NOAA Great Lakes Environmental Research Laboratory

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


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Working to improve Great Lakes modeling

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

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

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

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

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

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

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

 


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Scientists Work Around the Clock During Seasonal Lake Michigan Cruise

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

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

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

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

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

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

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

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

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Getting the MOCNESS ready.

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Chief scientist Hank Vanderploeg looks at some data.

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

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Paul prepares the fluoroprobe.

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


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Using Airplanes for Algal Bloom Prediction in Lake Erie

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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


Nowcast vs. Forecast: What’s the difference?

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

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


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


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Analyzing Algal Toxins in Near Real-Time

This morning, along side our partners at the University of Michigan’s Cooperative Institute for Limnology and Ecosystems Research (CILER), we deployed the very first Environmental Sample Processor (ESP) in a freshwater system.

An ESP is an autonomous robotic instrument that works as a ‘lab in a can’ in aquatic environments to collect water samples and analyze them for algal toxins. This allows for near real-time (only a couple of hours for remote analyzation as opposed to a day or more back at the lab) detection of harmful algal blooms (HABs) and their toxins. GLERL’s ESP—named the ESPniagara—will measure concentrations of Microcystin, the dominant algal toxin in the Great Lakes. It will also archive samples, allowing us to genetically detect Microcystis, the predominant HAB in the Great Lakes, back in the laboratory.

There are 17 ESPs throughout the world and the ESPniagara is the only one (so far) being used in freshwater. We’ve placed it near the Toledo drinking water intake in western Lake Erie to collect and analyze water and detect concentrations of toxins that may be a health risk to people swimming, boating or drinking Lake Erie water. We’ll post the data from the on our HABs and Hypoxia webpage  so that drinking water managers and other end users can make water quality/ public health decisions.

The goal of this research is to provide drinking water managers with data on algal toxicity before the water reaches municipal water intakes. ESPniagara will strengthen our ability to both detect and provide warning of potential human health impacts from toxins.

This research proves to be a great collaborative effort for GLERL, CILER, and our partners. The Monterey Bay Aquarium Research Institute (MBARI) first developed the ESP, which is now commercially manufactured by McLane Laboratories. GLERL purchased the ESPniagara with funding from EPA-Great Lakes Restoration Initiative. NOAA-National Centers for Coastal Ocean Science (NCCOS) developed the technology to detect Microcystins (an ELISA assay). NCCOS funding also supported previous work to demonstrate the viability of ESP technology to assist in monitoring and forecasting of HABs and their related toxins in the marine environment.

We plan to have the ESPniagara out in western Lake Erie for the next 30 days. Check back later this week and next for a few videos, photos, and some pretty cool data. For more information, check out our HABs and Hypoxia website and read up on the ESP.


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Tracking Changes in Great Lakes Temperature and Ice: New Approaches

In a new study, scientists from GLERL, the University of Michigan, and other institutions take a new look at changing ice cover and surface water temperature in the Great Lakes. The paper, set to be published in Climatic Change, is novel in two ways.

While previous research focused on changes in ice cover and temperature for each lake as a whole, this study reveals how different regions of the lakes are changing at different rates.

While many scientists agree that, over the long term, climate change will reduce ice cover in the Great Lakes, this paper shows that changes in ice cover since the 1970s may have been dominated by an abrupt decline in the late 1990s (coinciding with the strong 1997-1998 winter El Niño), rather than gradually declining over the whole period.

NOAA tracks ice cover and water surface temperature of the Great Lakes at a pretty fine spatial scale. Visit our CoastWatch site and you’ll see detailed maps of surface temperature and/or ice cover updated daily.

However, when studying long-term changes in temperature and ice cover on the lakes, the scientific community has used, in the past, either lakewide average temperature data or data from just a few buoys. We knew how each lake was changing overall, but not much more.

Now, for the first time, researchers are using our detailed data to look at the changes happening in different parts of each lake.

Using GIS (geographic information system) analysis tools, researchers calculated how fast ice cover and temperature were changing on average for each of thousands of small, square areas of the lakes (1.3 km2 for ice cover, and 1.8 km2 for temperature).

The maps below show the results. Changes in ice, on the left, are reported in the number of days of ice cover lost each year. Temperature changes are reported in degrees Celsius gained per year.

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Panel a shows the change in seasonal ice cover duration (d/yr) from 1973 to 2013, and panel b shows the change in summer surface water temperature (°C/yr) from 1994 to 2013. Maps from Mason, L.A., Riseng, C.M., Gronewold, A.D. et al. Climatic Change (2016). doi:10.1007/s10584-016-1721-2. Click image to enlarge.

The researchers also averaged these values across major subbasins of the lakes. Maps of those results are below. The color coding is the same, and again, ice cover is on the left while temperature is on the right.

Note: These subbasins aren’t random, and were outlined by scientists as a part of the Great Lakes Aquatic Habitat Framework (GLAHF), which is meeting a need (among other things) for lake study at intermediate spatial scales.

The panel on the left shows the change in seasonal ice cover duration (d/yr) from 1973 to 2013, and the panel on the right shows the change in summer surface water temperature (°C/yr) from 1994 to 2013. Maps created by Kaye LaFond for NOAA GLERL. Click image to enlarge.

Depth, prevailing winds, and currents all play a role in why some parts of the lakes are warming faster than others. A lot of information is lost if each lake is treated as a homogenous unit. With so much variation, it may not make sense for every region of the Great Lakes to use lakewide averages. Studying changes at a smaller scale could yield more useful information for local and regional decision makers.

The second part of the story has to do with how ice cover has changed in the lakes. Previous studies typically represent changes in ice cover as a long, slow decline from 1973 until today (that would be called a ‘linear trend’). However, when looking at the data more carefully, it seems the differences between the 70’s and today in many regions of the Great Lakes are better explained by a sudden jump (called a ‘change point’).

The figure below shows yearly data on ice cover for the central Lake Superior basin. It is overlaid with a linear trendline (the long, slow decline approach) as well as two flat lines, which represent the averages of the data before and after a certain point, the ‘change point’.

Annual ice cover duration (d/yr) for the central Lake Superior basin, overlaid on the left with a linear trend-line, and overlaid on the right with a change-point analysis. Graphic created by Kaye LaFond for NOAA GLERL. Click image to enlarge.

Statistical analyses show that the change point approach is much better fit for most subbasins of the Great Lakes. 

So what caused this sudden jump? Scientists aren’t sure, but the change points of the northernmost basins line up with the year 1998, which was a year with a very strong winter El Niño. This implies that changes in ice cover are due, at least in part, to the cyclical influence of the El Niño Southern Oscillation (ENSO).

All of this by no means implies that climate change didn’t have a hand in the overall decline, or that when there is a cyclical shift back upwards (this may have already happened in 2014) that pre-1998 ice cover conditions will be restored. The scientific consensus is that climate change is happening, and that it isn’t good for ice cover.

This research just asserts that within the larger and longer-term context of climate change, we need to recognize the smaller and shorter-term cycles that are likely to occur.


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UPDATE: GLERL Releases Drifter Buoys into Lake Erie

Update 08/09/2016: The buoys have drifted ashore and are being collected! The map below shows their full journey.

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This map shows the journey of the drifters from July 5, 2016 to August 5, 2016. Created by Kaye LaFond for NOAA GLERL. Click image to enlarge.

 

Original post 07/13/2016:

Last week, GLERL scientists released two mobile buoys with GPS tracking capabilities, known as ‘Lagrangian drifters’, into Lake Erie. We are now watching the buoys move around the lake with interest, and not just because it’s fun. The drifters help us test the accuracy of our Lake Erie hydrodynamics model, known as the Lake Erie Operational Forecasting System (LEOFS).

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This map shows the progress of the drifters as of July 13, 2016 08:19:00. Created by Kaye LaFond for NOAA GLERL. Click image to enlarge.

LEOFS is driven by meteorological data from a network of buoys, airports, coastal land stations, and weather forecasts which provide air temperatures, dew points, winds, and cloud cover.  The mathematical model then predicts water levels, temperatures, and currents (see below).

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An example of outputs from the Lake Erie Operational Forecast System (LEOFS)

 

We use these modeled currents to predict the path that something like, say, an algae bloom would take around the lake. In fact, this is the basis of our HAB tracker tool.

The strength of LEOFS is in how well the modeled currents match reality.  While there are a number of stationary buoys in Lake Erie, none provide realtime current measurements.  The drifters allow us to see how close we are getting to predicting the actual path an object would take.

Researchers will compare the actual paths of the drifters to the paths predicted by our model. This is a process known pretty universally as ‘in-situ validation’ (in-situ means “in place”). Comparing our models to reality helps us to continually improve them.

For more information and forecasts, see our Great Lakes Coastal Forecasting homepage.

For an up-to-date kmz file of the drifters (that opens as an animation in Google Earth), click here.