High-resolution climate simulations require tremendous computing resources and can generate massive datasets. At present, preserving the data from these simulations consumes vast storage resources at institutions such as the National Center for Atmospheric Research (NCAR). The historical data generation trends are economically unsustainable, and storage resources are already beginning to limit science objectives. To mitigate this problem, we investigate the use of data compression techniques on climate simulation data from the Community Earth System Model. Ultimately, to convince climate scientists to compress their simulation data, we must be able to demonstrate that the reconstructed data reveals the same mean climate as the original data, and this paper is a first step toward that goal. To that end, we develop an approach for verifying the climate data and use it to evaluate several compression algorithms. We find that the diversity of the climate data requires the individual treatment of variables, and, in doing so, the reconstructed data can fall within the natural variability of the system, while achieving compression rates of up to 5:1.

The Community Earth System Model (CESM), released in June 2010, incorporates new physical process and new numerical algorithm options, significantly enhancing simulation capabilities over its predecessor, the June 2004 release of the Community Climate System Model. CESM also includes enhanced performance tuning options and performance portability capabilities. This paper describes performance and performance scaling on both the Cray XT5 and the IBM BG/P for four representative production simulations, varying both problem size and enabled physical processes. The paper also describes preliminary performance results for high resolution simulations using over 200,000 processor cores, indicating the promise of ongoing work in numerical algorithms and where further work is required.