NOAA Great Lakes Environmental Research Laboratory

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

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Ice cover on the Great Lakes

The USCGC Mackinaw arrives in Duluth via Lake Superior. March 24, 2014

U.S. Coast Guard Cutter Mackinaw is an icebreaking vessel on the Great Lakes that assists in keeping channels and harbors open to navigation. Here, the USCGC Mackinaw arrives in Duluth via Lake Superior on March 24, 2014. Credit: NOAA
Ice formation on the Great Lakes is a clear sign of winter!

Looking back in time, the lakes were formed over several thousands of years as mile-thick layers of glacial ice advanced and retreated, scouring and sculpting the basin. The shape and drainage patterns of the basin were constantly changing from the ebb and flow of glacial meltwater and the rebound of the underlying land as the massive ice sheets retreated.

The amount and duration of ice cover varies widely from year to year. As part of our research, GLERL scientists are observing longterm changes in ice cover as a result of global warming. Studying, monitoring, and predicting ice coverage on the Great Lakes plays an important role in determining climate patterns, lake water levels, water movement patterns, water temperature, and spring algal blooms.

Doing research to improve forecasts is important for a variety of reasons.

Ice provides us a connection to the past and also serves as a measure of the harshness of current day winter weather. Understanding the major effect of ice on the Great Lakes is very important because ice cover impacts a range of benefits provided by the lakes—from hydropower generation to commercial shipping to the fishing industry. The ability to forecast and predict ice cover is also really important for recreational safety and rescue efforts, as well as for navigation, weather forecasting, adapting to lake level changes, and all sorts of ecosystem research. One great example of the importance of forecasting is illustrated by an incident that occurred in Lake Erie on a warm sunny day in February 2009 when a large ice floe broke away from the shoreline. The floating ice block stranded 134 anglers about 1,000 yards offshore and also resulted in the death of one man who fell into the water. While the ice on the western sections of the lake was nearly 2 feet thick, rising temperatures caused the ice to break up, and southerly wind gusts of 35 mph pushed the ice off shore. Having the ability to forecast how much ice cover there will be, where it may move, and what other factors like temperature, waves, or wind might play a role in what the ice is going to do, is incredibly important to a lot of users.

— GLERL’s 2017 Seasonal Ice Cover Projection for the Great Lakes —

GLERL’s ice climatologist, Jia Wang, along with partners from the Cooperative Institute for Limnology and Ecosystems Research, use two different methods to predict seasonal ice cover for the Great Lakes. One, a statistical regression model, uses mathematical relationships developed from historical observations to predict seasonal ice cover maximum based on the status of several global air masses that influence basin weather. This method forecasts that the maximum ice cover extent over the entire Great Lakes basin, will be 64%. The other forecast method, a 3-dimensional mechanistic model, is based on the laws of physics that govern atmospheric and hydrodynamic (how water moves) processes to predict ice growth in response to forecast weather conditions. This method predicts a maximum ice cover of 44% for the basin this year.

As you can see, the two methods have produced different answers. However, if you look at the last chart here, you’ll see that three of the lakes show good agreement between these two model types–Lakes Michigan, Erie, and Ontario. Continued research, along with the historical data we’ve been monitoring and documenting for over 40 years, will help GLERL scientists improve ice forecasts and, ultimately, improve our ability to adapt and remain resilient through change.

More information!

Below, is the most recent Great Lakes Surface Environmental Analysis (GLSEA) analysis of the Great Lakes Total Ice Cover. GLSEA is a digital map of the Great Lakes surface water temperature (see color bar on left) and ice cover (see grayscale bar on right), which is produced daily at GLERL by Great Lakes CoastWatch. It combines lake surface temperatures that are developed from satellite images and ice cover information provided by the National Ice Center (NIC). This image is the analysis of January 10, 2017 (13%). For the most current analysis, visit https://coastwatch.glerl.noaa.gov/glsea/cur/glsea_cur.png.

GLSEA total ice cover analysis for January 10, 2017

For technical information on GLERL’s ice forecasting program, check out our website here. 

You can also find much of the information in this post, and more, on this downloadable .pdf of the GLERL fact sheet on Great Lakes ice cover.

Want to see a really cool graphic showing the extent of the maximum ice cover on the Great Lakes for each year since 1973? You’ll find that here.

Great Lakes ice cover facts since 1973

94.7% ice coverage in 1979 is the maximum on record.

9.5% ice coverage in 2002 is the lowest on record.

11.5% ice coverage in 1998, a strong El Niño year.

The extreme ice cover in 2014 (92.5%) and 2015 (88.8%) were the first consecutive high ice cover years since the late 1970’s.

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On March 6, 2014, Great Lakes ice cover was 92.5%, putting winter 2014 into 2nd place in the record books for maximum ice cover. Satellite photo credit: NOAA Great Lakes CoastWatch and NASA.


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Vertical Water Temperature in Southern Lake Michigan

Since 1990, GLERL scientists have been measuring temperature in the middle of southern Lake Michigan (at approximately 42.68, -87.07). They’ve been using a vertical chain of instruments that measure temperature from top to bottom. This is one of the longest vertical temperature records in existence anywhere in the Great Lakes, and it reveals some interesting patterns about lake temperature and the seasons. We’ve created a static infographic as well as an interactive chart that allows you to zoom in on the data and get individual measurement values.

Below, check out our infographic explaining seasonal temperature profiles in Lake Michigan.

Click here to interactively explore Lake Michigan temperature data.

Click to see an infographic explaining Lake Michigan temperature data.

Lake Michigan temperature data infographic.

Hydrilla verticillata. Common Name: Hydrilla.


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Collaborative team identifies 16 high-risk Great Lakes invaders

NOAA’s Great Lakes Environmental Research Laboratory (GLERL) recently published a very detailed NOAA Technical Memorandum (GLERL-169), which identifies the potential for introduction (getting in), establishment (living and reproducing), and impact (changing the ecosystem in one way or another) of 67 species that were previously identified through peer-reviewed research as being highly likely to invade the Great Lakes basin. The study also identifies a subset of 16 species (5 plants, 6 fishes, 4 invertebrates), which should be considered the highest overall risk to the Great Lakes region (see photo gallery below).

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The tech memo—titled “A Risk Assessment of Potential Great Lakes Aquatic Invaders“— is the result of a large collaborative effort between partners all throughout the Great Lakes region. The paper was authored by Abigail Fusaro (Wayne State University), Rochelle Sturtevant (NOAA’s Great Lakes Sea Grant Network liaison with GLERL), Ed Rutherford (NOAA GLERL) and others—including 5 student co-authors and more than 30 students who contributed to the literature review, assessment of individual species, and editing of the final report.

A little history on this project

NOAA GLERL—in cooperation with United States Geological Survey (USGS)—has been tracking nonindigenous aquatic species (species that enter a body of water that is outside of the historical range, in other words, they’ve never lived there before) in the Great Lakes system and serving that information through the GLANSIS database  since 2003. Information in the GLANSIS database includes an overview of the species life history, ecology, and invasion history as well as maps of current distribution, comprehensive impact assessments and overviews of management options—all very useful and important information for tracking invaders. An enhancement to the database in 2011* gave researchers the ability to add information on species that pose a risk of invasion, but are not yet established in the Great Lakes. The addition of these assessments, which were previously published in peer-reviewed scientific literature, helps to identify the species that pose the highest overall risk (introduction + establishment + impact). This information is key in that it allows scientists and environmental managers to better monitor for invasions and make decisions about management options in a rapid response situation.

How this is unique

The risk assessment tools developed for GLANSIS apply a consistent approach across all taxonomic groups and vectors, and allow researchers to compare the potential impact of high-risk species with the realized impact of nonindigenous species that are already established.  The tech memo serves as documentation of these tools and approaches as well as examines cross-taxa patterns in risk.  An analysis of the risk assessment method itself and its results will appear in an upcoming issue of Management of Biological Invasions.

For more information on GLANSIS, please contact Rochelle Sturtevant, rochelle.sturtevant@noaa.gov, 734-741-2287.

*This was made possible with funding from the Great Lakes Restoration Initiative.

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Working to improve Great Lakes modeling

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The new two-way coupled model is driven by heat budget estimates (how much energy enters the system); that affects the water budget and how much energy is exchanged between a lake and the atmosphere along with large lake processes that are dynamic and seasonally variable.

The Great Lakes are more like inland seas. From the cold depths of Lake Superior fisheries to the shallow algae blooms of Lake Erie, the bodies of water differ greatly from one another. Yet they are all part of one climate system.

Up until now, atmospheric models and hydrodynamic models have remained separate to a large extent in the region, with only a few attempts to loosely couple them. In a new study, published this week in the Journal of Climate, an integrated model brings together climate and water models.

The collaborative work brought together researchers from Michigan Technological University, Loyola Marymount University, LimnoTech as well as GLERL scientist, Philip Chu. Pengfei Xue, an assistant professor of civil and environmental engineering at Michigan Tech, led the study through his work at the Great Lakes Research Center on campus.

“One of the important concepts in climate change, in addition to knowing the warming trend, is understanding that extreme events become more severe,” Xue says. “That is both a challenge and an important focus in regional climate modeling.”

To help understand climate change and other environmental issues, Xue and his team connected the dots between the air and water of the Great Lakes. The new model will be useful for climate predictions, habitat modeling for invasive species, oil spill mitigation and other environmental research.

To read more about this research, please visit a full version of this Michigan Tech news article, posted by Allison Mills at: http://www.mtu.edu/news/stories/2016/november/weather-storm-improving-great-lakes-modeling.html

 

A water sample taken in the presence of a beautiful sunset!


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Scientists Work Around the Clock During Seasonal Lake Michigan Cruise

Last month, scientists from GLERL, the Cooperative Institute for Limnology and Ecosystems Research (CILER), and other university partners took the research vessel Laurentian for a multi-day cruise on Lake Michigan as part of seasonal sampling to assess the spatial organization of the lower food web—spatial organization simply means the vertical and horizontal location where organisms hang out at different times of day, and the lower food web refers to small organisms at the bottom of the food chain.

The research goes on around the clock. Scientists work in shifts, taking turns sleeping and sampling. The Laurentian spends a full 24 hours at each monitoring station, sampling vertical slices of the water column. Sampling at these same stations has been going on since 2010, providing a long-term dataset that is essential for studying the impact of things like climate change and the establishment of invasive species.

Sampling focuses on planktonic (floating) organisms such as bacteria, phytoplankton (tiny plants), zooplankton (tiny animals), and larval fishes which feed on zooplankton. Many of the zooplankton migrate down into deep, dark, cold layers of the water column during the day to escape predators such as fish and other zooplankton. They return unseen to warm surface waters at night to feed on abundant phytoplankton. Knowing where everything is and who eats whom is important for understanding the system.

Our researchers use different sampling tools to study life at different scales. For example, our MOCNESS (Multiple Opening Closing Net Environmental Sampling System) is pretty good at catching larger organisms like larval fish, Mysis (opossum shrimp), and the like. The MOCNESS has a strobe flash system that stuns the organisms, making it easier to bring them into its multiple nets.

The PSS (Plankton Survey System) is a submersible V-Fin (vehicle for instrumentation) that is dragged behind the boat and measures zooplankton, chlorophyll (a measure of phytoplankton), dissolved oxygen, temperature, and light levels. Measurements are made at a very high spatial resolution from the top to the bottom of the water. At the same time fishery acoustics show where the fish are. Together, these two techniques allow us to see where much of the food web is located.

Water samples are taken at various depths and analyzed right on the boat. This is a good way to study microbes such as bacteria and very small phytoplankton. The lower food web has been pretty heavily altered by the grazing of quagga and zebra mussels. Specifically, the microbial food web (consisting of microbes such as bacteria and very small phytoplankton) makes up a larger component of the food web than before mussel invasion, and scientists are working to find out exactly how this has happened.

Check out the photos below for a glimpse of life in the field!

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Central Michigan University students Anthony and Allie are all smiles as they prepare to head out!

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Getting the MOCNESS ready.

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Chief scientist Hank Vanderploeg looks at some data.

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Filtering a water sample—filtering out the big stuff makes it easier to see microbes.

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Paul prepares the fluoroprobe.

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Taking a water sample in the presence of a beautiful sunset!

Example of the data collected by a hyperspectral sensor as it flew over a section of the Western Basin of Lake Erie, on September 19, 2016


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Using Airplanes for Algal Bloom Prediction in Lake Erie

How can airplanes help predict harmful algal blooms (HABs)?

For several years the National Oceanic and Atmospheric Administration (NOAA) has been using satellites to guide HAB forecasts. But, satellites have their limitations. For example, the Great Lakes region can be cloudy and satellite “cameras” can’t see through clouds. In western Lake Erie there are typically only about 20-30 usable cloud-free images during the HAB season, which limits our ability to make bloom predictions. Another challenge with satellites is that the resolution of images makes it difficult for scientists to “see” differences in the types of algae floating on the Lake Erie surface. After a big rainstorm, for instance, it is difficult to distinguish between muddy water flowing in from the Maumee River and algae that is already in the western basin.

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The resolution of satellite images makes it difficult to distinguish the types of algae floating on the surface of the water. We can detect different algae in the lake because each algae group (shown above) releases a different color pigment that we can ‘see’/ measure from the hyperspectral sensor.

To improve HABs forecasts, during the past two summers,  GLERL has been partnering with the Cooperative Institute for Limnology and Ecosystems Research (CILER) and Skypics to use a special hyperspectral sensor on an airplane-mounted camera. This weekly airborne campaign is coordinated with the weekly Lake Erie monitoring program. The monitoring program collects samples at multiple stations around western Lake Erie and the hyperspectral sensor captures images from those sampling stations on the same day. Comparing the field collected samples with what the sensor “sees” helps us to understand how well the sensor is working for HAB detection. Additionally, we coordinate with researchers at NASA’s Cleveland office, who are also flying their own airborne imaging sensor, to cross check our results with theirs for even more robust hyperspectral data validation and quality control.


Check out this short video clip of a HAB, taken by pilot, Zach Haslick, from Skypics, as seen from the window of his airplane, while flying the hyperspectral sensor over an area of Lake Erie.

Like satellites, hyperspectral sensors collect information on HAB location and size, but since our weekly hyperspectral flyovers are done below the clouds, the images are much higher resolution compared to satellites. Because of this, the hyperspectral sensors provide more accurate and detailed information on bloom concentration, extent, and even the types of algae present in the lake.

Hyperspectral sensors measure wavelengths, or color bands, released from chlorophyll color pigments in the HAB to detect color pigments that represent different types of algal groups. The process is similar to how the human eye detects wavelengths to create images but the hyperspectral sensor detects bands of wavelengths, or colors, at greater frequencies than what the human eye, or even satellites, can detect. The pigment detection information helps us determine what type of algae is present within blooms and whether or not toxins are present. In the long run, this will help us develop even more accurate HAB forecasts.

Success! This year the hyperspectral sensor detected a bloom that was not detected by a satellite!

On September 19, the hyperspectral flyover captured a HAB scum near a drinking water intake in Lake Erie that wasn’t visible from the satellite. Using the hyperspectral images, along with our HAB Tracker forecast tool to assess the potential of the scum to mix down into the lake (see images below), we were able to provide the drinking water intake manager with an early warning of a potential HAB moving near the intake.

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Hyperspectral sensing imagers offer drinking water intake managers a key resource for identifying the type and location of algal blooms near water intake systems, as was demonstrated on September 19. Now that the field season is over we have begun pouring over our data and will incorporate what we learned to improve our HAB Tracker forecast tool and, ultimately, provide better information to decision makers.

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GLERL scientists are also teaming up with other partners to test a variety of ways in which hyperspectral sensors can be useful in detecting HABs. In addition to the manned airplane studies, recently, along with a team from NASA Glenn Research Center and Sinclair Community College, researchers flew a UAS (Unmanned Aircraft System) with a hyperspectral sensor over the lower Maumee River/Maumee Bay area in Lake Erie (see the photo gallery above). Concurrently, researchers from the University of Toledo collected water samples for comparison. Not only useful for tracking HABs, this also demonstrates the successful use of a UAS for other types of environmental monitoring.

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Retrieval of new data from instruments in Manistique River will inform research and decision making

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During recent fieldwork, Dr. Philip Chu, scientist at NOAA’s Great Lakes Environmental Research Laboratory (GLERL) and Professor Chin Wu, from the University of Wisconsin Madison, retrieved six water level sensors and one Acoustic Doppler Current Profiler (ADCP) from the Manistique River—a 71.2 mile long river in the Upper Peninsula of Michigan that drains into Lake Michigan.

An ADCP measures water currents with sound by using the Doppler effect— sound wave has a higher frequency, or pitch, when it moves toward you than it does when it moves away. Think of the Doppler effect in action the next time you hear a speeding train pass you by. As the train moves toward you, the pitch of its whistle will be higher. As it moves away, it will be lower. The same effect happens as sound moves through water. The ADCP emits pulses of sounds that bounce off of particles moving through the water. Particles that are moving toward the sensor will produce a higher frequency than those moving away from the sensor. This effect allows the profiler to record data about sediment transport in the river.

After quality control and assurance procedures back in the lab, currents and water level data collected during this deployment, scientists will use the information to research the impacts of meteotsunamis, seiches, and flooding events on sediment transport through the river. The outcomes of this research will then will be used by organizations, such as the U.S. Army Corps of Engineers, for dredging operations on the river with the ultimate goal of improving water quality. (See the Great Lakes Water Quality Agreement for more on why the Manistique River is considered an “Area of Concern.”)

In addition, researchers will use this valuable field data while validating the NOAA next generation Lake Michigan-Huron Operational Forecasting System, one of the forecast systems within the Great Lakes Operational Forecasting System, or GLOFS. GLOFS is a prediction system that provides timely information to lake carriers, mariners, port and beach managers, emergency response teams, and recreational boaters, surfers, and anglers through both nowcast and forecast guidance.

Nowcast vs. Forecast: What’s the difference?

A nowcast is a description of the present lake conditions based on model simulations using observed meteorology. Nowcasts are generated every 6 hours and you can step backward in hourly increments to view conditions over the previous 48 hours, or view animations over this time period.

A forecast is a prediction of what will happen in the future. Our models use current lake conditions and predicted weather patterns to forecast the lake conditions for up to 5 days in the future. These forecasts are run twice daily, and you can step through these predictions in hourly increments, or view animations over this time period.

Professor Wu, along with Dr. Eric Anderson from GLERL, deployed these sensors earlier this summer. As with the majority of GLERL’s projects, this is a collaborative effort. Through the Cooperative Institute for Limnology and Ecosystems Research (CILER), this work is supported by NOAA National Marine and Fishery Service and funded by EPA Great Lakes Restoration Initiative. The University of Wisconsin is one of ten CILER Consortium partners.