Cloud-based framework for inter-comparing submesoscale-permitting realistic ocean models
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Published:2022-07-27
Issue:14
Volume:15
Page:5829-5856
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ISSN:1991-9603
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Container-title:Geoscientific Model Development
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language:en
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Short-container-title:Geosci. Model Dev.
Author:
Uchida TakayaORCID, Le Sommer JulienORCID, Stern Charles, Abernathey Ryan P.ORCID, Holdgraf Chris, Albert Aurélie, Brodeau Laurent, Chassignet Eric P., Xu Xiaobiao, Gula JonathanORCID, Roullet Guillaume, Koldunov NikolayORCID, Danilov Sergey, Wang QiangORCID, Menemenlis Dimitris, Bricaud Clément, Arbic Brian K.ORCID, Shriver Jay F.ORCID, Qiao Fangli, Xiao Bin, Biastoch ArneORCID, Schubert RenéORCID, Fox-Kemper BaylorORCID, Dewar William K., Wallcraft Alan
Abstract
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. 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.
Funder
Agence Nationale de la Recherche National Science Foundation Deutsche Forschungsgemeinschaft National Aeronautics and Space Administration Office of Naval Research National Oceanic and Atmospheric Administration National Natural Science Foundation of China Partnership for Advanced Computing in Europe AISBL Horizon 2020 Grand Équipement National De Calcul Intensif
Publisher
Copernicus GmbH
Reference94 articles.
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