This paper investigates the behavior of cooperative coevolutionary algorithms (CCEAs) under dynamic environments. The background of dynamic optimization and the approaches used in evolutionary algorithms (EAs) to address dynamic environments are first briefly reviewed. Two common approaches, including hypermutations and random immigrants, are incorporated into CCEAs to solve two dynamic problems: one moving peak problem and two moving peaks problem. The performance on these two problems under different change severities and different change periods are empirically compared with those of the EA counterparts. Experimental results indicate that using cooperative coevolutionary approach can generally provide a better performance than the EA counterparts. In particular, CCEA with the use of random immigrants consistently outperforms other algorithms we study. The reasons behind these observations are analyzed by studying the best-of-generation fitness against generations and the trajectories of best-of-generation individuals when tracking the moving optima in the search space.