Earth Virtualization Engines -- A Technical Perspective
This addresses the problem of limited access to and usability of climate data for a wide range of users, though it is incremental as it builds on existing models and techniques.
The paper discusses Earth Virtualization Engines (EVEs) as a technical approach to enhance climate change coping by providing interactive, accessible climate simulations and data through high-resolution physics-based models combined with machine learning, aiming to improve fidelity, efficiency, and interpretability of projections.
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.