## 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)

Out of curiosity I downloaded the station data from the Met Office Website for Oxford

http://www.metoffice.gov.uk/climate/uk/stationdata/

This shows the average temperature that Oxford has enjoyed. You can see the slope of the increase will be very dependent on start and finish years. 0.0077 deg c per year doesn’t seem a lot!

Here is the difference between the above average temperatures for the year compared with the Met Office released CRU data for Oxford. I have asked the Met Office to check my calculations.

The CRU data available only covers the years 1900-1980

The major deviations are for the years 1978, 1979, 1980.

UK Wind Power Generation for 3rd quarter 2010

The ten years with the hottest months from the Met Office Station Data.

Order of maximum temperature

Oxford back to 1853

2006 1983 1995 1911 1921 1976 1868 1947 2003 1874

Stornaway back to 1873

1899 1947 1997 1880 1955 2006 1933 1955 1976 1901

Durham back to 1880

2006 1995 1887 1947 1983 1975 1989 1901 1940 1990

Sheffield back to 1883

2006 1975 1983 1995 1976 1947 1921 1934 1995 1887

Eskdalemuir back to 1911

2006 1947 1955 1983 1989 1995 1975 1976 1934 1940