Cloud-based framework for inter-comparing submesoscale-permitting realistic ocean models
<p>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 networ...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-07-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/15/5829/2022/gmd-15-5829-2022.pdf |
| _version_ | 1811312692417265664 |
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| author | T. Uchida J. Le Sommer C. Stern R. P. Abernathey C. Holdgraf A. Albert L. Brodeau L. Brodeau E. P. Chassignet X. Xu J. Gula J. Gula G. Roullet N. Koldunov S. Danilov Q. Wang D. Menemenlis C. Bricaud B. K. Arbic J. F. Shriver F. Qiao B. Xiao A. Biastoch A. Biastoch R. Schubert R. Schubert B. Fox-Kemper W. K. Dewar W. K. Dewar A. Wallcraft |
| author_facet | T. Uchida J. Le Sommer C. Stern R. P. Abernathey C. Holdgraf A. Albert L. Brodeau L. Brodeau E. P. Chassignet X. Xu J. Gula J. Gula G. Roullet N. Koldunov S. Danilov Q. Wang D. Menemenlis C. Bricaud B. K. Arbic J. F. Shriver F. Qiao B. Xiao A. Biastoch A. Biastoch R. Schubert R. Schubert B. Fox-Kemper W. K. Dewar W. K. Dewar A. Wallcraft |
| author_sort | T. Uchida |
| collection | DOAJ |
| description | <p>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.</p> |
| first_indexed | 2024-04-13T10:41:19Z |
| format | Article |
| id | doaj.art-410e7dca56c44863b19f8edf164d36e3 |
| institution | Directory Open Access Journal |
| issn | 1991-959X 1991-9603 |
| language | English |
| last_indexed | 2024-04-13T10:41:19Z |
| publishDate | 2022-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj.art-410e7dca56c44863b19f8edf164d36e32022-12-22T02:49:55ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032022-07-01155829585610.5194/gmd-15-5829-2022Cloud-based framework for inter-comparing submesoscale-permitting realistic ocean modelsT. Uchida0J. Le Sommer1C. Stern2R. P. Abernathey3C. Holdgraf4A. Albert5L. Brodeau6L. Brodeau7E. P. Chassignet8X. Xu9J. Gula10J. Gula11G. Roullet12N. Koldunov13S. Danilov14Q. Wang15D. Menemenlis16C. Bricaud17B. K. Arbic18J. F. Shriver19F. Qiao20B. Xiao21A. Biastoch22A. Biastoch23R. Schubert24R. Schubert25B. Fox-Kemper26W. K. Dewar27W. K. Dewar28A. Wallcraft29Université Grenoble Alpes, CNRS, IRD, Grenoble-INP, Institut des Gêosciences de l’Environnement, Grenoble, FranceUniversité Grenoble Alpes, CNRS, IRD, Grenoble-INP, Institut des Gêosciences de l’Environnement, Grenoble, FranceLamont-Doherty Earth Observatory, Columbia University in the City of New York, New York City, USALamont-Doherty Earth Observatory, Columbia University in the City of New York, New York City, USA2i2c.org, Portland, Oregon, USAUniversité Grenoble Alpes, CNRS, IRD, Grenoble-INP, Institut des Gêosciences de l’Environnement, Grenoble, FranceOcean Next, Grenoble, FranceDatlas, Grenoble, FranceCenter for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, Florida, USACenter for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, Florida, USAUniv. Brest, CNRS, Ifremer, IRD, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, 29280, Plouzané, FranceInstitut Universitaire de France (IUF), Paris, FranceUniv. Brest, CNRS, Ifremer, IRD, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, 29280, Plouzané, FranceAlfred Wegener Institute (AWI), Helmholtz Center for Polar and Marine Research, Bremerhaven, GermanyAlfred Wegener Institute (AWI), Helmholtz Center for Polar and Marine Research, Bremerhaven, GermanyAlfred Wegener Institute (AWI), Helmholtz Center for Polar and Marine Research, Bremerhaven, GermanyJet Propulsion Laboratory, National Aeronautics and Space Administration (NASA), Palisades, California, USAMercator Ocean International, Toulouse, FranceDepartment of Earth and Environmental Sciences, University of Michigan, Ann Arbor, Michigan, USAOceanography Division, US Naval Research Laboratory, Hancock, Mississippi, USAFirst Institute of Oceanography, and Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao, ChinaFirst Institute of Oceanography, and Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao, ChinaGEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel, Kiel, GermanyDepartment of Ocean Circulation and Climate Dynamics, Kiel University, Kiel, GermanyUniv. Brest, CNRS, Ifremer, IRD, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, 29280, Plouzané, FranceGEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel, Kiel, GermanyDepartment of Earth, Environmental, and Planetary Sciences, Brown University, Providence, Rhode Island, USAUniversité Grenoble Alpes, CNRS, IRD, Grenoble-INP, Institut des Gêosciences de l’Environnement, Grenoble, FranceDepartment of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, Florida, USACenter for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, Florida, USA<p>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.</p>https://gmd.copernicus.org/articles/15/5829/2022/gmd-15-5829-2022.pdf |
| spellingShingle | T. Uchida J. Le Sommer C. Stern R. P. Abernathey C. Holdgraf A. Albert L. Brodeau L. Brodeau E. P. Chassignet X. Xu J. Gula J. Gula G. Roullet N. Koldunov S. Danilov Q. Wang D. Menemenlis C. Bricaud B. K. Arbic J. F. Shriver F. Qiao B. Xiao A. Biastoch A. Biastoch R. Schubert R. Schubert B. Fox-Kemper W. K. Dewar W. K. Dewar A. Wallcraft Cloud-based framework for inter-comparing submesoscale-permitting realistic ocean models Geoscientific Model Development |
| title | Cloud-based framework for inter-comparing submesoscale-permitting realistic ocean models |
| title_full | Cloud-based framework for inter-comparing submesoscale-permitting realistic ocean models |
| title_fullStr | Cloud-based framework for inter-comparing submesoscale-permitting realistic ocean models |
| title_full_unstemmed | Cloud-based framework for inter-comparing submesoscale-permitting realistic ocean models |
| title_short | Cloud-based framework for inter-comparing submesoscale-permitting realistic ocean models |
| title_sort | cloud based framework for inter comparing submesoscale permitting realistic ocean models |
| url | https://gmd.copernicus.org/articles/15/5829/2022/gmd-15-5829-2022.pdf |
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