This document summarizes an experiment that analyzed the relationship between annual increases in carbon dioxide levels in Mauna Loa, Hawaii from 1959-2011 and the frequency of major hurricanes in the Pacific Ocean from 1992-2009. The experiment found a negative correlation between the two variables, which did not support the hypothesis that higher carbon dioxide levels would increase hurricane frequency. Methodology included formatting data sets into a spreadsheet, graphing the variables, and determining the correlation coefficient and coefficient of determination to analyze the strength of the relationship between carbon dioxide increases and hurricane frequency.

1. The Annual Increase of Carbon Dioxide Levels in Mauna Loa, Hawaii in relation to the
Frequency of Major Hurricanes in the Pacific Ocean (1992- 2009)
Laura Rook
Fort Hays State University
January 11, 2013

2. Abstract
An experiment was performed to determine the correlation between the annual increase
of carbon dioxide in Mauna Loa, Hawaii and the frequency of major hurricanes in the Pacific
Ocean. The relationship between the two variables is important because it could allow scientists
to better predict when a major hurricane might occur. To determine the relationship between
carbon dioxide and major hurricanes, two sets of data were used. The first data set used
contained the number of hurricanes in the Pacific Ocean from the 1800s to 2011. The data set
was obtained from the National Oceanic and Atmospheric Administration and the Central Pacific
Hurricane Center. The second data set used contained carbon dioxide levels collected in Mauna
Loa, Hawaii from1959-2011. The data set was obtained from the National Climate Data Center
and the National Oceanic and Atmosphere Administration. The data was analyzed for the years
1992-2009 by determining a correlation coefficient as well as a coefficient of determination. It
was found that there is a negative correlation between the two variables which does not agree
with the hypothesis.

3. Introduction
The question of this project addresses how the annual increase of carbon dioxide affects
the frequency of major hurricanes in the Pacific Ocean. The level of carbon dioxide in the
atmosphere has increased by 35% over the last 150 years alone (Justus, Fletcher, 2001). The
objective of this project is to determine the correlation between the annual increase of carbon
dioxide in Mauna Loa, Hawaii and the frequency of major hurricanes in the Pacific Ocean from
1992 to 2009.
Topics similar to this project have been addressed before but the results were
inconclusive. In the past, scientists have researched whether global warming is a possible source
for an increase in hurricane activity in the Atlantic Ocean. However, the results varied.
According to a report discussing the causes and implications of an increase in Atlantic hurricane
activity, some scientists found an increase in hurricane activity due to global warming while
others found that there was a decrease in hurricane activity (Goldenberg, S. B., Landsea, C. W.,
Mestas-Nuñez, A. M., & Gray, W. M. (2001).
Carbon dioxide is a major component of global warming (Justus, Fletcher, 2001). Human
activities, such as the burning of fossil fuels, have contributed to increased atmospheric carbon
dioxide (CO2) and other trace greenhouse gases. If these gases continue to increase in the
atmosphere at the rate they are going, scientists believe the effects of global warming would
increase significantly through the Earth’s natural heat-trapping “greenhouse effect.” (Justus,

4. Fletcher, 2001). The increasing levels of carbon dioxide in the Earth’s atmosphere can also cause
an increase in precipitation and surface temperature of the oceans. (Andrews, Timothy, Forster,
Piers M, 2010). Global warming is also causing an underwater heat wave which in turn is
causing ocean temperatures to rise (House of Representatives, 2010). As the temperature of
water increases, it expands. During the last 40 years, this expansion has contributed to 25 percent
of the rising sea level (House of Representatives, 2010). Global warming is causing the sea level
to rise because increasing temperatures are melting ice in the ocean such as glaciers and ice
sheets (Solomon, S., Plattner, G.-K., Knutti, R., & Friedlingstein, P. (2009). A recent study used
current ice discharge data to propose that melting ice sheets will contribute to the rising sea level
by 1-2 meters by 2100 (Solomon, S., Plattner, G.-K., Knutti, R., & Friedlingstein, P. (2009).
Rising sea levels also pose a threat to coastal communities and wildlife by increasing the
vulnerability of tropical storms (House of Representatives, 2010).This is crucial to the
experiment considering that the primary controlling factor for tropical cyclone formation is sea
surface temperature (Anthes, Corell, Holland, Hurrell, MacCracken, Trenberth, 2006). As sea
surface temperatures are increasing, the likelihood of a hurricane increases because warm ocean
water of 82°F provides ample moisture and water vapor to the atmosphere to drive the hurricane
engine (US Department of Commerce, NOAA).
A number of scientists speculate that human activities, which have increased the level of
carbon dioxide (CO2) in the atmosphere by 35% over the last 150 years, are causing an increase
in global temperatures (Justus, Fletcher, 2001). Global temperatures have risen 0.6 C in the last
100 years, and are estimated to rise anywhere from 1.8 C to 7.1C over the next 100 years
(Justus, Fletcher, 2001). The relationships between tropical cyclones and global climate change
are scientifically complex, with great implications for society (Anthes, Corell, Holland, Hurrell,

5. MacCracken, Trenberth, 2006). An example of this is how a minimal rise in sea level can have
catastrophic consequences when major storms strike (Anthes, Corell, Holland, Hurrell,
MacCracken, Trenberth, 2006). Global-mean precipitation is an important part of the Earth’s
climate system. When the Earth’s global-mean precipitation changes, it can affect many aspects
of the climate system such as surface temperature of the ocean. When the surface temperature
changes, it can cause changes in water vapor, clouds, atmospheric stability and lapse rates. Thus,
changes in surface temperature can influence precipitation processes and lead to changes in
precipitation as well (Andrews, Forster, 2010). Changes in tropical cyclone activity are among
the more consequential results of global climate change. Storm intensity generally increases with
global warming but the results vary in different areas. Storm frequency tends to decreases in the
Southern Hemisphere and North Indian Ocean, but increases in the western North Pacific
(Emanuel, K., Sundararajan, R., & Williams, J. (2008). The five year time period from 1995 to
2000 had the highest level of hurricane activity on record in the North Atlantic Ocean
(Goldenberg, S. B., Landsea, C. W., Mestas-Nuñez, A. M., & Gray, W. M. (2001). The high
level of hurricane activity during the five year time period was the result of increases of North
Atlantic sea surface temperatures and decreases in vertical wind shear (Goldenberg, S. B.,
Landsea, C. W., Mestas-Nuñez, A. M., & Gray, W. M. (2001). The high level of hurricane
activity is expected to continue for another 10 to 40 years (Goldenberg, S. B., Landsea, C. W.,
Mestas-Nuñez, A. M., & Gray, W. M. (2001).
Methodology
As carbon dioxide levels increase in Mauna Loa, Hawaii (Figure 1), the frequency of
major hurricanes in the Pacific Ocean will also increase. This hypothesis was based on the fact

6. that carbon dioxide is a main component in global warming (Justus, Fletcher, 2001), and the
assumption that the effects of global warming have cause sea surface temperatures to increase.
Since the primary controlling factor for tropical cyclone formation is sea surface temperature
(Anthes, Corell, Holland, Hurrell, MacCracken, Trenberth, 2006), if the sea surface temperature
increased, so would the likelihood of a hurricane. The likelihood of a hurricane would increase
because warm ocean water of 82°F provides ample moisture and water vapor to the atmosphere
to drive the hurricane engine (US Department of Commerce, NOAA). Thus, as carbon dioxide
levels increase, the frequency of major hurricanes will also increase.
The hypothesis was first tested by formatting the two data sets into a Microsoft Excel
spreadsheet. From there the data sets were graphed as shown in Figure 1 and Figure 2, with the
year placed on the X-axis, and the increase of carbon dioxide along with the number of major
hurricanes on the Y-axis. The correlation between the two variables was determined by finding
the correlation coefficient, the coefficient of determination, and a critical value. A correlation
coefficient measures the strength and relationship between two variables (Bluman, A. G. (1995).
Possible values for a correlation coefficient range from negative one to positive one. A value of
negative one represents a strong negative linear correlation between the two variables whereas a
value of positive one represents a strong positive correlation between the two variables (Bluman,
A. G (1995). When there is a weak relationship between the variables the correlation coefficient
will be closer to zero (Bluman, A. G (1995). The symbol r stands for the correlation coefficient
and the coefficient of determination is represented by the symbol r2 (Cox, D. R., & Wermuth, N.
(1992). In order to find the critical value, the coefficient of determination value was multiplied
by 100. If the critical value was found to have a value of ninety five percent or greater than the
critical value was considered significant. However, if the critical value was lower than ninety

7. five percent then the critical value was not significant. If the value of the correlation coefficient
was found to be significant then the next step in analyzing the data was to find the equation of
the regression line. The regression line is the data’s line of best fit (Bluman, A. G. (1995).
Determining the equation of the regression line helps the researcher to see trends in the data and
make predictions upon the data (Bluman, A. G. (1995).
For this project, two data sets were used. The first was a table containing the number of
hurricanes in the Pacific Ocean for the years 1992 to 2009. The table provided the date and time
of the storm, latitude and longitude, and pressure and wind speed. The table also identified what
category the hurricane was classified as. For this project only hurricanes that reached a wind
speed of 100 mph or greater were classified as major hurricanes. The Saffir-Simpson scale for
categorizing hurricane wind speed and damage potential is being used more and more by
hurricane forecasters. The hurricane categories in the scale are associated by 1-min wind speeds
(Simiu, E., Vickery, P., & Kareem, A. (2007). According to the Saffir- Simpson Hurricane Wind
Scale, hurricanes reaching Category 3 and higher are considered major hurricanes because of
their potential for significant loss of life and damage. However, category 1 and 2 hurricanes are
still very dangerous. Hurricanes with a wind speed of 96 to 110 mph are classified as category 2
hurricanes (NOAA). Both data sets were found on the National Climatic Data Center online
database as well as National Oceanic and Atmospheric Administration and the Central Pacific
Hurricane Center online database.
The first step in processing the data was to format each data set into Microsoft Excel.
This was accomplished my manually typing each data set into an excel spreadsheet. Once both
sets of data were formatted into excel spreadsheets, the data was graphed to provide a visual aide
of the relationship between the two variables during the given time period (Figure 2). The type of

8. graph that was used was a scatter plot. A scatter plot was chosen because it best represented the
data. The next step in analyzing the data was to determine a correlation coefficient, a coefficient
of determination, and a critical value. The correlation coefficient was determined by using the
CORREL function in Microsoft Excel to find out if the correlation between the annual increase
of carbon dioxide and major hurricanes was positive or negative. The coefficient of
determination was found by squaring the correlation coefficient. The coefficient of determination
was needed to figure the percent of variability on how the frequency of major hurricanes in the
Pacific Ocean was affected by the annual mean increase of carbon dioxide in Mauna Loa,
Hawaii. The critical value was determined by multiplying the coefficient of determination by one
hundred to see whether or not there was a significant correlation.
Results
After processing and analyzing the data, the correlation coefficient between the two data
sets was found to be -0.1429. The correlation coefficient of -0.1429 produced a coefficient of
determination of 0.0204 and a critical value of 2.042%. There were multiple errors within the
data sets for the project. For the hurricane data, these errors included the accuracy of pressure
and wind speed. There were errors within the accuracy of pressure and wind speed because it
may be difficult to measure the exact pressure and wind speed at a specific time. Also, an error in
this data set is the number of hurricanes monitored. There is an error in the number of monitored
hurricanes because if a hurricane was not seen on radar, it would have not been included in the
data set. Also, hurricanes that did not reach a landmass may not have been reported and thus not
included in the data set. There was also an error within the carbon dioxide data however the data
collected from Mauna Loa gives the uncertainty within the data set. The uncertainty of the

9. annual increase of carbon dioxide in Mauna Loa, Hawaii for each year from 1992 to 2009 is
0.11.
Conclusions and Discussions
The objective of the project was successfully accomplished. The relationship between the
annual increase of carbon dioxide in Mauna Loa, Hawaii and the frequency of major hurricanes
in the Pacific Ocean from 1992 to 2009 was found. The hypothesis stated that as the annual level
of carbon dioxide increases in Mauna Loa, Hawaii, the frequency of major hurricanes in the
Pacific Ocean will also increase. The hypothesis predicted that there would be a positive
correlation between the two variables. However, the hypothesis was found to be incorrect .The
correlation coefficient was found to be -0.1429 which means that the variables had a negative
correlation, not a positive correlation. The negative correlation means that as carbon dioxide
levels are increasing, the frequency of major hurricanes is decreasing. The correlation coefficient
also falls below the critical value which was 2.042%. The research is limited by the number of
major hurricanes that have occurred between 1992 and 2009 in the Pacific Ocean. There were
only a total of fourteen major hurricanes during this time period and there were some years
where no hurricanes occurred.
There are many ways that the research for this project could be extended. First, hurricane
data from the Atlantic Ocean could be used as well as data from the Pacific Ocean. Using data
from both the Atlantic and Pacific Ocean would mean more hurricanes, which would allow the
research of this project to continue. While the results of this project showed that the correlation
between carbon dioxide and hurricanes in the pacific was negative, there may be a different
correlation between carbon dioxide and hurricanes in the Atlantic Ocean. Secondly, carbon
dioxide data collected from regions other than Mauna Loa, Hawaii could be used. This data

10. could be used instead of the Mauna Loa, Hawaii data or along with the Mauna Loa data. Carbon
dioxide trends could be different depending on the area where the carbon dioxide data was
collected. Carbon dioxide levels in one region might increase while carbon dioxide levels in
another region may decrease. Also, the levels of carbon dioxide in one area might not be as high
as carbon dioxide levels in another area. Another factor that could extend the research of this
project would be to add in more variables to the project. Possible variables that could extend the
research of this project might be precipitation and sea surface temperature. If precipitation and
sea surface temperature were accounted for in the project, scientists would be able to narrow
down exactly what factors affect the frequency of major hurricanes. This could be done by
finding the relationships between each variable and the frequency of major hurricanes. If a
variable was found to have no affect on the frequency of major hurricanes, this would lead
scientists in the right direction to find exactly what variables do affect the frequency of major
hurricanes. Both the precipitation data as well as sea surface temperature data could be found
through the National Climatic Data Center. Also, a possibility for extending the research of this
project would be to change the question of the project entirely. Instead of only focusing on the
relationship between carbon dioxide and major hurricanes, researchers could investigate the
relationship between all tropical systems and carbon dioxide. All tropical systems would include;
hurricanes, tropical depressions, and tropical storms. The results of researching the correlation
between all tropical systems and the annual increase on carbon dioxide could produce an entirely
different correlation all together. By expanding major hurricanes into all tropical systems could
result in a positive correlation rather than the negative correlation produced in the original
experiment. Another possibility for extending the research of this project would be to use a
broader range of data instead of only using data from 1992 until 2009. Using a broader range of

11. data would allow researchers to look at trends from tropical systems and carbon dioxide in the
past that could be used to predict trends in the future. Thus, using more data in the project could
produce different results than what was originally concluded in this research project.

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14. 0
0.5
1
1.5
2
2.5
3
3.5
1990 1995 2000 2005 2010
AnnualIncreaseofCarbonDioxideand#of
MajorHurricanes
Year
# of Hurricanes
Annual Increase of Carbon
Dioxide (ppm)
y = -0.0041x + 9.0358
R² = 0.0005
y = 0.0406x - 79.397
R² = 0.1341
0
0.5
1
1.5
2
2.5
3
3.5
1990 1995 2000 2005 2010
AnnualIncreaseofCarbonDioxideand#of
MajorHurricanes
Year
# of Hurricanes
Annual Increase of Carbon
Dioxide (ppm)
Linear (# of Hurricanes)
Linear (Annual Increase of
Carbon Dioxide (ppm))