NOAA Great Lakes Environmental Research Laboratory

The latest news and information about NOAA research in and around the Great Lakes

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NOAA GLERL collaborating with partners to monitor the Lake Huron ecosystem

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The NOAA Great Lakes Environmental Research Laboratory (GLERL) is participating in an international, multi-agency effort to study invasive species, water quality, fisheries, and climate change in Lake Huron this field season—pursuing key knowledge gaps in the ecosystem. The Coordinated Science and Monitoring Initiative (CSMI) coordinates across U.S. and Canadian agencies to conduct intensive sampling in one Great Lake per year, on a five-year cycle. The Great Lakes Restoration Initiative, which is administered by the U.S Environmental Protection Agency (EPA), is funding this research.

“While GLERL has had a long-term research program focused on Lake Michigan, we are using this initiative to advance long-term research on Lake Huron,” said GLERL Director Deborah Lee. “Invasive species, warming temperatures, and changes in nutrient loading are putting as much stress on Lake Huron as on Lake Michigan. We want to better understand the Lake Huron ecosystem and develop modeling tools to predict how the lake is changing.”

Henry Vanderploeg, Ph.D., chief of GLERL’s Ecosystem Dynamics research branch and lead researcher for GLERL’s efforts in the pelagic (open water) portion of the initiative comments, “GLERL plays a critical role in the CSMI, addressing key science questions. GLERL’s high frequency temporal and spatial sampling will help determine nutrient and energy flows from tributaries, nearshore to offshore. This type of data is critical to effectively manage Lake Huron for water quality and fish production.” Frequent spatial surveys are key to understanding food web connections throughout the seasons.

Researchers from GLERL  will expand upon their recent work in Lake Michigan (CSMI 2015) and past work in Huron (2012) to determine fine-scale food-web structure and function from phytoplankton to fishes along a nutrient-rich transect (from inner Saginaw Bay out to the 65-m deep Bay City Basin) and along a nutrient-poor transect (from inner Thunder Bay out to the Thunder Bay basin) during May, July, and September. GLERL will collect additional samples of fish larvae and zooplankton along both transects in June to help estimate larvae growth, diet, density, and mortality and to identify fish recruitment bottlenecks.

“GLERL was instrumental in establishing the long-term monitoring efforts that provide the foundation for current CSMI food-web studies,” said Ashley Elgin, Ph.D., research ecologist in the Ecosystem Dynamics research branch. Elgin serves as the NOAA representative on the CSMI Task Team, part of the Great Lakes Water Quality Act Annex 10, alongside partners from the U.S. Geological Survey (USGS), EPA, the U.S. Fish & Wildlife Service, Environment and Climate Change Canada, and the Ontario Ministries of Natural Resources and the Environment and Climate Change. This year, Elgin is conducting critical mussel growth field experiments in Lake Huron, expanding upon work she developed in Lake Michigan.  She will be addressing the following questions: (1) How does quagga mussel growth differ between regions with different nutrient inputs?; and (2) How do growth rates compare between Lakes Michigan and Huron? Elgin will also coordinate a whole-lake benthic survey, which will update the status of dreissenid mussels and other benthic-dwelling organisms in Lake Huron.  

GLERL’s key research partner, the Cooperative Institute for Great Lakes Research (CIGLR), will deploy a Slocum glider for a total of sixteen weeks to collect autonomous measurements of temperature, chlorophyll, colored dissolved organic matter (CDOM), and photosynthetically active radiation (PAR) between outer Saginaw Bay and open waters of the main basin.  Deployment times and coverage will be coordinated with other glider deployments by the EPA Office of Research and Development (ORD) and/or USGS Great Lakes Science Center, spatial research cruises, and periods of expected higher nutrient loads (i.e., following runoff events).  

CSMI research cruises began in late April and will continue through September. Researchers are using an impressive fleet of research vessels, including EPA’s 180-foot R/V Lake Guardian, GLERL’s 80-foot R/V Laurentian and 50-foot R/V Storm, and two large USGS research vessels, the R/V Articus and R/V Sterling. Sampling missions will also be conducted aboard Environment Canada’s Limnos across Lake Huron. The Laurentian is fitted out with a variety of advanced sensors and sampling gear, making it especially suitable for examining fine-scale spatial structure.

Scientists from the USGS Great Lakes Science Center, the Michigan Department of Natural Resources, and the University of Michigan are also participating in the Lake Huron CSMI.

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“Just Because the Blooms in Lake Erie Slow Down, Doesn’t Mean We Do”

NOAA GLERL harmful algal blooms research program featured on Detroit Public Television

As part of a series on The Blue Economy of the Great Lakes, NOAA’s Great Lakes Environmental Research Laboratory (GLERL) is featured in a short video, produced by Detroit Public Television (DPTV) and published on the DPTV website. The video, which features GLERL and its partners from the Cooperative Institute for Great Lakes Research (CIGLR, known formerly as CILER), describes the advanced technology GLERL uses to monitor, track, predict, and understand harmful algal blooms (HABs) in the Great Lakes. More specifically, the video focuses on efforts in Lake Erie, where over 400,000 people were affected by a 3-day shutdown of the Toledo drinking water treatment facility in 2014. Since then, GLERL and CIGLR have enhanced their HABs research program—much of which is made possible by funding from the Great Lakes Restoration Initiative, or GLRI—to include cutting-edge technologies such as the hyperspectral sensors and an Environmental Sample Processor (ESP), as well as experimental forecasting tools like the Lake Erie HAB Tracker.

In addition to online coverage, the video will be broadcast via DPTV at a future time, yet to be determined.

View the video above, or visit

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NOAA research to be highlighted at bi-national Conference on Great Lakes Research

Researchers from NOAA GLERL and the Cooperative Institute for Great Lakes Research (CIGLR), know formerly as CILER, along with other collaborators, will showcase their work on observations, advanced technologies, harmful algal blooms, modeling, forecasting, and more at the 60th Conference on Great Lakes Research on May 15-19th in Detroit, Michigan.

This year’s conference, held by the International Association for Great Lakes Research (IAGLR), is titled “From Cities to Farms: Shaping Great Lakes Ecosystems” and will feature over 660 presentations, plus a poster social with over 160 posters. GLERL and CIGLR authors will showcase their research in more than 50 presentations. In addition, GLERL scientists George Leshkevich, Ashley Baldridge Elgin; Henry Vanderploeg, Philip Chu, Eric Anderson, Brent Lofgren, Jia Wang, Steve Ruberg and Andrew Gronewold; CIGLR’s Dima Beletsky and Tom Johengen; and Great Lakes Sea Grant’s Rochelle Sturtevant will co-chair sessions throughout the week. These sessions include:

Here’s a schedule of where you’ll find our scientists and their research throughout the week (presentation titles linked to online abstract and poster titles linked to .pdf of poster, if available.) And, don’t forget to swing by the NOAA and CIGLR exhibitor tables for more information on our research programs, collaborations, or to grab a copy of one of our recent publications.

For more information you can find .pdf of the full abstract book on the IAGLR conference website.

GLERL and CIGLR posters and presentations during IAGLR 2017


Tuesday May 16, 2017

8:00A HALL, D.K., NGHIEM, S.V., GUNN, G.E., LESHKEVICH, G., HELFRICH, S.R., CRAWFORD, C.J., KEY, J.R., CZAJKOWSKI, K.P., RIGOR, I.G. and KIM, E.J. GLAWEX’17 – Snow and Ice Field and Aircraft Experiment in Michigan and the Great Lakes
8:40A NGHIEM, S.V., LESHKEVICH, G. and JACKSON, C. Great Lakes Satellite SAR Ice Type Classification and Its Relation to ICECON
9:00A SAYERS, M.J., RUBERG, S.A., LESHKEVICH, G., STUART, D.G., SHUCHMAN, R.A. and ADEN, S.T. Spatial and Temporal Patterns of Inherent Optical Properties in Western Lake Erie for 2015 and 2016
9:40A BOSSE, K.R., SHUCHMAN, R.A., SAYERS, M.J., SCHWAB, D.J. and LESHKEVICH, G. Developing A Daily Composite Product for Water Quality Parameters in the Great Lakes
2:40P LESHKEVICH, G. and LIU, S. Great Lakes CoastWatch – New Data Sets and New Data Servers
9:00A GILL, D.G., JOSHI, S.J., and ROWE, M. Understanding the Potential Utility of the HAB Tracker Forecast Model for Western Lake Erie Anglers
10:00A STOW, C.A., ROWE, M.D., RUBERG, S.A., JOHENGEN, T.H., ZHANG, H., BELETSKY, D., JOSHI, S.J., COLLINGSWORTH, P., MASON, D.M., KRAUS, R.T. and ANDERSON, E.J. Lake Erie Hypoxia Forecasting for Public Water Systems Decision Support
4:20P GLYSHAW, P., VANDERPLOEG, H.A., CAVALETTO, J.F., RUTHERFORD, E.S., WELLS, D.J., NASH, R.D.M. and GEFFEN, A.J. Potential effects of UV radiation on vertical distribution of zooplankton in Southeast Lake Michigan
4:40P CARRICK, H., RUDSTAM, L.G., WARNER, D. and VANDERPLOEG, H.A. Plankton dynamics in Lake Michigan along a near to offshore gradient in Lake Michigan
4:40P QIAN, S.S. and STOW, C.A. A Risk Forecasting Model of Cyanobacterial Toxin for Western Lake Erie

Wednesday, May 17, 2017

10:00A MARINO, J.A., PEACOR, S.D., BUNNELL, D.B., VANDERPLOEG, H.A., POTHOVEN, S.A., ELGIN, A.K., and IONIDES, E.L. Fitting models to field time series data to quantify Bythotrephes effects in Lake Michigan
10:20A ELGIN, A.K., BURLAKOVA, L.E., KARATAYEV, A.Y., MEHLER, K. and NALEPA, T.F. Quagga Mussel Body Condition and Size Distribution Inform Recent Lake Michigan Population Trends
11:20A VANDERPLOEG, H.A., SARNELLE, O., DENEF, V.J., CARRICK, H., ELGIN, A.K., ROWE, M.D., RUTHERFORD, E.S. and POTHOVEN, S.A. Food-web impacts of Dreissena are Context-dependent: Mapping out a New Research Agenda
2:00P STURTEVANT, R.A., BERGERON, D. and BUNTING-HOWARTH, K.E. Stakeholder Engagement in a Wicked World:Crude Oil Transport in the Great Lakes Region
8:20A ROWE, M.D., ANDERSON, E.J., RUBERG, S.A., MOEGLING, S., VERHAMME, E.M., BELETSKY, D., ZHANG, H., JOHENGEN, T.H. and STOW, C.A. Modeling Dissolved Oxygen Dynamics Near Drinking Water Intakes in the Central Basin of Lake Erie
8:40A HAWLEY, N., BELETSKY, D., WANG, J. and CHU, P. Time series measurements of ice thickness in Lake Erie, 2010-2011
10:00A ANDERSON, E.J., LANG, G.A., CHU, P., FUJISAKI-MANOME, A. and WANG, J. Development of the Next-Generation Lake Michigan-Huron Operational Forecast System (LMHOFS)
11:00A HOFFMAN, D.K., MCCARTHY, M.J., DAVIS, T.W., GOSSIAUX, D.C., BURTNER, A.M., JOHENGEN, T.H., PALLADINO, D.A., GARDNER, W.S., MYERS, J.A. and NEWELL, S.E. Water Column Ammonium Dynamics Affecting Harmful Cyanobacterial Blooms in Lake Erie
2:00P ZHANG, H., ROWE, M.D., JOHENGEN, T.H., ANDERSON, E.J. and RUBERG, S.A. Modeling succession of algal functional groups associated with Lake Erie harmful alga blooms
3:40P LIU, Q., ANDERSON, E.J. and BIDDANDA, B.A. A Physical-Biogeochemical Simulation of Muskegon Lake
3:40P LOFGREN, B.M. and XIAO, C. Influence of Greenhouse Gas Concentrations on Lake Phenology and Temperature Profiles
4:00P HU, H., WANG, J., LIU, H. and GOES, J. Simulation of Phytoplankton Distribution and Variation in the Bering-Chukchi Sea using a 3D Physica
4:20P RUTHERFORD, E.S., GEFFEN, A.J., NASH, R.D.M., WELLS, D.J., GLYSHAW, P., VANDERPLOEG, H.A., CAVALETTO, J.F. and MASON, D.M. Have Invasive Species Caused Changes in Larval Fish Density and Distribution in SE Lake Michigan?
4:40P FUJISAKI-MANOME, A., WANG, J. and ANDERSON, E.J. Modeled ice thickness in Lake Erie with different parameterizations of the ice strength
5:00P KESSLER, J.A., WANG, J., MANOME, A.F. and CHU, P. Modeling the Great Lakes with FVCOM+UGCICE

Thursday, May 18, 2017

8:00A WANG, J., KESSLER, J.A., HU, H., FUJISAKI-MANOME, A., CLITES, A., LOFGREN, B.M. and CHU, P. Seasonal forecast of Great Lakes ice cover using multi-variable regression and FVCOM+ice models
8:20A XUE, P., CHU, P., YE, X. and LANG, G.A. Improve Lake Erie Thermal Structure Predictions using Data Assimilative Hydrodynamic Model
8:40A CHU, P., ANDERSON, E.J., LOFGREN, B.M., GRONEWOLD, A.D., WANG, J., STOW, C.A., LANG, G.A., HUNTER, T. and CLITES, A. Towards an Integrated Environmental Modeling System for the Great Lakes
9:00A YE, X., XUE, P., PAL, J.S., LENTERS, J.D. and CHU, P. Coupling a Regional Climate Model with a 3-D Hydrodynamic Model over the Great Lakes
10:00A LUCIER, H.M., HAWLEY, N. and CHU, P. Developing a long-term database of water temperature measurements in the Great Lakes
2:00P PEI, L., HUNTER, T., BOLINGER, R. and GRONEWOLD, A.D. Applying Climate Change Projections in Great Lakes Regional Water Management Decisions
2:40P DAVIS, T.W., ROWE, M.D., ANDERSON, E.J., VANDERWOUDE, A., JOHENGEN, T.H., RUBERG, S.A., STUMPF, R.P. and DOUCETTE, G. Combining advanced technologies to develop an early warning system for HABs in western Lake Erie
3:00P MUZZI, R.W., RUBERG, S.A., BEADLE, K.S., CONSTANT, S.A., DAVIS, T.W., JOHENGEN, T.H., LUCIER, H.M. and VERHAMME, E.M. Observations of the distribution of phytoplankton during cyanobacteria blooms using an AVP
3:00P STURTEVANT, R.A., MARTINEZ, F., RUTHERFORD, E.S., ELGIN, A.K., SMITH, J.P. and ALSIP, P. Update on the Great Lakes Aquatic Nonindigenous Species Information System (GLANSIS)
3:40P VANDER WOUDE, A.J., MILLER, R.J., JOHENGEN, T.H. and RUBERG, S.A. Variability in Lake Erie by Integrating Hyperspectral Imagery, AUV’s and a Shipboard Underway System
4:00P STUART, D.G., BURTNER, A.M., JOHENGEN, T.H., MILLER, R.J., PALLADINO, D.A. and RUBERG, S.A. Trends In Nitrate, Phosphate And Bloom Indicators During The 2016 Western Lake Erie Field Season
4:20P RUBERG, S.A., CONSTANT, S.A., MUZZI, R.W., MILLER, R.J. and SMITH, J.P. Utilization of PostgreSQL Database for Real-Time Western Lake Erie Data Storage and Dissemination
4:40P JOHENGEN, T.H., PAIGE, K., RUBERG, S.A., TWISS, M.R. and PEARSON, R. State of the Science for Great Lakes Observations: Conclusions from the 2016 CILER Symposium
4:40P MASON, L., SAMPSON, K., DUGGER, A., GOCHIS, D., RISENG, C.M. and GRONEWOLD, A.D. Development of a new geospatial hydrofabric to support advanced hydrological modeling
5:00P LEE, D.H. The application of hydroclimate science to Lake Ontario-St. Lawrence River System regulation

Friday, May 19, 2017

8:00A FRY, L.M., GRONEWOLD, A.D., BOLINGER, R. and MUELLER, R. Assessment of Probabilistic 5-year Forecasts of Great Lakes Levels and Outflows for Hydropower
10:00A GRONEWOLD, A.D. and SMITH, J.P. Great Lakes water budget modelling and uncertainty estimation under a Bayesian MCMC framework
10:40A QUINN, F.H., CLITES, A. and GRONEWOLD, A.D. Reconciling Discontinuity of Temporal Flow Measurements for the Detroit River
11:20A LABUHN, K.A., CALAPPI, T.J., GRONEWOLD, A.D., ANDERSON, E.J. and KOWALSKI, P.J. Optimizing Water Levels in the Grass Island Pool for Hydropower Production on the Niagara River


Wednesday, May 17, 2017

Using the Fluoromarker Calcein to Assess Growth Rates of Quagga Mussels in situ.

MABREY, K. (1), GLYSHAW, P. (1) and ELGIN, A.K. (2) – (1) CILER University of Michigan, G110 Dana 440 Church St, Ann Arbor, MI, 48109, USA; (2) NOAA Great Lakes Environmental Research Laboratory, 1431 Beach St., Muskegon, MI, 49441, USA.

The quagga mussel (Dreissena r. bugensis) exerts a profound effect on the Lake Michigan food web. Understanding quagga mussel growth habits in situ will help us to better predict population growth and anticipate ecosystem effects. One technique used to mark mollusk species for growth experiments is exposure to the fluoromarker calcein. Studies on calcein are more common in marine conditions; less has been reported for freshwater species, let alone in natural environments. We conducted a field experiment at 45m in Lake Michigan to assess if calcein is an effective and noninvasive marking technique for quagga mussels. Mussels from two size groups were assigned to calcein or no calcein treatments, measured, then placed in replicate mesh cages attached to a tripod mooring platform 0.4m above the lakebed. We removed cages to remeasure mussels after 5 and 12 months. We captured fluorescent images using a dark box with a blue filter on the light source and a yellow filter on the camera. New shell growth beyond the calcein mark was visually delineated and measured using ImagePro Premier(v9.1) software. Preliminary results indicate that small and large quagga mussels respond differently to exposure to calcein. Studies using calcein will need to take these artifacts into account when measuring dreissenid mussel growth rates

Shock and Awe! Estimating Mysis Density and Catch Avoidance using the MOCNESS in SE Lake Michigan

WELLS, D.J.(1), PSAROUTHAKIS, Z.(1), RUTHERFORD, E.S.(2), CHIN, T.(1), VANDERPLOEG, H.A.(2), CAVALETTO, J.F.(2) and GLYSHAW, P.1(1) – (1) Cooperative Institute for Limnology and Ecosystems Research, 440 Church Street, Ann Arbor, MI, 48109, USA; (2) NOAA GLERL, 4840 S. State Rd., Ann Arbor, MI, 48108, USA

Mysis diluviana is a key member of Great Lakes aquatic food webs and is important prey for pelagic planktivores. Mysis are visual predators of zooplankton, and migrate diurnally from the lake bottom into the water column. Mysis biomass estimates are highly variable but critical for food web models that inform salmonid stocking decisions. In 2016, we evaluated catch efficiency of Mysis in a Multiple Opening/Closing Net and Environmental Sensing System (MOCNESS) with LED strobe lighting that is used to sample plankton and fish larvae in marine waters. We sampled Mysis density at a mid-depth (45 m) station in June and July, and at an offshore (110m) station in September off Muskegon, MI. We made replicate tows in thermally stratified depth layers during day and night, and compared Mysis densities sampled with the LED strobe on vs strobe off. There were no significant differences in Mysis density among depths in samples with strobe on or off in any month. Mysis were concentrated in dense layers in the metalimnion at night, and were highest in the hypolimnion during day. We conclude that the strobe flash light had no effect on catch avoidance by Mysis.

Forecasting Lake Levels Under Climate Change: Implications of Bias Correction.

CHANNELL, K.E.(1), GRONEWOLD, A.D.(2), XIAO, C.(3), ROOD, R.B.(1), LOFGREN, B.M.(2) and HUNTER, T.(2) – (1) University of Michigan Climate and Space Sciences and Engineering, Ann Arbor, MI, USA; (2) NOAA Great Lakes Environmental Research Laboratory, Ann Arbor, MI, USA; (3) Cooperative Institute for Limnology and Ecosystems Research, Ann Arbor, MI, USA.

Numerical models can provide a basis for projecting future water levels for the Great Lakes under climate change. Hydrological components critical to generating water supplies were extracted from the WRF/GFDL-CM3 downscaled climate model, and were then used to drive a routing model to produce water levels for the 21st century. A new method of bias correction was used to provide a more consistent representation of seasonality, trends, and variability, when compared to more conventional methods. Here we demonstrate the relative differences in hydrology projections from our method and previously used methods. Our results indicate that the bias correction method used is an important source of variability in water level projections. This is a source of variability that is perhaps just as important as choice of models, emission scenarios, etc., but is commonly overlooked.

Simulating and forecasting seasonal ice cover

JI, X.(1), ROOD, R.B.(1), DAHER, H.(2), GRONEWOLD, A.D.(2) and BOLINGER, R.(2)
(1) Climate and Space Science and Engineering, University of Michigan, 2455 Hayward St, Ann Arbor, MI, 48109, USA; (2) NOAA Great Lakes Environmental Research Laboratory, 4840 S State Rd, Ann Arbor, MI, 48108, USA.

Over the past several decades, dramatic changes in the spatial extent of seasonal and long-term ice cover have been documented for both marine and continential water bodies. Successfully projecting future changes in global ice cover requires an understanding of the drivers behing these historical changes. Here, we explore relationships between continental climate patterns and regional ice cover across the vast surface waters of the Great Lakes. Our findings indicate that abrupt historical changes in Great Lakes seasonal ice cover are coincident with historical changes in teleconnections, including both the El Nino Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). We find, in particular, that these teleconnections explain much of the ice cover decline in the late 1990s (coincident with the strong 1997-1998 winter El Nino) and the following persistent period of below-average period of ice that followed. We encode these relationships in a probabilistic model that provides seasonal projections of ice cover areal extent across the Great Lakes, as well as specific spatiotemporal patterns in ice cover at resolutions that align with critical regional human health and safety-related management decisions.

Implementation of the WRF-Hydro Model in the Great Lakes Region

XIAO, C.(1), LOFGREN, B.M.(2), GRONEWOLD, A.D.(2), GOCHIS, D.(3), MASON, L.(1) and PEI, L.(3) – (1) Cooperative Institute for Limnology and Ecosystems Research (CILER), University of Michigan, 4840 S. State Rd., Ann Arbor, MI, 48108, USA; (2) NOAA Great Lakes Environmental Research Laboratory, 4840 S. State Rd., Ann Arbor, MI, 48108, USA; (3) National Center for Atmospheric Research (NCAR), P.O. Box 3000, Boulder, CO, 80307, USA.

As a physics-based, spatially-distributed hydrologic modeling system, the community Weather Research and Forecasting model (WRF) hydrological extension package (WRF-Hydro) has been used in several streamflow prediction applications in the U.S. and around the world, including the National Water Model (NWM) at the newly established NOAA National Water Center. However, because of lack of consistency of the geofabric data along the U.S. and Canada borders, the Great Lakes basin is not entirely included in NWM, leaving a substantial gap for applying the national model to the water-dominated region. Thus, a specific effort has been devoted to implementing the WRF-Hydro modeling system in the Great Lakes basin, including preparing high-resolution terrain datasets, parameterizing lakes and reservoirs, and calibrating the model. Two experiments have been carried out to support implementation of the NWM in the Great Lakes basin: an offline WRF-Hydro simulation forced by NLDAS2 and a coupled WRF/WRF-Hydro simulation. The model results are validated against observations in terms of precipitation, runoff, soil moisture, channel flow, and land surface heat fluxes. Our preliminary study presented here shows that the WRF-Hydro model is capable of reproducing the land-hydro-air feedbacks in the Great Lakes region.

Investigation into Recent Meteotsunami Events in the Great Lakes.

ANDERSON, E.J.(1), BECHLE, A.J.(2), WU, C.H.(2), CHU, P.(1), MANN, G.E.(3), SCHWAB, D.J.(4) and LOMBARDY, K.(5) –  (1) NOAA GLERL, 4840 S. State Rd, Ann Arbor, MI, 48104, USA; (2)University of Wisconsin-Madison, Madison, WI, USA; (3) NWS WFO-Detroit, Detroit, MI, USA; (4) University of Michigan, Water Center, Ann Arbor, MI, USA; (5) NWS WFO-Cleveland, Cleveland, OH, USA.

Meteotsunami events have been documented in several countries around the world in the coastal ocean, semi-enclosed basins, and in the Great Lakes. In particular, investigations in the Great Lakes have raised the issue of dangers posed by enclosed basins due to the reflection and interaction of meteotsunami waves, in which the destructive waves can arrive several hours after the atmospheric disturbance has passed. This disassociation in time and space between the atmospheric disturbance and resultant meteotsunami wave can pose a significant threat to the public. In recent events in the Great Lakes, atmospheric conditions have induced meteotsunami waves in Lake Erie and Lake Superior. The resulting waves impacted swimmers, inundated a marina, flooded coastal communities. In this work, we attempt to explain the processes that led to these conditions through a combination of atmospheric and hydrodynamic modeling and an analysis of the observed meteorology. Results from a high-resolution atmospheric model and hydrodynamic model reveal the formation of destructive waves resulted from a combination of wave reflection, focusing, and edge waves, though important differences have been found between recent events in the Great Lakes.

Reconstructing evaporation over Lake Erie during the historic November 2014 lake effect snow event

FITZPATRICK, L.E.(1), FUJISAKI-MANOME, A.(1), GRONEWOLD, A.D.(2), ANDERSON, E.J.(2), SPENCE, C.(5), CHEN, J.(4), SHAO, C.(4), POSSELT, D.(3), WRIGHT, D.(3), LOFGREN, B.M.(2) and SCHWAB, D.J.(3),- (1)Cooperative Institute for Limnology and Environmental Research, Ann Arbor, MI, USA; (2)Great Lakes Environmental Research Laboratory, Ann Arbor, MI, USA; (3)University of Michigan, Ann Arbor, MI, USA; (4)Michigan State University, East Lansing, MI, USA; (5)Environment Climate Change Canada, Gatineau, QC, CANADA.

The extreme North American winter storm of November 2014 triggered a record lake effect snowfall (LES) event in southwest New York. This study examined the evaporation from Lake Erie during the record LES event between November 17th-20th, 2014, by reconstructing heat fluxes and evaporation rates over Lake Erie using the unstructured grid, Finite-Volume Community Ocean Model (FVCOM). Nine different model runs were conducted using combinations of three different flux algorithms and three different meteorological forcings. A few non-FVCOM model outputs were also included for further evaporation analysis. Model-simulated water temperature and meteorological forcing data were validated with buoy data at three locations in Lake Erie. The simulated sensible and latent heat fluxes were validated with the eddy covariance measurements at two offshore sites. The evaluation showed a significant increase in heat fluxes over three days, with the peak on the 18th of November. Snow water equivalent data from NOAA’s NOHRSC showed a spike in water content on the November 20th. The ensemble runs presented a variation in spatial pattern of evaporation, lake-wide average evaporation, and resulting cooling of the lake, however, the overall analysis showed significant evaporation from Lake Erie appeared to be the main contribution to the LES event.

MCMC modelling with JAGS and applications in the Great Lakes

SMITH, J.P.(1), MASON, L.(2), QIAN, S.S.(3) and GRONEWOLD, A.D.(4) – (1)CILER, G110 Dana Building 440 Church Street, Ann Arbor, MI, 48109-1041, USA; (2)University of Michigan, School of Natural Resources, 440 Church St., Ann Arbor, MI, 48109-1041, USA; (3)The University of Toledo, Department of Environmental Sciences, 2801 West Bancroft St., Toledo, OH, 43606-3390, USA; 4NOAA Great Lakes Environmental Research Laboratory, 4840 S. State Rd., Ann Arbor, MI, 48108-9719, USA.

Markov Chain Monte Carlo (MCMC) methods have made realistic modeling possible and are widely used in areas such as genetics, ecology, biostatistics, economics. Software packages, such as Just Another Gibbs Sampler (JAGS), have made MCMC accessible to many scientists, engineers, and other professionals looking to utilize the method. This high-level talk discusses the theory, the JAGS package, and a few applications including analyzing the effects of climate change on Great Lakes ice cover and water budget modelling.

Effect of light exposure and nutrients on buoyancy of Microcystis colonies.

MING, T.(1), VANDERPLOEG, H.A.(2), ROWE, M.D.(3), FANSLOW, D.L.(2), STRICKLER, J.R.(4), MILLER, R.J.(3), JOHENGEN, T.H.(3), DAVIS, T.W.(2) and GOSSIAUX, D.C.(2), – (1)School of Natural Resources & Environment, University of Michigan, 440 Church St., Ann Arbor, MI, 48109, USA; (2)NOAA Great Lakes Environmental Research Laboratory, 4840 S. State Rd., Ann Arbor, MI, 48108, USA; (3)Cooperative Institute for Limnology and Ecosystems Research, 440 Church St., Ann Arbor, MI, 48109, USA; (4)Great Lakes WATER Institute, University of Wisconsin – Milwaukee, 600 E. Greenfield Ave., Milwaukee, WI, 53204, USA.

Understanding the vertical distribution of Microcystis spp. is important for improving satellite-derived estimates of bloom biomass and for predicting the transport of blooms. For example, the Lake Erie HAB Tracker forecast model is initialized from satellite imagery, then predicts the transport and vertical distribution of harmful algal blooms (HABs) in Lake Erie over a five-day period. To improve vertical distribution predictions, we used novel videographic methods to determine effects of light intensity, colony size, as well as dissolved and particulate nutrient concentrations on the buoyant velocities of Microcystis colonies collected from western Lake Erie. We incubated whole water samples in two 2L borosilicate bottles in an outdoor incubator maintained at ambient lake temperatures. Light levels were varied to represent day and night conditions for a surface scum or turbulent mixed layer distributions. After an overnight dark adaption, subsamples from each bottle were collected in the morning and evening, then buoyant velocities were measured. In general, colonies were positively buoyant with rates increasing with colony size. However, varying nutrient and light conditions differentially impacted buoyancy rates of Microcystis colonies.

Skill Assessment of the Lake Erie HAB Tracker Forecast Model using Variable Spatial Neighborhoods.

OUYANG, W.(1), ROWE, M.D.(2) and ZHANG, H.(2), – (1)School of Natural Resources and Environment, University of Michigan, Dana Building 440 Church Street, Ann Arbor, MI, 48108, USA; (2) Cooperative Institute for Limnology and Ecosystems Research, University of Michigan, G110 Dana Building 440 Church Street, Ann Arbor, MI, 48108, USA.

Forecasts of harmful algal bloom (HAB) spatial distribution are useful to public water systems, anglers, and recreational boaters. The Lake Erie HAB Tracker model is initialized from satellite-derived HAB spatial distribution, then uses a hydrodynamic forecast to predict the transport and vertical distribution of HABs in Lake Erie over a 5-day period. The model was assessed previously using pixel-by-pixel skill statistics; however, such statistics produce large penalties for small spatial mismatch between simulated and observed fields. Here, we used an alternative approach, fractions skill score (FSS), which has been used in precipitation forecast skill assessment. FSS assesses model skills over a series of increasing spatial neighborhood sizes. Model skill may improve with increasing neighborhood size if spatial mismatch between simulated and observed fields is a problem. We calculated FSS for a series of 26 hindcast simulations from 2011. We compared model skill to a benchmark persistence forecast, which assumed no change from the initial satellite image. Model skill exceeded that of the persistence forecast initially, but the advantage decreased at day 7. Model skill was greatest at 1 km neighborhood size for days 1-2, but improved at neighborhood size of 3-5 km for days 3-6, a period when the forecast is more challenging.

Hydrodynamics of Western Lake Erie.

BELETSKY, D.(1), ANDERSON, E.J.(2) and BELETSKY, R.(1) – (1)University of Michigan, Ann Arbor, MI, USA; (2) NOAA GLERL, Ann Arbor, USA

Prediction of harmful algal blooms in Lake Erie depends on the accuracy of hydrodynamic models that provide information on lake circulation and temperature. Until recently, long-term observations of circulation in the western basin were practically non-existent, but in summer 2015 GLERL deployed 4 ADCPs that revealed highly variable circulation patterns. To evaluate model skill and better understand the dynamics of Lake Erie’s western basin, we compare FVCOM model results with current and temperature observations conducted in Lake Erie.

The Great Lakes Aquatic Nonindigenous Species Information System Watchlist.

ALSIP, P.(1), RICE, N.M.(2), IOTT, S.(1), STURTEVANT, R.A.(3), MARTINEZ, F.(4) and RUTHERFORD, E.S.(2), – (1)Cooperative Institute for Limnology and Ecosystems Research, 4840 South State Road, Ann Arbor, MI, 48108, USA; (2)NOAA Great Lakes Environmental Research Laboratory, 4840 South State Road, Ann Arbor, MI, 48108, USA; (3)Great Lakes Sea Grant Network, 4840 South State Road, Ann Arbor, MI, 48108, USA; (4)NOAA NCCOS, 4840 South State Road, Ann Arbor, MI, 48108, USA.

The Laurentian Great Lakes is one of the most heavily invaded aquatic systems in the world with over 180 documented aquatic nonindigenous species, the peak invasion rate was estimated to be 2.8 species introduced per year (1990-1995). While invasion rates have slowed in recent years, prevention remains the best defense. The Great Lakes Aquatic Nonindigenous Species Information System (GLANSIS) currently serves information for 67 species which have been identified through the peer-reviewed scientific literature as having some likelihood of invading the Great Lakes. This poster was developed primarily as an outreach tool to help scientists and citizens know what to look for in monitoring for the presence of these species. Monitoring is essential both to determine the success of prevention programs and to support early detection – early enough to make true rapid response and eradication feasible. The poster links back to the GLANSIS database, which includes further information about the potential for introduction, establishment, and impact of these species as well as more detailed information on how to identify the species and information on management options in the event they are detected.

Aerial photo survey improves NOAA GLERL’s Lake Erie ice model

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Understanding the duration, extent, and movement of Great Lakes ice is important for the Great Lakes maritime industry, public safety, and the recreational economy. Lake Erie is ice-prone, with maximum cover surpassing 80% many winters.

Multiple times a day throughout winter, GLERL’s 3D ice model predicts ice thickness and concentration on the surface of Lake Erie. The output is available to the public, but the model is under development, meaning that modelers still have research to do to get it to better reflect reality.

As our scientists make adjustments to the model, they need to compare its output with actual conditions so they know that it’s getting more accurate. So, on January 13th of this year, they sent a plane with a photographer to fly the edge of the lake and take photos of the ice.

The map below shows the ice model output for that day, along with the plane’s flight path and the location of the 172 aerial photos that were captured.

NOAA GLERL Lake Erie ice model output with all aerial photo survey locations -- January 13, 2017. Credit NOAA GLERL/Kaye LaFond.

NOAA GLERL Lake Erie ice model output with all aerial photo survey locations — January 13, 2017. Map Credit NOAA GLERL/Kaye LaFond.

These photos provide a detailed look at the sometimes complex ice formations on the lake, and let our scientists know if there are places where the model is falling short.

Often, the model output can also be compared to images and surface temperature measurements taken from satellites. That information goes into the GLSEA product on our website (this is separate from the ice model). GLSEA is useful to check the ice model with. However, it’s important to get this extra information.

“These photographs not only enable us to visualize the ice field when satellite data is not available, but also allow us to recognize the spatial scale or limit below which the model has difficulty in simulating the ice structures.” says Eric Anderson, an oceanographer at GLERL and one of the modelers.

 “This is particularly evident near the Canadian coastline just east of the Detroit River mouth, where shoreline ice and detached ice floes just beyond the shoreline are not captured by the model. These floes are not only often at a smaller spatial scale than the model grid, but also the fine scale mechanical processes that affect ice concentration and thickness in this region are not accurately represented by the model physics.”

Click through the images below to see how select photos compared to the model output. To see all 172 photos, check out our album on Flickr. The photos were taken by Zachary Haslick of Aerial Associates.

This gallery contains 10 photos


Lake Erie Hypoxia Forecasting Project Kicks Off With Stakeholder Workshop

A collaborative research team, led by Drs. Craig Stow of the National Oceanic and Atmospheric Administration’s Great Lakes Environmental Research Laboratory (NOAA GLERL) and Mark Rowe of the University of Michigan’s Cooperative Institute for Limnology and Ecosystems Research (CILER),  will be holding a workshop with key stakeholders for guidance on how a forecast model could help meet the needs for information on low oxygen conditions—or hypoxia—in Lake Erie. The workshop, coming up later this spring, kicks off a 5-year project that brings together inter-agency and university scientists to produce a forecasting system that will predict the location and movement of hypoxic water in Lake Erie. The project will link a hypoxia model to NOAA’s Lake Erie Operational Forecasting System (LEOFS) hydrodynamic model, which provides daily nowcast and 5-10 day forecasts of temperature and currents in Lake Erie.


Hypoxia occurs in the central basin of Lake Erie in July through September of most years. Low-oxygen water is an unfavorable habitat for fish, and may kill benthic organisms that provide food for fish. It is less well known, however, that hypoxic water can also upset drinking water treatment processes. Upwelling or seiche events can bring hypoxic water to water intakes along the shoreline, causing rapid changes in dissolved oxygen and associated water quality variables such as temperature, pH, dissolved organic matter, iron, and manganese. To maintain the quality of treated water, plant managers must adjust treatment in response to these changes. Hypoxia forecasts will provide several days advance notice of changing source water quality so that drinking water plant managers can be prepared to adjust treatment processes as needed.

While the hypoxia forecasting project will help to minimize the negative impacts of hypoxia, a parallel effort is occurring to address the root cause of this problem involving nutrient loading. Universities, state, federal, and Canadian agencies are collaborating to satisfy the goals of the Great Lakes Water Quality Agreement by reducing nutrient loads to Lake Erie, a primary stressor driving hypoxic conditions.

The upcoming stakeholder workshop on hypoxia will bring the research team together with stakeholders consisting of municipal drinking water plant managers from U.S. and Canadian facilities on Lake Erie, as well as representatives of state and local agencies. The group will learn about hypoxia and its effects, hear about the goals of the LEOFS-Hypoxia project, and provide input to the research team on their information needs. As the first in a series of meetings of the project’s Management Transition Advisory Group, this workshop will help identify the most useful data types and delivery mechanisms, laying the groundwork for the research team to design a forecasting tool that specifically addresses the needs of public water systems on Lake Erie.

The workshop will be held at Cleveland Water in Cleveland, Ohio. Representatives from Ohio Environmental Protection Agency (EPA), Ohio Department of Natural Resources, Ohio Sea Grant, townships and other local governments were also invited to attend.  

The LEOFS-Hypoxia project is a collaboration with the City of Cleveland Division of Water, Purdue University, and U. S. Geological Survey, with guidance from a management advisory group including representatives from Ohio public water systems, Ohio EPA, Great Lakes Observing System (GLOS), and NOAA. The work is supported by a $1.4 million award from the NOAA National Centers for Coastal Ocean Science (NCCOS) Center for Sponsored Coastal Ocean Research by a grant to NOAA GLERL and University of Michigan (award NA16NOS4780209).

Getting to the root cause of the problem
As part of an initiative conducted under the auspices of the Great Lakes Water Quality Agreement, Annex 4, the following forums, led by Dr. Craig Stow at GLERL, will focus on the linkage of nutrient loading to water quality degradation problems, such as hypoxia and harmful algal blooms.

  • 4/5-6: Nutrient Load Workshop
  • 5/9-10: Annex 4 (nutrients) Subcommittee Meeting

Scientists attending these workshops will apply long term research results to estimate nutrient inputs to Great Lakes waters and evaluate how well we are doing in reaching phosphorus load reduction targets established under Annex 4 of the GLWQA.

Additional Resources
NOAA GLERL Hypoxia web page:

Download the NOAA GLERL hypoxia infographic, here:

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Arrival of wave GLIDER SV2 platforms to expand GLERL data collection capacity in the Great Lakes

Left and bottom right: OSAT staff learning the ropes on the Wave GLIDER SV2 during a three-day training in Kawaihae, Hawaii. Top right: CILER’s Russ Miller (left) and GLERL’s Kyle Beadle (right) work in GLERL’s laboratory to prepare the newly acquired Wave GLIDERS for deployment.

GLERL’s OSAT (Observing Systems and Advance Technology) team, in collaboration with the Michigan Technological University’s (MTU) Great Lakes Research Center, is preparing to deploy the Wave GLIDER SV2 to expand its monitoring capacity in the Great Lakes. The Wave GLIDER functions as an autonomous surface vehicle that uses wave energy propulsion and communicates via Iridium satellite, providing real-time data back to users. This wave powered vehicle can be fitted with numerous instruments to collect data on a variety of physical characteristics of the lakes, including: waves, CTD (conductivity, temperature, depth), and currents. These data can be used for remote sensing algorithm validation. With the instrumentation on board, the Wave GLIDER  can continuously run transects throughout much of the year in all Great Lakes weather conditions and can be piloted and monitored by researchers at GLERL.

The two Liquid Robotics-designed Wave Glider SV2 platforms, to be deployed in the upcoming field season ,were surplused to GLERL by NOAA’s National Data Buoy Center (NBDC) in FY 2016. To ensure safe and reliable operation of these persistent, autonomous data collection platforms, Steve Constant and Steve Ruberg participated in a three-day training in Kawaihae, Hawaii at the Liquid Robotics Training Center this past January. They were accompanied by colleagues Russ Miller (Cooperative Institute for Limnology and Ecosystems Research (CILER)), Jamey Anderson (MTU), and Chris Pinnow (MTU). The training focused on instrument assembly, care, programming, piloting, and deployment and retrieval of the newly acquired wave glider units.

The vehicles, as currently configured, will be used for real-time observations supporting commercial shipping and validation of operational forecasts and satellite remote sensing products. Future applications include mapping of hypoxic zones impacting drinking water and acoustic fisheries parameters in U.S. coastal and Great Lakes regions.

NOAA booth at annual American Meteorological Society meeting.

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GLERL researchers heading to AMS 2017

The American Meteorological Society’s Annual Meeting (AMS 2017) is upon us and researchers from GLERL and CILER (the Cooperative Institute for Limnology and Ecosystems Research), along with other partners, are hitting the grounds running on Monday with posters and presentations on climate, ice, HABs, modeling, forecasting, transitioning research to ops, and more!

Here’s a schedule of where you’ll find us throughout the week. (GLERL and CILER researchers highlighted in italics. Poster titles linked to .pdf of poster, if available.) And, don’t forget to swing by the NOAA booth (#405) to check out all of the fantastic work that NOAA scientists are doing around the world!

GLERL and CILER posters and presentations during AMS 2017

Monday, 23 January 2017

The Great Lakes Adaptation Data Suite: Providing a Coherent Collection of Climate Data for the Great Lakes Region
Type: Poster
Location: 4E (Washington State Convention Center), Poster #1
Authors: Omar C. Gates, University of Michigan, Ann Arbor, MI; and K. Channell, D. Brown, W. Baule, D. J. Schwab, C. Riseng, and A. Gronewold

Abstract: Climate change impacts are a growing concern for researchers and adaptation professionals throughout society. These individuals look to different data sources in order to contemplate the challenges that are present from climate impacts. The use of observational data helps to understand which climatic factors exploit vulnerabilities and to develop solutions to make systems more resilient. However, non-uniform data collection and processing often hinders the progress towards such a goal because many publicly-accessible data sets are not readily usable to address the concern of climate impacts on societies. In the Great Lakes region, there is the added challenge of data quality and coverage issues for over-lake versus over-land observations. The creation of the Great Lakes Adaptation Data Suite (GLADS) aims to resolve these dilemmas by providing processed over-land and over-lake observations within one suite for the Great Lakes region of North America, and this data suite is provided to individuals with a vested interest in decision-making for climate resilience. This intent serves as a way for the GLADS to engage with individuals, from various backgrounds, that are interested in incorporating climate information into their work. Feedback from this audience will be analyzed to further improve the GLADS for use in decision-making. Further analysis will look at the connections among potential users and how they perceive the GLADS as being a useful tool for their research. By gaining perspective into the individuals’ expectations of the tool and their understanding of climate information, the GLADS will be able to accommodate the necessary steps for integrating climate information into decision-making processes to mitigate climate impacts.

Tuesday, 24 January 2017

Coupling Effects Between Unstructured WAVEWATCH III and FVCOM in Shallow Water Regions of the Great Lakes
Type: Presentation
Time: 9:15 AM
Locations: Conference Center: Chelan 4 (Washington State Convention Center )
Authors: Jian Kuang, IMSG@NOAA/NWS/NCEP, College Park, MD; and A. J. Van der Westhuysen, E. J. Anderson, G. Mann, A. Fujisaki, and J. G. W. Kelley

Abstract: The modeling of waves in shallow environments is challenging because of irregular coastlines and bathymetry, as well as complicated meteorological forcing. In this paper, we aim to provide insight into the physics of storm surge-wave interaction within shallow water regions of the Great Lakes under strong wind events. Extensive hindcast analysis using the 3D-circulation model FVCOM v3.2.2 and the third generation spectral wave model WAVEWATCH III v4.18 was conducted on unstructured meshes for each of the Great Lakes. The circulation and wave models are coupled through a file-transfer method and tested with various coupling intervals. We conducted tests for five short-term (storm length) test cases and three long-term (seasonal) test cases. Time series, spatial plots and statistics are provided. Data exchange of radiation stress, water elevation and ocean currents were tested in both two-way and one-way coupling regimes in order to assess the influence of each variable. Three types of wave current parametrizations will be discussed (surface layer, depth-averaged, and hybrid). The meteorological input forcing fields are 1km/4km/12km WRF model results with time interval of 1h for 4km/12km resolution and 10min for 1km resolution. Statistical analysis was performed in order to evaluate the model sensitivity on the unstructured domain in terms of wind, physics packages and surge-wave coupling effects. These efforts are towards an assessment of the model configuration with a view toward future operational implementation.

Linking Hydrologic and Coastal Hydrodynamic Models in the Great Lakes
Type: Presentation
Time: 2:00pm
Location: Conference Center – Chelan 4 (Washington State Convention Center)
Authors: Eric J. Anderson, NOAA/ERL/GLERL, Ann Arbor, MI; and A. Gronewold, L. Pei, C. Xiao, L. E. Fitzpatrick, B. M. Lofgren, P. Y. Chu, T. Hunter, D. J. Gochis, K. Sampson, and A. Dugger

Abstract: As the next-generation hydrologic and hydrodynamic forecast models are developed, a strong emphasis is placed on model coupling and the expansion to ecological forecasting in coastal regions. The next-generation NOAA Great Lakes Operational Forecast System (GLOFS) is being developed using the Finite Volume Community Ocean Model (FVCOM) to provide forecast guidance for traditional requirements such as navigation, search and rescue, and spill response, as well as to provide a physical backbone for ecological forecasts of harmful algal blooms, hypoxia, and pathogens. However, to date operational coastal hydrodynamic models have minimal or no linkage to hydrologic inflows and forecast information. As the new National Water Model (NWM) is developed using the Weather Research and Forecasting Hydrologic model (WRF-Hydro) to produce forecast stream flows at nearly 2.7 million locations, important questions arise about model coupling between the NWM and coastal models (e.g. FVCOM), how this linkage will impact forecast guidance in systems such as GLOFS, and how WRF-Hydro stream flows compare to existing products. In this study, we investigate hindcasted WRF-Hydro stream flows for the Great Lakes as compared to existing legacy research models. These hydrological stream flows are then linked with the next-generation FVCOM models, where the impacts to hydrodynamic forecast guidance can be evaluated. This study is a first step in coupling the next-generation NWM with NOAA’s operational coastal hydrodynamic models.

Regional Hydrological Response from Statistically Downscaled Future Climate Projections in the 21st Century
Type: Poster
Location: 4E (Washington State Convention Center), Poster #462
Authors: Lisi Pei, NOAA, Ann Arbor, MI; and A. Gronewold, T. Hunter, and R. Bolinger

Abstract: Understanding how future climate change signals propagate into hydrological response is critical for water supply forecasting and water resources management. To demonstrate how this understanding can be improved at regional scales, we studied the hydrological response of the Laurentian Great Lakes under future climate change scenarios in the 21st century using a conventional regional hydrological modeling system (the Great Lakes Advanced Hydrologic Prediction System, or GL-AHPS) forced by statistically downscaled CMIP5 (Coupled Model Intercomparison Project Phase 5) future projections. The Great Lakes serve as a unique case study because they constitute the largest bodies of fresh surface water on Earth, and because their basin is bisected by the international border between the United States and Canada, a feature that complicates water level and runoff modeling and forecasting. The GL-AHPS framework is specifically designed to address these unique challenges. Existing model validation results indicate that the GL-AHPS model framework provides reasonable simulation of historical seasonal water supplies, but has significant deficiencies on longer time scales. A major component of this study, therefore, includes reformulating key algorithms within the GL-AHPS system (including those governing evapotranspiration), and assessing the benefits of those improvements.

Reconstructing Evaporation over Lake Erie during the Historic November 2014 Lake Effect Snow Event
Type: Poster
Location: 4E (Washington State Convention Center), Poster #898
Authors: Lindsay E. Fitzpatrick, CILER, Ann Arbor, MI; and A. Manome, A. Gronewold, E. J. Anderson, C. Spence, J. Chen, C. Shao, D. M. Wright, B. M. Lofgren, C. Xiao, D. J. Posselt, and D. J. Schwab

Abstract: The extreme North American winter storm of November 2014 triggered a record lake effect snowfall event in southwest New York, which resulted in 14 fatalities, stranded motorists, and caused power outages. While the large-scale atmospheric conditions of the descending polar vortex are believed to be responsible for the significant lake effect snowfall over the region, to-date there has not yet been an assessment of how state-of-the-art numerical models performed in simulating evaporation from Lake Erie, which is tied to the accuracy in forecasting lake effect snow.

This study examined the evaporation from Lake Erie during the record lake effect snowfall event, November 17th-20th, 2014, by reconstructing heat fluxes and evaporation rates over Lake Erie using the unstructured grid, Finite-Volume Community Ocean Model (FVCOM). Nine different model runs were conducted using combinations of three different flux algorithms: the Met Flux Algorithm (COARE), a method routinely used at NOAA’s Great Lakes Environmental Research Laboratory (SOLAR), and the Los Alamos Sea Ice Model (CICE); and three different meteorological forcings: the Climate Forecast System version 2 Operational Analysis (CFSv2), Interpolated observations (Interp), and the High Resolution Rapid Refresh (HRRR). A few non-FVCOM model outputs were also included in the evaporation analysis from an atmospheric reanalysis (CFSv2) and the large lake thermodynamic model (LLTM). Model-simulated water temperature and meteorological forcing data (wind direction and air temperature) were validated with buoy data at three locations in Lake Erie. The simulated sensible and latent heat fluxes were validated with the eddy covariance measurements at two offshore sites; Long Point Lighthouse in north central Lake Erie and Toledo water crib intake in western Lake Erie. The evaluation showed a significant increase in heat fluxes over three days, with the peak on the 18th of November. Snow water equivalent data from the National Snow Analyses at the National Operational Hydrologic Remote Sensing Center showed a spike in water content on the 20th of November, two days after the peak heat fluxes. The ensemble runs presented a variation in spatial pattern of evaporation, lake-wide average evaporation, and resulting cooling of the lake. Overall, the evaporation tended to be larger in deep water than shallow water near the shore. The lake-wide average evaporations from CFSv2 and LLTM are significantly smaller than those from FVCOM. The variation among the nine FVCOM runs resulted in the 3D mean water temperature cooling in a range from 3 degrees C to 5 degrees C (6-10 EJ loss in heat content), implication for impacts on preconditioning for the upcoming ice season.

Projecting Water Levels of the Laurentian Great Lakes in the 21st Century from a Dynamical Downscaling Perspective
Type: Presentation
Time: 11:15 AM
Locations: 602 (Washington State Convention Center)
Authors: Chuliang Xiao, University of Michigan, CILER, Ann Arbor, MI; and B. M. Lofgren, J. Wang, P. Y. Chu, and A. Gronewold

Abstract: As the largest group of fresh surface water bodies on earth, the Laurentian Great Lakes have a significant influence on regional climate. Due to the limited spatial resolution of general circulation models (GCMs), the Great Lakes are generally ignored in GCMs. Thus, the technique of dynamical downscaling serves as a practical and important, but challenging solution to the problem of understanding climate impacts and hydrological response in this unique region. Here, we employed the Weather Research and Forecasting model (WRF) with an updated lake scheme to downscale from a GCM with two future greenhouse gas concentration scenarios in the 21st century. Historical validation shows that the WRF-Lake model, with a fine horizontal resolution and a 1-dimensional lake representation, improves the hydroclimatology simulation in terms of seasonal cycles of lake surface temperature, precipitation, and ice coverage. Based on the downscaling results, a hydrologic routing model is performed to project the Great Lakes’ water level changes in 21st century using net basin supply (NBS, calculated as the sum of over-lake precipitation, basin-wide runoff, and lake evaporation) as an input. As the lakes warm and lake ice diminishes, water levels are projected to have persistent and enhanced interannual variations in the presumed climate change. These changes have a range of potential socioeconomic impacts in the Great Lakes region, including changes in hydropower capacity, the length of the commercial shipping season, and the design life of coastal residences and infrastructure.

Wednesday, 25 January 2017

Simulating and Forecasting Seasonal Ice Cover
Type: Poster, #1147
Authors: Xiaolong Ji, University of Michigan, Ann Arbor, MI; and H. Daher, R. Bolinger, A. Gronewold, and R. B. Rood

Abstract: Over the past several decades, dramatic changes in the spatial extent of seasonal and long-term ice cover have been documented for both marine and continential (inland) water bodies. Successfully projecting (and planning for) future changes in global ice cover requires an understanding of the drivers behing these historical changes. Here, we explore relationships between continental climate patterns and regional ice cover across the vast surface waters of the Laurentian Great Lakes. The Great Lakes constitute the largest collective surface of freshwater on Earth, and seasonal variability in ice cover is closely linked with lake heat content, energy fluxes, and water levels (all of which have strong linkages with ecological and socioeconomic stability in the region). Our findings indicate that abrupt historical changes in Great Lakes seasonal ice cover are coincident with historical changes in teleconnections, including both the El Nino Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). We find, in particular, that these teleconnections explain much of the ice cover decline in the late 1990s (coincident with the strong 1997-1998 winter El Nino) and the following persistent period of below-average period of ice that followed. We encode these relationships in a probabilistic model that provides seasonal projections of ice cover areal extent across the Great Lakes, as well as specific spatiotemporal patterns in ice cover at resolutions that align with critical regional human health and safety-related management decisions.

What Does It Take to Transition Six Forecasting Systems into Operations in Ten Years? — Lessons Learned, Myths and Facts
Type: Presentation
Time: 11:15 AM
Location: 608 (Washington State Convention Center)
Authors: Philip Y. Chu, GLERL, Ann Arbor, MI; and E. J. Anderson, G. Lang, J. G. W. Kelley, E. Myers, A. Zhang, J. Xu, and Y. Chen

Abstract: NOAA Great Lakes Operational Forecasting System (GLOFS), developed by the Great Lakes Environmental Research Laboratory and National Ocean Service, has been operational since 2005. A project to upgrade GLOFS, using FVCOM as the core 3-D oceanographic forecast model, has been conducted during the past 3 years involving GLERL, NOS/CSDL and CO-OPS and NCEP Central Operations. The 1st phase of this project has been completed with the operational implementation of a new GLOFS version for Lake Erie on NOAA’s Weather and Climate Operational Supercomputer System in May 2016.

Many lessons were learned from transitioning six forecasting systems to operations in 10 years. On the technical aspects which include hardware, software, systems — we found that keys to successful transition are on 1) methods to harden the software infrastructure to make a robust, automated system; 2) backup and alternative procedures for handling missing or corrupted input data; 3) standardized validation and skill assessment metrics; 4) preparation of complete documentation including validation test reports, standard operating procedures (SOP), and software user guides; 5) adequate near-real-time observations of discharge, and water levels to provide LBCs for the system and 6) field projects in the Great Lakes (i.e. IFYGL) to provide surface and subsurface data for the evaluation of the forecast models during development and testing. In particular, program source codes need to be frozen during the testing, validation and the transition period with proper version control.

In addition to the technical aspects, a successful system transition from the research/development stage into operations also involves non-technical aspects, such as commitment from senior leadership, frequent communications among all involved parties on progress and milestones, training sessions for the system operators and user engagement workshops for the end users.

Applying WRF-Hydro in the Great Lakes Basin: Offline Simulations in the Seasonal Hydrological Responses
Type: Presentation
Time: 4:45 PM
Location: Conference Center – Chelan 2 (Washington State Convention Center )
Authors: Lisi Pei, NOAA, Ann Arbor, MI; and A. Gronewold, D. J. Gochis, K. Sampson, A. Dugger, C. Xiao, L. Mason, B. M. Lofgren, and P. Y. Chu

Abstract: As a unified atmosphere-land hydrological modeling system, the WRF-Hydro (Weather Research and Forecasting model Hydrological modeling extension package) framework is being employed by the NOAA-National Water Center (NWC, Tuscaloosa, AL) to provide streamflow forecasting over the entire CONUS in 250 m resolution from hourly to monthly scale. Currently, efforts are focused on tests and an operational forecast launch on August 16th, 2016. But due to inconsistencies in the land surface hydrographic datasets between U.S. and Canada over the Great Lakes Basin, many of the tributaries feeding the Great Lakes and the major channels connecting the Great Lakes (including the Niagara, St. Clair, and Detroit Rivers) are missing or poorly represented in the current NWC streamflow forecasting domain. Improvements in the model’s current representation of lake physics and stream routing are also critical for WRF-Hydro to adequately simulate the Great Lakes water budget and Great Lakes coastal water levels. To customize WRF-Hydro to the Laurentian Great Lakes Basin using protocols consistent with those used for the current CONUS operational domain, the NOAA-Great Lakes Environmental Research Laboratory has partnered with the National Center for Atmospheric Research (NCAR) and other agencies to develop land surface hydrographic datasets and compatible stream routing grids that connect to the current CONUS operational domain. This research group is also conducting 1-km resolution offline tests with WRF-Hydro based on current best available bi-national land surface geographic datasets to examine the model’s ability to simulate seasonal hydrological response over the Great Lakes (runoff and land-atmosphere fluxes) with its coupled overland flow terrain-routing module, subsurface lateral flow module and channel flow (runoff) module.

Thursday, 26 January 2017

Using the Next-Generation Great Lakes Operational Forecasting System (GLOFS) to Predict Harmful Algal Bloom (HAB) Transport with the HAB Tracker
Type: Presentation
Time: 3:30 PM
Location: 611 (Washington State Convention Center)
Authors: Eric J. Anderson, NOAA/ERL/GLERL, Ann Arbor, MI; and M. Rowe, J. Xu, A. Zhang, G. Lang, J. G. W. Kelley, and R. Stumpf

Abstract: Harmful algal blooms (HAB) plague coastal environments around the world, and particularly in the United States in areas such as the Great Lakes, Florida, Washington, and Maine. In the Great Lakes, shallow embayments such as the western basin of Lake Erie have experienced a period of increasing HAB intensity in recent years, including an event in 2014 where high toxicity levels resulted in a drinking water restriction to nearly 400,000 residents. In order to help decision makers and the public respond to these events, an experimental model has been developed short-term forecasts of HAB concentration and transport. The HAB Tracker uses the next-generation NOAA Lake Erie Operational Forecast System (LEOFS), which is based on the Finite Volume Community Ocean Model (FVCOM). The new FVCOM-based LEOFS model produces hydrodynamic forecast guidance out to 5 days using meteorology from the 3-km HRRR and 2.5 km NDFD. An experimental version of this model also extends the forecast horizon out to 10 days using forecasted meteorology from the GFS. Hourly hydrodynamic conditions (currents, diffusivity, water temperature) are supplied to a three-dimensional Lagrangian particle trajectory model that has been developed to predict HAB transport and vertical migration through the water column. Initial conditions are provided by satellite remote sensing of surface chlorophyll concentration, when available, in which previous nowcasts are used to fill gaps in satellite-derived HAB extent and extend surface concentrations into the water column to produce a three-dimensional field of HAB concentration. In-situ observations of microcystis concentration provide a calibration of particle buoyancy (i.e. colony migration) and a basis for model validation. Results show the three-dimensional HAB Tracker has improved forecast skill out to 10 days over two-dimensional surface concentration forecast products and is better than a persistence forecast out to 5 days.