The Significance of Quality Assurance within Model Intercomparison Projects at the World Data Centre for Climate (WDCC)

The classical goals of a quality assessment system in the data life cycle are (1) to encourage data creators to improve their quality assessment procedures to reach the next quality level and (2) enable data consumers to decide, whether a dataset has a quality that is sufficient for usage in the target application, i.e. to appraise the data usability for their own purpose.

As the data volumes of projects and the interdisciplinarity of data usage grow, the need for homogeneous structure and standardised notation of data and metadata increases. This third aspect is especially valid for the data repositories, as they manage data through machine agents. So checks for homogeneity and consistency in early parts of the workflow become essential to cope with today's data volumes.

Selected parts of the workflow in the model intercomparison project CMIP5 and the archival of the data for the interdiscipliary user community of the IPCC-DDC AR5 and the associated quality checks are reviewed. We compare data and metadata checks and relate different types of checks to their positions in the data life cycle.

The project's data citation approach is included in the discussion, with focus on temporal aspects of the time necessary to comply with the project's requirements for formal data citations and the demand for the availability of such data citations.

In order to make different quality assessments of projects comparable, WDCC developed a generic Quality Assessment System. Based on the self-assessment approach of a maturity matrix, an objective and uniform quality level system for all data at WDCC is derived which consists of five maturity quality levels.