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.
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.
“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.”
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.
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!
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
If you live in the Great Lakes basin and have been on or even near a road recently, you might be feeling unreasonably ragey at the mere mention of lake effect snow. We get it. But bear with us, because we’re doing some cool science we’d like to tell you about. It may even make your commute easier someday, or at least more predictable.
GLERL scientists are working with researchers at the University of Michigan’s Cooperative Institute for Great Lakes Research (CIGLR), the National Weather Service, and NOAA’s Earth Systems Research Laboratory (ESRL) to make lake effect snow forecasts in the Great Lakes better.
NOAA’s high resolution rapid refresh (HRRR) model is the most commonly used weather model for predicting lake effect snow. An experimental version runs on a beastly high-performance computer at ESRL in Colorado, and predicts a whole list of atmospheric variables (including snowfall) every 15 minutes. The model relies on water surface temperature data, collected via satellite, to make its predictions. It’s important to give the model accurate water surface temperatures to estimate evaporation across the Great Lakes, since it is the main driver of lake effect snow.
Unfortunately, satellite temperature data has limitations. If clouds keep satellites from measuring the temperature at a specific location, the weather model will just use the most recent measurement it has. Since it’s especially cloudy in the Great Lakes during the lake effect snow season (late fall and early winter), that data could be days old. Because lake temperatures are changing quite rapidly this time of year, days-old data just doesn’t cut it.
As it turns out, GLERL already has a model that predicts Great Lakes surface temperature pretty well. The Great Lakes Operational Forecast System (GLOFS) spits out lake surface temperatures every hour. If we tell the weather model to use GLOFS output instead of satellite data, it has the potential to do a far better job of forecasting lake effect snow.
Linking two models like this is called “coupling”. GLOFS actually already uses input from HRRR—wind, air temperature, pressure, clouds and humidity data all inform GLOFS’ predictions. We’re just coupling the models in both directions. HRRR will send its output to GLOFS, GLOFS will “talk back” with its own predictions of water surface temperature (and ice cover), and HRRR will produce a (hopefully) more informed prediction of lake effect snow.
Initial results are promising. We used the coupled models to do a ‘hindcast’ (a forecast for the past) to predict lake effect snow for a major event over Lake Erie in November of 2014. They did a significantly better job than without coupling. The figure below shows the difference.
The coupled models improved cumulative snow water equivalent forecasts. Red shows where the model increased snowfall.
You’ll notice a band of blue on the southeastern edge of Lake Erie, indicating that the coupled models predicted less lake effect snow in that area. There’s a band of orange directly to the north of it, where the coupled models predicted more lake effect snow. What you’re seeing is the coupled model predicting the same band of snow, but further north, closer to where it actually fell.
That storm slammed the city of Buffalo, New York, killing 13 people. Better lake effect snow predictions have the potential to save lives.
GLERL and partners will be doing further testing this winter, and if it works out, the model coupling will be carried over in research-to-operations transitions.
It’s nearly winter here in the Great Lakes—our buoys are in the warehouse, our boats are making their way onto dry land, and folks in the lab are working hard to assess observed data, experiments, and other results from this field season.
This is a retrospective animation showing the predicted surface chlorophyll concentrations estimated by the Experimental Lake Erie HAB Tracker model during the 2018 season. Surface chlorophyll concentrations are an indicator of the likely presence of HABs. For more information about how the HAB Tracker forecast model is produced and can be interpreted, visit our About the HAB Tracker webpage.
The harmful algal bloom (HAB) season is also long over in the region. The final Lake Erie HAB Bulletin was sent out on Oct. 11, as the Microcystis had declined in satellite imagery and toxins decreased to low detection limits in samples. In the seasonal assessment, sent out by NOAA’s Centers for Coastal Ocean Science on Oct. 26, it was determined that the season saw a relatively mild bloom—despite its early arrival in the lake—and the bloom’s severity was significantly less than that which was predicted earlier in the season. These bulletins and outlooks are compiled using several models. Over the winter, the teams working on the models take what they learn from the previous season, and update their models for future use.
Aerial photograph of the harmful algal bloom in Western Basin of Lake Erie on July 2, 2018, (Photo Credit: Aerial Associates Photography, Inc. by Zachary Haslick). Pilots from Aerodata have been flying over Lake Erie this summer to map out the general scope of the algal blooms. In addition to these amazing photos, during the flyovers, additional images are taken by a hyperspectral imager (mounted on the back of the aircraft) to improve our understanding of how to map and detect HABs. The lead researcher for this project is Dr. Andrea VanderWoude, a NOAA contractor and remote sensing specialist with Cherokee Nation Businesses. For more images, check out our album on Flickr.
This lab work is super important for understanding the drivers of toxic algae in the Great Lakes. For instance, in a new study released this month, researchers looking at samples from previous years found that “ . . . the initial buildup of blooms can happen at a much higher rate and over a larger spatial extent than would otherwise be possible, due to the broad presence of viable cells in sediments throughout the lake,” according to the lead author Christine Kitchens, a research technician at CIGLR, who works here in the GLERL lab. This type of new information can be incorporated into the models used to make the annual bloom forecasts.
As you can see, our work doesn’t end when the field season is over. In spring 2019, when the boats and buoys are back in the water and samples are being drawn from the lakes, researchers will already have a jump on their work, having spent the winter months analyzing previous years, preparing, and applying what they’ve learned to the latest version of the Experimental HAB Tracker, advanced observing technologies, and cutting-edge research on harmful algal blooms in the Great Lakes.
Eric Anderson, GLERL oceanographer, used to study the movement of fluid inside bone tissue — now he studies the movement of water in the Great Lakes.
Eric Anderson is NOAA GLERL’s resident oceanographer (but his Twitter handle is @lakeographer—you should trademark that one, Eric). At its core, his research centers around the movement of water. You might have seen our animations of currents in the Straits of Mackinac, or of meteotsunamis coming across Lake Michigan — he’s the guy behind those computer models.
Some cool things about Eric are that he plays the banjo, that he used to study the movement of fluid inside bone tissue, and that he’s quick to remind us people were watching the Straits of Mackinac millennia before his computer models existed. Read on to learn more cool things!
How would you describe your job?
My research is on hydrodynamics, which is a fancy way of saying the moving physical aspects of the water in the Great Lakes—things like currents, temperatures, ice, and waves. Most of my day is built around looking at measurements of the water and air and then developing computer models that simulate how the lakes respond to different weather conditions. This field of science is particularly helpful in safe navigation of the lakes, responding to contaminant spills, search and rescue operations, and understanding how the ecosystem responds to different lake conditions.
What is the most interesting thing you’ve accomplished in your job?
Maybe the most rewarding has been working on the Straits of Mackinac. It’s one of the most beautiful spots in the Great Lakes, but also one of the most dynamic, with high-speed currents changing every few days, if not hours. A groundswell of attention to the Straits in the last several years has pushed the public to get more engaged and learn about the conditions in the Straits, and I’ve been glad to help where I can.
As part of this work, we’ve found some 1600’s-era [settler] written accounts of the currents in the Straits. We also know that [Indigenous] people have been watching the Straits for thousands of years, and it’s rewarding to continue this thread of knowledge.
What do you feel is the most significant challenge in your field today?
It seems like the hardest thing is to communicate the science. People are starved for information, and there’s a real love out there for learning about the Great Lakes. All we can do is to try and keep the flow of information getting out to the folks who care, and just as important, to those who don’t think they care. When you see environmental science covered in the news, it’s usually reporting on something negative or even catastrophic, which is certainly important, but there are pretty cool discoveries being made routinely, big and small, and those don’t often seem to make it to the headlines. We have to keep working hard to make sure these stories make it out, and at the same time keep our ears open to the concerns that people have for the lakes.
Where do you find inspiration? Where do your ideas come from in your research or other endeavors in your job?
Inspiration is everywhere. Try to hike up to a good vantage point overlooking the lake, like the dunes or a bluff, and not feel inspired. More often, though, inspiration comes from talking with other people, whether scientists, students, or interested members of the public. I can’t think of a time where I’ve given a public seminar and not walked away with a new question or idea to investigate. People’s enthusiasm and bond with the Great Lakes is infectious, and so I try to tap into that as often as I can.
Two meteotsunamis, large waves caused by storm systems, came across Lake Michigan on April 13, 2018. Eric Anderson models meteotsunamis in his role as oceanographer at NOAA GLERL.
How would you advise high school students interested in science as a career path, or someone interested in your particular field?
I took somewhat of a winding career path to get where I’m at with GLERL, working in car assembly plants and then on the nano-fluidic flow inside bone tissue before ending up in physical oceanography. I didn’t really know what I wanted in high school or college, but I knew physics and math were where I felt at home. So I found a way to learn the fundamentals that I’ve been able to apply in each of these jobs, and that allowed me to explore different parts of science and engineering. Not everyone will have the same chances or opportunities, but if you can find a way to really solidify the fundamentals and just as importantly seek out a breadth of experiences, you’ll be in a better position when those opportunities do come along.
What do you like to do when you AREN’T sciencing?
I’m either hanging out with family, playing music, or talking with someone about how I wish I was playing more music.
What do you wish people knew about scientists or research?
By and large, science is curiosity driven, often fueled by the scientist’s own enthusiasm, and in my case also by the interests of the public. Whether it’s a new discovery, or re-codifying or quantifying something that others have observed for millennia, there’s no agenda here other than to understand what’s happening around us and share whatever pieces we can make sense of. I’ll add a sweeping generalization that scientists love to talk about their research, so don’t be afraid to ask.
The Great Lakes, along with their connecting waterways and watersheds, make up the largest lake system on the planet—more than 20% of the world’s surface freshwater! Water levels on the lakes change in response to a number of factors, and these changes can happen quickly. Changing water levels can have both positive and negative impacts on shipping, fisheries, tourism, and coastal infrastructure like roads, piers, and wetlands.
Currently, water levels on all of the Great Lakes are above their monthly averages, and have been developing since the spring of 2013, when a record-setting two-year rise in water levelsbegan on the upper Great Lakes. Extreme conditions in spring of 2017 produced flooding and widespread damage at the downstream end of the basin—Lake Ontario and the St. Lawrence River. In case you missed it, check out our infographicon this flooding event.
So, what’s happening now that it’s winter?
As we entered the late fall-early winter of 2017-2018, a warm weather pattern had forecasters looking toward a fairly warm winter. However, in late December, the conditions changed and a much colder than normal weather pattern took many folks living in the Great Lakes by surprise. Much like how water levels can change quickly in the Great Lakes, so can ice cover. Due to frigid air temperatures, between December 20 and January 7, total ice cover on the lakes jumped 26.3%. Lake Erie alone jumped up to nearly 90%!
After January 7th, ice coverage dropped a bit as the air temperatures warmed, then rose again as temperatures went back down, showing again how vulnerable the lakes are to even the slightest changes. Compare where we are now to where we were 2 years ago at this time, and you’ll easily see how variable seasonal ice cover can be in the Great Lakes.
What’s the outlook for ice and water levels?
Below, you’ll find what GLERL researchers expect to see for ice cover this winter, as well as the U.S. Army Corps’ water levels forecast into Spring 2018. Be sure to read further to find out more about the science that goes into these predictions!
On 1/3/2018, NOAA’s Great Lakes Environmental Research Laboratory updated the maximum 2018 Great Lakes basinwide ice cover projection to 60%. The long-term average is 55%. The updated forecast reflects changes in teleconnection patterns (large air masses that determine our regional weather) since early December 2017—movement from a strong to a weak La Nina, a negative to a positive Pacific Decadal Oscillation, and a positive to a negative North Atlantic Oscillation. These patterns combine to create colder than average conditions for the Great Lakes.
—Water Levels forecast into spring 2018—
According to the most recent weekly water level update from the U.S. Army Corps, water levels for all of the Great Lakes continue to be above monthly average levels and above last year’s levels at this time. All of the lakes have declined in the last month. Note that ice developing in the channels and on the lake surface can cause large changes in daily levels during the winter, especially for Lake St. Clair. Over the next month, Lake Superior and Lake Michigan-Huron are expected to continue their seasonal decline. Lake St.Clair, Lake Erie, Lake Ontario are expected to begin their seasonal rise.
More information on water levels and ice cover forecasting
How are water levels predicted in the Great Lakes?
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.
At GLERL, research on water levels in the Great Lakes analyzes all of the components of the Great Lakes water budget. The information we gather is used to improve forecast models. The infographic below goes into more detail about the Great Lakes water budget.
How does winter ice cover affect water levels?
As mentioned in the recently released Quarterly Climate Impacts and Outlook for the Great Lakes, water levels in the Great Lakes tend to decline in late fall and early winter, mainly due to reduced runoff and streamflow combined with higher over-lake evaporation caused by the temperature difference between air and water. Factors such as surface water temperatures, long stretches of cold or warm air temperatures, and winds all impact the amount of lake ice cover as well as extreme winter events, such as lake-effect snow—which we’ve already seen plenty of this winter—and vice versa. All of these factors influence winter water levels in the Great Lakes. The timing and magnitude of snow melt and spring runoff will be major players in the spring rise.
GLERL researcher, George Leshkevich, drilling through the ice in Green Bay, Lake Michigan.
NOAA’s Great Lakes Environmental Research Laboratory (GLERL) is on the cutting edge of using satellite remote sensing to monitor different types of ice as well as the ice cover extent. To make this possible, an algorithm—a mathematical calculation developed at GLERL to retrieve major Great Lakes ice types from satellite synthetic aperture radar (SAR) data—has been transferred to NOAA’s National Environmental Satellite, Data, and Information Service (NESDIS) for evaluation for operational implementation.
Once operational, the algorithm for Great Lakes ice cover mapping holds multiple applications that will advance marine resource management, lake fisheries and ecosystem studies, Great Lakes climatology, and ice cover information distribution (winter navigation). Anticipated users of the ice mapping results include the U.S. Coast Guard (USCG), U.S. National Ice Center (NIC), and the National Weather Service (NWS).
For satellite retrieval of key parameters (translation of satellite imagery into information on ice types and extent), it is necessary to develop algorithms specific to the Great Lakes owing to several factors:
Ocean algorithms often do not work well in time or space on the Great Lakes
Ocean algorithms often are not tuned to the parameters needed by Great Lakes stakeholders (e.g. ice types)
Vast difference exists in resolution and spatial coverage needs
Physical properties of freshwater differ from those of saltwater
The relatively high spatial and temporal resolution (level of detail) of SAR measurements, with its all-weather, day/night sensing capabilities, make it well-suited to map and monitor Great Lakes ice cover for operational activities. Using GLERL and Jet Propulsion Lab’s (JPL) measured library of calibrated polarimetric C-band SAR ice backscatter signatures, an algorithm was developed to classify and map major Great Lakes ice types using satellite C-band SAR data (see graphic below, Methodology for Great Lakes Ice Classification prototype).
ICECON (ice condition index) for the Great Lakes—a risk assessment tool recently developed for the Coast Guard—incorporates several physical factors including temperature, wind speed and direction, currents, ice type, ice thickness, and snow to determine 6 categories of ice severity for icebreaking operations and ship transit. To support the ICECON ice severity index, the SAR ice type classification algorithm was modified to output ice types or groups of ice types, such as brash ice and pancake ice to adhere to and visualize the U.S. Coast Guards 6 ICECON categories. Ranges of ice thickness were assigned to each ice type category based on published freshwater ice nomenclature and extensive field data collection. GLERL plans to perform a demonstration/evaluation of the ICECON tool for the Coast Guard this winter.
Mapping and monitoring Great Lakes ice cover advances NOAA’s goals for a Weather-Ready Nation and Resilient Coastal Communities and Economies, and Safe Navigation. Results from this project, conducted in collaboration with Son V. Nghiem (NASA/Jet Propulsion Laboratory), will be made available to the user community via the NOAA Great Lakes CoastWatch website (https://coastwatch.glerl.noaa.gov).
Measuring different ice types on Green Bay used to validate the ICECON (ice type classification) Scale in a RADARSAT-2 synthetic aperture radar (SAR) scene taken on February 26, 2017.