Articles | Volume 19, issue 17
https://doi.org/10.5194/gmd-19-8427-2026
© Author(s) 2026. 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-19-8427-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
10 Sep 2026
Development and technical paper |
| 10 Sep 2026
| 10 Sep 2026
Development of a high-resolution coupled SHiELD-MOM6-LM4 model – Part 2: Model overview, coupling technique, and evaluation of hydrological extremes during Hurricane Helene
Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA
Sergey Malyshev
Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, NJ, USA
Zhihong Tan
Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA
Elena Shevliakova
Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, NJ, USA
Kun Gao
Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA
Lucas Harris
Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, NJ, USA
Rusty Benson
Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, NJ, USA
William Cooke
Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, NJ, USA
Niki Zadeh
Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, NJ, USA
Lauren Chilutti
Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, NJ, USA
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Joseph Mouallem, Lucas Harris, and Rusty Benson
Geosci. Model Dev., 15, 4355–4371, https://doi.org/10.5194/gmd-15-4355-2022, https://doi.org/10.5194/gmd-15-4355-2022, 2022
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Kai-Yuan Cheng, Lucas M. Harris, and Yong Qiang Sun
Geosci. Model Dev., 15, 1097–1105, https://doi.org/10.5194/gmd-15-1097-2022, https://doi.org/10.5194/gmd-15-1097-2022, 2022
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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
Alfieri, L., Burek, P., Dutra, E., Krzeminski, B., Muraro, D., Thielen, J., and Pappenberger, F.: GloFAS – global ensemble streamflow forecasting and flood early warning, Hydrol. Earth Syst. Sci., 17, 1161–1175, https://doi.org/10.5194/hess-17-1161-2013, 2013. a
Balaji, V.: The Flexible Modeling System, Springer Berlin Heidelberg, Berlin, Heidelberg, 33–41, ISBN 978-3-642-23360-9, https://doi.org/10.1007/978-3-642-23360-9_5, 2012. a
Balaji, V. and Langenhorst, A.: ESM Workflow, Springer Berlin Heidelberg, Berlin, Heidelberg, 5–13, ISBN 978-3-642-23932-8, https://doi.org/10.1007/978-3-642-23932-8_2, 2012. a
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, ISBN 978-0-444-52206-1, https://doi.org/10.1016/B978-044452206-1/50021-5, 2006. a
Balsamo, G., Beljaars, A. C. M., Scipal, K., Viterbo, P., van den Hurk, B., Hirschi, M., and Betts, A. K.: A Revised Hydrology for the ECMWF Model: Verification from Field Site to Terrestrial Water Storage and Impact in the Integrated Forecast System, J. Hydrometeorol., 10, 623–643, https://doi.org/10.1175/2008JHM1068.1, 2009. a
Best, M. J., Beljaars, A., Polcher, J., and Viterbo, P.: A Proposed Structure for Coupling Tiled Surfaces with the Planetary Boundary Layer, J. Hydrometeorol., 5, 1271–1278, https://doi.org/10.1175/JHM-382.1, 2004. a
Boussetta, S., Balsamo, G., Arduini, G., Dutra, E., McNorton, J., Choulga, M., Agustí-Panareda, A., Beljaars, A., Wedi, N., Munõz-Sabater, J., de Rosnay, P., Sandu, I., Hadade, I., Carver, G., Mazzetti, C., Prudhomme, C., Yamazaki, D., and Zsoter, E.: ECLand: The ECMWF Land Surface Modelling System, Atmosphere, 12, 723, https://doi.org/10.3390/atmos12060723, 2021. 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
Cosgrove, B., Gochis, D., Flowers, T., Dugger, A., Ogden, F., Graziano, T., Clark, E., Cabell, R., Casiday, N., Cui, Z., Eicher, K., Fall, G., Feng, X., Fitzgerald, K., Frazier, N., George, C., Gibbs, R., Hernandez, L., Johnson, D., Jones, R., Karsten, L., Kefelegn, H., Kitzmiller, D., Lee, H., Liu, Y., Mashriqui, H., Mattern, D., McCluskey, A., McCreight, J. L., McDaniel, R., Midekisa, A., Newman, A., Pan, L., Pham, C., RafieeiNasab, A., Rasmussen, R., Read, L., Rezaeianzadeh, M., Salas, F., Sang, D., Sampson, K., Schneider, T., Shi, Q., Sood, G., Wood, A., Wu, W., Yates, D., Yu, W., and Zhang, Y.: NOAA's National Water Model: Advancing operational hydrology through continental-scale modeling, JAWRA J. Ame.n Water Resour. Assoc., 60, 247–272, https://doi.org/10.1111/1752-1688.13184, 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
Du, J.: NCEP/EMC 4KM Gridded Data (GRIB) Stage IV Data. Version 1.0, NSF NCAR Earth Observing Laboratory [data set], https://doi.org/10.5065/D6PG1QDD, 2011. 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, b
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
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. 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, https://doi.org/10.1175/JCLI-D-15-0389.s1, 2016. a
Held, I. M.: Surface fluxes, implicit time stepping, and the exchange grid: The structure of the surface exchange module, Zenodo, https://doi.org/10.5281/zenodo.18090279, 2004. 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
Koster, R. D., Dirmeyer, P. A., Guo, Z., Bonan, G., Chan, E., Cox, P., Gordon, C. T., Kanae, S., Kowalczyk, E., Lawrence, D., Liu, P., Lu, C.-H., Malyshev, S., McAvaney, B., Mitchell, K., Mocko, D., Oki, T., Oleson, K., Pitman, A., Sud, Y. C., Taylor, C. M., Verseghy, D., Vasic, R., Xue, Y., and Yamada, T.: Regions of Strong Coupling Between Soil Moisture and Precipitation, Science, 305, 1138–1140, https://doi.org/10.1126/science.1100217, 2004. a
Lamichhane, K., Karki, S., Sharma, K., Khadka, B., Acharya, B., Biswakarma, K., Adhikari, S., Kc, R., Danegulu, A., Bhattarai, S., Regmi, A., Subedi, M., and Bhattarai, P. K.: Unraveling the causes and impacts of increasing flood disasters in the kathmandu valley: Lessons from the unprecedented September 2024 floods, Nat. Hazards Res., 5, 875–897, https://doi.org/10.1016/j.nhres.2025.04.001, 2025. a
Lawrence, D., Fisher, R., Koven, C., Oleson, K., Swenson, S., Bonan, G., Collier, N., Ghimire, B., van Kampenhout, L., Kennedy, D., Kluzek, E., Lawrence, P., Li, F., Li, H., Lombardozzi, D., Riley, W., Sacks, W., Shi, M., Vertenstein, M., Wieder, W., Xu, C., Ali, A., Badger, A., Bisht, G., van den Broeke, M., Brunke, M., Burns, S., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J., Flanner, M., Fox, A., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox, R., Kumar, S., Lenaerts, J., Leung, L., Lipscomb, W., Lu, Y., Pandey, A., Pelletier, J., Perket, J., Randerson, J., Ricciuto, D., Sanderson, B., Slater, A., Subin, Z., Tang, J., Thomas, R., Val Martin, M., and Zeng, X.: The Community Land Model 85 Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty, J. Adv. Model. Earth Sy., 11, 4245–4287, https://doi.org/10.1029/2018MS001583, 2019. 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
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
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
Miralles, D. G., Gentine, P., Seneviratne, S. I., and Teuling, A. J.: Land–atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges, Ann. New York Acad. Sci., 1436, 19–35, https://doi.org/10.1111/nyas.13912, 2019. a
Mogensen, K., Magnusson, L., and Bidlot, J.-R.: The IFS Single-Executable Coupling Strategy, Technical Memorandum 673, European Centre for Medium-Range Weather Forecasts (ECMWF), https://doi.org/10.21957/rfplwzuol, 2012. 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-LM4 model – Part 2: Model overview, coupling technique, and evaluation of hydrological extremes during Hurricane Helene, Zenodo [code/data set], https://doi.org/10.5281/zenodo.21542868, 2026. 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
Mouallem, J., Gao, K., Reichl, B. G., Chilutti, L., Harris, L., Benson, R., Zadeh, N., Chen, J., Chen, J.-H., and Zhang, C.: Development of a high-resolution coupled SHiELD-MOM6 model – Part 1: Model overview, coupling technique, and validation in a regional setup, Geosci. Model Dev., 18, 6461–6478, https://doi.org/10.5194/gmd-18-6461-2025, 2025. a, b, c, d, e, f
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
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016. a
Pollard, R. T., Rhines, P. B., and Thompson, R. O. R. Y.: The deepening of the wind-Mixed layer, Geophys. Fluid Dynam., 4, 381–404, https://doi.org/10.1080/03091927208236105, 1973. 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
Roberts, C. D., Senan, R., Molteni, F., Boussetta, S., Mayer, M., and Keeley, S. P. E.: Climate model configurations of the ECMWF Integrated Forecasting System (ECMWF-IFS cycle 43r1) for HighResMIP, Geosci. Model Dev., 11, 3681–3712, https://doi.org/10.5194/gmd-11-3681-2018, 2018. a
Santos, L., Mouallem, J., and S. Peixoto, P.: 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
Seneviratne, S., Lüthi, D., Litschi, M., and Schär, C: Land–atmosphere coupling and climate change in Europe, Nature, 443, 205–209, https://doi.org/10.1038/nature05095, 2006. a
UCAR: Using CTSM with WRF – CTSM documentation, https://escomp.github.io/CTSM/lilac/specific-atm-models/wrf.html (last access: 6 September 2026), 2020. a
Vuuren D., Kriegler, E., O'Neill, B., Ebi, K., Riahi, K., Carter, T., Edmonds, J., Hallegatte, S., Kram, T., Mathur, R., and Winkler, H.: A new scenario framework for Climate Change Research: scenario matrix architecture, Climatic Change, 122, 373–386, https://doi.org/10.1007/s10584-013-0906-1, 2014. a
Worthen, D., Wang, J., Montuoro, R., Heinzeller, D., Li, B., Theurich, G., Turuncoglu, U., Rosen, D., Jovic, D., Curtis, B., Mahajan, R., Lei, H., Richert, A., Chawla, A., Wang, J., Meixner, J., Abdolali, A., Masarik, M., Pan, L., Barlage, M., Liu, B., Vertenstein, M., Craig, T., Benson, R., Robinson, T., Clune, T., Jiang, W., Barton, N., Vandenberghe, G., Potts, M., Kim, J., Perlin, N., Book, C., Bernardet, L., Yang, F., Sun, S., Kim, H.-S., Baker, B., Huang, J., Jeon, C.-H., and Stajner, I.: Coupling Infrastructure Capability in UFS Weather Model, Office Note 519, National Centers for Environmental Prediction, https://doi.org/10.25923/dvv2-3g03, 2024. a
Yamazaki, D., Kanae, S., Kim, H., and Oki, T.: A Physically Based Description of Floodplain Inundation Dynamics in a Global River Routing Model, Water Resour. Res., 47, W04501, https://doi.org/10.1029/2010WR009726, 2011. a
Zhao, M., Golaz, J.-C., Held, I. M., Guo, H., Balaji, V., Benson, R., Chen, J.-H., Chen, X., Donner, L. J., Dunne, J. P., Durachta, J. W., Fan, S.-M., Friedenreich, S. M., Garner, S. T., Ginoux, P., Harris, L. M., Horowitz, L. W., Krasting, J. P., Langenhost, A. R., Liang, Z., Lin, P., Lin, S.-J., Malyshev, S., Mason, E., Milly, P. C. D., Ming, Y., Naik, V., Paulot, F., Paynter, D., Phillips, P. J., Radkhakrishnan, A., Ramaswamy, V., Robinson, T., Schwarzcopf, M. D., Seman, C. J., Shevliakova, E., Shen, Z., Shin, H., Silvers, L. G., Wilson, J. R., Winton, M., Wittenberg, A. T., Wyman, B. L., and Xiang, B.: The GFDL Global Atmosphere and Land Model AM4.0/LM4.0: 2. Model Description, Sensitivity Studies, and Tuning Strategies, J. Adv. Model. Earth Sy., 10, 735–769, https://doi.org/10.1002/2017ms001209, 2018. 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, 2022a. a
Zhou, L., Harris, L., Chen, J.-H., Gao, K., Guo, H., Xiang, B., Tong, M., Huff, J. J., and Morin, M.: Improving Global Weather Prediction in GFDL SHiELD Through an Upgraded GFDL Cloud Microphysics Scheme, J. Adv. Model. Earth Sy., 14, https://doi.org/10.1029/2021MS002971, 2022b. a, b
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 integrate GFDL's (Geophysical Fluid Dynamics Laboratory) land model in the high resolution global atmospheric model SHiELD (System for High-resolution modeling for Earth-to-Local Domains), unlocking new capabilities in simulating new suite of land-atmosphere conditions and extreme hydrological processes such as flooding. A single global simulation captures Hurricane Helene's 2024 landfall, including soil saturation, runoff generation, and river flooding in Western North Carolina, while simultaneously capturing an independent extreme rainfall and flooding event in Nepal.
We integrate GFDL's (Geophysical Fluid Dynamics Laboratory) land model in the high resolution...
