund, M., V. Eyring, G.-Camps-Valls, P. Freidlingstein, P. Gentine, & M. Reichstein, Constraining uncertainty in projected gross primary production with machine learning. JGR: Biogeosc. 125, e2019JG005619, doi:10.1029/2019JG005619 (2020).
  • Kraft, B., Jung, M., Körner, M., Koirala, S., & Reichstein, M. (2021). Towards hybrid modeling of the global hydrological cycle. Hydrology and Earth System Sciences Discussions, 1-40.
  • Kraft, B., Jung, M., Körner, M., & Reichstein, M. (2020). Hybrid modeling: Fusion of a deep approach and physics-based model for global hydrological modeling. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences43, 1537-1544.

Supporting the USMILE proposal

  • Eyring, V., Cox, P. M., Flato, G. M., Gleckler, P. J., Abramowitz, G., Caldwell, P., Collins, W. D., Gier, B., Hall, A. D., Hoffman, F. M., Hurtt, G. C., Jahn, A., Jones, C. D., Klein, S. A., Krasting, J., Kwiatkowski, L., Lorenz, R., Maloney, E., Meehl, G. A., Pendergrass, A., Pincus, R., Ruane, A. C., Russell, J. L., Sanderson, B., Santer, B., Sherwood, S. C., Simpson, I., Stouffer, R. & Williamson, M. S. Taking climate model evaluation to the next level. Nature Clim. Change 9, 102–110 (2019).
  • Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G. & Yacalis, G. Could Machine Learning Break the Convection Parameterization Deadlock? Geophysical Research Letters 45, 5742-5751, doi:10.1029/2018gl078202 (2018).
  • Reichstein, M., Camps-Valls, G., Stevens, B., Denzler, J., Carvalhais, N., Jung, M. & Prabhat. Deep learning and process understanding for data-driven Earth System Science. Nature 566, 195–204 (2019).