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Domain science experts are commonly limited by computational efficiency of their code and hardware resources available for execution of desired simulations. Here, we detail a collaboration between domain scientists focused on simulating an ensemble of climate and human management decisions to drive environmental (e.g., water quality) and economic (e.g., crop yield) outcomes. Briefly, the domain scientists developed a message passing interface to execute the formerly serial code across a number of processors, anticipating significant performance improvement by moving to a cluster computing environment from their desktop machines. The code is both too complex to efficiently re-code from scratch and has a shared codebase that must continue to function on desktop machines as well as the parallel implementation. However, inefficiencies in the code caused the LUSTRE filesystem to bottleneck performance for all users. The domain scientists collaborated with Indiana University's Science Applications and Performance Tuning and High Performance File System teams to address the unforeseen performance limitations. The non-linear process of testing software advances and hardware performance is a model of the failures and successes that can be anticipated in similar applications. Ultimately, through a series of iterative software and hardware advances the team worked collaboratively to increase performance of the code, cluster, and file system to enable more than 100-fold increases in performance. As a result, the domain science is able to assess ensembles of climate and human forcing on the model, and sensitivities of ecologically and economically important outcomes of intensively managed agricultural landscapes.