FN Archimer Export Format PT J TI Cloud-based framework for inter-comparing submesoscale-permitting realistic ocean models BT AF 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 AS 1:1;2:1;3:2;4:2;5:3;6:1;7:4,5;8:6;9:6;10:7,8;11:7;12:9;13:9;14:9;15:10;16:11;17:12;18:13;19:14;20:14;21:15,16;22:7,15;23:17;24:1,18;25:6; FF 1:;2:;3:;4:;5:;6:;7:;8:;9:;10:;11:;12:;13:;14:;15:;16:;17:;18:;19:;20:;21:;22:;23:;24:;25:; C1 Université Grenoble Alpes, CNRS, IRD, Grenoble-INP, Institut des Gêosciences de l’Environnement, Grenoble, France Lamont-Doherty Earth Observatory, Columbia University in the City of New York, New York City, USA 2i2c.org, Portland, Oregon, USA Ocean Next, Grenoble, France Datlas, Grenoble, France Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, Florida, USA Univ. Brest, CNRS, Ifremer, IRD, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, 29280, Plouzané, France Institut Universitaire de France (IUF), Paris, France Alfred Wegener Institute (AWI), Helmholtz Center for Polar and Marine Research, Bremerhaven, Germany Jet Propulsion Laboratory, National Aeronautics and Space Administration (NASA), Palisades, California, USA Mercator Ocean International, Toulouse, France Department of Earth and Environmental Sciences, University of Michigan, Ann Arbor, Michigan, USA Oceanography Division, US Naval Research Laboratory, Hancock, Mississippi, USA First Institute of Oceanography, and Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao, China GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel, Kiel, Germany Department of Ocean Circulation and Climate Dynamics, Kiel University, Kiel, Germany Department of Earth, Environmental, and Planetary Sciences, Brown University, Providence, Rhode Island, USA Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, Florida, USA C2 UNIV GRENOBLE ALPES, FRANCE UNIV COLUMBIA, USA 2I2C.ORG, USA OCEAN NEXT, FRANCE DATLAS, FRANCE UNIV FLORIDA STATE, USA UBO, FRANCE INST UNIV FRANCE, FRANCE INST A WEGENER, GERMANY JET PROP LAB, USA MERCATOR OCEAN, FRANCE UNIV MICHIGAN, USA NRL, USA FIO, CHINA IFM GEOMAR, GERMANY UNIV KIEL, GERMANY UNIV BROWN, USA UNIV FLORIDA STATE, USA UM LOPS IN WOS Cotutelle UMR DOAJ copubli-france copubli-europe copubli-univ-france copubli-int-hors-europe copubli-sud IF 5.1 TC 8 UR https://archimer.ifremer.fr/doc/00787/89881/95366.pdf https://archimer.ifremer.fr/doc/00787/89881/95367.pdf LA English DT Article AB 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. PY 2022 PD JUN SO Geoscientific Model Development SN 1991-959X PU Copernicus GmbH VL 15 IS 14 UT 000830460900001 BP 5829 EP 5856 DI 10.5194/gmd-15-5829-2022 ID 89881 ER EF