Recent trends in climate modeling find in GRID computing a powerful way to achieve results by sharing computing and data distributed resources. In particular, ensemble prediction is based on the generation of multiple simulations from perturbed model conditions to sample the existing uncertainties. In this work, we present a GRID application consisting of a state-of-the-art climate model (CAM). The main goal of the application is providing a user-friendly platform to run ensemble-based predictions on the GRID. This requires managing a complex workflow involving long-term jobs and data management in a user-transparent way. In doing so, we identified the weaknesses of current GRID middleware tools and developed a robust workflow by merging the optimal existing applications with an underlying self-developed workflow.
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