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