• Constraining Uncertainty in Projected Gross Primary Production With Machine Learning 

      Schlund, ManuelORCIDiD; Eyring, VeronikaORCIDiD; Camps‐Valls, Gustau; Friedlingstein, Pierre; Gentine, PierreORCIDiD; Reichstein, MarkusORCIDiD (Journal of Geophysical Research: Biogeosciences, 2020-11-21)
      The terrestrial biosphere is currently slowing down global warming by absorbing about 30% of human emissions of carbon dioxide (CO2). The largest flux of the terrestrial carbon uptake is gross primary production (GPP) ...
    • Deep Learning Based Cloud Cover Parameterization for ICON 

      Grundner, ArthurORCIDiD; Beucler, TomORCIDiD; Gentine, PierreORCIDiD; Iglesias‐Suarez, FernandoORCIDiD; Giorgetta, Marco A.ORCIDiD; Eyring, VeronikaORCIDiD (Journal of Advances in Modeling Earth Systems, 2022-12-14)
      A promising approach to improve cloud parameterizations within climate models and thus climate projections is to use deep learning in combination with training data from storm‐resolving model (SRM) simulations. The ICOsahedral ...
    • Non‐Linear Dimensionality Reduction With a Variational Encoder Decoder to Understand Convective Processes in Climate Models 

      Behrens, GunnarORCIDiD; Beucler, TomORCIDiD; Gentine, PierreORCIDiD; Iglesias‐Suarez, FernandoORCIDiD; Pritchard, MichaelORCIDiD; Eyring, VeronikaORCIDiD (Journal of Advances in Modeling Earth Systems, 2022-08-13)
      Deep learning can accurately represent sub‐grid‐scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack interpretability due to large internal ...