Great Lakes Forecasting

Great Lakes forecasts predict future conditions that affect people’s lives and support informed decision making.

Conditions in the Great Lakes affect the daily lives of tens of millions of people, impact the multi-trillion dollar regional economy, and influence resource management decisions. Predictions of future water level fluctuations, ice extent, snowfall, waves, temperature and more facilitate planning and decision making that promote safety and efficiency. Using the real-time data from buoys and other observing systems in the Great Lakes, we are producing model forecasts and predictions for the benefit of society.

With our partners at NOAA GLERL, CIGLR is developing and improving Great Lakes forecasts and hindcasts. Our activities in this area include:

1. Modeling Great Lakes Ice

Ice cover in the Great Lakes plays a key role not only in regional weather, but also in environmental quality and economic prosperity. Our ability to model and predict Great Lakes ice cover has broad implications for forecasting winter weather, understanding lake water levels, providing information for safe ice-related recreation, and helping inform decisions for Great Lakes commercial shipping and fishing industries. By using models that include the Finite-Volume Community Ocean Model for Great Lakes ice circulation (FVCOM+ice) and comparing observational data from the Arctic, we improving Great Lakes ice forecasts/hindcasts and gaining insight into how changing climates might impact future Great Lakes ice cover.

2. Simulating Water Levels

In light of future climate scenarios, CIGLR is engaging in research to improve Great Lakes water budget estimates and water level simulations. The consequences of water level change for shipping, commerce, and human safety have been magnified by the persistently low lake levels in the Great Lakes during 1999–2012, the recent rebound since 2014, and coastal flooding of Lake Ontario in 2017. We are using the Weather Research and Forecasting (WRF) model to conduct historical (1975 – 2005) and future (2006 – 2100) water level projections. We are also supporting the International Joint Commission’s (IJC) need for understanding water levels and future water supplies in the Great Lakes, by creating a robust historical dataset and water budget estimate for each Great Lake that explains changes in observed water levels.

3. Predicting Lake Effect Snow

Lake-effect snow (LES) is one of the most hazardous weather events in the Great Lakes region, creating dangerous transportation conditions and closing schools and businesses. However, it also drives the local economy during winter in many areas that rely on winter-related outdoor recreation (ice-fishing, skiing) for jobs. Forecasting LES events accurately both in timing and in the amount of snowfall is currently very difficult. To help with this problem, CIGLR is leading a project with National Weather Service (NWS) forecasters to reduce the uncertainties in their models and improve the LES forecasts, ice predictions, and visibility forecasts. These improvements will be incorporated into short-term weather forecasts, producing  more timely and accurate predictions and increasing community preparedness for severe winter weather events.

Stay up-to-date on the most recent news and scientific media generated from our Great Lakes Forecasting research here:


Alves, J.H., A. Chawla, H.L. Tolman, D.J. Schwab, G.A. Lang and G. Mann. 2014. The operational implementation of a Great Lakes wave forecasting system at NOAA/NCEP. Weather and Forecasting. 29(6):1473-1497. (DOI:10.1175/WAF-D-12-00049.1). Alves_etal.pdf

Anderson, E.J., D.J. Schwab and G.A. Lang. 2010. A real-time hydraulic and hydrodynamic model for the St. Clair River, Lake St. Clair, Detroit River System. Journal of Hydraulic Engineering. (DOI:10.1061/ASCEHY.1943-7900.0000203). Anderson_etal.pdf

Bai, X., J. Wang, D.J. Schwab, Y. Yang, L. Luo, G.A. Leshkevich and S. Liu. 2013. Modeling 1993-2008 climatology of seasonal general circulation and thermal structure in the Great Lakes using FVCOM. Ocean Modelling. 65:40-63. (DOI: 10.1016/j.ocemod.2013.02.003). Bai_etal.pdf

Benjamin, S.G., S.S. Weygandt, J.M. Brown, M. Hu, C.R. Alexander, T.G. Smirnova, J.B. Olson, E.P. James, D.C. Dowell, G.A. Grell, H. Lin, S.E. Peckham, T.L. Smith, W.R. Moninger and J.S. Kenyon. 2016a. A North American Hourly Assimilation and Model Forecast Cycle: The Rapid Refresh. American Meterological Society. 144:1669-1694. (DOI:10.1175/MWR-D-15-0242.1). Benjamin_etal.pdf

Benjamin, S.G., J.M. Brown and T.G. Smirnova. 2016b. Explicit Precipitation-Type Diagnosis from a Model Using a Mixed-Phase Bulk Cloud–Precipitation Microphysics Parameterization. Weather Forecast. 31:609-619. (DOI:10.1175/WAF-D-15-0136.1). Benjamin2_etal.pdf

Cable, R.N., D. Beletsky, R. Beletsky, K. Wigginton, B.W. Locke and M.B. Duhaime. 2017. Distribution and modeled transport of plastic pollution in the Great Lakes, the world’s largest freshwater resource. Frontiers in Environmental Science. 5:1-18. (DOI:10.3389/fenvs.2017.00045). Cable_etal.pdf

Clites, A.H., J.P. Smith, T.S. Hunter and A.D. Gronewold. 2014. Visualizing relationships between hydrology, climate, and water level fluctuations on Earth’s largest system of lakes. Journal of Great Lakes Research. 40(3):807-811. (DOI:10.1016/j.jglr.2014.05.014). Clites_etal

Clites, A.H., J. Wang, K.B. Campbell, A.D. Gronewold, R.A. Assel, X. Bai and G.A. Leshkevich. 2014. Cold water and high ice cover on Great Lakes in spring. EOS, Transactions of the American Geophysical Union. 95(34):305-306. (DOI:10.1002/2014EO340001). Clites2_etal.pdf

Fry, L.M., T.S. Hunter, M.S. Phanikumar, V. Fortin and A.D. Gronewold. 2013. Identifying streamgage networks for maximizing the effectiveness of regional water balance modeling. Water Resources Research. 49:1-12. (DOI:10.1002/wrcr.20233). Fry_etal.pdf

Fujisaki-Manome, A., L.E. Fitzpatrick, A.D. Gronewold, E.J. Anderson, B.M. Lofgren, C. Spence, J. Chen, C. Shao, D.M. Wright and C. Xiao. 2017. Turbulent Heat Fluxes during an Extreme Lake Effect Snow Event. Journal of Hydrometeorology. (DOI:10.1175/JHM-D-17-0062.1). Fujisaki-Manome_etal.pdf

Fujisaki-Manome, A., J. Wang, X. Bai, G. Leshkevich and B. Lofgren. 2013. Model-simulated interannual variability of Lake Erie ice cover, circulation, and thermal structure in response to atmospheric forcing 2003-2012. Journal of Geophysical Research. 118:4286-4304. (DOI:10.1002/jgrc.20312). Fujisaki-Manome2_etal.pdf

Gao, G., C. Chen, J. Qi and R.C. Beardsley. 2011. An unstructured‐grid, finite‐volume sea ice model: Development, validation, and application. Journal of Geophysical Research. 116:1-15. C00D04, (DOI:10.1029/2010JC006688). Gao_etal.pdf

Gronewold, A.D.,  E.J. Anderson, B. Lofgren, P.D. Blanken, J. Wang, J. Smith, T. Hunter, G. Lang, C.A. Stow, D. Beletsky and J. Bratton. 2015. Impacts of extreme 2013–2014 winter conditions on Lake Michigan’s fall heat content, surface temperature, and evaporation. Geophysical Research Letters. 42:3364-3370. (DOI:10.1002/2015GL063799). Gronewold_etal.pdf

Gronewold, A.D., J. Bruxer, D. Durnford, J.P. Smith, A.H. Clites, F. Seglenieks, T.S. Hunter, S.S. Qian and V. Fortin. 2016. Hydrological drivers of record-setting water level rise on Earth’s largest lake system. Water Resources Research. 52:4026-4042. (DOI: 10.1002/2015WR0182090). Gronewold2_etal.pdf

Gronewold, A.D., A.H. Clites, J.P. Smith and T.S. Hunter. 2013. A dynamic graphical interface for visualizing projected, measured, and reconstructed surface water elevations on the earth’s largest lakes. Environmental Modelling and Software. 49:34-39. (DOI:10.1016/j.envsoft.2013.07.003). Gronewold3_etal.pdf

Holman, K.D., A. Gronewold, M. Notaro and A. Zarrin. 2012. Improving historical precipitation estimates over the Lake Superior basin. Geophysical Research Letters. 39:L03405. (DOI:10.1029/2011GL050468). Holman_etal.pdf

Hu, H., J. Wang, H. Liu and J. Goes. 2016. Simulation of phytoplankton distribution and variation in the Bering-Chukchi Sea using a 3-D physical-biological model. Journal of Geophysical Research: Oceans. 121:4041-4055. (DOI:10.1002/2016JC011692). Hu_etal.pdf

Hunter, T.S., A.H. Clites, K.B. Campbell and Andrew D. Gronewold. 2015. Development and application of a North American Great Lakeshydrometeorological database – Part I: Precipitation, evaporation, runoff, and air temperature. 41:65-77. Journal of Great Lakes Reearch. (DOI:10.1016/j.jglr.2014.12.006). Hunter_etal.pdf

Kult, J.M., L.M. Fry, A.D. Gronewold and W. Choi. 2014. Regionalization of hydrologic response in the Great Lakes basin: Considerations of temporal scales of analysis. Journal of Hydrology. 519:2224-2237. (DOI:10.1016/j.jhydrol.2014.09.083). Kult_etal.pdf

Lenters, J.D., J.B. Anderton, P.D. Blanken, C. Spence and A.E. Suyker. 2013. Assessing the Impacts of Climate Variability and Change on Great Lakes Evaporation: Implications for water levels and the need for a coordinated observation network. 2011 Project Reports. D. Brown, D. Bidwell, L. Briley, eds. Available from Great Lakes Integrated Sciences and Assessments (GLISA) Center. Lenters_etal.pdf

Lofgren, B.M., A.D. Gronewold, A. Acciaioli, J. Cherry, A. Steiner and D. Watkins. 2013. Methodological Approaches to Projecting the Hydrologic Impacts of Climate Change. Earth Interactions. 17:1-19. (DOI:10.1175/2013EI000532.1). Lofgren_etal.pdf

Luo, L., J. Wang, D.J. Schwab, H. Vanderploeg, G. Leshkevich, X. Bai, H. Hu and D. Wang. 2012. Simulating the 1998 spring bloom in Lake Michigan using a coupled physical-biological Model. Journal of Geophysical Research.117:1-14. (DOI:10.1029/2012JC008216). Luo_etal.pdf

Mason, L.A., C.M. Riseng, A.D. Gronewold, E.S. Rutherford, J. Wang, A. Clites, S.D.P. Smith and P.B. McIntyre. 2016. Fine-scale spatial variation in ice cover and surface temperature trends across the surface of the Laurentian Great Lakes. Climate Change. 138:71-83. (DOI:10.1007/s10584-016-1721-2). Mason_etal.pdf

Nguyen, T.D., N. Hawley and M.S. Phanikumar. 2017. Ice cover, winter circulation, and exchange in Saginaw Bay and Lake Huron. Limnology and Oceanography. 62:376-393. (DOI:10.1002/lno.10431). Nguyen_etal.pdf

Smith, J.P., T.S. Hunter, A.H. Clites, C.A. Stow, T. Slawecki, G.C. Muhr and A.D. Gronewold. 2016. An expandable web-based platform for visually analyzing basin-scale hydro-climate time series data. Environmental Modelling and Software. 78:97-105. (DOI:10.1016/j.envsoft.2015.12.005). Smith_etal.pdf

Wang, J., X. Bai, H. Hu, A. Clites, M. Colton and B. Lofgren. 2012. Temporal and spatial variability of  Great Lakes ice cover, 1973-2010. Journal of Climate. (DOI:10.1175/2011JCLI4066.1). Wang_etal.pdf

Wang, J., H. Hu, D. Schwab, G. Leshkevich, D. Beletsky, N. Hawley and A. Clites. 2010. Development of the Great Lakes Ice-circulation Model (GLIM): Application to Lake Erie in 2003-2004. Journal of Great Lakes Research. 36:425-436. (DOI:10.1016/j.jglr.2010.04.002). Wang2_etal.pdf

Wang, J., K. Mizobata, X. Bai, H. Hu, M. Jin, Y. Yu, M. Ikeda, W. Johnson, W. Perie and A. Fujisaki. 2014. A modeling study of coastal circulation and landfast ice in the nearshore Beaufort and Chukchi Seas using CIOM. Journal of Geophysical Research: Oceans. 119(6):3285-3312. (DOI:10.1002/2013JC009258). Wang3_etal.pdf

Wohlleben, T., A. Tivy, W. Stroeve, W. Meier, F. Fetterer, J. Wang and R.A. Assel. 2013. Computing and representing sea ice trends: Toward a community consensus. EOS, Transactions of the American Geophysical Union. 94(40):352. (DOI:10.1002/2013EO400006). Wohlleben_etal.pdf

Xiao, C., B.M. Lofgren, J. Wang and P.Y. Chu. 2016. Improving the lake scheme within a coupled WRF-Lake model in the Great Lakes. Journal of Advances in Modeling Earth Systems. 8:1969-1985. (DOI:10.1002/2016MS000717). Xiao_etal.pdf

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