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

Type Article
Date 2022-07
Language English
Author(s) Uchida TakayaORCID1, Le Sommer JulienORCID1, Stern Charles2, Abernathey Ryan P.ORCID2, Holdgraf Chris3, Albert Aurélie1, Brodeau Laurent4, 5, Chassignet Eric P.6, Xu Xiaobiao6, Gula JonathanORCID7, 8, Roullet Guillaume7, Koldunov NikolayORCID9, Danilov Sergey9, Wang QiangORCID9, Menemenlis Dimitris10, Bricaud Clément11, Arbic Brian K.ORCID12, Shriver Jay F.ORCID13, Qiao Fangli14, Xiao Bin14, Biastoch ArneORCID15, 16, Schubert RenéORCID7, 15, Fox-Kemper BaylorORCID17, Dewar William K.1, 18, Wallcraft Alan6
Affiliation(s) 1 : Université Grenoble Alpes, CNRS, IRD, Grenoble-INP, Institut des Gêosciences de l’Environnement, Grenoble, France
2 : Lamont-Doherty Earth Observatory, Columbia University in the City of New York, New York City, USA
3 :, Portland, Oregon, USA
4 : Ocean Next, Grenoble, France
5 : Datlas, Grenoble, France
6 : Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, Florida, USA
7 : Univ. Brest, CNRS, Ifremer, IRD, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, 29280, Plouzané, France
8 : Institut Universitaire de France (IUF), Paris, France
9 : Alfred Wegener Institute (AWI), Helmholtz Center for Polar and Marine Research, Bremerhaven, Germany
10 : Jet Propulsion Laboratory, National Aeronautics and Space Administration (NASA), Palisades, California, USA
11 : Mercator Ocean International, Toulouse, France
12 : Department of Earth and Environmental Sciences, University of Michigan, Ann Arbor, Michigan, USA
13 : Oceanography Division, US Naval Research Laboratory, Hancock, Mississippi, USA
14 : First Institute of Oceanography, and Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao, China
15 : GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel, Kiel, Germany
16 : Department of Ocean Circulation and Climate Dynamics, Kiel University, Kiel, Germany
17 : Department of Earth, Environmental, and Planetary Sciences, Brown University, Providence, Rhode Island, USA
18 : Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, Florida, USA
Source Geoscientific Model Development (1991-959X) (Copernicus GmbH), 2022-07 , Vol. 15 , N. 14 , P. 5829-5856
DOI 10.5194/gmd-15-5829-2022
WOS© Times Cited 3

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. It remains, nonetheless, 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 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.

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Uchida Takaya, Le Sommer Julien, Stern Charles, Abernathey Ryan P., Holdgraf Chris, Albert Aurélie, Brodeau Laurent, Chassignet Eric P., Xu Xiaobiao, Gula Jonathan, Roullet Guillaume, Koldunov Nikolay, Danilov Sergey, Wang Qiang, Menemenlis Dimitris, Bricaud Clément, Arbic Brian K., Shriver Jay F., Qiao Fangli, Xiao Bin, Biastoch Arne, Schubert René, Fox-Kemper Baylor, Dewar William K., Wallcraft Alan (2022). Cloud-based framework for inter-comparing submesoscale-permitting realistic ocean models. Geoscientific Model Development, 15(14), 5829-5856. Publisher's official version : , Open Access version :