Classification of Lake Michigan snow days for estimation of the lake‐effect contribution to the downward trend in November snowfall

Clark, Craig A.; Goebbert, Kevin H.; Ganesh-Babu, Bharath; Young, Allison M.; Heinlein, Kaitlyn; Casas, Eleanor; Guchte, Andrew P. VanDe; Krull, Alexander J.; Sefcovic, Zachary; Connelly, Ryan; Caruthers, A.; Haynes, Matthew; Zigner, Katelyn; Fingerle, Sarah; Cade, Evan; DeRolf, Timothy; Schletz, Samantha

doi:10.1002/joc.6542

Cited by 5 publications

(5 citation statements)

References 37 publications

(85 reference statements)

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“…Storms were visually assessed using reanalysis images similar to the procedures used by Clark et al . (2020) and using meteorological reports from Syracuse Hancock International Airport and the methods outlined by Suriano and Leathers (2017a) (Table 1). Archived 3‐hr United States (CONUS) Analyses images were accessed from the Weather Prediction Center (WPC) from 00Z May 1, 2005 to June 30, 2015 (Table 1).…”

Section: Methods and Resultsmentioning

confidence: 99%

A classification scheme for identifying snowstorms affecting central New York State

Hartnett

2020

Intl Journal of Climatology

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The Great Lakes region experiences anomalously high seasonal snowfall totals relative to similar latitudes. Although lake‐effect snowstorms are common in this region, snowfall occurs from a variety of storm types. This study examines snowstorms in a subsection of the Lake Ontario basin to develop a classification scheme to categorize the different types of snowstorms affecting the region. From 1985 to 2015, there were 11 different snowstorm types to affect the study area. The classification system was used to assess the frequency of, and snowfall produced by the different storm types within the eastern Great Lakes region. From the classification, snowstorms were categorized as either non‐direct cyclonic storms (NDCS) or direct cyclonic storms (DCS). Lake‐effect snowstorms, a type of NDCS, were the most frequent storm (35.1% of all storms) and accounted for approximately 39.4% of the snowfall. Most lake‐effect storms (37.7%) produced moderate snowfall totals (10.2–25.3 cm), yet heavy snowfall storms (≥25.4 cm) contributed significantly (ρ ≤ .05) more to seasonal snowfall totals than lighter snowfall storms. Direct cyclonic clippers forming over high latitudes of northwestern Canada, were the most frequent DCS in Central New York (11.3% of all storms), with nearly three quarters of the storms originating over Alberta. These storms only contributed 9.2% of the seasonal snowfall in the study area, compared to 12.7% from direct cyclonic Nor'easters forming near the east coast of North America. Although Nor'easters occur less frequently than clippers, when they do occur, they tend to produce heavy widespread snowfall across the region. The classification system proposed can be modified to accommodate snow basins across the globe. Classifying snowstorms will help determine the seasonal snowfall contribution from different storms and aid in future climate predictions, as individual snowstorm types may respond differently to a warming global climate.

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Section: Methods and Resultsmentioning

confidence: 99%

A classification scheme for identifying snowstorms affecting central New York State

Hartnett

2020

Intl Journal of Climatology

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“…Since the formation of lake-effect snowstorms and non-lake-effect snowstorms are fundamentally different, a warming climate may have contrasting influences on these storms. This is especially important for storms that occur near the beginning or end of the snowfall season, as recent studies have noted a transition from snow to rain in these storms (Schmidlin et al, 1987;Groisman and Easterling, 1994;Miner and Fritsch, 1997;Knowles et al, 2006;Pierce and Cayan, 2013;Kluver and Leathers, 2015;Clark et al, 2020). Since the potential trajectory of future snowfall varies according to the type of storm, for accurate snowfall predictions, models need to decipher the relative contributions of different snowstorm types to the seasonal snowfall total.…”

Section: Discussionmentioning

confidence: 99%

“…Multiple researchers have suggested that lake-effect snow exhibits sub-seasonal variability in its prevalence (Strommen and Harman, 1978;Niziol et al, 1995;Veals and Steenburgh, 2015;Clark et al, 2016Clark et al, , 2020Fairman et al, 2016). Harrington and Dewey (1987) found that lake-effect snowstorms are most common from November to January, when the lake surface is the warmest, resulting in large surface to 850 hPa lapse rates and minimal ice cover.…”

Section: Sub-seasonal Lake-effect Snow Contributionsmentioning

confidence: 99%

The Seasonal Snowfall Contributions of Different Snowstorm Types in Central New York State

Hartnett

2021

Front. Water

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Located at the eastern extent of the Great Lakes snowbelt, Central New York averages some of the highest annual snowfall totals east of the Rocky Mountains. This is in large part due to the variety of snowstorms that affect the region including lake-effect storms, coastal storms, and overrunning storms. Previous estimates suggest that lake-effect snowstorms account for approximately half of the seasonal snow in the Great Lakes basin, but ignore the spatial variability that exists within the region. Therefore, this study examines the seasonal snowfall contributions of the different snowstorm types to affect Central New York. Results suggest that although lake-effect snowstorms are the dominant snowstorm type in the region, their seasonal snowfall contributions vary between 13 and 48%. Although lake-effect snowstorms produce more snow during the peak and mid-seasons, their relative contribution is greatest during the early and mid-winter seasons. Generally, higher contributions occur near the Tug Hill Plateau, with lower contributions in southern Central New York. Instead, snowfall in southern Central New York is mostly dominated by Nor'easters (16–35%), with lesser contributions from Rocky lows (14–29%). Overrunning storms that originate in Canada (e.g., Alberta clippers) and non-cyclonic storms contribute the least to seasonal snowfall totals across Central New York; however, they are often the catalyst for lake-effect snowstorms in the region, as they advect continental polar air masses that destabilize across the lake. Understanding the actual snowfall contribution from different snowstorm types is needed for future climate predictions. Since the potential trajectory of future snowfall varies according to the type of storm, climate models must accurately predict the type of storm that is producing the snow.

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“…Furthermore, above freezing inland surface temperatures and weak CAA also factored into why LES activity was suppressed. As [21,59] notes, ample CAA is needed to create large vertical temperature gradients which generate turbulent heat and moisture fluxes and destabilize the overlying polar air mass. Cluster 1 mesoscale patterns were observed, albeit to a lesser degree, with Cluster 2.…”

Section: Summary and Future Workmentioning

confidence: 99%

“…Future research will further investigate these meteorological traits through the development of a diagnostic objective classification model that categorizes LES and non-LES clippers based on results from this study. Reference [59] demonstrated that the climatological spatial snowfall patterns over Lake Michigan contain enough of a synoptic signal to objectively classify LES from synoptically driven snowfall. The authors plan to further this work by developing a machine learning based classifier using the results of this work.…”

Section: Summary and Future Workmentioning

confidence: 99%

Structure and Evolution of Non-Lake-Effect Snow Producing Alberta Clippers

Wiley

Mercer

2021

Atmosphere

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Alberta Clippers (clippers) have long been associated with lake-effect snow (LES) events due to their frequent passage over the Great Lakes basin. However, not all clippers produce LES, and no research has inquired into which synoptic fields most influence LES formation. This study analyzes clippers during non-LES situations to further knowledge on which atmospheric variables most regulate LES development on the synoptic scale. As no such database currently exists, a clipper repository is developed using National Centers for Environmental Prediction Reanalysis data. The repository is then cross referenced with a previously developed LES repository to identify clippers responsible for LES. Composite synoptic-scale patterns were then constructed on the remaining non-LES clippers to identify synoptic conditions that ultimately inhibited LES formation. This analysis is supplemented by an assessment of lake surface conditions in each composite to evaluate how influential the lake characteristics were in the suppression of LES activity. In total, 51 non-LES clippers were identified, tracked, and separated into three composite map types that exhibited unique storm track and spatial characteristics. Permutation testing revealed that lake surface conditions were not significantly (p ≤ 0.05) different between LES and non-LES associated clippers implying the main LES inhibition factors were meteorological.

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Classification of Lake Michigan snow days for estimation of the lake‐effect contribution to the downward trend in November snowfall

Cited by 5 publications

References 37 publications

A classification scheme for identifying snowstorms affecting central New York State

A classification scheme for identifying snowstorms affecting central New York State

The Seasonal Snowfall Contributions of Different Snowstorm Types in Central New York State

Structure and Evolution of Non-Lake-Effect Snow Producing Alberta Clippers

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