Articles | Volume 18, issue 18
https://doi.org/10.5194/gmd-18-6461-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-6461-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development of a high-resolution coupled SHiELD-MOM6 model – Part 1: Model overview, coupling technique, and validation in a regional setup
Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA
Kun Gao
Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA
Brandon G. Reichl
Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, NJ, USA
Lauren Chilutti
Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, NJ, USA
Lucas Harris
Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, NJ, USA
Rusty Benson
Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, NJ, USA
Niki Zadeh
Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, NJ, USA
Jing Chen
Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA
Jan-Huey Chen
Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, NJ, USA
Cheng Zhang
Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA
Department of Civil and Environmental Engineering, Rowan University, Glassboro, NJ, USA
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Andrew C. Ross, Charles A. Stock, Alistair Adcroft, Enrique Curchitser, Robert Hallberg, Matthew J. Harrison, Katherine Hedstrom, Niki Zadeh, Michael Alexander, Wenhao Chen, Elizabeth J. Drenkard, Hubert du Pontavice, Raphael Dussin, Fabian Gomez, Jasmin G. John, Dujuan Kang, Diane Lavoie, Laure Resplandy, Alizée Roobaert, Vincent Saba, Sang-Ik Shin, Samantha Siedlecki, and James Simkins
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Cited articles
Adcroft, A., Anderson, W., Balaji, V., Blanton, C., Bushuk, M., Dufour, C. O., Dunne, J. P., Griffies, S. M., Hallberg, R., Harrison, M. J., Held, I. M., Jansen, M. F., John, J. G., Krasting, J. P., Langenhorst, A. R., Legg, S., Liang, Z., McHugh, C., Radhakrishnan, A., Reichl, B. G., Rosati, T., Samuels, B. L., Shao, A., Stouffer, R., Winton, M., Wittenberg, A. T., Xiang, B., Zadeh, N., and Zhang, R.: The GFDL Global Ocean and Sea Ice Model OM4.0: Model Description and Simulation Features, J. Adv. Model. Earth Sy., 11, 3167–3211, https://doi.org/10.1029/2019MS001726, 2019. a, b
Balaji, V., Anderson, J., Held, I., Winton, M., Durachta, J., Malyshev, S., and Stouffer, R. J.: - The Exchange Grid: A mechanism for data exchange between Earth System components on independent grids, in: Parallel Computational Fluid Dynamics 2005, edited by: Deane, A., Ecer, A., McDonough, J., Satofuka, N., Brenner, G., Emerson, D. R., Periaux, J., and Tromeur-Dervout, D., Elsevier, Amsterdam, 179–186, https://doi.org/10.1016/B978-044452206-1/50021-5, 2006. a, b
Bender, M. A., Ginis, I., and Kurihara, Y.: Numerical simulations of tropical cyclone-ocean interaction with a high-resolution coupled model, J. Geophys. Res.-Atmos., 98, 23245–23263, https://doi.org/10.1029/93JD02370, 1993. a
Black, T. L., Abeles, J. A., Blake, B. T., Jovic, D., Rogers, E., Zhang, X., Aligo, E. A., Dawson, L. C., Lin, Y., Strobach, E., Shafran, P. C., and Carley, J. R.: A Limited Area Modeling Capability for the Finite-Volume Cubed-Sphere (FV3) Dynamical Core and Comparison With a Global Two-Way Nest, J. Adv. Model. Earth Sy., 13, e2021MS002483, https://doi.org/10.1029/2021MS002483, 2021. a, b
Bolot, M., Harris, L. M., Cheng, K.-Y., Merlis, T. M., Blossey, P. N., Bretherton, C. S., Clark, S. K., Kaltenbaugh, A., Zhou, L., and Fueglistaler, S.: Kilometer-scale global warming simulations and active sensors reveal changes in tropical deep convection, npj Climate and Atmospheric Science, 6, 209, https://doi.org/10.1038/s41612-023-00525-w, 2023. a
Chen, X., Andronova, N., Leer, B. V., Penner, J. E., Boyd, J. P., Jablonowski, C., and Lin, S. J.: A control-volume model of the compressible Euler equations with a vertical Lagrangian coordinate, Mon. Weather Rev., 141, 2526–2544, https://doi.org/10.1175/MWR-D-12-00129.1, 2013. a
Cheng, K.-Y., Lin, S.-J., Harris, L., and Zhou, L.: Supercells and Tornado-Like Vortices in an Idealized Global Atmosphere Model, Earth and Space Science, 11, e2023EA003368, https://doi.org/10.1029/2023EA003368, 2024. a
Delworth, T. L., Cooke, W. F., Adcroft, A., Bushuk, M., Chen, J.-H., Dunne, K. A., Ginoux, P., Gudgel, R., Hallberg, R. W., Harris, L., Harrison, M. J., Johnson, N., Kapnick, S. B., Lin, S.-J., Lu, F., Malyshev, S., Milly, P. C., Murakami, H., Naik, V., Pascale, S., Paynter, D., Rosati, A., Schwarzkopf, M., Shevliakova, E., Underwood, S., Wittenberg, A. T., Xiang, B., Yang, X., Zeng, F., Zhang, H., Zhang, L., and Zhao, M.: SPEAR: The Next Generation GFDL Modeling System for Seasonal to Multidecadal Prediction and Projection, J. Adv. Model. Earth Sy., 12, e2019MS001895, https://doi.org/10.1029/2019MS001895, 2020. a
Dunne, J. P., Horowitz, L. W., Adcroft, A. J., Ginoux, P., Held, I. M., John, J. G., Krasting, J. P., Malyshev, S., Naik, V., Paulot, F., Shevliakova, E., Stock, C. A., Zadeh, N., Balaji, V., Blanton, C., Dunne, K. A., Dupuis, C., Durachta, J., Dussin, R., Gauthier, P. P. G., Griffies, S. M., Guo, H., Hallberg, R. W., Harrison, M., He, J., Hurlin, W., McHugh, C., Menzel, R., Milly, P. C. D., Nikonov, S., Paynter, D. J., Ploshay, J., Radhakrishnan, A., Rand, K., Reichl, B. G., Robinson, T., Schwarzkopf, D. M., Sentman, L. T., Underwood, S., Vahlenkamp, H., Winton, M., Wittenberg, A. T., Wyman, B., Zeng, Y., and Zhao, M.: The GFDL Earth System Model Version 4.1 (GFDL-ESM 4.1): Overall Coupled Model Description and Simulation Characteristics, J. Adv. Model. Earth Sy., 12, e2019MS002015, https://doi.org/10.1029/2019MS002015, 2020. a
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model, J. Geophys. Res.-Atmos., 108, https://doi.org/10.1029/2002JD003296, 2003. a
Gao, K., Harris, L., Zhou, L., Bender, M., and Morin, M.: On the Sensitivity of Hurricane Intensity and Structure to Horizontal Tracer Advection Schemes in FV3, J. Atmos. Sci., 78, 3007–3021, https://doi.org/10.1175/JAS-D-20-0331.1, 2021. a, b
Gao, K., Harris, L., Bender, M., Chen, J.-H., Zhou, L., and Knutson, T.: Regulating Fine-Scale Resolved Convection in High-Resolution Models for Better Hurricane Track Prediction, Geophys. Res. Lett., 50, e2023GL103329, https://doi.org/10.1029/2023GL103329, 2023. a, b
Gao, K., Mouallem, J., and Harris, L.: What Are the Finger-Like Clouds in the Hurricane Inner-Core Region?, Geophys. Res. Lett., 51, e2024GL110810, https://doi.org/10.1029/2024GL110810, 2024. a, b
GLORYS12V1: Global Ocean Reanalysis Products (GLORYS12V1), Copernicus Marine Environment Monitoring Service (CMEMS), https://doi.org/10.48670/moi-00021, 2018. a
Griffies, S. M., Adcroft, A., and Hallberg, R. W.: A Primer on the Vertical Lagrangian-Remap Method in Ocean Models Based on Finite Volume Generalized Vertical Coordinates, J. Adv. Model. Earth Sy., 12, e2019MS001954, https://doi.org/10.1029/2019MS001954, 2020. a
Han, J. and Bretherton, C. S.: TKE-Based Moist Eddy-Diffusivity Mass-Flux (EDMF) Parameterization for Vertical Turbulent Mixing, Weather Forecast., 34, 869–886, https://doi.org/10.1175/WAF-D-18-0146.1, 2019. a
Han, J., Wang, W., Kwon, Y. C., Hong, S.-Y., Tallapragada, V., and Yang, F.: Updates in the NCEP GFS Cumulus Convection Schemes with Scale and Aerosol Awareness, Weather Forecast., 32, 2005–2017, https://doi.org/10.1175/WAF-D-17-0046.1, 2017. a, b
Harris, L., Zhou, L., Lin, S. J., Chen, J. H., Chen, X., Gao, K., Morin, M., Rees, S., Sun, Y., Tong, M., Xiang, B., Bender, M., Benson, R., Cheng, K. Y., Clark, S., Elbert, O. D., Hazelton, A., Huff, J. J., Kaltenbaugh, A., Liang, Z., Marchok, T., Shin, H. H., and Stern, W.: GFDL SHiELD: A Unified System for Weather-to-Seasonal Prediction, J. Adv. Model. Earth Sy., 12, 1–25, https://doi.org/10.1029/2020MS002223, 2020. a, b
Harris, L., Zhou, L., Kaltenbaugh, A., Clark, S., Cheng, K.-Y., and Bretherton, C.: A Global Survey of Rotating Convective Updrafts in the GFDL X-SHiELD 2021 Global Storm Resolving Model, J. Geophys. Res.-Atmos., 128, e2022JD037823, https://doi.org/10.1029/2022JD037823, 2023. a
Harris, L. M. and Lin, S. J.: A two-way nested global-regional dynamical core on the cubed-sphere grid, Mon. Weather Rev., 141, 283–306, https://doi.org/10.1175/MWR-D-11-00201.1, 2013. a
Harris, L. M., Lin, S.-J., and Tu, C.: High-Resolution Climate Simulations Using GFDL HiRAM with a Stretched Global Grid, J. Climate, 29, 4293–4314, https://doi.org/10.1175/JCLI-D-15-0389.s1, 2016. a
Harris, L. M., Rees, S. L., Morin, M., Zhou, L., and Stern, W. F.: Explicit Prediction of Continental Convection in a Skillful Variable-Resolution Global Model, J. Adv. Model. Earth Sy., 11, 1847–1869, https://doi.org/10.1029/2018MS001542, 2019. a
Held, I.: The Monin-Obukhov Module for FMS, Copernicus Marine Environment Monitoring Service (CMEMS), https://github.com/NOAA-GFDL/FMS/tree/main/monin_obukhov (last access: 22 September 2025), 2001. a
Held, I. M., Guo, H., Adcroft, A., Dunne, J. P., Horowitz, L. W., Krasting, J., Shevliakova, E., Winton, M., Zhao, M., Bushuk, M., Wittenberg, A. T., Wyman, B., Xiang, B., Zhang, R., Anderson, W., Balaji, V., Donner, L., Dunne, K., Durachta, J., Gauthier, P. P. G., Ginoux, P., Golaz, J.-C., Griffies, S. M., Hallberg, R., Harris, L., Harrison, M., Hurlin, W., John, J., Lin, P., Lin, S.-J., Malyshev, S., Menzel, R., Milly, P. C. D., Ming, Y., Naik, V., Paynter, D., Paulot, F., Ramaswamy, V., Reichl, B., Robinson, T., Rosati, A., Seman, C., Silvers, L. G., Underwood, S., and Zadeh, N.: Structure and Performance of GFDL's CM4.0 Climate Model, J. Adv. Model. Earth Sy., 11, 3691–3727, https://doi.org/10.1029/2019MS001829, 2019. a
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2008JD009944, 2008. a
Jackson, L., Hallberg, R., and Legg, S.: A Parameterization of Shear-Driven Turbulence for Ocean Climate Models, J. Phys. Oceanogr., 38, 1033–1053, https://doi.org/10.1175/2007JPO3779.1, 2008. a
Kaltenbaugh, A., Harris, L., Cheng, K.-Y., Zhou, L., Morrin, M., and Stern, W.: Using GFDL C-SHiELD for the prediction of convective storms during the 2021 spring and summer, NOAA technical memorandum OAR GFDL, 2022-001, https://doi.org/10.25923/ednx-rm34, 2022. a
Lellouche, J.-M., Greiner, E., Bourdallé-Badie, R., Garric, G., Melet, A., Drévillon, M., Bricaud, C., Hamon, M., Le Galloudec, O., Regnier, C., Candela, T., Testut, C.-E., Gasparin, F., Ruggiero, G., Benkiran, M., Drillet, Y., and Le Traon, P.-Y.: The Copernicus Global ° Oceanic and Sea Ice GLORYS12 Reanalysis, Front. Earth Sci., 9, https://doi.org/10.3389/feart.2021.698876, 2021. a
Li, Q., Fox-Kemper, B., Breivik, Ø., and Webb, A.: Statistical models of global Langmuir mixing, Ocean Model., 113, 95–114, https://doi.org/10.1016/j.ocemod.2017.03.016, 2017. a
Lin, S. J.: A “vertically Lagrangian” finite-volume dynamical core for global models, Mon. Weather Rev., 132, 2293–2307, https://doi.org/10.1175/1520-0493(2004)132<2293:AVLFDC>2.0.CO;2, 2004. a, b
Lin, S.-J. and Rood, R. B.: Multidimensional Flux-Form Semi-Lagrangian Transport Schemes, Mon. Weather Rev., 124, 2046–2070, https://doi.org/10.1175/1520-0493(1996)124<2046:MFFSLT>2.0.CO;2, 1996. a, b
Lin, S.-J. and Rood, R. B.: An explicit flux-form semi-Lagrangian shallow-water model on the sphere, Q. J. Roy. Meteor. Soc., 123, 2477–2498, https://doi.org/10.1002/qj.49712354416, 1997. a, b
Merlis, T. M., Cheng, K.-Y., Guendelman, I., Harris, L., Bretherton, C. S., Bolot, M., Zhou, L., Kaltenbaugh, A., Clark, S. K., Vecchi, G. A., and Fueglistaler, S.: Climate sensitivity and relative humidity changes in global storm-resolving model simulations of climate change, Science Advances, 10, eadn5217, https://doi.org/10.1126/sciadv.adn5217, 2024a. a
Merlis, T. M., Guendelman, I., Cheng, K.-Y., Harris, L., Chen, Y.-T., Bretherton, C. S., Bolot, M., Zhou, L., Kaltenbaugh, A., Clark, S. K., and Fueglistaler, S.: The Vertical Structure of Tropical Temperature Change in Global Storm-Resolving Model Simulations of Climate Change, Geophys. Res. Lett., 51, e2024GL111549, https://doi.org/10.1029/2024GL111549, 2024b. a
Mouallem, J.: Running SHiELD with GFDL's FMS full coupler infrastructure, NOAA technical memorandum OAR GFDL, 2024-002, https://doi.org/10.25923/ezfm-az21, 2024. a
Mouallem, J.: Development of a High-Resolution Coupled SHiELD- MOM6 Model. Part I – Model Overview, Coupling Technique, and Validation in a Regional Setup [data set and code], https://doi.org/10.5281/zenodo.15178709, 2025. a
Mouallem, J., Harris, L., and Benson, R.: Multiple same-level and telescoping nesting in GFDL's dynamical core, Geosci. Model Dev., 15, 4355–4371, https://doi.org/10.5194/gmd-15-4355-2022, 2022. a
Mouallem, J., Harris, L., and Chen, X.: Implementation of the Novel Duo-Grid in GFDL's FV3 Dynamical Core, J. Adv. Model. Earth Sy., 15, e2023MS003712, https://doi.org/10.1029/2023MS003712, 2023. a
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res.-Atmos., 116, https://doi.org/10.1029/2010JD015139, 2011. a
Pollard, R. T., Rhines, P. B., and Thompson, R. O. R. Y.: The deepening of the wind-Mixed layer, Geophysical Fluid Dynamics, 4, 381–404, https://doi.org/10.1080/03091927208236105, 1973. a
Price, J. F.: Upper Ocean Response to a Hurricane, J. Phys. Oceanogr., 11, 153–175, https://doi.org/10.1175/1520-0485(1981)011<0153:UORTAH>2.0.CO;2, 1981. a
Putman, W. M. and Lin, S. J.: Finite-volume transport on various cubed-sphere grids, J. Comput. Phys., 227, 55–78, https://doi.org/10.1016/j.jcp.2007.07.022, 2007. a
Ramstrom, W., Zhang, X., Ahern, K., and Gopalakrishnan, S.: Implementation of storm-following nest for the next-generation Hurricane Analysis and Forecast System (HAFS), Front. Earth Sci., 12, https://doi.org/10.3389/feart.2024.1419233, 2024. a
Reed, K. A. and Jablonowski, C.: Idealized tropical cyclone simulations of intermediate complexity: A test case for AGCMs, J. Adv. Model. Earth Sy., 4, https://doi.org/10.1029/2011MS000099, 2012. a
Reichl, B. G. and Hallberg, R.: A simplified energetics based planetary boundary layer (ePBL) approach for ocean climate simulations, Ocean Model., 132, 112–129, https://doi.org/10.1016/j.ocemod.2018.10.004, 2018. a
Reichl, B. G. and Li, Q.: A Parameterization with a Constrained Potential Energy Conversion Rate of Vertical Mixing Due to Langmuir Turbulence, J. Phys. Oceanogr., 49, 2935–2959, https://doi.org/10.1175/JPO-D-18-0258.1, 2019. a
Reichl, B. G., Wittenberg, A. T., Griffies, S. M., and Adcroft, A.: Improving Equatorial Upper Ocean Vertical Mixing in the NOAA/GFDL OM4 Model, Earth and Space Science, 11, e2023EA003485, https://doi.org/10.1029/2023EA003485, 2024. a
Santos, L. F., Mouallem, J., and Peixoto, P. S.: Analysis of finite-volume transport schemes on cubed-sphere grids and an accurate scheme for divergent winds, J. Comput. Phys., 522, 113618, https://doi.org/10.1016/j.jcp.2024.113618, 2025. a
Zhang, J. A., Nolan, D. S., Rogers, R. F., and Tallapragada, V.: Evaluating the Impact of Improvements in the Boundary Layer Parameterization on Hurricane Intensity and Structure Forecasts in HWRF, Mon. Weather Rev., 143, 3136–3155, https://doi.org/10.1175/MWR-D-14-00339.1, 2015. a
Zhou, L., Lin, S.-J., Chen, J.-H., Harris, L. M., Chen, X., and Rees, S. L.: Toward Convective-Scale Prediction within the Next Generation Global Prediction System, B. Am. Meteorol. Soc., 100, 1225–1243, https://doi.org/10.1175/BAMS-D-17-0246.1, 2019. a
Zhou, L., Harris, L., and Chen, J.-H.: The GFDL Cloud Microphysics Parameterization, NOAA technical memorandum OAR GFDL, 2022-002, https://doi.org/10.25923/pz3c-8b96, 2022. a
Zhou, L., Harris, L., Chen, J.-H., Gao, K., Cheng, K.-Y., Tong, M., Kaltenbaugh, A., Morin, M., Mouallem, J., Chilutti, L., and Johnston, L.: Bridging the Gap Between Global Weather Prediction and Global Storm-Resolving Simulation: Introducing the GFDL 6.5 km SHiELD, J. Adv. Model. Earth Sy., 16, e2024MS004430, https://doi.org/10.1029/2024MS004430, 2024. a
Short summary
We introduce a new high-resolution model that couples the atmosphere and ocean to better simulate extreme weather events. It combines the Geophysical Fluid Dynamics Laboratory (GFDL) advanced atmospheric and ocean models with a powerful coupling system that enables robust and efficient two-way interactions. Simulations show that the model accurately captures hurricane behavior and its impact on the ocean. It also runs efficiently on supercomputers. This model represents a key step toward improving extreme weather forecasts.
We introduce a new high-resolution model that couples the atmosphere and ocean to better...