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Climate Modelling

The Department of Climate Modelling belongs to Faculty 1 of the University of Bremen and is part of the Institute of Environmental Physics (IUP). In cooperation with the department Earth System Model Evaluation and Analysis located at the Institute of Atmospheric Physics of the German Aerospace Center (DLR), plays a pioneering role in the development and application of machine learning (ML) techniques in combination with Earth observations to improve Earth system models (ESMs). Its overarching goal is to reduce long-standing systematic model errors and, through these hybrid (physics + ML) Earth system models, to provide robust climate projections, as well as reliable information to support climate adaptation and mitigation strategies. The department contributes to the international development of the Earth System Model Evaluation Tool (ESMValTool), which drives the routine and comprehensive evaluation of Earth system models against Earth observations.

Research Focus Areas

  • Improving Earth system models through machine learning in combination with Earth observation data
  • Reducing uncertainties in climate predictions using ML and Earth observations
  • Developing innovative benchmarks for traditional and hybrid (physics + ML) Earth system models
  • Identifying and reducing systematic errors in climate models and formulating recommendations for targeted model improvements
  • Enhancing process understanding and parameterizations of climate-relevant processes through machine learning
  • Developing and applying ML-based methods for regional climate projections and uncertainty quantification
  • Advancing and applying the ESMValTool for the systematic evaluation of Earth system models using observations
  • Contributing to international model intercomparison projects, in particular the Coupled Model Intercomparison Project (CMIP) under the World Climate Research Programme (WCRP)

Core Tools and Data Resources

  • Machine learning methods for improving and analyzing climate models
  • Eyring Group GitHub Repository – development hub for machine-learning-based Earth system modelling tools, hybrid (Physics + ML) approaches, and evaluation workflows
  • ESMValTool for comprehensive evaluation of Earth system models with Earth observations – a community-developed, open-source software package for the comprehensive evaluation of Earth system models with Earth observations
  • Simulations with the ICON (Icosahedral Nonhydrostatic) climate model
  • Climate simulations from international model intercomparison projects (e.g., CMIP)
  • Earth observations and reanalysis products for model evaluation and development

Collaborations and International Networks

The department collaborates closely with the Earth System Model Evaluation and Analysis located at the Institute of Atmospheric Physics of the German Aerospace Center (DLR; Head: Prof. Veronika Eyring). Close collaborations also exist with the National Center for Atmospheric Research (NCAR) in Boulder, CO, USA. The department is actively involved in international research activities of the World Climate Research Programme (WCRP), contributing substantially to CMIP, and regularly participates in major climate and ozone assessments of the Intergovernmental Panel on Climate Change (IPCC) and the World Meteorological Organization (WMO).

Selected Publications:

  • Lindenlaub, L., Weigel, K., Hassler, B., Jones, C., and Eyring, V.: Characteristics ofagricultural droughts in CMIP6 historical simulations and future projections, Earth Syst.Dynam., 17, 81–105, https://doi.org/10.5194/esd-17-81-2026, 2026.
  • Behrens, G., Beucler, T., Iglesias-Suarez, F., Yu, S., Gentine, P., Schwabe, M. and Eyring, V.: Simulating atmospheric processes in Earth system models and quantifying uncertainties with deep learning multi-member and stochastic parameterizations. Journal of Advances in Modeling Earth Systems, 17, e2024MS004272. https://doi.org/10.1029/2024MS004272 , 2025.
  • Hafner, K, Iglesias-Suarez F., Shamekh S., Gentine P., Giorgetta M. A., Pincus R., Eyring V.: Interpretable machine learning-based radiation emulation for ICON. Journal of Geophysical Research: Machine Learning and Computation, 2, e2024JH000501. https://doi.org/10.1029/2024JH000501 , 2025.
  • Eyring, V., Collins, W.D., Gentine, P., Barnes, E.A., Barreiro, M., Beucler, T., Bocquet, M., Bretherton, C.S., Christensen, H.M., Gagne, D.J., Hall, D., Hammerling, D., Hoyer, S., Iglesias-Suarez, F., Lopez-Gomez, I., McGraw, M.C., Meehl, G.A., Molina, M.J., Monteleoni,C., Mueller, J., Pritchard, M.S., Rolnick, D., Runge, J., Stier, P., Watt-Meyer, O., Weigel, K., Yu, R., Zanna, L., Pushing the frontiers in climate modelling and analysis with machine learning, Nat. Clim. Chang., https://doi.org/10.1038/s41558-024-02095-y , 2024.
  • Eyring, V., P. Gentine, G. Camps-Valls, D. M. Lawrence, and M. Reichstein: AI-empowered next-generation multiscale climate modelling for mitigation and adaptation, Nat. Geosci., https://doi.org/10.1038/s41561-024-01527-w, 2024.
  • Gier, B. K., Schlund, M., Friedlingstein, P., Jones, C. D., Jones, C., Zaehle, S., and Eyring, V.: Representation of the terrestrial carbon cycle in CMIP6, Biogeosciences, 21, 5321–5360, https://doi.org/10.5194/bg-21-5321-2024, 2024.
  • Galytska, E., Weigel, K., Handorf, D., Jaiser, R., Köhler, R., Runge, J., & Eyring, V.: Evaluating causal Arctic-midlatitude teleconnections in CMIP6. Journal of Geophysical Research: Atmospheres, 128, e2022JD037978. , https://doi.org/10.1029/2022JD037978, 2023.
  • Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937-1958, doi:10.5194/gmd-9-1937-2016, 2016.
  • Eyring, V., Righi, M., Lauer, A., Evaldsson, M., Wenzel, S., Jones, C., Anav, A., Andrews, O.,Cionni, I., Davin, E. L., Deser, C., Ehbrecht, C., Friedlingstein, P., Gleckler, P., Gottschaldt, K.-D., Hagemann, S., Juckes, M., Kindermann, S., Krasting, J., Kunert, D., Levine, R., Loew, A., Mäkelä, J., Martin, G., Mason, E., Phillips, A. S., Read, S., Rio, C., Roehrig, R., Senftleben, D., Sterl, A., van Ulft, L. H., Walton, J., Wang, S., and Williams, K. D.: ESMValTool (v1.0) – a community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP, Geosci. Model Dev., 9, 1747-1802, doi:10.5194/gmd-9-1747-2016, 2016.
  • Eyring, V.,Gleckler, P. J., Heinze, C., Stouffer, R. J., Taylor, K. E., Balaji, V., Guilyardi, E., Joussaume, S., Kindermann, S., Lawrence, B. N., Meehl, G. A., Righi, M., and Williams, D. N.: Towards improved and more routine Earth system model evaluation in CMIP, Earth Syst. Dynam., 7, 813-830, doi:10.5194/esd-7-813-2016, 2016.
  • Wenzel, S., Cox, P. M., Eyring, V.,and Friedlingstein, P.: Projected land photosynthesis constrained by changes in the seasonal cycle of atmospheric CO2, Nature, doi: 10.1038/nature19772, 2016.

Contact:
Prof. Dr. Veronika Eyring