Forecasting Arctic sea ice is a complex, open problem in polar science, exacerbated by climate change and Arctic amplification. Arctic sea ice dynamics are inherently interconnected to atmosphere and ocean dynamics. This means that coupled climate models are currently the best available tools to model and forecast sea ice. However, climate models consistently underrepresent sea ice processes on subseasonal timescales, contributing to forecasting challenges. In this study, we evaluate the Community Earth System Model Version Two's (CESM2) skill in representing Arctic sea ice by comparing model output to reanalysis and observational data. Doing so establishes CESM2's viability as a tool for studying Arctic sea ice processes and identifies places where model improvements are needed. While sea ice processes in CESM2 have been evaluated on annual and climate timescales, its performance on subseasonal timescales has not. We analyze CESM2's performance in representing subseasonal sea ice variability by comparing its temporal and spatial interannual variability to observations. We also evaluate CESM2's performance at capturing very rapid sea ice loss events (VRILEs). VRILEs are substantial sea ice loss events that occur on the timescale of days. We find that CESM2 does a poor job of representing VRILEs and has less interannual variability than observations.