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Climate modelling on the GRID Experiences in the EU-project EELA

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 sequence of two state-of-the-art climate models (one global model and one regional model), operable through a web portal (based on Genius). The main goal of the application is providing ensemble-based regional predictions. This requires managing a complex workflow involving long-term jobs and job dependencies in a user-transparent way. In doing so, we identified the weaknesses of current middleware tools and developed a robust workflow by merging the optimal existing applications with an underlying self-developed workflow application based on the communication with metadata catalogs (currently AMGA) storing application status and dynamic model output generation.
As an illustrative scientific challenge, the application is applied to study the El Niño phenomenon, by simulating an El Niño year with different forcing conditions and analyzing the precipitation response over south-american countries subject to flooding risk.