Towards Specialized Supercomputers for Climate Sciences: Computational Requirements of the Icosahedral Nonhydrostatic Weather and Climate Model

TL;DR

This paper analyzes the computational needs of the ICON climate model, emphasizing the necessity for specialized supercomputers to improve high-resolution climate predictions and integrating machine learning for efficiency.

Contribution

It presents a detailed requirements model for ICON, highlighting the need for specialized supercomputers and proposing machine learning techniques to enhance climate simulation accuracy.

Findings

Computational demands for km-scale simulations are outlined.

Machine learning can improve model accuracy and efficiency.

Guidelines for designing future supercomputers for climate science.

Abstract

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.