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

1

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.

2

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.

3

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.

4

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.

5

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

6

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

7

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

8

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

9

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.

10

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).

11

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.

12

“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,

13

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

14

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.

15

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.

16

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

17

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 RollingCorrelation CorrelationSnow/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 YearCorrelation CorrelationSnow/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

18

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.

19

RESULTS

Year Snowfall

5 year

10 year

20 year

Lake Erie Temp

5 year

10 year

20 year

Ice Coverage %

5 year

10 year

20 year

1930 331931 331932 401933 331934 32 34.

21935 33 34.

21936 32 341937 36 33.

21938 33 33.

21939 34 33.

633.9

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

34.15

1950 10 16.62

41 35.6

35.3

34.55

1951 30.8 19.62

33 35.6

34.8

34.55

Year Snowf 5 10 20 Lake 5 10 20 Ice 5 10 20

20

all year year year Erie Temp

year

year

year Coverage %

year

year

year

1952 20.5 21.26

35 35.6

34.3

34.3

1953 26 19.36

37 36.2

34.8

34.5

1954 17.5 20.96

35 36.2

35.1

34.65

1955 23.5 23.66

20.14

36 35.2

35.4

34.8

1956 18 21.1 20.36

33 35.2

35.4

34.85

1957 43.5 25.7 23.48

37 35.6

35.6

34.9

1958 26.3 25.76

22.56

39 36 36.1

35.2

1959 38.5 29.96

25.46

32 35.4

35.8

35.1

1960 31.3 31.52

27.59

36 35.4

35.3

35.3

1961 17.1 31.34

26.22

32 35.2

35.2

35

1962 9.7 24.58

25.14

33 34.4

35 34.65

1963 21 23.52

24.64

32 33 34.5

34.65

1964 35.2 22.86

26.41

32 33 34.2

34.65

1965 38.7 24.34

27.93

24.035

36 33 34.2

34.8

1966 46.4 30.2 30.77

25.565

37 34 34.6

35

1967 13.3 30.92

27.75

25.615

34 34.2

34.3

34.95

1968 36 33.92

28.72

25.64 35 34.8

33.9

35

1969 40.2 34.92

28.89

27.175

32 34.8

33.9

34.85

1970 29.2 33.02

28.68

28.135

33 34.2

33.6

34.45

1971 31.5 30.04

30.12

28.17 35 33.8

33.9

34.55

Year Snowfall

5 year

10 year

20 year

Lake Erie

5 yea

10 yea

20 year

Ice Coverag

5 yea

10 yea

20 year

21

Temp

r r e % r r

1972 28.1 33 31.96

28.55 39 34.8

34.5

34.75

1973 15.7 28.94

31.43

28.035

37 35.2

35 34.75

95

1974 17 24.3 29.61

28.01 35 35.8

35.3

34.75

89

1975 13 21.06

27.04

27.485

37 36.6

35.4

34.8 80

1976 49 24.56

27.3 29.035

38 37.2

35.5

35.05

95

1977 42.5 27.44

30.22

28.985

32 35.8

35.3

34.8 100 91.8

1978 57.1 35.72

32.33

30.525

34 35.2

35.2

34.55

100 92.8

1979 27.7 37.86

31.08

29.985

35 35.2

35.5

34.7 100 95

1980 11.1 37.48

29.27

28.975

39 35.6

36.1

34.85

94 97.8

1981 32.2 34.12

29.34

29.73 32 34.4

35.8

34.85

96 98

1982 38.7 33.36

30.4 31.18 33 34.6

35.2

34.85

99 97.8

94.8

1983 13.5 24.64

30.18

30.805

41 36 35.6

35.3 41 86 89.4

1984 16 22.3 30.08

29.845

32 35.4

35.3

35.3 95 85 90

1985 34.5 26.98

32.23

29.635

41 35.8

35.7

35.55

96 85.4

91.6

1986 25.7 25.68

29.9 28.6 33 36 35.2

35.35

95 85.2

91.6

1987 17.6 21.46

27.41

28.815

40 37.4

36 35.65

88 83 90.4

1988 22.9 23.34

23.99

28.16 38 36.8

36.4

35.8 91 93 89.5

1989 7.1 21.56

21.93

26.505

34 37.2

36.3

35.9 95 93 89

1990 12.9 17.24

22.11

25.69 33 35.6

35.7

35.9 86 91 88.2

1991 19.7 16.04

20.86

25.1 39 36.8

36.4

36.1 45 81 83.1

1992 30.1 18.54

20 25.2 35 35.8

36.6

35.9 94 82.2

82.6

88.7

Year Snowfall

5 year

10 year

20 year

Lake Erie Temp

5 year

10 year

20 year

Ice Coverage %

5 year

10 year

20 year

22

1993 20.5 18.06

20.7 25.44 33 34.8

35.8

35.7 95 83 88 88.7

1994 34.6 23.56

22.56

26.32 32 34.4

35.8

35.55

100 84 88.5

89.25

1995 22.4 25.46

21.35

26.79 40 35.8

35.7

35.7 96 86 88.5

90.05

1996 30.1 27.54

21.79

25.845

32 34.4

35.6

35.4 100 97 89 90.3

1997 23.6 26.24

22.39

24.9 35 34.4

35.1

35.55

100 98.2

90.2

90.3

1998 8.1 23.76

20.91

22.45 36 35 34.9

35.65

7 80.6

81.8

85.65

1999 42.6 25.36

24.46

23.195

37 36 35.2

35.75

76 75.8

79.9

84.45

2000 37.1 28.3 26.88

24.495

36 35.2

35.5

35.6 91 74.8

80.4

84.3

2001 25.3 27.34

27.44

24.15 32 35.2

34.8

35.6 94 73.6

85.3

84.2

2002 6.8 23.98

25.11

22.555

35 35.2

34.8

35.7 13 56.2

77.2

79.9

2003 67.6 35.88

29.82

25.26 34 34.8

34.9

35.35

95 73.8

77.2

82.6

2004 51.6 37.68

31.52

27.04 37 34.8

35.4

35.6 94 77.4

76.6

82.55

2005 35.5 37.36

32.83

27.09 35 34.6

34.9

35.3 92 77.6

76.2

82.35

2006 10.3 34.36

30.85

26.32 34 35 35.1

35.35

21 63 68.3

78.65

2007 49.4 42.88

33.43

27.91 43 36.6

35.9

35.5 95 79.4

67.8

79

2008 38.1 36.98

36.43

28.67 37 37.2

36 35.45

93 79 76.4

79.1

2009 58.9 38.44

38.06

31.26 35 36.8

35.8

35.5 95 79.2

78.3

79.1

2010 50.9 41.52

39.44

33.16 36 37 35.8

35.65

93 79.4

78.5

79.45

2011 49.7 49.4 41.88

34.66 33 36.8

35.9

35.35

95 94.2

78.6

81.95

2012 28.4 45.2 44.04

34.575

39 36 36.3

35.55

13 77.8

78.6

77.9

2013 27.2 43.02

40 34.91 38 36.2

36.7

35.8 84 76 77.5

77.35

2014 50.2 41.28

39.86

35.69

23

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.

24

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.

25

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.

26

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.

27

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.

28

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.

29

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.

30

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 YearFive Year Rolling

AverageTen Year Rolling

AverageTwenty Year Rolling

Average

Average35.16666667 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

31

Year by YearFive Year Rolling

AverageTen Year Rolling

AverageTwenty Year Rolling

Average

Average83.31707317 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 (%)

32

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).

33

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.

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 19782 1989 1946 1992 1966 20093 1998 1984 1996 1976 20034 1949 1974 1951 20075 1962 1961 1960 20116 1950 1954 1971 20147 2006 1987 1981 20108 1980 1956 1985 20049 1947 1991 1994

10 1990 1952 196411 1975 1993 194812 1967 1963 200513 1983 1995 196814 1988 200015 1955 200816 1997 195917 2001 196518 1986 198219 1953 196920 1958 197721 2013 199922 197923 197224 2012

1976.769 1971.462 1975.769 1993.125 1996.667

Standard Deviation Distribution Year by Year Snowfall (in)

Average Year

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

34

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.

35

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 20122 1950 1952 1959 2005 20113 1990 1987 1971 19804 1993 1989 1966 20045 1992 1984 1967 19796 1953 1964 1961 20097 1951 1988 1960 20148 1954 1963 1972 20109 1975 1994 1970 2007

10 1955 1982 201311 1998 196812 2002 198113 1974 200614 1965 196915 1976 197816 1962 200317 198318 199919 199520 198621 195722 195823 199724 198525 200126 197727 199628 200029

1972.111 1980.107 1972 2002.9 2011.5

Standard Deviation Distribution Five Year Rolling Average Snowfall (in)

Average Year

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

36

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.

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 20142 1955 1988 1968 2009 20133 1956 1999 1969 2010 20114 1993 1963 1980 20125 1991 2002 19816 1998 1962 19747 1995 1959 20038 1996 1961 19869 1989 1964 1984

10 1990 2000 197111 1997 1975 198312 1994 1976 197713 1958 1987 198214 2001 196615 1960 200616 1967 197917 1965 197318 200419 197220 198521 197822 200523 2007242526272829

1984.923 1975.647 1979.077 2009 2012.5

Standard Deviation Distribution Ten Year Rolling Average Snowfall (in)

Average Year

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

37

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.

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 20122 2002 1991 1969 2009 20113 1999 1992 1975 2010 20134 1965 2003 2007 20145 2001 1993 19746 2000 1966 19737 1967 19708 1968 19889 1990 1971

10 1996 197211 1994 198612 2006 200813 1989 198714 1995 198015 2004 197716 2005 197617 198518 198119 198420 197921 197822 198323242526272829

Average Year 1994.167 1991 1983.462 2000.333 2012.5

Standard Deviation Distribution Twenty Year Rolling Average Snowfall (in)

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.

38

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 19502 1936 1931 1955 1976 19833 1940 1933 1960 1988 19854 1943 1935 1965 2013 20075 1944 1938 1998 19586 1959 1945 2000 19727 1961 1946 2010 19808 1963 1951 1953 19919 1964 1956 1957 2012

10 1969 1962 1966 193211 1977 1970 1973 194212 1981 1982 1975 198713 1984 1986 1999 199514 1994 1990 200415 1996 1993 200816 2001 201117 193918 194819 196720 197821 198922 200323 200624 194725 194926 195227 195428 196829 197130 197431 197932 199233 199734 200235 200536 2009

1958.077 1951.154 1972.923 1975.923 1981.25

Standard Deviation Distribution Year by Year Lake Erie Temperature (Degrees F)

Average Year

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

39

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.

40

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 19872 1963 1935 1955 20073 1964 1967 1956 19884 1965 1970 1961 19915 1937 1962 1973 20096 1938 1981 1978 20117 1940 1994 1979 20108 1948 1996 2000 19769 1939 1997 2001 1989

10 1971 1941 2002 200811 1936 1982 194212 1946 2005 195913 1949 1944 196014 1966 1968 198415 1969 195016 1972 195117 1993 195218 2003 195719 2004 198020 1945 199021 1998 197422 2006 197723 198524 199225 199526 195827 198328 198629 199930 201231 195332 195433 2013343536

1949.462 1969.846 1969.923 1996.4 1987

Standard Deviation Distribution Five Year Rolling Average Erie Temperature (Degrees F)

Average Year

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

41

each standard deviation range in order from least to greatest are as follows, 1949,

1969, 1969, 1996, and 1987.

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 20132 1940 1963 1997 20113 1939 1972 2006 19874 1968 1966 1961 20085 1969 1951 1978 19586 1971 1953 1982 19807 1943 2001 1986 19898 1944 2002 1999 20129 1945 1998 1950 1988

10 1947 2003 1960 199111 1964 2005 1974 199212 1965 1962 197713 1941 1973 198414 1942 195515 1946 195616 1948 197517 1952 200418 1967 197619 197920 200021 195722 198323 199624 198525 199026 199527 195928 198129 199330 199431 200932 201033343536

1954.308 1976.769 1977.538 1993 2013

Standard Deviation Distribution Ten Year Rolling Average Lake Erie Temperature (Degrees F)

Average Year

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

42

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 19872 1952 1979 1960 19983 1970 1972 1983 20104 1953 1973 1984 19935 1950 1974 2005 19956 1951 1955 1986 20027 1971 1965 2003 19998 1978 1975 2006 19889 1954 1977 2011 2013

10 1962 1956 1996 198911 1963 1969 2008 199012 1980 2007 199213 1981 2009 199114 1982 198515 1957 199416 1967 199717 1961 201218 1966 200019 1968 200120 1976 200421 1959222324252627282930313233343536

Average Year 1959.364 1970.769 1993.538 1995.923 #DIV/0!

Standard Deviation Distribution Twenty Year Rolling Average Lake Erie Temperature (Degrees F)

43

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 20132 2002 1991 1975 19903 2012 19874 2006 19745 19886 20007 20058 20089 2010

10 198011 199212 200113 200414 197315 197616 198417 198618 198919 199320 200321 200722 200923 201124 198125 198526 199527 198228 197729 197830 197931 199432 199633 1997

2004.5 1987 1987 1996.308 #DIV/0! #DIV/0!

Standard Deviation Distribution Year by Year Ice Coverage (%)

Average Year

44

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 20112 2006 2003 1999 1984 19793 2013 1986 19964 2004 1985 19805 2005 1983 19826 2012 1995 19817 2008 1990 19978 2009 19779 2007 1978

10 2010 198811 1998 198912 199113 199214 198715 1993161718192021222324252627282930313233

Average Year 2004 2002 2003.692 1986.273 1989.429 #DIV/0!

Standard Deviation Distribution Five Year Rolling Average Ice Coverage (%)

45

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.

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 19972 2006 2004 1993 19873 2002 1990 19854 2003 1994 19865 2013 1995 19826 2009 19897 2010 19968 2011 19839 2012 1988

10 1999 198411 200012 199813 199214 199115161718192021222324252627282930313233

Average Year 2006.5 2005 2004.692 1991.3 1987.4 #DIV/0!

Standard Deviation Distribution Ten Year Rolling Average Ice Coverage (%)

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.

46

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 19922 2006 2008 2000 19933 2009 1999 19944 2010 1998 19955 2002 19966 2011 19977 20058 20049 2003

101112131415161718192021222324252627282930313233

Average Year 2013 2009 2006.556 1999.5 1994.5 #DIV/0!

Standard Deviation Distribution Twenty Year Rolling Average Ice Coverage (%)

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.

47

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.

48

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

49

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.

50

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52