Abstract

In the last 30 years, earth system modeling has become increasingly data-intensive. The Community Earth System Model (CESM) response to the next Intergovernmental Panel on Climate Change (IPCC) assessment report (AR6) may require close to 1 Billion CPU hours of computation and generate up to 12 P... Show moreIn the last 30 years, earth system modeling has become increasingly data-intensive. The Community Earth System Model (CESM) response to the next Intergovernmental Panel on Climate Change (IPCC) assessment report (AR6) may require close to 1 Billion CPU hours of computation and generate up to 12 PB of raw data for post-processing. Existing post-processing tools are serial-only and impossibly slow with this much data. To improve the post-processing performance, our team has adopted a strategy of targeted replacement of the "bottleneck software" with light-weight parallel Python alternatives. This allows maximum impact with the least disruption to the CESM community and the shortest delivery time. We developed two light-weight parallel Python tools: one to convert model output from time-slice to time-series format, and one to perform fast time-averaging of time-series data. We present the motivation, approach, and results of these two tools, and our plans for future research and development. Show less