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 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.
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 timingof 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.
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.
With a network of experimental buoys that are constantly recording new data every few minutes, the amount of data the NOAA Great Lakes Environmental Research Laboratory (GLERL) has collected in the past 15 years is massive – and prepping it all to be archived in an official data repository is no small task. This year, thanks to the hard work of GLERL’s data managers and engineers, the Great Lakes environmental data collected by NOAA GLERL’s real-time buoys has been archived with NOAA’s National Centers for Environmental Information (NCEI) data repository. NCEI hosts and provides public access to one of the most significant archives of oceanic, atmospheric and geophysical data in the world.
A NOAA GLERL Real-Time Coastal Observation Network (ReCON) buoy in Lake Michigan.
An ever-growing collection of Great Lakes data
This real-time Great Lakes observational data archived in NCEI has been collected over time by sensors on coastal buoys as part of GLERL’s Real-Time Coastal Observation Network (ReCON). Each of ReCON’s 16 buoy stations collects meteorological data and provides sub-surface measurements of chemical, biological, and physical parameters (things like wave height, dissolved oxygen, chlorophyll, and water temperature). Totaling an impressive 2,055 data files, this data spans 15 years – from the inception of the first ReCON station in 2004 through the end of the 2019 field season. The data collected by GLERL’s ReCON buoys in the past 15 years are unique and valuable, and now that they are properly processed and easily accessible in the NCEI archive, they can be used in a variety of ways.
Using historic data to improve scientific models
While the near real-time info that our experimental ReCON buoys provide is great for helping you decide whether to hit the water for a day of boating or fishing, their usefulness doesn’t stop there. This Great Lakes ReCON data – both old and new – is incredibly useful to state and federal resource managers, educators, and researchers. For example, scientists can use the historic datasets to test the accuracy of their models, a process known as ‘hindcasting.’ When using archived data for hindcasting, researchers enter data for past events into their model to see how well the model’s output matches the known results. One cool example of hindcasting is the animation below that shows the Lake Superior wind and wave conditions that led to the sinking of the Edmund Fitzgerald in 1975.
Animation created with hindcasting that shows significant wave height and wind field, final voyage of the Edmund Fitzgerald, Nov 9-11, 1975.
As for the fact that ReCON data is collected in near real-time, these convenient same-day measurements can help determine whether or not a hypoxic (low oxygen) event will occur, detect nutrients contributing to harmful algal blooms, and even provide crucial data to the NOAA National Weather Service for coastal forecasting.
Water intake crib off the coast of Lake Erie in Cleveland, Ohio. Real-time data collected by NOAA GLERL’s ReCON buoys can help warn water intake managers of potential hypoxic events, which can affect drinking water quality.
Putting our data to the test
NOAA GLERL data manager Lacey Mason and marine engineer Ron Muzzi are in charge of preparing and submitting the data to NOAA’s NCEI data repository. Preparing the data to be archived involves performing quality assurance checks to ensure that it meets the Integrated Ocean Observing System’s (IOOS) standards set specifically for real-time oceanographic data. All of the data undergoes multiple quality tests before being archived, and each data point is flagged to indicate its reliability – whether it passed all tests, is suspect, or failed one or more tests.
In addition to being available on NOAA GLERL’s website and now the NOAA NCEI data repository, GLERL’s real-time buoy data can also be found on NOAA’s National Data Buoy Center website. The NCEI archive is fully updated with all of GLERL’s real-time data through 2019, and GLERL will continue to add new data to the archive on a yearly basis. The archived data can be accessed from the link here: https://doi.org/10.25921/jvks-b587.
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.
Have you ever wanted to explore Great Lakes shipwrecks and the underwater life that lives around them? Now you can, right from home! This recent feature from Detroit Public TV’s Great Lakes Now program introduces viewers to the world of shipwrecks in NOAA’s Thunder Bay National Marine Sanctuary, as well as their relationship with the surrounding natural environment. Originally recorded on October 12, 2020, Great Lakes Now Program Director Sandra Svoboda talks with NOAA GLERL Research Ecologist Ashley Elgin and NOAA Maritime Archaeologist Stephanie Gandulla about this unique sanctuary in northern Lake Huron.
Divers approach a shipwreck on the bottom of Lake Huron in Thunder Bay National Marine Sanctuary. Invasive mussels completely cover the railing of the ship.
Elgin discusses some of the fascinating ways that these shipwrecks intersect with the ecology around them, from the impacts invasive species have on the wrecks to the differences in fauna surrounding shallow wrecks versus deep ones. Gandulla focuses on the historic perspective of the sanctuary and its shipwrecks, helping viewers dive into the “history under the waves” with underwater footage of the shipwrecks themselves.
The video also features a preview of the new PBS documentary series Age of Nature, which leads Elgin and Gandulla to discuss the differences between shipwrecks and their ecosystems in the ocean and the Great Lakes. Watch the full video below!
Plus, here are a few more photos from the breathtaking views of Thunder Bay National Marine Sanctuary:
Paddleboarders explore a shallow shipwreck from the surface in Thunder Bay National Marine Sanctuary in northern Lake Huron. The clearness of the water is enhanced by filtration from the invasive mussels that Research Ecologist Ashley Elgin studies at NOAA GLERL.A propeller from the Monohansett shipwreck rests on the Lake Huron floor in Thunder Bay National Marine Sanctuary.The dilapidated deck of the shipwreck E.B. Allen is mostly covered in invasive mussels as it sits on the bottom of Lake Huron in Thunder Bay National Marine Sanctuary. The bubbles from a diver’s oxygen tank can be seen rising up from inside the wreck.
With over 180 non-native aquatic species currently present in the Great Lakes and potential new invaders on the horizon, keeping track of the impacts and risks that these organisms pose is an ongoing challenge. The Great Lakes Aquatic Nonindigenous Species Information System (GLANSIS), a one-stop shop for information about aquatic nonindigenous species, hosts data on the historical and ongoing effects of aquatic organisms introduced to the region. Two NOAA Technical Memos serve as the underlying risk and impact assessments for the GLANSIS database and researchers recently completed important annual updates to these documents (TM-161c and TM-169c). These updates allow researchers to stay current on potential risk and impacts of these organisms to the ecosystem.
GLANSIS technical memos contain the risk and impact information provided by the database.
What are the GLANSIS tech memos?
NOAA Technical Memos are used for the timely documentation and communication of raw data, preliminary results of scientific studies, or interim reports that may not have received formal external peer reviews in the style of academic journal articles or manuscripts. These numbered publications are publically available online in PDF format, and serve as important research documentation and reference material.
The GLANSIS team updates two different tech memos every year by reviewing and synthesizing the scientific literature on invasive — or potentially invasive — aquatic species. The first, TM GLERL-161et seq., “An Impact Assessment of Great Lakes Aquatic Nonindigenous Species”, provides the updated impact assessments for nonindigenous species documented as reproducing and overwintering in the Great Lakes, focusing on their ecological, socio-economic, and beneficial impacts to the region. The second, TM GLERL-169 et seq., “A Risk Assessment of Potential Great Lakes Aquatic Invaders” documents species that have been identified as likely to become invasive if introduced to the Great Lakes region. The 2019 updates document the updated impact assessments for 89 of the 188 nonindigenous species (TM-161c) and four assessments were updated and eight new species were added for potential invasives (TM-169c).
Why are the tech memos updated every year, and why are they important?
The GLANSIS technical memos provide transparent, publicly-available documentation of the risk assessment process that underlies the species profiles in the database. Not only do they provide the summary information available in the website, they also share all the original sources and the details of the specific semi-quantitative analysis behind declaring particular species ‘high-impact’. New and improved data on aquatic invasive species is being published all the time, and documenting annual updates to risk and impact assessments helps to keep the GLANSIS database up-to-date and allows researchers to track the changes in the state of knowledge for specific species through the years. Each update takes a new look at how the latest data influences larger-scale patterns and trends. Unlike a website, where old versions are overwritten by the new, the technical memos provide a stable, citable reference point.
The GLANSIS tech memos can be read in full at the NOAA Great Lakes Environmental Research Lab’s publications page. To learn more about GLANSIS, check out https://www.glerl.noaa.gov/glansis/ and explore the site for yourself.
CIGLR’s Russ Miller deploying glider in Lake Huron, June 2017
NOAA Great Lakes Environmental Research Laboratory (GLERL) and the Cooperative Institute for Great Lakes Research (CIGLR) recently posted eight years’ worth of Great Lakes autonomous underwater vehicle (AUV), or “glider data ” on NOAA’s Integrated Ocean Observing System (IOOS) Underwater Glider Data Assembly Center (DAC) map. The map is a collaborative effort and includes current and historical glider missions dating back to 2005 from around the planet. This data is useful to government agencies, researchers, environmental managers, and citizens who use Great Lakes data for better understanding the characteristics of Great Lakes water.
CIGLR glider just before a deployment in Lake Michigan at the NOAA GLERL Lake Michigan Field Station in Muskegon, MI.
The collection and analysis of this data is a close collaboration between NOAA GLERL, CIGLR and partner institutions. CIGLR owns and operates the glider, and it is deployed using NOAA GLERL vessels. Data managers and researchers from both organizations are working together to make this data as useful and accessible as possible. This cooperative project, which has been funded by the Great Lakes Observing System (GLOS; a part of the IOOS program), aims to support science, public safety, and security through the use of unmanned systems (UxS).
Glider Tech Specs
This glider is buoyancy-driven, meaning it controls its depth in the water by inflating and deflating a “bladder” that in turn makes it sink or float. It typically operates at around 30 meters (100 feet) below the lake surface, but can go as deep as 200 meters (650 feet) when needed. While the glider is able to work on it’s own, scientists wirelessly communicate with it regularly throughout its journey when it’s at the surface. It’s programmed to resurface regularly for check-ins, so we always know right where it is and we can even instruct it to change its mission path if necessary. It may only travel an average of 1 kilometer (0.6 miles) per hour, but its missions can last up to 60 days and provide us with amazing data sets to help answer questions about the Great Lakes ecosystem. Check out the video below from NOAA’s Ocean Service and visit this fact page for more on how the glider works.
The importance of data collection
With every deployment, the glider measures the water’s physical properties such as temperature, mineral content, pressure, and salinity. (Yes, even the Great Lakes have a tiny bit of salinity!) It also measures biological properties such as chlorophyll fluorescence and concentrations of dissolved organic matter, which indicate the region’s level of primary biological productivity (the amount of organic matter produced by phytoplankton in the water). Phytoplankton might be tiny, but their productivity is extremely important to the lakes’ ecosystems because it provides nutrients to the rest of the food web.
CIGLR glider floating just below the surface of the water.
When you piece together all these day-to-day measurements, you can use them to study seasonal changes such as movement of the thermocline – or steep temperature gradient in the lake – which can impact the rate of biological activity in the spring and summer. The size and intensity of spring algal blooms and occasional “whiting events” (accumulations of calcium carbonate particles in the water due to increased biological productivity) are other examples of seasonal biological phenomena the glider can observe. The glider collects high-quality data efficiently and cost-effectively, day and night in all weather conditions, ultimately allowing us to collect more data in a shorter amount of time than is possible with traditional ship-based methods. The robust datasets it gives us advance our understanding of Great Lakes processes on short-term, seasonal, and annual timescales — and lay a foundation for observing changes in the lakes over several decades.
This map shows NOAA GLERL/CIGLR underwater glider pathways in southern Lake Michigan, available on NOAA’s Integrated Ocean Observing System (IOOS) Underwater Glider Data Assembly Center map. A long-term series of Lake Michigan observations in the southern basin of Lake Michigan began in 2012, criss-crossing between Muskegon, Milwaukee. This complements data collected by the NOAA National Data Center Station 45007, as well as temperature string in the southern basin of the lake, connecting the observations of NOAA GLERL and University of Wisconsin-Madison.
Glider paths shown on the maps include all deployment from 2012-2019. These paths expand observations collected by Federal and University research vessels in the same regions of the Great Lakes, through the use of other tools, such as NOAA GLERL’s Plankton Survey System (PSS) and Multiple Opening and Closing Net and Environmental Sampling System (MOCNESS). It is important to have a long period of observations from many types of collection across the lakes to better understand how things like water temperature at different depths, inputs from rivers, and seasonal changes to other characteristics of the water affect the ecosystem.This information is useful in understanding the impacts of invasive species, harmful algal blooms, and our changing climate.
This map shows NOAA GLERL/CIGLR underwater glider pathways in the Great Lakes, available on NOAA’s Integrated Ocean Observing System (IOOS) Underwater Glider Data Assembly Center map. In 2013, 2015, 2017, and 2018, glider deployments were chosen to complement ship- and glider-based observations of the Environmental Protection Agency (EPA), NOAA, United States Geological Survey (USGS), and Coordinated Science and Monitoring Initiative (CSMI) in Lakes Michigan, Ontario and Huron. Lake Erie is too shallow for effective use of this glider, and Lake Superior has been monitored by EPA and University of Minnesota Large Lakes Observatory gliders.
Future deployments and collaboration
Planning is currently underway for future missions in the Great Lakes and potential applications for the glider’s wide variety of data. The glider will also be used this year on Lake Michigan for research and observations during the 2020 Cooperative Science and Monitoring Initiative (CSMI), a binational effort to coordinate science and monitoring activities in one of the five Great Lakes each year. This year’s CSMI research will likely use the glider to gain a better understanding of water quality in the lake’s nearshore regions – the area in the water from where waves begin to break, up to the lowest water point on the beach. With great partners like CIGLR and GLOS, the future is bright for NOAA’s underwater glider explorations.
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!
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 MediaResearchers 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 MediaNOAA 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 MediaNOAA GLERL Marine Engineer Kyle Beadle controls the ROV in order to observe sinkholes from the R/V Storm. Photo: Great Lakes Outreach MediaNOAA 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
Ice conditions in Lake Superior under a clear blue sky near Grand Marais. March 24, 2014. Credit: NOAA
Editor’s Note: This blog post was updated on February 4, 2020 to reflect an updated Seasonal Ice Forecast. Please be sure to read the entire update for more information on this active area of research at NOAA GLERL!
As many of us in the Great Lakes community start to don our parkas and break out the snow shovels, we know the splashing waves on our shorelines will soon be replaced with ice. And, with near-record high water levels in the lakes this year, the question of how ice and water levels will affect coastal communities in the months ahead looms large.
The role of ice in the Great Lakes water budget
To start, we know that evaporation plays a major role in water levels by withdrawing water that enters the lakes from precipitation and runoff. So, high evaporation contributes to lower water levels, and low evaporation contributes to higher water levels. (For more on the Great Lakes water budget, check out this infographic.) Traditional thinking is that high ice cover forms a “cap” that leads to decreased evaporation of lake water. However, we now know that the relationship between ice, evaporation, and water levels is more complex than that.
While this assessment on Great Lakes evaporation from Great Lakes Integrated Sciences & Assessments explains that high ice cover is still associated with less evaporation the following spring, it also reports that evaporation rates before winter have an effect on how much ice forms in the first place. Specifically, it explains that high evaporation rates in the fall correspond with high ice cover the following winter. So just as ice cover can influence evaporation, the reverse is true as well – a much different story than the one-way street it was previously thought to be.
A look at 2020 ice cover: NOAA GLERL’s observations & predictions
On January 1st, 2020, the total Great Lakes ice cover was 1.3%. That’s about a third as much ice as around the same time last year, and barely anything compared to early 2018, when it was already about 20%. You’ll see in the figure below that shallow, protected bays tend to freeze first, especially ones that are located in the northern Great Lakes region. So it makes sense that most of the ice so far is in the bays of Lake Superior, followed by northern bays in Lakes Michigan and Huron like Green Bay and Georgian Bay.
Click here for more comparisons like this on GLERL’s website
GLERL conducts research on ice cover forecasting on two different time scales: short-term (1-5 days) and seasonal. GLERL’s short-term ice forecasting is part of the upgrade to the Great Lakes Operational Forecast System (GLOFS), a set of models currently being transitioned to operations at the National Ocean Service to predict things like currents, water temperature, water levels, and ice. The ice nowcast and forecast products (concentration, thickness, velocity) have been tested for the past several years and will soon become operational (available for the general public).
GLERL’s ice climatologist, Jia Wang, produces an experimental annual projection for Great Lakes ice cover using a statistical model that predicts maximum Great Lakes ice cover percentages for the entire season. This model’s prediction is based on the predicted behaviors of four global-scale air masses: ENSO (El Nino and Southern Oscillation), NAO (North Atlantic Oscillation), PDO (Pacific Decadal Oscillation), and AMO (Atlantic Multidecadal Oscillation). While they’re all pretty far away from the Great Lakes, past research has shown that these air masses — or global teleconnections — heavily influence the year-to-year variability of Great Lakes ice cover.
Based on this experimental model’s results, NOAA GLERL projects this Great Lakes ice cover this winter to be around 47%. That’s almost 9% below the long-term average of 55.7%. Here’s the preliminary projection broken down by lake:
Lake Superior: 54%
Lake Michigan: 41%
Lake Huron: 66%
Lake Erie: 80%
Lake Ontario: 32%
Updated Seasonal Ice Forecast
On 1/24/2020, GLERL researchers reran the experimental ice forecast model. The reason for the revised ice projection was due to significant deviation in the actual teleconnection indices from what was predicted in November. Because the model uses these predicted teleconnections to predict ice cover, it is important to use the most accurate values. The experimental ice forecast model is rerun to get updated values. NOAA GLERL’s research towards improving our capabilities for ice forecasting is ongoing. This research product continues to evolve as we gain understanding of the complex climatic drivers for the Great Lakes Region.
Lake Superior = 46-54% Lake Michigan = 23-42% Lake Huron = 47-72% Lake Ontario = 16-36% Lake Erie = 67-74%
Why did the 2020 Great Lakes ice cover forecast change? / What factors go into this forecast?
The experimental Great Lakes ice forecast is initially calculated in November and is based on a model run based on the forecasted teleconnection patterns for December, January and February. If the teleconnection values diverge from the forecast (that is, the climate did not act as predicted) then the experimental ice forecast model is updated with the latest information on expected teleconnection indices for the remainder of the winter.
What does the ice coverage range mean?
Because there is uncertainty with this experimental forecast, two different versions of the model are used. Version “a ” uses only 4 teleconnection patterns (NAO, AMO, ENSO, PDO) as variables (inputs). Version “b” also includes observed November lake surface temperature (LST) as a variable. November and December LST were shown to be equally well correlated with ice cover but by using November LST, it enables the forecast to be made a month earlier.
Ice cover is low right now, what could happen that would increase ice coverage?
A lot can still happen as there are many weeks of winter left. Historically, much of the major freezing happens in February. However, if temperature continues to remain abnormally warm, it is unlikely ice cover would reach these values.
Why is it important to continue this research?
NOAA GLERL continues to refine the ice forecast model, active research designed to improve the Great Lakes ice forecast. We plan to improve the forecast skill by adding the cumulative freezing degree days since December 1, and update the forecast every two weeks throughout the ice season.
Predicting Great Lakes water levels
Forecasts of Great Lakes monthly-average water levels are based on computer models, including some from NOAA GLERL, along with more than 150 years of data from past weather and water level conditions. The official 6-month forecast is produced each month through a binational partnership between the U.S. Army Corps of Engineers and Environment and Climate Change Canada.
Want to know more about GLERL’s ice research? Visit our ice cover webpage for current conditions, forecasts, historical data, and more!
Great Lakes ice cover facts since 1973
94.7% ice coverage in 1979 is the maximum on record.
9.5% ice coverage in 2002 is the lowest on record.
11.5% ice coverage in 1998, a strong El Niño year.
The extreme ice cover in 2014 (92.5%) and 2015 (88.8%) were the first consecutive high ice cover years since the late 1970’s.
On March 6, 2014, Great Lakes ice cover was 92.5%, putting winter 2014 into 2nd place in the record books for maximum ice cover. Satellite photo credit: NOAA Great Lakes CoastWatch and NASA.
NOAA Great Lakes Environmental Research Laboratory 
