2023/09/16 by Torsten Hoefler, Hoefler, Torsten, Björn Stevens +37 · 2 citations
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Atmospheric and Oceanic Physics (physics.ao-ph) #Computational Engineering #Computers and Society (cs.CY) #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #FOS: Physical sciences #Finance #Physics and Society (physics.soc-ph) #Scientific Computing and Data Management #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2309.09002
openalex publication_date 2023/09/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Participants of the Berlin Summit on Earth Virtualization Engines (EVEs) discussed ideas and concepts to improve our ability to cope with climate change. EVEs aim to provide interactive and accessible climate simulations and data for a wide range of users. They combine high-resolution physics-based models with machine learning techniques to improve the fidelity, efficiency, and interpretability of climate projections. At their core, EVEs offer a federated data layer that enables simple and fast access to exabyte-sized climate data through simple interfaces. In this article, we summarize the technical challenges and opportunities for developing EVEs, and argue that they are essential for addressing the consequences of climate change.