Articles | Volume 11, issue 9
Geosci. Model Dev., 11, 3659–3680, 2018

Special issue: Coupled Model Intercomparison Project Phase 6 (CMIP6) Experimental...

Geosci. Model Dev., 11, 3659–3680, 2018

Development and technical paper 11 Sep 2018

Development and technical paper | 11 Sep 2018

Requirements for a global data infrastructure in support of CMIP6

Venkatramani Balaji1,2, Karl E. Taylor3, Martin Juckes4, Bryan N. Lawrence5,4, Paul J. Durack3, Michael Lautenschlager6, Chris Blanton7,2, Luca Cinquini8, Sébastien Denvil9, Mark Elkington10, Francesca Guglielmo9, Eric Guilyardi9,4, David Hassell4, Slava Kharin11, Stefan Kindermann6, Sergey Nikonov1,2, Aparna Radhakrishnan7,2, Martina Stockhause6, Tobias Weigel6, and Dean Williams3 Venkatramani Balaji et al.
  • 1Princeton University, Cooperative Institute of Climate Science, Princeton, NJ 08540, USA
  • 2NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA
  • 3PCMDI, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
  • 4Science and Technology Facilities Council, Abingdon, UK
  • 5National Centre for Atmospheric Science, University of Reading, Reading, UK
  • 6Deutsches KlimaRechenZentrum GmbH, Hamburg, Germany
  • 7Engility Corporation, NJ 08540, USA
  • 8Jet Propulsion Laboratory (JPL), 4800 Oak Grove Drive, Pasadena, CA 91109, USA
  • 9Institut Pierre Simon Laplace, CNRS/UPMC, Paris, France
  • 10Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
  • 11Canadian Centre for Climate Modelling and Analysis, Atmospheric Environment Service, University of Victoria, Victoria, BC, Canada

Abstract. The World Climate Research Programme (WCRP)'s Working Group on Climate Modelling (WGCM) Infrastructure Panel (WIP) was formed in 2014 in response to the explosive growth in size and complexity of Coupled Model Intercomparison Projects (CMIPs) between CMIP3 (2005–2006) and CMIP5 (2011–2012). This article presents the WIP recommendations for the global data infrastructure needed to support CMIP design, future growth, and evolution. Developed in close coordination with those who build and run the existing infrastructure (the Earth System Grid Federation; ESGF), the recommendations are based on several principles beginning with the need to separate requirements, implementation, and operations. Other important principles include the consideration of the diversity of community needs around data – a data ecosystem – the importance of provenance, the need for automation, and the obligation to measure costs and benefits.

This paper concentrates on requirements, recognizing the diversity of communities involved (modelers, analysts, software developers, and downstream users). Such requirements include the need for scientific reproducibility and accountability alongside the need to record and track data usage. One key element is to generate a dataset-centric rather than system-centric focus, with an aim to making the infrastructure less prone to systemic failure.

With these overarching principles and requirements, the WIP has produced a set of position papers, which are summarized in the latter pages of this document. They provide specifications for managing and delivering model output, including strategies for replication and versioning, licensing, data quality assurance, citation, long-term archiving, and dataset tracking. They also describe a new and more formal approach for specifying what data, and associated metadata, should be saved, which enables future data volumes to be estimated, particularly for well-defined projects such as CMIP6.

The paper concludes with a future facing consideration of the global data infrastructure evolution that follows from the blurring of boundaries between climate and weather, and the changing nature of published scientific results in the digital age.

Short summary
We present recommendations for the global data infrastructure needed to support CMIP scientific design and its future growth and evolution. We follow a dataset-centric design less prone to systemic failure. Scientific publication in the digital age is evolving to make data a primary scientific output, alongside articles. We design toward that future scientific data ecosystem, informed by the need for reproducibility, data provenance, future data technologies, and measures of costs and benefits.