1. Global Temperature Increases – Does Cooling Have a Statistical Basis for the last Three Decades?
By Thomas Christian
EAS 4803

2. 1978-2010 Ground Data (on Left) vs
Ground Data (on Left) vs. Satellite Data (on Right) with Least Squares Regression and Error Bars
p =
p =

3. Principal Component Regression with Ground Temperature Data on x-axis and Satellite Temperature Data on y-axis plotting for data from December 1978 until March 2010
Line in Black is the
first PC regression
line
Line in Blue is the
Reduced major axis
regression
Line in Green is the
least-squares
Lines in Magenta
Represent 95%
Confidence error
bars
pc = ; ls = ; rma =

4. 1998-2008 Ground Data (on Left) vs
Ground Data (on Left) vs. Satellite Data (on Right) with Least Squares Regression and Error Bars
p =
p =
When Ground vs. Satellite Temperature Data is looked at for the decade – a slight negative trend can be found for the Satellite Data

5. Principal Component Regression with Ground Temperature Data on x-axis and Satellite Temperature Data on y-axis plotting for data from December 1998 until 2008
Black Line is the
first PC regression
line
Blue Line is the
Reduced major axis
regression
Green Line is the least-
squares regression
Magenta Lines
Represent 95%
Confidence error
bars
pc = ; ls = ; rma =

6. March 2009-March 2010 Ground Data (on Left) vs
March 2009-March 2010 Ground Data (on Left) vs. Satellite Data (on Right) with Least Squares Regression and Error Bars
p =
p = 1.0e+003 *

7. Principal Component Regression with Ground Temperature Data on x-axis and Satellite Temperature Data on y-axis plotting for data from March 2009 until March 2010
Black Line is the
first PC regression
line
Blue Line is the
Reduced major axis
regression
Green Line is the
least-squares
Magenta Lines
Represent 95%
Confidence error
bars
pc = ; ls = ; rma =

8. Conclusions
When data is cherry picked – cooling can be found, however this signal is limited and not pronounced.
There is no statistical basis for global cooling. The global mean temperature is increasing and has been for the last 30 years which were analyzed in this project. PCR was used because the sum of the products of vertical and horizontal distances is minimized There is no bias for x and y.
Data sources:
GLB.Ts.txt
and
uahncdc.lt