Articles | Volume 18, issue 13
https://doi.org/10.5194/gmd-18-4293-2025
© 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-18-4293-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
16 Jul 2025
Model description paper |
| 16 Jul 2025
| 16 Jul 2025
Enhancing winter climate simulations of the Great Lakes: insights from a new coupled lake–ice–atmosphere (CLIAv1) system on the importance of integrating 3D hydrodynamics with a regional climate model
Department of Civil, Environmental and Geospatial Engineering, Michigan Technological University, Houghton, MI, USA
Great Lakes Research Center, Michigan Technological University, Houghton, MI, USA
Environmental Science Division, Argonne National Laboratory, Lemont, IL, USA
Chenfu Huang
Great Lakes Research Center, Michigan Technological University, Houghton, MI, USA
Yafang Zhong
Cooperative Institute for Meteorological Satellite Studies, Space Science and Engineering Center, University of Wisconsin-Madison, Madison, WI, USA
Michael Notaro
Nelson Institute Center for Climatic Research, University of Wisconsin-Madison, Madison, WI, USA
Miraj B. Kayastha
Department of Civil, Environmental and Geospatial Engineering, Michigan Technological University, Houghton, MI, USA
Xing Zhou
Department of Civil, Environmental and Geospatial Engineering, Michigan Technological University, Houghton, MI, USA
Chuyan Zhao
Great Lakes Research Center, Michigan Technological University, Houghton, MI, USA
Christa Peters-Lidard
National Aeronautics and Space Administration Goddard Space Flight Center, Greenbelt, MD, USA
Carlos Cruz
National Aeronautics and Space Administration Goddard Space Flight Center, Greenbelt, MD, USA
Eric Kemp
National Aeronautics and Space Administration Goddard Space Flight Center, Greenbelt, MD, USA
Related authors
Chunyong Jung, Pengfei Xue, Chenfu Huang, William Pringle, Mrinal Biswas, Geeta Nain, and Jiali Wang
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-47, https://doi.org/10.5194/wes-2025-47, 2025
Revised manuscript under review for WES
Short summary
Short summary
This study introduces a system that combines weather, ocean, and wave models to better understand their interactions during tropical storms and their impact on offshore structures like wind turbines. Tested using Hurricane Henri (2021), the system improves storm predictions by including how waves and ocean cooling affect storm strength and wind patterns. The results show this approach helps assess risks to offshore infrastructure during severe weather, making it more accurate and reliable.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Short summary
We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Pengfei Xue, Xinyu Ye, Jeremy S. Pal, Philip Y. Chu, Miraj B. Kayastha, and Chenfu Huang
Geosci. Model Dev., 15, 4425–4446, https://doi.org/10.5194/gmd-15-4425-2022, https://doi.org/10.5194/gmd-15-4425-2022, 2022
Short summary
Short summary
The Great Lakes are the world's largest freshwater system. They are a key element in regional climate influencing local weather patterns and climate processes. Many of these complex processes are regulated by interactions of the atmosphere, lake, ice, and surrounding land areas. This study presents a Great Lakes climate change projection that employed the two-way coupling of a regional climate model with a 3-D lake model (GLARM) to resolve 3-D hydrodynamics essential for large lakes.
Gabriëlle J. M. De Lannoy, Louise Busschaert, Michel Bechtold, Niccolò Lanfranco, Shannon de Roos, Zdenko Heyvaert, Jonas Mortelmans, Samuel A. Scherrer, Maxime Van den Bossche, Sujay Kumar, David M. Mocko, Eric Kemp, Lee Heng, Pasquale Steduto, and Dirk Raes
EGUsphere, https://doi.org/10.5194/egusphere-2025-4417, https://doi.org/10.5194/egusphere-2025-4417, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
To facilitate regional crop growth simulations at any spatial resolution, with a range of different input sources for meteorology, soil and crop parameters, we have incorporated the AquaCrop model into the NASA Land Information System. This system also facilitates the assimilation of satellite data to update the crop and water conditions during model simulations. We present three exploratory applications to highlight the possibilities and pathways for future research on crop estimation.
Chunyong Jung, Pengfei Xue, Chenfu Huang, William Pringle, Mrinal Biswas, Geeta Nain, and Jiali Wang
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-47, https://doi.org/10.5194/wes-2025-47, 2025
Revised manuscript under review for WES
Short summary
Short summary
This study introduces a system that combines weather, ocean, and wave models to better understand their interactions during tropical storms and their impact on offshore structures like wind turbines. Tested using Hurricane Henri (2021), the system improves storm predictions by including how waves and ocean cooling affect storm strength and wind patterns. The results show this approach helps assess risks to offshore infrastructure during severe weather, making it more accurate and reliable.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Short summary
We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Gifford H. Miller, Simon L. Pendleton, Alexandra Jahn, Yafang Zhong, John T. Andrews, Scott J. Lehman, Jason P. Briner, Jonathan H. Raberg, Helga Bueltmann, Martha Raynolds, Áslaug Geirsdóttir, and John R. Southon
Clim. Past, 19, 2341–2360, https://doi.org/10.5194/cp-19-2341-2023, https://doi.org/10.5194/cp-19-2341-2023, 2023
Short summary
Short summary
Receding Arctic ice caps reveal moss killed by earlier ice expansions; 186 moss kill dates from 71 ice caps cluster at 250–450, 850–1000 and 1240–1500 CE and continued expanding 1500–1880 CE, as recorded by regions of sparse vegetation cover, when ice caps covered > 11 000 km2 but < 100 km2 at present. The 1880 CE state approached conditions expected during the start of an ice age; climate models suggest this was only reversed by anthropogenic alterations to the planetary energy balance.
Amy McNally, Jossy Jacob, Kristi Arsenault, Kimberly Slinski, Daniel P. Sarmiento, Andrew Hoell, Shahriar Pervez, James Rowland, Mike Budde, Sujay Kumar, Christa Peters-Lidard, and James P. Verdin
Earth Syst. Sci. Data, 14, 3115–3135, https://doi.org/10.5194/essd-14-3115-2022, https://doi.org/10.5194/essd-14-3115-2022, 2022
Short summary
Short summary
The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) global and Central Asia data streams described here generate routine estimates of snow, soil moisture, runoff, and other variables useful for tracking water availability. These data are hosted by NASA and USGS data portals for public use.
Pengfei Xue, Xinyu Ye, Jeremy S. Pal, Philip Y. Chu, Miraj B. Kayastha, and Chenfu Huang
Geosci. Model Dev., 15, 4425–4446, https://doi.org/10.5194/gmd-15-4425-2022, https://doi.org/10.5194/gmd-15-4425-2022, 2022
Short summary
Short summary
The Great Lakes are the world's largest freshwater system. They are a key element in regional climate influencing local weather patterns and climate processes. Many of these complex processes are regulated by interactions of the atmosphere, lake, ice, and surrounding land areas. This study presents a Great Lakes climate change projection that employed the two-way coupling of a regional climate model with a 3-D lake model (GLARM) to resolve 3-D hydrodynamics essential for large lakes.
Wanshu Nie, Sujay V. Kumar, Kristi R. Arsenault, Christa D. Peters-Lidard, Iliana E. Mladenova, Karim Bergaoui, Abheera Hazra, Benjamin F. Zaitchik, Sarith P. Mahanama, Rachael McDonnell, David M. Mocko, and Mahdi Navari
Hydrol. Earth Syst. Sci., 26, 2365–2386, https://doi.org/10.5194/hess-26-2365-2022, https://doi.org/10.5194/hess-26-2365-2022, 2022
Short summary
Short summary
The MENA (Middle East and North Africa) region faces significant food and water insecurity and hydrological hazards. Here we investigate the value of assimilating remote sensing data sets into an Earth system model to help build an effective drought monitoring system and support risk mitigation and management by countries in the region. We highlight incorporating satellite-informed vegetation conditions into the model as being one of the key processes for a successful application for the region.
Cited articles
Assel, A. A.: An ice-cover climatology for Lake Erie and Lake Superior for the winter seasons 1897–1898 to 1982–1983, Int. J. Climatol., 10, 731–748, 1990.
Assel, R. A.: Classification of Annual Great Lakes Ice Cycles: Winters of 1973–2002, J. Climate, 18, 4895, https://doi.org/10.1175/JCLI3571.1, 2005.
Ballentine, R. J., Stamm, A. J., Chermack, E. E., Byrd, G. P., and Schleede, D.: Mesoscale model simulation of the 4–5 January 1995 lake-effect snowstorm, Weather Forecast., 13, 893–920, 1998.
Bennington, V., Notaro, M., and Holman, K. D.: Improving Climate Sensitivity of Deep Lakes within a Regional Climate Model and Its Impact on Simulated Climate, J. Climate, 27, 2886–2911, https://doi.org/10.1175/jcli-d-13-00110.1, 2014.
Bitz, C. M. and Lipscomb, W. H.: An energy-conserving thermodynamic model of sea ice, J. Geophys. Res.-Oceans, 104, 15669–15677, 1999.
Blanken, P. D., Spence, C., Hedstrom, N., and Lenters, J. D.: Evaporation from Lake Superior: 1. Physical controls and processes, J. Great Lakes Res., 37, 707–716, 2011.
Briley, L. and Jorns, J.: Great Lakes Climate Modeling Workshop report. Great Lakes Integrated Sciences and Assessments (GLISA), University of Michigan, https://glisa.umich.edu/project/2021-great-lakes-climatemodeling-workshop/ (last access: 12 July 2025), 2021.
Briley, L. J., Rood, R. B., and Notaro, M.: Large lakes in climate models: A Great Lakes case study on the usability of CMIP5, J. Great Lakes Res., 47, 405–418, https://doi.org/10.1016/j.jglr.2021.01.010, 2021.
Brown, L. C. and Duguay, C. R.: The response and role of ice cover in lake-climate interactions, Prog. Phys. Geogr., 34, 671–704, 2010.
Bryan, A. M., Steiner, A. L., and Posselt, D. J.: Regional modeling of surface-atmosphere interactions and their impact on Great Lakes hydroclimate, J. Geophys. Res.-Atmos., 120, 1044–1064, https://doi.org/10.1002/2014JD022316, 2015.
Bullock, O. R., Alapaty, K., Herwehe, J. A., Mallard, M. S., Otte, T. L., Gilliam, R. C., and Nolte, C. G.: An observation-based investigation of nudging in WRF for downscaling surface climate information to 12-km grid spacing, J. Appl. Meteorol. Clim., 53, 20–33, 2014.
Cannon, D., Wang, J., Fujisaki-Manome, A., Kessler, J., Ruberg, S., and Constant, S.: Investigating Multidecadal Trends in Ice Cover and Subsurface Temperatures in the Laurentian Great Lakes Using a Coupled Hydrodynamic–Ice Model, J. Climate, 37, 1249–1276, https://doi.org/10.1175/JCLI-D-23-0092.1, 2024.
Changnon Jr., S. A. and Jones, D. M. A.: Review of the influences of the Great Lakes on weather, Water Resour. Res., 8, 360–371, https://doi.org/10.1029/WR008i002p00360, 1972.
Chen, C., Beardsley, R. C., Cowles, G., Qi, J., Lai, Z., Gao, G., Stuebe, D., Xu, Q., Xue, P., Ge, J., and Ji, R.: An unstructured-grid, finite-volume community ocean model: FVCOM user manual, Cambridge, MA, USA, Sea Grant College Program, Massachusetts Institute of Technology, https://web.archive.org/web/20161229211546id_/http://fvcom.smast.umassd.edu/wp-content/uploads/2013/11/MITSG_12-25.pdf (last access: 10 July 2025), 2012.
Chin, M., Rood, R. B., Lin, S. J., Müller, J. F., and Thompson, A. M.: Atmospheric sulfur cycle simulated in the global model GOCART: Model description and global properties, J. Geophys. Res.-Atmos., 105, 24671–24687, 2000.
Chuang, H.-Y. and Sousounis, P. J.: The impact of the prevailing synoptic situation on the lake-aggregate effect, Mon. Weather Rev., 131, 990–1010, 2003.
CoastWatch Great Lakes Node: Sea surface temperature (SST) from Great Lakes Surface Environmental Analysis (GLSEA), geodetic coordinate system (LAT, LON), 1995–2023, NOAA Great Lakes Environmental Research Laboratory [data set], https://apps.glerl.noaa.gov/erddap/files/GLSEA_GCS/ (last access: 14 July 2024), 2024a.
CoastWatch Great Lakes Node: Ice concentration from Great Lakes Surface Environmental Analysis (GLSEA) and NIC, geodetic coordinate system (LAT, LON), 1995–present, NOAA Great Lakes Environmental Research Laboratory [data set], https://apps.glerl.noaa.gov/erddap/files/GL_Ice_Concentration_GCS/ (last acces: 14 July 2024), 2024b.
Collins, W. D., Rasch, P. J., Boville, B. A., Hack, J. J., McCaa, J. R., Williamson, D. L., Kiehl, J. T., Briegleb, B., Bitz, C., Lin, S. J., and Zhang, M.: Description of the NCAR community atmosphere model (CAM 3.0), NCAR Tech. Note NCAR/TN-464+ STR, 226, 1326–1334, 2004.
Colucci, S. J.: winter cyclone frequencies over the eastern United States and adjacent western Atlantic, 1964–1973: Student paper – First place winner of The Father James B. Macelwane Annual Award in Meteorology, announced at the Annual Meeting of the AMS, Philadelphia, Pa., 21 January 1976, B. Am. Meteorol. Soc., 57, 548–553, 1976.
Craig, A., Valcke, S., and Coquart, L.: Development and performance of a new version of the OASIS coupler, OASIS3-MCT_3.0, Geosci. Model Dev., 10, 3297–3308, https://doi.org/10.5194/gmd-10-3297-2017, 2017.
Crossman, E. J. and Cudmore, B. C.: Biodiversity of the fishes of the Laurentian Great Lakes: a great lakes fishery commission project, Ital. J. Zool., 65, 357–361, 1998.
Delaney, F. and Milner, G.: The State of Climate Modeling in the Great Lakes Basin – A Synthesis in Support of a Workshop held on June 27, 2019 in Arr Arbor, MI, https://climateconnections.ca/app/uploads/2020/05/The-State-of-Climate-Modeling-in-the-Great-Lakes-Basin_Sept132019.pdf (last access: 13 July 2025), 2019.
Eichenlaub, V. L.: Weather and climate of the Great Lakes region [USA], University of Notre Dame Press, ISBN 978-0268019303, 1978.
EPA (Environmental Protection Agency): State of the Great Lakes 2011. EPA 950-R-13-002, available at: https://archive.epa.gov/solec/web/pdf/sogl-2011-technical-report-en.pdf (last access: 14 July 2025), 2014.
Fang, X. and Stefan, H. G.: Long-term lake water temperature and ice cover simulations/measurements, Cold Reg. Sci. Technol., 24, 289–304, https://doi.org/10.1016/0165-232X(95)00019-8, 1996.
Gao, G., Chen, C., Qi, J., and Beardsley, R. C.: An unstructured-grid, finite-volume sea ice model: Development, validation, and application. J. Geophys. Res.-Oceans, 116, C00D04, https://doi.org/10.1029/2010JC006688, 2011.
Gao, Y., Fu, J. S., Drake, J., Liu, Y., and Lamarque, J.-F.: Projected changes of extreme weather events in the eastern United States based on a high resolution climate modeling system, Environ. Res. Lett., 7, 044025, https://doi.org/10.1088/1748-9326/7/4/044025, 2012.
Gerbush, M. R., Kristovich, D. A., and Laird, N. F.: Mesoscale boundary layer and heat flux variations over pack ice–covered Lake Erie, J. Appl. Meteorol. Clim., 47, 668–682, 2008.
Giorgi, F. and Gutowski Jr., W. J.: Regional Dynamical Downscaling and the CORDEX Initiative, Annu. Rev. Environ. Resour., 40, 467–490, https://doi.org/10.1146/annurev-environ-102014-021217, 2015.
GLEN – Great Lakes Evaporation Network: GLEN Level 1 eddy covariance data for Lake Superior, Superior Watershed Partnership, https://superiorwatersheds.org/GLEN/ (last access: 13 June 2024), 2024.
GLSEA (NOAA Great Lakes Surface Environmental Analysis): Sea Surface Temperature (SST) from Great Lakes Surface Environmental Analysis (GLSEA) [data set], https://coastwatch.glerl.noaa.gov/erddap/files/GLSEA_GCS/ (last access: 9 November 2023), 2023.
Goudsmit, G. H., Burchard, H., Peeters, F., and Wüest, A.: Application of k-ϵ turbulence models to enclosed basins: The role of internal seiches, J. Geophys. Res.-Oceans, 107, 23-21–23-13, 2002.
Gu, H., Jin, J., Wu, Y., Ek, M. B., and Subin, Z. M.: Calibration and validation of lake surface temperature simulations with the coupled WRF-lake model, Climatic Change, 129, 471–483, 2015.
Hanrahan, J., Langlois, J., Cornell, L., Huang, H., Winter, J. M., Clemins, P.J., Beckage, B. and Bruyère, C.: Examining the Impacts of Great Lakes Temperature Perturbations on Simulated Precipitation in the Northeastern United States, J. Appl. Meteorol. Clim., 60, 935–949, 2021.
Holman, K. D., Gronewold, A., Notaro, M., and Zarrin, A.: Improving historical precipitation estimates over the Lake Superior basin, Geophys. Res. Lett., 39, L03405, https://doi.org/10.1029/2011GL050468, 2012.
Hostetler, S. W. and Bartlein, P. J.: Simulation of lake evaporation with application to modeling lake level variations of Harney-Malheur Lake, Oregon, Water Resour. Res., 26, 2603–2612, https://doi.org/10.1029/WR026i010p02603, 1990.
Huang, C.: Lake model code for the manuscript “On the Importance of Coupling a 3D Hydrodynamic Model with a Regional Climate Model in Simulating the Great Lakes Winter Climate”, Zenodo [software], https://doi.org/10.5281/zenodo.12746348, 2024a.
Huang, C.: NU-WRF (v11) code for the manuscript “On the Importance of Coupling a 3D Hydrodynamic Model with a Regional Climate Model in Simulating the Great Lakes Winter Climate”, Zenodo [software], https://doi.org/10.5281/zenodo.12746306, 2024b.
Hunke, E. C. and Dukowicz, J. K.: An elastic–viscous–plastic model for sea ice dynamics, J. Phys. Oceanogr., 27, 1849–1867, 1997.
Hunke, E. C., Lipscomb, W. H., Turner, A. K., Jeffery, N., and Elliott, S.: Cice: the los alamos sea ice model documentation and software user's manual version 4.1 la-cc-06-012, T-3 Fluid Dynamics Group, Los Alamos National Laboratory, 675, 500, 2010.
Hutson, A., Fujisaki-Manome, A., and Lofgren, B.: Testing the Sensitivity of a WRF-based Great Lakes Regional Climate Model to Cumulus Parameterization and Spectral Nudging, J. Hydrometeorol., 25, 1007–1025, https://doi.org/10.1175/JHM-D-22-0234.1, 2024.
Kain, J. S.: The Kain–Fritsch convective parameterization: an update, J. Appl. Meteorol., 43, 170–181, 2004.
Kain, J. S. and Fritsch, J. M.: A one-dimensional entraining/detraining plume model and its application in convective parameterization, J. Atmos. Sci., 47, 2784–2802, 1990.
Kayastha, M. B., Huang, C., Wang, J., Pringle, W. J., Chakraborty, T. C., Yang, Z., Hetland, R. D., Qian, Y., and Xue, P.: Insights on Simulating Summer Warming of the Great Lakes: Understanding the Behavior of a Newly Developed Coupled Lake-Atmosphere Modeling System, J. Adv. Model. Earth Sy., 15, e2023MS003620, https://doi.org/10.1029/2023MS003620, 2023.
Kristovich, D. A. R. and Laird, N. F.: Observations of Widespread Lake-Effect Cloudiness: Influences of Lake Surface Temperature and Upwind Conditions, Weather Forecast., 13, 811–821, https://doi.org/10.1175/1520-0434(1998)013<0811:Oowlec>2.0.Co;2, 1998.
Kumar, S. V., Peters-Lidard, C. D., Tian, Y., Houser, P. R., Geiger, J., Olden, S., Lighty, L., Eastman, J. L., Doty, B., Dirmeyer, P., and Adams, J.: Land information system: An interoperable framework for high resolution land surface modeling, Environ. Modell. Softw., 21, 1402–1415, 2006.
Launder, B. E. and Spalding, D. B.: The numerical computation of turbulent flows, Comput. Meth. Appl. Mech. Eng., 3, 269–289, https://doi.org/10.1016/0045-7825(74)90029-2, 1974.
Lenters, J., Anderton, J., Blanken, P., Spence, C., and Suyker, A.: Assessing the Impacts of Climate Variability and Change on Great Lakes Evaporation. 2011 Project Reports, edited by: Brown, D., Bidwell, D., and Briley, L., Great Lakes Integrated Sciences and Assessments (GLISA) Center, https://glisa.umich.edu/wp-content/uploads/2021/02/GLISA_Lake_Evaporation_Lenters_Final.pdf (last access: 14 July 2025), 2013.
Leon, L. F., Lam, D., Schertzer, W., and Swayne, D.: Lake and climate models linkage: a 3-D hydrodynamic contribution, Adv. Geosci., 4, 57–62, https://doi.org/10.5194/adgeo-4-57-2005, 2005.
Leon, L. F., Lam, D. C. L., Schertzer, W. M., Swayne, D. A., and Imberger, J.: Towards coupling a 3D hydrodynamic lake model with the Canadian regional climate model: simulation on Great Slave Lake, Environ. Modell. Softw., 22, 787–796, 2007.
Lofgren, B. M.: Simulation of atmospheric and lake conditions in the Laurentian Great Lakes region using the Coupled Hydrosphere-Atmosphere Research Model (CHARM), NOAA Technical Memorandum GLERL-165 [Technical memorandum], NOAA Great Lakes Environmental Research Laboratory. https://repository.library.noaa.gov/view/noaa/11169, (last access: 14 July 2025), 2014.
Mallard, M. S., Nolte, C. G., Spero, T. L., Bullock, O. R., Alapaty, K., Herwehe, J. A., Gula, J., and Bowden, J. H.: Technical challenges and solutions in representing lakes when using WRF in downscaling applications, Geosci. Model Dev., 8, 1085–1096, https://doi.org/10.5194/gmd-8-1085-2015, 2015.
Mallard, M. S., Nolte, C. G., Bullock, O. R., Spero, T. L., and Gula, J.: Using a coupled lake model with WRF for dynamical downscaling, J. Geophys. Res.-Atmos., 119, 7193–7208, 2014.
Martynov, A., Sushama, L., and Laprise, R.: Simulation of temperate freezing lakes by one-dimensional lake models: performance assessment for interactive coupling with regional climate models, Boreal Environ. Res., 15, 143–164, 2010.
Martynov, A., Sushama, L., Laprise, R., Winger, K., and Dugas, B.: Interactive lakes in the Canadian Regional Climate Model, version 5: the role of lakes in the regional climate of North America, Tellus A, 64, 16226, https://doi.org/10.3402/tellusa.v64i0.16226, 2012.
Matsui, T., Iguchi, T., Li, X., Han, M., Tao, W. K., Petersen, W., L'Ecuyer, T., Meneghini, R., Olson, W., Kummerow, C. D., and Hou, A. Y.: GPM satellite simulator over ground validation sites, B. Am. Meteorol. Soc., 94, 1653–1660, 2013.
Matsui, T., Santanello, J., Shi, J. J., Tao, W. K., Wu, D., Peters-Lidard, C., Kemp, E., Chin, M., Starr, D., Sekiguchi, M., and Aires, F.: Introducing multisensor satellite radiance-based evaluation for regional Earth system modeling, J. Geophys. Res.-Atmos., 119, 8450–8475, 2014.
Mellor, G. L. and Yamada, T.: Development of a turbulence closure model for geophysical fluid problems, Rev. Geophys., 20, 851–875, https://doi.org/10.1029/RG020i004p00851, 1982.
Minallah, S. and Steiner, A. L.: The effects of lake representation on the regional hydroclimate in the ECMWF reanalyses, Mon. Weather Rev., 149, 1747–1766, 2021.
Mironov, D., Heise, E., Kourzeneva, E., Ritter, B., Schneider, N., and Terzhevik, A.: Implementation of the lake parameterisation scheme FLake into the numerical weather prediction model COSMO, Boreal Environ. Res., 15, 218–230, 2010.
Mitchell, K.: The community Noah land-surface model (LSM) user's guide: Public release version 2.7.1, NOAA Technical Memorandum GLERL-165 [Technical memorandum], NOAA Great Lakes Environmental Research Laboratory, https://ral.ucar.edu/document-or-file/noah-lsm-users-guide (last access: 14 July 2025), 2005.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, https://doi.org/10.1029/97JD00237, 1997.
Mooney, P., Mulligan, F., and Fealy, R.: Evaluation of the sensitivity of the weather research and forecasting model to parameterization schemes for regional climates of Europe over the period 1990–1995, J. Climate, 26, 1002–1017, 2013.
Morrison, H., Thompson, G., and Tatarskii, V.: Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one-and two-moment schemes, Mon. Weather Rev., 137, 991–1007, 2009.
Moukomla, S. and Blanken, P. D.: The estimation of the North American Great Lakes turbulent fluxes using satellite remote sensing and MERRA reanalysis data, Remote Sens., 9, 141, https://doi.org/10.3390/rs9020141, 2017.
Nakanish, M.: Improvement of the Mellor–Yamada turbulence closure model based on large-eddy simulation data, Bound.-Lay. Meteorol., 99, 349–378, 2001.
Nakanishi, M. and Niino, H.: An improved Mellor–Yamada level-3 model: Its numerical stability and application to a regional prediction of advection fog, Bound.-Lay. Meteorol., 119, 397–407, 2006.
Nakanishi, M. and Niino, H.: Development of an improved turbulence closure model for the atmospheric boundary layer, J. Meteorol. Soc. Jpn. Ser. II, 87, 895–912, 2009.
Niziol, T. A., Snyder, W. R., and Waldstreicher, J. S.: Winter weather forecasting throughout the eastern United States. Part IV: Lake effect snow, Weather Forecast., 10, 61–77, 1995.
Notaro, M., Bennington, V., and Vavrus, S.: Dynamically Downscaled Projections of Lake-Effect Snow in the Great Lakes Basin*, J. Climate, 28, 1661–1684, https://doi.org/10.1175/JCLI-D-14-00467.1, 2015.
Notaro, M., Holman, K., Zarrin, A., Fluck, E., Vavrus, S., and Bennington, V.: Influence of the Laurentian Great Lakes on Regional Climate, J. Climate, 26, 789–804, https://doi.org/10.1175/jcli-d-12-00140.1, 2013a.
Notaro, M., Zarrin, A., Vavrus, S., and Bennington, V.: Simulation of Heavy Lake-Effect Snowstorms across the Great Lakes Basin by RegCM4: Synoptic Climatology and Variability*, Mon. Weather Rev., 141, 1990–2014, https://doi.org/10.1175/mwr-d-11-00369.1, 2013b.
Notaro, M., Zhong, Y., Xue, P., Peters-Lidard, C., Cruz, C., Kemp, E., Kristovich, D., Kulie, M., Wang, J., Huang, C., and Vavrus, S. J.: Cold Season Performance of the NU-WRF Regional Climate Model in the Great Lakes Region, J. Hydrometeorol., 22, 2423–2454, https://doi.org/10.1175/JHM-D-21-0025.1, 2021.
Oleson, K., Lawrence, D., and Bonan, G. B.: Technical description of version 4.5 of the Community Land Model (CLM), Ncar Tech. Note NCAR/TN-503+STR, National Center for Atmospheric Research, Boulder, 2013.
Perroud, M., Goyette, S., Martynov, A., Beniston, M., and Annevillec, O.: Simulation of multiannual thermal profiles in deep Lake Geneva: A comparison of one-dimensional lake models, Limnol. Oceanogr., 54, 1574–1594, 2009.
Peters-Lidard, C. D., Houser, P. R., Tian, Y., Kumar, S. V., Geiger, J., Olden, S., Lighty, L., Doty, B., Dirmeyer, P., Adams, J., and Mitchell, K.: High-performance Earth system modeling with NASA/GSFC's Land Information System, Innovations in Systems and Software Engineering, 3, 157–165, 2007.
Peters-Lidard, C. D., Kemp, E. M., Matsui, T., Santanello Jr., J. A., Kumar, S. V., Jacob, J. P., Clune, T., Tao, W. K., Chin, M., Hou, A., and Case, J. L.: Integrated modeling of aerosol, cloud, precipitation and land processes at satellite-resolved scales, Environ. Modell. Softw., 67, 149–159, 2015.
Petterssen, S. and Calabrese, P. A.: On some weather influences due to warming of the air by the Great Lakes in winter, J. Atmos. Sci., 16, 646–652, 1959.
Rau, E., Vaccaro, L., Riseng, C., and Read, J. G.: The Dynamic Great Lakes Economy Employment Trends from 2009 to 2018, https://repository.library.noaa.gov/view/noaa/38612 (last access: 14 July 2025), 2020.
Riley, M. J. and Stefan, H. G.: MINLAKE: A dynamic lake water quality simulation model, Ecol. Model., 43, 155–182, 1988.
Schwab, D. J., Leshkevich, G. A., and Muhr, G. C.: Automated Mapping of Surface Water Temperature in the Great Lakes, J. Great Lakes Res., 25, 468–481, https://doi.org/10.1016/S0380-1330(99)70755-0, 1999.
Scott, R. W. and Huff, F. A.: Impacts of the Great Lakes on Regional Climate Conditions, J. Great Lakes Res., 22, 845–863, https://doi.org/10.1016/S0380-1330(96)71006-7, 1996.
Sharma, A., Hamlet, A. F., Fernando, H. J. S., Catlett, C. E., Horton, D. E., Kotamarthi, V. R., Kristovich, D. A. R., Packman, A. I., Tank, J. L., and Wuebbles, D. J.: The Need for an Integrated Land-Lake-Atmosphere Modeling System, Exemplified by North America's Great Lakes Region, Earth's Future, 6, 1366–1379, https://doi.org/10.1029/2018ef000870, 2018.
Shi, J. J., Matsui, T., Tao, W. K., Tan, Q., Peters-Lidard, C., Chin, M., Pickering, K., Guy, N., Lang, S., and Kemp, E. M.: Implementation of an aerosol–cloud-microphysics–radiation coupling into the NASA unified WRF: Simulation results for the 6–7 August 2006 AMMA special observing period, Q. J. Roy. Meteor. Soc., 140, 2158–2175, 2014.
Shi, Q. and Xue, P.: Impact of Lake Surface Temperature Variations on Lake Effect Snow Over the Great Lakes Region, J. Geophys. Res.-Atmos., 124, 12553–12567, https://doi.org/10.1029/2019jd031261, 2019.
Smagorinsky, J.: General Circulation Experiments with the Primitive Equations: I. The Basic Experiment, Mon. Weather Rev., 91, 99–164, https://doi.org/10.1175/1520-0493(1963)091<0099:Gcewtp>2.3.Co;2, 1963.
Song, Y., Semazzi, F. H., Xie, L., and Ogallo, L. J.: A coupled regional climate model for the Lake Victoria basin of East Africa, Int. J. Climatol., 24, 57–75, 2004.
Spence, C., Blanken, P. D., Hedstrom, N., Fortin, V., and Wilson, H.: Evaporation from Lake Superior: 2. Spatial distribution and variability, J. Great Lakes Res., 37, 717–724, https://doi.org/10.1016/j.jglr.2011.08.013, 2011.
Spence, C., Blanken, P. D., Lenters, J. D., and Hedstrom, N.: The importance of spring and autumn atmospheric conditions for the evaporation regime of Lake Superior, J. Hydrometeorol., 14, 1647–1658, https://doi.org/10.1175/JHM-D-12-0170.1, 2013.
Spero, T. L., Nolte, C. G., Bowden, J. H., Mallard, M. S., and Herwehe, J. A.: The impact of incongruous lake temperatures on regional climate extremes downscaled from the CMIP5 archive using the WRF model, J. Climate, 29, 839–853, 2016.
Stepanenko, V. and Lykossov, V.: Numerical modeling of heat and moisture transfer processes in a system lake-soil, Russ. Meteorol. Hydro., 3, 95–104, 2005.
Stepanenko, V., Machul'Skaya, E., Glagolev, M., and Lykossov, V.: Numerical modeling of methane emissions from lakes in the permafrost zone, Izv. Atmos. Ocean. Phy., 47, 252–264, 2011.
Stepanenko, V. M., Goyette, S., Martynov, A., Perroud, M., Fang, X., and Mironov, D.: First steps of a lake model intercomparison project: LakeMIP, Boreal Environ. Res., 15, 191–202, 2010.
Subin, Z. M., Riley, W. J., and Mironov, D.: An improved lake model for climate simulations: Model structure, evaluation, and sensitivity analyses in CESM1, J. Adv. Model. Earth Sy., 4, M02001, https://doi.org/10.1029/2011MS000072, 2012.
Sun, L., Liang, X.-Z., and Xia, M.: Developing the Coupled CWRF-FVCOM Modeling System to Understand and Predict Atmosphere-Watershed Interactions Over the Great Lakes Region, J. Adv. Model. Earth Sy., 12, e2020MS002319, https://doi.org/10.1029/2020MS002319, 2020.
Todorovich, P.: America's emerging megaregions and implications for a national growth strategy, International Journal of Public Sector Management, 22, 221–234, 2009.
Turuncoglu, U. U., Giuliani, G., Elguindi, N., and Giorgi, F.: Modelling the Caspian Sea and its catchment area using a coupled regional atmosphere-ocean model (RegCM4-ROMS): model design and preliminary results, Geosci. Model Dev., 6, 283–299, https://doi.org/10.5194/gmd-6-283-2013, 2013.
Vaccaro, L. and Read, J.: Vital to Our Nation's Economy: Great Lakes Jobs, https://www.michiganseagrant.org/wp-content/uploads/2018/10/11-203-Great-Lakes-Jobs-report.pdf (last access: 14 July 2024), 2011.
Valcke, S.: The OASIS3 coupler: a European climate modelling community software, Geosci. Model Dev., 6, 373–388, https://doi.org/10.5194/gmd-6-373-2013, 2013.
Wang, J., Bai, X., Hu, H., Clites, A., Colton, M., and Lofgren, B.: Temporal and Spatial Variability of Great Lakes Ice Cover, 1973–2010*, J. Climate, 25, 1318–1329, https://doi.org/10.1175/2011jcli4066.1, 2012.
Wang, J., Xue, P., Pringle, W., Yang, Z., and Qian, Y.: Impacts of Lake Surface Temperature on the Summer Climate Over the Great Lakes Region, J. Geophys. Res.-Atmos., 127, e2021JD036231, https://doi.org/10.1029/2021JD036231, 2022.
Woolway, R. I., Anderson, E. J., and Albergel, C.: Rapidly expanding lake heatwaves under climate change, Environ. Res. Lett., 16, 094013, https://doi.org/10.1088/1748-9326/ac1a3a, 2021.
Xiao, C., Lofgren, B. M., Wang, J., and Chu, P. Y.: Improving the lake scheme within a coupled WRF-lake model in the Laurentian Great Lakes, J. Adv. Model. Earth Sy., 8, 1969–1985, https://doi.org/10.1002/2016MS000717, 2016.
Xue, P., Schwab, D. J., and Hu, S.: An investigation of the thermal response to meteorological forcing in a hydrodynamic model of Lake Superior, J. Geophys. Res.-Oceans, 120, 5233–5253, https://doi.org/10.1002/2015JC010740, 2015.
Xue, P., Pal, J. S., Ye, X., Lenters, J. D., Huang, C., and Chu, P. Y.: Improving the Simulation of Large Lakes in Regional Climate Modeling: Two-Way Lake–Atmosphere Coupling with a 3D Hydrodynamic Model of the Great Lakes, J. Climate, 30, 1605–1627, https://doi.org/10.1175/jcli-d-16-0225.1, 2017.
Xue, P., Ye, X., Pal, J. S., Chu, P. Y., Kayastha, M. B., and Huang, C.: Climate projections over the Great Lakes Region: using two-way coupling of a regional climate model with a 3-D lake model, Geosci. Model Dev., 15, 4425–4446, https://doi.org/10.5194/gmd-15-4425-2022, 2022.
Yang, T. Y., Kessler, J., Mason, L., Chu, P. Y., and Wang, J.: A consistent Great Lakes ice cover digital data set for winters 1973–2019, Sci. Data, 7, 259, https://doi.org/10.1038/s41597-020-00603-1, 2020.
Ye, X., Chu, P. Y., Anderson, E. J., Huang, C., Lang, G. A., and Xue, P.: Improved thermal structure simulation and optimized sampling strategy for Lake Erie using a data assimilative model, J. Great Lakes Res., 46, 144–158, https://doi.org/10.1016/j.jglr.2019.10.018, 2020.
Yeates, P. and Imberger, J.: Pseudo two-dimensional simulations of internal and boundary fluxes in stratified lakes and reservoirs, Int. J. River Basin Manage., 1, 297–319, 2003.
Zhong, Y., Notaro, M., Vavrus, S. J., and Foster, M. J.: Recent accelerated warming of the Laurentian Great Lakes: Physical drivers, Limnol. Oceanogr., 61, 1762–1786, https://doi.org/10.1002/lno.10331, 2016.
Short summary
This study introduces a new 3D lake–ice–atmosphere coupled model that significantly improves winter climate simulations for the Great Lakes compared to traditional 1D lake model coupling. The key contribution is the identification of critical hydrodynamic processes – ice transport, heat advection, and shear-driven turbulence production – that influence lake thermal structure and ice cover and explain the superior performance of 3D lake models to their 1D counterparts.
This study introduces a new 3D lake–ice–atmosphere coupled model that significantly improves...
