With exascale computing on the horizon, the performance variability of I/O systems presents a key challenge in sustaining high performance. In many HPC applications, I/O is performed concurrently by all processes; this produces I/O bursts, which causes resource contention and substantial variability of I/O performance, significantly impacting the overall application performance. We here utilize the IOR benchmark to explore the influence of user-configurable parameters of the I/O stack on performance variability. We then propose a new approach, called Damaris, leveraging dedicated I/O cores on each multicore SMP node to efficiently perform asynchronous data processing and I/O. We evaluate our approach on two different platforms with the CM1 atmospheric model, one of the BlueWaters HPC applications. By gathering data into large files while avoiding synchronization between cores, our solution increases the I/O throughput by a factor of 6, hides all I/O-related costs, and enables a 600% compression ratio without any additional overhead.