scholarly journals Synoptic Climatology of Lake-Effect Snow Events off the Western Great Lakes

Climate ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 43
Author(s):  
Jake Wiley ◽  
Andrew Mercer
Keyword(s):  
Great Lakes ◽  
Large Scale ◽  
Lake Michigan ◽  
Lake Superior ◽  
Principal Component ◽  
Forecast Model ◽  
Synoptic Climatology ◽  
Synoptic Scale ◽  
Ongoing Research ◽  
Lake Effect

As the mesoscale dynamics of lake-effect snow (LES) are becoming better understood, recent and ongoing research is beginning to focus on the large-scale environments conducive to LES. Synoptic-scale composites are constructed for Lake Michigan and Lake Superior LES events by employing an LES case repository for these regions within the U.S. North American Regional Reanalysis (NARR) data for each LES event were used to construct synoptic maps of dominant LES patterns for each lake. These maps were formulated using a previously implemented composite technique that blends principal component analysis with a k-means cluster analysis. A sample case from each resulting cluster was also selected and simulated using the Advanced Weather Research and Forecast model to obtain an example mesoscale depiction of the LES environment. The study revealed four synoptic setups for Lake Michigan and three for Lake Superior whose primary differences were discrepancies in a surface pressure dipole structure previously linked with Great Lakes LES. These subtle synoptic-scale differences suggested that while overall LES impacts were driven more by the mesoscale conditions for these lakes, synoptic-scale conditions still provided important insight into the character of LES forcing mechanisms, primarily the steering flow and air–lake thermodynamics.

scholarly journals An Updated Synoptic Climatology of Lake Erie and Lake Ontario Heavy Lake-Effect Snow Events

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 872
Author(s):  
Jake Wiley ◽  
Andrew Mercer
Keyword(s):  
Composite Structures ◽  
Large Scale ◽  
Wrf Model ◽  
Principal Component ◽  
Synoptic Climatology ◽  
Composite Support ◽  
The North ◽  
Lake Effect ◽  
Upper Level ◽  
Regional Reanalysis

Lake-effect snow (LES) storms pose numerous hazards, including extreme snowfall and blizzard conditions, and insight into the large-scale precursor conditions associated with LES can aid local forecasters and potentially allow risks to be mitigated. In this study, a synoptic climatology of severe LES events over Lakes Erie and Ontario was created using an updated methodology based on previous studies with similar research objectives. Principal component analysis (PCA) coupled with cluster analysis (CA) was performed on a case set of LES events from a study domain encompassing both lakes, grouping LES events with similar spatial characteristics into the primary composite structures for LES. Synoptic scale composites were constructed for each cluster using the North American Regional Reanalysis (NARR). Additionally, one case from each cluster was simulated using the Weather Research and Forecast (WRF) model to analyze mesoscale conditions associated with each of the clusters. Three synoptic setups were identified that consisted of discrepancies, mostly in the surface fields, from a common pattern previously identified as being conducive to LES, which features a dipole and upper-level low pressure anomaly located near the Hudson Bay. Mesoscale conditions associated with each composite support differing LES impacts constrained to individual lakes or a combination of both.

scholarly journals Lake-to-Lake Cloud Bands: Frequencies and Locations

2007 ◽  
Vol 135 (12) ◽  
pp. 4202-4213 ◽  
Author(s):  
Yarice Rodriguez ◽  
David A. R. Kristovich ◽  
Mark R. Hjelmfelt
Keyword(s):  
High Resolution ◽  
Great Lakes ◽  
Satellite Imagery ◽  
Lake Superior ◽  
Great Lakes Region ◽  
Lake Effect ◽  
Time Period ◽  
High Resolution Satellite Imagery

Abstract Premodification of the atmosphere by upwind lakes is known to influence lake-effect snowstorm intensity and locations over downwind lakes. This study highlights perhaps the most visible manifestation of the link between convection over two or more of the Great Lakes lake-to-lake (L2L) cloud bands. Emphasis is placed on L2L cloud bands observed in high-resolution satellite imagery on 2 December 2003. These L2L cloud bands developed over Lake Superior and were modified as they passed over Lakes Michigan and Erie and intervening land areas. This event is put into a longer-term context through documentation of the frequency with which lake-effect and, particularly, L2L cloud bands occurred over a 5-yr time period over different areas of the Great Lakes region.

Paleohydrology of the upper Laurentian Great Lakes from the late glacial to early Holocene

2009 ◽  
Vol 71 (3) ◽  
pp. 397-408 ◽  
Author(s):  
Andy Breckenridge ◽  
Thomas C. Johnson
Keyword(s):  
Great Lakes ◽  
Lake Michigan ◽  
Lake Superior ◽  
Late Glacial ◽  
Lake Huron ◽  
Michigan Basin ◽  
Great Lakes Basin ◽  
Lake Agassiz ◽  
Sharp Drop ◽  
Freshwater Fluxes

AbstractBetween 10,500 and 9000 cal yr BP, δ18O values of benthic ostracodes within glaciolacustrine varves from Lake Superior range from − 18 to − 22‰ PDB. In contrast, coeval ostracode and bivalve records from the Lake Huron and Lake Michigan basins are characterized by extreme δ18O variations, ranging from values that reflect a source that is primarily glacial (∼ − 20‰ PDB) to much higher values characteristic of a regional meteoric source (∼ − 5‰ PDB). Re-evaluated age models for the Huron and Michigan records yield a more consistent δ18O stratigraphy. The striking feature of these records is a sharp drop in δ18O values between 9400 and 9000 cal yr BP. In the Huron basin, this low δ18O excursion was ascribed to the late Stanley lowstand, and in the Lake Michigan basin to Lake Agassiz flooding. Catastrophic flooding from Lake Agassiz is likely, but a second possibility is that the low δ18O excursion records the switching of overflow from the Lake Superior basin from an undocumented northern outlet back into the Great Lakes basin. Quantifying freshwater fluxes for this system remains difficult because the benthic ostracodes in the glaciolacustrine varves of Lake Superior and Lake Agassiz may not record the average δ18O value of surface water.

Fecundity of Coho Salmon (Oncorhynchus kisutch) from the Great Lakes and a Comparison with Ocean Salmon

1976 ◽  
Vol 33 (5) ◽  
pp. 1150-1155 ◽  
Author(s):  
Thomas M. Stauffer
Keyword(s):  
Great Lakes ◽  
Early Life ◽  
First Generation ◽  
Lake Michigan ◽  
Coho Salmon ◽  
Pacific Salmon ◽  
Lake Superior ◽  
Oncorhynchus Kisutch ◽  
Early Life History ◽  
Freshwater Salmon

I measured fecundity of coho salmon (Oncorhynchus kisutch) that matured in the Great Lakes to make comparisons with Pacific Ocean coho salmon and among groups of Great Lakes salmon. Numbers of eggs produced (1600–3500) by Great Lakes salmon were comparable to production (1500–3300) by Pacific salmon of similar size. Average egg diameters of Lake Michigan (7.1–7.4 mm) and Pacific salmon (6.1–7.4 mm) were also comparable but Lake Superior eggs were smaller (5.1–5.4 mm). Fecundity of second generation freshwater salmon which originated from Lake Michigan eggs was similar to that of the first generation which originated from Pacific eggs because the average numbers (2938–3243) and diameters (7.1–7.4 mm) of eggs produced were about the same. On the average, Lake Michigan salmon contained more (2938) and larger (7.1-mm diam) eggs than did Lake Superior salmon (2150 and 5.1-mm diam) of the same year-class and early life history.

scholarly journals Nearshore Benthic Mapping in the Great Lakes: A Multi-Agency Data Integration Approach in Southwest Lake Michigan

2021 ◽  
Vol 13 (15) ◽  
pp. 3026
Author(s):  
Molly K. Reif ◽  
Brandon S. Krumwiede ◽  
Steven E. Brown ◽  
Ethan J. Theuerkauf ◽  
Joseph H. Harwood
Keyword(s):  
Great Lakes ◽  
Satellite Data ◽  
Analytical Methods ◽  
Classification Accuracy ◽  
Large Scale ◽  
Lake Michigan ◽  
Remotely Sensed Data ◽  
Ecosystem Monitoring ◽  
Tier 1 ◽  
Benthic Substrate

The Laurentian Great Lakes comprise the largest assemblage of inland waterbodies in North America, with vast geographic, environmentally complex nearshore benthic substrate and associated habitat. The Great Lakes Water Quality Agreement, originally signed in 1972, aims to help restore and protect the basin, and ecosystem monitoring is a primary objective to support adaptive management, environmental policy, and decision making. Yet, monitoring ecosystem trends remains challenging, potentially hindering progress in lake management and restoration. Consistent, high-resolution maps of nearshore substrate and associated habitat are fundamental to support management needs, and the nexus of high-quality remotely sensed data with improvements to analytical methods are increasing opportunities for large-scale nearshore benthic mapping at project-relevant spatial resolutions. This study attempts to advance the integration of high-fidelity data (airborne imagery and lidar, satellite imagery, in situ observations, etc.) and machine learning to identify and classify nearshore benthic substrate and associated habitat using a case study in southwest Lake Michigan along Illinois Beach State Park, Illinois, USA. Data inputs and analytical methods were evaluated to better understand their implications with respect to the Coastal and Marine Ecological Classification Standard (CMECS) classification hierarchy, resulting in an approach that could be easily applied to other shallow coastal environments. Classification of substrate and biotic components were iteratively classified in two Tiers in which classes with increasing specificity were identified using different combinations of airborne and satellite data inputs. Classification accuracy assessments revealed that for the Tier 1 substrate component (3 classes), average overall accuracy was 90.10 ± 0.60% for 24 airborne data combinations and 89.77 ± 1.02% for 12 satellite data combinations, whereas the Tier 1 biotic component (2 classes) average overall accuracy was 93.58 ± 0.91% for 24 airborne data combinations and 92.67 ± 0.71% for 11 satellite data combinations. The Tier 2 result for the substrate component (2 classes) was 93.28% for 2 airborne data combinations and 95.25% for the biotic component (2 classes). The study builds on foundational efforts to move towards a more integrated data approach, whereby data strengths and limitations for mapping nearshore benthic substrate and associated habitat, expressed through classification accuracy, were evaluated within the context of the CMECS classification hierarchy, and has direct applicability to critical monitoring needs in the Great Lakes.

scholarly journals Spatiotemporal Snowfall Variability in the Lake Michigan Region: How is Warming Affecting Wintertime Snowfall?

2016 ◽  
Vol 55 (8) ◽  
pp. 1813-1830 ◽  
Author(s):  
Craig A. Clark ◽  
Travis J. Elless ◽  
Anthony W. Lyza ◽  
Bharath Ganesh-Babu ◽  
Dana M. Koning ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Serial Correlation ◽  
Lake Michigan ◽  
Principal Component ◽  
Temporal Behavior ◽  
Temperature Peak ◽  
Spatiotemporal Structure ◽  
Teleconnection Patterns ◽  
Lake Effect ◽  
Trend Reversal

AbstractThis study has investigated the spatiotemporal structure and changes in Lake Michigan snowfall for the period 1950–2013. With data quality caveats acknowledged, a larger envelope of stations was included than in previous studies to explore the data using time series analysis, principal component analysis, and geographic information systems. Results indicate warming in recent decades, a near-dearth of serial correlation, midwinter dependence on teleconnection patterns, strong sensitivity of snowfall to temperature, peak snowfall variability and dependence on temperature within the lake-effect belt, an increasing fraction of seasonal snowfall occurring from December to February, and temporal behavior consistent with the previously reported trend reversal in snowfall.

scholarly journals Periodic structures of Great Lakes levels using wavelet analysis

2011 ◽  
Vol 59 (1) ◽  
pp. 24-35 ◽  
Author(s):  
Taner Cengiz
Keyword(s):  
Wavelet Transform ◽  
Great Lakes ◽  
Lake Level ◽  
Lake Erie ◽  
Lake Michigan ◽  
Periodic Structures ◽  
Lake Superior ◽  
Great Lakes Region ◽  
Continuous Wavelet ◽  
Lake Levels

Periodic structures of Great Lakes levels using wavelet analysisThe recently advanced approach of wavelet transforms is applied to the analysis of lake levels. The aim of this study is to investigate the variability of lake levels in four lakes in the Great Lakes region where the method of continuous wavelet transform and global spectra are used. The analysis of lake-level variations in the time-scale domain incorporates the method of continuous wavelet transform and the global spectrum. Four lake levels, Lake Erie, Lake Michigan, Lake Ontario, and Lake Superior in the Great Lakes region were selected for the analysis. Monthly lake level records at selected locations were analyzed by wavelet transform for the period 1919 to 2004. The periodic structures of the Great Lakes levels revealed a spectrum between the 1-year and 43- year scale level. It is found that major lake levels periodicities are generally the annual cycle. Lake Michigan levels show different periodicities from Lake Erie and Lake Superior and Lake Ontario levels. Lake Michigan showed generally long-term (more than 10 years) periodicities. It was shown that the Michigan Lake shows much stronger influences of inter-annual atmospheric variability than the other three lakes. The other result was that some interesting correlations between global spectrums of the lake levels from the same climatic region were found.

scholarly journals Lessons from Inter-Comparison of Decadal Climate Simulations and Observations for the Midwest U.S. and Great Lakes Region

Atmosphere ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 266
Author(s):  
Ashish Sharma ◽  
Alan F. Hamlet ◽  
Harindra J.S. Fernando
Keyword(s):  
Great Lakes ◽  
Large Scale ◽  
Climate Modeling ◽  
Wrf Model ◽  
Warm Season ◽  
Great Lakes Region ◽  
Lake Effect ◽  
Simulated Precipitation ◽  
Decadal Climate ◽  
Meteorological Impact

Even with advances in climate modeling, meteorological impact assessment remains elusive, and decision-makers are forced to operate with potentially malinformed predictions. In this article, we investigate the dependence of the Weather Research and Forecasting (WRF) model simulated precipitation and temperature at 12- and 4-km horizontal resolutions and compare it with 32-km NARR data and 1/16th-degree gridded observations for the Midwest U.S. and Great Lakes region from 1991 to 2000. We used daily climatology, inter-annual variability, percentile, and dry days as metrics for inter-comparison for precipitation. We also calculated the summer and winter daily seasonal minimum, maximum, and average temperature to delineate the temperature trends. Results showed that NARR data is a useful precipitation product for mean warm season and summer climatological studies, but performs extremely poorly for winter and cold seasons for this region. WRF model simulations at 12- and 4-km horizontal resolutions were able to capture the lake-effect precipitation successfully when driven by observed lake surface temperatures. Simulations at 4-km showed negative bias in capturing precipitation without convective parameterization but captured the number of dry days and 99th percentile precipitation extremes well. Overall, our study cautions against hastily pushing for increasingly higher resolution in climate studies, and highlights the need for the careful selection of large-scale boundary forcing data.

Profundal Testate Amoebae (Arcellacea) of Lake Superior and Lake Michigan

2020 ◽  
Vol 50 (1) ◽  
pp. 3-10 ◽  
Author(s):  
Krystyna M. Kornecki ◽  
Miriam E. Katz
Keyword(s):  
Pilot Study ◽  
Great Lakes ◽  
Water Depth ◽  
Lake Michigan ◽  
Lake Superior ◽  
Testate Amoebae ◽  
Sediment Surface ◽  
Valuable Insight ◽  
Water Interface ◽  
Large Lake

Abstract Sediment surface death assemblages of recent testate amoebae (Arcellacea) are reported from nine sites in Lake Superior and Lake Michigan. These are the first profundal sediment-water interface samples of testate amoebae described from either of the Great Lakes which provide valuable insight on deep-water, large-lake assemblages. Centropixid strains were present to abundant in shallower, nearshore sites (up to 66 m water depth). Assemblages at depths >40 m were dominated by Difflugia oblonga “tenuis.” The shallowest sample (26 m) was dominated by Centropyxis aculeata “discoides” and Difflugia oblonga “tenuis.” Over 100 tests per sample were observed from >100 m. Density of tests appears to be constrained by lithology rather than water depth. The deepest site (325 m) yielded low foraminiferal abundances. This pilot study provides a first step towards documenting the distribution of testate amoebae in the Great Lakes.

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