Climate Change and the Earth’s Magnetic Poles,
A Possible Connection
Author: Kerton, Adrian K.
Source: Energy & Environment, Volume 20, Numbers 1-2, January 2009 , pp. 75-83(9)
Publisher: Multi-Science Publishing Co Ltd
Abstract:
Many natural mechanisms have been proposed for climate change during the past millennia, however, none of these appears to have accounted for the change in global temperature seen over the second half of the last century. As such the rise in temperature has been attributed to man made mechanisms. Analysis of the movement of the Earth’s magnetic poles over the last 105 years demonstrates strong correlations between the position of the north magnetic, and geomagnetic poles, and both northern hemisphere and global temperatures. Although these correlations are surprising, a statistical analysis shows there is a less than one percent chance they are random, but it is not clear how movements of the poles affect climate. Links between changes in the Earth’s magnetic field and climate change, have been proposed previously although the exact mechanism is disputed. These include: The Earth’s magnetic field affects the energy transfer rates from the solar wind to the Earth’s atmosphere which in turn affects the North Atlantic Oscillation. Movement of the poles changes the geographic distribution of galactic and solar cosmic rays, moving them to particularly climate sensitive areas. Changes in distribution of ultraviolet rays resulting from the movement of the magnetic field, may result in increases in the death rates of carbon sinking oceanic plant life such as phytoplankton.
Keywords: MAGNETIC POLES; DRIFT; CLIMATE; COSMIC RAYS
Document Type: Research article
DOI: 10.1260/095830509787689286
Although correlation does not prove cause, there is a significant body of evidence in paleomagnetic studies linking aspects of the Earth’s magnetic field with climate.
Here are 3 of the graphs from the paper which you can download as a pdf below. I would be grateful for any comments.
Download pdf E-E_Clr_Abstracts
I am indebted to Professor Jan Veizer for his help and guidance in writing my first scientific paper.
Since the paper was published I am indebted to Dr. Simon Bray who ran the Spearman Pearson Product Moment test on my correlations.
Dr Simon Bray, Senior Teaching Fellow, School of Biological Sciences, Visiting Researcher
Centre for Environmental Sciences, School of Civil Engineering and the Environment
University of Southampton
1. Northern Hemisphere N. Pole Deviations 1. For correlation between Lat North and Anomaly, values are: R = 0.794. P = <0.001 (Highly significant). Data were normal – correlation was Pearson Product Moment method. Polynomial regression at 2nd order gave Rsq value of 0.7024 – see Figure 1
2. For correlation between Long West and Anomaly, values are:
R = 0.839. P = <0.001 (Highly significant). Data were normal – correlation was Pearson Product Moment method.
Polynomial regression at 2nd order gave Rsq value of 0.708 – see Figure 2
3. For correlation between Long West normalised and Anomaly, values are:
R = 0.839. P = <0.001 (Highly significant). Data were normal – correlation was Pearson Product Moment method.
Polynomial regression at 2nd order gave Rsq value of 0.708 – see Figure 3
Note same values for regression and correlation as unnormalised – see fig 2.
4. For correlation between Lat North normalised and Anomaly, values are:
R = 0.792. P = <0.001 (Highly significant). Data were normal – correlation was Pearson Product Moment method.
Polynomial regression at 2nd order gave Rsq value of 0.698 – see Figure 4
Note values for regression and correlation very similar to unnormalised (correlation mildly less, see Fig. 1)
5. Global Tem Geomagnetic N. Pole Deviation
For correlation between Long West and Anomaly, values are:
R = 0.741. P = <0.001 (Highly significant). Data were normal – correlation was Pearson Product Moment method.
Polynomial regression at 2nd order gave Rsq value of 0.8391 (very good!) – see Figure 5
6. For correlation between Lat North and Anomaly, values are:
R = 0.880. P = <0.001 (Highly significant). Data were normal – correlation was Pearson Product Moment method.
Polynomial regression at 2nd order gave Rsq value of 0.7848 – see Figure 6
7. For correlation between Long West Normalised and Temp, values are:
R = 0.739. P = <0.001 (Highly significant). Data were normal – correlation was Pearson Product Moment method.
Polynomial regression at 2nd order gave Rsq value of 0.838 – see Fig 7 (correlation and regression very slightly less than unnormalised – see Figure 5).
8. For correlation between Lat Normalised and Temp Anomaly, values are:
R = 0.882. P = <0.001 (Highly significant). Data were normal – correlation was Pearson Product Moment method.
Polynomial regression at 2nd order gave Rsq value of 0.7892 – see Figure 8 (slightly better than unnormalised – see Fig 6)
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Adrian Kerton 