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  • Irish Chronicles Document Links Between Volcanoes and Weather
    A study of over 40,000 written entries in Irish Annals and ice core measurements shows a strong corr...Read more

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    Irish Chronicles Document Links Between Volcanoes and Weather

    A study of over 40,000 written entries in Irish Annals and ice core measurements shows a strong correlation between the occurrence of volcanic eruptions and extreme cold weather in Ireland over a 1200 year period. Data analyzed in this study cover the period from 431 to 1649, during which time up to 48 volcanic eruptions are identified in Greenland ice core records through deposition of volcanic sulfate in annual layers of ice. You can find the study (open access), published on 6 June 2013 in IOP Publishing's journal Environmental Research Letters, at http://iopscience.iop.org/1748-9326/8/2/024035/article. Find out more about how volcanoes can influence climate.
  • EF-5 Tornado in El Reno, Oklahoma Widest Ever Recorded in US
    The EF-5 tornado that hit El Reno, Oklahoma on May 31st was the widest ever recorded in the US, acco...Read more

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    EF-5 Tornado in El Reno, Oklahoma Widest Ever Recorded in US

    The EF-5 tornado that hit El Reno, Oklahoma on May 31st was the widest ever recorded in the US, according to the National Weather Service in Norman Oklahoma. The tornado, which remained on the ground for 40 minutes and reached 2.6 miles across (4.2 km), took the lives of 18 people including storm chasers Tim Samaras, Paul Samaras and Carl Young. For more information on the tornado, visit http://ow.ly/i/2hfDG.
  • Atmospheric CO2 Level Tops 400 ppm
    During the week of May 13th, the CO2 level at the Mauna Loa Observatory in Hawaii topped 400 ppm rep...Read more

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    Atmospheric CO2 Level Tops 400 ppm

    During the week of May 13th, the CO2 level at the Mauna Loa Observatory in Hawaii topped 400 ppm repeatedly. Daily levels of CO2 can vary due to weather, and there are seasonal trends as well. The level of atmospheric greenhouse gases continues to increase, now over 120 ppm since the Industrial Revolution began. For more on the Keeling Curve, see http://keelingcurve.ucsd.edu/. Find out more about greenhouse gases and warming.
One might wonder if the time spent studying for a test affects test score. These two variables would probably be positively related. As time studying for a test goes up, test score probably goes up too.
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Correlations in Science

It's important in their work for scientists to know if two sets of data (or variables) are related to each other. For example, you might wonder if the amount of time a student spends reading the Windows to the Universe website is related to the grade that student gets in his or her science classes. How would you test this? How would you express your results in a way that would clearly tell other people what sort of relationship there is between these two variables?

One of the most common ways a scientist does this is by noting correlation. Correlation tells if two different variables vary together - that is, if one goes up, does the other one also go up? Correlation is usually calculated using a formula that results in a number ranging from -1 to 1.

A correlation of -1 says that the two variables are inversely related. As one variable increases the other always decreases. For example, the total sales in a given day for an ice cream truck and the total snowfall for that same day might have a correlation close to -1. On days with lots of snow, not many people are buying ice cream from the truck. On days where the ice cream truck’s sales are really high, it’s probably not snowing.

A correlation of 1 says that the two variables are directly related. That is as one variable goes up the other does also. For example, the amount of time a basketball player spends practicing is usually closely related to the number of points he or she scores in games. This correlation would be close to 1.

Many times a correlation will be close to 0. This means that there is no obvious relationship between the two variables.

It’s important to remember that even when two variables are correlated, this does not mean that a change in one variable causes the other one to change. It just means they’re related. For instance, when it’s raining you can see people using umbrellas a lot more often, and you can see cars using their wipers a lot more often. So umbrella use and windshield wiper use are correlated, but neither causes the other. We don’t use umbrellas because other people are using wipers, or vice versa. We use both because it’s raining.

Last modified January 25, 2008 by Jennifer Bergman.

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