1. 1
Declining Lake Erie Ice vs. Increasing Chardon, Ohio Snowfall
A Master Thesis
Submitted to the Faculty
Of
American Public University
By
Gregory Allan Pristov
In Partial Fulfillment of the
Requirements for the Degree
Of Master of Science
November 2016
American Public University
Charles Town, WV

2. 2
The author hereby grants the American Public University System the right to display
these contents for educational purposes.
The author assumes total responsibility for meeting the requirements set by United
States copyright law for the inclusion of any materials that are not the author’s creation
or in the public domain.
© Copyright 2016 by Gregory Allan Pristov
All rights reserved.

3. 3
DEDICATION
I dedicate this to my loving, supportive, and most importantly, patient wife.
Without the sacrifices that she has made along the way, completion of this Master’s
degree would not have been achievable.

4. 4
ACKNOWLEDGEMENTS
I would like to recognize my Father, James Pristov, Swim Coach, Thomas Sill,
and Undergraduate Advisor/Professor Dr. David Orosz for their guidance, voice of
reason, and genuine care that was provided to get me through my early life. Without
these men, I would have never learned the value of sacrifice. There have been many
wonderful people along the way, all of which have been able to help me come to the
realization of how little I actually know, which is most important of all.

5. 5
ABSTRACT OF THESIS
Declining Lake Erie Ice vs. Increasing Chardon, Ohio SnowfallBy
Gregory Allan Pristov
American Public University System, July 23, 2016
Charles Town, West Virginia
Doctor Elizabeth D’Andrea, Thesis Professor
This research was conducted to determine if global climate change has had an
impact on snowfall in Chardon, OH for the month of January. Statistics for snowfall,
percent ice coverage, and lake temperature were collected and analyzed to determine if
a correlation was present. The data analysis shows that there has been a linear
correlation between warming Lake Erie temperatures and Lake Effect snow events in
Chardon over the last century. With Chardon lying within the Snowbelt, an increase in
lake temperatures, combined with decrease in total lake ice coverage has had a direct
impact on the heat exchange necessary to produce a Lake Effect Snow event. The
results from this paper can help to educate the public that their community is being
directly impacted by the change in climate. An increase in cold weather events would
oftentimes be thought of as an argument against warming temperatures, whereas it is
directly tied to the trend of increasing temperatures.

6. 6
TABLE OF CONTENTS
CHAPTER PAGE
I. INTRODUCTION 9
II. LITERATURE REVIEW 12
III. METHODOLOGY 17
IV. RESULTS 20
V. LIMITATIONS OF STUDY 48
VI. DISCUSSION 49
VII. LIST OF REFERENCES 51

7. 7
LIST OF TABLES
TABLE PAGE
1. Correlation 18
2. Master Table of Data 20
3. Standard Deviations – Snowfall 30
4. Standard Deviations – Temperature 30
5. Standard Deviations – Ice Coverage 31
6. Normal Distribution – Year by Year, Snow 34
7. Normal Distribution – Five Year Rolling Average, Snow 36
8. Normal Distribution – Ten Year Rolling Average, Snow 37
9. Normal Distribution – Twenty Year Rolling Average, Snow 38
10. Normal Distribution – Year by Year, Temperature 39
11. Normal Distribution – Five Year Rolling Average, Temperature 41
12. Normal Distribution – Ten Year Rolling Average, Temperature 42
13. Normal Distribution – Twenty Year Rolling Average, Temperature 43
14. Normal Distribution – Year by Year, Ice 44
15. Normal Distribution – Five Year Rolling Average, Ice 45
16. Normal Distribution – Ten Year Rolling Average, Ice 46
17. Normal Distribution – Twenty Year Rolling Average, Ice 47

8. 8
LIST OF FIGURES
FIGURE PAGE
A. Year by Year, Snowfall vs. Ice 24
B. Five Year Rolling Average, Snowfall vs. Ice 25
C. Ten Year Rolling Average, Snowfall vs. Ice 26
D. Twenty Year Rolling Average, Snowfall vs. Ice 27
E. Year by Year, Temperature 28
F. Five Year Rolling Average, Temperature 28
G. Ten Year Rolling Average, Temperature 29
H. Twenty Year Rolling Average, Temperature 30

9. 9
INTRODUCTOIN
Snow, “precipitation in the form of small white ice crystals formed directly from
the water vapor of the air at a temperature of less than 32 degrees Fahrenheit” (Snow,
2016). Snow can come in many different intensities, patterns, and can be formed in
different ways, each having a set of criteria to earn its respective title. A blizzard is
defined by heavy snows blowing at a speed of at least 35 miles per hour. This snow
does not need to be falling, but could get picked up from the ground by the wind, or a
ground blizzard, it also requires a visibility of no greater than one quarter of a mile and
must last a minimum of three hours (Oblack, 2016). Another type of snow event that can
be experienced an Ice Storm. This snow event is characterized by the falling of rain,
which transitions to ice as it reaches the surface that it lands on, causing a build-up of
heavy ice on whatever it lands on. Nor’easters occur during times when the wind blows
from the Northeast, bringing with it blizzards and thunderstorms (Oblack, 2016).
Lake Effect Snow is exactly what it sounds like, a snow event as a result of
conditions surrounding a body of water. There are some criteria that must be met prior
to a snow event being given the title of Lake Effect Snow. For starters, the body of water
must meet a standard for size (greater than 62 miles wide). The water body/lake must
not be frozen over. In order for the heat transfer that occurs during the formation of a
Lake Effect Snow event, water must be able to provide moisture to the atmosphere. The
heat transfer that takes place during this evolution occurs due to a minimum
temperature differential between the water and air of 23 degrees F, although this is the
minimum that the difference must be, a larger delta creates a more extreme event. To
accompany all of these criteria, wind speed and direction play the final role in

10. 10
determining the extent of the Lake Effect Snow event. Lighter winds will allow for
greater accumulation of water into the air. These are the minimum criteria that must be
satisfied in order for a snow event to be crowned one of Lake Effect (Beware, 2016).
The process which occurs in order for the evolution to take place is fairly straight
forward. Cold air (below freezing) makes its way over the body of water, in our case,
Lake Erie. Due to the warm temperatures of the water, moisture is sucked up by the
passing air. The warmer water causes the moisture saturated air rise above the colder,
unsaturated air (due to density). With increasing elevation, the air cools, allowing
precipitation to form. Upon arrival at the shore, the land slows the speed of the wind.
This causes a pile up of the moisture saturated air. This, combined with the elevation
increase associate with moving from body of water to land mass causes the moisture
heavy air to further condense, starting the Lake Effect Snow event (Beware, 2016).
Taking these criteria into consideration, Lake Erie fits the needs to be ready for a
Lake Effect Snow event. Lake Erie, which received its name the Iroquoian tribe, “Erige”
or cat, was deemed fitting to describe the violent and unpredictable nature surrounding
the tendencies of the Lake (Lake Erie, 2016) With a maximum length of 241 miles (East
to West), and width of 57 miles North to South), when winds are blowing in the right
direction, Lake Effect Snow is able to be produced (Lake Erie, 2016). Chardon, Ohio is
located in North Eastern Ohio, 10 miles south of Lake Erie, at an elevation of
approximately 1129 feet above sea level (Map of Chardon, 2016). Lake Erie has an
elevation of approximately 571 feet above sea level (Lake Erie Fact, 2016), making the
elevation difference between the City and the Lake a difference of 558 feet.

11. 11
One would think that Lake Effect Snow events are common in nature. In reality,
there are exactly three regions in the world that meet all of the criteria to create a true
Lake Effect Snow event. These areas are the Great Lakes region, the area East of the
Hudson Bay, and the West Coast of the Japanese islands of Honshu and Hokkaido
(Beware, 2016).
The reason that the month of January was chosen to analyze for a change in
Lake Effect Snow is due to a couple of different factors. Historically, in January, Lake
Erie has been either completely, or nearly completely frozen over by the end of the
month. Since this is a large transition in the ecosystem, to view the changes
surrounding that alteration can tell a tale of epic proportions. Another reason for
choosing January is that the air temperatures are cold enough during this time to
capture the moisture coming off of the lake if it isn’t frozen over.
Of the Great Lakes, Lake Erie is both the most Southern, and shallowest of the
five. These two factors allow Lake Erie to become the warmest in the summer, while still
providing the opportunity to freeze over in the winter. With an average depth of 62 feet,
the Lake has been able to reach temperatures upwards of 85 degrees F in the summer,
and an average in the low 70’s (Zimmermann, 2013).

12. 12
LITERATURE REVIEW
Currently, there is not an abundant source of articles pertaining to Lake Effect
Snow off of Lake Erie. The majority of my research came from the National Weather
Data Bases that are available. There are some useful articles however that explain the
relationship between Lake Effect Snow and Lake temperatures/ice coverage of the
lakes. These articles have been used to explain how the process works, not as much to
prove that global warming/climate change is impacting the variables being studied.
In the article “Increasing Great Lake–Effect Snowfall during the Twentieth
Century: A Regional Response to Global Warming?” (2003) the relationship between air
temperatures, water temperatures, lake ice cover, and Lake Effect Snow are studied in
order to see the increase of Lake Effect Snow vs. the stability of non-Lake Effect Snow
fall. It is shown that an increase of 1.9 cm per year for total Lake Effect snowfall has
occurred on average from the year 1915 through the year 2000 in Syracuse, NY.
Although this does not directly correlate to my research, it shows that there is an
increase of Lake Effect Snow occurring in a nearby region of the United States.
The article “The Influence of Ice Cover on Two Lake-Effect Snow Events over
Lake Erie,” (2008) focusses on the ice cover on Lake Erie during the winter months, and
its impact on Lake Effect Snow. It is stated that Lake Erie is normally over 90 percent
ice covered by early January. The Article states that not only the amount of surface ice
coverage of the lake, but thickness of the ice also has an effect on Lake Effect snow as
well.

13. 13
“Mesoscale modeling of lake effect snow over Lake Erie - sensitivity to
convection, microphysics and the water temperature” by N. E. Theeuwes; G. J.
Steeneveld; F. Krikken; A. A. M. Holtslag discusses the science behind Lake Effect
Snow. The information that has been obtained from this article was utilized during the
early parts of the paper, where the reader is informed about why Lake Effect Snow
occurs.
Mathieu R Gerbush, David A R Kristovich, and Neil F Laird wrote a paper titled
“Mesoscale Boundary Layer and Heat Flux Variations over Pack Ice-Covered Lake
Erie”, which discusses the effects that snow coverages has on snow events off of Lake
Erie. This paper focusses on the presence of ice coverage, vs. the common trend of a
decrease in Lake Ice Coverage. The lack of Ice causes problems for predicting Lake
Effect Snow.
The article “Climatology of Lake-Effect Precipitation Events over Lake Tahoe and
Pyramid Lake” by Neil Laird, Alicia M Bentley, Sara A Ganetis, Andrew Stieneke, and
Samantha A Tushaus discusses Lake Effect precipitation in California and Nevada.
Although different than the climate in the Lake Erie and Northeastern Ohio areas, the
characteristics of warmer lake and colder air temperatures are a defining statistics to
accompany the events discussed. The paper goes into surface air temperatures, wind
speeds, and lake-air temperature differences. The wind speed was studied to determine
length of the snow events.
“Contributions of Lake-Effect Periods to the Cool-Season Hydroclimate of the
Great Salt Lake Basin” by Kristen N Yeager, W James Steenburgh, and Trevor I Alcott
focusses on the lake effect events revolving around the cool season. Unlike my paper,

14. 14
which focusses on the month of January, and is much later in the cold cycle, this paper
deals with lake effect events starting as early as September and as late as May.
The article “The response and role of ice cover in lake-climate interactions,” By:
Brown and Duguay, talks about the relationship between Lakes in General (Great Lakes
Included), and the interactions with the surrounding climate. It talks about how ice is
formed on Lakes, and how the Great Lakes Ice formation has been pushed back, and
the break up has been pushed forward, shortening the period of ice cover over the
Great Lakes. Also, it is stated that ice thickness is associated with snow cover as well
as playing a role in lake effect snow. This article has been used to help explain how the
Lake Effect Snow is influenced based on the variable of ice coverage.
“Lake Erie Facts” by Kim Ann Zimmermann (2013) has provided information on
important details regarding Lake Erie. Some of the key information obtained from this
article include the dimensions of Lake Erie, which are important when determining if a
snow event can be considered one of Lake Effect or not. It also discusses how Lake
Erie is more likely to freeze over than the other Great Lakes due to the shallowness of
the body of water. On the opposite end of the spectrum, due to a combination of depth
and location, Lake Erie is able to become the hottest during the summer months as
well. It is stated that “Lake-effect snow has a huge impact on the surrounding
communities”.
The Article “Understanding Winter Storm Types and Intesity”, by Rachelle Oblack
(2016) discusses and informs readers of the different types of snow events that can
occur. To many, snow is snow, end of story. My paper however discusses the trending

15. 15
of one type of snow event, Lake Effect. This article has been of benefit, as I have used
to discuss the differences between snow events.
The book “Understandable Statistics”, by Charles Henry Brase and Corrinne
Pellillo Brase was used throughout this paper when dealing with data analysis. Being a
paper focused on the quantitative analysis and correlation of snowfall and ice coverage,
formulas provided in this literature was utilized in the analysis process. This book has
played a pivotal role in the completion of the thesis.
Using the article “Global Warming and the Great Lakes”, which was read on the
National Wildlife Federation’s website, I have been able to take information regarding
consequences of global warming that make the trends discussed in this paper relevant.
With warming Lake Erie temperatures, impacts will be seen both immediately and in the
future. My hope for this paper is to educate people regarding the importance of the
trends associated with Lake Erie temperature, with a primary focus on snowfall.
However, information from this article has been utilized in the discussion to point out
that snowfall isn’t the only consequence associated with increasing water temperatures.
Similar to the prior article, “Changes in Precipitation” from The Climate Change
Clearing House, will be used to discuss impacts that the increase in snowfall will have
on the environment and communities to be affected. It is stated that due to global
warming, an increase in precipitation will be seen in many areas, which will lead to
increased snow pack and flooding in some regions.

16. 16
The next three websites have provided the backbone for my research. Being a
paper centered on data, these National Weather sources are extremely valuable for the
construction of my research.
Great Lakes Ice Cover, (2014) has vital information including the Maximum ice
coverage of Lake Erie. This is one of the sources that I have utilized for my thesis. This
website has various other useful data including monthly ice coverage from 2003 to
2013. This is not an article, but simply a source of well-respected data from the National
Oceanic and Atmospheric Association.
Weather History for Chardon, OH, (2014), has been a provider for the majority of
data used throughout the paper. With this website, I was able to look up the total
snowfall within the Snow Belt of Ohio for the years 1945 through 2014. This website has
largely contributed in my research when I compare the data for snowfall vs. lake
temperature vs. ice coverage from year to year. This website also provides more data
such as total precipitation throughout the year, but I am mainly interested in the data
regarding snowfall. Without this source, the paper would not exist. Taking raw data from
the website, different statistical analyses were used to interpret the data obtained.
Lake Erie and Lake Ontario Water Temperatures, (2014), has allowed me to look
up the temperature of Lake Erie on any date from January 1st, 1927, until current time.
This is the third source that I used when comparing the three variables for my research
paper. The temperatures of the lake are one of the primary focusses of this paper. With
data supporting the claim that temperatures of the lake are rising, I have used the data
to compare the statement that it is also causing an increase in snowfall in Chardon,
Ohio.

17. 17
METHODOLOGY
For the analysis of Lake Effect Snow vs. Lake Erie Temperature and Ice
Coverage, I have taken a quantitative approach. Using linear comparisons of yearly
data, coupled with linear comparisons of rolling averages for data including five year,
ten year, and twenty year rolling averages, it can be readily and clearly seen that Lake
Effect Snow in Chardon, Ohio has been steadily rising. Contributing to the rising snow,
Lake Erie Temperatures have been falling, causing an equally steady decline in Lake
Erie Ice Coverage for the month of January. This data has been obtained and analyzed
for the years 1930 through 2013 for Lake Erie Temperatures. For Snowfall in Chardon,
Ohio, data has been obtained and analyzed for the years 1946 through 2014. The data
for Lake Erie Ice Coverage (percent) has been obtained and analyzed for the years
1973 through 2013.
Taking the data, the excel average function was used “=average(x:y)” to calculate the
rolling averages. For example, “=average(year1:year5), =average(year2:year6),
=average(year3:year7), etc…” this was performed for the five year, ten year, and twenty
year rolling averages. The data that would be shown for the five year rolling average for
2010 would include the average of the years 2006 through 2010. The average for 2010
for the twenty year rolling average would include data from 1991 through 2010. This
was utilized to show how not only individual data points seem to be trending in a
changing direction, rather the average temperature, ice coverage and snow fall all
changing as well over time. This is a more telling story. After obtaining all of this data, it
was dissected and used to construct scatter plots within Microsoft Excel. Trend lines

18. 18
were added to these scatter plots, and the graphs were extended out to show
projections for twenty years prior and twenty years post the time of data obtained.
Further utilizing Microsoft Excel, the CORREL function was used to determine
the linear correlation between Snow and Ice, Snow and Temperature, and Temperature
and Ice. This was once again performed for the year to year, five year rolling average,
ten year rolling average, and twenty year rolling averages. The CORREL function is
used to determine a correlation coefficient between two sets of variables/data sets. A
correlation coefficient of +1 would indicate a perfect correlation, i.e. as X raises by one,
Y raises by one. A value of -1 would indicate a perfectly negative relationship, i.e. as X
raises by one, Y decreases by one. With this knowledge, as the values for correlation
coefficient near + or – 1, the relationship grows stronger.
20 Year Rolling 10 Year Rolling
Correlation Correlation
Snow/Ice -0.55 Snow/Ice -0.46
Snow/Temp -0.15 Snow/Temp 0.04
Temp/Ice 0.21 Temp/Ice 0.04
5 Year Rolling Year by Year
Correlation Correlation
Snow/Ice -0.03 Snow/Ice 0.44
Snow/Temp 0.04 Snow/Temp -0.02
Temp/Ice -0.05 Temp/Ice -0.22
With the assumption that as lake ice coverage decreases, snowfall would
increase, the rolling averages all support this assumption. As the rolling average
increases, the negative correlation observed between Snow and Ice Coverage grows to
a correlation coefficient of -0.55. However, the year by year analysis of the correlation

19. 19
between snow and ice coverage shows a positive relationship. With a correlation
coefficient of 0.44, the year by year comparison would indicate that as ice increases,
snowfall increases as well.
For the correlation of snowfall vs lake temperature, I received results indicating
little correlation, as the correlation coefficient hovered on either side of 0. The 20 year
rolling average had the largest value at -0.15. For the comparison of Lake Temperature
and Ice Coverage, the year by year analysis and 20 year rolling average had opposite
results. At -0.22 and 0.21 for their respective correlation coefficients, the data falls on
the weaker end of a definitive linear correlation.

20. 20
RESULTS
Year Snowf
all
5
year
10
year
20
year
Lake
Erie
Tem
p
5
yea
r
10
yea
r
20
year
Ice
Coverag
e %
5
yea
r
10
yea
r
20
year
1930 33
1931 33
1932 40
1933 33
1934 32 34.
2
1935 33 34.
2
1936 32 34
1937 36 33.
2
1938 33 33.
2
1939 34 33.
6
33.
9
1940 32 33.
4
33.
8
1941 38 34.
6
34.
3
1942 40 35.
4
34.
3
1943 32 35.
2
34.
2
1944 32 34.
8
34.
2
1945 33 35 34.
2
1946 15.8 33 34 34.
3
1947 12.3 35 33 34.
2
1948 35.5 34 33.
4
34.
3
1949 9.5 35 34 34.
4
34.1
5
1950 10 16.6
2
41 35.
6
35.
3
34.5
5
1951 30.8 19.6
2
33 35.
6
34.
8
34.5
5

21. 21
Year Snowf
all
5
year
10
year
20
year
Lake
Erie
Tem
p
5
yea
r
10
yea
r
20
year
Ice
Coverag
e %
5
yea
r
10
yea
r
20
year
1952 20.5 21.2
6
35 35.
6
34.
3
34.3
1953 26 19.3
6
37 36.
2
34.
8
34.5
1954 17.5 20.9
6
35 36.
2
35.
1
34.6
5
1955 23.5 23.6
6
20.1
4
36 35.
2
35.
4
34.8
1956 18 21.1 20.3
6
33 35.
2
35.
4
34.8
5
1957 43.5 25.7 23.4
8
37 35.
6
35.
6
34.9
1958 26.3 25.7
6
22.5
6
39 36 36.
1
35.2
1959 38.5 29.9
6
25.4
6
32 35.
4
35.
8
35.1
1960 31.3 31.5
2
27.5
9
36 35.
4
35.
3
35.3
1961 17.1 31.3
4
26.2
2
32 35.
2
35.
2
35
1962 9.7 24.5
8
25.1
4
33 34.
4
35 34.6
5
1963 21 23.5
2
24.6
4
32 33 34.
5
34.6
5
1964 35.2 22.8
6
26.4
1
32 33 34.
2
34.6
5
1965 38.7 24.3
4
27.9
3
24.03
5
36 33 34.
2
34.8
1966 46.4 30.2 30.7
7
25.56
5
37 34 34.
6
35
1967 13.3 30.9
2
27.7
5
25.61
5
34 34.
2
34.
3
34.9
5
1968 36 33.9
2
28.7
2
25.64 35 34.
8
33.
9
35
1969 40.2 34.9
2
28.8
9
27.17
5
32 34.
8
33.
9
34.8
5
1970 29.2 33.0
2
28.6
8
28.13
5
33 34.
2
33.
6
34.4
5
1971 31.5 30.0
4
30.1
2
28.17 35 33.
8
33.
9
34.5
5

22. 22
Year Snowf
all
5
year
10
year
20
year
Lake
Erie
Tem
p
5
yea
r
10
yea
r
20
year
Ice
Coverag
e %
5
yea
r
10
yea
r
20
year
1972 28.1 33 31.9
6
28.55 39 34.
8
34.
5
34.7
5
1973 15.7 28.9
4
31.4
3
28.03
5
37 35.
2
35 34.7
5
95
1974 17 24.3 29.6
1
28.01 35 35.
8
35.
3
34.7
5
89
1975 13 21.0
6
27.0
4
27.48
5
37 36.
6
35.
4
34.8 80
1976 49 24.5
6
27.3 29.03
5
38 37.
2
35.
5
35.0
5
95
1977 42.5 27.4
4
30.2
2
28.98
5
32 35.
8
35.
3
34.8 100 91.
8
1978 57.1 35.7
2
32.3
3
30.52
5
34 35.
2
35.
2
34.5
5
100 92.
8
1979 27.7 37.8
6
31.0
8
29.98
5
35 35.
2
35.
5
34.7 100 95
1980 11.1 37.4
8
29.2
7
28.97
5
39 35.
6
36.
1
34.8
5
94 97.
8
1981 32.2 34.1
2
29.3
4
29.73 32 34.
4
35.
8
34.8
5
96 98
1982 38.7 33.3
6
30.4 31.18 33 34.
6
35.
2
34.8
5
99 97.
8
94.
8
1983 13.5 24.6
4
30.1
8
30.80
5
41 36 35.
6
35.3 41 86 89.
4
1984 16 22.3 30.0
8
29.84
5
32 35.
4
35.
3
35.3 95 85 90
1985 34.5 26.9
8
32.2
3
29.63
5
41 35.
8
35.
7
35.5
5
96 85.
4
91.
6
1986 25.7 25.6
8
29.9 28.6 33 36 35.
2
35.3
5
95 85.
2
91.
6
1987 17.6 21.4
6
27.4
1
28.81
5
40 37.
4
36 35.6
5
88 83 90.
4
1988 22.9 23.3
4
23.9
9
28.16 38 36.
8
36.
4
35.8 91 93 89.
5
1989 7.1 21.5
6
21.9
3
26.50
5
34 37.
2
36.
3
35.9 95 93 89
1990 12.9 17.2
4
22.1
1
25.69 33 35.
6
35.
7
35.9 86 91 88.
2
1991 19.7 16.0
4
20.8
6
25.1 39 36.
8
36.
4
36.1 45 81 83.
1
1992 30.1 18.5
4
20 25.2 35 35.
8
36.
6
35.9 94 82.
2
82.
6
88.7

23. 23
Year Snowf
all
5
year
10
year
20
year
Lake
Erie
Temp
5
year
10
year
20
year
Ice Coverage
%
5
year
10
year
20
year
1993 20.5 18.0
6
20.7 25.44 33 34.
8
35.
8
35.7 95 83 88 88.7
1994 34.6 23.5
6
22.5
6
26.32 32 34.
4
35.
8
35.5
5
100 84 88.
5
89.2
5
1995 22.4 25.4
6
21.3
5
26.79 40 35.
8
35.
7
35.7 96 86 88.
5
90.0
5
1996 30.1 27.5
4
21.7
9
25.84
5
32 34.
4
35.
6
35.4 100 97 89 90.3
1997 23.6 26.2
4
22.3
9
24.9 35 34.
4
35.
1
35.5
5
100 98.
2
90.
2
90.3
1998 8.1 23.7
6
20.9
1
22.45 36 35 34.
9
35.6
5
7 80.
6
81.
8
85.6
5
1999 42.6 25.3
6
24.4
6
23.19
5
37 36 35.
2
35.7
5
76 75.
8
79.
9
84.4
5
2000 37.1 28.3 26.8
8
24.49
5
36 35.
2
35.
5
35.6 91 74.
8
80.
4
84.3
2001 25.3 27.3
4
27.4
4
24.15 32 35.
2
34.
8
35.6 94 73.
6
85.
3
84.2
2002 6.8 23.9
8
25.1
1
22.55
5
35 35.
2
34.
8
35.7 13 56.
2
77.
2
79.9
2003 67.6 35.8
8
29.8
2
25.26 34 34.
8
34.
9
35.3
5
95 73.
8
77.
2
82.6
2004 51.6 37.6
8
31.5
2
27.04 37 34.
8
35.
4
35.6 94 77.
4
76.
6
82.5
5
2005 35.5 37.3
6
32.8
3
27.09 35 34.
6
34.
9
35.3 92 77.
6
76.
2
82.3
5
2006 10.3 34.3
6
30.8
5
26.32 34 35 35.
1
35.3
5
21 63 68.
3
78.6
5
2007 49.4 42.8
8
33.4
3
27.91 43 36.
6
35.
9
35.5 95 79.
4
67.
8
79
2008 38.1 36.9
8
36.4
3
28.67 37 37.
2
36 35.4
5
93 79 76.
4
79.1
2009 58.9 38.4
4
38.0
6
31.26 35 36.
8
35.
8
35.5 95 79.
2
78.
3
79.1
2010 50.9 41.5
2
39.4
4
33.16 36 37 35.
8
35.6
5
93 79.
4
78.
5
79.4
5
2011 49.7 49.4 41.8
8
34.66 33 36.
8
35.
9
35.3
5
95 94.
2
78.
6
81.9
5
2012 28.4 45.2 44.0
4
34.57
5
39 36 36.
3
35.5
5
13 77.
8
78.
6
77.9
2013 27.2 43.0
2
40 34.91 38 36.
2
36.
7
35.8 84 76 77.
5
77.3
5
2014 50.2 41.2
8
39.8
6
35.69

24. 24
It can be seen that for the year by year results for snowfall and ice coverage, that
there is a declining trend for ice and increasing trend for snowfall. There are peaks early
in the snowfall history, as can be seen in the late 1970’s where a peak greater than it
isn’t seen until into the 2000’s. Likewise, there are low points for ice coverage
throughout the entirety of the graph. The declining trend is attributed to the more
extreme low points of the 2000’s.

25. 25
Starting to determine the change in the average for snowfall and ice
coverage over time, the five year rolling average was compiled. It can be seen that
there is almost a sin curve trend associated with the snowfall for the five year rolling
average. This can be seen with increases and decreases in the five year rolling average
throughout the time of the study. Currently, we are in what looks to be an increasing
trend. As for the ice coverage, there has been a steady decline over time for the five
yea rolling average. There are peaks along the way, with each new peak being shorter
than the one prior.

26. 26
For the ten year rolling average, we can really see how the average for both
snowfall and ice coverage are changing, or if they are. Analyzing ten rolling data points,
if changes are occurring, we would see them. The data shows that snowfall for
Chardon, Ohio started on an increasing trend throughout the 50’s, 60’s, 70’s and the
80’s. during the early 90’s there was a dip in snowfall, that had since followed with a
rapid trend of increasing snowfall. As for ice coverage, there has continued to be a
decrease in the ten year average throughout the 80’s –present time.

27. 27
The twenty year average gives us a true understanding of overall change. As we
can see, unlike the more precise graphs, the snowfall average for twenty years is barely
rising. The highest point on the graph comes at the present time, indicating that over the
past twenty years, we have an average snowfall for Chardon greater than any other
twenty year period since the 1960’s. The twenty year average for ice coverage can now
be seen without the peaks from outliers. This shows a steady decline in the average ice
coverage since the early 1990’s. In the month of January, we now have an average ice
coverage of Lake Erie of less than 80 percent.

28. 28
Analyzing the year by year data for Lake Erie temperature for the month of
January, there does not seem to be much of a trend. With frequent peaks in
temperature, followed by dips in temperature in following years, no real patter makes
itself readily apparent. There does seem to be higher peaks, and at a more frequent
interval as time goes on.

29. 29
Taking the five year rolling average, a pattern starts to make itself known. The
trend line associated with the graph shows an incline in the average temperature over
time. The peaks, although spread evenly, have begun to grow taller over time, indicating
higher average temperatures for the five year periods. At the same time, the dips in
temperature have also grown to higher temperatures as well.
The ten year rolling average starts to bring the data together. As peaks become
less frequent, and less severe, we can see how the average Lake Erie temperature for
January is beginning to change. We had a ten year average low in the late 60’ or early
70’s. This was followed by a sharp incline in the 70’s, which became somewhat
sporadic for the next 20 years. The early 2000’s seemed to be on a declining trend,
when a sharp trend in the warming Lake Erie temperatures began. We are currently
have the highest ten year rolling average since the 1940’s, when the data started being
obtained.

30. 30
The most telling story of what is going on with the weather is the twenty year
average. We can tell by using this data, that there is an actual trend, and not just
random warm points throughout history. With a twenty year average low in the 1940’s of
approximately 34.1 degrees Fahrenheit, the average temperature peaked in the 1990’s
at over 36 degrees. We ae currently sitting around 35.75 degrees, and are in a time of
increased trending for Lake Erie temperatures in the month of January. Overall, over the
last 70 years, the average Lake Erie temperature for the month of January has risen at
a consistent, and now predictable rate. By the year 2040, it would not be a shock to
have the average lake temperature over 36.5 degrees Fahrenheit, with peaks well
above that.

31. 31
Average 28.67101 28.68308 28.42133 27.9174
Std
Dev. 14.18841 7.665893 5.670116 3.126888
Bin
Range -13.8942 5.685398 11.41099 18.53674
σ (-3) 0.294199 13.35129 17.0811 21.66362
σ (-2) 14.48261 21.01718 22.75122 24.79051
σ (-1) 28.67101 28.68308 28.42133 27.9174
σ (+1) 42.85942 36.34897 34.09145 31.04429
σ (+2) 57.04783 44.01486 39.76157 34.17118
σ (+3) 71.23624 51.68076 45.43168 37.29806
Year by Year
Five Year Rolling
Average
Ten Year Rolling
Average
Twenty Year Rolling
Average
Average
35.16666667 35.1375 35.136 35.16077
Std
Dev.
2.776117308 1.106297 0.754203 0.471408
Bin
Range
26.83831474 31.81861 32.87339 33.74654
σ (-3)
29.61443205 32.92491 33.62759 34.21795
σ (-2)
32.39054936 34.0312 34.3818 34.68936
σ (-1)
35.16666667 35.1375 35.136 35.16077
σ (+1)
37.94278397 36.2438 35.8902 35.63218
σ (+2)
40.71890128 37.35009 36.64441 36.10359
σ (+3)
43.49501859 38.45639 37.39861 36.57499

32. 32
Year by Year
Five Year Rolling
Average
Ten Year Rolling
Average
Twenty Year Rolling
Average
Average
83.31707317 83.89189 83.21875 83.4455
Std Dev.
26.25970204 9.73945 6.9299 4.44859
Bin
Range
4.537967051 54.67354 62.42905 70.0997
σ (-3)
30.79766909 64.41299 69.35895 74.5483
σ (-2)
57.05737113 74.15244 76.28885 78.9969
σ (-1)
83.31707317 83.89189 83.21875 83.4455
σ (+1)
109.5767752 93.63134 90.14865 87.894
σ (+2)
135.8364773 103.3708 97.07855 92.3426
σ (+3)
162.0961793 113.1102 104.0084 96.7912
Blue- Snowfall (in.)
Green- Lake Ice Temperature (deg. F)
Yellow- Lake Ice Coverage (%)

33. 33
On top of the linear correlation used to analyze the data obtained, averages and
standard deviations were calculated for the year by year, five year rolling average, ten
year rolling average, and twenty year rolling average data sets. An average or mean is
defined as (Mean = Sum of all entries / Number of entries) (Brase, 2010). The standard
deviation for the data sets were obtained using the “STDEV.S” function in Microsoft
Excel, which estimates the standard deviation based on the samples provided. A
standard deviation is a measurement data with its position relative to the mean. For
every standard deviation away from the mean, an expected number of sample points
will fall within that range. For example, approximately 34 percent of all samples will fall
within the range of zero and one standard deviation. For the range of one to two
standard deviations approximately 13.5 percent of all samples will fall in this range, and
for greater than three standard deviations, less than 0.1 percent of all samples will make
up the values that fall here. The distribution of samples relative to the mean and
standard deviations should make a bell curve or normal curve. A normal or bell curve
has defining characteristics. These are as follows: “The curve is bell-shaped, with the
highest point over the mean” (Brase 2010), “The curve is symmetrical about a vertical
line through the mean” (Brase, 2010), “The curve approaches the horizontal axis but
never touches or crosses it” (Brase, 2010), “The inflection points between cupping
upward and downward occur above mean plus one standard deviation and mean minus
one standard deviation” (Brase, 2010), and “The are under the entire curve is 1” (Brase,
2010).

34. 34
I have analyzed the data for each of the data sets for the standard deviation and
mean. With this data, I have determined how many data points fall within each standard
deviation, and the years associated with each data point.
For the Year by Year analysis of standard deviation for snowfall per year, you
can see that zero years fell less than three standard deviations. Thirteen years fell
between negative two and negative three standard deviations. For these points, all
snowfall totals for these years were between 0.29 inches and 14.48 inches for their
respective years. The average year for this sample set was 1976.77. For the range of
σ (-3) σ (-2) σ (-1) σ (+1) σ (+2) σ (+3)
0.294199 14.48261 28.67101 42.85942 57.04783 71.23624
1 2002 1973 1970 1957 1978
2 1989 1946 1992 1966 2009
3 1998 1984 1996 1976 2003
4 1949 1974 1951 2007
5 1962 1961 1960 2011
6 1950 1954 1971 2014
7 2006 1987 1981 2010
8 1980 1956 1985 2004
9 1947 1991 1994
10 1990 1952 1964
11 1975 1993 1948
12 1967 1963 2005
13 1983 1995 1968
14 1988 2000
15 1955 2008
16 1997 1959
17 2001 1965
18 1986 1982
19 1953 1969
20 1958 1977
21 2013 1999
22 1979
23 1972
24 2012
1976.769 1971.462 1975.769 1993.125 1996.667
Standard Deviation Distribution Year by Year Snowfall (in)
Average Year

35. 35
negative one to negative two standard deviations, 24 data points made up the total. The
range for these values was between 14.48 inches and 28.67 inches per year, and the
average year for this data set was 1971.46. For the range of negative one to positive
one standard deviation, we had a total of 21 data points, ranging from 28.67 inches to
42.86 inches. The average year for this data set was 1975.77. For the range of positive
one to positive two standard deviations, eight years fell in this range, which was 42.86
inches to 57.05 inches. The average year was 1993.13. For the final data set, we had
three years, which fell between 57.05 and 71.23 inches of total snowfall for January,
with an average year of 1996.67. Upon further analysis of this data, you can see that as
we have a value greater than the mean, the average year increases, indicating a trend
of increased snowfall over time.

36. 36
Looking at the standard deviation distribution, you can see that the data forms
what looks similar to the standard bell/normal curve as expected. Between negative one
and negative two standard deviations, 13.35 to 21.02 inches, we had eight years with
an average of 1972.11 for the year. For the second range, we had 29 data points, with
an average year of 1980.11. The snowfall for these years fell between 20.12 and 28.68
inches respectfully. The third group of years was between 28.68 and 36.35 inches per
year, and had an average year of 1972. For the ranger of 36.35 to 44.01 inches, the
σ (-3) σ (-2) σ (-1) σ (+1) σ (+2) σ (+3)
13.35129 21.01718 28.68308 36.34897 44.01486 51.68076
1 1991 1956 1973 2008 2012
2 1950 1952 1959 2005 2011
3 1990 1987 1971 1980
4 1993 1989 1966 2004
5 1992 1984 1967 1979
6 1953 1964 1961 2009
7 1951 1988 1960 2014
8 1954 1963 1972 2010
9 1975 1994 1970 2007
10 1955 1982 2013
11 1998 1968
12 2002 1981
13 1974 2006
14 1965 1969
15 1976 1978
16 1962 2003
17 1983
18 1999
19 1995
20 1986
21 1957
22 1958
23 1997
24 1985
25 2001
26 1977
27 1996
28 2000
29
1972.111 1980.107 1972 2002.9 2011.5
Standard Deviation Distribution Five Year Rolling Average Snowfall (in)
Average Year

37. 37
average year for the set of ten was 2002.9. For the final set, there were two years,
indicating the years that had the furthest snowfall totals above the mean. The average
of these two points was 2011.5.
The ten year rolling average follows similar trends to the year by year and five
year rolling average distributions. With the data set being highest above the mean
snowfall also being the highest average year. The eight years that had the most
σ (-3) σ (-2) σ (-1) σ (+1) σ (+2) σ (+3)
17.0811 22.75122 28.42133 34.09145 39.76157 45.43168
1 1992 1957 1970 2008 2014
2 1955 1988 1968 2009 2013
3 1956 1999 1969 2010 2011
4 1993 1963 1980 2012
5 1991 2002 1981
6 1998 1962 1974
7 1995 1959 2003
8 1996 1961 1986
9 1989 1964 1984
10 1990 2000 1971
11 1997 1975 1983
12 1994 1976 1977
13 1958 1987 1982
14 2001 1966
15 1960 2006
16 1967 1979
17 1965 1973
18 2004
19 1972
20 1985
21 1978
22 2005
23 2007
24
25
26
27
28
29
1984.923 1975.647 1979.077 2009 2012.5
Standard Deviation Distribution Ten Year Rolling Average Snowfall (in)
Average Year

38. 38
snowfall for Chardon, Ohio were the years 2007 – 2014. Of the ten years with the most
average snowfall, nine of them fall after the year 2005.
The twenty year rolling average for snowfall had similar results to the ten year
rolling average. Common throughout all data sets was that the two average years
furthest above the mean happened to also be the latest average years. This supports
the statement that there has been an increase in snowfall in Chardon, Ohio over time.
σ (-3) σ (-2) σ (-1) σ (+1) σ (+2) σ (+3)
21.66362 24.79051 27.9174 31.04429 34.17118 37.29806
1 1998 1997 2005 1982 2012
2 2002 1991 1969 2009 2011
3 1999 1992 1975 2010 2013
4 1965 2003 2007 2014
5 2001 1993 1974
6 2000 1966 1973
7 1967 1970
8 1968 1988
9 1990 1971
10 1996 1972
11 1994 1986
12 2006 2008
13 1989 1987
14 1995 1980
15 2004 1977
16 2005 1976
17 1985
18 1981
19 1984
20 1979
21 1978
22 1983
23
24
25
26
27
28
29
Average Year 1994.167 1991 1983.462 2000.333 2012.5
Standard Deviation Distribution Twenty Year Rolling Average Snowfall (in)

39. 39
The year by year distribution for Lake Erie temperatures is a great indicator of the
warming temperatures that can be observed for the lake. I had sixteen years make up
an average date of 1958, which fell between negative three and negative two standard
deviation points. This data range had an average temperature between 29.61 and 32.39
σ (-3) σ (-2) σ (-1) σ (+1) σ (+2) σ (+3)
29.61443 32.39055 35.16667 37.94278 40.7189 43.49502
1 1934 1930 1937 1941 1950
2 1936 1931 1955 1976 1983
3 1940 1933 1960 1988 1985
4 1943 1935 1965 2013 2007
5 1944 1938 1998 1958
6 1959 1945 2000 1972
7 1961 1946 2010 1980
8 1963 1951 1953 1991
9 1964 1956 1957 2012
10 1969 1962 1966 1932
11 1977 1970 1973 1942
12 1981 1982 1975 1987
13 1984 1986 1999 1995
14 1994 1990 2004
15 1996 1993 2008
16 2001 2011
17 1939
18 1948
19 1967
20 1978
21 1989
22 2003
23 2006
24 1947
25 1949
26 1952
27 1954
28 1968
29 1971
30 1974
31 1979
32 1992
33 1997
34 2002
35 2005
36 2009
1958.077 1951.154 1972.923 1975.923 1981.25
Standard Deviation Distribution Year by Year Lake Erie Temperature (Degrees F)
Average Year

40. 40
degrees Fahrenheit for Lake Erie for the month of January. For the set that fell between
negative two and negative one standard deviation, we had a total of 36 years, which
averaged out to be 1951. Although this is a lower average year, the date still falls below
the average for Lake Erie temperatures. As we go to the right side of the distribution
curve, the data begins to climb. With average years of 1972, 1975 and 1981, as the
water gets warmer, the average year climbs as well.

41. 41
Similar to the data for the year by year analysis, as the five year rolling average
year increases, the temperature of the lake follows a similar trend. This holds true for all
ranges, with the exception of the warmest average year of 1987. This is a lone data
point, and falls within the range of 37.35 and 38.45 degrees Fahrenheit. The years for
σ (-3) σ (-2) σ (-1) σ (+1) σ (+2) σ (+3)
32.92491 34.0312 35.1375 36.2438 37.35009 38.45639
1 1947 1934 1943 1975 1987
2 1963 1935 1955 2007
3 1964 1967 1956 1988
4 1965 1970 1961 1991
5 1937 1962 1973 2009
6 1938 1981 1978 2011
7 1940 1994 1979 2010
8 1948 1996 2000 1976
9 1939 1997 2001 1989
10 1971 1941 2002 2008
11 1936 1982 1942
12 1946 2005 1959
13 1949 1944 1960
14 1966 1968 1984
15 1969 1950
16 1972 1951
17 1993 1952
18 2003 1957
19 2004 1980
20 1945 1990
21 1998 1974
22 2006 1977
23 1985
24 1992
25 1995
26 1958
27 1983
28 1986
29 1999
30 2012
31 1953
32 1954
33 2013
34
35
36
1949.462 1969.846 1969.923 1996.4 1987
Standard Deviation Distribution Five Year Rolling Average Erie Temperature (Degrees F)
Average Year

42. 42
each standard deviation range in order from least to greatest are as follows, 1949,
1969, 1969, 1996, and 1987.
Similar to the five year rolling average, the ten year rolling average saw an
increase in average year as water temperatures grew higher. Unlike the five year
σ (-3) σ (-2) σ (-1) σ (+1) σ (+2) σ (+3)
33.62759 34.3818 35.136 35.8902 36.64441 37.39861
1 1970 1949 1954 2007 2013
2 1940 1963 1997 2011
3 1939 1972 2006 1987
4 1968 1966 1961 2008
5 1969 1951 1978 1958
6 1971 1953 1982 1980
7 1943 2001 1986 1989
8 1944 2002 1999 2012
9 1945 1998 1950 1988
10 1947 2003 1960 1991
11 1964 2005 1974 1992
12 1965 1962 1977
13 1941 1973 1984
14 1942 1955
15 1946 1956
16 1948 1975
17 1952 2004
18 1967 1976
19 1979
20 2000
21 1957
22 1983
23 1996
24 1985
25 1990
26 1995
27 1959
28 1981
29 1993
30 1994
31 2009
32 2010
33
34
35
36
1954.308 1976.769 1977.538 1993 2013
Standard Deviation Distribution Ten Year Rolling Average Lake Erie Temperature (Degrees F)
Average Year

43. 43
however, the highest water temperature range had an average year of 2013 vs the 1987
from the five year. This is still a single point, but shows that as time increases, the
average of the lake temperatures are increasing as well.
σ (-3) σ (-2) σ (-1) σ (+1) σ (+2) σ (+3)
34.21795 34.68936 35.16077 35.63218 36.10359 36.57499
1 1949 1964 1958 1987
2 1952 1979 1960 1998
3 1970 1972 1983 2010
4 1953 1973 1984 1993
5 1950 1974 2005 1995
6 1951 1955 1986 2002
7 1971 1965 2003 1999
8 1978 1975 2006 1988
9 1954 1977 2011 2013
10 1962 1956 1996 1989
11 1963 1969 2008 1990
12 1980 2007 1992
13 1981 2009 1991
14 1982 1985
15 1957 1994
16 1967 1997
17 1961 2012
18 1966 2000
19 1968 2001
20 1976 2004
21 1959
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
Average Year 1959.364 1970.769 1993.538 1995.923 #DIV/0!
Standard Deviation Distribution Twenty Year Rolling Average Lake Erie Temperature (Degrees F)

44. 44
Similar to the other rolling averages, the twenty year rolling average saw a
normal distribution of data points. As is the trend, as the average year increases, the
water temperature for Lake Erie increases as well. Starting with the coldest Lak Erie
temperatures, 1959, 1970, 1993, and 1995 were the average years as water
temperature ranges increased. This follows the trend of increasing water temperatures
over time, ultimately changing how we define the average temperature for the lake for
the month of January.
σ (-3) σ (-2) σ (-1) σ (+1) σ (+2) σ (+3)
30.79767 57.05737 83.31707 109.5768 135.8365 162.0962
1 1998 1983 1999 2013
2 2002 1991 1975 1990
3 2012 1987
4 2006 1974
5 1988
6 2000
7 2005
8 2008
9 2010
10 1980
11 1992
12 2001
13 2004
14 1973
15 1976
16 1984
17 1986
18 1989
19 1993
20 2003
21 2007
22 2009
23 2011
24 1981
25 1985
26 1995
27 1982
28 1977
29 1978
30 1979
31 1994
32 1996
33 1997
2004.5 1987 1987 1996.308 #DIV/0! #DIV/0!
Standard Deviation Distribution Year by Year Ice Coverage (%)
Average Year

45. 45
For the year by year analysis of ice coverage of Lake Erie, 33 of the 44 data
points fell within the negative one to positive one standard deviation range. This means
that the vast majority of all data collected fell just on either side of the average for ice
coverage. The average year for the large cluster was 1996. The lowest ice coverage
with an average below 30.79 percent ice coverage had three years after 2006, and only
one year less than the year 2000, being 1998. With all of the low points in ice coverage
coming within the recent past, this is a trend that ice coverage is declining over time.
σ (-3) σ (-2) σ (-1) σ (+1) σ (+2) σ (+3)
64.41299 74.15244 83.89189 93.63134 103.3708 113.1102
1 2002 2001 2000 1994 2011
2 2006 2003 1999 1984 1979
3 2013 1986 1996
4 2004 1985 1980
5 2005 1983 1982
6 2012 1995 1981
7 2008 1990 1997
8 2009 1977
9 2007 1978
10 2010 1988
11 1998 1989
12 1991
13 1992
14 1987
15 1993
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
Average Year 2004 2002 2003.692 1986.273 1989.429 #DIV/0!
Standard Deviation Distribution Five Year Rolling Average Ice Coverage (%)

46. 46
The data for the five year rolling average took more of the common shape for the
normal distribution. We can see that as time goes on, the ice coverage percent drops
off. With the highest ice coverage range having an average year of 1989, and the lowest
ice coverage range having an average year of 2004.
It can be readily observed that as ice coverage on Lake Erie drops, the year
increases. As time goes on, ice coverage on the lake for the month of January is seeing
a steady decline. The impact that this can have on an ecosystem has the potential to
play a crucial role in the future of the area.
σ (-3) σ (-2) σ (-1) σ (+1) σ (+2) σ (+3)
69.35895 76.28885 83.21875 90.14865 97.07855 104.0084
1 2007 2005 2008 2001 1997
2 2006 2004 1993 1987
3 2002 1990 1985
4 2003 1994 1986
5 2013 1995 1982
6 2009 1989
7 2010 1996
8 2011 1983
9 2012 1988
10 1999 1984
11 2000
12 1998
13 1992
14 1991
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
Average Year 2006.5 2005 2004.692 1991.3 1987.4 #DIV/0!
Standard Deviation Distribution Ten Year Rolling Average Ice Coverage (%)

47. 47
For the twenty year rolling average normal distribution, the vast majority of later
dates come after the year 2000, with not a single sample that has a value of greater
than the mean for ice coverage being prior to the bench mark. The average year per
rolling average increases as the ice coverage decreases, which is what he have seen
throughout this paper.
σ (-3) σ (-2) σ (-1) σ (+1) σ (+2) σ (+3)
74.54827 78.99686 83.44545 87.89405 92.34264 96.79123
1 2013 2012 2007 2001 1992
2 2006 2008 2000 1993
3 2009 1999 1994
4 2010 1998 1995
5 2002 1996
6 2011 1997
7 2005
8 2004
9 2003
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
Average Year 2013 2009 2006.556 1999.5 1994.5 #DIV/0!
Standard Deviation Distribution Twenty Year Rolling Average Ice Coverage (%)

48. 48
Limitations of Study
One of the major limitations of this study was a lack of data. The data that was
obtained is useful to show trends over the last fifty to eighty years, depending on the
subject being analyzed. It would have been preferable to have data that extended
further into the past. That being said, the data that was obtained provided valuable
information as to the recent trends of Lake Erie temperatures, ice coverage, and
snowfall for the month of January for Chardon, Ohio. Furthermore, much more than Ice,
Temperature, and Snowfall account for the creation of Lake Effect snow events. Had
the direction of wind, speed of wind, and temperature of air been analyzed, it would
have shown a complete picture. With the information that I obtained and analyzed, I
have been able to put together trends, and averages to show that there has been a
change. This provides valuable information for predicting the future of our Lake Effect
snow averages and ice coverage, but does not give a clear picture of the direct
correlations. The lake can be as cold or warm as possible, if the wind isn’t blowing in the
right direction, Lake Effect snow will not fall in Chardon, Ohio. For the purpose of
showing that Chardon, Ohio is seeing an increase in snow over time, and Lake Erie is
seeing a trend of warming waters, combined with decreased ice coverage, the data
obtained shows this clearly.

49. 49
DISCUSSION
Growing up in Northeastern Ohio and visiting my great grandparents during the
weekends (who lived in Chardon, Ohio), we dealt with what is called Lake Effect Snow.
Throughout our teenage years, we used to debate in school about the reality of global
warming. One of the common arguments that was often used to dispute the existence of
global warming was that if the world was getting warmer, why are we getting more
snow, and therefore, it was a false proclamation. Back then, we had no idea that there
was actually a trend of increased Lake Effect Snow occurring in our own back yards.
The data that has been gathered and analyzed depicts a telling story. During our
lifetime, we have had a steady incline in Lake Erie Temperatures, which has had a
direct impact on the decline in Lake Erie Ice Coverage, contributing to an increase in
Lake Effect snow in Chardon, Ohio.
The trends can be seen by just looking at the year to year changes, but the real
changes can be seen when you analyze how the average for each of the above criteria
is changing. With the average temperature, ice coverage and snowfall all changing, the
extreme events of the past will become less and less of outliers.
Although this paper has focused the impacts that warming Lake Erie
temperatures are having on Snowfall totals in Chardon, Ohio, there are other impacts
that will affect the communities surrounding the lake. One of the first impacts that can
be seen is as increase in plant and animal life that thrives in the warmer waters (Global
Warming, 2016). This will in turn lead to pushing the natural organisms out of their
habitat, leading to changing of the ecosystem as a whole. One such animal that thrives

50. 50
in the warmer waters that we are seeing include the invasive species of Zebra Mussels.
These mussels have begun shaping not only the lake, but industries in the area as well.
At just the Perry Nuclear Power Plant alone, every summer an extensive project is
performed to kill the zebra mussels that have made the cooling water system their
home. Impacts such as this will be seen in the coming times, and they will not only be
consisting of more sever and frequent Lake Effect Snow.
The increase in snowfall will present its own set of new challenges. Every year,
snow falls over the area. Throughout the winter, this snow piles up, waiting for the warm
spring air to come and melt it. If the trends continue of increased snowfall in the years to
come, the potential for floods will begin to increase. With amounts of snow being melted
in the years to come, an increase in flooding could be predicted (Changes, 2009).
These floods will have direct impacts on those who are affected, and will have impacts
that are not often thought about. The flood water will cause increased erosion in the
areas where the water levels rise, potentially devastating communities and taking
houses, cars, and roadways with them in the process.
The data that has been collected and analyzed show that there is a trend of
increasing Lake Erie temperatures, decrease of ice coverage for Lake Erie, and
increasing snowfall for Chardon, Ohio for the month of January. This data is only for one
month of the year. If these trends are seen throughout multiple months, or entire years,
they problems associated with these changes will only be expanded. Just how a
marathon is completed one step at a time, environmental disasters are created one
degree/snowflake at a time.

51. 51
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