NOAA Great Lakes Environmental Research Laboratory

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


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New algorithm to map Great Lakes ice cover

Leshkvich sampling ice

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).

 

ice-types

ICECON Scale

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.

 


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Update on Lake Erie hypoxia forecasting stakeholder workshop (May 23, 2017)

Researchers partner with drinking water plant managers to forecast hypoxia in Lake Erie

By Devin Gill, Cooperative Institute for Great Lakes Research and Kristin Schrader, Great Lakes Observation Systems

Lake Erie’s “dead zone” not only impacts the lake’s ecosystem, but also poses challenges for managers of drinking water treatment facilities. The Lake Erie dead zone is a region of the central basin where oxygen levels within the water become extremely low, creating a condition known as hypoxia. Great Lakes researchers are sharing their scientific expertise to help managers be fully prepared for threats to drinking water resulting from hypoxic conditions.

Scientists from NOAA GLERL, Cooperative Institute for Great Lakes Research (CIGLR) and the Great Lakes Observing System (GLOS) met on May 23 in Cleveland, Ohio with water plant managers from the southern shore of Lake Erie for a stakeholder engagement workshop to discuss the hypoxia issue. An important focus of the workshop was the development of a new hypoxia forecast model that will act as an early warning system when hypoxic water has the potential to enter intakes of water treatment facilities. The depletion of oxygen in hypoxic water occurs when the water column stratifies (separates into warm and cold layers that don’t mix). Oxygen in the lower, cold layer becomes depleted from the lack of mixing with the upper (warm) layer that is exposed to air, as well as from the decomposition of organic matter (dead plants and animals) in the lower layer. The process of hypoxia is illustrated by GLERL’s infographic, The Story of Hypoxia.

Stakeholders who attended the workshop explained that water treatment operators must be prepared to respond quickly during a hypoxic event to ensure that drinking water quality standards are met. Hypoxic water often is associated with low pH and elevated manganese and iron. Manganese can cause discoloration of treated water, while low pH may require adjustment to avoid corrosion of water distribution pipes, which can introduce lead and copper into the water.

At the workshop, researchers shared information on lake processes that contribute to hypoxia and on development of the Lake Erie Operational Forecasting System that provides nowcasts and forecasting guidance of water levels, currents, and water temperature out to 120 hours, and is updated 4 times a day. Information was also shared on preliminary hypoxia modeling results that simulated an upwelling event (wind-driven motion in the Great Lakes, pushing cooler water towards the lake surface, replacing the warmer surface water) that brought hypoxic water to several water plant intakes in September, 2016. Water plant managers reported that advance notice of a potential upwelling event that could bring hypoxic water to their intakes would be useful to alert staff and potentially increase the frequency of testing for manganese.

Dr. Mark Rowe from University of Michigan, CIGLR, researcher and co-lead on this initiative, comments on the value of this hypoxia stakeholder engagement workshop: “At both NOAA and the University of Michigan, there is an increasing focus on co-design of research, which refers to involving the end-users of research results throughout the entire project, from concept to conclusion. If we succeed, a new forecast model will be developed that will be run by the operational branch of NOAA. This can only happen if there is a group of users who request it. This workshop provided critical information to the researchers regarding the needs of the water plants, while also informing water plant managers on how forecast models could potentially help them plan their operations, and on the latest scientific understanding of hypoxia in Lake Erie. ”

Stakeholder Scott Moegling, Water Quality Manager at City of Cleveland Division of Water, also recognizes the value of  engagement between the stakeholders and the Great Lakes researchers. Moegling points out that “the drinking water plant managers not only benefit from sharing of operational information and research, but also by establishing lines of communication between water utilities and researchers that help identify common areas of interest. The end result—researchers providing products that can be immediately used by water utilities—is of obvious interest to the water treatment industry on Lake Erie.”  Moegling also views the GLERL/CIGLR research on the hypoxia forecast model as holding great potential in predicting hypoxic conditions in Lake Erie and believes that once the model is developed and calibrated, there may be a number of other possibilities for highly useful applications.

In addition to sharing the latest research on hypoxia, the stakeholder engagement workshop provided a forum for water plant managers to share information with each other on how to recognize hypoxic events and efficiently adjust water treatment processes. Researchers at CIGLR and NOAA GLERL are committed to conduct research that serves society, and will continue to work with this stakeholder group over the course of the five-year project to develop a hypoxia forecast model that meets their needs.

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“Just Because the Blooms in Lake Erie Slow Down, Doesn’t Mean We Do”

NOAA GLERL harmful algal blooms research program featured on Detroit Public Television

As part of a series on The Blue Economy of the Great Lakes, NOAA’s Great Lakes Environmental Research Laboratory (GLERL) is featured in a short video, produced by Detroit Public Television (DPTV) and published on the DPTV website. The video, which features GLERL and its partners from the Cooperative Institute for Great Lakes Research (CIGLR, known formerly as CILER), describes the advanced technology GLERL uses to monitor, track, predict, and understand harmful algal blooms (HABs) in the Great Lakes. More specifically, the video focuses on efforts in Lake Erie, where over 400,000 people were affected by a 3-day shutdown of the Toledo drinking water treatment facility in 2014. Since then, GLERL and CIGLR have enhanced their HABs research program—much of which is made possible by funding from the Great Lakes Restoration Initiative, or GLRI—to include cutting-edge technologies such as the hyperspectral sensors and an Environmental Sample Processor (ESP), as well as experimental forecasting tools like the Lake Erie HAB Tracker.

In addition to online coverage, the video will be broadcast via DPTV at a future time, yet to be determined.

View the video above, or visit http://bit.ly/2pK2g0J.

Aerial photo survey improves NOAA GLERL’s Lake Erie ice model

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Understanding the duration, extent, and movement of Great Lakes ice is important for the Great Lakes maritime industry, public safety, and the recreational economy. Lake Erie is ice-prone, with maximum cover surpassing 80% many winters.

Multiple times a day throughout winter, GLERL’s 3D ice model predicts ice thickness and concentration on the surface of Lake Erie. The output is available to the public, but the model is under development, meaning that modelers still have research to do to get it to better reflect reality.

As our scientists make adjustments to the model, they need to compare its output with actual conditions so they know that it’s getting more accurate. So, on January 13th of this year, they sent a plane with a photographer to fly the edge of the lake and take photos of the ice.

The map below shows the ice model output for that day, along with the plane’s flight path and the location of the 172 aerial photos that were captured.

NOAA GLERL Lake Erie ice model output with all aerial photo survey locations -- January 13, 2017. Credit NOAA GLERL/Kaye LaFond.

NOAA GLERL Lake Erie ice model output with all aerial photo survey locations — January 13, 2017. Map Credit NOAA GLERL/Kaye LaFond.

These photos provide a detailed look at the sometimes complex ice formations on the lake, and let our scientists know if there are places where the model is falling short.

Often, the model output can also be compared to images and surface temperature measurements taken from satellites. That information goes into the GLSEA product on our website (this is separate from the ice model). GLSEA is useful to check the ice model with. However, it’s important to get this extra information.

“These photographs not only enable us to visualize the ice field when satellite data is not available, but also allow us to recognize the spatial scale or limit below which the model has difficulty in simulating the ice structures.” says Eric Anderson, an oceanographer at GLERL and one of the modelers.

 “This is particularly evident near the Canadian coastline just east of the Detroit River mouth, where shoreline ice and detached ice floes just beyond the shoreline are not captured by the model. These floes are not only often at a smaller spatial scale than the model grid, but also the fine scale mechanical processes that affect ice concentration and thickness in this region are not accurately represented by the model physics.”

Click through the images below to see how select photos compared to the model output. To see all 172 photos, check out our album on Flickr. The photos were taken by Zachary Haslick of Aerial Associates.

This gallery contains 10 photos


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Lake Erie Hypoxia Forecasting Project Kicks Off With Stakeholder Workshop

A collaborative research team, led by Drs. Craig Stow of the National Oceanic and Atmospheric Administration’s Great Lakes Environmental Research Laboratory (NOAA GLERL) and Mark Rowe of the University of Michigan’s Cooperative Institute for Limnology and Ecosystems Research (CILER),  will be holding a workshop with key stakeholders for guidance on how a forecast model could help meet the needs for information on low oxygen conditions—or hypoxia—in Lake Erie. The workshop, coming up later this spring, kicks off a 5-year project that brings together inter-agency and university scientists to produce a forecasting system that will predict the location and movement of hypoxic water in Lake Erie. The project will link a hypoxia model to NOAA’s Lake Erie Operational Forecasting System (LEOFS) hydrodynamic model, which provides daily nowcast and 5-10 day forecasts of temperature and currents in Lake Erie.

HypoxiaDiagram

Hypoxia occurs in the central basin of Lake Erie in July through September of most years. Low-oxygen water is an unfavorable habitat for fish, and may kill benthic organisms that provide food for fish. It is less well known, however, that hypoxic water can also upset drinking water treatment processes. Upwelling or seiche events can bring hypoxic water to water intakes along the shoreline, causing rapid changes in dissolved oxygen and associated 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. Hypoxia forecasts will provide several days advance notice of changing source water quality so that drinking water plant managers can be prepared to adjust treatment processes as needed.

While the hypoxia forecasting project will help to minimize the negative impacts of hypoxia, a parallel effort is occurring to address the root cause of this problem involving nutrient loading. Universities, state, federal, and Canadian agencies are collaborating to satisfy the goals of the Great Lakes Water Quality Agreement by reducing nutrient loads to Lake Erie, a primary stressor driving hypoxic conditions.

The upcoming stakeholder workshop on hypoxia will bring the research team together with stakeholders consisting of municipal drinking water plant managers from U.S. and Canadian facilities on Lake Erie, as well as representatives of state and local agencies. The group will learn about hypoxia and its effects, hear about the goals of the LEOFS-Hypoxia project, and provide input to the research team on their information needs. As the first in a series of meetings of the project’s Management Transition Advisory Group, this workshop will help identify the most useful data types and delivery mechanisms, laying the groundwork for the research team to design a forecasting tool that specifically addresses the needs of public water systems on Lake Erie.

The workshop will be held at Cleveland Water in Cleveland, Ohio. Representatives from Ohio Environmental Protection Agency (EPA), Ohio Department of Natural Resources, Ohio Sea Grant, townships and other local governments were also invited to attend.  

The LEOFS-Hypoxia project is a collaboration with the City of Cleveland Division of Water, Purdue University, and U. S. Geological Survey, with guidance from a management advisory group including representatives from Ohio public water systems, Ohio EPA, Great Lakes Observing System (GLOS), and NOAA. The work is supported by a $1.4 million award from the NOAA National Centers for Coastal Ocean Science (NCCOS) Center for Sponsored Coastal Ocean Research by a grant to NOAA GLERL and University of Michigan (award NA16NOS4780209).

Getting to the root cause of the problem
As part of an initiative conducted under the auspices of the Great Lakes Water Quality Agreement, Annex 4, the following forums, led by Dr. Craig Stow at GLERL, will focus on the linkage of nutrient loading to water quality degradation problems, such as hypoxia and harmful algal blooms.

  • 4/5-6: Nutrient Load Workshop
  • 5/9-10: Annex 4 (nutrients) Subcommittee Meeting

Scientists attending these workshops will apply long term research results to estimate nutrient inputs to Great Lakes waters and evaluate how well we are doing in reaching phosphorus load reduction targets established under Annex 4 of the GLWQA.

Additional Resources
NOAA GLERL Hypoxia web page: https://www.glerl.noaa.gov/res/HABs_and_Hypoxia/hypoxiaWarningSystem.html

Download the NOAA GLERL hypoxia infographic, here:

NOAA booth at annual American Meteorological Society meeting.


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GLERL researchers heading to AMS 2017

The American Meteorological Society’s Annual Meeting (AMS 2017) is upon us and researchers from GLERL and CILER (the Cooperative Institute for Limnology and Ecosystems Research), along with other partners, are hitting the grounds running on Monday with posters and presentations on climate, ice, HABs, modeling, forecasting, transitioning research to ops, and more!

Here’s a schedule of where you’ll find us throughout the week. (GLERL and CILER researchers highlighted in italics. Poster titles linked to .pdf of poster, if available.) And, don’t forget to swing by the NOAA booth (#405) to check out all of the fantastic work that NOAA scientists are doing around the world!


GLERL and CILER posters and presentations during AMS 2017

Monday, 23 January 2017

The Great Lakes Adaptation Data Suite: Providing a Coherent Collection of Climate Data for the Great Lakes Region
Type: Poster
Location: 4E (Washington State Convention Center), Poster #1
Authors: Omar C. Gates, University of Michigan, Ann Arbor, MI; and K. Channell, D. Brown, W. Baule, D. J. Schwab, C. Riseng, and A. Gronewold

Abstract: Climate change impacts are a growing concern for researchers and adaptation professionals throughout society. These individuals look to different data sources in order to contemplate the challenges that are present from climate impacts. The use of observational data helps to understand which climatic factors exploit vulnerabilities and to develop solutions to make systems more resilient. However, non-uniform data collection and processing often hinders the progress towards such a goal because many publicly-accessible data sets are not readily usable to address the concern of climate impacts on societies. In the Great Lakes region, there is the added challenge of data quality and coverage issues for over-lake versus over-land observations. The creation of the Great Lakes Adaptation Data Suite (GLADS) aims to resolve these dilemmas by providing processed over-land and over-lake observations within one suite for the Great Lakes region of North America, and this data suite is provided to individuals with a vested interest in decision-making for climate resilience. This intent serves as a way for the GLADS to engage with individuals, from various backgrounds, that are interested in incorporating climate information into their work. Feedback from this audience will be analyzed to further improve the GLADS for use in decision-making. Further analysis will look at the connections among potential users and how they perceive the GLADS as being a useful tool for their research. By gaining perspective into the individuals’ expectations of the tool and their understanding of climate information, the GLADS will be able to accommodate the necessary steps for integrating climate information into decision-making processes to mitigate climate impacts.

Tuesday, 24 January 2017

Coupling Effects Between Unstructured WAVEWATCH III and FVCOM in Shallow Water Regions of the Great Lakes
Type: Presentation
Time: 9:15 AM
Locations: Conference Center: Chelan 4 (Washington State Convention Center )
Authors: Jian Kuang, IMSG@NOAA/NWS/NCEP, College Park, MD; and A. J. Van der Westhuysen, E. J. Anderson, G. Mann, A. Fujisaki, and J. G. W. Kelley

Abstract: The modeling of waves in shallow environments is challenging because of irregular coastlines and bathymetry, as well as complicated meteorological forcing. In this paper, we aim to provide insight into the physics of storm surge-wave interaction within shallow water regions of the Great Lakes under strong wind events. Extensive hindcast analysis using the 3D-circulation model FVCOM v3.2.2 and the third generation spectral wave model WAVEWATCH III v4.18 was conducted on unstructured meshes for each of the Great Lakes. The circulation and wave models are coupled through a file-transfer method and tested with various coupling intervals. We conducted tests for five short-term (storm length) test cases and three long-term (seasonal) test cases. Time series, spatial plots and statistics are provided. Data exchange of radiation stress, water elevation and ocean currents were tested in both two-way and one-way coupling regimes in order to assess the influence of each variable. Three types of wave current parametrizations will be discussed (surface layer, depth-averaged, and hybrid). The meteorological input forcing fields are 1km/4km/12km WRF model results with time interval of 1h for 4km/12km resolution and 10min for 1km resolution. Statistical analysis was performed in order to evaluate the model sensitivity on the unstructured domain in terms of wind, physics packages and surge-wave coupling effects. These efforts are towards an assessment of the model configuration with a view toward future operational implementation.

Linking Hydrologic and Coastal Hydrodynamic Models in the Great Lakes
Type: Presentation
Time: 2:00pm
Location: Conference Center – Chelan 4 (Washington State Convention Center)
Authors: Eric J. Anderson, NOAA/ERL/GLERL, Ann Arbor, MI; and A. Gronewold, L. Pei, C. Xiao, L. E. Fitzpatrick, B. M. Lofgren, P. Y. Chu, T. Hunter, D. J. Gochis, K. Sampson, and A. Dugger

Abstract: As the next-generation hydrologic and hydrodynamic forecast models are developed, a strong emphasis is placed on model coupling and the expansion to ecological forecasting in coastal regions. The next-generation NOAA Great Lakes Operational Forecast System (GLOFS) is being developed using the Finite Volume Community Ocean Model (FVCOM) to provide forecast guidance for traditional requirements such as navigation, search and rescue, and spill response, as well as to provide a physical backbone for ecological forecasts of harmful algal blooms, hypoxia, and pathogens. However, to date operational coastal hydrodynamic models have minimal or no linkage to hydrologic inflows and forecast information. As the new National Water Model (NWM) is developed using the Weather Research and Forecasting Hydrologic model (WRF-Hydro) to produce forecast stream flows at nearly 2.7 million locations, important questions arise about model coupling between the NWM and coastal models (e.g. FVCOM), how this linkage will impact forecast guidance in systems such as GLOFS, and how WRF-Hydro stream flows compare to existing products. In this study, we investigate hindcasted WRF-Hydro stream flows for the Great Lakes as compared to existing legacy research models. These hydrological stream flows are then linked with the next-generation FVCOM models, where the impacts to hydrodynamic forecast guidance can be evaluated. This study is a first step in coupling the next-generation NWM with NOAA’s operational coastal hydrodynamic models.

Regional Hydrological Response from Statistically Downscaled Future Climate Projections in the 21st Century
Type: Poster
Location: 4E (Washington State Convention Center), Poster #462
Authors: Lisi Pei, NOAA, Ann Arbor, MI; and A. Gronewold, T. Hunter, and R. Bolinger

Abstract: Understanding how future climate change signals propagate into hydrological response is critical for water supply forecasting and water resources management. To demonstrate how this understanding can be improved at regional scales, we studied the hydrological response of the Laurentian Great Lakes under future climate change scenarios in the 21st century using a conventional regional hydrological modeling system (the Great Lakes Advanced Hydrologic Prediction System, or GL-AHPS) forced by statistically downscaled CMIP5 (Coupled Model Intercomparison Project Phase 5) future projections. The Great Lakes serve as a unique case study because they constitute the largest bodies of fresh surface water on Earth, and because their basin is bisected by the international border between the United States and Canada, a feature that complicates water level and runoff modeling and forecasting. The GL-AHPS framework is specifically designed to address these unique challenges. Existing model validation results indicate that the GL-AHPS model framework provides reasonable simulation of historical seasonal water supplies, but has significant deficiencies on longer time scales. A major component of this study, therefore, includes reformulating key algorithms within the GL-AHPS system (including those governing evapotranspiration), and assessing the benefits of those improvements.

Reconstructing Evaporation over Lake Erie during the Historic November 2014 Lake Effect Snow Event
Type: Poster
Location: 4E (Washington State Convention Center), Poster #898
Authors: Lindsay E. Fitzpatrick, CILER, Ann Arbor, MI; and A. Manome, A. Gronewold, E. J. Anderson, C. Spence, J. Chen, C. Shao, D. M. Wright, B. M. Lofgren, C. Xiao, D. J. Posselt, and D. J. Schwab

Abstract: The extreme North American winter storm of November 2014 triggered a record lake effect snowfall event in southwest New York, which resulted in 14 fatalities, stranded motorists, and caused power outages. While the large-scale atmospheric conditions of the descending polar vortex are believed to be responsible for the significant lake effect snowfall over the region, to-date there has not yet been an assessment of how state-of-the-art numerical models performed in simulating evaporation from Lake Erie, which is tied to the accuracy in forecasting lake effect snow.

This study examined the evaporation from Lake Erie during the record lake effect snowfall event, November 17th-20th, 2014, by reconstructing heat fluxes and evaporation rates over Lake Erie using the unstructured grid, Finite-Volume Community Ocean Model (FVCOM). Nine different model runs were conducted using combinations of three different flux algorithms: the Met Flux Algorithm (COARE), a method routinely used at NOAA’s Great Lakes Environmental Research Laboratory (SOLAR), and the Los Alamos Sea Ice Model (CICE); and three different meteorological forcings: the Climate Forecast System version 2 Operational Analysis (CFSv2), Interpolated observations (Interp), and the High Resolution Rapid Refresh (HRRR). A few non-FVCOM model outputs were also included in the evaporation analysis from an atmospheric reanalysis (CFSv2) and the large lake thermodynamic model (LLTM). Model-simulated water temperature and meteorological forcing data (wind direction and air temperature) were validated with buoy data at three locations in Lake Erie. The simulated sensible and latent heat fluxes were validated with the eddy covariance measurements at two offshore sites; Long Point Lighthouse in north central Lake Erie and Toledo water crib intake in western Lake Erie. The evaluation showed a significant increase in heat fluxes over three days, with the peak on the 18th of November. Snow water equivalent data from the National Snow Analyses at the National Operational Hydrologic Remote Sensing Center showed a spike in water content on the 20th of November, two days after the peak heat fluxes. The ensemble runs presented a variation in spatial pattern of evaporation, lake-wide average evaporation, and resulting cooling of the lake. Overall, the evaporation tended to be larger in deep water than shallow water near the shore. The lake-wide average evaporations from CFSv2 and LLTM are significantly smaller than those from FVCOM. The variation among the nine FVCOM runs resulted in the 3D mean water temperature cooling in a range from 3 degrees C to 5 degrees C (6-10 EJ loss in heat content), implication for impacts on preconditioning for the upcoming ice season.

Projecting Water Levels of the Laurentian Great Lakes in the 21st Century from a Dynamical Downscaling Perspective
Type: Presentation
Time: 11:15 AM
Locations: 602 (Washington State Convention Center)
Authors: Chuliang Xiao, University of Michigan, CILER, Ann Arbor, MI; and B. M. Lofgren, J. Wang, P. Y. Chu, and A. Gronewold

Abstract: As the largest group of fresh surface water bodies on earth, the Laurentian Great Lakes have a significant influence on regional climate. Due to the limited spatial resolution of general circulation models (GCMs), the Great Lakes are generally ignored in GCMs. Thus, the technique of dynamical downscaling serves as a practical and important, but challenging solution to the problem of understanding climate impacts and hydrological response in this unique region. Here, we employed the Weather Research and Forecasting model (WRF) with an updated lake scheme to downscale from a GCM with two future greenhouse gas concentration scenarios in the 21st century. Historical validation shows that the WRF-Lake model, with a fine horizontal resolution and a 1-dimensional lake representation, improves the hydroclimatology simulation in terms of seasonal cycles of lake surface temperature, precipitation, and ice coverage. Based on the downscaling results, a hydrologic routing model is performed to project the Great Lakes’ water level changes in 21st century using net basin supply (NBS, calculated as the sum of over-lake precipitation, basin-wide runoff, and lake evaporation) as an input. As the lakes warm and lake ice diminishes, water levels are projected to have persistent and enhanced interannual variations in the presumed climate change. These changes have a range of potential socioeconomic impacts in the Great Lakes region, including changes in hydropower capacity, the length of the commercial shipping season, and the design life of coastal residences and infrastructure.

Wednesday, 25 January 2017

Simulating and Forecasting Seasonal Ice Cover
Type: Poster, #1147
Authors: Xiaolong Ji, University of Michigan, Ann Arbor, MI; and H. Daher, R. Bolinger, A. Gronewold, and R. B. Rood

Abstract: Over the past several decades, dramatic changes in the spatial extent of seasonal and long-term ice cover have been documented for both marine and continential (inland) water bodies. Successfully projecting (and planning for) future changes in global ice cover requires an understanding of the drivers behing these historical changes. Here, we explore relationships between continental climate patterns and regional ice cover across the vast surface waters of the Laurentian Great Lakes. The Great Lakes constitute the largest collective surface of freshwater on Earth, and seasonal variability in ice cover is closely linked with lake heat content, energy fluxes, and water levels (all of which have strong linkages with ecological and socioeconomic stability in the region). Our findings indicate that abrupt historical changes in Great Lakes seasonal ice cover are coincident with historical changes in teleconnections, including both the El Nino Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). We find, in particular, that these teleconnections explain much of the ice cover decline in the late 1990s (coincident with the strong 1997-1998 winter El Nino) and the following persistent period of below-average period of ice that followed. We encode these relationships in a probabilistic model that provides seasonal projections of ice cover areal extent across the Great Lakes, as well as specific spatiotemporal patterns in ice cover at resolutions that align with critical regional human health and safety-related management decisions.

What Does It Take to Transition Six Forecasting Systems into Operations in Ten Years? — Lessons Learned, Myths and Facts
Type: Presentation
Time: 11:15 AM
Location: 608 (Washington State Convention Center)
Authors: Philip Y. Chu, GLERL, Ann Arbor, MI; and E. J. Anderson, G. Lang, J. G. W. Kelley, E. Myers, A. Zhang, J. Xu, and Y. Chen

Abstract: NOAA Great Lakes Operational Forecasting System (GLOFS), developed by the Great Lakes Environmental Research Laboratory and National Ocean Service, has been operational since 2005. A project to upgrade GLOFS, using FVCOM as the core 3-D oceanographic forecast model, has been conducted during the past 3 years involving GLERL, NOS/CSDL and CO-OPS and NCEP Central Operations. The 1st phase of this project has been completed with the operational implementation of a new GLOFS version for Lake Erie on NOAA’s Weather and Climate Operational Supercomputer System in May 2016.

Many lessons were learned from transitioning six forecasting systems to operations in 10 years. On the technical aspects which include hardware, software, systems — we found that keys to successful transition are on 1) methods to harden the software infrastructure to make a robust, automated system; 2) backup and alternative procedures for handling missing or corrupted input data; 3) standardized validation and skill assessment metrics; 4) preparation of complete documentation including validation test reports, standard operating procedures (SOP), and software user guides; 5) adequate near-real-time observations of discharge, and water levels to provide LBCs for the system and 6) field projects in the Great Lakes (i.e. IFYGL) to provide surface and subsurface data for the evaluation of the forecast models during development and testing. In particular, program source codes need to be frozen during the testing, validation and the transition period with proper version control.

In addition to the technical aspects, a successful system transition from the research/development stage into operations also involves non-technical aspects, such as commitment from senior leadership, frequent communications among all involved parties on progress and milestones, training sessions for the system operators and user engagement workshops for the end users.

Applying WRF-Hydro in the Great Lakes Basin: Offline Simulations in the Seasonal Hydrological Responses
Type: Presentation
Time: 4:45 PM
Location: Conference Center – Chelan 2 (Washington State Convention Center )
Authors: Lisi Pei, NOAA, Ann Arbor, MI; and A. Gronewold, D. J. Gochis, K. Sampson, A. Dugger, C. Xiao, L. Mason, B. M. Lofgren, and P. Y. Chu

Abstract: As a unified atmosphere-land hydrological modeling system, the WRF-Hydro (Weather Research and Forecasting model Hydrological modeling extension package) framework is being employed by the NOAA-National Water Center (NWC, Tuscaloosa, AL) to provide streamflow forecasting over the entire CONUS in 250 m resolution from hourly to monthly scale. Currently, efforts are focused on tests and an operational forecast launch on August 16th, 2016. But due to inconsistencies in the land surface hydrographic datasets between U.S. and Canada over the Great Lakes Basin, many of the tributaries feeding the Great Lakes and the major channels connecting the Great Lakes (including the Niagara, St. Clair, and Detroit Rivers) are missing or poorly represented in the current NWC streamflow forecasting domain. Improvements in the model’s current representation of lake physics and stream routing are also critical for WRF-Hydro to adequately simulate the Great Lakes water budget and Great Lakes coastal water levels. To customize WRF-Hydro to the Laurentian Great Lakes Basin using protocols consistent with those used for the current CONUS operational domain, the NOAA-Great Lakes Environmental Research Laboratory has partnered with the National Center for Atmospheric Research (NCAR) and other agencies to develop land surface hydrographic datasets and compatible stream routing grids that connect to the current CONUS operational domain. This research group is also conducting 1-km resolution offline tests with WRF-Hydro based on current best available bi-national land surface geographic datasets to examine the model’s ability to simulate seasonal hydrological response over the Great Lakes (runoff and land-atmosphere fluxes) with its coupled overland flow terrain-routing module, subsurface lateral flow module and channel flow (runoff) module.

Thursday, 26 January 2017

Using the Next-Generation Great Lakes Operational Forecasting System (GLOFS) to Predict Harmful Algal Bloom (HAB) Transport with the HAB Tracker
Type: Presentation
Time: 3:30 PM
Location: 611 (Washington State Convention Center)
Authors: Eric J. Anderson, NOAA/ERL/GLERL, Ann Arbor, MI; and M. Rowe, J. Xu, A. Zhang, G. Lang, J. G. W. Kelley, and R. Stumpf

Abstract: Harmful algal blooms (HAB) plague coastal environments around the world, and particularly in the United States in areas such as the Great Lakes, Florida, Washington, and Maine. In the Great Lakes, shallow embayments such as the western basin of Lake Erie have experienced a period of increasing HAB intensity in recent years, including an event in 2014 where high toxicity levels resulted in a drinking water restriction to nearly 400,000 residents. In order to help decision makers and the public respond to these events, an experimental model has been developed short-term forecasts of HAB concentration and transport. The HAB Tracker uses the next-generation NOAA Lake Erie Operational Forecast System (LEOFS), which is based on the Finite Volume Community Ocean Model (FVCOM). The new FVCOM-based LEOFS model produces hydrodynamic forecast guidance out to 5 days using meteorology from the 3-km HRRR and 2.5 km NDFD. An experimental version of this model also extends the forecast horizon out to 10 days using forecasted meteorology from the GFS. Hourly hydrodynamic conditions (currents, diffusivity, water temperature) are supplied to a three-dimensional Lagrangian particle trajectory model that has been developed to predict HAB transport and vertical migration through the water column. Initial conditions are provided by satellite remote sensing of surface chlorophyll concentration, when available, in which previous nowcasts are used to fill gaps in satellite-derived HAB extent and extend surface concentrations into the water column to produce a three-dimensional field of HAB concentration. In-situ observations of microcystis concentration provide a calibration of particle buoyancy (i.e. colony migration) and a basis for model validation. Results show the three-dimensional HAB Tracker has improved forecast skill out to 10 days over two-dimensional surface concentration forecast products and is better than a persistence forecast out to 5 days.


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Ice cover on the Great Lakes

The USCGC Mackinaw arrives in Duluth via Lake Superior. March 24, 2014

U.S. Coast Guard Cutter Mackinaw is an icebreaking vessel on the Great Lakes that assists in keeping channels and harbors open to navigation. Here, the USCGC Mackinaw arrives in Duluth via Lake Superior on March 24, 2014. Credit: NOAA
Ice formation on the Great Lakes is a clear sign of winter!

Looking back in time, the lakes were formed over several thousands of years as mile-thick layers of glacial ice advanced and retreated, scouring and sculpting the basin. The shape and drainage patterns of the basin were constantly changing from the ebb and flow of glacial meltwater and the rebound of the underlying land as the massive ice sheets retreated.

The amount and duration of ice cover varies widely from year to year. As part of our research, GLERL scientists are observing longterm changes in ice cover as a result of global warming. 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, and spring algal blooms.

Doing research to improve forecasts is important for a variety of reasons.

Ice provides us a connection to the past and also serves as a measure of the harshness of current day winter weather. Understanding the major effect of ice on the Great Lakes is very important because ice cover impacts a range of benefits provided by the lakes—from hydropower generation to commercial shipping to the fishing industry. The ability to forecast and predict ice cover is also really important for recreational safety and rescue efforts, as well as for navigation, weather forecasting, adapting to lake level changes, and all sorts of ecosystem research. One great example of the importance of forecasting is illustrated by an incident that occurred in Lake Erie on a warm sunny day in February 2009 when a large ice floe broke away from the shoreline. The floating ice block stranded 134 anglers about 1,000 yards offshore and also resulted in the death of one man who fell into the water. While the ice on the western sections of the lake was nearly 2 feet thick, rising temperatures caused the ice to break up, and southerly wind gusts of 35 mph pushed the ice off shore. Having the ability to forecast how much ice cover there will be, where it may move, and what other factors like temperature, waves, or wind might play a role in what the ice is going to do, is incredibly important to a lot of users.

— GLERL’s 2017 Seasonal Ice Cover Projection for the Great Lakes —

GLERL’s ice climatologist, Jia Wang, along with partners from the Cooperative Institute for Limnology and Ecosystems Research, use two different methods to predict seasonal ice cover for the Great Lakes. One, a statistical regression model, uses mathematical relationships developed from historical observations to predict seasonal ice cover maximum based on the status of several global air masses that influence basin weather. This method forecasts that the maximum ice cover extent over the entire Great Lakes basin, will be 64%. The other forecast method, a 3-dimensional mechanistic model, is based on the laws of physics that govern atmospheric and hydrodynamic (how water moves) processes to predict ice growth in response to forecast weather conditions. This method predicts a maximum ice cover of 44% for the basin this year.

As you can see, the two methods have produced different answers. However, if you look at the last chart here, you’ll see that three of the lakes show good agreement between these two model types–Lakes Michigan, Erie, and Ontario. Continued research, along with the historical data we’ve been monitoring and documenting for over 40 years, will help GLERL scientists improve ice forecasts and, ultimately, improve our ability to adapt and remain resilient through change.


More information!

Below, is the most recent Great Lakes Surface Environmental Analysis (GLSEA) analysis of the Great Lakes Total Ice Cover. GLSEA is a digital map of the Great Lakes surface water temperature (see color bar on left) and ice cover (see grayscale bar on right), which is produced daily at GLERL by Great Lakes CoastWatch. It combines lake surface temperatures that are developed from satellite images and ice cover information provided by the National Ice Center (NIC). This image is the analysis of January 10, 2017 (13%). For the most current analysis, visit https://coastwatch.glerl.noaa.gov/glsea/cur/glsea_cur.png.

GLSEA total ice cover analysis for January 10, 2017

For technical information on GLERL’s ice forecasting program, check out our website here. 

You can also find much of the information in this post, and more, on this downloadable .pdf of the GLERL fact sheet on Great Lakes ice cover.

Want to see a really cool graphic showing the extent of the maximum ice cover on the Great Lakes for each year since 1973? You’ll find that here.


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.

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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.