67 citations · 170 across the 6 of their papers we have counts for
4 papers · 1 filter
Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties with Deep Learning Multi-Member and Stochastic Parameterizations
Gunnar Behrens, Tom Beucler, Fernando Iglesias-Suarez +5
Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here…
Causally-informed deep learning to improve climate models and projections
Fernando Iglesias-Suarez, Pierre Gentine, Breixo Solino-Fernandez +4
Climate models are essential to understand and project climate change, yet long-standing biases and uncertainties in their projections remain. This is largely associated with the r…
Non-Linear Dimensionality Reduction with a Variational Encoder Decoder to Understand Convective Processes in Climate Models
Gunnar Behrens, Tom Beucler, Pierre Gentine +3
Deep learning can accurately represent sub-grid-scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack…
Deep Learning Based Cloud Cover Parameterization for ICON
Arthur Grundner, Tom Beucler, Pierre Gentine +3
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-reso…