Atmospheric physics

Publications on climate modelling and quantum computing

*by student I (co)-advised
  1. *Klamt, J., M. Schwabe, A. Lauer, M. A. Giorgetta, T. Mauritsen, V. Eyring: A Machine Learning-based Planetary Boundary Layer Height Scheme for ICON-A Learned from Vertically Highly Resolved Simulations, submitted (2025). https://doi.org/10.22541/essoar.176676912.24375190/v1
  2. Christiansen, P. J., D. Ohl de Mello, C. Brügmann, S. Hien, F. Herbort, M. Kiffner, L. Pastori, V. Eyring, M. Schwabe: Quantum Bayesian Optimization for the Automatic Tuning of Lorenz-96 as a Surrogate Climate Model, submitted (2025). https://doi.org/10.48550/arXiv.2512.20437
  3. Schwabe, M., L. Pastori, V. Sarandrea, V. Eyring: Quantum Machine Learning for Climate Modelling. Accepted by IEEE Conf. Quantum Artificial Intelligence (2025). https://doi.org/10.48550/arXiv.2512.14208
  4. *Heuer, H., T. Beucler, M. Schwabe, J. Savre, M. Schlund, V. Eyring: Beyond the Training Data: Confidence-Guided Mixing of Parameterizations in a Hybrid AI-Climate Model, submitted (2025). https://doi.org/10.48550/arXiv.2510.08107
  5. Pastori L., V. Eyring, M. Schwabe: Fisher Information, Training and Bias in Fourier Regression Models, submitted (2025). https://doi.org/10.48550/arXiv.2510.06945
  6. *Sarauer, E., M. Schwabe, P. Weiss, A. Lauer, P. Stier, V. Eyring: A physics-informed machine learning parameterization for cloud microphysics in ICON. Environmental Data Science 4, e40 (2025). https://doi.org/10.1017/eds.2025.10016
  7. Schwabe, M., L. Pastori, I. de Vega, P. Gentine, L. Iapichino, V. Lahtinen, M. Leib, J. M. Lorenz, V. Eyring: Opportunities and challenges of quantum computing for climate modeling. Environmental Data Science 4, e35 (2025). https://doi.org/10.1017/eds.2025.10010
  8. Bonnet, P., L. Pastori, M. Schwabe, M. Giorgetta, F. Iglesias-Suarez, V. Eyring: Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching. Geoscientific Model Development 18(12), 3681–3706 (2025). https://doi.org/10.5194/gmd-18-3681-2025
  9. Behrens, G., T. Beucler, F. Iglesias-Suarez, S. Yu, P. Gentine, M. Pritchard, M. Schwabe, V. Eyring: Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties With Deep Learning Multi-Member and Stochastic Parameterizations. JAMES 17 (4), e2024MS004272 (2025). https://doi.org/10.1029/2024MS004272
  10. Pastori L., A. Grundner, V. Eyring, M. Schwabe: Quantum Neural Networks for Cloud Cover Parameterizations in Climate Models, submitted (2025). https://doi.org/10.48550/arXiv.2502.10131
  11. *Sarauer, E., M. Schwabe, P. Weiss, A. Lauer, P. Stier, V. Eyring: Physics-informed Machine Learning-based Cloud Microphysics parameterization for Earth System Models. The Twelfth International Conference on Learning Representations. Workshop: Tackling Climate Change with Machine Learning (2024) https://elib.dlr.de/204924/1/paper.pdf
  12. *Heuer, H., M. Schwabe, P. Gentine, M. A. Giorgetta, V. Eyring. Interpretable multiscale Machine Learning-Based Parameterizations of Convection for ICON. JAMES 16 (8), e2024MS004398 (2024). https://doi.org/10.1029/2024MS004398

Presentations on climate modelling at international workshops and conferences (to be updated)

Talks unless otherwise noted
  1. Schwabe, M., Bonnet, P., Grundner, A., Schlund, M. and Eyring, V.: "ML developments for ICON and Evaluation with ESMValTool". ICON Seamless Workshop, 10.-11.08.2023, Offenbach, Deutschland (2023)
  2. Schwabe, M. and Eyring, V. "Machine learning for improved understanding and projections of climate change." TRR 165/181 Conference on ”Scale interactions, data-driven modeling, and uncertainty in weather and climate”, 27.-30. Oct. 2023, Ingolstadt. (2023) (invited)
  3. Schwabe, M., Pastori, L., Dogra, L., Klamt, J., Sarauer, E., Eyring, V.: Quantum Machine Learning for Climate Science. Workshop on Applications of Quantum Computing, 10.-11. Jul. 2023, Garching (2023) (Poster, invited)
  4. Schwabe, M. and Shamekh, S. – Teaser Talk: Hybrid Modelling For Atmosphere , USMILE General Assembly, Valencia, Spain (2022) (invited)
  5. Schwabe, M., Behrens, G., Beucler, T., Iglesias-Suarez, F., Gentine, P., Giorgetta, M., Grundner, A., Pritchard, M., Eyring, V.: "Interpretable AI – two examples", ELLIS Workshop, Valencia, Spain (2022)
  6. Schwabe, M., Grundner, A., Gentine, P., Giorgetta, M. A., Rapp, M., Eyring, V.: "Machine Learning based gravity wave parameterizations for ICON", 2022 SPARC Gravity Wave Symposium, online (Poster) (2022)