WUWT readers may remember way back when…I posted this from Joe D’Aleo:
Warming Trend: PDO And Solar Correlate Better Than CO2
Joe wrote then:
Clearly the US annual temperatures over the last century have correlated far better with cycles in the sun and oceans than carbon dioxide. The correlation with carbon dioxide seems to have vanished or even reversed in the last decade.
There’s a new paper by Paulo Cesar Soares in the International Journal of Geosciences supporting Joe’s idea, and it is full and open access. See link below.
Warming Power of CO2 and H2O: Correlations with Temperature Changes
Author: Paulo Cesar Soares
ABSTRACT
The dramatic and threatening environmental changes announced for the next decades are the result of models whose main drive factor of climatic changes is the increasing carbon dioxide in the atmosphere. Although taken as a premise, the hypothesis does not have verifiable consistence. The comparison of temperature changes and CO2 changes in the atmosphere is made for a large diversity of conditions, with the same data used to model climate changes. Correlation of historical series of data is the main approach. CO2 changes are closely related to temperature.
Warmer seasons or triennial phases are followed by an atmosphere that is rich in CO2, reflecting the gas solving or exsolving from water, and not photosynthesis activity. Interannual correlations between the variables are good. A weak dominance of temperature changes precedence, relative to CO2 changes, indicate that the main effect is the CO2 increase in the atmosphere due to temperature rising. Decreasing temperature is not followed by CO2 decrease, which indicates a different route for the CO2 capture by the oceans, not by gas re-absorption. Monthly changes have no correspondence as would be expected if the warming was an important absorption-radiation effect of the CO2 increase.
The anthropogenic wasting of fossil fuel CO2 to the atmosphere shows no relation with the temperature changes even in an annual basis. The absence of immediate relation between CO2 and temperature is evidence that rising its mix ratio in the atmosphere will not imply more absorption and time residence of energy over the Earth surface. This is explained because band absorption is nearly all done with historic CO2 values. Unlike CO2, water vapor in the atmosphere is rising in tune with temperature changes, even in a monthly scale. The rising energy absorption of vapor is reducing the outcoming long wave radiation window and amplifying warming regionally and in a different way around the globe.
…
From the conclusion:
The main conclusion one arrives at the analysis is that CO2 has not a causal relation with global warming and it is not powerful enough to cause the historical changes in temperature that were observed. The main argument is the absence of immediate correlation between CO2 changes preceding temperature either for global or local changes. The greenhouse effect of the CO2 is very small compared to the water vapor because the absorbing effect is already realized with its historical values. So, the reduction of the outcoming long wave radiation window is not a consequence of current enrichment or even of a possible double ratio of CO2. The absence of correlation between temperature changes and the immense and variable volume of CO2 waste by fuel burning is explained by the weak power of additional carbon dioxide in the atmosphere to reduce the outcoming window of long wave radiation. This effect is well performed by atmosphere humidity due to known increase insolation and vapor content in atmosphere.
The role of vapor is reinforced when it is observed that the regions with a great difference between potential and actual specific humidity are the ones with high temperature increase, like continental areas in mid to high latitudes. The main implication is that temperature increase predictions based on CO2 driving models are not reliable.
If the warmer power of solar irradiation is the independent driver for decadal and multidecadal cycles, the expected changes in insolation and no increase in green- house power may imply the recurrence of multidecadal cool phase, recalling the years of the third quarter of past century, before a new warming wave. The last decade stable temperature seems to be the turning point.
Full Text (PDF, 1794KB) PP.102-112 DOI: 10.4236/ijg.2010.13014
RACullen;
Nah. It’s much easier to hit HRs in warm weather/air. I’m serious. Ask any pro ball player.
Speaking of correlation, remember the cell phone scare?
http://www.guardian.co.uk/science/blog/2010/dec/20/mobile-phone-radiation-correlation
And remember Erin Brockovich?
http://www.dailymail.co.uk/femail/article-1343559/She-single-mother-claimed-town-poisoned-water-supply–Erin-Brockovich-wrong.html
I posted a big comment on this from another location and hadn’t entered my e-mail , so it blew my post away. That’s a software bug. If you make an error like that so you click on “back” to return to the pre-post view so you can add the e-mail; why does it blow your whole post away. I couldn’t be bothered re-entering all that I wrote.
So here’s the short version.
1/ Linearly scale (magnify) the magenta.
2/ Down shift the magenta origin.
3/ Voilla !! the correlation is very much better than is shown in the plot above.
4/ And the correlation of green with magenta or blueblack isn’t improved one iota.
5/ And that software bug is still there.
John F. Hultquist: It took me years to realize what the word “pattern” meant in a discussion of PDO. Maybe I need to explain the word pattern, like paisley or plaid or checkerboard. A map of a checkerboard pattern in the North Pacific might get the point across. That sounds like a quick and fun little post when I’m done with the one I’m working on. Thanks for the idea. Or I simply make the following clarification:
The PDO is a statistically manufactured dataset that basically represents the pattern of the SST anomalies of the North Pacific north of 20N, and I’m using the word pattern in the same way that checkboards and plaids are patterns.
@EFS_Junior says:
January 1, 2011 at 4:38 pm
I smell a rat!
——————————————————————-
Forget the spoon – do not try to bend the spoon — that’s impossible
just taste the soup 😉
Oooooooooh, what a shame. And the ‘global warmers’ dressed so nice.
“latitude says:
January 1, 2011 at 4:29 pm
I know it’s a lot harder to get CO2 into water, than it is to get CO2 out of water.
Don’t know why, it just is what it is, and never really questioned it until now.
Anyone got an idea of why that is? ”
The process is more complicated than it first appears, depending on many things, including the amounts involved, temperature, pressure, acidity. The reversible conversion of the CO2 to carbonic acid/bicarbonate (and vice-versa) is a (relatively slow) reaction that has the effect of “removing” some of the CO2 to bicarbonate, so allowing more CO2 to dissolve (and the amount converted varies with pH). When CO2 is released to the atmosphere from solution in water the reverse happens. This is one of the most important, yet rather slow, reactions for living organisms and they have evolved a “perfect” enzyme to do it: carbonic anhydrase.
So what they are saying is that the 91 ( !!!!!!!!!!!!!! ) Norwegian representatives in Cancun waste airplane tickets and tax-payers money. What a surprise.
Lack of a causal relationship for CO2 driving global warming is a drum I’ve been beating for years now. Even the data Al Gore presents illustrates this point to the naked eye, i.e without crosscorreletion time series analysis. To be causal, the stimulus must always occur before the response. Hence events can be strongly correlated without either one being causal of the other. Ordinary correlation analysis does not take relative timing of events into account.
The problem is that strong correlation is often misunderstood to imply causality. This misunderstanding is not only in the layman’s mind, but also in many scientists’ minds. For example, the headline author for the article above obviously does not understand this distinction – the article body speaks to causality; the headline speaks to correlation!
A proper way to illustrate a necessary, but not sufficient, condition for confirming causality is to compute the crosscorrelation function for the CO2 concentration and global temperature time series. If the correlation time at which this crosscorreleation function peaks places CO2 concentration ahead of global temperature, then this necessary condition is met, and causality remains a possibility. If this necessary condition is not met, then the causality premise is false.
Sometimes whether or not the causality premise false is apparent without crosscorrelation analysis by viewing the (time series) data itself. Such is the case for data that Al Gore presents. The plot of CO2 concentration and global temperature data over time, scaled such that the peaks for each appear approximately the same size on the plot, shows CO2 concentration change almost always following, in distincting to leading, global temperature change. Thus the premise of CO2 concentration being the cause of global temperature is clearly false.
Of course, the statistical explanation above is overly simplified in that uncertainty of the CO2 concentration and global temperature measurement data, and the many assumptions of such an analysis must be taken into account. However, the above explanation covers the essence of the matter.
Hi All,
I just downloaded the Hadcrut3v dataset and plotted the annual averages in a spreadsheet. If you use the full dataset there is a clear upward trend. The graph used at the top of this post only covers 1996 till 2008–only 12 years. Climate normals are generally considered to be 30 years. Also, the graph I have does not match the temperature plot on that first chart. Does anyone have any ideas about this?
please write in 5th grade englinsh, otherwise you efforts are wasted forever, no matter how important your points.
Dear MRB,
If by chance your comment applies to my submission, please be assured that I tried my darnedest to give both the fifth grade English explanation as well as the graduate level introductory explanation from a time series analysis/stochastic analysis/ statistics course. I appreciate your making me reconsider the communication issue here.
It is unfortunate that more than fifth grade math is sometimes required to resolve subtle issues. However, the core concept – “To be causal, the stimulus must always occur before the response.” – should be easily within the intellectual capacity of a competent fifth grader.
Maybe a more simple paraphrase of this core concept would be – “For event A to to be the possible cause of another event, B; event A must take place before event B.”
If you can formulate a still more elementary explanation, I would welcome the collaboration. I’m sure the readers would benefit.
As a statistician, how am I supposed to believe the results in such an article? The first moment of the series analyzed is not specified/quantified, yet there are statements about the second moment (correlation). These depend on how the first moment (mean/trend) is computed. Please let the math/stats people do the math/stats analysis.