EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Deep Learning for Climate Model Output Statistics

Michael Steininger1, Daniel Abel2, Katrin Ziegler2, Anna Krause1, Heiko Paeth2, and Andreas Hotho1
Michael Steininger et al.
  • 1Chair of Data Science, Department of Computer Science, University of Würzburg, Würzburg, Germany ({steininger,anna.krause,hotho}
  • 2Chair of Physical Geography, Department of Geography and Geology, University of Würzburg, Würzburg, Germany ({daniel.abel,katrin.ziegler,heiko.paeth}

Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitation. Model output statistics (MOS) reduce these errors by fitting the model output to observational data with machine learning. In this work, we explore the feasibility and potential of deep learning with convolutional neural networks (CNNs) for MOS. We propose the CNN architecture ConvMOS specifically designed for reducing errors in climate model outputs and apply it to the climate model REMO. Our results show a considerable reduction of errors and mostly improved performance compared to three commonly used MOS approaches.

How to cite: Steininger, M., Abel, D., Ziegler, K., Krause, A., Paeth, H., and Hotho, A.: Deep Learning for Climate Model Output Statistics, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2175,, 2021.

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