GLOBAL WARMING AND HURRICANE CORRELATION

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GLOBAL WARMING AND HURRICANE CORRELATION

• BY THE SHARK TEAM

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Null Hypothesis

• There Is No Correlation Between Global Warming
  And Hurricane Frequency And Intensity

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Global Warming Indicator

• Average global temperature deviation data from
  1899 until present is used as the global warming
  indicator in all correlations and statistical
  analysis.
• We consider the signature of global warming to be
  present in the temperature data from 1973 until
  2005.

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Hurricane Frequency

• The data shows a relative steady frequency of
  hurricanes until a distinct increase in the last
  decade

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Hurricane Pressure

• and also a substantial decrease in the average
  hurricane pressure

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Analysis Procedures

• ANOVA between the last two decades of hurricane
  frequency looking for a significant difference
• Correlation between average global temperature
  deviation from 1899 and hurricane frequency
• Correlation between temperature from 1987 until
  present with the hurricane frequency

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This graph shows

• Average hurricane strength as measured by
  category has not changed much over this time
  span.
• However, there is a sharp increase in hurricane
  frequency after 1994 after a long period of
  downward trend.

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ANOVA analysis

• We attempted to quantify this change in frequency
  using an ANOVA between the years 82-94 and
  95-05.

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ANOVA Results

• 82-94 yields an average of 3.6 hurricanes per
  year. 95-05 has an average of 7.85.
• The difference in means was significant with
  p.017.

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ANOVA interpretation

• This means that there is a significant change in
  frequency in the last 10 years compared to the
  previous 10.

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Regression

• Since we are using global mean temperature as our
  measure of global warming, it seems logical to
  look for a correlation between the temperature
  and hurricane frequency.

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Regression Analysis

• To this end, we ran two regressions.
• The first was for the all the data, the second
  was from 73 on.

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First regression

• The first regression yielded an F-score of 39.1
  with 105 degrees of freedom. This yields a
  p-value of 9e-9, which is very highly
  significant.

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But

• Obviously, there are residuals about the linear
  fit that are non-random, especially a clump
  around 0 on the X-axis.

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Explanation

• If you look at the first graph, we can see that
  hurricane frequency has a peak that corresponds
  with about a fifteen year lag behind the global
  temperature.

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More explanation

• This lag means that for any change in our X value
  (temperature), there will be a time of about 15
  years before our Y values change, which will
  cause a clump in the data.

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This means

• There is about a fifteen year lag behind the
  global warming signal.
• Which means that the system hasnt fully
  responded to the increase in global temperature.

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More meaning

• The data shows an approximately stable slope in
  temperature increase over the last 20 years.
  Running a regression with hurricane frequency
  should yield a good linear model with some
  predictive power for future hurricane frequency
  for the next 15 years.

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87 on Temp/Freq. Reg.
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Prediction

• Using a least squares fit the the temperature
  data from 73-05, we get a prediction of .77
  degrees from the 1899 mean temp and a prediction
  of about 15 hurricanes for 2020 up from 14 in
  2005.

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THE END

• In conclusion, the analysis of the hurricane data
  and global temperatureallows us to reject our
  null hypothesis that the two variables arent
  correlated.

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But

• The correlations also have a high standard errors
  in our slope which when factored in give a 95
  confidence interval for hurricane frequency of
  15 to 52. Obviously, this range is not physical,
  which leads us to conclude

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More but

• 1) We used a poor proxy for global warming
• or
• 2) There hasnt been enough time for the last
  uptick of temperature to show in the frequency of
  hurricanes.