Submitted as: methods for assessment of models
08 Feb 2022
Submitted as: methods for assessment of models | 08 Feb 2022
Status: a revised version of this preprint is currently under review for the journal GMD.

Cloud-based framework for inter-comparing submesoscale permitting realistic ocean models

Takaya Uchida1, Julien Le Sommer1, Charles Stern2, Ryan Abernathey2, Chris Holdgraf3, Aurélie Albert1, Laurent Brodeau4,5, Eric Chassignet6, Xiaobiao Xu6, Jonathan Gula7,8, Guillaume Roullet7, Nikolay Koldunov9, Sergey Danilov9, Qiang Wang9, Dimitris Menemenlis10, Clément Bricaud11, Brian Arbic12, Jay Shriver13, Fangli Qiao14, Bin Xiao14, Arne Biastoch15,16, René Schubert7,15, Baylor Fox-Kemper17, and William Dewar1,18 Takaya Uchida et al.
  • 1Université Grenoble Alpes, CNRS, IRD, Grenoble-INP, Institut des Géosciences de l’Environnement, France
  • 2Lamont-Doherty Earth Observatory, Columbia University in the City of New York, USA
  •, USA
  • 4Ocean Next, Grenoble, France
  • 5Datlas, Grenoble, France
  • 6Center for Ocean-Atmospheric Prediction Studies, Florida State University, USA
  • 7Univ. Brest, CNRS, Ifremer, IRD, Laboratoire d’Océanographie Physique et Spatiale (LOPS), IUEM, 29280, Plouzané, France
  • 8Institut Universitaire de France (IUF), Paris, France
  • 9Alfred Wegener Institute (AWI), Helmholtz Center for Polar and Marine Research, Germany
  • 10Jet Propulsion Laboratory, National Aeronautics and Space Administration (NASA), USA
  • 11Mercator Ocean International, France
  • 12Department of Earth and Environmental Sciences, University of Michigan, USA
  • 13Oceanography Division, US Naval Research Laboratory, USA
  • 14First Institute of Oceanography, and Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao, China
  • 15GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel, Germany
  • 16Kiel University, Kiel, Germany
  • 17Department of Earth, Environmental, and Planetary Sciences, Brown University, USA
  • 18Department of Earth, Ocean and Atmospheric Science, Florida State University, USA

Abstract. With the increase in computational power, ocean models with kilometer-scale resolution have emerged over the last decade. These models have been used for quantifying the energetic exchanges between spatial scales, informing the design of eddy parametrizations and preparing observing networks. The increase in resolution, however, has drastically increased the size of model outputs, making it difficult to transfer and analyze the data. Nonetheless, it is of primary importance to assess more systematically the realism of these models. Here, we showcase a cloud-based analysis framework proposed by the Pangeo Project that aims to tackle such distribution and analysis challenges. We analyze the output of eight submesoscale-permitting simulations, all on the cloud, for a crossover region of the upcoming Surface Water and Ocean Topography (SWOT) altimeter mission near the Gulf Stream separation. The models used in this study are run with the NEMO, CROCO, MITgcm, HYCOM, FESOM and FIO-COM code bases. The cloud-based analysis framework: i) minimizes the cost of duplicating and storing ghost copies of data, and ii) allows for seamless sharing of analysis results amongst collaborators. We describe the framework and provide example analyses (e.g., sea-surface height variability, submesoscale vertical buoyancy fluxes, and comparison to predictions from the mixed-layer instability parametrization). Basin-to-global scale, submesoscale-permitting models are still at their early stage of development; their cost and carbon footprints are also rather large. It would, therefore, benefit the community to document the different model configurations for future best practices. We also argue that an emphasis on data analysis strategies would be crucial for improving the models themselves.

Takaya Uchida et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-27', Stephen M. Griffies, 11 Feb 2022
    • AC1: 'Reply on RC1', Takaya Uchida, 03 May 2022
  • RC2: 'Comment on gmd-2022-27', Mike Bell, 14 Mar 2022
    • AC2: 'Reply on RC2', Takaya Uchida, 03 May 2022
  • CC1: 'Comment on gmd-2022-27', Andy Hogg, 04 Apr 2022
    • AC3: 'Reply on CC1', Takaya Uchida, 03 May 2022
  • RC3: 'Comment on gmd-2022-27', Joel Hirschi, 05 Apr 2022
    • AC4: 'Reply on RC3', Takaya Uchida, 03 May 2022
  • RC4: 'Comment on gmd-2022-27', Andy Hogg, 06 Apr 2022
    • AC5: 'Reply on RC4', Takaya Uchida, 03 May 2022

Takaya Uchida et al.

Takaya Uchida et al.


Total article views: 758 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
566 170 22 758 5 4
  • HTML: 566
  • PDF: 170
  • XML: 22
  • Total: 758
  • BibTeX: 5
  • EndNote: 4
Views and downloads (calculated since 08 Feb 2022)
Cumulative views and downloads (calculated since 08 Feb 2022)

Viewed (geographical distribution)

Total article views: 701 (including HTML, PDF, and XML) Thereof 701 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 20 May 2022
Short summary
Ocean and climate scientists have used numerical simulations as a tool to examine the ocean and climate system ever since the 1970s. Since then, owing to the continuous increase in computational power and advances in numerical methods, we have been able to simulate increasing complex phenomena. The fidelity of the simulations in representing the phenomena remains a core issue in the ocean science community. Here, we propose a cloud-based framework to inter-compare and assess such simulations.