The computational and energy cost of simulation and storage for climate science: lessons from CMIP6

• Main Authors: Acosta, Mario C, Palomas, Sergi, Paronuzzi Ticco, Stella V, Utrera, Gladys, Biercamp, Joachim, Bretonniere, Pierre-Antoine, Budich, Reinhard, Castrillo, Miguel, Caubel, Arnaud, Doblas-Reyes, Francisco, Epicoco, Italo, Fladrich, Uwe, Joussaume, Sylvie, Gupta, Alok Kumar, Lawrence, Bryan, Le Sager, Philippe, Lister, Grenville, Moine, Marie-Pierre, Rioual, Jean-Christophe, Valcke, Sophie, Zadeh, Niki, Balaji, Venkatramani
• Format: Article
• Language: English

The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts aimed at better understanding the past, present, and future of climate changes in a multi-model context. A total of 21 model intercomparison projects (MIPs) were endorsed in its sixth phase (CMIP6), which included 190 different experiments that were used to simulate 40 000 years and produced around 40 PB of data in total. This paper presents the main findings obtained from the CPMIP (the Computational Performance Model Intercomparison Project), a collection of a common set of metrics, specifically designed for assessing climate model performance. These metrics were exclusively collected from the production runs of experiments used in CMIP6 and primarily from institutions within the IS-ENES3 consortium. The document presents the full set of CPMIP metrics per institution and experiment, including a detailed analysis and discussion of each of the measurements. During the analysis, we found a positive correlation between the core hours needed, the complexity of the models, and the resolution used. Likewise, we show that between 5 %–15 % of the execution cost is spent in the coupling between independent components, and it only gets worse by increasing the number of resources. From the data, it is clear that queue times have a great impact on the actual speed achieved and have a huge variability across different institutions, ranging from none to up to 78 % execution overhead. Furthermore, our evaluation shows that the estimated carbon footprint of running such big simulations within the IS-ENES3 consortium is 1692 t of CO2 equivalent. As a result of the collection, we contribute to the creation of a comprehensive database for future community reference, establishing a benchmark for evaluation and facilitating the multi-model, multi-platform comparisons crucial for understanding climate modelling performance. Given the diverse range of applications, configurations, and hardware utilised, further work is required for the standardisation and formulation of general rules. The paper concludes with recommendations for future exercises aimed at addressing the encountered challenges which will facilitate more collections of a similar nature.