NOAA Great Lakes Environmental Research Laboratory

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

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Q&A with NOAA scientists: Causes and impacts of 2024’s historically low Great Lakes ice cover

Many people have questions about the historically low Great Lakes ice cover this winter, and we’ve got answers! NOAA GLERL’s Bryan Mroczka (Physical Scientist) and Andrea Vander Woude (Integrated Physical and Ecological Modeling and Forecasting Branch Chief) answer the following frequently asked questions regarding the causes and impacts of this year’s low ice cover.

What’s driving the lack of ice? Is El Niño involved somehow?

The long-term trend shows a decline in ice cover in the Great Lakes region over the past several decades. Ice cover has been decreasing by approximately 5 percent per decade, for a 25 percent total decrease between 1973 and 2023. In addition, the length of the Great Lakes ice season has decreased by approximately 27 days on average over the same period.

Annual maximum ice cover on the Great Lakes, 1973-2023.

Factors that drive the lack of ice are climatic variables such as the El Niño-Southern Oscillation (ENSO) along the Equatorial Pacific in addition to other global oceanic oscillations. These atmospheric patterns in the ocean influence weather patterns in the Great Lakes, driving the climatic response of the lakes. Increases in air temperatures are responsible for the lack of ice in addition to the “heat memory” of the lakes as they retain heat from the summer season temperatures. 

While El Niño may have exacerbated the extreme low ice seen this year, the increased frequency of low ice years across the lakes is tied to generally warmer winter conditions, defined by fewer and generally shorter intrusion of arctic air into the region. While much of the Continental U.S. has seen a warming trend during the winter months, the upper Midwest/Great Lakes have seen some of the most dramatic warming.

Color-coded chart showing maximum annual ice cover percentage on the Great Lakes from 1973-2024. Bar colors indicate El Niño strength for each year, ranging from "very strong El Niño" to "El Niño not present"
This graph shows maximum ice concentration every year from 1973-2024, with color-coding to show El Niño strength each year. Note that 2024’s maximum ice cover of 16% is as of mid-February, and is subject to change if ice cover increases later in the season.

An important factor in a season’s ice potential across the Great Lakes is the weather patterns influencing the region during December. December is what we would consider a “priming” month, in which the first arctic air masses cool the lakes and begin the ice generation process within enclosed bays and along the shoreline. Recently, we have seen a multitude of Decembers exhibit above-average temperatures, including significantly above-average temperatures this winter in particular. The lack of early season cold air, and resulting late start to the ice generation season makes later significant gains in ice concentration harder to achieve.

How does the lack of ice impact the Great Lakes ecosystem, as well as towns and cities on the lakes? 

Ice is an important element for the ecosystems, economy, and coastal resilience across the Great Lakes. Ice is a natural part of the Great Lakes yearly cycle and many animal species, from microbial to larger fauna, rely on the ice for protecting young and harboring eggs. The Great Lakes also see most of their significant storms and large wave events during the colder months of late fall through winter. The shorebound ice sheets act as an important buffer against these waves, protecting the coast from erosion and damage to shoreline infrastructure. In years with very low ice, such as this one, the coast becomes more susceptible to the full onslaught of wave energy.

An ice shelf and pieces of floating ice line a residential seawall on Lake Huron on a sunny day.
Ice on the Lake Huron shoreline near Oscoda, MI on January 27, 2024. Credit: Clarice Farina

The economy of the Great Lakes can see negative and positive outcomes from a very low ice year. Two of the more important wintertime recreational sports in the Great Lakes include ice-fishing and snowmobiling. When the ice is scarce and thin, the ability to partake in ice-fishing is significantly reduced both spatially and temporally. When it comes to snowmobiling, warmer winters will generally result in more rain events compared to normal, as well as reduced snow cover and lower quality snow. 

One “silver lining” for the Great Lakes economy that may result from a low ice year, is a boost to the shipping industry. Low ice years are likely to extend the shipping season across the lakes, and may extend the season significantly if the locks are not hampered by significant ice. 

Is there still time for the ice to return before spring?

The ice season in the Great Lakes typically extends until the end of March, and the maximum ice cover for the year comes near the end of February to early March. The clear trend is one of decreasing ice, but it is still too early to determine how this year will ultimately compare to past years and the long term average. 

Winter is not close to being over, and periods of new ice generation are almost certain as we head through the next month. The longer term pattern into early March does suggest that a few bouts of arctic air will reach the Great Lakes, but similar to earlier portions of this winter, there does not appear to be a signal for any long term below average temperature events. The colder air events ahead are more likely to be short-lived (several days), and not long enough for significant gains in ice concentration. It is certainly possible that we’ll see the ice concentrations climb out of the current historic lows before the end of the month, but a major pattern shift (currently not in the forecast) would be required to drive ice concentrations out of below-normal realms for any of the lakes before the spring. 

How do low ice levels impact evaporation, water levels, and lake effect snow?

While Great Lakes water levels are generally lowest in the winter, most of the evaporation from the lakes actually happens in the fall. This is because evaporation is driven by a large difference between the air temperature and the water temperature, which happens in the fall when the air cools down but the water is still holding onto its summer heat. The graphic below illustrates the seasonal cycles that Great Lakes water levels undergo every year.

Graphic showing land in the background and water in the foreground, divided into four panels corresponding with the seasons. Text describes the water level changes throughout the year: Winter low, spring rise, summer peak, and fall decline.

As of right now, we are not seeing any significant impacts to water levels due to the lower ice. Water levels are essentially the same (within one inch) as the values we were seeing at this time last year, and running just a touch above the long term average. The U.S. Army Corps of Engineers is forecasting very little change in water levels for the next 6 months. The lakes are almost ice-free, but we are also not seeing any significant degree or duration of arctic air. Despite the lack of ice, the water temperatures are still cold – just a few degrees above freezing – so the generally small difference in water temperature and air temperature means that evaporation levels are kept in check.

One might assume that the lakes remaining ice-free might increase the amounts of lake effect snow, and this is possible given there is still a steady supply of colder air supportive of driving the lake effect. However, this winter, the lack of cold air arriving over the region has reduced the lake effect snow events, and promoted significant melting between events.

Why do NOAA GLERL’s ice records only go back to 1973?

The early 1970s is when we first had reliable satellite data with which to construct more accurate and complete datasets. Before the satellite era, information during the winter about ice concentration away from the shoreline was very limited. This is why we only use the 51-year dataset for our calculations, as this represents the highest quality data.

Learn more about this year’s low Great Lakes ice

Current and historical ice cover data from NOAA GLERL

NOAA Research: Great Lakes ice cover reaches historic low

Climate.gov: Ice coverage nearly nonexistent across the Great Lakes, as the historical peak approaches

Download images and graphics from this article on our 2024 ice Flickr album


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NOAA GLERL Deputy Director Jesse Feyen receives AMS STAC 2023 Coastal Environment Committee Outstanding Service Award

Congratulations to NOAA Great Lakes Environmental Research Laboratory Deputy Director Jesse Feyen on receiving an American Meteorological Society (AMS) award this week! Dr. Feyen was awarded the Scientific and Technological Activities Commission (STAC) 2023 Committee on Coastal Environment Outstanding Service Award.

NOAA GLERL Deputy Director Jesse Feyen (center) accepts his award with Yun Qian (Pacific Northwest National Laboratory, left) and Greg Dusek (NOAA National Ocean Service, right).

Remarks from the AMS awards ceremony:

Dr. Feyen is receiving this award for serving the AMS Committee on the Coastal Environment with exceptional distinction for 6 years. Dr. Feyen served as the committee chair from 2020 – 2023, a period which was obviously characterized by COVID and the challenges that COVID and virtual world posed to our ability to plan and host our symposium at the AMS Annual Meeting.

Under Jesse’s leadership the committee successfully adapted to the first ever fully virtual Annual Meeting, and the first ever Hybrid Annual Meeting. Through these challenges we saw a growth in the coastal symposium, both in the number and diversity of attendees, up to this year, where we had the greatest number of presentations and posters we have ever had at our symposium.

His leadership also resulted in a larger and more diverse committee – in career stage, background, race and gender – resulting in the outstanding committee which put together the symposium you are at today.

On top of all that, even after his tenure, Jesse has continued to serve and support the committee and been an invaluable resource to Yun and I as we stepped into the chair and vice-chair roles.

Congratulations, Deputy Director Feyen, from all of us at NOAA!


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Lake effect snow: What, why and how?

As fall comes to a close, those who live and work in the Great Lakes region are wondering what weather this winter has in store. An El Niño Advisory is currently in effect, which means El Niño conditions have developed and are expected to continue. So, what does that mean for the Great Lakes? Will we still see lake effect snow?

An El Niño shifts the odds towards warmer, drier weather across the region – but that certainly doesn’t mean no snow! Whether or not there’s an El Niño, lake effect snow events can and do occur in the Great Lakes every year. In fact, we’re already experiencing them this year in places like Buffalo, NY and western Michigan.

During El Niño winters, the polar jet stream tends to stay to the north of the Great Lakes region, while the Pacific jet stream remains across the southern U.S. With the Great Lakes positioned between the storm tracks, warmer and possibly drier conditions can develop during El Niño events.

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.

Graphic via NOAA National Weather Service

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. 

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.

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 difficult to do during the winter, as conditions are too rough and dangerous for most research vessels and buoys. (However, NOAA is making progress towards expanding our Great Lakes winter observation capabilities!)

Satellite measurements can also be hard to come by, as the Great Lakes region is notoriously cloudy in the winter. It’s not uncommon to go for over a week without usable imagery.

Lake Effect Snow animation: This mid-December 2016 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.

NOAA 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 the Cooperative Institute for Great Lakes Research (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.

Research published by CIGLR, GLERL and other research partners, “Improvements to lake-effect snow forecasts using a one-way air-lake model coupling approach,” is part of a 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.

Our lake effect snow research continues

Our lake effect modeling research is ongoing, and NOAA GLERL, CIGLR, NOAA NWS Detroit, the NOAA Global Systems Laboratory continue to address the complex challenges and our studies build upon each other to improve modeling of lake-effect snow events. A future 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. 

Related news articles and blog posts: 

Improving Lake Effect Snow Forecasts

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

Related research papers: 

Fujisaki-Manome et al. (2022) Forecasting lake-/sea-effect snowstorms, advancement, and challenges

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 Lakes Operational 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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Underwater robots significantly advance our ability to study Lake Erie’s harmful algal blooms

Newly published research from the NOAA Great Lakes Environmental Research Laboratory (GLERL), the Cooperative Institute for Great Lakes Research (CIGLR), and partners reveals that using underwater robots could significantly advance scientists’ ability to study the harmful algal blooms (HABs) that appear in the Great Lakes and oceans every summer. You may remember reading about NOAA’s collaborative fieldwork in 2019 that used these robots to detect toxins in Lake Erie’s harmful algal bloom. Three years later, the findings from this pioneering research come bearing good news!

This autonomous underwater vehicle (AUV), known as “Makai,” visited the Great Lakes from the Monterey Bay Aquarium Research Institute (MBARI) to help scientists study Lake Erie’s harmful algal bloom. Credit: Steve Ruberg, NOAA GLERL

What are HABs, and how do we study them?

HABs occur when colonies of algae grow out of control and produce toxic or harmful effects on people, fish, shellfish, marine mammals and birds. Western Lake Erie in particular has been plagued by intensified HABs over the past decade. These blooms consist of cyanobacteria, or blue-green algae, which are capable of producing toxins that endanger human and animal health, compromise drinking water supplies, foul coastlines, and impact communities and businesses that depend on the lake. 

Harmful algal bloom in western Lake Erie in October, 2011. Credit: NOAA Great Lakes CoastWatch

The underwater robot used in this research project is known as a long-range autonomous underwater vehicle, or LRAUV. As the name suggests, the LRAUV is built to travel long distances beneath the water’s surface, collecting data for an extended period of time. LRAUVs are useful research tools, as they can collect high-quality data more efficiently and cost-effectively than scientists taking samples from a ship or along the shore. They can be deployed day and night in all weather conditions, and can provide more detailed information to researchers and drinking water managers than other monitoring methods.

For this project, NOAA and CIGLR teamed up with the Monterey Bay Aquarium Research Institute and university partners to equip an LRAUV with a 3rd Generation (3G) Environmental Sample Processor (ESP) — a mobile version of what has previously been known as NOAA’s “lab in a can.” The 3G ESP’s job is to measure microcystin, a potent liver toxin produced by the cyanobacteria that cause harmful algal blooms in the Great Lakes. In just a few hours, the 3G ESP can collect and analyze water samples from the bloom with the same methods that scientists use to analyze samples back at the lab. It does this with the use of ‘omics, a collective suite of technologies used to analyze biological molecules such as DNA, RNA, proteins, or metabolites. These technologies can be used to identify the algal species that produce HABs, understand their behavior, and predict shifts in their population structure.

NOAA and partners deployed the LRAUV-3G ESP in Lake Erie to autonomously measure microcystin, a potent liver toxin produced by the cyanobacteria that cause harmful algal blooms in the Great Lakes. Photo credit: NOAA

Did this robot step up to the challenge?

Before widely adopting the use of the LRAUV-3G ESP to study Lake Erie HABs, scientists had to ensure that the data these instruments collect is accurate and reliable. A main goal of the new publication was to assess how dependable the LRAUV-3G ESP’s data is compared to data that was collected and analyzed by humans.

The authors used a variety of parameters to assess the vehicle’s performance of ‘omics tests on samples it collected from the HAB. They ultimately found that the LRAUV-3G ESP successfully performed flexible, autonomous sampling across a wide range of HAB conditions, and the results indicated equivalency between autonomous and manual methods. In fact, no significant differences were found between LRAUV-3G ESP and manual sample collection and handling methods in the 12 parameters tested. In other words, this robot passed the test!

Left: Scientists retrieve the LRAUV-3G ESP from its mission to measure algal toxins in Lake Erie. Photo credit: NOAA AOML. Right: First author Paul Den Uyl (CIGLR) CIGLR retrieves 3G ESP filters for analysis of Lake Erie microbial community DNA. Photo Credit: Kelly Godwin.

One of the most exciting aspects of this research is that it shows that scientists can use an autonomous sampling platform to replicate traditional ship-based sampling, and they can do so in a particularly challenging environment (Lake Erie’s shallow western basin) where HABs are a serious health concern. Using this instrument in Lake Erie’s shallow waters presented another challenge for the scientists involved. In response to the lake’s challenges, researchers worked on the LRAUV’s buoyancy to ensure that the instrument didn’t drag across the ground. With this technology – sampling DNA and measuring toxins on an autonomous platform – NOAA and partners may be able to provide an early warning system for HABs in the future.

The 3rd Generation Environmental Sample Processor demonstrates engineering advancements from the first and second generation ESPs. Photo credit: NOAA GLERL.

Partners on this research came from far and wide to conduct this important research:

  • National Oceanic and Atmospheric Administration (NOAA)
    • NOAA Atlantic Oceanographic and Meteorological Laboratory (AOML), Ocean Chemistry and Ecosystems Division
    • NOAA Great Lakes Environmental Research Laboratory
    • NOAA National Centers for Coastal Ocean Science (NCCOS)
    • NOAA Southwest Fisheries Science Center
  • Cooperative Institute for Great Lakes Research (CIGLR), University of Michigan
  • Northern Gulf Institute, Mississippi State University
  • Monterey Bay Aquarium Research Institute (MBARI)
  • Department of Earth and Environmental Sciences, University of Michigan

Explore more photos of this research on NOAA GLERL’s Flickr page.


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Decades in the making, NOAA’s newest Lake Superior and Lake Ontario forecast systems become fully operational

Did you know that NOAA operates a forecasting system that predicts water conditions on the Great Lakes? Whether you’re wondering about a lake’s temperature, currents, or water level changes, NOAA’s got you covered! This fall, NOAA implemented newly updated versions of the Lake Superior and Lake Ontario portions of this system, and added ice forecasts to all five lakes.

Lake Michigan waves at the St. Joseph North Pier Lighthouses following superstorm Sandy. October 29, 2012. Credit: S. Lashley, NOAA NWS.

GLOFS forecasts Great Lakes conditions

The publicly available Great Lakes Operational Forecast System (GLOFS) is a NOAA automated, model-based prediction system aimed at providing improved predictions of these conditions in the five Great Lakes (Erie, Michigan, Superior, Huron and Ontario) for the commercial, recreation, and emergency response communities. GLOFS models use current lake conditions and predicted weather patterns to forecast the lake conditions for up to five days (120 hours) in the future. GLOFS predictions enable users to increase the margin of safety and maximize the efficiency of commerce throughout the Great Lakes.

NOAA’s National Weather Service (NWS) and National Ocean Service (NOS) work together to run GLOFS operationally on NOAA’s High Performance Computing System. By running on NOAA’s High Performance Computing System, GLOFS has direct access to National Weather Service operational meteorological products that are required for reliable and timely operations.

A major update for Lakes Ontario and Superior

A key goal of NOAA’s Research branch is to continually make forecasts better, and GLERL scientists play a major role in improving the models that constitute GLOFS. Like the rest of GLOFS, the Lake Ontario and Lake Superior portions – Lake Ontario Operational Forecast System (LOOFS) and Lake Superior Operational Forecast System (LSOFS) – were originally based on the Princeton Ocean Model. As of October 2022, they’ve now been upgraded with higher-resolution versions that are based on a newer computer model.

MODIS satellite images of Lakes Superior (left) and Ontario (right) in March 2021.

The new LOOFS and LSOFS use the Finite Volume Community Ocean Model (FVCOM), coupled with an unstructured grid version of the Los Alamos Sea Ice model (CICE). The new model provides users with higher resolution of nowcast (near-present conditions) and forecast guidance of water levels, currents, water temperature, ice concentration, ice thickness and ice velocity out to 120 hours in the future, and it updates four times per day. By invoking advanced model schemes and algorithms, LOOFS and LSOFS are expected to generate a more accurate model output than their former versions. 

Before they were ready to become operational, the new versions of LOOFS and LSOFS were run experimentally at GLERL for several years, where they underwent extensive testing and evaluation. GLERL played a key role in developing these models and ran them as part of the Great Lakes Coastal Forecasting System (GLCFS) – an experimental version of GLOFS that GLERL uses to prepare new models to become operational.

With this transition, the GLOFS models for all five Great Lakes have now been upgraded to FVCOM versions, as the Lake Erie model was upgraded in 2016, and the Lake Michigan-Huron model was upgraded in 2019. A new FVCOM-based model for the Huron-Erie Corridor, which includes Lake St. Clair and both the St. Clair and Detroit Rivers, is scheduled to be added to GLOFS in 2023. Read more about the LOOFS and LSOFS transition here.

These output maps from the current 3rd generation GLOFS show Lake Superior wind speed
and direction (top) and Lake Ontario water temperatures (bottom).

GLERL has been improving GLOFS for over 30 years

GLOFS is based on the Great Lakes Forecasting System, originally developed by The Ohio State University (OSU) and GLERL in the late 1980s and 1990s under the direction of Dr. Keith Bedford (OSU) and Dr. David Schwab (NOAA GLERL). The original forecasting systems utilized the Princeton Ocean Model (POM) and used a set of uniformly structured bathymetric grids. The first routine nowcast, using a low-resolution grid for Lake Erie, began at OSU in 1992.

Starting in 2002, GLERL’s semi-operational GLCFS was expanded to five lakes using medium-resolution grids (5 – 10 km) and 48-hr forecasts were added. This version was successfully transferred from research to operations at NOAA NOS in 2010. The transition to operations at NOAA NOS was a joint effort between NOAA GLERL, NOS Center for Operational Oceanographic Products and Services (CO-OPS) and NOS Office of Coast Survey (OSC) Coast Survey Development Laboratory (CSDL), private industry, and academia (OSU).

NOAA GLERL has continued to make improvements to the experimental GLCFS; these include increasing the grid resolution (2 – 10 km), adding ice dampening and an ice model, and extending the forecasts to 120 hours during the period of 2006-2014 (generation 2). The current 3rd generation of the GLOFS is what you see run by NOS today, with a resolution of 200m to 2.5km and producing 120-hour forecasts.

The development and implementation of LSOFS and LOOFS is a joint project across several NOAA offices and external partners. 

  • NOAA National Ocean Service Center for Operational Oceanographic Products and Services
  • NOAA NOS Office of Coast Survey
  • NOAA Office of Oceanic and Atmospheric Research Great Lakes Environmental Research Laboratory
  • Finite Volume Community Ocean Model development group at the University of Massachusetts Dartmouth
  • NOAA National Weather Service National Centers for Environmental Prediction Central Operations


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Lessons from Lake Huron: A look back at NOAA GLERL’s 2022 fieldwork for the Cooperative Science and Monitoring Initiative

Every summer, NOAA GLERL scientists travel far and wide across the Great Lakes region to study the biological, chemical, and physical properties of these amazing lakes. A portion of this fieldwork contributes to a larger project called the Cooperative Science and Monitoring Initiative – or CSMI – which helps us take a deeper dive into studying a different Great Lake each year. Instituted under the 2012 Great Lakes Water Quality Agreement, CSMI is a multi-agency, international effort to coordinate science and monitoring activities in one of the five Great Lakes each year to generate data and information for environmental management agencies.

MODIS satellite image of Lake Huron on May 18, 2021. Credit: NOAA Great Lakes CoastWatch Node.

Each Great Lake gets a “CSMI year” once every five years, and 2022 was Lake Huron’s turn to shine. Sitting right at the center of the Great Lakes region, Lake Huron is shared by the state of Michigan and the Canadian province of Ontario. It’s the second largest of the Great Lakes and ranks as the fourth largest lake in the world by surface area. Lake Huron provides economically and culturally important services, including a productive fishery, a source of clean drinking water, and natural beauty that supports a significant tourism industry. It’s also home to Thunder Bay National Marine Sanctuary, the first ever NOAA National Marine Sanctuary to be established in the Great Lakes.

GLERL’s fieldwork for this year’s Lake Huron CSMI efforts focused on benthic and spatial surveys in Thunder Bay and Saginaw Bay. Here’s a look back at some of the highlights!

GLERL scientists Ashley Elgin and Rachel Orzechowski rinse down sediments collected by a Ponar grab.

NOAA GLERL has been conducting benthic (lake bottom) research in the Great Lakes since 1980, during which time we have built an unparalleled record of the arrival and expansion of invasive zebra and quagga mussels. CSMI provides the perfect opportunity to expand on this knowledge. Surveying the lake bottom allows us to track the population dynamics of these mussels, follow their impacts on native species, and also monitor for any new invasive benthic species. 
GLERL scientist Paul Glyshaw collects Ponar samples onboard the Fisheries and Oceans Canada/Canadian Coast Guard vessel Limnos for mussel length-weight analysis.

In June, July, and August of this year, GLERL conducted surveys that will allow us to update the status of invasive dreissenid mussels and other benthos of Lake Huron. As an exciting bonus, our benthic surveys in Saginaw Bay and Thunder Bay even received dive support from Thunder Bay NMS to supplement the samples collected with Ponar grabs.

Thunder Bay NMS divers Stephanie Gandulla and John Bright support GLERL’s benthic survey on board the R/V 5503.
The large metal claw used for a Ponar grab is no match for a mussel-covered rock like this, which is why we need NOAA’s Thunder Bay NMS Divers to support the benthic survey.

In the truly collaborative fashion that CSMI is known for, GLERL scientists maximized time on these cruises by collecting samples for several federal and university collaborators in addition to conducting our mussel survey.  For example, mussels and sediments went to the U.S. Geological Survey for mercury analysis, and researchers from the University of Michigan will be looking for mussel environmental DNA in water samples.

This sediment sample from Saginaw Bay has many benthic inverts present, including dreissenid mussels, chironomids, water mites, amphipods, and a snail. 
Paul Glyshaw collects and filters Lake Huron water onboard the Fisheries and Oceans Canada/Canadian Coast Guard vessel Limnos to measure carbon content. This helps us address potential impacts of climate change on the lake, including acidification, changes to production, and altered biogeochemical processes.

Plus, GLERL also teamed up with the U.S. Environmental Protection Agency, Fisheries and Oceans Canada (DFO), and the Canadian Coast Guard in a whole lake-benthic survey, during which GLERL assessed mussel body condition, mussel reproduction, inorganic carbon measures, and collected water for eDNA across the lake. In true CSMI spirit, DFO stepped up and supported the benthic survey when the EPA R/V Lake Guardian became unavailable. 

Fisheries and Oceans Canada/Canadian Coast Guard vessel Limnos pulls into Port Huron for the Lake Huron Benthic survey.

In addition to surveying what’s happening on the lake floor, GLERL also conducted an intensive spatial survey through CSMI to study Lake Huron’s food web in the area between Thunder Bay and Saginaw Bay. With a special focus on studying the interactions between larval fish and plankton, one of the key instruments used was GLERL’s Plankton Survey System (PSS). This high-tech piece of equipment is a towed multi-sensor platform capable of measuring turbidity, chlorophyll a, photosynthetically active radiation (PAR), conductivity, temperature, and zooplankton spatial distributions.

GLERL scientists use the PSS on Lake Michigan in the mid 2000s.

The plots below show a nearshore to offshore view of Lake Huron’s biological data measured by the PSS, like water temperature, dissolved oxygen, and chlorophyll, and plankton distribution. Check out more PSS plots from this spatial survey here.

While the PSS instrument was collecting data below the waves, lots of mayflies were catching a ride on this research cruise!

Now that the fieldwork is complete, the next step for GLERL’s CSMI work is to process our samples and analyze our data to continue building our knowledge of Lake Huron. Stay tuned in 2023, when CSMI heads east to study Lake Ontario!

For more CSMI information, data, and findings, visit greatlakescsmi.org. Plus, check out this related CSMI project in which GLERL and CIGLR developed an Experimental Biophysical Modeling Forecast System for Lakes Michigan and Huron.


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NOAA Wave Glider Camaro Gathers Key Data During 25-Day Cruise in Lake Superior

The NOAA Great Lakes Environmental Research Laboratory (GLERL) and Michigan Technological University (MTU) Great Lakes Research Center recently teamed up on the deployment of a wave glider in Lake Superior. The chemical and biological data collected will help researchers understand more about the Lake Superior foodweb and also be used to validate satellite information.

Autonomous wave glider that was recently deployed into Lake Superior by the MTU Great Lakes Research Center. Credit: Sarah Atkinson/Michigan Tech

Information gathered by autonomous vehicles, such as the wave glider, helps fine-tune satellite algorithms (instructions that tell a satellite how to interpret what it’s seeing). Satellites are a great tool for observing the lakes, as they provide a broader view than that from the ground. Researchers create Great Lakes-specific algorithms because those used in the ocean often do not work well in the lakes. The data collected by the wave glider will help validate the algorithms and allow researchers to understand more about the lakes, such as primary productivity (See MTU’s blog post for more.)

A team of researchers from MTU deployed the wave glider on August 30, 2021 and it spent 25 days surveying the lake and collecting data. The plan is to make the data public through the National Centers for Environmental Information (NCEI) so that information can be used in many ways including model development.

Path of the wave glider deployed on August 30th, 2021 and recovered on September 22, 2021 off the eastern coast of the Keweenaw Peninsula, near Bete Grise.

“It is a privilege for the Great Lakes Research Center to collaborate with NOAA GLERL on the wave glider experiment in Lake Superior, a first of its kind,” said Andrew Barnard, director of Michigan Tech’s Great Lakes Research Center. “This project continues to build a strong partnership between our organizations to push the boundaries of autonomy and sensing technologies. These new technologies in the Great Lakes support a better understanding of the physical processes in the lakes and will directly result in improved management insight for policy makers.”

Steve Ruberg of NOAA GLERL is thrilled with the MTU partnership as it expands our ability to collect data throughout the lakes. “Uncrewed vehicles give us the persistent large spatial observational capability to get in situ observations that will allow us to validate Great Lakes remote sensing.”

Left to right: Michigan Tech R/V Agassiz Jamey Anderson, assistant director of marine operations, Michigan Tech Great Lakes Research Center; Tim Havens, incoming director of the Great Lakes Research Center (January 2022) and John Lenters, associate research scientist at the Great Lakes Research Center ready the wave glider for deployment. Credit: Sarah Atkinson/Michigan Tech

This research project is a part of the Environmental Protection Agency’s Cooperative Science and Monitoring Initiative (CSMI). Federal and state agencies, tribal groups, non-governmental organizations and academic researchers from the United States and Canada team up yearly to assess conditions in one of the five Great Lakes. The survey focuses on a series of research areas that are tailored to the unique challenges and data needs associated with each lake.


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From safe drinking water to sustainable fisheries, NOAA GLERL’s Experimental Lake Erie Hypoxia Forecast is even more useful than anticipated

Four years ago, NOAA’s Great Lakes Environmental Research Laboratory (GLERL) and the Cooperative Institute for Great Lakes Research (CIGLR) began providing an Experimental Lake Erie Hypoxia Forecast Model to warn stakeholders of low-oxygen upwelling events that can cause water quality problems for over 2 million residents of northern Ohio. Now in its fifth year, this forecast model has turned out to serve additional purposes that NOAA’s scientists hadn’t even considered – including maintaining sustainable fisheries and solving a smelly mystery!

Water intake crib off the coast of Lake Erie in Cleveland, Ohio. By forecasting potential hypoxic upwelling events that could impact water quality, NOAA GLERL’s Experimental Hypoxia Forecast Model helps drinking water plant managers be prepared to adjust their treatment processes as needed.

Providing critical warnings to keep drinking water safe

Hypoxia – a state of low oxygen – occurs in the deep waters of Lake Erie’s central basin in July through September of most years. Low-oxygen water is an unfavorable habitat for fish, and may kill bottom-dwelling organisms that provide food for fish. While the hypoxic water generally stays near the lake floor, changes in wind and water currents can create upwelling events, in which this zone of low oxygen is brought to the surface along the coast.

Once it creeps into shallower parts of the lake, hypoxic water can upset drinking water treatment processes at water intakes along the shoreline. Hypoxic upwelling events cause rapid changes in 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. NOAA GLERL’s Experimental Hypoxia Forecast Model provides several days of advance notice that water quality is changing, so that drinking water plant managers can be prepared to adjust their treatment processes as needed.

This infographic from NOAA GLERL describes how hypoxia occurs in large bodies of water like the Great Lakes.

Plot twist: Benefiting more than just our water supply

NOAA GLERL’s Experimental Lake Erie Hypoxia Forecast has proven to be incredibly successful in its original goal – but our scientists were surprised to learn that its usefulness didn’t stop there. Recent stakeholder interviews conducted by CIGLR Stakeholder Engagement Specialist Devin Gill revealed that, in addition to helping manage the drinking water treatment process, the forecast has also become an unexpectedly vital tool for managing Lake Erie’s fisheries. 

One agency that makes use of the experimental hypoxia forecast is the Ohio Department of Natural Resources (DNR). The Ohio DNR is responsible for generating population estimates for Lake Erie’s yellow perch and walleye – estimates that ultimately help determine official catch limits to maintain the lake’s sustainable fisheries. 

“Large aggregations of fish may seek refuge at the edges of the hypoxic zone,” says Ann Marie Gorman, a fisheries biologist with the Ohio DNR’s Fairport Harbor Fisheries Research Station. “Our office tracks the location of the lake’s cold bottom water using the NOAA GLERL Hypoxia Forecast Model, and we may modify the timing of some of our surveys to minimize the potential impact of hypoxia on the results. Overall, the NOAA GLERL Hypoxia Forecast Model has become an integral tool for our survey planning.”

Understanding fish behaviors in response to hypoxia is important to conducting accurate population surveys of Lake Erie’s fish species. The ability of NOAA GLERL’s hypoxia forecast to warn fisheries managers of potential survey bias from these hypoxic events helps to save time, money, and energy that may have otherwise been used to conduct unsuccessful trawling surveys in hypoxic zones.

NOAA GLERL’s Experimental Hypoxia Forecast Model helps to guide the planning of trawling surveys like this one conducted by the Ohio Department of Natural Resources. Consulting the forecast helps the Ohio DNR to minimize the potential impact of hypoxia on survey results, which are used to set catch limits that keep Lake Erie’s fisheries sustainable. Photo credit: Ohio Department of Natural Resources.

Richard Kraus, a supervisory research fish biologist with the United States Geological Survey (USGS) Great Lakes Science Center Field Station in Ohio, also uses the experimental hypoxia forecast for his work with Lake Erie’s fisheries. Kraus explains that in Lake Erie, several cold-water fish species rely on finding refuge in colder, deeper waters of the lake – waters that are not impacted by warmer summer air temperatures. However, the presence of hypoxic zones in these deeper waters can impact how much refuge is available for these fish. As hypoxia reduces refuge habitats for cold-water species, chronic effects on growth and reproduction may develop, and in severe circumstances fish kills sometimes occur. The NOAA GLERL Hypoxia Forecast Model is instrumental in predicting where these potential ecosystem impacts could occur, in turn helping fisheries managers determine sustainable catch limits for each fish species in question.

The experimental forecast was also found to be useful to commercial and recreational fishers, who use the forecast to gauge the distribution of yellow perch in relation to hypoxic zones. Fishers can utilize the forecast on a daily basis to determine where to launch their boats, and where to search for aggregations of fish, depending on the hypoxia forecast for that day.

Plus, it’s not just routine fisheries management and recreation that the Experimental Hypoxia Forecast helps improve. In early September, it helped solve the mystery of a strange, foul smell coming from Lake Erie near Cleveland, Ohio, and fish kills associated with it. These phenomena resulted in many public inquiries regarding suspected gas leaks or pollutant spills. Thanks to the forecast, public officials knew that an upwelling of hypoxic water had recently occurred, likely carrying sulfur and nitrogen compounds that caused the stench, and were able to quickly eliminate other possible causes.

Half a decade in the making

Since it began in 2017, this NOAA project has grown into much more than just a computer model. The Experimental Lake Erie Hypoxia Forecast model was developed as a five-year project (2017-2021) with funding from NOAA’s Coastal Hypoxia Research Program, and is an extension of the Lake Erie Operational Forecasting System at NOAA’s Center for Operational Oceanographic Products and Services. Co-led by NOAA GLERL research scientists Drs. Mark Rowe and Craig Stow, and CIGLR’s Dr. Casey Godwin, project scientists provide an email update to public water systems, fisheries managers, and other stakeholders ahead of likely hypoxic events that contains links to the experimental forecast website and other useful NOAA webpages.

Map from the NOAA GLERL Experimental Lake Erie Hypoxia Forecast Model showing predicted change in Lake Erie temperature (top) and dissolved oxygen (bottom) during a three-day hypoxic upwelling event from August 31 to September 2, 2021.

Partners on this project include Ohio public water systems (including the cities of Cleveland and Avon Lake), NOAA’s National Ocean Service, and the Great Lakes Observing System. Special thanks to Devin Gill from the Cooperative Institute for Great Lakes Research for contributing stakeholder interview findings for this article.


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New wave buoy will provide data to support wave and flood forecasting on Lake Champlain

The NOAA Great Lakes Environmental Research Laboratory (GLERL) and partners recently deployed a buoy in Lake Champlain that will measure the lake’s wave heights to assess the accuracy of a new experimental model for the lake. This is part of a five-year NOAA GLERL project that will improve public safety on Lake Champlain by contributing to flood preparedness and response around the shores of the lake. Wave conditions are critical to public safety both for recreational and commercial activities on the lake – such as for boats, harbors, and beaches – but also for predicting coastal flood impacts at the shoreline where waves can run up and significantly impact infrastructure.

Left: Newly deployed NOAA buoy in Lake Champlain. Credit: University of Vermont FEMC staff. Top right: NOAA GLERL partners at the University of Vermont’s Forest Ecosystem Monitoring Cooperative (FEMC) deployed the buoy on Lake Champlain in May 2021. Credit: University of Vermont FEMC staff. Bottom right: Sunset on Lake Champlain. Credit: Dan Titze, CIGLR.

The project is a major collaborative effort by bi-national, federal, and university partners of NOAA GLERL. Partners at the University of Vermont’s Forest Ecosystem Monitoring Cooperative (FEMC) deployed the seasonal buoy in May, and the Coastal Data Information Program (CDIP) at the University of San Diego Scripps Institute of Oceanography receives the data, manages its quality control, and posts it to NOAA’s National Data Buoy Center (NDBC) website. Researchers at the Cooperative Institute for Great Lakes Research (CIGLR) are currently leading the development of a wave model for Lake Champlain, which is providing experimental forecasts on the GLERL website.

The buoy is located in the middle of the lake near Schuyler Reef, where it will remain until late fall, and is collecting wave height observations that will be used to validate NOAA’s WAVEWATCH III model for Lake Champlain. The experimental model’s output data will be compared to the buoy’s observed data, which will help scientists assess how well the model performs.

Location of the new NOAA Lake Champlain wave buoy, depicted by a yellow diamond. Map credit: NOAA National Data Buoy Center.

The buoy’s environmental data can be found on the CDIP website, and on the buoy’s page on the NOAA NDBC website. The buoy and the experimental wave model will be a helpful new tool for the region’s National Weather Service Weather Forecast Office in Burlington, Vermont, which provides lake forecasts including wave data to mariners in the region.

In addition to regional weather forecasters and local mariners, this buoy’s data will also be useful to emergency managers in the counties and cities around Lake Champlain and the Richelieu River, as well as the NOAA National Centers for Environmental Prediction which will transition the WAVEWATCH III model to operations.

This project is funded by the International Joint Commission’s Lake Champlain-Richelieu River (LCRR) Study Board. The International Joint Commission (IJC) is a bi-national organization established by the governments of the United States and Canada under the Boundary Waters Treaty of 1909. It oversees activities affecting the extensive waters and waterways along the Canada–United States border. The IJC’s LCRR Study Board was created in 2016 to undertake a study of the causes, impacts, risks, and potential solutions to flooding in the LCRR basin.


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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.