Managing and monitoring of ensemble climate prediction experiments with Autosubmit

One of the main challenges for the climate science is how to efficiently perform vast numbers of simulations of the Earth system on a large variety of supercomputers. In particular, the climate community has developed complex computational systems to obtain climate predictions. A huge amount of computational resources are needed to produce the simulations, as well as to deal with the data incoming to and outcoming from the models. Regardless of the platform, climate simulations typically consist in hundreds of programs or scripts whose workflow can be complex. In this paper, Autosubmit, a Python-based tool that allows creating, launching and monitoring climate experiments with the EC-Earth ESM and the NEMO ocean model, is introduced. The experiment is defined as a sequence of jobs that Autosubmit remotely submits and manages in a transparent way for the user. Autosubmit could be expanded to perform any ensemble climate experiment on any computing platform to ensure the efficient handling of highly-dependent jobs, an optimum use of available computing resources, and a user-friendly management of the experiments, including creation, start, stop, restart, live monitoring and reproduction.