With exascale computing on the horizon, the performance variability of I/O systems represents a key challenge in sustaining high performance. In many HPC applications, I/O is concurrently performed by all processes, which leads to I/O bursts. This causes resource contention and substantial variability of I/O performance, which significantly impacts the overall application performance. In this paper, we first explore the influence of user-configurable parameters and I/O approaches on write performance variability. We then propose a new approach, called Damaris, which leverages 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 including the Kraken Cray XT5 supercomputer, with the CM1 atmospheric model, which is one of the target HPC applications for the Blue Waters project. By gathering data into large files while avoiding synchronization between cores, our solution brings several benefits: 1) it increases the sustained write throughput by a factor of almost 15; 2) it provides almost 70\% overall application speedup on 9K cores; 3) it fully hides I/O-related costs; 4) it enables a 600\% compression ratio without any additional overhead, leading to a major reduction of storage requirements.