Recently after some conversations with a former chemical engineer who provided me with some insight, I’ve come to the conclusion that many engineers have difficulty with many of the premises of AGW theory because in their “this has to work or people die” world of exacting standards, the AGW argument doesn’t hold up well by their standards of performance.
Today I was surprised to learn that one of the foremost and world famous engineers on the planet, Burt Rutan, has become an active climate skeptic. You may be familiar with some of Rutan’s work through his company, Scaled Composites:
Thanks to WUWT reader Dale Knutsen, I was provided a PowerPoint file recently by email presented by Mr. Rutan at the Oshkosh fly-in convention on July 29th, 2009 and again on August 1st, 2009. It has also now been posted online by an associate of Mr. Rutan’s.
There were a number of familiar things in the PowerPoint, including data plots from one of the USHCN stations I personally surveyed and highlighted, Santa Rosa, NM. Rutan had an interest in it because of the GISS adjustment to the data. For him, the whole argument is about the data. He says about his presentation in slide #3:
Not a Climatologist’s study; more from the view of a flight test guy who has spent a lifetime in data analysis/interpretation.
In the notes of his PowerPoint on slide #3, Rutan tells us why he thinks this way(emphasis mine):
My study is NOT as a climatologist, but from a completely different prospective in which I am an expert.
Complex data from disparate sources can be processed and presented in very different ways, and to “prove” many different theories.
For decades, as a professional experimental test engineer, I have analyzed experimental data and watched others massage and present data. I became a cynic; My conclusion – “if someone is aggressively selling a technical product who’s merits are dependant on complex experimental data, he is likely lying”. That is true whether the product is an airplane or a Carbon Credit.
Now since I’m sure people like foaming Joe Romm will immediately come out to label Mr. Rutan as a denier/delayer/generally bad person, one must be careful to note that Mr. Rutan is not your average denier/delayer. He’s “green”. Oh horrors, a “green denier”! Where have we seen that before?
From his PowerPoint, here’s his house, note the energy efficient earth walled design.
In his PowerPoint notes he says about his green interests:
My house was Nov 89 Pop Science Cover story; “World’s Most Efficient House”. Its big advantage is in the desert summer. It is all-electric and it uses more energy in the relatively mild winters than in the harsh summers – just the opposite of my neighbors.
The property has provisions for converting to self-sustaining (house and plug-in hybrid car) via adding wind generator and solar panels when it becomes cost effective to do so.
Testing Solar Water Heat in the 70s at RAF; the Rutan Aircraft Factory was converted to solar-heated water in the 70s, when others were only focused on gasoline costs.
My all electric EV-1 was best car I ever owned. Primary car for 7 years, all-electric with an 85 mile range. I was very sad (just like the guy shown) when the leased cars were recalled and crushed by General Motors. I will buy a real hybrid when one becomes available (plug-in with elect-range>60 miles). The Prius “hybrid” is not a hybrid, since it is fueled only by gasoline. A Plug-in Hybrid can be fueled with both gas and electricity. You might even see a ‘plug-in hybrid airplane’ in my future.
Interest is technology, not tree-hugging
Well that right there is reason enough to put all sorts or nasty labels on the man. Welcome to the club Burt, we are proud to have you!
Rutan’s closing observations slide is interesting:
And, in his notes he makes this mention:
Is the debate over? – The loudest Alarmist says the debate is over. However, “It is error only, and not truth, that shrinks from inquiry”.
I think by the “loudest alarmist” he means Al Gore.
And his final slide:
Rutan’s PowerPoint file is posted at:
http://rps3.com/Pages/Burt_Rutan_on_Climate_Change.htm
For those that don’t have PowerPoint, I’ve converted it to a PDF file for easy and immediate reading online which you can download here.
I wonder if in conversations with his biggest client, Virgin’s Richard Branson, he ever mentions Gore and their joint project? I’d love to be a fly on the wall for that conversation.
Reading this thread has made me wary of the pronouncements of engineers. One self proclaimed engineer claimed that after looking into climate models (presumably GCMs, which is what most people mean when referring to “climate models”) he found them to be not physical, but statistical in nature. If PolitiFact had investigated this claim it would have rated “Pants on Fire”. GCMs are physical in nature, parametrizations for sub grid scale processes and all.
Another poster (Sowell?) claims that the models suffer from a problem of trying to scale up (what?). This is silly on it’s face. GCMs (the G stands for general and the C stands for circulation) represent as best we can the distilled knowledge that we have about how the climate operates. While I see that there are gaps in our knowledge (dual ITCZ being one oddity) these are not due to scaling problems.
Sowell at one point claims that the IPCC suffers from a scaling problem. This is about as silly a statement as I can see. The IPCC is a giant literature review which attempts to distill what is known about the climate system. What is meant by a “scaling problem” in this context makes no sense at all.
evanjones makes repeated silly claims, most of them resting on the claim of “The Great Pacific Climatic Shift” of the mid 1970’s. This is a shift in general ENSO conditions from SOI + to SOI -. The problem with making this argument is that there have been many shifts from a predominantly La Nina regime to a predominantly El Nino regime in the historical record and none have resulted in the sort of warming the planet has seen in the last 35 years. At this point I will refer you to the book “El Nino, Storming Through History” which provides an excellent documentation of the raw data.
For those of you who are not aware of such things, we are currently in an El Nino condition. The current dynamical models (which have performed better than the statistical models recently) predict a continuing strengthening of El Nino resulting in a moderate El Nino by early 2010. IANACS, nor do I play one on TV, but at this point I will make a naive prediction: 2010 will be the warmest or second warmest year on record. I base this on the steady warming trend which exists in the surface record which has been shown at Open Mind ad nauseum and the fact that an El Nino generally results in an injection of energy into the climate system.
Finally, as an engineer myself, I see my job as applying well supported scientific results to products being set loose on the population. As such, I work several steps back from the cutting edge of science. I would hate to see researchers subject to the same standard as my work. If such was the case, science would probably ceases to exist.
RW (06:26:08) :
First of all, I apologise for using the word ‘fabrication’. I didn’t say that the graph was a fabrication, but I shouldn’t have used that word.
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You have now given a link that discloses the source and methodology behind that graph, so that’s good. As you suggested, I have plotted the data and I get a similar result. I’ll come to that in a moment.
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It seems there are two questions about this graph:
1. Does the graph actually show a linear relationship between temperature and CO2?
2. If the relationship is indeed linear, does it prove a causal relationship between the two?
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I’ll start with the second question first. The graph was produced by a blogger named Robert Grumbine. There’s nothing wrong with science being done by bloggers, and Grumbine seems to know what he’s talking about.
Grumbine concedes that the warming up to 1950 was natural, so the first half of the graph has nothing to do with CO2, and yet it looks similarly linear. A reasonable conclusion, therefore, is that linearity is no proof of a causal effect. This is hardly surprising, as many things, particularly over a restricted time period, tend to change in a linear fashion.
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Hadley/CRU state that the warming up to about 1975 can be explained by natural forcings (this is part of their ‘two graph’ proof of AGW). If Hadley is correct, then the first three-quarters of that graph had nothing to do with CO2.
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On a longer scale, Professor Akasufo makes a good case that the global temperature has been increasing in a roughly linear fashion since the end of the Little Ice Age.
http://people.iarc.uaf.edu/~sakasofu/pdf/two_natural_components_recent_climate_change.pdf
If he is right, and all the warming was natural, then you would still see a roughly linear relationship between CO2 and temperature.
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Grumbine states that if the two graphs are straight lines then the correlation is perfect. That may be technically correct, but it doesn’t make much sense, because straight lines don’t have any features. However, if the graph had lots of features (going up and down in different parts) and the features on both lines are virtually identical, then you could certainly say there is a high degree of correlation.
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The problem with the CO2 curve is that it is nearly featureless. In contrast, the temperature curve is feature-rich. Now, if the CO2 had lots of features, sometimes going up and sometimes going down, and if the temperature curve had very similar features, then I would say that there was very high correlation. I would also be forced to conclude that there was a very strong causal relation between the two. Providing the data showed that the up and down temperature changes did not occur *before* the corresponding CO2 changes, then I would conclude that in all probability the temperature was being driven by the CO2. But this is hypothetical, as the CO2 is nearly featureless, and can almost be approximated by a simple straight line.
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Fortunately nature has performed an experiment over the last half million years that has been captured by the ice cores. Particularly due to the ice ages and other climate changes, the CO2 record is extremely feature-rich. And so is the temperature record. The obvious correlation between the two is startling, as Gore makes clear in An Inconvenient Truth. Unfortunately for Gore, we now know that the temperature changes occur roughly 1000 years before the matching CO2 changes, so CO2 could not have been the driver. The CO2 was changing due to the slowly-changing ocean temperatures.
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If, as seems to be the case, the ice cores show no evidence of CO2 driving the temperature, then the only reasonable hypothesis is that CO2 has a negligible effect on temperature. Of course, simple physics shows that a doubling of CO2 should lead to roughly a one degree rise, but climate is complex and full of negative and positive feedbacks. Clearly, in the long run negative feedback dominates, otherwise we wouldn’t be having this discussion today. If so, then it’s quite possible that negative feedbacks reduce the basic greenhouse effect by a large amount, thus making CO2 an insignificant climate driver.
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Of course, as one of Rutan’s slides demonstrate, over periods of tens and hundreds of millions of years there is essentially no correlation between CO2 and temperature. Also, during most of the Earth’s history when there was life, CO2 levels were on average far higher than they are today.
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So, to summarise, I think an apparently linear relationship between CO2 and temperature is absolutely no proof of a causal relationship.
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And now to the first question: does that graph actually demonstrate a linear relationship between CO2 and temperature? I was initially somewhat – shall we say – sceptical when I saw that graph. If you look at the two graphs of CO2 and temperature it’s obvious that they’re very different. All that you can say is that they both went up during the 20th century – but then, a lot of things went up.
It’s curious that Grumbine doesn’t use filtered data, so that there is a large spread of noise. Possibly this band of noise gives a slightly misleading impression. Also he includes a straight trend line, which can have the effect of deceiving the eye into seeing a straight line when in fact it doesn’t exist. I wondered how it would look with filtered data and that’s one reason why I did actually plot the data. I used Mauna Loa and HADCRUT3. As Mauna Loa starts around 1958, that’s the period covered. Grumbine’s plot probably starts around 1900.
Here’s my first plot, which uses unfiltered data.
http://www.kline.demon.co.uk/AANoFiltering.jpg
Blue is CO2, red is temperature,green is the ‘linear’ plot (temperature versus CO2), with CO2 on the horizontal scale.
It looks similar to Grumbine’s. However, the flat portion corresponding to the last ten years is more prominent. Even with the noise you can see that it’s not all that linear.
The other two plots are for one and four years rolling average:
http://www.kline.demon.co.uk/AAOneYearFilter.jpg
http://www.kline.demon.co.uk/AAFourYearFilter.jpg
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You can see that, with filtering, the green curve is similar to the red temperature curve. This is not surprising. If the CO2 curve is close to a straight line then the red and green lines will be similar, as essentially the amount of CO2 is proportional to time.
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So, if we apply a bit of filtering to clean it up, then the ‘linear’ curve looks essentially the same as the original temperature curve, which is highly non-linear. Indeed, maybe a third of the curve shows a negative relationship, with temperatures going down as CO2 goes up.
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If you simply look at the curves of CO2 and temperature you can see a lack of correlation between the two. Most dramatically, although CO2 has steadily increased there has been no corresponding increase in temperature over the last ten years or so. The Grumbine graph, once it has been cleaned up to reduce the noise, simply confirms the lack of correlation.
Chris
evanmjones:
“However, when you run it along date lines (unlike your graph) you get multidecadal rises and falls that correlate with the rises and falls of the multidecadal oceanic-atmospheric cycles, while the rise in CO2 is a simple curve. I do not dispute the numbers in your graph, I merely note the arrangement, which is not strictly chronological.”
As you have still not given any numbers, I conclude that you are fully aware that your claims have no mathematical basis. If you think they do, just show us the numbers. If you refuse to quantify your assertions, it makes it pointless trying to have a scientific discussion with you.
Your talk of ‘decoupling the years’ doesn’t make sense; you calculate a correlation between two variables by plotting one against the other, and a third variable like time is irrelevant. I’ve no idea what your educational background is but I can tell you that you are coming to erroneous conclusions because you are looking at the problem in a fundamentally incorrect way.
As for whether we face an emergency, that is an inherently very interesting question which is well worth investigating. However, if you still deny, despite the data being right there in front of you, that there is a strong correlation between CO2 and temperature, and a weaker correlation between oscillation indexes and temperature, then you are not conversing within the domain of science and you might as well be typing in Mandarin.
Rattus Norvegicus (00:16:39) :
Climate scientists who write and run GCMs are just about to learn what the engineers have known for many decades. Big models of big systems have have big failures, especially when the big systems have features that are not understood or are not represented in the big models.
Sadly, if you are an engineer, it appears you do not understand what scale-up is and how important it is. One simply does not start by constructing the biggest facility first. Yet those who write GCMs ignore this.
Ah well.
Nature bats last.
Chris Wright:
“First of all, I apologise for using the word ‘fabrication’. I didn’t say that the graph was a fabrication, but I shouldn’t have used that word”
You did, in fact, directly accuse me of fabricating the graph. But OK, I accept your apology.
“It seems there are two questions about this graph:
1. Does the graph actually show a linear relationship between temperature and CO2?
2. If the relationship is indeed linear, does it prove a causal relationship between the two?”
These are not the questions to be asked. There is no attempt to show a linear relation, and nor is one expected. The graph shows a strong correlation between CO2 levels and temperature – nothing more, nothing less.
“I’ll start with the second question first. The graph was produced by a blogger named Robert Grumbine. There’s nothing wrong with science being done by bloggers, and Grumbine seems to know what he’s talking about.”
Do you know who Robert Grumbine is?
“Grumbine concedes that the warming up to 1950 was natural, so the first half of the graph has nothing to do with CO2, and yet it looks similarly linear.”
In this one sentence, you misrepresent Grumbine and misunderstand his graph. His very simple study implies that before 1950, CO2 was a significant but not dominant contributor to climate change. His graph has CO2 concentrations on the x-axis, not time, so the whole of the graph has everything to do with CO2.
“Hadley/CRU state that the warming up to about 1975 can be explained by natural forcings (this is part of their ‘two graph’ proof of AGW). If Hadley is correct, then the first three-quarters of that graph had nothing to do with CO2.”
Again, you seem to be misrepresenting Hadley and misunderstanding the graph. Here’s a quote from a Hadley Centre Technical Note entitled “Estimation of natural and anthropogenic contributions to 20th Century Temperature Change”: We found that the effects of well-mixed greenhouse gases, other anthropogenic effects (largely the indirect effect of sulphate aerosols), and natural causes (solar irradiance changes and volcanic eruptions) could be detected in the record of surface temperature change during the entire 20th century. You seem to think that climate change is either 100% natural or 100% anthropogenic, with some sudden switch between the two. This is not the case.
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“Grumbine states that if the two graphs are straight lines then the correlation is perfect. That may be technically correct, but it doesn’t make much sense, because straight lines don’t have any features.”
It makes perfect sense and it’s a simple matter of mathematical definition.
“However, if the graph had lots of features (going up and down in different parts) and the features on both lines are virtually identical, then you could certainly say there is a high degree of correlation.”
True, and not in any way incompatible with the previous statement.
“The problem with the CO2 curve is that it is nearly featureless. In contrast, the temperature curve is feature-rich.”
This is not a “problem”. It’s an observation. To put it in the simplest of terms, CO2 is a driver of climate, not of weather. The climate is fairly monotonically warming. Weather continues as it always will.
“Now, if the CO2 had lots of features, sometimes going up and sometimes going down, and if the temperature curve had very similar features, then I would say that there was very high correlation.”
Like evanmjones, you seem to prefer words to mathematics. We have plotted T v CO2. We have fitted a line. We have calculated the statistics. The correlation is very high (R²=0.78). Nothing more to be said.
“If, as seems to be the case, the ice cores show no evidence of CO2 driving the temperature, then the only reasonable hypothesis is that CO2 has a negligible effect on temperature.”
A wildly erroneous conclusion. Just because something hasn’t been the primary cause of climate change doesn’t mean it can’t. There is no evidence that I know of that an opaque sphere has ever enclosed the Earth. Your logic would lead you to conclude that if humans were building such a sphere, then it wouldn’t affect the climate.
“Clearly, in the long run negative feedback dominates, otherwise we wouldn’t be having this discussion today.”
Non sequitur.
“If so, then it’s quite possible that negative feedbacks reduce the basic greenhouse effect by a large amount, thus making CO2 an insignificant climate driver.”
Like evanmjones you seem to think that feedbacks operate selectively, depending on the dominant forcing. This is physically impossible.
“Here’s my first plot, which uses unfiltered data.
http://www.kline.demon.co.uk/AANoFiltering.jpg
Blue is CO2, red is temperature,green is the ‘linear’ plot (temperature versus CO2), with CO2 on the horizontal scale.”
It looks similar to Grumbine’s. However, the flat portion corresponding to the last ten years is more prominent. Even with the noise you can see that it’s not all that linear.
The other two plots are for one and four years rolling average:
http://www.kline.demon.co.uk/AAOneYearFilter.jpg
http://www.kline.demon.co.uk/AAFourYearFilter.jpg”
None of these are what Grumbine used to produce his graph. He used annual averages. Nor have you fitted any lines or calculated any correlation coefficients.
“So, if we apply a bit of filtering to clean it up, then the ‘linear’ curve looks essentially the same as the original temperature curve, which is highly non-linear. Indeed, maybe a third of the curve shows a negative relationship, with temperatures going down as CO2 goes up.”
One does not “apply a bit of filtering to clean it up” without good reason.
“If you simply look at the curves of CO2 and temperature you can see a lack of correlation between the two. Most dramatically, although CO2 has steadily increased there has been no corresponding increase in temperature over the last ten years or so. The Grumbine graph, once it has been cleaned up to reduce the noise, simply confirms the lack of correlation.”
It’s truly amazing that you can look at two curves that clearly correlate, and a graph that shows and quantifies that correlation, and still see no correlation. This is pure denial. There is no good reason to take 12 month or 48 month moving averages but in fact, if you do, the correlation is strengthened. For monthly CO2 concentrations and temperature anomalies, I calculate R⊃2=0.64. For 12 month trailing averages of both, R⊃2 = 0.80, and for 48 month trailing averages, it’s 0.93. The Grumbine graph does not need “cleaning up to reduce the noise”, and if you do so you in fact artificially inflate the correlation, rather than (as you were attempting to) reducing it.
That makes sense to me too, Morten
RW (08:25:53) :
Okay, I’ll concede one point. Grumbine was actually quoting IPCC, that the forcing due to CO2 was significantly less prior to 1950. He didn’t say it was zero prior to 1950.
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Unfortunately it looks like Hadley has removed their ‘proof’, probably for good reason. You may be familiar with it. They showed two graphs. The first was for purely natural forcing and it matched actual temperatures very well until about 1975, but after that the measured temperature went up much higher. The second graph included the effect of CO2 and matched perfectly, before and after 1975. This ‘proved’ that CO2 forcing was required to properly account for the total warming. But the whole point was that natural forcings accurately accounted for the warming until around 1975. I think I have some slides from Hadley that give an account of this, but I can’t dig them up right now.
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It seems the fundamental argument is this: does that graph provide proof of cause and effect? I don’t think it does. If, as I said, both graphs had lots of features and they corresponded well, then, yes, that would be proof of cause and effect. But there are essentially no features that correspond, primarily because the CO2 has virtually no features. By contrast, graphs linking solar activity and temperature, for example, do have significant features that correspond well.
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Suppose you have three graphs: one showing the ice core CO2/temperature, the second graph showing solar activity/temperature (e.g. Svensmark), and the third graph showing the 20th century CO2/temperature. Suppose you showed them to the proverbial man in the street and asked him to rank them in terms of cause and effect. I think his first choice would be the ice cores, as there are many features that match almost perfectly. His second choice would be solar activity/temperature, as there are significant features that match but not quite so perfectly. And his third choice – a distant third – would be the 20th century record. There’s no visible correlation except that they both go up.
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It would be easy to make lots of similar graphs that show a similar relationship, but between variables that have no causal link e.g. the distance to a galaxy and the average salary of climate scientists. Both are increasing but there’s no causal link at all.
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“One does not “apply a bit of filtering to clean it up” without good reason.”
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Oh, come on, I would think the reason is obvious. Graphs in all sciences are often filtered to remove the noise so that the underlying trend is clearer. You could say it’s a bit like removing the weather to reveal the climate. Have you never used filtering of any kind on your own graphs?
I used monthly data because that’s what I had. And monthly data has to be better because it has higher resolution.
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“A wildly erroneous conclusion. Just because something hasn’t been the primary cause of climate change doesn’t mean it can’t.”
(with respect to the ice cores).
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Unfortunately we can’t do climate experiments, so all we have is the historical record. And probably the best records come from the ice cores.
I’m sorry, but if you claim a certain value for the CO2 forcing and the ice cores don’t support that, then you’re wrong. The ice cores are a perfect test because, as I’ve said, they are feature rich and therefore provide plenty of opportunities to demonstrate any causal relationships. The features match almost perfectly, but with a thousand year delay due to the inertia of the oceans. If you can’t see a signal in the ice cores that confirm your assumed forcing then it probably doesn’t exist. Almost certainly temperatures would respond to CO2 very quickly, perhaps just a year or so, so you would expect to see a clear and almost instantaneous response. But, as far as I’m aware, there’s no sign of that.
I prefer to base my beliefs on solid measurements and not on the output of computer models whose predictions are consistently wrong.
Chris
Roger Sowell (07:40:01) :
Had you missed the models of smaller scale and/or more simplified circulation systems, e.g., small-scale process models, regional weather models, mesoscale models, etc., developed over the past several decades?
if you want to see comments from just about all of the “Warmites” Gore has enlisted since his “inconvenient lies” movie, visit http://www.current.com and search for things like “global warming”.
enjoy the abscence of logic and science in the “arguments” and “discussions.”
it’s enough to make a grown engineer cry.
Rattus Norvegicus (00:16:39) :
I am the engineer who characterized the GCM’s as statistical. Although they claim to be physical, the constants they use in presumed feedback were selected based on how well the models hindcast. This is there fatal flaw and why I don’t believe they have any predictive value. When you notice a correlation, you should then investigate the physical relationships to determine cause and effect; not just try different mathematical relationships and time lags.
Do you realize that the “best” models (ones that hindcast best) assumed a huge positive feedback from clouds? They assumed that small increases in air temperature would reduce cloud cover. There are certainly ways to test this and they have all failed. If this were so, there would be markedly less cloud cover in the warmer areas of earth.
Why does cloud cover vary? Maybe cosmic rays, maybe chaos. Either way, if you want to predict weather and climate better, we should research cloud dynamics. When I found out the GCM’s used all sorts of positive feedback fudge factors to exaggerate the climate sensitivity by a factor of five, like Burt Rutan, I knew it was BS.
When they explain all the texture in the temperature record and there is a residual 1K climate sensitivity with a very high correlation, I might take them seriously. Of course once you explain everything but a linear trend, you might look to other roughly linear factors like land use change.
oms (16:08:25) :
“Had you missed the models of smaller scale and/or more simplified circulation systems, e.g., small-scale process models, regional weather models, mesoscale models, etc., developed over the past several decades?”
No, I had not missed that. Been following along for quite some time. And noticed along the way how wonderfully accurate the “small-scale process models” were; and am completely confident in the regional weather predictions from the “regional weather models” (they NEVER miss, do they?); and the infallibly accurate predictions from the other models, too. I thought of all that accuracy every time I shoveled 12 inches of “partly cloudy” from my driveway, and just laughed when I was drenched in rain on a “fair and sunny” day, predicted by those models. Oh, what? Not the same models? You mean CLIMATE models! Of course…the ones that failed to properly predict anything so far…like the recent run of cool temperatures in Los Angeles…the complete blanket of snow across Canada this past winter…snow in Buenos Aires…absence of record hurricanes…sea levels refusing to rise as predicted…ice growing and growing globally…THOSE models!
Engineers don’t have the luxury to be wrong 99 percent of the time. We adhere to things like fundamentals.
By the way, to all you climate scientists out there. We engineers (with the lawyers’ assistance for permits and such) are going to design and build the processes to combat your faked CO2 crisis – carbon capture and sequestration, cap and trade, renewable energy as replacement for fossil fuels, bio-fuels for transportation, and all the rest. And the economies in the world that are forced to do these things are going to crumble, with people unemployed, cold, and hungry. Liberal state governments, and federal governments, will increase tax rates to compensate for falling revenues as businesses fail. Tax-payers will soon revolt. The non-participating economies will continue to consume fossil fuels and grow their economies.
And the engineers will be pointing straight at the climate scientists with their faked CO2 crisis, properly laying the blame entirely at your feet.
This is not a game.
Roger Sowell (21:12:51) :
Any references to regional weather models or small scale process studies using DNS which are wrong 99% of the time? Are they all in the same category?
Do you use models for engineering applications? Do you develop models at all?
Riiiight, I’m not a climate scientist, but I say Go For It.
Chris Wright: you use monthly data because its higher resolution “has to be better”, but then you take moving averages to “remove the noise so that the underlying trend is clearer”? You’re just doing random things to the data with no meaningful justification. The funny thing is, you’re inflating the correlation between the two variables, when clearly what you hope to do is reduce it. You’ve written almost 2000 words describing Grumbine’s graph and your own, but you haven’t actually calculated a single number. The numbers of interest are what I gave – R² (a measure of how well two variables correlate) for T vs CO2 since 1958 is 0.64 for monthly data, 0.78 for annual, 0.80 for 12 month moving averages and 0.93 for 48 month moving averages. Your waffle is a laborious and confused attempt to deny the staggeringly obvious correlation that exists between global temperature and CO2 concentrations.
Folks, CO2 and temperature do not correlate: click. Notice the R^2 non-correlation. Which stands to reason, because as beneficial carbon dioxide increases, the planet’s temperature doesn’t follow. Thus the CO2=AGW conjecture fails.
Folks, despite the repeated efforts of many people, Smokey cannot get it into his head that climate cannot be measured over ten years, and thinks that the more times he posts exactly the same graph, the less wrong it gets. Smokey’s belief system crumbles under the slightest scrutiny, as you can see with this graph.
REPLY: To be fair, you are pretty stubborn yourself on your own belief system. There’s plenty of stubbornness to go around. – A
RW:
Nice strawman there. Sorry that the AGW conjecture still fails. The question was about the non-correlation between CO2 and temperature, not about the definition of climate.
[snip smooookey don’t make me turn this board around! ~ charles the mo
therderator]Smokey – you fail, yet again.
A – what’s stubbornness got to do with anything? Smokey is wrong – that is all. What do you think of his endless stream of graphs that show only a carefully picked small segment of the available data? Do you think they are worthwhile, or irrelevant?
REPLY: I think looking at recent trends is instructive, the relevancy increases with time.
Burt Rutan is someone I’ve admired. Spaceship One was such an accomplishment. As I read the presentation here I found that his values, and conclusions based on looking at the science, agreed with mine. I was surprised to see him say, though, that we will basically never run out of oil. That seems to be an “out there” assertion. I realize we’re finding new reserves, but were they not created millions of years ago? Of course processes continue which will create oil in the future, but unless I see data saying otherwise I think we’re using it up faster than it’s being created, which necessarily leads to a conclusion that we will run out one day in the far off future.
Reading some of the alarmist literature, I’m struck by how speculative and/or how math-intensive it is, and how little is based on observations that have gone through rigorous scrutiny. It seems as though the “alarmist train” is being run by mathematicians and statisticians, not scientists. Mathematics and simplistic statistical analysis can create the illusion of absolute truth in the real world. I sometimes think that some math-minded people are knowingly pulling a con-game on the world at large, because so many people are mathematically illiterate and do not understand the nature of the relationship between math and science.
I also notice that alarmists strangely avoid going to the heart of the matter. I’ve proposed two times to alarmists, “Why don’t we look at the tropospheric temperature record, where the greenhouse effect actually takes place?” They talk around it. They talk about the surface temperature record, and the CO2 record and say, “See how they correlate, and how the relationship fits well with the established formula for radiative forcing?”. Or they say, “Look at the stratosphere and how it’s cooling. That’s evidence of greenhouse warming.” I can see the rationale for that conclusion, but it’s not the end of the story. There’s a mystery that they do not want to acknowledge. If we look at the troposphere itself there’s a slight warming, but from what I’ve seen, even using a temperature data set that an alarmist referred to me, it’s less than .1 degrees C from the 1980s to recently, way less than the climate models say it should be.
What I find with the alarmists is the same thing I’ve heard is true of people who believe in creationism: They demand an explanation, even if none that is rigorously rational exists. So they grasp for the one, no matter how flimsy, that agrees with their world view and seems to make the most sense at the moment. They use mathematics and statistics to reassure their belief, to give the imprimatur of “truth”. It’s all based on a belief that “We affect the world”. Yes we do, but to what extent is the question. To them our effect is as large as the world. The evidence says maybe the scope of the effect of industrialization on temperature is as large as the world, but the magnitude of the influence is so small that it could not lead to the catastrophe that alarmists predict.
Notice:
going offline for some time to come, sorry about answers owed.
Burt Rutan’s presentation on Anthropogenic Global Warming in the forum in Oshkosh was music to my ears. My epiphany on the subject came when I saw the documentary “The Great Global Warming Swindle” on TV in Europe in 2007. I have been looking for this documentary to play on American TV ever since. The qualifications of the scientists that participated in it, and the cohesiveness of their data from widely different fields, clearly debunk both the claim of scientific consensus and manmade CO2 as the cause of climate change.
You can view the documentary at the following link:
“http://www.moviesfoundonline.com/great_global_warming_swindle.php”
ref: RW, 21.08.2009… re: http://www.kline.demon.co.uk/AAFourYearFilter.jpg
and the red line is very similar to the blue line, but shifted ten years to the right…
interesting.
sorry… red line is GREEN line shifted ten years to the right… look at the shape.
[ok to edit previous post and delete this one.
thanks!