"CO2 variations show little correlation with our planet's climate on
long, medium and even short time scales." R. Timothy Patterson,
Professor Of Geology, Director Ottawa-Carleton Geoscience Center,
Carleton University, Canada

The reason the carbon industry propaganda
Bonzo and other fossil fools post comes
from minor publications is that larger
newspapers and magazines do check their
facts before publishing.

On Apr 23, 10:23 pm, "0NBZ0" <0N...@doooooooooooooooodoooooooooo.com
wrote:
[ . . . ]

"CO2 variations show little correlation with our planet's climate on
long, medium and even short time scales." R. Timothy Patterson,
Professor Of Geology, Director Ottawa-Carleton Geoscience Center,
Carleton University, Canada

Like almost everything Bonzo posts,
this is a flat out lie. As pointed
out below, atmospheric CO2 concentration
shows a very strong correlation with
global land and sea mean surface temperatures.

-------

CO2 or Sunspots: Statistical Correlation Chooses

Statistical correlation is a powerful technique with
very many uses. It produces "R squared" a measure of
whether two series of measures trend together.

(Those who are new to statistical correlation and
"R squared" will find a tutorial on the subject here:

http://mathworld.wolfram.com/Correlation.html

http://mathworld.wolfram.com/CorrelationCoefficient.html

Item 20 in the above shows R squared for several graphed
relationships.)

When applied to a time series of global mean surface
temperatures and data from prospective global warming
causes covering the same time period, correlation can
help locate the cause of the observed global warming.
Low "R squared" values, those near zero, can, by
themselves, totally rule out a prospective cause.
High "R squared" values indicate that a prospective
cause is very likely, but do not, by themselves,
'prove' something caused the warming. (Experimental
science rarely 'proves' something like a mathematical
proof does.)

Below are directly observed data for global mean surface
temperature, CO2 concentration, and sunspots for the last
50 years. This is as long as the longest directly
observed record of atmospheric CO2 concentration.

The R^2 value for the correlation of CO2 and planetary
surface temperature is 0.78. The simple rising
line showing heating for increasing CO2 explains a
lot of the variance in the global mean temperature.
The relationship between CO2 and global temperature
is very strong and the anthropogenic greenhouse gas
radiative forcing theory is well supported by these
data.

The R^2 value for sunspots and and planetary
surface temperature is very near zero. These data
clearly do not support any relationship between
sunspot numbers and global mean surface temperature
over the last 50 years. It is very unlikely that
sunspots have anything to do with the current
global warming.

This test applies very easily to all other claims for
global warming causes. It will quickly separate the
wheat from the chaff.

-.-. --.- Roger Coppock

=-=-=-=-=-=-= The Data =-=-=-=-=-=-> The global mean surface "Temp"erature data are the GISS
adjusted J-D yearly land and sea average, available from
NASA at:

http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt

The "CO2" data are the yearly averages of the monthly data
from the Keeling curve measured at Mauna Loa, available at:

ftp://ftp.cmdl.noaa.gov/ccg/co2/trends/co2_mm_mlo.txt

"Sunspots" are the yearly averages of the monthly means
in the NOAA NGDC "MONTHLY" file. They are available at
the FTP site accessed through this web page:

http://www.ngdc.noaa.gov/stp/SOLAR/SSN/ssn.html

Year Temp CO2 Sunspots
1958 14.08 315.33 184.5917
1959 14.06 315.98 158.75
1960 13.99 316.91 112.275
1961 14.08 317.65 53.8833
1962 14.04 318.46 37.6
1963 14.08 318.99 27.8917
1964 13.79 319.20 10.2
1965 13.89 320.03 15.0583
1966 13.97 321.37 46.875
1967 14.00 322.18 93.6667
1968 13.96 323.05 105.8917
1969 14.08 324.62 105.5583
1970 14.03 325.68 104.6917
1971 13.90 326.32 66.65
1972 14.00 327.46 68.9333
1973 14.14 329.68 38.15
1974 13.92 330.17 34.4083
1975 13.95 331.14 15.4583
1976 13.84 332.06 12.55
1977 14.13 333.78 27.4833
1978 14.02 335.40 92.6583
1979 14.09 336.78 155.275
1980 14.18 338.70 154.65
1981 14.27 340.11 140.45
1982 14.05 340.98 116.2917
1983 14.26 342.84 66.6333
1984 14.09 344.20 45.85
1985 14.06 345.87 17.9417
1986 14.13 347.19 13.4
1987 14.27 348.98 29.225
1988 14.31 351.45 100
1989 14.19 352.89 157.7917
1990 14.38 354.16 142.2917
1991 14.35 355.48 145.775
1992 14.12 356.27 94.4833
1993 14.14 356.96 54.7333
1994 14.24 358.63 29.8667
1995 14.38 360.63 17.5
1996 14.30 362.37 8.625
1997 14.40 363.47 21.4833
1998 14.57 366.50 64.2083
1999 14.33 368.14 93.175
2000 14.33 369.41 119.5333
2001 14.48 371.07 110.925
2002 14.56 373.16 104.0917
2003 14.55 375.80 63.5667
2004 14.49 377.55 40.4417
2005 14.62 379.75 29.7833
2006 14.54 381.85 15.1833
2007 14.57 383.72 7.5417

=-=-=-=-=-=-= "R" Program Outputs =-=-=-=-=-=-> The following are outputs of the "R" statistical program:
For information on "R," please see:

http://www.r-project.org/

--------

Call:
lm(formula = Temp ~ CO2, data = aframe)

Residuals:
Min 1Q Median 3Q Max
-0.2316612 -0.0805322 0.0185249 0.0763159 0.1798386

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.10008e+01 2.41721e-01 45.5103 < 2.22e-16 ***
CO2 9.24797e-03 7.01018e-04 13.1922 < 2.22e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.101321 on 48 degrees of freedom
Multiple R-Squared: 0.783817, Adjusted R-squared: 0.779313
F-statistic: 174.034 on 1 and 48 DF, p-value: < 2.220e-16

--------

Call:
lm(formula = Temp ~ Sunspots, data = aframe)

Residuals:
Min 1Q Median 3Q Max
-0.3909495 -0.1523184 -0.0514594 0.1445919 0.4380756

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.41804e+01 5.39054e-02 263.06149 < 2e-16 ***
Sunspots 4.97803e-05 6.18766e-04 0.08045 0.93621
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.217902 on 48 degrees of freedom
Multiple R-Squared: 0.000134823, Adjusted R-squared: -0.0206957
F-statistic: 0.00647235 on 1 and 48 DF, p-value: 0.936213

Roger you know that you cannot prove anything with this
argument.You have not included the other natural processes
that affect Global temperature.

Are you still banging on about this?

"CO2 variations show little correlation with our planet's climate on
long, medium and even short time scales." R. Timothy Patterson,
Professor Of Geology, Director Ottawa-Carleton Geoscience Center,
Carleton University, Canada
Like almost everything Bonzo posts,

INVESTOR'S BUSINESS DAILY

April 23, 2008



http://ibdeditorial.com/IBDArticles.aspx?id=293843193434228



Climate Change: A former NASA astronaut says the same solar phenomenon
that doomed Napoleon's army may soon stop Al Gore's march to glory cold.
Prepare for the big chill.



Napoleon's retreat from Moscow is a legendary military disaster. While
historians and military buffs note the toll the Russian winter took on
La Grande Armee, few if any appreciate the role solar activity, or the
lack of it, played in one of the great military reversals in history.



Geophysicist Phil Chapman, the first Australian to become a NASA
astronaut, and who served as mission specialist on the Apollo 14 lunar
mission, writes in the Down Under newspaper the Australian that "the
rout of Napoleon's Grand Army from Moscow was at least partly due to the
lack of sunspots."



This is more than a historical footnote. The same pattern of solar
activity that doomed Napoleon is occurring as we speak.



The sun goes through a series of 11-year cycles in which sunspots
fluctuate in both number and intensity, greatly influencing Earth's
climate and weather. The end of each cycle is called a solar minimum,
where sunspot activity is at a low point. Activity usually picks up
after that as each new cycle begins.



As Chapman notes, the most recent minimum occurred in March 2007.
Sunspot activity should have increased shortly after that but sunspot
activity has remained at a virtual standstill.



If you log on to www.spaceweather.com, you will see a current picture of
the sun from the U.S. Solar and Heliospheric Observatory (SOHO) with but
a single tiny sunspot, dubbed number 992. The previous time a cycle was
delayed like this, according to Chapman, was during what was called the
Dalton Minimum, a particularly cold period that lasted several decades
starting in 1790. "Northern winters became ferocious," he says.



The success of Napoleon's march was not in the stars, at least not in
the one closest to the Earth.



This has been a winter of record cold and record snowfalls. The four
major agencies tracking Earth's temperature, including NASA's Goddard
Institute, report the earth cooled 0.7C in 2007, the fastest decline in
the age of instrumentation, putting us back to where the Earth was in
1930.



It snowed in Baghdad for the first time in centuries, and Chapman says
"the extent of Antarctic sea ice . . . was the greatest on record since
James Cook discovered the place in 1770."



So far this year, SOHO has detected just three sunspots, including
number 992, which appeared on Monday. One was found in January and
lasted only two days. Another appeared earlier this month but vanished
within 24 hours. There should be more, many more. At its peak, the sun
should look like a teenager's face before the prom.



Kenneth Tapping, a solar researcher and project director for Canada's
National Research Council, oversees the operation of a 60-year-old radio
telescope that he calls a "stethoscope for the sun."



Tapping reports no change in the sun's magnetic field so far this cycle
and warns that if the sun remains quiet for another year or two, it may
indicate another repeat of that period of drastic cooling of the Earth,
bringing massive snowfall and severe weather to the Northern Hemisphere.



Chapman says the temperate climate we now enjoy is the exception, not
the rule. We are currently in an interglacial period, the Holocene.
"Under normal conditions," he says, "most of North America and Europe
(is) buried under about 1.5 kilometers of ice."



It takes about a 12-degrees Celsius decline in average global
temperature to trigger glaciation. If last year's decline continued for
the next 20, the total drop would be 14 C, more than enough.



Al Gore says disastrous warming is imminent. But as we look through the
solar telescope with NASA astronaut Chapman and others, we have to ask
ourselves - what's wrong with this picture?

The reason the carbon industry propaganda
Bonzo and other fossil fools post comes
from minor publications is that larger
newspapers and magazines do check their
facts before publishing.

On Apr 23, 10:23 pm, "0NBZ0" <0N...@doooooooooooooooodoooooooooo.com
wrote:
[ . . . ]

"CO2 variations show little correlation with our planet's climate on
long, medium and even short time scales." R. Timothy Patterson,
Professor Of Geology, Director Ottawa-Carleton Geoscience Center,
Carleton University, Canada

Like almost everything Bonzo posts,
this is a flat out lie.  As pointed
out below, atmospheric CO2 concentration
shows a very strong correlation with
global land and sea mean surface temperatures.

-------

CO2 or Sunspots: Statistical Correlation Chooses

Statistical correlation is a powerful technique with
very many uses.  It produces "R squared" a measure of
whether two series of measures trend together.

(Those who are new to statistical correlation and
"R squared" will find a tutorial on the subject here:

http://mathworld.wolfram.com/Correlation.html

http://mathworld.wolfram.com/CorrelationCoefficient.html

Item 20 in the above shows R squared for several graphed
relationships.)

When applied to a time series of global mean surface
temperatures and data from prospective global warming
causes covering the same time period, correlation can
help locate the cause of the observed global warming.
Low "R squared" values, those near zero, can, by
themselves, totally rule out a prospective cause.
High "R squared" values indicate that a prospective
cause is very likely, but do not, by themselves,
'prove' something caused the warming.  (Experimental
science rarely 'proves' something like a mathematical
proof does.)

Below are directly observed data for global mean surface
temperature, CO2 concentration, and sunspots for the last
50 years.  This is as long as the longest directly
observed record of atmospheric CO2 concentration.

The R^2 value for the correlation of CO2 and planetary
surface temperature is 0.78.  The simple rising
line showing heating for increasing CO2 explains a
lot of the variance in the global mean temperature.
The relationship between CO2 and global temperature
is very strong and the anthropogenic greenhouse gas
radiative forcing theory is well supported by these
data.

The R^2 value for sunspots and and planetary
surface temperature is very near zero.  These data
clearly do not support any relationship between
sunspot numbers and global mean surface temperature
over the last 50 years.  It is very unlikely that
sunspots have anything to do with the current
global warming.

This test applies very easily to all other claims for
global warming causes.  It will quickly separate the
wheat from the chaff.

-.-. --.-  Roger Coppock

=-=-=-=-=-=-= The Data =-=-=-=-=-=-> The global mean surface "Temp"erature data are the GISS
adjusted J-D yearly land and sea average, available from
NASA at:

http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt

The "CO2" data are the yearly averages of the monthly data
from the Keeling curve measured at Mauna Loa, available at:

ftp://ftp.cmdl.noaa.gov/ccg/co2/trends/co2_mm_mlo.txt

"Sunspots" are the yearly averages of the monthly means
in the NOAA NGDC "MONTHLY" file.  They are available at
the FTP site accessed through this web page:

http://www.ngdc.noaa.gov/stp/SOLAR/SSN/ssn.html

Year Temp  CO2    Sunspots
1958 14.08 315.33 184.5917
1959 14.06 315.98 158.75
1960 13.99 316.91 112.275
1961 14.08 317.65 53.8833
1962 14.04 318.46 37.6
1963 14.08 318.99 27.8917
1964 13.79 319.20 10.2
1965 13.89 320.03 15.0583
1966 13.97 321.37 46.875
1967 14.00 322.18 93.6667
1968 13.96 323.05 105.8917
1969 14.08 324.62 105.5583
1970 14.03 325.68 104.6917
1971 13.90 326.32 66.65
1972 14.00 327.46 68.9333
1973 14.14 329.68 38.15
1974 13.92 330.17 34.4083
1975 13.95 331.14 15.4583
1976 13.84 332.06 12.55
1977 14.13 333.78 27.4833
1978 14.02 335.40 92.6583
1979 14.09 336.78 155.275
1980 14.18 338.70 154.65
1981 14.27 340.11 140.45
1982 14.05 340.98 116.2917
1983 14.26 342.84 66.6333
1984 14.09 344.20 45.85
1985 14.06 345.87 17.9417
1986 14.13 347.19 13.4
1987 14.27 348.98 29.225
1988 14.31 351.45 100
1989 14.19 352.89 157.7917
1990 14.38 354.16 142.2917
1991 14.35 355.48 145.775
1992 14.12 356.27 94.4833
1993 14.14 356.96 54.7333
1994 14.24 358.63 29.8667
1995 14.38 360.63 17.5
1996 14.30 362.37 8.625
1997 14.40 363.47 21.4833
1998 14.57 366.50 64.2083
1999 14.33 368.14 93.175
2000 14.33 369.41 119.5333
2001 14.48 371.07 110.925
2002 14.56 373.16 104.0917
2003 14.55 375.80 63.5667
2004 14.49 377.55 40.4417
2005 14.62 379.75 29.7833
2006 14.54 381.85 15.1833
2007 14.57 383.72 7.5417

=-=-=-=-=-=-= "R" Program Outputs =-=-=-=-=-=-> The following are outputs of the "R" statistical program:
For information on "R," please see:

   http://www.r-project.org/

--------

Call:
lm(formula = Temp ~ CO2, data = aframe)

Residuals:
       Min         1Q     Median         3Q        Max
-0.2316612 -0.0805322  0.0185249  0.0763159  0.1798386

Coefficients:
               Estimate  Std. Error t value   Pr(>|t|)
(Intercept) 1.10008e+01 2.41721e-01 45.5103 < 2.22e-16 ***
CO2         9.24797e-03 7.01018e-04 13.1922 < 2.22e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.101321 on 48 degrees of freedom
Multiple R-Squared: 0.783817,   Adjusted R-squared: 0.779313
F-statistic: 174.034 on 1 and 48 DF,  p-value: < 2.220e-16

--------

Call:
lm(formula = Temp ~ Sunspots, data = aframe)

Residuals:
       Min         1Q     Median         3Q        Max
-0.3909495 -0.1523184 -0.0514594  0.1445919  0.4380756

Coefficients:
               Estimate  Std. Error   t value Pr(>|t|)
(Intercept) 1.41804e+01 5.39054e-02 263.06149  < 2e-16 ***
Sunspots    4.97803e-05 6.18766e-04   0.08045  0.93621
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.217902 on 48 degrees of freedom
Multiple R-Squared: 0.000134823,        Adjusted R-squared: -0.0206957
F-statistic: 0.00647235 on 1 and 48 DF,  p-value: 0.936213

Roger Coppock wrote:
On Apr 24, 1:43 am, Steven <> wrote:
[ . . . ]
Are you still banging on about this?

Get used to it Steven, the fossil fools
on this forum repeat the same old obvious
lies for years.

Have you EVER changed any minds?
Perhaps you do not have the truth on your side.

On Apr 24, 1:43 am, Steven <> wrote:
[ . . . ]
Are you still banging on about this?

Get used to it Steven, the fossil fools
on this forum repeat the same old obvious
lies for years.

On Apr 24, 1:43 am, Steven <> wrote:
[ . . . ]

Are you still banging on about this?

Get used to it Steven, the fossil fools
on this forum repeat the same old obvious
lies for years.

INVESTOR'S BUSINESS DAILY

April 23, 2008



http://ibdeditorial.com/IBDArticles.aspx?id=293843193434228



Climate Change: A former NASA astronaut says the same solar phenomenon
that doomed Napoleon's army may soon stop Al Gore's march to glory
cold. Prepare for the big chill.



Napoleon's retreat from Moscow is a legendary military disaster. While
historians and military buffs note the toll the Russian winter took on
La Grande Armee, few if any appreciate the role solar activity, or the
lack of it, played in one of the great military reversals in history.



Geophysicist Phil Chapman, the first Australian to become a NASA
astronaut, and who served as mission specialist on the Apollo 14 lunar
mission, writes in the Down Under newspaper the Australian that "the
rout of Napoleon's Grand Army from Moscow was at least partly due to
the lack of sunspots."

"Roger Coppock" <rcoppock@adnc.com> wrote in message
news:2b87154e-0507-497a-8045-19ef8b5bea45@r9g2000prd.googlegroups.com...
On Apr 23, 10:23 pm, "0NBZ0" <0N...@doooooooooooooooodoooooooooo.com
wrote:
[ . . . ]
"CO2 variations show little correlation with our planet's climate on
long, medium and even short time scales." R. Timothy Patterson,
Professor Of Geology, Director Ottawa-Carleton Geoscience Center,
Carleton University, Canada
Like almost everything Bonzo posts,
this is a flat out lie.
***********************


Yes professor, I'm sure we all believe you .... NOT!

On Apr 24, 7:05 am, Roger Coppock <rcopp...@adnc.com> wrote:
The reason the carbon industry propaganda
Bonzo and other fossil fools post comes
from minor publications is that larger
newspapers and magazines do check their
facts before publishing.

On Apr 23, 10:23 pm, "0NBZ0" <0N...@doooooooooooooooodoooooooooo.com
wrote:
[ . . . ]

"CO2 variations show little correlation with our planet's climate on
long, medium and even short time scales." R. Timothy Patterson,
Professor Of Geology, Director Ottawa-Carleton Geoscience Center,
Carleton University, Canada

Like almost everything Bonzo posts,
this is a flat out lie. As pointed
out below, atmospheric CO2 concentration
shows a very strong correlation with
global land and sea mean surface temperatures.

-------

CO2 or Sunspots: Statistical Correlation Chooses

Statistical correlation is a powerful technique with
very many uses. It produces "R squared" a measure of
whether two series of measures trend together.

(Those who are new to statistical correlation and
"R squared" will find a tutorial on the subject here:

http://mathworld.wolfram.com/Correlation.html

http://mathworld.wolfram.com/CorrelationCoefficient.html

Item 20 in the above shows R squared for several graphed
relationships.)

When applied to a time series of global mean surface
temperatures and data from prospective global warming
causes covering the same time period, correlation can
help locate the cause of the observed global warming.
Low "R squared" values, those near zero, can, by
themselves, totally rule out a prospective cause.
High "R squared" values indicate that a prospective
cause is very likely, but do not, by themselves,
'prove' something caused the warming. (Experimental
science rarely 'proves' something like a mathematical
proof does.)

Below are directly observed data for global mean surface
temperature, CO2 concentration, and sunspots for the last
50 years. This is as long as the longest directly
observed record of atmospheric CO2 concentration.

The R^2 value for the correlation of CO2 and planetary
surface temperature is 0.78. The simple rising
line showing heating for increasing CO2 explains a
lot of the variance in the global mean temperature.
The relationship between CO2 and global temperature
is very strong and the anthropogenic greenhouse gas
radiative forcing theory is well supported by these
data.

The R^2 value for sunspots and and planetary
surface temperature is very near zero. These data
clearly do not support any relationship between
sunspot numbers and global mean surface temperature
over the last 50 years. It is very unlikely that
sunspots have anything to do with the current
global warming.

This test applies very easily to all other claims for
global warming causes. It will quickly separate the
wheat from the chaff.

-.-. --.- Roger Coppock

=-=-=-=-=-=-= The Data =-=-=-=-=-=-=
The global mean surface "Temp"erature data are the GISS
adjusted J-D yearly land and sea average, available from
NASA at:

http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt

The "CO2" data are the yearly averages of the monthly data
from the Keeling curve measured at Mauna Loa, available at:

ftp://ftp.cmdl.noaa.gov/ccg/co2/trends/co2_mm_mlo.txt

"Sunspots" are the yearly averages of the monthly means
in the NOAA NGDC "MONTHLY" file. They are available at
the FTP site accessed through this web page:

http://www.ngdc.noaa.gov/stp/SOLAR/SSN/ssn.html

Year Temp CO2 Sunspots
1958 14.08 315.33 184.5917
1959 14.06 315.98 158.75
1960 13.99 316.91 112.275
1961 14.08 317.65 53.8833
1962 14.04 318.46 37.6
1963 14.08 318.99 27.8917
1964 13.79 319.20 10.2
1965 13.89 320.03 15.0583
1966 13.97 321.37 46.875
1967 14.00 322.18 93.6667
1968 13.96 323.05 105.8917
1969 14.08 324.62 105.5583
1970 14.03 325.68 104.6917
1971 13.90 326.32 66.65
1972 14.00 327.46 68.9333
1973 14.14 329.68 38.15
1974 13.92 330.17 34.4083
1975 13.95 331.14 15.4583
1976 13.84 332.06 12.55
1977 14.13 333.78 27.4833
1978 14.02 335.40 92.6583
1979 14.09 336.78 155.275
1980 14.18 338.70 154.65
1981 14.27 340.11 140.45
1982 14.05 340.98 116.2917
1983 14.26 342.84 66.6333
1984 14.09 344.20 45.85
1985 14.06 345.87 17.9417
1986 14.13 347.19 13.4
1987 14.27 348.98 29.225
1988 14.31 351.45 100
1989 14.19 352.89 157.7917
1990 14.38 354.16 142.2917
1991 14.35 355.48 145.775
1992 14.12 356.27 94.4833
1993 14.14 356.96 54.7333
1994 14.24 358.63 29.8667
1995 14.38 360.63 17.5
1996 14.30 362.37 8.625
1997 14.40 363.47 21.4833
1998 14.57 366.50 64.2083
1999 14.33 368.14 93.175
2000 14.33 369.41 119.5333
2001 14.48 371.07 110.925
2002 14.56 373.16 104.0917
2003 14.55 375.80 63.5667
2004 14.49 377.55 40.4417
2005 14.62 379.75 29.7833
2006 14.54 381.85 15.1833
2007 14.57 383.72 7.5417

=-=-=-=-=-=-= "R" Program Outputs =-=-=-=-=-=-=
The following are outputs of the "R" statistical program:
For information on "R," please see:

http://www.r-project.org/

--------

Call:
lm(formula = Temp ~ CO2, data = aframe)

Residuals:
Min 1Q Median 3Q Max
-0.2316612 -0.0805322 0.0185249 0.0763159 0.1798386

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.10008e+01 2.41721e-01 45.5103 < 2.22e-16 ***
CO2 9.24797e-03 7.01018e-04 13.1922 < 2.22e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.101321 on 48 degrees of freedom
Multiple R-Squared: 0.783817, Adjusted R-squared: 0.779313
F-statistic: 174.034 on 1 and 48 DF, p-value: < 2.220e-16

--------

Call:
lm(formula = Temp ~ Sunspots, data = aframe)

Residuals:
Min 1Q Median 3Q Max
-0.3909495 -0.1523184 -0.0514594 0.1445919 0.4380756

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.41804e+01 5.39054e-02 263.06149 < 2e-16 ***
Sunspots 4.97803e-05 6.18766e-04 0.08045 0.93621
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.217902 on 48 degrees of freedom
Multiple R-Squared: 0.000134823, Adjusted R-squared:
-0.0206957 F-statistic: 0.00647235 on 1 and 48 DF, p-value: 0.936213

Roger you know that you cannot prove anything with this
argument.You have not included the other natural processes
that affect Global temperature .
You are a CHARLATAN your statistics are the sort that
Disraeli complained about.

On 24 Apr, 08:05, Roger Coppock <rcopp...@adnc.com> wrote:
The reason the carbon industry propaganda
Bonzo and other fossil fools post comes
from minor publications is that larger
newspapers and magazines do check their
facts before publishing.

On Apr 23, 10:23 pm, "0NBZ0" <0N...@doooooooooooooooodoooooooooo.com
wrote:
[ . . . ]

"CO2 variations show little correlation with our planet's climate on
long, medium and even short time scales." R. Timothy Patterson,
Professor Of Geology, Director Ottawa-Carleton Geoscience Center,
Carleton University, Canada

Like almost everything Bonzo posts,
this is a flat out lie. As pointed
out below, atmospheric CO2 concentration
shows a very strong correlation with
global land and sea mean surface temperatures.

-------

CO2 or Sunspots: Statistical Correlation Chooses

Statistical correlation is a powerful technique with
very many uses. It produces "R squared" a measure of
whether two series of measures trend together.

(Those who are new to statistical correlation and
"R squared" will find a tutorial on the subject here:

http://mathworld.wolfram.com/Correlation.html

http://mathworld.wolfram.com/CorrelationCoefficient.html

Item 20 in the above shows R squared for several graphed
relationships.)

When applied to a time series of global mean surface
temperatures and data from prospective global warming
causes covering the same time period, correlation can
help locate the cause of the observed global warming.
Low "R squared" values, those near zero, can, by
themselves, totally rule out a prospective cause.
High "R squared" values indicate that a prospective
cause is very likely, but do not, by themselves,
'prove' something caused the warming. (Experimental
science rarely 'proves' something like a mathematical
proof does.)

Below are directly observed data for global mean surface
temperature, CO2 concentration, and sunspots for the last
50 years. This is as long as the longest directly
observed record of atmospheric CO2 concentration.

The R^2 value for the correlation of CO2 and planetary
surface temperature is 0.78. The simple rising
line showing heating for increasing CO2 explains a
lot of the variance in the global mean temperature.
The relationship between CO2 and global temperature
is very strong and the anthropogenic greenhouse gas
radiative forcing theory is well supported by these
data.

The R^2 value for sunspots and and planetary
surface temperature is very near zero. These data
clearly do not support any relationship between
sunspot numbers and global mean surface temperature
over the last 50 years. It is very unlikely that
sunspots have anything to do with the current
global warming.

This test applies very easily to all other claims for
global warming causes. It will quickly separate the
wheat from the chaff.

-.-. --.- Roger Coppock

=-=-=-=-=-=-= The Data =-=-=-=-=-=-=
The global mean surface "Temp"erature data are the GISS
adjusted J-D yearly land and sea average, available from
NASA at:

http://data.giss.nasa.gov/gistemp/tabledata/GLB.Ts+dSST.txt

The "CO2" data are the yearly averages of the monthly data
from the Keeling curve measured at Mauna Loa, available at:

ftp://ftp.cmdl.noaa.gov/ccg/co2/trends/co2_mm_mlo.txt

"Sunspots" are the yearly averages of the monthly means
in the NOAA NGDC "MONTHLY" file. They are available at
the FTP site accessed through this web page:

http://www.ngdc.noaa.gov/stp/SOLAR/SSN/ssn.html

Year Temp CO2 Sunspots
1958 14.08 315.33 184.5917
1959 14.06 315.98 158.75
1960 13.99 316.91 112.275
1961 14.08 317.65 53.8833
1962 14.04 318.46 37.6
1963 14.08 318.99 27.8917
1964 13.79 319.20 10.2
1965 13.89 320.03 15.0583
1966 13.97 321.37 46.875
1967 14.00 322.18 93.6667
1968 13.96 323.05 105.8917
1969 14.08 324.62 105.5583
1970 14.03 325.68 104.6917
1971 13.90 326.32 66.65
1972 14.00 327.46 68.9333
1973 14.14 329.68 38.15
1974 13.92 330.17 34.4083
1975 13.95 331.14 15.4583
1976 13.84 332.06 12.55
1977 14.13 333.78 27.4833
1978 14.02 335.40 92.6583
1979 14.09 336.78 155.275
1980 14.18 338.70 154.65
1981 14.27 340.11 140.45
1982 14.05 340.98 116.2917
1983 14.26 342.84 66.6333
1984 14.09 344.20 45.85
1985 14.06 345.87 17.9417
1986 14.13 347.19 13.4
1987 14.27 348.98 29.225
1988 14.31 351.45 100
1989 14.19 352.89 157.7917
1990 14.38 354.16 142.2917
1991 14.35 355.48 145.775
1992 14.12 356.27 94.4833
1993 14.14 356.96 54.7333
1994 14.24 358.63 29.8667
1995 14.38 360.63 17.5
1996 14.30 362.37 8.625
1997 14.40 363.47 21.4833
1998 14.57 366.50 64.2083
1999 14.33 368.14 93.175
2000 14.33 369.41 119.5333
2001 14.48 371.07 110.925
2002 14.56 373.16 104.0917
2003 14.55 375.80 63.5667
2004 14.49 377.55 40.4417
2005 14.62 379.75 29.7833
2006 14.54 381.85 15.1833
2007 14.57 383.72 7.5417

=-=-=-=-=-=-= "R" Program Outputs =-=-=-=-=-=-=
The following are outputs of the "R" statistical program:
For information on "R," please see:

http://www.r-project.org/

--------

Call:
lm(formula = Temp ~ CO2, data = aframe)

Residuals:
Min 1Q Median 3Q Max
-0.2316612 -0.0805322 0.0185249 0.0763159 0.1798386

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.10008e+01 2.41721e-01 45.5103 < 2.22e-16 ***
CO2 9.24797e-03 7.01018e-04 13.1922 < 2.22e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.101321 on 48 degrees of freedom
Multiple R-Squared: 0.783817, Adjusted R-squared: 0.779313
F-statistic: 174.034 on 1 and 48 DF, p-value: < 2.220e-16

--------

Call:
lm(formula = Temp ~ Sunspots, data = aframe)

Residuals:
Min 1Q Median 3Q Max
-0.3909495 -0.1523184 -0.0514594 0.1445919 0.4380756

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.41804e+01 5.39054e-02 263.06149 < 2e-16 ***
Sunspots 4.97803e-05 6.18766e-04 0.08045 0.93621
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.217902 on 48 degrees of freedom
Multiple R-Squared: 0.000134823, Adjusted R-squared: -0.0206957
F-statistic: 0.00647235 on 1 and 48 DF, p-value: 0.936213

Chery picked data leaving out the temperature drop from 1940 and 2007.

Temp has a better correlation with SO2 than CO2 post war.