The Effect of Global Temperature Increase on Lake-Effect Snowfall
Downwind of Lake Erie
A thesis presented to
the faculty of
the College of Arts and Sciences of Ohio University
In partial fulfillment
of the requirements for the degree
Master of Arts
Michael R. Ferian
March 2009
© 2009 Michael R. Ferian. All Rights Reserved
2
This thesis titled
The Effect of Global Temperature Increase on Lake-Effect Snowfall
Downwind of Lake Erie
by
MICHAEL R. FERIAN
has been approved for
the Department of Geography
and the College of Arts and Sciences by
_________________________________________
Dorothy Sack
Professor of Geography
________________________________________
Benjamin M. Ogles
Dean, College of Arts and Sciences
3
ABSTRACT
FERIAN, MICHAEL R., M.A., March 2009, Geography
The Effect of Global Temperature Increase on Lake-effect Snowfall Downwind of Lake
Erie (89 pp.)
Director of Thesis: Dorothy Sack
Lake-effect snowfall is a large contributor to yearly precipitation in the Great
Lakes region, affecting the water budget as well as local economies. Air temperature is
an important variable for lake-effect snowfall production, as it determines the
temperature disparity that is needed between the water and the air. Recent increasing
trends in air temperature have potential implications for lake-effect snowfall production.
This thesis examines seasonal temperatures and lake-effect snowfall totals since 1950 for
Cleveland, OH and Buffalo, NY to determine how seasonal temperature trends have
affected yearly lake-effect snowfall outputs. Also, the effect of increased temperatures
on snowfall patterns within winter seasons is analyzed. This provides a good case study
for the examination of lake-effect snowfall with respect to air temperature and what
might be expected in years to come. It was found that lake-effect snowfall will continue
to increase until the mean winter temperature of a particular area rises above freezing.
Approved: _____________________________________________________
Dorothy Sack
Professor of Geography
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ACKNOWLEDGEMENTS
I wish to thank my advisor, Dorothy Sack, whose patience was greatly
appreciated during times of struggle and frustration. Although Meteorology is not her
area of expertise, she took the time to understand concepts and offer the best advice
possible when problems arose. Her ability to ease the inherent stress of writing a thesis,
while establishing the importance of maintaining a diligent work ethic helped immensely
during this process. I would also like to express my appreciation for her assistance in
completing the final steps of thesis process, as I had moved to the west coast, making the
processes slightly more difficult.
I would also like to thank my committee members, Dr. James Lein and Dr. Tim
Anderson, who always made themselves available when necessary, and especially for
taking time during their holiday to hear my oral defense. In addition, I would like to
thank Dr. Ron Isaac and Chris Towe for their support and guidance throughout my
graduate education and tenure as Associate Director of Scalia Lab. The advice they
offered and life lessons they taught are invaluable.
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TABLE OF CONTENTS
ABSTRACT ........................................................................................................................ 3
ACKNOWLEDGEMENTS ................................................................................................ 4
LIST OF TABLES .............................................................................................................. 7
LIST OF FIGURES ............................................................................................................ 9
CHAPTER 1: Research Problem ..................................................................................... 11
1.1 Introduction .................................................................................................... 11
1.2 Significance.................................................................................................... 13
1.3 Research Questions ........................................................................................ 14
1.4 Hypothesis...................................................................................................... 14
CHAPTER 2: Literature Review ..................................................................................... 16
2.1 Temperature Trends and Lake Ice Cover........................................................ 16
2.2 Lake-effect Locations .................................................................................... 17
2.2.1 United States and Canada ................................................................... 18
2.2.2 Lake Erie ............................................................................................. 21
2.3 Temperature Trends and Lake Effect Snow .................................................. 25
CHAPTER 3: Study Area ................................................................................................ 28
3.1 The Great Lakes ............................................................................................. 28
3.2 Lake Erie ........................................................................................................ 29
3.3 Cleveland, OH................................................................................................ 33
3.4 Buffalo, NY.................................................................................................... 35
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CHAPTER 4: Methodology…………………………………………………………….37
4.1 Data Collection………………………………………………………………38
4.1.1 Air Temperature Data……………………………………………..38
4.1.2 Lake-effect Snowfall Data………………………………………...39
4.1.3 Lake Ice Cover…………………………………………………….41
4.2 Data Analysis………………………………………………………………...41
CHAPTER 5: Results……………………………………………………………………44
5.1 Lake-effect Snowfall Totals…………………………………………………44
5.2 Seasonal Air Temperatures and Lake-effect Snowfall Totals……………….48
5.2.1 Cleveland, OH……………………………………………………..48
5.2.2 Buffalo, NY………………………………………………………..60
5.3 Interseasonal Snowfall Trends……………………………………………....71
CHAPTER 6: Discussion……………………………………………………………….77
CHAPTER 7: Conclusions……………………………………………………………...82
REFERENCES…………………………………………………………………………..85
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LIST OF TABLES
Page
Table 1: North American Great Lakes Attributes…………………………………….....29
Table 2: Air Temperature Data Terms and Definitions…………………………………40
Table 3: Snowfall Data Terms and Definitions…………………………………………41
Table 4: Statistical Analyses for Research Question One………………………………43
Table 5: Statistical Analyses for Research Question Two………………………………43
Table 6: Total Lake-effect Snowfall…………………………………………………….45
6a: Early Period…………………………………………………………………..45
6b: Later Period…………………………………………………………………..45
Table 7: Mean Seasonal, Normal Mean Seasonal, and Deviation from Normal Mean
Seasonal Temperatures for Cleveland, OH……………………………………49
Table 8: Total Snowfall, Normal Snowfall, and Snowfall Deviation for Cleveland,
OH……………………………………………………………………………...51
Table 9: Seasonal Temperature Deviations from Normal and Lake-effect Snowfall
Deviations from Normal for Cleveland, OH…………………………………..52
Table 10: Seasonal Temperature Departures Correlation to Snowfall Departures for
Cleveland, OH………………………………………………………………...56
Table 11: Mean Seasonal, Normal Mean Seasonal, and Deviation from Normal Mean
Seasonal Temperatures for Buffalo, NY……………………………………..61
Table 12: Snowfall, Normal Snowfall, and Snowfall Deviation for Buffalo, NY………63
Table 13: Seasonal Temperature Deviations from Normal and Lake-effect Snowfall
Deviations from Normal for Buffalo, NY……………………………………64
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Table 14: Seasonal Temperature Departures Correlation to Snowfall Departures for
Buffalo, NY…………………………………………………………………...67
Table 15: Monthly Snowfall, Yearly Snowfall, and Correlation Coefficients for
Cleveland, OH………………………………………………………………...73
15a: Early Period………………………………………………………………...73
15b: Later Period………………………………………………………………...74
Table 16: Monthly Snowfall, Yearly Snowfall, and Correlation Coefficients for
Buffalo, NY…………………………………………………………………...75
16a: Early Period………………………………………………………………...75
16b: Later Period………………………………………………………………...76
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LIST OF FIGURES
Page
Figure 1: The North American Great Lakes Basin……………………………………...23
Figure 2: Lake Erie Bathymetry………………………………………………………...24
Figure 3: Lake Erie Annual Maximum Ice Cover………………………………………24
Figure 4: Lake-effect and Non-lake-effect Sites………………………………………...32
Figure 5: Northeast Ohio Snowbelt……………………………………………………..34
Figure 6: Digital Elevation Model of Northeast Ohio…………………………………..35
Figure 7: Yearly Lake-effect Snow Totals and Trends………………………………….46
7a: Cleveland, OH……………………………………………………………….46
7b: Buffalo, NY…………………………………………………………………46
Figure 8: Yearly Lake-effect Snowfall Totals for Cleveland and Buffalo……………...47
Figure 9: Mean Seasonal Temperatures for Cleveland, OH…………………………….53
9a: Winter………………………………………………………………………..53
9b: Spring………………………………………………………………………..53
9c: Summer……………………………………………………………………...54
9d: Fall…………………………………………………………………………..54
Figure 10: Winter, Summer, Spring, and Fall Correlation Scatter Plots for Cleveland...56
Figure 11: Temperature Deviations and Snowfall Deviations for Cleveland…………...58
11a: Winter………………………………………………………………………58
11b: Summer…………………………………………………………………….59
Figure 12: Mean Seasonal Temperatures for Buffalo, NY……………………………...65
12a: Spring………………………………………………………………………65
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12b: Winter……………………………………………………………………...65
12c: Summer…………………………………………………………………….66
12d: Fall…………………………………………………………………………66
Figure 13: Winter, Fall, Summer, and Spring Correlation Scatter Plots for Buffalo…...68
Figure 14: Winter Temperature Deviations and Snowfall Deviations for Buffalo……..70
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CHAPTER 1
RESEARCH PROBLEM
1.1 Introduction
Lake-effect snow refers to a weather phenomenon that occurs only in certain
locations throughout the world. Some favored locations include the North American Great
Lakes, the Great Salt Lake of Utah, the Finger Lakes in New York, Lake Winnipeg in
Manitoba, and Lake Baikal in Russia. A similar effect occurs near the Chesapeake,
Delaware, and Massachusetts Bays, as well as the Sea of Japan and the Baltic Sea.
Requirements include a cold enough climate to produce snow on land adjacent to a large
body of water. When cold air traverses a large, warmer body of water, the air of contrasting
temperatures mixes and pulls moisture upward, causing cooling, condensation, and
eventually snow. These types of precipitation events have produced some of the most
significant snowfall totals on record. One particular five-day event at the end of 2001
dumped more than 330 cm on Montague, New York, near Lake Ontario, making it one of the
snowiest storms on record (NOAA 2007). These very large amounts of lake-effect snows
typically fall in localized areas that lie downwind of the adjacent lake. Favored areas around
the North American Great Lakes are known as the lake-effect “snow belts” and are generally
located south and east of their respective lake because the wind generally prevails to that
direction during the winter months. In addition to being in close proximity and downwind of
the lake, snow belts are typically situated in areas of higher elevation that cause additional
uplift of air, thereby assisting in the formation of precipitation.
Several factors are known to contribute to the formation of lake-effect snow.
These include the presence of an upper level low pressure system, cold air aloft, and wind
12
directions that will maximize the fetch, which is the distance over water traversed by the
air (Niziol 1987). However it is important that temperature trends are conducive to lakeeffect formation over an entire winter season. While it is imperative that temperatures
remain warm enough to deter the lake from freezing, it is also essential for them to
remain cold enough to create a temperature disparity between the air and the lake.
The importance of temperature trends for the occurrence of lake-effect snowfall
has prompted interest in global temperature change and its effect on regional and local
temperature patterns. Global temperatures have consistently risen since 1975 and a
global temperature anomaly of +0.42˚C has occurred between 1850 and 2006 (Brohan et
al. 2006). In conjunction with this trend, a study by Burnett et al. (2003) concluded that
Great Lake temperatures have also been increasing since 1850, accounting for later
average first freezes and earlier average ice melts. Average yearly lake-effect snowfall
totals in the Great Lakes region have increased since 1931 and the most notable increases
have occurred since the mid 1970s (Burnett et al. 2003). Monthly snowfall trends within
each winter season, however, have not been previously studied.
The purpose of this thesis is to examine lake-effect snowfall totals downwind of
Lake Erie to determine their degree of correlation with increasing temperature trends.
This is accomplished using mean seasonal temperatures and yearly lake-effect snowfall
totals. Monthly snowfall totals within lake-effect seasons are also explored to determine
any significant changes to seasonal snowfall patterns throughout time. If a close
association between temperature and lake-effect snowfall is found, it could lead to greater
accuracy in snowfall forecasts for such regions.
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1.2 Significance
The local water cycle is typically adjusted to incorporate the average amounts of
snow and snowmelt, and problems can occur as a result of large lake-effect snowfalls. A
large lake-effect snowfall season can disrupt the local water budget and be detrimental to
the agricultural and fishing industries. An unusually active lake-effect season can cause
flooding when the snow melts, delaying growing or fishing seasons. A similar effect may
be seen if an anomalously calm lake-effect season occurs, resulting in a decrease in
groundwater recharge or surface runoff.
Other industries that are important to local and regional economies in snow belts
thrive on snowfall. Examples include snow removal and the manufacture and sales of
winter-related merchandise. Workers in these industries rely on heavy winter snows in
order to sustain a certain level of income. Winter recreation sectors of the economy,
particularly ski resorts, also benefit from the large amounts of snow often associated with
the lake-effect.
Forecasting lake-effect snow events presents a major challenge, even for the most
esteemed winter weather experts. Because many continuously changing factors
determine the intensity and the exact locations of the snowfall, the degree of confidence
in a forecast is rarely high, and it has been especially difficult to predict of severity of an
entire lake-effect snow season as it approaches. Establishing a quantitative relationship
between temperature variables and lake-effect snowfall could aid in estimating the
severity of an upcoming lake-effect snowfall season. This information would allow
people in various industries to prepare accordingly.
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1.3 Research Questions
The nature of the physical connection between the rise in both global and lake
temperature and lake-effect snowfall totals raises debate. On one hand, it would be
logical to assume that increased air temperatures would deter the lake from freezing,
allowing for a longer lake-effect snow season. This would tend to increase lake-effect
snowfall totals. In another sense, it seems acceptable to assume that increased air
temperatures would reduce the amount of sub-freezing days, which would decrease lakeeffect snowfall totals. General seasonal temperature trends may be important in creating
an ideal situation for lake-effect snow development. Because large bodies of water like
Lake Erie are slow at responding to heat changes, it is possible that temperature trends in
other seasons are causing lakes to stay warmer in winter. This, in conjunction with
winters that still experience substantial sub-freezing days, is a perfect situation for lakeeffect snow formation.
To fully explore this topic, it is important to analyze interseasonal temperature
trends as well as monthly snowfall trends within different winter seasons for the Lake
Erie study area. For this reason, two research questions are proposed. First, which
seasons have experienced the greatest temperature increases, and how do seasonal
temperatures correlate with lake-effect snowfall totals? Second, what are the monthly
snowfall trends within lake-effect seasons, and can they be attributed to monthly,
seasonal, or annual temperature trends?
1.4 Hypothesis
Water warms and cools at a much slower rate than the atmosphere and land.
Therefore, there is a time lag for the lake temperatures to be affected by variations in air
15
temperature. For this reason, I hypothesize that air temperatures in summer months
preceding a given winter season will most significantly dictate lake-effect snow totals in
that winter season. I also postulate that even though winter temperatures have been
increasing, enough below-freezing days remain in the study area to produce significant
amounts of lake-effect snow. However, because of the rising temperatures, Lake Erie
will freeze less often, creating greater amounts of snow in the middle of the winter
season, when the lake would have been frozen in the past.
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CHAPTER 2
LITERATURE REVIEW
2.1 Temperature Trends and Lake Ice Cover
Since 1850, the average annual global temperature has increased by
approximately 0.85˚C (Brohan et al. 2006). Since the 1970s, the earth’s mean annual
temperature has increased on average by 0.17˚C per decade with some of the highest
yearly averages occurring within the last fifteen years (Balling 2003). According to a
study by Magnuson et al. (2000) on lake and river temperature trends in the Northern
Hemisphere, from 1846 to 1995 air temperatures near the lakes they studied have
increased by approximately 1.8˚C. That research also determined that the studied lakes
and rivers froze approximately eight days later and broke up approximately ten days
earlier by the end of the 150 year period (Magnuson et al. 2000). These numbers are
consistent with those pertaining to the North American Great Lakes since 1850 (Assel et
al. 1995). This longer period of open water can have a significant impact on the lakeeffect snow season because for a substantial amount of lake-effect snow to occur, a
considerable quantity of liquid water needs to be evaporated into the atmosphere. Ice
cover on the lakes severely limits that essential component. A decreasing trend in the
number of days with ice cover translates into a greater potential for lake-effect snow to
occur.
Hanson et al. (1992) studied the ice cover trends in the Great Lakes region from
1955 to 1989 by examining spring runoff in the St. Lawrence River system. They
noticed that runoff started earlier and earlier in more recent years, signifying shorter
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winters and longer springs. Hansen et al. (1992) determined that 10 to 15 fewer days of
ice cover were occurring by the end of the period. The study attributes this decrease in
ice cover to increased springtime temperatures in the region (Hanson et al. 1992). This is
a feasible explanation because increased temperatures at the end of the winter season or
the beginning of the spring season would increase the rapidity of the melting process.
Temperature trends during other seasons were not analyzed or discussed.
2.2 Lake-effect Locations
Lake-effect precipitation occurs in certain locations throughout the world.
Favored lake-effect locations in North America exist in the United States and Canada,
near the Great Salt Lake and the Great Lakes. Other areas throughout the world also
experience a similar phenomenon, including locations to the lee of Lake Baikal in Russia,
the Baltic Sea, and the Sea of Japan. The common characteristic that makes these areas
prime for lake-effect development is that they exist in the mid-latitude region of the
Northern Hemisphere. The mid-latitudes contain the areas lying between 30˚ and 60˚
North or South latitude. This region experiences different air masses throughout the year,
as opposed to tropical or polar regions where weather is more static. During the spring
and summer in the mid-latitudes, rising air temperatures and a higher sun angle heat these
bodies of water. Once winter approaches, air temperatures quickly drop, but the
temperature of the water remains warm enough to create the temperature disparity
essential for lake-effect development (Sousounis 2000).
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2.2.1 United States and Canada
The North American Great Lakes are the most notorious producers of lake-effect
precipitation in the United States. Areas to the lee of Lake Superior, Lake Huron, Lake
Ontario, Lake Michigan, and Lake Erie experience lake-effect precipitation each year.
Lake-effect snow accounts for approximately 30% to 50% of the total annual snowfall in
the Great Lakes region (Bates 1993). Because the wind generally blows from west to
east in this region, the most commonly affected areas are located just south and east of
the lakes. The development of lake-effect snow near the Great Lakes is dependent upon
the presence of several meteorological conditions. Cold air must be in place over the
warmer, unfrozen lake water. Significant lake-effect snowfall is experienced when the
difference in temperature at the 850-millibar (mb) level, which is approximately 1500 m
above the surface, and at the surface of the water is 13˚C or greater (Hjelmfelt 1989). A
large-scale uplift mechanism, such as a trough of low pressure, must accompany the cold
air mass. This allows for the warm, moist air near the lake surface to vertically mix with
the colder air above it. Wind directions at the surface and at the 700-mb level, which is
approximately 3100 m above the surface, dictate the orientation, intensity, and duration
of the snow bands. The wind at the 700-mb level is referred to as the “steering wind,” so
snow bands will generally move in the direction to which the 700-mb wind prevails
(Hjelmfelt 1989). The duration and intensity of snow bands are maximized when the
direction of the prevailing surface wind is similar to the direction of the 700-mb wind,
indicating minimal directional wind shear. Significant lake-effect snow is possible when
the wind direction at the 700-mb level and at the surface differ by less than 60˚ (Hjelmfelt
1989).
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When these conditions occur, topographic characteristics near the Great Lakes
can enhance lake-effect snowfall. Orographic features are not necessary for lake-effect
development, but more elevated areas typically see higher snowfall totals. The presence
of hills or mountains provides an additional uplift mechanism for the warmer, moister air
near the surface, increasing the amount of vertical mixing and water vapor in the
atmosphere. Hill (1971) studied lake-effect snowfall with respect to orography to the lee
of Lake Erie and Lake Ontario and approximated a 25-50 cm annual increase in snowfall
per 100 m increase in elevation (Hill 1971). Hjelmfelt (1992) studied the effect of
orography on lake-effect snowfall totals to the lee of Lake Michigan and recorded a
disparity of several millimeters per hour between elevated and non-elevated areas
(Hjelmfelt 1992). This may seem negligible, but the elevation gradient near Lake
Michigan is minimal, compared to areas to the lee of Lake Erie and Lake Ontario (Figure
1). Because of the minimal elevation gradient near Lake Michigan, Hjelmfelt (1992)
concluded that the approximation suggested by Hill (1992) is feasible.
Although lake-effect snowfall is the focus of this thesis, lake-effect precipitation
is not limited to snowfall. Lake-effect rain events are also a contributor to annual
precipitation totals in the favored locations. Lake-effect rain develops under similar
synoptic conditions as lake-effect snow and is also enhanced by topographical features.
The distinguishing factor between a lake-effect snow and rain event is the surface
temperature. When the temperature disparity between the 850-mb level and the lake
surface temperature is sufficient for lake-effect development, snow will occur when the
surface temperature is near or below freezing, while rain will occur when the surface
temperature is above freezing. The latter most often is the case from late summer to late
20
fall. During this period the mean lake temperature is warmer than the mean air
temperature. However, the mean air temperature is not cold enough to produce snow.
For example, the mean water temperature of Lake Erie near Buffalo exceeds the mean air
temperature of Buffalo from September to April (Miner et al. 1997). This denotes the
duration of Buffalo’s lake-effect precipitation season because the mean water temperature
is warmer than the mean air temperature during that period. From September to early
November Buffalo’s mean air temperature is above freezing, so lake-effect precipitation
predominately falls as rain (Miner 1997). However, anomalously cold air outbreaks do
occur during the early lake-effect season, allowing for snow or mixed precipitation
events.
Another significant producer of lake-effect precipitation is the Great Salt Lake in
Utah. The Great Salt Lake is 120 km long and 45 km wide, making it the largest lake in
the United States west of the Great Lakes (Steenburgh et al. 2000). The threshold values
necessary for producing significant lake-effect snow that have been identified near the
Great Lakes are similar for the Great Salt Lake (Steenburgh et al. 2000). However, the
Great Salt Lake differs from the Great Lakes in several respects. Except for some
freshwater inlets, the Great Salt Lake is primarily salt water. The high salinity of the
water prevents the lake from freezing during the winter (Carpenter 1993). Also, the
Great Salt Lake is much shallower than the Great Lakes. The average depth of the Great
Salt like is 4.8 m and the maximum depth is only 10.5 m (Steenburgh et al. 2000). In
comparison, the deepest Great Lake, Lake Superior, has an average depth of 148 m, and
the shallowest Great Lake, Lake Erie, has an average depth of 19 m (Assel et al. 2003).
Because the Great Salt Lake is so shallow, water temperatures trends are in sync with air
21
temperature trends. Both the maximum and minimum air and water temperatures are
typically experienced on August 1 and February 1, respectively (Steenburgh et al. 2000).
Due to the high salinity of the water and the shallow nature of the lake, the lake-effect
snow season is prolonged. Lake-effect snow can be experienced to the lee of the Great
Salt Lake from early fall to late spring (Steenburgh et al. 2000), while the lake-effect
snow season near the Great Lakes begins in late fall and lasts through early spring.
Although the lake-effect season is prolonged near the Great Salt Lake, the size
and shape of the lake is a limiting factor to the frequency and intensity of lake-effect
snow storms. A minimum fetch of 120 km is required for the production of significant
lake-effect snow (Hjelmfelt 1989). While the size of the Great Lakes allows for multiple
wind directions to produce a fetch of this magnitude, only a north-northwest wind over
the Great Salt Lake will produce a fetch of 120 km (Carpenter 1993). Other wind
directions minimize the fetch, often producing light snow showers or snow flurries
(Carpenter 1993). However, the topography of the region can compensate for the
minimal fetch. The large elevation gradient that exists south and west of the lake aids in
the convergence of air near the surface, increasing the amount of vertical mixing of warm
and cold air (Carpenter 1993). Similar to the effect of topography near the Great Lakes,
this enhances the intensity and duration of lake-effect snow bands.
2.2.2 Lake Erie
Although Lake Erie is the southernmost Great Lake (Figure 1), on average it
experiences the greatest amount of ice cover. This is due to its shallow nature. The
average depth of Lake Erie is only 18.9 m, compared to Lake Superior, the deepest Great
22
Lake, which has an average depth of 147.2 m (Environmental Protection Agency 2006).
Within a lake basin, deeper areas freeze at a much slower rate than shallower areas. The
western portion of the Lake Erie, near Cleveland, OH, is the shallowest, while the eastern
portion, near Buffalo, NY, is the deepest (Figure 2). Therefore, the western part of the
lake tends to freeze earlier and more often than the eastern part. Assel et al. (2003) used
historic records to determine the maximum percentage of ice cover on the surface of each
Great Lake each year from 1963 to 2001. Lake Erie proved to have the highest average
annual percentage, 87%, during that period. However, in recent years the percentage has
decreased (Figure 3). The lowest annual maximum ice cover percentage for Lake Erie
was observed in 1998 at 5%, and the average of the annual percentages for the last four
years of the study was the lowest of any four-year period (Assel et al. 2003).
The recent decline in ice cover has been popularly attributed to the increasing
annual air temperatures, which affect the thermal cycle of the lakes. During a typical
winter, Lake Erie surface temperatures are lowest when they are just above 0˚C in
February (Schertzer et al. 1987), indicating that the lake surface will typically be
completely frozen during most of February. This, in turn, implies that lake-effect
snowfall totals would be minimized during that month. From January to February and
from late February to mid-March the lake surface temperature is near 1˚C, allowing for a
mix of ice and water (Schertzer et al. 1987). Development of lake-effect snow is
problematic under these conditions, but is still possible with the right combination of
synoptic conditions. Due to the temperature stratification of the lake water that takes
place during the summer, the lake can store large amounts of heat. Lake Erie experiences
its maximum heat storage potential during the month of August, while in mid-September
23
the lake stratification begins to break down (Schertzer et al. 1987). For this reason, air
temperatures during the summer months are important for determining the amount of heat
stored by the lake, which has implications for lake temperatures in subsequent months, as
well as for the upcoming lake-effect snow season.
Figure 1. The North American Great Lakes Basin (United States Environmental Protection Agency).
24
Figure 2. Lake Erie Bathymetry (National Geophysical Data Center 2008)
Figure 3. Lake Erie Annual Maximum Ice Cover (United States Environmental Protection Agency 2007).
25
2.3 Temperature Trends and Lake-Effect Snow
Increasing trends in lake-effect snowfall during the 20th century have been well
documented and provide the framework for this study. The Great Lakes region is the
only area in the United States that has shown a consistent increase in snowfall from 1945
to 1985 (Leathers et al. 1992). This suggests that lake-effect snowfall is increasing,
creating the positive trend in snowfall totals in that region. Davis et al. (2000) studied
precipitation around the Great Lakes and concluded that lake-effect snowfall is highly
sensitive to larger scale processes, such as global warming. Rising snowfall trends, in
conjunction with increasing air temperatures, is also noted in a study by Burnett et al.
(2003). They examined regional snowfall trends and lake-effect snowfall trends for all of
the Great Lakes. It was concluded that in the years since 1931, cold season snowfall has
been negatively correlated with temperature and colder winters have produced heavier
lake-effect snow totals. However, temperature trends throughout the other seasons were
not assessed (Burnett et al. 2003). Temperature trends in other seasons could influence
the overall snowfall trend, as well as the snowfall trends within each winter season. For
this reason, it is imperative to collect temperature data for all seasons for the study period
when assessing snowfall trends. This was accomplished in a previous study by Bolsenga
and Norton (1993), in which an increasing snowfall trend was acknowledged and
seasonal temperature trends in the Great Lakes Basin from 1901 to 1987 were assessed.
Those authors concluded that although over the study period each season experienced
some temperature increase, winter temperatures rose less (Bolsenga and Norton 1993).
The increase in lake-effect snowfall was confirmed in another study by Norton and
Bolsenga (1993), in which snowfall increases were attributed to lake-effect snow rather
26
than synoptic snowfall. Norton and Bolsenga (1993) found that the temperatures in both
the spring and winter seasons had not increased as much as those of the other two
seasons, and this may be responsible for increased lake-effect snow totals (Norton and
Bolsenga 1993).
Seasonal temperature trends in recent years, especially those for winter, have
implications for predicting future lake-effect snowfall trends. Mean annual temperatures
have increased, but seasonal temperature trends differ depending on the season. A debate
exists as to whether an overall increase in annual temperatures will continue to correlate
to increased lake-effect snowfall. One side of the debate, supported by Burnett et al.
(2003), states that rising annual temperatures will continue to produce increased snowfall
totals throughout the next century. This is based on the assumption that reduced ice
cover will provide the necessary energy, while the winters will stay cold enough to
produce lake-effect snow. This is reinforced by the findings of Bolsenga and Norton
(1993), which show that winter temperatures are increasing at a slower rate than those for
other seasons. In this scenario, Lake Erie would experience greater snowfall in the future
because it would freeze less often than it has in the past. Other sources propose that lakeeffect snowfall intensity and frequency will begin to decrease within a few decades if
mean winter temperatures rise above freezing, severely diminishing the number of days
in which lake-effect snow could be produced. Kunkel et al. (2000) found that air
temperature was the most significant variable in determining lake-effect snowfall totals
and predicted that the southern Great Lakes could undergo approximately a 50%
reduction in lake-effect snowfall by the end of the twenty-first century. Similar studies
agree with this notion (Crowe 1985; Cohen and Allsop 1988). This argument suggests
27
that the margins of Lake Erie would experience a decreased in snowfall due to the
inherently warmer temperatures of its latitudinal location. In other words, the mean
winter temperature will rise above freezing, implying fewer days in which lake-effect can
occur.
28
CHAPTER 3
STUDY AREA
3.1 The Great Lakes
The origin of the North American Great Lakes is attributed to the advance and
retreat of glaciers. This is based on the presence of glacial landforms and sediment
deposits discovered in the Great Lakes region (Farrand 1988). Glaciers advanced
southward into the present day Great Lakes region approximately 2 million years ago
(Farrand 1988). The glaciers followed a system of existing bedrock valleys because they
offered the least resistance to their advance. The valleys consisted of soft bedrock, such
as limestone and shale, easily eroded and deposited by the glaciers (Larson et al. 2001).
When the glaciers retreated approximately 14,000 years ago, the melt-water filled the
areas of softer bedrock that were carved by the glaciers. Approximately 4,000 years ago
the glaciers retreated completely, creating the Great Lakes (Farrand 1998). Although the
shape of the lakes has remained similar since their formation, it is believed that the lakes
have become deeper due to additional scouring of the surface bedrock (Larson et al.
2001).
The Great Lakes system, which includes Lakes Superior, Michigan, Huron, Erie,
and Ontario, is the largest source of freshwater on earth (Table 1). It accounts for
approximately 84% of North America’s freshwater and 21% of the world’s freshwater
(Environmental Protection Agency 2008). They provide a source for domestic water
needs for approximately 40 million people in the United States and Canada (MacDonaphDumler et al. 2006). They also accommodate large industries, such as manufacturing,
29
shipping, recreation and tourism, and fishing that fuel the local and regional economies.
For example, throughout the Great Lakes-St. Lawrence River system, over 100 ports
exist and close to 200 million tons of cargo are shipped internationally, regionally, and
locally per year (Quinn 2003). This equates to revenues of approximately $3 billion
annually (Lindeberg and Albercook 2000). The commercial fishing industry is valued at
almost $50 million annually, and the recreation fishing industry is a large boost to the
economy, as millions of anglers spend billions of dollars to fish the Great Lakes (Kling et
al. 2003)
Great Lake
Totals
Feature
Units
Superior
Michigan
Huron
Erie
Ontario
Average Depth
meters
147
85
59
19
86
Maximum Depth
meters
406
282
229
64
244
Volume
km3
12,100
4,920
3,540
484
1,640
22,684
Water Area
km2
82,100
57,800
59,600
25,700
18,960
244,160
Land Drainage Area
km2
127,700
118,000
134,100
78,000
64,030
521,830
Shoreline Length
km
4,385
2,633
6,157
1,402
1,146
17,017
Retention Time
years
191
99
22
2.6
6
Table 1. Attributes of the Great Lakes (Environmental Protection Agency 2008).
3.2 Lake Erie
Lake Erie is recognized as the oldest Great Lake because it was the first to form
as the glaciers retreated to the north. Relative to the other Great Lakes, the Lake Erie
basin is primarily flat. However, differences in bedrock and landforms exist across the
30
lake basin. The western basin, having a maximum depth of 11 m, is the shallowest Lake
Erie basin (Bolsenga et al. 1993). This portion of the lake is known for its network of
islands and reefs that exist among the otherwise flat bottom. Many of the islands contain
large sand deposits, which have formed shoreline beaches (Larson et al. 2001). The
majority of the shoreline and adjacent land near the western basin consist of marshy
flatlands (Larson et al. 2001). The soil there consists primarily of impervious silt and
clay, making it a fertile area for cultivation (Bolsenga et al. 1993). The central basin has
a maximum depth of 19m and is void of any major landforms, making it the flattest part
of the Lake Erie basin (Bolsenga et al. 1993). The majority of the northern shoreline in
the central basin is similar to the western basin. However, areas on the southern
shoreline consist of shale or clay bluffs derived from glacial drift (Larson et al. 2001).
The eastern basin is the deepest of the Lake Erie Basins with a maximum depth of 64 m.
This is due to the bowl-shape of the lake bottom in that area (Bolsenga et al. 1993). The
northern shore of the eastern basin consists of low marshlands, similar to the western
basin shorelines. However, the southern shoreline consists of permeable soils made of
rock. These areas are used to build infrastructure, such as highways and residential
complexes (Bolsenga et al. 1993).
The climate in the Lake Erie basin is greatly dependent on the season and
proximity to the lake. Because the Great Lakes are massive bodies of water, air masses
are easily modified while traversing the lakes. This creates microclimates, experienced in
localized areas in close proximity to the lake. These microclimates are especially present
downwind of Lake Erie because its shallow nature causes surface temperatures to
fluctuate more throughout a given year than the other Great Lakes. This effect can be
31
experienced with respect to air temperature in both the summer and winter. For example,
because the mean water temperature in summer is less than the mean air temperature,
areas closer to the lake will experience cooler daily temperatures than areas a few miles
inland. Also, during the summer warmer air over the cooler lake surface waters helps to
stabilize the atmosphere, intensifying high pressure systems (Bates et al. 1993). During
the winter, the mean water temperature is warmer than the mean air temperature, so areas
close to the lake experience warmer daily temperatures than inland locations. Cooler air
overriding the warmer lake water in winter creates instability, intensifying low pressure
systems (Bates et al. 1993).
Although all of the Great Lakes are major lake-effect snow producers, Lake Erie
was chosen for this thesis because its shallow depth, combined with its geographical
location, creates a unique situation. Because Lake Erie is the shallowest Great Lake, it
typically freezes earlier, and therefore, experiences the shortest lake-effect snow season
(Niziol et al. 1995). However, because it is the most southern Great Lake, it is more
vulnerable to experiencing warmer temperatures as a result of global temperature
increases. For these reasons, trends may be better observed in this area of the Great
Lakes. Also, a number of large cities are located along Lake Erie’s banks. The major
cities downwind of Lake Erie chosen for lake-effect snowfall sites are Cleveland, OH,
and Buffalo, NY. Data are readily available for these cities and possible effects of
increased lake-effect snow would impact a large population. Approximately 1.8 million
people reside within Lake Erie’s primary snow belt (Schmidlin 1993). In addition to
Cleveland and Buffalo, seven smaller, regional sites were chosen and deemed as nonlake-effect sites (Figure 4). The non-lake-effect sites include Detroit, MI, Toledo, OH,
32
Akron, OH, Youngstown, OH, Olean, NY, Elmira, NY, and Penn Yan, NY. These sites
were chosen so that they surround the lake-effect sites, but are not included in the
traditional snow belt areas. These sites were also chosen because of their snowfall data
availability. Each of these non-lake-effect sites contained data from 1950 to present,
which was sufficient for this study. Other sites may have been more favorable based on
their location, but data were available only for recent years. The careful selection of
these non-lake-effect sites allows for the determination between lake-effect snow events
and regional, or synoptic, snow events.
Figure 4. Map of Lake-effect and Non-Lake-effect Sites
33
3.3 Cleveland, OH
Cleveland is located at 41.52˚N latitude and 81.68˚W longitude. The city lies on
the south-central shore of Lake Erie in Cuyahoga County of northeastern Ohio (Figure 5).
According to the 2000 United States Census, Cleveland had a population of 478,403,
making it the thirty-third largest city in the country. Cleveland is known for its expansive
metropolitan area, which, according to the 2000 census, ranked fourteenth in the nation
(United States Census Bureau 2000). This sets up the potential for many people to be
affected by lake-effect snows in the greater Cleveland area.
Because of the changing contours of Lake Erie shoreline in northeastern Ohio,
different wind directions dictate which areas are most likely to experience the majority of
the snow for that particular event. The heaviest snow bands develop when westerly
winds prevail because they have the greatest fetch. A westerly wind direction favors the
traditional northeastern Ohio snow belt, the higher terrain east of Cleveland. The primary
snow belt refers to the areas to the lee of Lake Erie that receive mean annual snowfall
totals of 200 cm or greater (Schmidlin 1993). Chardon, OH, located in the heart of the
northeastern Ohio snow belt, is 360 m above sea level, while Cleveland is 183 m above
sea level (National Weather Service 2008) (Figure 6). If the wind changes to a
northwesterly direction snow bands are typically less intense due to a smaller fetch, but a
greater area is affected. This wind direction favors not only the primary snow belt, but
also the secondary snow belt, which includes the counties directly to the south of the
primary snow belt (Schmidlin 1993). Cleveland-Hopkins International Airport, where
the snowfall totals were collected for this study, is located approximately 19 kilometers
southwest of the city in the secondary snow belt area. The elevation there is 236 m above
34
sea level. Lake-effect can also develop under a northerly wind, but because the fetch is
minimal resulting snow bands are fairly weak. When the wind comes from the
southwest, snow bands are pushed offshore and the snow begins to subside in northeast
Ohio.
Figure 5. Northeast Ohio Snowbelt
35
Figure 6. Digital Elevation Model of Northeast Ohio
3.4 Buffalo, NY
Buffalo is located at 42.91˚N latitude and 78.87˚W longitude. The city lies on the
far eastern shore of Lake Erie in Erie County of western New York. Together, the city of
36
Buffalo and its metropolitan area have approximately half the population of Cleveland
and its metropolitan area (United States Census Bureau 2000). However, unlike
Cleveland, Buffalo is located in the heart of the snow belt in western New York and
typically experiences heavier lake-effect snow than Cleveland. The Greater Buffalo
International Airport, where the snowfall data were collected for this study, is located
approximately 17 kilometers northeast of the city and has an elevation of 217 m. The
Buffalo airport also resides within the primary snow belt in western New York Because
of Buffalo’s location with respect to Lake Erie, a southwest wind will have a fetch that is
almost the entire length of the lake. This allows for heavy snow squalls that persist for a
long duration. Lake-effect snow bands can also affect the Buffalo area when the wind is
westerly and northwesterly, but as winds become more northerly snowfall intensity
decreases and the snow bands affect locations to the southwest of the Buffalo area.
37
CHAPTER 4
METHODOLOGY
The data collection and analysis methods described in this section were conducted
to explore the two proposed research questions for both lake-effect sites, Cleveland and
Buffalo. The first research question explores seasonal temperature trends for a range of
study years and determines if lake-effect snow totals correlate with seasonal
temperatures. The second question considers asks monthly lake-effect snowfall trends
within winter seasons and investigates whether they can be attributed to monthly,
seasonal, or annual temperature trends. Analysis of the first research question required
the retrieval of temperature data from the lake-effect sites and separation of the studied
years into an early period and a late period to determine temperature trends. In addition,
snowfall data from both the lake-effect and non-lake-effect sites were needed to calculate
lake-effect snow totals for the lake-effect sites. The effect of these trends on snowfall
totals will be assessed only for the late period. Analysis of the second research question
required the use of the acquired snowfall data and compared monthly snowfall trends
between the early period and the late period. The beginning of the entire period was
chosen at 1950 to the limited history of the non-lake-effect sites’ snowfall records. The
dates for the early and late periods were chosen based on the fact that global temperatures
began to increase steadily during the mid-1970s. The collected temperature and snowfall
data were derived and analyzed through visual and statistical means to help answer the
questions posed.
38
4.1 Data Collection
4.1.1 Air Temperature Data
Air temperature data for this thesis were obtained for Cleveland and Buffalo from
the National Climatic Data Center (NCDC 2007). Temperatures in this thesis are
expressed in degrees Fahrenheit, as collected from the NCDC. First, mean monthly
temperatures were acquired for each month for the entire period. These values represent
the mean of the observed temperatures throughout a given month (Table 2). For
example, the mean monthly temperature for March, 1995, is the mean temperature for
March 1 through March 30 of 1995. Then, normal mean monthly temperatures, also
provided by the NCDC, were collected for each month for the entire period. These
values represent the cumulative mean temperature for a given month based on each
previous year in the NCDC database, which began in 1928. For example, the normal
mean temperature for March, 1995 is the average of the mean temperatures of March
1928 through March 1994. The study period for this thesis is 1950-2006, so the years
from 1928 to 1949 only serve to provide a sufficient amount of temperature data to obtain
normal temperatures for the years within the study period. Because this thesis assesses
seasonal temperature trends, both the mean monthly temperatures and the normal mean
monthly temperatures were converted into mean seasonal and normal mean seasonal
temperatures using March, April, and May as spring, and so on. For each season for each
year the mean monthly and normal mean monthly temperatures for the corresponding
months were simply added and divided by 3 to convert to seasonal values. The normal
mean seasonal temperatures were subtracted from the mean seasonal temperatures to
obtain deviations from the normal mean seasonal temperatures. This deviation value
39
helped to depict the temperature trends of seasons and made comparisons between
different seasons and different years possible.
4.1.2 Lake-Effect Snowfall Data
For this thesis, the lake-effect snow season is defined as consisting of the months
of October through April following the work of Burnett et al. (2003). Snowfall amounts
in inches were collected from the NCDC for each day throughout the lake-effect snow
season during the entire period for the lake-effect and non-lake-effect sites. These data
consist of total snowfall, meaning they incorporate both synoptic and lake-effect events.
Because this thesis is concerned only with lake-effect snowfall, the data required
filtering. Using both the non-lake-effect sites and archived surface maps from the
National Oceanic and Atmospheric Administration (NOAA 2007), each day in which the
lake-effect sites received snowfall was analyzed and deemed either a lake-effect or a nonlake-effect event. Although this method of deciphering between lake-effect and nonlake-effect events is effective and widely practiced, some degree of error most likely
exists for a couple reasons. First, some of the non-lake-effect sites, such as Akron, OH,
are not located in the favored lake-effect snow belts, but they are close enough that they
may experience some lake-effect snow each year. However, such sites were used
because their data were available since 1950, as opposed to other sites that may have
been located in a more favorable area, but had limited data. Second, because lake-effect
often develops after the passage of a synoptic event, it is difficult to determine exactly
when the lake-effect event began.
40
The lake-effect snowfall amounts were recorded separately and totaled for each
month. The monthly totals were added to obtain yearly snowfall totals (Table 3). Yearly
snowfall seasons are dated coinciding with the start of the lake-effect season. For
example, the 1995 snowfall season represents October through December, 1995, and
January through April, 1996. The yearly snowfall totals were used to obtain a normal
snowfall value for each year during the later period, 1976-2006. The normal snowfall
values were obtained by calculating a running average for each previous year. For
example, the normal snowfall for the lake-effect snow season of 1995 is the average of
the yearly snowfall totals from 1950 through 1994. Then, deviation from normal
snowfall was calculated for each year during the later period by subtracting the normal
snowfall value from the yearly total.
Term
Definition
observed mean temperature for a given month of a given
mean monthly temperature year
normal mean monthly
average mean temperature for a given month based on the
temperature
entire database record
mean seasonal
mean monthly temperatures converted into mean seasonal
temperature
temperatures (using March, April, May as Spring, etc.)
normal mean seasonal
normal mean monthly temperatures converted into normal
temperature
mean seasonal temperatures
deviation from the normal
mean seasonal
mean seasonal temperature minus normal mean seasonal
temperature
temperature
Table 2. Air Temperature Data Terms and Definitions.
41
Term
yearly snowfall
normal snowfall
deviation from the normal
snowfall
Definition
total lake-effect snow accumulation in a given year
yearly snowfall averaged over previous years (back to
1950)
yearly snowfall minus normal snowfall
Table 3. Snowfall Data Terms and Definitions.
4.1.3 Lake Ice Cover
Although air temperature is used as the main variable in determining lake-effect
snow in this particular study, lake ice data can help explain lake-effect snowfall
anomalies. Because yearly ice cover is a product of observed air temperatures throughout
all seasons, these data also help to explain the effect of seasonal temperature trends on the
lake temperature, and thus, lake-effect snowfall totals. Daily ice charts were obtained
from the NOAA Great Lakes Ice Atlas (NOAA 2006). These were used in conjunction
with ice data presented in Assel et al. (2003) to further explain statistical findings and
trends among observed temperatures and snowfall totals.
4.2 Data Analysis
For the first proposed research question, mean seasonal temperatures were
compared from the early period to the late period visually to determine the seasons that
have experienced the greatest temperature increases. Then, the air temperature deviation
values and the snowfall deviation values were analyzed, only for the late period. The
deviation values allowed for a consistent way to associate temperature trends to
precipitation trends. Comparisons would have been more difficult to conduct if the
42
temperature and snowfall data would have been left in their raw forms. To investigate
the association between seasonal temperatures and lake-effect snowfall totals, correlation
and bivariate linear regression were applied using deviations from normal mean seasonal
temperatures as the independent variable and the deviations from normal snowfall as the
dependent variable (Table 4). This method was employed to determine the season or
seasons that have the strongest association with yearly snowfall during the late period.
Scatter plot representations of these findings were also used to add a visual understanding
to the values and trends.
For the second proposed research question, monthly snowfall totals were analyzed
for the early and late periods using bivariate correlation and linear regression. These
statistics were applied to each period using the monthly snowfall totals as the
independent variable and yearly snowfall totals as the dependent variable (Table 5). This
method was employed in order to decipher the most statistically significant months in
determining yearly snowfall totals from the early to late period. The results from these
analyses were used in conjunction with the temperature and snowfall statistical findings
and ice data to further develop possible explanations and discussion.
43
Period
Assessed
19762006
Analysis Type
bivariate
correlation and
linear regression
Independent Variable
deviation from normal
mean seasonal
temperatures
Dependent
Confidence
Variable
Interval
deviation from
normal
snowfall
95%
Table 4. Statistical Analyses Conducted for First Proposed Research Question for Cleveland and Buffalo.
Period
Assessed
Analysis Type
Independent
Variable
Dependent
Variable
Confidence
Interval
19501975
bivariate correlation and
linear regression
monthly
snowfall totals
yearly
snowfall totals
95%
19762006
bivariate correlation and
linear regression
monthly
snowfall totals
yearly
snowfall totals
95%
Table 5. Statistical Analyses Conducted for Second Proposed Research Question for Cleveland and Buffalo
44
CHAPTER 5
RESULTS
5.1 Lake-effect Snowfall Totals
The first step in analyzing the results is to confirm or deny the findings of studies,
such as Burnett et al. (2003) and Bolsenga and Norton (1993), that lake-effect snowfall
has been increasing in recent years. The lake-effect snowfall totals for Cleveland (Table
6a) and Buffalo (Table 6b) were graphed to visually represent the trends (Figure 7). The
trend line on each graph gives an indication as to the snowfall trend. The graphs show
that LE snowfall has been increasing since 1950 for both cities. However, the increasing
trend is more obvious for Cleveland (Figure 7a). Also, the majority of years with highly
anomalous snowfall totals in Cleveland have occurred since 1990. This is not as evident
in Buffalo, where snowfall totals have been more consistent throughout the study period
(Figure 7b). The slope of the trend line for Cleveland snowfall is .222, which means that
since 1950 snowfall is increasing on average by approximately 2.22 inches per decade.
The slope of the trend line for Buffalo snowfall is .173. This equates to an average
increasing snowfall trend of approximately 1.73 inches per decade. Although Buffalo’s
snowfall has not been increasing as fast as Cleveland’s, it should be noted that Buffalo
experiences much higher yearly lake-effect snowfall totals than Cleveland (Figure 8).
45
YEAR
CLEVELAND
BUFFALO
YEAR
CLEVELAND
BUFFALO
1950
22.7
38.3
1976
27.8
120.4
1951
47.9
43.6
1977
26.1
55.3
1952
14.1
43.1
1978
15.4
35.0
1953
48.4
55.7
1979
10.3
40.9
1954
31.3
40.0
1980
28.1
31.7
35.7
52.9
1955
28.5
39.0
1981
1956
27.8
55.9
1982
9.4
24.6
1957
20.0
67.0
1983
20.6
60.5
1958
21.8
48.4
1984
29.3
65.2
1959
23.6
26.2
1985
29.4
70.5
1960
15.4
56.7
1986
9.9
20.0
1961
11.8
60.7
1987
24.1
28.2
1962
27.7
38.6
1988
24.4
40.6
23.5
34.2
1963
13.5
23.4
1989
1964
11.8
41.5
1990
26.7
31.0
1965
19.7
58.1
1991
26.3
37.0
1966
17.3
27.0
1992
39.2
20.9
1967
14.6
44.1
1993
33.2
50.5
1968
20.7
57.4
1994
20.5
58.8
1969
19.9
64.5
1995
52.0
84.7
1970
16.0
38.9
1996
15.0
68.1
14.8
28.4
1971
16.9
56.5
1997
1972
26.6
21.4
1998
17.9
48.1
1973
15.3
44.0
1999
30.0
25.3
1974
27.1
43.4
2000
28.6
90.6
1975
20.9
36.4
2001
23.8
87.9
2002
61.7
57.9
2003
47.9
60.6
2004
47.1
48.4
2005
30.0
48.7
2006
53.1
55.9
Table 6a. Total Lake-effect snowfall
(in) for the early period
Table 6b. Total Lake-effect snowfall
(in) for the later period
46
Lake-effect Snowfall (in.)
Yearly Lake-effect Snowfall Totals for Cleveland, OH
70
60
50
40
30
20
10
0
1940
1960
1980
2000
2020
Year
.
Figure 7a. Yearly Lake-effect Snowfall Totals and Trend for Cleveland, OH.
Lake-effect Snowfall (in.)
Yearly Lake-effect Snowfall Totals for Buffalo, NY
140
120
100
80
60
40
20
0
1940
1960
1980
Year
Figure 7b. Yearly Lake-effect Snowfall Totals and Trend for Buffalo, NY.
2000
.
47
140
120
Cleveland
100
Buffalo
80
60
40
20
04
01
20
98
20
95
19
92
19
89
19
86
19
83
19
80
19
77
19
74
19
71
19
68
19
65
19
62
19
59
19
56
19
19
19
19
53
0
50
Lake-effect Snowfall Totals (in.)
Yearly Lake-effect Snowfall Totals for Cleveland and Buffalo
Year
Figure 8. Yearly Lake-effect Snowfall Totals for Cleveland and Buffalo.
Throughout the entire period, the average yearly lake-effect snowfall for
Cleveland was 25.7 inches. Buffalo’s yearly average was almost twice that amount at
48.3 inches. In fact, there were only 4 years in which the snowfall total in Cleveland
exceeded Buffalo’s average snowfall. Three of these four years were after 1995, with
2002 having the highest total, 61.7 inches. Buffalo’s highest lake-effect snowfall total,
120.4 inches, occurred in 1976. However, the next three highest amounts happened after
1995. There were only five years in which Cleveland’s lake-effect snowfall total
exceeded Buffalo’s total. Two of the lowest yearly snowfall totals in Cleveland were
observed in the early 1980s, with the lowest total, 9.4 inches, occurring in 1982.
48
Buffalo’s lowest total was 20 inches, which occurred in 1986. Cleveland observed its
second lowest snowfall total, 9.9 inches, during that year.
5.2 Seasonal Air Temperatures and Lake-effect Snowfall Totals (Research Question
One)
5.2.1 Cleveland, OH
For each season throughout the late period, mean seasonal temperatures and
normal mean seasonal temperatures were used to calculate a deviation from the normal
mean seasonal temperature (Table 7). For each year throughout the late period, yearly
snowfall and normal snowfall were used to calculate a deviation from the normal
snowfall (Table 8). Using the deviation values for both temperature and snowfall (Table
10), visual and statistical approaches were implemented.
Throughout the late period, three of the four seasons experienced a positive
average temperature deviation from normal (Table 9). Winter showed the largest
deviation, with an average temperature deviation of +0.5˚F, followed by spring and
summer, with average deviations of +0.4˚F and +0.3˚F, respectively. Fall, which had an
average deviation of -0.1˚F, was the only season that experienced a negative deviation.
Similar trends are evident for each season when analyzing the mean seasonal
temperatures from the early period to the late period. From 1950 to 1975, the average
mean winter temperature was 28.5˚F, and from 1976 to 2006 the average mean winter
temperature increased to 29.0˚F. The graph of mean winter temperatures from 19502006 indicates this increasing trend (Figure 9a). During the early period, the average
49
mean spring temperature was 47.8˚F, which increased to 48.6˚F during the late period
(Figure 9b). The average mean summer temperature also increased from 70.4˚F to 70.7˚F
from the early to late period (Figure 9c). The fall temperature trend showed a decline, as
the average mean temperature from the early period to the late period decreased from
53.5˚F to 53.2˚F (Figure 9d).
Year
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Mean
Spring
49.8
52.0
46.3
48.8
46.1
47.4
48.9
47.9
43.1
51.4
50.0
50.4
48.4
47.0
49.2
53.4
47.5
46.8
47.2
48.6
44.6
45.6
51.6
48.6
50.6
48.5
47.8
48.5
50.5
45.5
49.3
Normal
Mean
Spring
47.8
47.8
48.0
47.9
48.0
47.9
47.9
47.9
47.9
47.8
47.9
47.9
48.0
48.0
48.0
48.0
48.1
48.1
48.1
48.1
48.1
48.0
48.0
48.0
48.0
48.1
48.1
48.1
48.1
48.1
48.1
Spring
Deviation
2.0
4.1
-1.7
0.9
-1.9
-0.5
1.0
0.0
-4.8
3.7
2.2
2.4
0.4
-1.0
1.3
5.4
-0.7
-1.3
-0.9
0.6
-3.5
-2.4
3.7
0.5
2.5
0.4
-0.3
0.4
2.4
-2.6
1.2
Year
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Mean
Summer
69.8
68.7
71.4
69.8
69.8
69.8
68.5
72.6
69.6
67.6
69.8
72.1
73.0
70.9
69.5
72.8
67.5
72.0
70.8
74.9
69.9
68.8
70.7
71.8
68.5
70.8
73.2
70.9
68.7
74.3
70.9
Normal
Mean
Summer
70.4
70.4
70.3
70.4
70.3
70.3
70.3
70.3
70.3
70.3
70.2
70.2
70.3
70.3
70.4
70.3
70.4
70.3
70.4
70.4
70.5
70.5
70.4
70.4
70.5
70.4
70.4
70.5
70.5
70.5
70.5
Summer
Deviation
-0.6
-1.7
1.1
-0.5
-0.5
-0.5
-1.8
2.4
-0.7
-2.7
-0.5
1.8
2.7
0.6
-0.8
2.5
-2.9
1.7
0.4
4.6
-0.5
-1.7
0.2
1.4
-2.0
0.4
2.7
0.4
-1.8
3.9
0.4
Table 7. Mean Seasonal, Normal Mean Seasonal, and Deviation from Normal Mean Seasonal
Temperatures
50
Table 7 (Continued)
Year
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Mean
Fall
47.6
54.5
55.5
53.2
50.7
51.7
54.5
54.1
52.8
55.0
53.9
52.4
51.6
53.0
54.1
53.5
51.7
51.8
55.4
52.7
51.4
51.6
55.1
54.5
52.5
54.9
53.9
54.3
54.6
55.4
52.4
Normal
Mean
Fall
53.5
53.3
53.3
53.4
53.4
53.3
53.3
53.3
53.3
53.3
53.4
53.4
53.3
53.3
53.3
53.3
53.3
53.3
53.2
53.3
53.3
53.2
53.2
53.2
53.3
53.3
53.3
53.3
53.3
53.3
53.4
Fall
Deviation
-5.9
1.2
2.2
-0.2
-2.7
-1.7
1.2
0.8
-0.6
1.7
0.5
-1.0
-1.7
-0.3
0.8
0.2
-1.6
-1.5
2.2
-0.6
-1.8
-1.7
1.9
1.2
-0.7
1.7
0.6
1.0
1.3
2.1
-1.0
Year
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Mean
Winter
20.1
23.4
27.0
28.4
24.5
31.7
31.9
23.1
30.7
27.4
28.0
32.0
33.7
26.4
32.8
30.9
26.6
29.2
30.2
28.7
35.8
31.7
32.6
31.2
30.3
27.6
27.3
30.6
33.4
25.8
25.0
Normal
Mean
Winter
28.8
28.5
28.3
28.2
28.1
28.1
28.0
28.2
28.1
28.1
28.1
28.1
28.1
28.2
28.3
28.3
28.4
28.5
28.4
28.5
28.4
28.5
28.7
28.7
28.8
28.8
28.9
28.8
28.8
28.8
28.9
Winter
Deviation
-9.1
-6.5
-3.3
-1.1
-1.4
-2.9
7.1
-2.1
-0.6
-1.5
2.1
0.6
2.7
1.5
3.7
4.2
2.2
-3.1
2.9
-1.9
3.2
6.9
4.3
2.8
-1.4
6.5
-3.4
-0.5
0.9
3.3
0.5
51
Year
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Snowfall
27.8
26.1
15.4
10.3
28.1
35.7
9.4
20.6
29.3
29.4
9.9
24.1
24.4
23.5
26.7
26.3
39.2
33.2
20.5
52
15
14.8
17.9
30
28.6
23.8
61.7
47.9
47.1
30
53.1
Normal Snowfall
22.4
22.6
22.7
22.4
22.0
22.2
22.6
22.2
22.2
22.4
22.6
22.3
22.3
22.4
22.4
22.5
22.6
23.0
23.2
23.1
23.8
23.6
23.4
23.3
23.4
23.5
23.5
24.2
24.7
25.1
25.2
Snowfall Deviation
5.4
3.5
-7.3
-12.1
6.1
13.5
-13.2
-1.6
7.1
7.0
-12.7
1.8
2.1
1.1
4.3
3.8
16.6
10.2
-2.7
28.9
-8.8
-8.8
-5.5
6.7
5.2
0.3
38.2
23.7
22.4
4.9
27.9
Table 8. Total Snowfall, Normal Snowfall, and Snowfall Deviation for Cleveland, OH
52
YEAR
SPRING
SUMMER
FALL
WINTER
SNOW
1976
2.0
-0.6
-5.9
-9.1
5.4
1977
4.1
-1.7
1.2
-6.5
3.5
1978
-1.7
1.1
2.2
-3.3
-7.3
1979
0.9
-0.5
-0.2
-1.1
-12.1
1980
-1.9
-0.5
-2.7
-1.4
6.1
1981
-0.5
-0.5
-1.7
-2.9
13.5
1982
1.0
-1.8
1.2
7.1
-13.2
1983
0.0
2.4
0.8
-2.1
-1.6
1984
-4.8
-0.7
-0.6
-0.6
7.1
1985
3.7
-2.7
1.7
-1.5
7.0
1986
2.2
-0.5
0.5
2.1
-12.7
1987
2.4
1.8
-1.0
0.6
1.8
1988
0.4
2.7
-1.7
2.7
2.1
1989
-1.0
0.6
-0.3
1.5
1.1
1990
1.3
-0.8
0.8
3.7
4.3
1991
5.4
2.5
0.2
4.2
3.8
1992
-0.7
-2.9
-1.6
2.2
16.6
1993
-1.3
1.7
-1.5
-3.1
10.2
1994
-0.9
0.4
2.2
2.9
-2.7
1995
0.6
4.6
-0.6
-1.9
28.9
1996
-3.5
-0.5
-1.8
3.2
-8.8
1997
-2.4
-1.7
-1.7
6.9
-8.8
1998
3.7
0.2
1.9
4.3
-5.5
1999
0.5
1.4
1.2
2.8
6.7
2000
2.5
-2.0
-0.7
-1.4
5.2
2001
0.4
0.4
1.7
6.5
0.3
2002
-0.3
2.7
0.6
-3.4
38.2
2003
0.4
0.4
1.0
-0.5
23.7
2004
2.4
-1.8
1.3
0.9
22.4
2005
-2.6
3.9
2.1
3.3
4.9
2006
1.2
0.4
-1.0
0.5
27.9
Average
Deviation
0.4
0.3
-0.1
0.5
5.4
Table 9. Seasonal Temperature Deviations from Normal and Lake-effect Snowfall
Deviations from Normal for Cleveland, OH
53
Temperature (F)
Mean Winter Temperatures 1950-2006
33.0
28.0
23.0
19
50
19
54
19
58
19
62
19
66
19
70
19
74
19
78
19
82
19
86
19
90
19
94
19
98
20
02
20
06
18.0
Year
Figure 9a. Mean Winter Temperatures from 1950-2006 for Cleveland, OH
Mean Spring Temperatures 1950-2006
Temperature (F)
54.0
52.0
50.0
48.0
46.0
44.0
Year
Figure 9b. Mean Spring Temperatures from 1950-2006 for Cleveland, OH.
2006
2002
1998
1994
1990
1986
1982
1978
1974
1970
1966
1962
1958
1954
1950
42.0
54
Mean Summer Temperatures 1950-2006
Temperature (F)
76.0
74.0
72.0
70.0
68.0
19
50
19
54
19
58
19
62
19
66
19
70
19
74
19
78
19
82
19
86
19
90
19
94
19
98
20
02
20
06
66.0
Year
Figure 9c. Mean Summer Temperatures from 1950-2006 for Cleveland, OH
Mean Fall Temperatures 1950-2006
Temperature (F)
58.0
56.0
54.0
52.0
50.0
48.0
19
50
19
54
19
58
19
62
19
66
19
70
19
74
19
78
19
82
19
86
19
90
19
94
19
98
20
02
20
06
46.0
Year
Figure 9d. Mean Fall Temperatures from 1950-2006 for Cleveland, OH.
55
Using SPSS, bivariate correlation and linear regression were used to indicate
seasons whose temperatures are most significant in determining yearly lake-effect
snowfall totals. Because there are many other meteorological variables that can explain
the variance in lake-effect snowfall totals besides temperature, the regression model did
not perform well as indicated by an R value of 0.429. However, the correlation analysis
and associated scatter plots indicate that winter and summer temperature deviations had
the most significant association with yearly snowfall deviations (Table 10). As expected,
winter showed a negative correlation of -0.358, and summer showed a positive
correlation of 0.245. Although these correlations are not astounding, they exhibited a
greater association than the other two seasons, as evident by the more defined linear
trends of their scatter plots (Figure 10). A trend line having a slope close to 0 indicates a
weaker correlation. Winter’s negative correlation coefficient implies that when winter
temperatures are colder than normal, higher snowfall totals occur, and vice versa. This is
evident when analyzing the graphs of winter temperature deviations and yearly snowfall
deviations through the late period (Figure 11a). For the most part, when an upward spike
in snowfall is evident, a downward spike in temperature occurs, and vice versa. The
temperature maximums and minimums are more subtle than the snowfall maximums and
minimums because the values don’t deviate as greatly as snowfall deviations. Summer’s
positive correlation coefficient implies that warmer summer temperatures correlate to
increased snowfall totals, and vice versa (Figure 11b).
56
Significance
0.86
0.201
0.982
0.085
Spring
Summer
Fall
Winter
Correlation Coefficient
0.245
0.35
-0.005
-0.358
Table 10. Seasonal Temperature Departures Correlation to Snowfall Departures for Cleveland, OH.
Snowfall Deviations v. Winter Temperature Deviations
Snowfall Deviation
40.0
30.0
20.0
10.0
0.0
‐10.0
‐20.0
‐7.0
‐2.0
3.0
Temperature Deviation
Figure 10. Winter, Summer, Spring, and Fall Correlation Scatter Plots
8.0
57
Figure 10 (Continued)
Snowfall Deviations v. Summer Temperature Deviations
Snowfall Deviation
40.0
30.0
20.0
10.0
0.0
‐10.0
‐20.0
‐4.0
‐2.0
0.0
2.0
4.0
6.0
Temperature Deviation
Snowfall Deviations v. Spring Temperature Deviations
Snowfall Deviation
40.0
30.0
20.0
10.0
0.0
‐10.0
‐20.0
‐6.0
‐4.0
‐2.0
0.0
2.0
4.0
6.0
Temperature Deviation
.
58
Figure 10 (Continued)
Snowfall Deviations v. Fall Temperature Deviations
40.0
Snowfall Deviation
30.0
20.0
10.0
0.0
‐10.0
‐20.0
‐7.0
‐5.0
‐3.0
‐1.0
1.0
3.0
Temperature Deviation
.
Winter Temperature Deviations and Snowfall Deviations
Deviation
40.0
30.0
Snowfall
20.0
Temperature
10.0
0.0
-10.0
19
76
19
78
19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00
20
02
20
04
20
06
-20.0
Year
Figure 11a. Winter Temperature Deviations and Snowfall Deviations.
59
Summer Temperature Deviations and Snowfall Deviations
40.0
Temperature
Deviation
30.0
Snowfall
20.0
10.0
0.0
-10.0
Year
Figure 11b. Summer Temperature Deviations and Snowfall Deviations.
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
1976
-20.0
60
5.2.2 Buffalo, NY
For each season throughout the late period, mean seasonal temperatures and
normal mean seasonal temperatures were used to calculate a deviation from the normal
mean seasonal temperature (Table 11). For each year throughout the late period, yearly
snowfall and normal snowfall were used to calculate a deviation from the normal
snowfall (Table 12). Using the deviation values for both temperature and snowfall (Table
13), visual and statistical approaches were implemented.
Similar to the results from Cleveland, three of the four seasons exhibited an
increasing temperature trend in Buffalo. Spring had the highest average temperature
deviation of +0.7˚F, followed by winter and summer, with average temperature
deviations of +0.6 and +0.3, respectively. From 1950 to 1975 the average mean spring
temperature was 44.6˚F and from 1976 to 2006 the average mean spring temperature
increased to 45.8˚F. The graph of mean spring temperatures from 1950 to 2006 indicates
this trend (Figure 12a). The average mean winter temperature increased from 26.4˚F to
26.9˚F from the early period to the late period (Figure 12b). The average mean summer
temperature also increased from 68.7˚F to 69.1˚F from the early period to late period
(Figure 12c). Fall was the only season in which mean temperatures did not show a
significant increasing trend. The graph of mean fall temperatures (Figure 12d) shows that
mean temperatures remained nearly steady through the entire period.
61
Year
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Spring
45.7
49.0
42.7
46.5
45.3
45.8
45.0
44.7
42.6
48.2
47.9
49.4
47.0
43.3
46.8
50.9
44.2
45.0
45.4
45.6
41.9
42.0
48.7
45.6
47.2
45.7
44.1
44.0
47.1
43.2
47.7
Spring
Mean
44.6
44.7
44.8
44.7
44.8
44.8
44.9
44.9
44.9
44.8
44.9
45.0
45.1
45.1
45.1
45.1
45.3
45.2
45.2
45.2
45.2
45.2
45.1
45.2
45.2
45.2
45.2
45.2
45.2
45.2
45.2
Spring
Deviation
1.1
4.4
-2.1
1.7
0.5
1.0
0.2
-0.1
-2.3
3.4
3.0
4.4
1.9
-1.8
1.7
5.7
-1.0
-0.2
0.2
0.4
-3.3
-3.2
3.6
0.4
2.0
0.5
-1.2
-1.3
1.9
-2.0
2.5
Year
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Summer
67.9
68.2
68.6
68.4
68.7
69.3
66.3
71.0
69.5
67.2
67.7
70.7
70.4
68.6
69.5
70.7
65.5
70.5
70.1
71.9
68.9
67.4
68.7
70.2
66.8
69.9
70.6
68.0
66.6
73.2
70.6
Summer
Mean
68.7
68.7
68.7
68.7
68.7
68.7
68.7
68.6
68.7
68.7
68.7
68.6
68.7
68.7
68.7
68.8
68.8
68.7
68.8
68.8
68.9
68.9
68.8
68.8
68.9
68.8
68.8
68.9
68.9
68.8
68.9
Summer
Deviation
-0.8
-0.5
-0.1
-0.2
0.1
0.7
-2.4
2.4
0.8
-1.5
-1.0
2.0
1.7
-0.1
0.8
1.9
-3.3
1.7
1.3
3.1
0.1
-1.5
-0.1
1.4
-2.0
1.1
1.8
-0.9
-2.2
4.4
1.7
Table 11. Mean Seasonal, Normal Mean Seasonal, and Deviation from Normal Mean Seasonal
Temperature for Buffalo, NY
62
Table 11 (Continued)
Year
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Mean
Fall
46.8
51.8
50.2
52.0
50.2
49.8
52.4
52.1
50.2
52.9
50.1
51.3
50.7
50.1
52.5
51.5
49.9
49.4
53.1
50.2
49.9
49.5
52.8
52.8
50.8
54.2
51.9
51.6
53.1
54.0
52.6
Normal
Mean
Fall
51.6
51.4
51.5
51.5
51.4
51.3
51.3
51.3
51.3
51.2
51.3
51.2
51.0
51.1
51.0
51.1
51.1
51.1
51.1
51.1
51.1
50.9
50.8
50.9
50.9
51.0
51.0
51.2
51.2
51.3
51.4
Fall
Deviation
-4.8
0.4
-1.3
0.5
-1.3
-1.5
1.1
0.8
-1.1
1.7
-1.1
0.1
-0.4
-1.0
1.5
0.4
-1.2
-1.7
2.0
-0.9
-1.1
-1.4
2.0
1.8
-0.2
3.2
0.8
0.3
1.8
2.7
1.1
Year
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Mean
Winter
20.1
21.3
21.9
26.8
25.8
23.1
31.4
25.6
27.2
25.2
27.8
28.4
28.0
26.7
30.3
28.7
27.4
23.2
28.6
23.7
29.4
32.3
29.9
28.5
25.8
32.9
22.7
25.4
26.3
30.0
28.2
Normal
Mean
Winter
26.4
26.2
26.0
25.8
25.9
25.9
25.8
26.0
25.9
26.0
26.0
26.0
26.1
26.1
26.1
26.2
26.3
26.3
26.3
26.3
26.2
26.3
26.4
26.5
26.6
26.5
26.7
26.6
26.6
26.6
26.6
Winter
Deviation
-6.3
-4.9
-4.1
1.0
0.0
-2.7
5.6
-0.3
1.2
-0.8
1.9
2.4
1.9
0.6
4.2
2.5
1.1
-3.1
2.3
-2.6
3.2
6.0
3.5
2.0
-0.8
6.4
-3.9
-1.2
-0.3
3.4
1.6
63
Year
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Snowfall
120.4
55.3
35
40.9
31.7
52.9
24.6
60.5
65.2
70.5
20
28.2
40.6
34.2
31
37
20.9
50.5
58.8
84.7
68.1
28.4
48.1
25.3
90.6
87.9
57.9
60.6
48.4
48.7
55.9
Normal Snowfall
45.0
47.8
48.1
47.6
47.4
46.9
47.1
46.4
46.8
47.3
48.0
47.2
46.7
46.6
46.2
45.9
45.7
45.1
45.2
45.5
46.4
46.8
46.4
46.5
46.1
46.9
47.7
47.9
48.1
48.1
48.2
Snowfall Deviation
75.4
7.5
-13.1
-6.7
-15.7
6.0
-22.5
14.1
18.4
23.2
-28.0
-19.0
-6.1
-12.4
-15.2
-8.9
-24.8
5.4
13.6
39.2
21.7
-18.4
1.7
-21.2
44.5
41.0
10.2
12.7
0.3
0.6
7.7
Table 12. Observed Yearly Snowfall totals, Normal Snowfall and Snowfall Deviation for Buffalo, NY.
64
YEAR
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Average
Deviation
SPRING
1.1
4.4
-2.1
1.7
0.5
1.0
0.2
-0.1
-2.3
3.4
3.0
4.4
1.9
-1.8
1.7
5.7
-1.0
-0.2
0.2
0.4
-3.3
-3.2
3.6
0.4
2.0
0.5
-1.2
-1.3
1.9
-2.0
2.5
0.7
SUMMER
-0.8
-0.5
-0.1
-0.2
0.1
0.7
-2.4
2.4
0.8
-1.5
-1.0
2.0
1.7
-0.1
0.8
1.9
-3.3
1.7
1.3
3.1
0.1
-1.5
-0.1
1.4
-2.0
1.1
1.8
-0.9
-2.2
4.4
1.7
0.3
FALL
-4.8
0.4
-1.3
0.5
-1.3
-1.5
1.1
0.8
-1.1
1.7
-1.1
0.1
-0.4
-1.0
1.5
0.4
-1.2
-1.7
2.0
-0.9
-1.1
-1.4
2.0
1.8
-0.2
3.2
0.8
0.3
1.8
2.7
1.1
0.1
WINTER
-6.3
-4.9
-4.1
1.0
0.0
-2.7
5.6
-0.3
1.2
-0.8
1.9
2.4
1.9
0.6
4.2
2.5
1.1
-3.1
2.3
-2.6
3.2
6.0
3.5
2.0
-0.8
6.4
-3.9
-1.2
-0.3
3.4
1.6
0.6
SNOW
75.4
7.5
-13.1
-6.7
-15.7
6.0
-22.5
14.1
18.4
23.2
-28.0
-19.0
-6.1
-12.4
-15.2
-8.9
-24.8
5.4
13.6
39.2
21.7
-18.4
1.7
-21.2
44.5
41.0
10.2
12.7
0.3
0.6
7.7
4.2
Table 13. Seasonal Temperature Deviations from Normal and Lake-effect Snowfall Deviations from
Normal for Buffalo, NY.
65
Mean Spring Temperatures 1950-2006
Temperature (F)
52.0
50.0
48.0
46.0
44.0
42.0
19
50
19
54
19
58
19
62
19
66
19
70
19
74
19
78
19
82
19
86
19
90
19
94
19
98
20
02
20
06
40.0
Year
Figure 12a. Mean Spring Temperatures from 1950-2006 for Buffalo, NY.
35.0
33.0
31.0
29.0
27.0
25.0
23.0
21.0
19.0
19
50
19
54
19
58
19
62
19
66
19
70
19
74
19
78
19
82
19
86
19
90
19
94
19
98
20
02
20
06
Temperature (F)
Mean Winter Temperatures 1950-2006
Year
Figure 12b. Mean Winter Temperatures from 1950-2006 for Buffalo, NY.
66
74.0
73.0
72.0
71.0
70.0
69.0
68.0
67.0
66.0
65.0
64.0
19
50
19
54
19
58
19
62
19
66
19
70
19
74
19
78
19
82
19
86
19
90
19
94
19
98
20
02
20
06
Temperature (F)
Mean Summer Temperatures 1950-2006
Year
Figure 12c. Mean Summer Temperatures from 1950-2006 for Buffalo, NY.
56.0
55.0
54.0
53.0
52.0
51.0
50.0
49.0
48.0
47.0
46.0
19
50
19
54
19
58
19
62
19
66
19
70
19
74
19
78
19
82
19
86
19
90
19
94
19
98
20
02
20
06
Temperature (F)
Mean Fall Temperatures 1950-2006
Year
Figure 12d. Mean Fall Temperatures from 1950-2006 for Buffalo, NY.
67
A bivariate correlation and linear regression was implemented to analyze
temperature deviations and snowfall deviations for Buffalo. Similar to Cleveland, the
linear regression model did not perform well; therefore, the correlation coefficients and
associated scatter plots were used to analyze the connections between seasonal
temperatures and snowfall. Winter temperature deviations showed the greatest
association with snowfall deviations, as the correlation coefficient was -0.40 (Table 14).
Aside from winter, the next most significant season was fall. However, it had a fairly
unimpressive correlation coefficient of -0.175. No other seasons exhibited a significant
association, as evident by scatter plot trends (Figure 13). Winter’s negative correlation
implies that a colder than normal winter season would equate to higher lake-effect
snowfall totals, and vice versa. The graphs of temperature deviations and snowfall
deviations through the late period reflect this (Figure 14), especially during two time
periods throughout the study period. First, the highest snowfall total occurred at the
beginning of the study period, which coincides with the coldest winter season observed.
Second, each year from 1986 to 1992 experienced a negative snowfall deviation. A
period of above normal winter temperatures coincides with these lesser snowfall totals.
Spring
Summer
Fall
Winter
Significance
0.772
0.593
0.885
0.058
Correlation Coefficient
-0.024
0.102
-0.175
-0.4
Table 14. Seasonal Temperature Departures correlation to Snowfall Departures for Buffalo, NY.
68
Figure 13. Winter, Fall, Summer, and Spring Correlation Scatter Plots.
Snowfall Deviations v. Winter Temperature Deviations
Snowfall Deviation
85.0
65.0
45.0
25.0
5.0
‐15.0
‐35.0
‐6.5
‐4.5
‐2.5
‐0.5
1.5
3.5
5.5
Temperature Deviation
.
Snowfall Deviations v. Fall Temperature Deviations
Snowfall Deviation
85.0
65.0
45.0
25.0
5.0
‐15.0
‐35.0
‐5.0
‐3.0
‐1.0
1.0
3.0
Temperature Deviation
.
69
Figure 13. Winter, Fall, Summer, and Spring Correlation Scatter Plots. (continued)
Snowfall Deviations v. Summer Temperature Deviations
Snowfall Deviation
40.0
30.0
20.0
10.0
0.0
‐10.0
‐20.0
‐4.0
‐2.0
0.0
2.0
4.0
6.0
Temperature Deviation
.
Snowfall Deviations v. Spring Temperature Deviations
Snowfall Deviation
90.0
70.0
50.0
30.0
10.0
‐10.0
‐30.0
‐5.0
‐3.0
‐1.0
1.0
3.0
5.0
7.0
Temperature Deviation
.
70
Winter Temperature Deviations and Snowfall Deviations
70.0
Deviation
Temperature
50.0
Snowfall
30.0
10.0
-10.0
19
76
19
78
19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00
20
02
20
04
20
06
-30.0
Year
Figure 14. Winter Temperature Deviations and Snowfall Deviations
71
5.3 Interseasonal Snowfall Trends (Research Question two)
To answer the second proposed research question, monthly snowfall totals were
analyzed statistically. This was done for both the early period and the late period for
Cleveland and Buffalo to determine potential differing patterns in monthly snowfall
trends. To determine how monthly snowfall trends within winter seasons have changed
between time periods, monthly snowfall totals and yearly snowfall totals for the early and
late periods were analyzed using correlation and linear regression. Because monthly
snowfall totals are directly associated with yearly totals, the regression model performed
very well for both periods, with adjusted R values close to 1. All months exhibited high
significance because any amount of snow accumulation in a given month contributes to
the yearly totals. The correlation analysis indicated the months that are most instrumental
in determining yearly snowfall totals. For Cleveland, during the early period, November,
December, and January, and March snowfall exhibited high positive correlations, while
October, February, and April snowfall were not as important in determining yearly
snowfall totals (Table 15a). The correlation analysis for the late period showed a
changing trend. During this period, December, January, February, and April snowfall
were highly correlated, while October, November, and March snowfall showed less
association with yearly snowfall totals (Table 15b). It is important to note two changes
that occurred from the early period to late period. First, during the early period
November had the highest correlation and during the late period it showed little
relevance. Second, February snowfall exhibited a low correlation during the early period,
but it had a greater influence on yearly snowfall totals during the late period.
72
The results of the linear regression and correlation analyses of monthly snowfall
and yearly snowfall in Buffalo were dissimilar to the results of the analyses in Cleveland.
During both periods, Buffalo’s October snowfall was very minimal, so the regression
model deemed October as insignificant. All other months were significant. The
correlation coefficients indicate the degree to which each month’s snowfall is associated
with yearly snowfall. During the early period, January snowfall exhibited the highest
correlation, accompanied by November and March snowfall, which also showed a high
association. December, March, and April snowfall showed weak correlation to yearly
snowfall totals (Table 16a). During the late period, December snowfall showed the
highest correlation, followed by November and January snowfall. February, March, and
April snowfall were weakly correlated to yearly snowfall during the late period (Table
16b).
73
YEAR
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
OCT
0.0
0.0
0.8
0.0
6.4
0.0
0.0
2.5
0.0
0.0
0.0
0.0
8.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.5
0.0
NOV
1.0
16.0
0.5
4.7
0.2
2.4
7.4
1.5
1.3
1.0
4.1
0.9
0.0
0.1
0.5
1.2
0.0
3.1
6.8
1.8
4.1
1.0
3.3
3.1
5.3
2.3
DEC
5.9
11.5
1.0
3.1
8.4
8.3
5.7
5.0
5.1
3.0
4.0
1.0
13.2
6.1
1.0
1.0
6.3
1.8
4.4
4.2
2.0
1.2
4.8
5.0
8.8
6.1
JAN
4.1
5.1
2.6
11.9
7.8
5.5
4.3
2.3
1.2
2.5
2.8
1.0
1.5
2.1
5.3
9.0
1.0
2.5
1.4
3.4
2.1
2.3
7.1
1.3
5.2
8.9
FEB
2.1
3.0
1.2
11.2
1.9
7.6
0.0
5.6
1.0
9.8
2.9
3.3
3.2
3.1
3.6
4.4
8.5
6.7
1.6
1.5
1.5
5.1
9.6
1.5
3.5
0.5
MAR
9.0
4.5
5.0
17.5
6.6
3.4
8.4
2.9
13.2
6.8
0.6
5.6
1.5
1.6
1.0
2.6
1.5
0.5
6.5
8.8
5.8
5.0
1.0
1.4
2.0
2.0
APR
0.6
7.8
3.0
0.0
0.0
1.3
2.0
0.2
0.0
0.5
1.0
0.0
0.3
0.5
0.4
1.5
0.0
0.0
0.0
0.2
0.5
2.3
0.8
3.0
0.8
1.1
Correlation
Coefficient
0.20
0.60
0.55
0.58
0.29
0.47
0.37
TOTAL
22.7
47.9
14.1
48.4
31.3
28.5
27.8
20.0
21.8
23.6
15.4
11.8
27.7
13.5
11.8
19.7
17.3
14.6
20.7
19.9
16.0
16.9
26.6
15.3
27.1
20.9
Table 15a. Monthly snowfall (in), Yearly snowfall (in), and Correlation Coefficients for the early period
for Cleveland, OH.
74
YEAR
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
OCT
1.6
0.0
0.0
0.2
0.0
4.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.0
0.0
0.0
0.0
0.0
0.0
NOV
8.0
7.6
0.0
0.5
2.2
1.0
0.8
6.0
1.7
0.0
0.4
0.0
0.8
5.9
0.0
0.2
6.9
1.8
0.0
5.6
2.4
3.3
0.1
0.0
1.5
0.0
1.9
1.3
0.0
2.8
1.4
DEC
10.0
5.9
1.0
1.0
4.4
9.1
1.0
2.8
0.3
11.4
0.6
1.5
4.1
11.2
1.2
6.1
5.8
15.5
0.0
13.3
2.0
3.9
4.7
5.8
4.5
1.7
16.2
15.0
18.6
10.3
9.5
JAN
3.8
8.5
6.6
3.8
7.2
9.3
6.3
1.1
13.5
14.3
1.0
2.0
4.0
0.2
12.1
9.3
1.6
13.3
6.3
4.9
3.8
3.4
7.8
12.7
5.4
5.0
24.1
21.2
10.8
2.1
17.4
FEB
3.7
2.2
6.9
3.3
1.6
1.0
0.3
6.1
8.9
1.6
4.8
9.5
8.5
2.1
9.3
4.0
21.1
1.2
13.5
6.9
2.8
0.2
2.5
4.0
1.0
9.5
18.8
3.9
3.7
13.7
9.6
MAR
0.0
1.7
0.5
1.5
12.7
5.3
0.2
4.6
0.0
2.1
3.1
9.2
2.2
0.8
4.1
2.5
3.8
1.0
0.5
11.1
3.2
4.0
2.8
6.9
15.9
4.4
0.7
5.3
11.1
1.1
3.2
APR
0.7
0.2
0.4
0.0
0.0
6.0
0.8
0.0
4.9
0.0
0.0
1.9
4.8
3.3
0.0
4.2
0.0
0.2
0.2
10.2
0.8
0.0
0.0
0.6
0.3
2.2
0.0
1.2
2.9
0.0
12.0
Correlation
Coefficient
0.07
0.14
0.77
0.65
0.40
0.26
0.48
TOTAL
27.8
26.1
15.4
10.3
28.1
35.7
9.4
20.6
29.3
29.4
9.9
24.1
24.4
23.5
26.7
26.3
39.2
33.2
20.5
52.0
15.0
14.8
17.9
30.0
28.6
23.8
61.7
47.9
47.1
30.0
53.1
Figure 15b. Monthly snowfall (in), Yearly snowfall (in), and Correlation Coefficients for the late period for
Cleveland, OH.
75
YEAR
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
NOV
8.9
3.5
11.2
6.0
0.0
20.0
3.5
16.9
7.5
4.4
14.1
3.3
2.0
0.0
5.1
8.2
5.4
16.2
2.0
16.1
2.6
13.9
1.5
2.2
16.3
3.0
DEC
12.7
17.5
12.4
5.5
13.9
10.0
21.8
3.8
9.0
4.5
21.2
26.6
14.1
8.4
11.4
4.6
6.1
9.3
7.4
11.4
13.3
7.7
5.8
10.2
3.7
10.0
JAN
7.9
13.0
3.6
9.0
22.8
3.0
24.2
11.8
16.9
11.2
12.7
21.1
12.6
7.0
5.5
19.6
5.9
7.2
27.3
21.7
10.5
17.4
5.6
12.2
3.8
6.2
FEB
2.5
2.0
9.8
11.7
1.3
4.0
2.0
32.0
2.4
5.1
1.4
7.7
7.9
2.5
8.2
14.6
8.6
10.0
10.1
8.3
3.1
6.1
2.1
12.4
11.2
6.2
MAR
6.3
7.0
3.5
23.5
2.0
2.0
1.0
2.5
12.6
1.0
1.3
2.0
2.0
2.5
11.3
8.2
1.0
1.4
7.7
7.0
9.4
9.8
2.0
5.6
4.3
9.9
APR
0.0
0.6
2.6
0.0
0.0
0.0
3.4
0.0
0.0
0.0
5.0
0.0
0.0
3.0
0.0
2.9
0.0
0.0
2.9
0.0
0.0
1.6
2.4
1.4
4.1
1.1
Correlation
Coefficients
0.41
0.24
0.60
0.47
0.27
0.09
TOTAL
38.3
43.6
43.1
55.7
40.0
39.0
55.9
67.0
48.4
26.2
56.7
60.7
38.6
23.4
41.5
58.1
27.0
44.1
57.4
64.5
38.9
56.5
21.4
44.0
43.4
36.4
Figure 16a. Monthly snowfall (in), Yearly snowfall (in), and Correlation Coefficients for the early period
for Buffalo, NY.
76
YEAR
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
NOV
27.4
9.0
0.5
12.5
2.4
1.1
13.7
14.4
0.3
4.7
9.2
0.7
0.6
2.8
0.7
13.7
7.1
3.6
0.9
5.0
9.4
8.8
0.2
0.0
42.0
0.0
3.4
2.7
0.2
15.1
2.1
DEC
42.3
18.6
1.0
13.5
13.8
9.3
3.0
35.2
3.8
51.7
1.0
1.7
5.0
14.4
6.4
7.6
2.0
24.8
5.3
46.9
14.8
2.2
11.3
6.4
31.3
61.1
24.0
14.8
19.4
6.0
3.0
JAN
29.9
19.5
18.0
3.6
5.8
33.9
3.4
2.5
47.9
8.1
5.0
1.6
3.8
6.5
11.8
7.2
3.2
7.0
21.0
7.9
27.5
5.1
32.5
0.8
8.6
7.6
18.0
31.3
13.1
4.4
8.8
FEB
18.0
6.1
11.5
7.5
2.2
2.7
2.3
3.6
10.1
4.1
3.5
18.1
22.8
5.4
9.2
3.4
6.0
4.1
30.7
8.3
3.2
1.8
1.3
9.5
4.6
4.0
7.8
4.3
10.8
21.7
16.9
MAR
1.5
2.1
2.4
3.8
7.5
2.5
2.2
3.9
3.1
1.9
1.3
6.1
7.3
1.0
2.9
4.0
2.3
5.1
0.9
14.4
11.2
10.5
1.8
5.4
4.1
15.2
4.7
4.6
3.7
1.5
1.7
APR
1.3
0.0
1.6
0.0
0.0
3.4
0.0
0.9
0.0
0.0
0.0
0.0
1.1
4.1
0.0
1.1
0.3
5.9
0.0
2.2
2.0
0.0
1.0
3.2
0.0
0.0
0.0
2.9
1.2
0.0
0.8
Correlation
Coefficient
0.42
0.76
0.42
0.12
0.23
0.02
TOTAL
120.4
55.3
35.0
40.9
31.7
52.9
24.6
60.5
65.2
70.5
20.0
28.2
40.6
34.2
31.0
37.0
20.9
50.5
58.8
84.7
68.1
28.4
48.1
25.3
90.6
87.9
57.9
60.6
48.4
48.7
55.9
Figure 16b . Monthly snowfall (in), Yearly snowfall (in), and Correlation Coefficients for the late period
for Buffalo, NY.
77
CHAPTER 6
DISCUSSION
Results from both Cleveland and Buffalo show that lake-effect snowfall totals
have been increasing from 1950 to 2006. At both sites average mean temperatures for
each season, with the exception of fall, have increased from the early period, 1950-1975
to the late period, 1976-2006. Although summer temperature deviations showed a
slightly significant correlation to snowfall in Cleveland, winter temperature deviations
showed the most significant correlation to snowfall deviations in Cleveland and Buffalo
because winter temperatures are more directly related to lake-effect development than the
temperatures of the other seasons. Also, because other meteorological parameters that
induce lake-effect snow were not introduced into the regression model, temperatures in
seasons other than winter were not identified as directly associated to lake-effect totals.
However, it is logical to assume that increased spring, summer, and winter temperatures
are directly associated with lake water temperatures, which plays a major role in lakeeffect snow development. Increased spring and summer temperatures will cause higher
lake temperatures, while the near normal fall temperatures will be unable to counteract
the heating of the lake before the winter season commences.
Analysis of some extreme lake-effect seasons helps to put the effect of seasonal
temperatures on snowfall totals into perspective. Cleveland experienced its highest lakeeffect snow total, 61.7 inches, in 2002. Several contributing factors occurred that year,
causing the anomalously high lake-effect snow totals. First, the mean summer
temperature was 2.7˚F above normal. The mean fall temperature, whose average from
78
the early to late period experienced an overall decrease, was also above normal. Because
of these warmer air temperatures, the water temperature of Lake Erie was still above 40˚F
well into December and did not slip below 34˚F throughout the majority of the lake
during that winter (National Weather Service 2008). For this reason, only 10-15% of the
surface of the lake froze (Assel et al. 2003), so a considerable amount of moisture was
available to fuel lake-effect snow development throughout the entire lake-effect season.
In addition, the mean winter temperature in 2002 was 3.4˚F below normal, implying a
considerable number of cold days in which lake-effect could develop. On the other end
of the spectrum, one of Buffalo’s lowest lake-effect totals, 24.6 inches, which accounted
for a -22.5 inch deviation from normal, occurred in 1982. A significant cause of this
snowfall deficit was an abnormally low mean summer temperature. The mean summer
temperature in 1982 was 2.4˚F below normal, while the spring and fall temperatures were
near normal. The below normal summer temperatures translated into near freezing lake
water temperatures by the beginning of January (National Weather Service 2008) and an
almost completely frozen surface from January to mid-March. Over 50% of the lakeeffect snowfall fell during the Month of November before the lake cooled. In addition,
the mean winter temperature was 5.6˚F above normal, implying a lack of below freezing
days, which inhibits lake-effect snow development.
Although winter temperatures are negatively correlated to snowfall totals,
increasing winter temperatures can lead to increased snowfall totals. This is feasible as
long as the mean winter temperature remains below freezing. Accompanied by higher
spring and summer temperatures, increased winter temperatures would prevent Lake Erie
from freezing. If the mean winter temperature were to remain below freezing, there
79
would be enough days to produce significant lake-effect snow throughout the entire
winter season. From the early period to the late period the average mean winter
temperature in Cleveland increased from 28.5˚F to 29.0˚F and in Buffalo increased from
26.4˚F to 26.9˚F. During the early period Cleveland experienced 3 years with a mean
winter temperature above freezing and only 4 years above freezing during the late period.
Buffalo experienced 1 year with a mean winter temperature above freezing during the
early period and only 2 years above freezing during the late period. These numbers
support the findings of both Burnett et al. (2003) and Kunkel et al. 2000 regarding
increasing air temperatures and the future of lake-effect snowfall. Burnett et al. (2003)
found that increasing air temperatures will continue to produce significant snowfall.
However, Kunkel et al. (2000) believed that winter temperatures will become too warm
for significant lake-effect production. Based on the average mean winter temperatures
and assuming a similar rate of mean temperature increase, it is feasible that significant
lake-effect will continue to occur through the next century, especially in areas such as
Buffalo where mean winter temperatures are colder than Cleveland. However, at this rate
of temperature increase, the mean winter temperature in more southern areas, such as
Cleveland, will rise above freezing during the next century, which will be a great
detriment to lake-effect snow production.
Based on the observed seasonal temperature trends, monthly snowfall trends from
the early period to the late period in Cleveland were consistent with the hypothesis that
snowfall totals will increase mid to late winter during the late period due to warmer lake
waters and reduced ice cover. During the early period, November, December, January,
and March snowfall had the highest correlation to yearly snowfall totals, while October,
80
February, and April snowfall were weakly associated. During the late period, December,
January, February, and April snowfall were highly correlated to yearly snowfall totals,
while October, November, and March snowfall were very weakly correlated. Months
that had similar correlations during both periods were October, December, and January.
October was weakly correlated in both periods because mean temperatures during this
month are simply not cold enough for significant lake-effect snow development.
December and January were significant to yearly snowfall totals in both periods because
the average mean temperatures during those months were below freezing during both
periods, while Lake Erie does not typically freeze until February.
Months that showed drastic changes in correlations coefficients from the early to
late periods were November and February. During the early period November snowfall
had the highest correlation to yearly snowfall and February had one of the lowest
correlations, while during the late period November had the lowest correlation and
February’s correlation increased. This occurrence is attributed to observed temperature
trends within each period. The average mean November temperature was almost 1˚F
cooler during the early period than the late, which implies a greater number of days in
which temperatures would have been conducive to lake-effect snow development,
especially given the relatively warm lake water during that month. Although February
has the coldest average mean temperature, which would be conducive to lake-effect
development, the lake was almost completely frozen during this month throughout the
early period. Each year from 1950-1975 at least 80% of Lake Erie was frozen throughout
February, which hindered lake-effect development during that month. During the late
period, November snowfall was so weakly correlated to total snowfall because the
81
number of days, in which conditions were favorable for lake-effect development, was
reduced due to increased temperatures. The increased temperatures during the late period
also translated into reduced lake ice cover. From 1976-2006 there were several winters
in which less than 50% of Lake Erie was frozen, with some of the lowest observed ice
cover occurring during the late 1990s (Assel et al. 2003). Greater moisture availability,
accompanied by mean temperatures well below freezing, accounted for the increased
association of February snowfall to total snowfall during the late period.
Buffalo’s monthly snowfall trends were not consistent with Cleveland’s, nor were
they consistent with expected trends based on observed temperature trends from the early
to late period. There are two major issues that stand out in the results. First, during the
early period, December snowfall is weakly correlated to total snowfall. During the late
period, December snowfall has the highest association to total snowfall. This differs
from Cleveland because there December snowfall was significant during both periods
due to cold air temperatures and warm enough lake temperatures. Second, unlike in
Cleveland, the correlation of February snowfall to total snowfall in Buffalo during the
late period did not increase. In fact, the correlation decreased, opposite to what would be
expected. The inconsistent results are difficult to analyze based solely on observed
temperature trends. It is believed that there are other, localized factors that are causing
these sporadic results in Buffalo.
82
CHAPTER 7
CONCLUSION
In conclusion, an in depth look at temperatures and lake-effect snowfall in
Cleveland, OH and Buffalo, NY confirmed the findings of previous studies that both are,
indeed, increasing. At both sites winter and spring temperatures increased the most from
the early period to the later period. Summer temperatures showed a small degree of
increase, while fall temperatures remained steady. This is inconsistent with and Bolsenga
(1993), who found that spring and winter temperatures in the Great Lakes region rose less
than in summer and fall. This inconsistency is most likely due to the much larger study
area and shorter study period they employed in their study, as they assessed the entire
Great Lakes region only from 1951-1980.
Although Buffalo experiences more lake-effect snow on average per year,
Cleveland’s lake-effect snowfall totals have been increasing at a higher rate. This can be
attributed to the difference in air temperature and lake water dynamics between
Cleveland and Buffalo. Mean seasonal temperatures are a couple degrees warmer in
Cleveland and the lake water is much shallower in the western basin of Lake Erie,
making it more susceptible to increased air temperatures. The lake is not affected as
much by changing temperatures in the eastern basin because it is much deeper; therefore,
lake-effect snowfall totals in Buffalo have been increasing at a lower rate.
At both study sites winter temperatures displayed a high negative correlation with
yearly lake-effect snowfall totals. The majority of years with abnormally high lake-effect
snow accumulations experienced abnormally low winter temperatures, and vice versa.
83
Summer showed a fairly significant positive correlation with yearly snowfall totals.
Years, in which higher than normal summer temperatures were, coupled with lower than
normal winter temperatures, experienced the greatest lake-effect snowfall totals.
Seasonal temperatures throughout a given year affect lake water temperatures and ice
formation during the winter season. This has proven extremely important to lake-effect
development as some of the years with the highest lake-effect totals occurred when
higher than normal lake water temperatures persisted with minimal ice coverage. It is
evident that summer temperatures are critical in determining the lake conditions during
the winter, not only based on the statistical findings, but also based on the fact that many
of the years experiencing warmer lake waters, less ice formation, and higher snowfall
totals also experienced colder than normal winter temperatures. This implies that
summer temperatures have a long-term effect on the lake, while winter temperatures have
more of a short-term influence, dictating whether air temperatures are favorable for lakeeffect development.
If similar temperature trends occur over the next several decades a continued
increase in lake-effect snow can be expected. As evident in Cleveland, snowfall is
expected to decrease in the early stages of the lake-effect snowfall season and increase
during the later portion of the season, as lake ice coverage continues to decline as
temperatures rise. Summer temperatures will continue to be above normal, and although
winter temperatures will increase, significant lake-effect snow will take place until the
mean winter temperature rises above freezing. At that point, which could occur as early
as 2100, a decline in lake-effect snowfall would take place. The southern areas
throughout the Great Lakes snow belt will be affected first because their mean winter
84
temperatures are currently much warmer than more northern areas. However, this
decreasing trend in lake-effect snowfall would eventually affect the entire Great Lakes
region, given that global temperature increases continue to have such a profound impact
on the region.
85
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