Earth Virtualization Engines -- A Technical Perspective

TL;DR

Earth Virtualization Engines (EVEs) are advanced climate simulation platforms combining physics-based models and machine learning to provide accessible, high-fidelity climate data crucial for addressing climate change challenges.

Contribution

This paper presents a technical perspective on EVEs, highlighting their architecture, challenges, and potential to improve climate data accessibility and simulation fidelity.

Findings

EVEs enable federated access to exabyte-scale climate data.

They integrate physics models with machine learning for better projections.

EVEs are essential tools for climate change mitigation and adaptation.

Abstract

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.