NOAA Great Lakes Environmental Research Laboratory

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


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Looking back: The ups and downs of Great Lakes ice cover in 2021

Ice formations cover a pier on the Lake Michigan shoreline in Holland, MI. February 27, 2021. Credit: Clarice Farina.

It’s no secret that the Great Lakes had a wild ride in terms of ice cover this past winter. From a slow start that led to near-record low ice cover in January, to the sudden widespread freeze just a few weeks later, here’s a look back at how ice cover on the lakes has fluctuated during the 2020-2021 ice season.

As we highlighted in our last blog post on historic ice data, January 2021 had the second-lowest overall Great Lakes ice cover on record since 1973 (with the very lowest being January 2002). For all five individual lakes, January 2021 was in the top five lowest ice-cover Januarys since 1973.

This graph shows average Great Lakes ice cover for the month of January every year from 1973 to 2021, organized by lowest ice cover (far left) to highest ice cover (far right). Credit: NOAA GLERL.

Starting out at 10.65% on February 1st, ice cover rose dramatically over the next three weeks with the region’s extreme cold weather. Growing quickly and steadily, total Great Lakes ice cover finally topped out at 45.84% on February 19th. But with air temperatures warming back up shortly afterwards, this spike was short-lived. Within a week it was back down to around 20% and continued to taper off, falling below 1% on April 3rd and reaching 0.1% on April 20.

This graph shows Great Lakes ice cover in 2021 (black line) compared to the historical average ice cover from 1973-2020 (red line). Credit: NOAA GLERL.

This Winter vs. The Long-Term Average

While all five lakes were far below their January average, each one did something a little different during February, when compared to its 1973-2020 average. The following graphs show this winter’s ice cover (black line) vs. the 1973-2020 average (red line) for each lake.⁣

Lake Erie ice cover jumped dramatically up to 81% in the second week of February, well above its average seasonal peak of around 65%. It stayed above 75% for about two weeks until falling back down below its average at the beginning of March.


Lake Michigan ice cover increased steadily throughout February, with its highest percentage being 33% on February 18th — only briefly staying above its average for that time period. It dropped off quickly the following week, then decreased gradually throughout March.

Lake Superior spent about a week in mid-February above its average ice cover for those days, peaking at about 51% on February 19th. Similar to Lake Michigan, it only stayed above its average for a short interval before rapidly falling back down under 20%.

Lake Ontario ice cover took a while to ramp up, staying below 10% until mid-February. It reached maximum ice cover on February 18th, topping out at about 21% – slightly higher than its average for that day.


Lake Huron was the only lake that did not reach above-average ice cover for the entire winter. Its peak ice cover was 48% on February 20th, which was about the same as its average for that time of year.

Melting into Spring

Throughout March, ice cover on all five lakes continued to decrease steadily, with the exception of a spike in ice cover around the second week of the month likely due to fluctuations in air temperature. For Lakes Erie and Ontario, this short-lived jump was enough to get them back up near their average early March ice cover for a few days. 

As for the timing of each lake’s peak 2021 ice cover compared with the average, Lakes Erie, Michigan, Huron, and Ontario all peaked later than their average, while Lake Superior is the only one that peaked earlier than its average.

Ice covers the Lake Huron shoreline in Oscoda, MI on February 15, 2021. Credit: G. Farina, NOAA GLERL.

This winter’s maximum seasonal ice cover of 45.8% is just 7.5% less than the long-term average of 53.3%. While it’s below the average, it’s still more than double the 2020 seasonal maximum of 19.5% ice cover, but is just over half the 2019 seasonal maximum of 80.9%. With so much year-to-year variability, forecasting ice cover each year can be incredibly difficult. NOAA GLERL’s experimental ice forecast, updated in mid-February, predicted Great Lakes ice cover in 2021 to peak at 38% – not too far off from what it really was. NOAA GLERL continues to analyze both current and historical data to refine the ice forecast model, working to actively improve our experimental Great Lakes ice forecast each year.

This graph shows annual maximum ice cover on the Great Lakes each year from 1973 to 2021. Credit: NOAA GLERL.

For more on NOAA GLERL’s Great Lakes ice cover research and forecasting, visit our ice homepage here: https://go.usa.gov/xsRnM⁣

⁣Plus, access these graphs plus more Great Lakes CoastWatch graphs & data here: https://go.usa.gov/xsRnt⁣

Flat, jagged pieces of ice float in Lake Huron near Oscoda, MI on February 15, 2021. Credit: G. Farina, NOAA GLERL.


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Five decades of Great Lakes ice cover data – and where to find it

Understanding the major effects of ice on the Great Lakes is crucial. Ice cover impacts a range of societal benefits provided by the lakes, from hydropower generation to commercial shipping to the fishing industry. The amount of ice cover varies from year to year, as well as how long it remains on the lakes. With almost five full decades of ice data to look at, GLERL scientists are observing long-term changes in ice cover as a result of climate change. 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 structure, and spring plankton blooms.

Maximum ice cover on the Great Lakes every year from 1973 to 2018. Credit: NOAA GLERL.

NOAA GLERL has been exploring the relationships between ice cover, lake thermal structure, and regional climate for over 30 years through the use of historical model simulations and observations of ice cover, surface water temperature, and other variables. Weekly ice cover imaging products produced by the Canadian Ice Service (CIS) started in 1973. Beginning in 1989, the U.S. National Ice Center (NIC) produced Great Lakes ice cover charts that combined both Canadian and U.S. satellite imagery. Today, these products are downloaded and processed at GLERL by our CoastWatch program, a nationwide NOAA program within which GLERL functions as the Great Lakes regional node. In this capacity, GLERL uses near real-time satellite data to produce and deliver products that support environmental decision-making and ongoing research. While the Great Lakes CoastWatch Program is a great resource for near real-time ice cover data, historical data is just as important – and that’s where GLERL’s Great Lakes Ice Cover Database comes in. Originally archived by GLERL through the National Snow & Ice Data Center, the Great Lakes Ice Cover Database houses data that dates back to 1973 and continues to be updated daily during the ice season every year.

Ice caves on Lake Michigan’s Glen Haven beach in 2005. Credit: National Parks Service.

Even though the CIS and NIC are the ones who actually collect Great Lakes ice cover data, GLERL plays the important role of re-processing this ice data into more accessible file formats, making it readily usable to anyone who needs it. Agencies and organizations that have used ice cover data from GLERL in the past include the NASA Earth Observatory, U.S. Army Corps of Engineers, U.S. Coast Guard, and National Geographic. Types of data requested might include historic minimum and maximum ice coverage for certain regions or lakes, or dates of the first and last ice cover in a region from year to year. This information can be helpful for managers in industries like energy production and commercial shipping.

This graph shows annual maximum ice coverage on the Great Lakes every year from 1973 to 2020. The red dashed line marks the long-term average maximum ice cover of 53.3%. Credit: NOAA GLERL.

GLERL scientists can also use this historic ice cover data to analyze how current ice cover conditions compare with previous years. For example, here’s how the ice cover during January 2021 stacks up against data for past Januarys:

  • Lake Michigan and the five-lake average had their second lowest January ice cover (with January 2002 being the first lowest).
  • The other lakes are all in the top five lowest ice cover for the month of January.
  • Six out of ten of the Januarys with the lowest ice cover have occurred during the last decade for the five-lake average (though 2014 was fourth highest January ice cover).
This graph shows average Great Lakes ice cover for the month of January every year from 1973 to 2021, organized by lowest ice cover (far left) to highest ice cover (far right). Credit: NOAA GLERL.

GLERL is also working to make this data more user-friendly for anyone looking to utilize it. This recent paper from GLERL and the Cooperative Institute for Great Lakes Research (CIGLR) describes the scientists’ efforts to standardize two existing formats of historic ice cover data. The authors explain that “technology has improved and the needs of users have evolved, so Great Lakes ice cover datasets have been upgraded several times in both spatial and temporal resolutions.” The paper documents the steps the authors took to reprocess the data in order to make it more consistent and accessible, which ultimately makes it easier for users to study long-term trends.

Timeline of ice chart evolution and frequency, from the research paper described above (Yang et al 2020). Credit: Ting-Yi Yang, Cooperative Institute for Great Lakes Research.

Whether you’re looking for decades of Great Lakes ice data or just a few days, GLERL’s got you covered! Looking for more Great Lakes ice cover information? Visit our ice cover homepage here.

MODIS satellite image of ice cover on the Great Lakes, March 16, 2014. Credit: NOAA Great Lakes CoastWatch.


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Lake effect snow: What, why and how?

Winter is nearly here — and those who live and work in the Great Lakes region are already wondering what the winter of 2021 has in store. Early indications suggest a La Niña winter pattern, which shifts the odds towards cooler, wetter weather with more ice cover. 

More snow and ice can mean more fun, and can be great for winter sports like ice fishing, snowmobiling and skiing. Unfortunately, it can also mean severe weather events involving ice and snow. In the Great Lakes region, snow comes via the usual low pressure systems, but we also can get lake effect snow. 

Average location of the jet stream and typical temperature and precipitation impacts during La Niña winter over North America. Map by Fiona Martin for NOAA Climate.gov.

What is lake effect snow?

In the Great Lakes region, hazardous winter weather often happens when cold air descends from the Arctic region. Lake effect snow is different from a low pressure snow storm in that it is a much more localized and sometimes very rapid and intense snow event. As a cold, dry air mass moves over the unfrozen and relatively warm waters of the Great Lakes, warmth and moisture from the lakes are transferred into the atmosphere. This moisture then gets dumped downwind as snow.

Lake Effect Snow Can Be Dangerous

Lake effect snow storms can be very dangerous. For example, 13 people were killed by a storm that took place November 17-19, 2014 in Buffalo, New York. During the storm, more than five  feet of snow fell over areas just east of Buffalo, with mere inches falling just a few miles away to the north. Not only were lives lost, but the storm disrupted travel and transportation, downed trees and damaged roofs, and caused widespread power outages. Improving  lake effect snow forecasts is critical because of the many ways lake effect snow conditions affect commerce, recreation, and community safety.

Lake Effect Snow animation: Mid-December 2016 The lake effect snow EVENT resulted in extremely heavy snow across Michigan, Ohio, upstate New York as well as the province of Ontario east of Lake Superior and Huron.

Why is lake effect snow so hard to forecast?

There are a number of factors that make lake effect snow forecasting difficult. The widths of lake-effect snowfall bands are usually less than 3 miles — a very small width that makes them difficult to pinpoint in models. The types of field measurements scientists need to make forecasts better are also hard to come by, especially in the winter!  We would like to take frequent lake temperature and lake ice measurements but that is currently not possible to do during the winter, as conditions are too rough and dangerous for research vessels and buoys. Satellite measurements can also be hard to come by. The Great Lakes region is notoriously cloudy in the winter –  it’s not uncommon to go for over a week without usable imagery. 

MODIS satellite image of a lake effect snow event in the Great Lakes, caused by extensive evaporation as cold air moves over the relatively warm lakes. November 20, 2014. Credit: NOAA Great Lakes CoastWatch.

GLERL and CIGLR work to improve lake effect snow forecasting

Currently, NOAA Great Lakes operational models provide guidance for lake effect snow forecasts and scientists at NOAA GLERL and CIGLR are conducting studies to improve them. 

They use data from lake effect snow events in the past and compare how a new model performs relative to an existing model.  One way to improve forecast model predictions is through a model coupling approach, or linking two models so that they can communicate with each other. When they are linked, the models can share their outputs with each other and produce a better prediction in the end. 

Our lake effect snow research continues

Our lake effect modeling research is ongoing, and GLERL, CIGLR, NWS Detroit, the NOAA Global Systems Laboratory continue to address the complex challenges and and our studies build upon each other to improve modeling of lake-effect snow events. A new focus will be on running the models on a smaller grid scale and continuing to work to improve temperature estimates as both are key to forecasting accuracy.

A recent study, published by CIGLR and GLERL and other research partners, Improvements to lake-effect snow forecasts using a one-way air-lake model coupling approach,” is the latest in a recent series of studies* (see list below) that help to make lake effect snow forecasts better. This study takes a closer look at how rapid changes in Great Lakes temperatures and ice impact regional atmospheric conditions and lake-effect snow. Rapidly changing Great Lake surface conditions during lake effect snow events are not accounted for in existing operational weather forecast models. The scientists identified a new practical approach for how models communicate that does a better job of capturing rapidly cooling lake temperatures and ice formation. This research can result in improved forecasts of weather and lake conditions. The models connect and work together effectively and yet add very little computational cost. The advantage to this approach in an operational setting is that computational resources can be distributed across multiple systems.

Study model run: This panel of images shows model runs that looks at data from a lake effect snow event from January 2018 with and without the new type of model coupling. The image on the far right labeled Dynamic – Control Jan 06 shows the differences in air temperature (red = warmer, blue = colder) and wind (black arrows) when the models are coupled. The areas in color show how the new model coupling changed the model output considerably and improved the forecast.

Related news articles and blog posts:

From the CIGLR Winter 2020 eNewsletter – Improving Lake Effect Snow Forecasts

NOAA Research News, April 2019 NOAA research yields better lake-effect snow forecasts

NOAA GLERL Blog, 2018 – Improving lake effect snow forecasts by making models talk to each other

Related research papers: 

Fujisaki-Manome et al. (2020) Improvements to lake-effect snow forecasts using a one-way air-lake model coupling approach. 

Anderson et al. (2019) Ice Forecasting in the Next-Generation Great LakesOperational Forecast System (GLOFS) 

Fujisaki-Manome et al. (2017) Turbulent Heat Fluxes during an Extreme Lake-Effect Snow Event

Xue et al. (2016) Improving the Simulation of Large Lakes in Regional Climate Modeling: Two-Way Lake–Atmosphere Coupling with a 3D Hydrodynamic Model of the Great Lakes


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Sinkhole Science: Groundwater in the Great Lakes

If you followed our fieldwork last summer, you probably remember hearing about our research on the fascinating sinkholes and microbial communities that lie at the bottom of northern Lake Huron off the coast of Alpena, MI. Now you can experience this research as a short film!

NOAA GLERL has partnered with Great Lakes Outreach Media to create a short film entitled Sinkhole Science: Groundwater in the Great Lakes. It was recently featured on Detroit Public Television’s Great Lakes Now program as well as the Thunder Bay National Marine Sanctuary’s International Film Festival. 

In the film, you’ll learn how NOAA GLERL’s Observation Systems and Advanced Technology (OSAT) branch studies how these sinkholes impact the water levels and ecosystems of the Great Lakes. GLERL’s OSAT Program Leader Steve Ruberg explains the high-tech gadgets involved in this research, including a remotely operated vehicle (ROV), a tilt-based current sensor, and temperature strings to determine vertical movement of groundwater entering the lakes through the sinkholes.

Hit “play” to dive into the exciting world of GLERL’s sinkhole science!

Researchers from NOAA GLERL’s Observation Systems and Advanced Technology team set out on the R/V Storm to study sinkholes on the floor of northern Lake Huron off the coast of Alpena, MI. Photo: Great Lakes Outreach Media
Researchers on NOAA GLERL’s R/V Storm deploy a remotely operated vehicle (ROV) to observe sinkholes at the bottom of Lake Huron off the coast of Alpena, MI. Photo: Great Lakes Outreach Media
NOAA GLERL’s OSAT Program Lead Steve Ruberg and Instrument Specialist Steven Constant observe a sinkhole via live video feed from the ROV. Photo: Great Lakes Outreach Media
NOAA GLERL Marine Engineer Kyle Beadle controls the ROV in order to observe sinkholes from the R/V Storm. Photo: Great Lakes Outreach Media
NOAA GLERL Instrument Specialist Steven Constant and Vessel Captain Travis Smith monitor the ROV as it dives beneath the surface to observe a sinkhole. Photo: Great Lakes Outreach Media


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Millions of Microbes: The Unexpected Inhabitants of Lake Huron’s Underwater Sinkholes

When most people think of sinkholes, a massive cavity in the ground opening up and swallowing a car is what usually comes to mind. But when scientists at the NOAA Great Lakes Environmental Research Laboratory (GLERL) hear “sinkholes,” their minds jump to an unusual place — the bottom of a Great Lake.

Aerial view of research boat on green water
Researchers on GLERL’s R/V Storm study sinkholes in northern Lake Huron off the coast of Alpena, Michigan. (Credit: David J Ruck/Great Lakes Outreach Media)

Thousands of years ago, off the coast of Alpena, Michigan, patches of ground beneath Lake Huron collapsed to form a series of underwater sinkholes — some measuring hundreds of feet across and up to 60 feet deep. You may have read this NOAA.gov article about how these sinkholes are contributing water to Lake Huron, but did you know they also support a huge kingdom of microorganisms?

Microbes might be tiny, but they’re one of the biggest research topics in the Great Lakes. They thrive near the sinkholes because the groundwater seeping in has the perfect chemistry for their survival: low oxygen levels and lots of chloride and sulfate, which come from the dissolved limestone underlying the lake. These factors make the sinkholes inhospitable for fish and other wildlife normally found in the Great Lakes, which means these microbes have a much easier time surviving there than other creatures. With perfect living conditions and little competition, they’re so abundant that they form purple, green, and white microbial mats that cover the lake floor like a colorful carpet.

Floor of Lake Huron covered by purple and white microbial mats with bubbles in them.
Purple microbial mats in the Middle Island Sinkhole in Lake Huron, June 2019. Small hills and “fingers” like this one in the mats are caused by gases like methane and hydrogen sulfide bubbling up beneath them. (Credit: Phil Hartmeyer, NOAA Thunder Bay National Marine Sanctuary)

Scientists at GLERL are collaborating with partners from the University of Michigan and Grand Valley State University to see just what these microscopic lake dwellers can teach us. This video by Great Lakes Outreach Media highlights how they can even give us a deeper insight into the history of Earth itself.

Associate Professor Greg Dick from the University of Michigan discusses cyanobacteria’s important role in Earth science. This clip is from Great Lakes Outreach Media’s upcoming documentary, “The Erie Situation.”

Some sinkholes are so deep that sunlight can’t reach them, but that doesn’t stop some microbes from calling them home. They’re able to live their entire lives in complete darkness, because they get their energy from the added minerals in the water rather than from sunlight — a process called chemosynthesis. But whether they need sunlight or not, several of the microbial species present have proven to be full of surprises.

“In the near-shore systems, the cyanobacteria we found have DNA signatures that come closest to comparing to the cyanobacteria found at the bottom of a lake in Antarctica. So that’s a strange coincidence,” said Steve Ruberg, the scientist in charge of sinkhole research at GLERL. “Some of the other bacteria we’ve found in the deeper systems have only been found off the coast of Africa.”

Fish sitting on a rock, which is covered by purple and white microbes
A burbot resting on rocks covered in purple and white microbial mats inside the Middle Island sinkhole in Lake Huron. (Credit: Phil Hartmeyer, NOAA Thunder Bay National Marine Sanctuary)

The particular sinkholes we’re studying are located within NOAA’s Thunder Bay National Marine Sanctuary, an area of Lake Huron that’s federally protected for the purpose of preserving nearly 200 shipwrecks. In fact, the only reason we know about these sinkholes is because they were discovered by accident only 18 years ago, on a research cruise documenting the shipwrecks.

Close up of rocks covered in  purple, white and green microbes on the bottom of Lake Huron, with a diver in the background.
A diver observes the purple, white and green microbes covering rocks in Lake Huron’s Middle Island Sinkhole (Credit: Phil Hartmeyer, NOAA Thunder Bay National Marine Sanctuary)

So why did this microbial paradise come into existence in the first place? The story goes back much further than the sinkholes’ discovery in 2001. About 400 million years ago, before the Great Lakes even existed, a layer of limestone bedrock formed beneath what is now Lake Huron. Then around 10,000 years ago, underground caves were created when a chemical reaction between the limestone and acidic groundwater dissolved away holes in the bedrock. All that was left were weakly supported “ceilings” that eventually collapsed into the sinkholes we — and the microbes — know and love today.

Close up of rocks covered in purple, white and green microbes on the floor of Lake Huron
Purple cyanobacteria and white chemosynthetic mats on the floor of Lake Huron with Lowell Instruments current meter. (Credit: Phil Hartmeyer, NOAA Thunder Bay National Marine Sanctuary)

Since Lakes Michigan and Erie have the same limestone bedrock as Lake Huron, GLERL scientists think these lakes could be home to more of these fascinating underwater features. So while the excitement of this fieldwork has died down for the year, our research on Great Lakes sinkholes and their tiny inhabitants is far from over.


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Special Two-Day Science Translation Session at IAGLR 2019

This June, fellow researchers from around the world will gather in Brockport, New York, on the shores of the Erie Canal for IAGLR’s 62nd annual Conference on Great Lakes ResearchHosted by The College at Brockport, State University of New York, the conference will feature four days of scientific sessions and speakers focusing on the theme Large Lakes Research: Connecting People and Ideas. Mark your calendars for June 10-14, 2019. You won’t want to miss it! 

During the conference there will be a special two-day session that highlights the importance of science translation.  The session, Beyond Peer Review: Why You Must Connect Your Science to Stakeholders (and how to do it), will consist of several components—17 formal presentations, a moderated panel discussion, a synthesis discussion with Q&A, as well as a Skills Café. Conference attendees are welcome to join us for any and all portions of this session. We hope to see you there!


Day 1 – Tuesday, June 11th

On Tuesday, the SciComm session will include 12 presentations and a panel moderated discussion with science communication thought leaders Peter Annin (Author), Andrea Densham (Shedd), Sandra Svoboda (DPTV) & TJ Pignataro (Buffalo News). The panelists will explore what they see happening now and what they think the future looks like for connecting people and ideas for large lakes research.

Tuesday Morning (Edwards Hall Room 103)

Tuesday Afternoon (Edwards Hall Room 103)

  • 1:40-3:20 pm – 5 presentations
  • 3:20-3:40 pm – Break
  • 3:40-5:20 pm – 4 presentations

Day 2 – Wednesday,  June 12th

On Wednesday, the SciComm session will continue with 5 formal presentations, and a synthesis discussion in the morning and a Skills Café in the afternoon.

Wednesday Morning (Edwards Hall Room 103)

  • 8:00-9:20 am – 3 presentations
  • 9:20-9:40 am – Break
  • 9:40-10:20 am – 2 presentations
  • 10:20-11:00 am – Interactive Synthesis Discussion and Question & Answer Session with Peter Annin (Author), Andrea Densham (Shedd), Sandra Svoboda (DPTV) & TJ Pignataro (Buffalo News)

Wednesday Afternoon (Edwards Hall Room 102)

  • 1:40 – 5:00 pm – Skills Café  –  This series of short interactive workshops will allow participants to practice a variety of skills that will make them more effective at communicating the “so what” of their research to lay – but key – audiences. 

The Panel: Hear the latest from science communication thought leaders!

Peter Annin, Author and Director of the Mary Griggs Burke Center for Freshwater Innovation

Peter Annin is the director of the Mary Griggs Burke Center for Freshwater Innovation and the author of The Great Lakes Water Wars, the definitive work on the Great Lakes water diversion controversy. Before coming to Northland College in 2015, Peter served as a reporter at Newsweek, the associate director of the Institute for Journalism and Natural Resources, and the managing director of the University of Notre Dame’s Environmental Change Initiative. He continues to report on the Great Lakes water diversion issue and has published a second edition of The Great Lakes Water Wars. 

Andrea Densham, Senior Director of Conservation and Advocacy at the Shedd Aquarium

Andrea Densham joined Shedd Aquarium in 2017 to lead the newly launched Conservation Policy and Advocacy team. Created to enhance Shedd’s position as a policy expert, Densham’s team develops and implements the institution’s policy goals. A government affairs thought leader and advisor, she brings more than 20 years of experience in not-for-profit management, strategic planning, research, and public policy and advocacy.

 

TJ Pignataro, Environmental Reporter for the Buffalo News

T.J. Pignataro has been a staff reporter for The Buffalo News for more than 20 years and the environment and weather reporter since 2013. He holds a juris doctor degree from SUNY Buffalo Law School and is completing his Certificate in Weather Forecasting this spring from the Pennsylvania State University’s Department of Meteorology and Atmospheric Science. TJ uses Twitter to convey Great Lakes environmental news, weather emergencies and Great Lakes science in plain language. 

Sandra Svoboda, Program Director, Great Lakes Now, Detroit Public Television

A nine-month stint with The Associated Press brought Sandra to Detroit … 29 years ago. She earned a bachelor’s in journalism from Indiana University and holds two master’s degrees from Wayne State, one in public administration and one in library and information science. The Special Libraries Association IT Division recognized her research with its 2018 Joe Ann Clifton Student Award for her paper on how Detroit voting dynamics can inform citizen engagement strategies. Sandra has worked for The (Toledo) Blade covering education/children’s issues, Detroit’s Metro Times and FEMA, where she deployed to Louisiana to help coordinate/communicate about community rebuilding/planning efforts for/after disasters. Sandra has won awards for broadcast, print, digital and community engagement work from the Michigan Associated Press, the Michigan Association of Broadcasters, Association of Alternative Newsweeklies, State Bar of Michigan, Michigan Press Association and Society of Professional Journalists-Detroit chapter, and Wayne State’s public administration program recognized her with the Distinguished Alumni Award in 2015 for her work covering Detroit’s bankruptcy. She has taught communications, writing, public policy, and political science at Wayne State University and the University of Michigan-Dearborn. As the Great Lakes region has always been her home Sandra has traveled between Minnesota and Tadoussac, Quebec, both on the water and on land. A competitive sailor, she races hundreds of miles each season on the Great Lakes, and once threw out a pitch at a Detroit Tigers game as recognition of her win with her team at the U.S. Women’s Match Racing Championship. She’s also eaten Asian carp as part of her coverage of invasive species.


The Skills Café: Get help communicating your research!

WHO: Do people’s eyes glaze over when you begin to talk about your research? Do you believe your research has the ability to make a difference, but you’re not sure how to get others excited about it too? Then this session is for you! For the researcher looking to improve their accessibility in attaining broader impacts; the early career professional seeking tips on how to set theirselves apart in a competitive market; the passionate scientist looking for ways to ensure their work makes an impact . . . the Skills Cafe is your opportunity to grow and try new things in a fun and supportive setting.

WHAT: This series of short interactive workshops will allow participants to practice a variety of skills that will make them more effective at communicating the “so what” of their research to lay—but key—audiences. Get tips on interacting with the media, hone your speaking skills, get feedback from a mock interview, and learn from the trials and tribulations of your peers!

WHEN: 1:40-5:00 pm on Wednesday, June 12.

WHERE: Edwards Hall, Room 102

For more information and a detailed schedule of activities stop by the NOAA exhibitor booth.


About IAGLR 2019: The 2019 International Association for Great Lakes Research Conference is hosted by The College at Brockport, State University of New York, June 10-14, 2019. The conference will feature four days of scientific sessions and speakers focusing on the theme “Large Lakes Research: Connecting People and Ideas.”

map of great lakes showing colors of model output


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Improving lake effect snow forecasts by making models talk to each other

If you live in the Great Lakes basin and have been on or even near a road recently, you might be feeling unreasonably ragey at the mere mention of lake effect snow. We get it. But bear with us, because we’re doing some cool science we’d like to tell you about. It may even make your commute easier someday, or at least more predictable.

GLERL scientists are working with researchers at the University of Michigan’s Cooperative Institute for Great Lakes Research (CIGLR), the National Weather Service, and NOAA’s Earth Systems Research Laboratory (ESRL) to make lake effect snow forecasts in the Great Lakes better.

NOAA’s high resolution rapid refresh (HRRR) model is the most commonly used weather model for predicting lake effect snow. An experimental version runs on a beastly high-performance computer at ESRL in Colorado, and predicts a whole list of atmospheric variables (including snowfall) every 15 minutes. The model relies on water surface temperature data, collected via satellite, to make its predictions. It’s important to give the model accurate water surface temperatures to estimate evaporation across the Great Lakes, since it is the main driver of lake effect snow.

Unfortunately, satellite temperature data has limitations. If clouds keep satellites from measuring the temperature at a specific location, the weather model will just use the most recent measurement it has. Since it’s especially cloudy in the Great Lakes during the lake effect snow season (late fall and early winter), that data could be days old. Because lake temperatures are changing quite rapidly this time of year, days-old data just doesn’t cut it.

As it turns out, GLERL already has a model that predicts Great Lakes surface temperature pretty well. The Great Lakes Operational Forecast System (GLOFS) spits out lake surface temperatures every hour. If we tell the weather model to use GLOFS output instead of satellite data, it has the potential to do a far better job of forecasting lake effect snow.

Linking two models like this is called “coupling”. GLOFS actually already uses input from HRRR—wind, air temperature, pressure, clouds and humidity data all inform GLOFS’ predictions. We’re just coupling the models in both directions. HRRR will send its output to GLOFS, GLOFS will “talk back” with its own predictions of water surface temperature (and ice cover), and HRRR will produce a (hopefully) more informed prediction of lake effect snow.

Initial results are promising. We used the coupled models to do a ‘hindcast’ (a forecast for the past) to predict lake effect snow for a major event over Lake Erie in November of 2014. They did a significantly better job than without coupling. The figure below shows the difference.

The coupled models improved cumulative snow water equivalent forecasts. Red shows where the model increased snowfall.

You’ll notice a band of blue on the southeastern edge of Lake Erie, indicating that the coupled models predicted less lake effect snow in that area. There’s a band of orange directly to the north of it, where the coupled models predicted more lake effect snow. What you’re seeing is the coupled model predicting the same band of snow, but further north, closer to where it actually fell.

That storm slammed the city of Buffalo, New York, killing 13 people. Better lake effect snow predictions have the potential to save lives.

GLERL and partners will be doing further testing this winter, and if it works out, the model coupling will be carried over in research-to-operations transitions.


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The HAB season is over, but the work goes on

It’s nearly winter here in the Great Lakes—our buoys are in the warehouse, our boats are making their way onto dry land, and folks in the lab are working hard to assess observed data, experiments, and other results from this field season.

habtracker2018

This is a retrospective animation showing the predicted surface chlorophyll concentrations estimated by the Experimental Lake Erie HAB Tracker model during the 2018 season. Surface chlorophyll concentrations are an indicator of the likely presence of HABs. For more information about how the HAB Tracker forecast model is produced and can be interpreted, visit our About the HAB Tracker webpage.

The harmful algal bloom (HAB) season is also long over in the region. The final Lake Erie HAB Bulletin was sent out on Oct. 11, as the Microcystis had declined in satellite imagery and toxins decreased to low detection limits in samples. In the seasonal assessment, sent out by NOAA’s Centers for Coastal Ocean Science on Oct. 26, it was determined that the season saw a relatively mild bloom—despite its early arrival in the lake—and the bloom’s severity was significantly less than that which was predicted earlier in the season. These bulletins and outlooks are compiled using several models. Over the winter, the teams working on the models take what they learn from the previous season, and update their models for future use.

Back in the lab, the HABs team—researchers from both GLERL and the Cooperative Institute for Great Lakes Research (CIGLR)—will spend the winter analyzing data they collected through a variety of observing systems. This summer was packed with the use of new observing technologies, like hyperspectral cameras and the Environmental Sample Processor (in case you missed it, check out this fun photo story of the experimental deployment of a 3rd generation ESP). In addition, GLERL and CIGLR staff maintained a weekly sampling program program, from which scientists are analyzing and archiving samples and conducting experiments.

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Aerial photograph of the harmful algal bloom in Western Basin of Lake Erie on July 2, 2018, (Photo Credit: Aerial Associates Photography, Inc. by Zachary Haslick). Pilots from Aerodata have been flying over Lake Erie this summer to map out the general scope of the algal blooms. In addition to these amazing photos, during the flyovers, additional images are taken by a hyperspectral imager (mounted on the back of the aircraft) to improve our understanding of how to map and detect HABs. The lead researcher for this project is Dr. Andrea VanderWoude, a NOAA contractor and remote sensing specialist with Cherokee Nation Businesses. For more images, check out our album on Flickr.

This lab work is super important for understanding the drivers of toxic algae in the Great Lakes. For instance, in a new study released this month, researchers looking at samples from previous years found that “ . . . the initial buildup of blooms can happen at a much higher rate and over a larger spatial extent than would otherwise be possible, due to the broad presence of viable cells in sediments throughout the lake,” according to the lead author Christine Kitchens, a research technician at CIGLR, who works here in the GLERL lab. This type of new information can be incorporated into the models used to make the annual bloom forecasts.

As you can see, our work doesn’t end when the field season is over.  In spring 2019, when the boats and buoys are back in the water and samples are being drawn from the lakes, researchers will already have a jump on their work, having spent the winter months analyzing previous years, preparing, and applying what they’ve learned to the latest version of the Experimental HAB Tracker, advanced observing technologies, and cutting-edge research on harmful algal blooms in the Great Lakes.


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Photo story: Using an AUV to track algae in Lake Erie

In late July and early September, during the peak of the 2018 harmful algal bloom in the Western Basin of Lake Erie, NOAA GLERL, NOAA National Centers for Coastal Ocean Science (NCCOS), NOAA Atlantic Oceanographic and Meteorological Laboratory (AOML) and CIGLR researchers teamed up with a group of scientists and engineers from the Monterey Bay Research Institute (MBARI). Their mission: to test how well a third-generation environmental sample processor (3GESP), mounted inside a long-range autonomous underwater vehicle (LRAUV), can track and analyze toxic algae in the Western Basin of Lake Erie. You can read more about the purpose of this project in this great news story by MBARI’s Kim Fulton-Bennett.

Below is a photo story showing all (well, much) of the hard work that went into this test deployment.

First, the new gear had to be shipped from California to the GLERL laboratory in Ann Arbor, Michigan.

 

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Upon arrival, Jim Birch, Director of the MBARI SURF (Sensors Underwater Research of the Future) Center, & Bill Ussler, MBARI biogeochemist, got straight to work in GLERL’s Marine Instrumentation Lab.

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The inside of the 3G ESP has a lot of moving parts. Since this is the first time the team is testing it in freshwater, before it can go out, everything needs to be fine-tuned to work in a variety of conditions in Lake Erie (more on that later.)

So. Many. Moving. Parts.

 

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Once everything is in working order, the 3GESP gets inserted into an LRAUV or long-range autonomous underwater vehicle (the torpedo-looking thing). This gives the 3GESP the ability to move around in the water all by itself once researchers have set parameters for it. The team has named this particular vehicle, Makai, which is Hawaiian for “toward or by the sea.” Seems appropriate! That’s Brian Kieft, MBARI software engineer, on the right. He plays a crucial role in making sure Makai does her job.

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All hands on deck for a few more tweaks.

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Once everything is installed tightly, helium is added into the canister to check for leaks. CIGLR engineer, Russ Miller, is working with Jim to fill it up.

Now, the team is ready to head out to Lake Erie. Here’s where things start to get exciting!

 

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Before the team sets Makai free to track the algal bloom in the Western Basin of Lake Erie, they must first check her ballast and trim. This is especially important for such a shallow lake (relative to where the team has been testing this technology in the deep canyons of of Monterey Bay off the coast of California.)

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Brian has to do all of the hard work.

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Because, science.

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Time to load Makai onto the NOAA vessel, which is stationed in La Salle, Michigan. Captain Kent Baker, a contractor with NOAA, is in the background operating the crane. Kent takes NOAA and CIGLR researchers and technicians out to bi-weekly sampling stations, helps deploy buoys and other instrumentation, and is at the ready for pretty much anything that needs to happen in Lake Erie.

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Once she’s all settled onto the boat, the team takes Makai to the first deployment location.

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The inaugural deployment was set to match up with the bi-weekly sampling stations.

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Look closely and you’ll see Makai off on her way!

Makai and the team spent nearly two weeks tracking, sampling, adjusting, and learning about using this technology to track algal toxins in Lake Erie.

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The team used the images from GLERL’s Experimental Lake Erie Harmful Algal Bloom (HAB) Tracker to determine where to send Makai.

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Then, they would determine how many samples to take, and program her to go to specific waypoints.

Remember when we said this Lake Erie mission will be different than the ones the team has performed in Monterey Bay? Well, here’s one example of how.

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After a few hours of no communication, and a little hunting, this is how the team found Makai. Two problems here: One, with the propellor up and the nose down, Makai cannot transmit data, including her location, as the transmitter only works above water. And, two, well . . .

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The reason she was nose down in the first place is because Lake Erie is pretty shallow, and she’d taken on quite a bit of mud.

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Once she was all cleaned up, the team set Makai out again to complete the rest of her mission.

Once the deployment was over, the research didn’t stop there.

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Archive samples were taken so that folks back in the lab could further analyze them.

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Here’s GLERL’s Observing Systems and Advanced Technology (OSAT) branch chief, Steve Ruberg (left), along with Paul Den Uyl, a researcher with CIGLR, helping Bill extract the sample filters from the cartridges.

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The filters are being collected for analysis of DNA. The DNA will be extracted from each filter and analyzed. We’re looking at absolute quantity of known microcystin producing toxin genes in samples collected, information on bacterial community composition, and information on eukaryotic organism community composition. The samples will also analyzed through shotgun sequencing. This is where all of the genes in the sample are turned into human readable information and can be combined to make what can be thought of as an organism’s genetic instruction guide (what genes it has). This information will be very helpful in better understanding what causes the algae to be toxic (not all algae is toxic).

 


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Photo story: Taking a closer look at how invasive mussels are changing the Great Lakes food web

The invasion of zebra and quagga mussels in the Great Lakes is taking a toll on the ecosystem. To investigate these ecological changes, scientists from GLERL and the Cooperative Institute for Great Lakes Research (CIGLR) are doing experimentation on how quagga mussels affect the lower food web by filtering large amounts of phytoplankton out of the water.  Scientists are also investigating how mussel feeding and excretion of nutrients drive harmful algal blooms (HABs) in growth stimulation, extent, location, and toxicity.

The following experimental activities are being conducted under controlled conditions to look for changes in living and nonliving things in the water before and after quagga mussel feeding.

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Scientists are using quagga mussels captured from Lakes Michigan and Erie to understand how invasive mussels impact the lower food web. Prior to experimentation, the mussels are housed in cages where they graze on phytoplankton in water kept at the same temperature as the lakes. This helps acclimate them to natural lake conditions.

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The research team, led GLERL’s Hank Vanderploeg (front right), coordinates the different phases of the experiment. By filtering water before and after quagga mussel feeding, team members learn about the effect of these mussels on levels of phytoplankton (as measured by chlorophyll), nutrients (phosphorus and nitrogen), particulate matter, carbon, bacteria, and genetic material.

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CIGLR research associates, Glenn Carter and Paul Glyshaw, pour lake water into sample bottles for processing at different stages of the experiment.

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GLERL’s, Joann Cavaletto, pours lake water from the graduated cylinder into the filter funnel. She is filtering for particulate phosphorus samples. She also measures total chlorophyll and fractionated chlorophyll based on 3 size fractions; >20 µm, between 20 µm and 2 µm, and between 2 µm and 0.7 µm.

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GLERL’s Dave Fanslow, operates the FluoroProbe displaying the level of pigments from different phytoplankton throughout the feeding experiment: pre-feeding of quagga mussel, progression of feeding on an hourly basis, and final measurements at the end of the experiment. The FluoroProbe measurements determine the concentration of pigments, such as chlorophyll, that quagga mussels filter out of the water throughout the experiment.

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The FluoroProbe emits highly specific wavelengths of light using an LED array, which then trigger a fluorescence response in algae pigments and allow the immediate classification of green and blue green algae, cryptomonads, and diatoms.

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University of Michigan scientists, Vincent Denef (left and upper right, kneeling in bottom right) and Nikesh Dahal (standing in bottom right), filter water before and after quagga mussel feeding. They are looking at changes in the bacterial community based on the genetic composition of groups, focusing on the variability of toxic production in cyanobacteria in harmful algal blooms. Following the filtration phase of the experiment, they will conduct DNA and RNA sequencing for toxicity gene expression in the cyanobacteria.