  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.
