Sustainable pathways towards net zero Switzerland

September 1, 2023
In Progress
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Climate change increasingly affects society and ecosystems, including biodiversity, and also impacts energy security in Switzerland. Climate-related weather extremes of unprecedented levels and increased weather variability on different timescales may lead to the intermittency of energy supply. Combined with changes in hydropower, these require the design of more resilient future energy systems that operate reliably even in extreme situations and years. In the scope of the SPEED2ZERO initiative, we use state-of-the-art machine learning approaches, like generative adversarial networks (GANs), variational autoencoders (VAEs), normalizing flows, and diffusion models, to name a few, as well as Earth System Model emulators to model spatially explicit temperature, precipitation, and climate extremes, combined with sampling approaches that enable the emulation of high-dimensional density distributions. These models can learn from high-resolution climate model simulations and generate new weather patterns at much lower computational cost. The project will provide crucial inputs for modeling platforms and enable the generation of climate storylines, i.e., realizations of specific extremes and compound extremes, e.g., periods that are exceptionally hot and dry at the same time.



SDSC Team:
Maxim Samarin
Shirin Goshtasbpour
Michele Volpi
Guillaume Obozinski

PI | Partners:

ETH Zurich, Institute for Atmospheric and Climate Science:

  • Prof. Dr. Reto Knutti
  • Dr. Cyril Brunner

More info

ETH Zurich, Seminar for Statistics:

  • Prof. Dr. Nicolai Meinshausen
  • Maybritt Schillinger

More info



Climate projections on regional scales are indispensable for deriving successful policies to address challenges related to energy, biodiversity, and climate change. However, typical global circulation models (GCMs) simulate climate variables like temperature or precipitation on spatial resolutions of 100 to 300 km and, thus, can only provide coarse estimates. To include regional-scale processes and local characteristics, regional climate models (RCMs) downscale outputs of GCMs to higher spatial resolutions of typically 10 to 30 km. Still, these RCM simulations can be computationally demanding and exhibit biases in the model prediction compared to actual temperature and other climate variable observations. To address these shortcomings, we develop multivariate generative downscaling approaches to generate regional-scale climate patterns from coarse-scale GCM inputs.

Proposed Approach / Solution

As a starting point for achieving the goals within the SPEED2ZERO initiative topic areas depicted in Figure 1, the SDSC's involvement focuses on developing multivariate generative downscaling approaches based on recent promising results in applying state-of-the-art architectures like GANs or diffusion models. As illustrated in Figure 2, the goal is to design an RCM emulator capable of providing several plausible climate pattern predictions, which are closer to actual observations of temperature and other climate variables.


Machine learning is increasingly becoming more important in climate modeling. In that regard, our projects will contribute state-of-the-art methods for working on challenges related to energy, biodiversity, and climate change. The SPEED2ZERO initiative will generate crucial scientific insight and develop technology, toolboxes, scenarios, and action plans with interactive visualizations to enable a sustainable transformation to a net zero greenhouse gases and biodiversity-positive Switzerland.

Figure 1: SPEED2ZERO focuses on the areas of net zero greenhouse gas emissions, energy, biodiversity, and climate change.
Figure 2: An important tool to achieve the goals in SPEED2ZERO is downscaling from coarse-scale global circulation model inputs to regional-scale outputs.



Additional resources


  1. Sun, Y., Deng, K., Ren, K., Liu, J., Deng, C., & Jin, Y. (2024). Deep learning in statistical downscaling for deriving high spatial resolution gridded meteorological data: A systematic review. ISPRS Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2023.12.011
  2. Ling, F., Ouyang, L., Larbi, B. R., Luo, J.-J., Han, T., Zhong, X., & Bai, L. (2024). Is artificial intelligence providing the second revolution for weather forecasting? ArXiv. https://arxiv.org/abs/2401.16669
  3. Fischer, E. M., Beyerle, U., Bloin-Wibe, L., Gessner, C., Humphrey, V., Lehner, F., Pendergrass, A. G., Sippel, S., Zeder, J., & Knutti, R. (2023). Storylines for unprecedented heatwaves based on ensemble boosting. Nature Communications, 14(4643). https://doi.org/10.1038/s41467-023-40112-4
  4. Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Alet, F., Ravuri, S., Ewalds, T., Eaton-Rosen, Z., Hu, W., Merose, A., Hoyer, S., Holland, G., Vinyals, O., Stott, J., Pritzel, A., Mohamed, S., & Battaglia, P. (2023). Learning skillful medium-range global weather forecasting. Science, 382(1416-1421). https://doi.org/10.1126/science.adi2336
  5. Price, I., Sanchez-Gonzalez, A., Alet, F., Andersson, T. R., El-Kadi, A., Masters, D., Ewalds, T., Stott, J., Mohamed, S., Battaglia, P., Lam, R., & Willson, M. (2023). GenCast: Diffusion-based ensemble forecasting for medium-range weather. ArXiv. https://arxiv.org/abs/2312.15796


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