We discuss the computational challenges and requirements for high-resolution climate simulations using the Icosahedral Nonhydrostatic Weather and Climate Model (ICON). We define a detailed requirements model for ICON which emphasizes the need for specialized supercomputers to accurately predict climate change impacts and extreme weather events. Based on the requirements model, we outline computational demands for km-scale simulations, and suggests machine learning techniques to enhance model accuracy and efficiency. Our findings aim to guide the design of future supercomputers for advanced climate science.

翻译：本文讨论了使用二十面体非静力天气与气候模型（ICON）进行高分辨率气候模拟所面临的计算挑战与需求。我们为ICON建立了一个详细的需求模型，该模型强调了需要专用超级计算机来准确预测气候变化影响与极端天气事件。基于此需求模型，我们概述了公里级模拟的计算需求，并提出了利用机器学习技术提升模型精度与效率的途径。我们的研究旨在为未来先进气候科学超级计算机的设计提供指导。