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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 5
Geosci. Model Dev., 11, 1799-1821, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 11, 1799-1821, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Review and perspective paper 08 May 2018

Review and perspective paper | 08 May 2018

Crossing the chasm: how to develop weather and climate models for next generation computers?

Bryan N. Lawrence1,2,3, Michael Rezny4, Reinhard Budich5, Peter Bauer6, Jörg Behrens7, Mick Carter8, Willem Deconinck6, Rupert Ford9, Christopher Maynard8, Steven Mullerworth8, Carlos Osuna10, Andrew Porter9, Kim Serradell11, Sophie Valcke12, Nils Wedi6, and Simon Wilson1,2,8 Bryan N. Lawrence et al.
  • 1Department of Meteorology, University of Reading, Reading, UK
  • 2National Centre of Atmospheric Science, Reading, UK
  • 3STFC Rutherford Appleton Laboratory, Didcot, UK
  • 4Monash University, Melbourne, Australia
  • 5Max Planck Institute for Meteorology, Hamburg, Germany
  • 6ECMWF, Reading, UK
  • 7DKRZ, Hamburg, Germany
  • 8Met Office, Exeter, UK
  • 9STFC Hartree Centre, Daresbury Laboratory, Daresbury, UK
  • 10ETH, Zurich, Switzerland
  • 11Barcelona Supercomputing Center, Barcelona, Spain
  • 12Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique, Toulouse, France

Abstract. Weather and climate models are complex pieces of software which include many individual components, each of which is evolving under pressure to exploit advances in computing to enhance some combination of a range of possible improvements (higher spatio-temporal resolution, increased fidelity in terms of resolved processes, more quantification of uncertainty, etc.). However, after many years of a relatively stable computing environment with little choice in processing architecture or programming paradigm (basically X86 processors using MPI for parallelism), the existing menu of processor choices includes significant diversity, and more is on the horizon. This computational diversity, coupled with ever increasing software complexity, leads to the very real possibility that weather and climate modelling will arrive at a chasm which will separate scientific aspiration from our ability to develop and/or rapidly adapt codes to the available hardware.

In this paper we review the hardware and software trends which are leading us towards this chasm, before describing current progress in addressing some of the tools which we may be able to use to bridge the chasm. This brief introduction to current tools and plans is followed by a discussion outlining the scientific requirements for quality model codes which have satisfactory performance and portability, while simultaneously supporting productive scientific evolution. We assert that the existing method of incremental model improvements employing small steps which adjust to the changing hardware environment is likely to be inadequate for crossing the chasm between aspiration and hardware at a satisfactory pace, in part because institutions cannot have all the relevant expertise in house. Instead, we outline a methodology based on large community efforts in engineering and standardisation, which will depend on identifying a taxonomy of key activities – perhaps based on existing efforts to develop domain-specific languages, identify common patterns in weather and climate codes, and develop community approaches to commonly needed tools and libraries – and then collaboratively building up those key components. Such a collaborative approach will depend on institutions, projects, and individuals adopting new interdependencies and ways of working.

Publications Copernicus
Short summary
Weather and climate models consist of complex software evolving in response to both scientific requirements and changing computing hardware. After years of relatively stable hardware, more diversity is arriving. It is possible that this hardware diversity and the pace of change may lead to an inability for modelling groups to manage their software development. This chasm between aspiration and reality may need to be bridged by large community efforts rather than traditional in-house efforts.
Weather and climate models consist of complex software evolving in response to both scientific...