At worst, the discovery shows what to look for when trying to predict average global temperature and shows that the IPCC have been looking at the wrong thing.

The threat to freedom and prosperity specifically referred to misguided actions that some politicians were advocating (Cap and Trade) based on the misinformation. With climategate that threat has been substantially reduced.

Without the threat to freedom and prosperity the discoveries matter little except as an early warning to the coming cold.

]]>the core of your argument is:

“I’m right because my model fits the data”

This is however a fallacious argument, the way you derived the model it would have been remarkable if you DIDN’T get a fit.

Like I have said before,Using your methods, I could show an equally convincing correlation between global temps and the price of pork belly futures, it obviously wouldn’t disprove AGW.

This is an entirely different argument than the climate scientists are making. They have established a phenomena based physics, specifically, emission-absorption spectra. They are then developing models to try and estimate the effect that phenomena will have in the future. They are not saying “we’re right because our model fits the last 100 yrs of data”. which is exactly what you are saying.

Now, I’m not convinced that AGW is real, and I’m certainly not convinced that if it is the consequences would be as bad as some say. But I base my opinions on the science, science can not be judged to be right or wrong based on the effects it has on our political stances.

while we were going back and forth over the math, you thought that an appropriate point would be:

“climate change is a mistake that takes away freedom and prosperity”

Science is not right or wrong based on whether or not the policies resulting from that science would be desirable. Vaccines are undesirable from a political standpoint, they cost taxpayer money and the government must take away peoples freedoms in order to coerce people into getting vaccinated. This is unfortunate, but it does not make the entire field of immunology wrong.

So, are you judging this based on the science? or based on the effect that science would have on your political stances?

if the former is true, then why even bother throwing out that last bit about freedom when you knew it was irrelevant to the discussion?

]]>The IPCC GCMs have failed to predict as shown e.g. at http://scienceandpublicpolicy.org/images/stories/papers/originals/co2_report_july_09.pdf .

The model presented in the October 14 pdf at http://climaterealists.com/index.php?tid=145&linkbox=true accurately predicts average global temperatures since 1895. Unknowns in the model were determined using measured data from prior to 1975.

Since 2000 the atmospheric carbon dioxide level has increased 18.8% of the increase from 1800 to 2000. According to the average of the five reporting agencies, the average global temperature has not changed much for several years and during the seven years from 2002 through 2008 the trend shows a DECREASE of 1.8°C/century. This SEPARATION between the increasing carbon dioxide level and not-increasing average global temperature is outside of the ‘limits’ of all of the predictions of the IPCC and ‘consensus’ of Climate Scientists. The separation has been increasing at an average rate of about 2% per year since 2000. It corroborates the lack of connection between atmospheric carbon dioxide increase and average global temperature.

As the atmospheric carbon dioxide level continues to increase and the average global temperature doesn’t it is becoming more and more apparent that the IPCC and many Climate Scientists have made an egregious mistake and a whole lot of people have been misled.

Without human caused global warming there is no human-caused climate change. Any activity to reduce atmospheric carbon dioxide to reduce climate change is a mistake that takes away freedom and prosperity

]]>But more importantly, I am quite convinced that there is a fatal flaw in your logic.

If you have two plausible causes, not mutually exclusive, then showing that the system’s response can be explained using only one of the causes does not “prove” that the other cause did not significantly contribute to the system’s response. This is especially the case when your model has been fitted to the data, making it entirely unremarkable that such a correlation occurs.

My “two guys pushing on a rock” analogy holds. I pictured the rock as moving, the observation was its acceleration (though I don’t see how this is relevant). I also never assumed that “each of the causes contributes a part of the force”, in fact, the example possible model I gave was:

“We cold easily derive a model where you have no effect on the rock (I push with 100 N, you push with 0 N)”

My rock example is really just a way to illustrate my point. The real problem here is that the core of your argument is logically fallacious. Even if I stipulated for the sake of argument, that the model was flawless, it would still not “disprove” or even cast doubt on AGW.

If you wanted to accomplish this goal (and if we’re being honest, your goal should be to find the truth, not to disprove AGW,… but anyways…), you would need to show a similarly strong correlation between the empirical data and a model whose parameters were established completely independent of that data.

]]>It is becoming apparent why tens of billions of dollars have been spent in futile efforts to prove that added CO2 caused Global Warming while an unpaid engineer with a desk-top computer and using simple engineering analysis has discovered the real cause of the temperature run-up in the 20th century. With this discovery, changes to ghg levels have been found to have no significant effect on climate and Natural Climate Change has been verified. This does not show that added ghgs have zero effect. It does show that the temperature anomalies can be accurately calculated by ignoring any effect from changes to the level of ghgs.

The validity of any model depends on its predictive ability. The AOGCMs that the IPCC uses have been ‘trained’ relentlessly to fit more than a century of data (how many dof were used to do that ‘training’?). They have failed miserably to predict anything since about 2001. All of the unknowns in the ‘model’ presented in the October 14 pdf at http://climaterealists.com/index.php?tid=145&linkbox=true could be determined using data prior to about 1975. This model gives an excellent prediction of temperatures after then.

However, just because this model does a phenomenal job of ‘predicting’ average global temperatures since 1895 does not mean that it will continue to do so forever. The ‘effective ocean turnover’, although overwhelmingly dominated by the (time-integral of the) PDO index, is actually the net effect of multiple ocean turnovers that happened to coincide in a way that resulted in the observed pattern for the 20th century and so far in the 21st century. Perhaps these individual ocean turnovers can be sorted out and time constants determined so that the net ocean turnover can be projected into the future. If the sunspot count can also be predicted than this model can be used to predict future average global temperature. Lacking any of this, the projection shown on the graph remains the best estimate of the lower limit to the future temperature trend. If the quiet sun continues, the trend of actual measurements of average global temperature should be close to that lower limit.

Your analogy of the rock is an interesting one. I can think of two issues with it: 1) Our rock moves i.e. the average global temperature changes and 2) the assumption must be that each of the ’causes’ contributes a part of the ‘force’. The alternative is the illogical assumption that one or both of the ’causes’ is sharply nonlinear. Since one of the ‘forces’ accounts for all of the change the other ‘force’ must be insignificant.

]]>Firstly, the term “null hypothesis” is commonly used in science outside of statistics to refer to the fact that one’s base assumption must be that nothing extraordinary is occurring. Its a similar concept to Occam’s razor, or Carl Sagan’s “extraordinary claims require extraordinary evidence” mantra. Secondly, if we wanted to pick nits, I think a rather strong argument could be made that all observational and experimental science is rooted in statistics… but I digress…

I did miss that he considered radiative heat loss, though the lack of an independently determined emissivity coefficient invalidates the fact that the energy balance results in the correct temperature. In other words, you can’t choose the emissivity so that the temperatures agree with the empirical data and then argue that the model is valid because of that agreement.

The term “degrees of freedom” is commonly used in the systems modeling literature to refer to the number of parameters the optimization algorithm is allowed to vary in order to minimize the cost function. “Orthogonal dimension in the optimization parameter space” is also appropriate, but it just doesn’t role off the tongue as nicely as DOF.

Which leads us to the real problem with the model now that the infinitely increasing temperature has been dealt with. We can nit pick on the exact number, but in creating this model, the author selected somewhere between 4 and 6 separate parameters specifically so that the model would resemble the empirical data. It is therefore entirely unremarkable that model resembles said empirical data. This is extremely important because the claim boils down to: “the model matches the data, therefore AGW is bunk”.

Like I said before, give me any historical data set (poll results, sports stats, price of tea in china, etc…) the ability to fool around with integrals and derivatives, and 4 or 5 free parameters, and I could produce a correlation at least as convincing as the one presented in this article. Would that “Disprove” AGW?

In order to make an argument like that for a model, the parameters need to be arrived at independently of the data set you are trying to match. You can’t cheat by looking at the answer key to come up with your own answer. For example, instead of choosing the emissivity so the the temps turned out right, you would need to arrive at the emissivity either via some measurement, or through some independent calculation such as doing a weighted average of the emissivities of the various gasses in the atmosphere.

Don’t get me wrong, we optimize model parameters in order to match model outputs with empirical data all the time. But we do this in order to extract system parameters such as pole-zero locations, nonlinear phase plots, damping ratios, etc… We would never optimize a model to fit the empirical evidence, and then use that fit as evidence for some argument. Of course it fits, you rigged the game ahead of time so that it practically HAD to turn out that way.

All that being said, I have a problem with the basic logic behind the argument. Given that there are two plausible causes that are not mutually exclusive, can the fact that a model can be derived to explain the response with only one cause be used to “prove” that the other cause has had no effect? I don’t think so.

To give an example, lets say you and I were both pushing on a giant rock. Observations show that the net force on the rock is 100 N. We cold easily derive a model where you have no effect on the rock (I push with 100 N, you push with 0 N). But that doesn’t “prove” you weren’t pushing on the rock. Its just as likely that I wasn’t pushing at all, or that we were both pushing with 50N each, or any of the other infinite number of possible combinations.

What I’m saying is that since sun spots and infrared absorption are both plausible climate forcing mechanisms, and they aren’t mutually exclusive, then showing that a model can explain the data without invoking one of those two causes doesn’t disprove the effect of the other.

]]>First, you assert that I said something that I didn’t say and then you berate me for having said it. How does that work? I continuously search for credible evidence of whether I am right or not even when I am ‘certain’. Given that, maybe I should weasel-word it a bit and say I am very nearly certain. No, I’m certain . . .

Statistical analysis was not used or needed in this work so reference to ‘null hypothesis’ is not relevant.

Apparently you did not spend enough time looking at the paper and then jumped to erroneous conclusions. You overlooked that the ‘energy out’, proportional to the fourth power of average global absolute temperature, was subtracted from the ‘energy in’ leaving the energy change which is proportional to average global temperature change. The flaw is in your failure to accurately read and understand what is written.

Perhaps the following description of the research will be easier to understand: According to the first law of thermodynamics, energy in minus energy out equals change in energy stored. The historical coincidence of low sunspot count with low temperature suggests the hypothesis that there is a connection. To get to energy and the first law, take the time-integral of sunspot count. NOAA has a site that gives a daily average sunspot count for each year so, to integrate with a time step of a year, just add the numbers for each year up to the year of interest. That would be ‘energy in’.

The rate that energy is radiated from the planet is proportional to the fourth power of the absolute temperature. Multiply this by the number of years to get to the same ‘year of interest’. The result of this is ‘energy out’ and the difference is energy change or net energy. Then multiply the energy change by a constant to make the net energy from 1700 to about 1940 have a fairly level trend. That requires a constant of 0.00000000636 which in scientific notation is written 6.36E-9.

The difference, energy in minus energy out, is the change in energy stored in the planet. The change in energy of the planet is indicated by the average global temperature. To get values close to the temperature anomalies, divide the ‘change in energy’ by 4000. Subtract a constant value of 0.4 from each of these values that you calculate to offset the data to overlay measured temperature anomalies. Graph this and you will discover a temperature run-up in the last half of the 20th century.

The planet’s temperature is dominated by the temperature of the oceans. The oceans circulate and ‘turn over’ so that sometimes warmer temperatures are at the surface and sometimes colder temperatures, so measured average surface temperatures can go up and down without any change in the overall average energy that the oceans contain. Assume an oscillating temperature trend factor due to effective ocean turnover that starts at +0.45 in 1877 and declines linearly for 32 years to zero in 1909 then increases linearly for 32 years back to +0.45 in 1941, then back down linearly again to zero in 1973, linearly up to 0.45 in 2005 and linearly down to zero again in 2037.

Now add the anomalies calculated from sunspot energy to the anomalies calculated from effective ocean oscillation and you will discover that you have done a remarkable job of tracking the measured temperatures of the entire 20th century and on into the 21st century. Alternatively, go to the October 14 pdf at http://climaterealists.com/index.php?tid=145&linkbox=true to see the resulting graph showing an overlay of the calculated and measured temperatures. The temperature decline from 1941 to 1973 was about 57% less than it would have been without the high sunspot-count time-integral during that period. The average global temperature increase from 1973 to 2005 was about 47% more than it would have been without the high sunspot-count time-integral during that period. It was not necessary to consider any contribution from CO2 or any other greenhouse gas.

There are actually 4 ‘constants’ that were discovered during the research. A fifth value is applied as a simple offset on the graph to show how accurately the calculated temperature anomalies match the measured temperature anomalies.

6.36E-9 is required to make temperatures from 1700 to about 1940 have a level trend.

The 4000 value accounts for the effective thermal capacitance to “get values close to temperature anomalies”.

The third and fourth constants that were discovered during the research account for effective ocean turnover oscillation and do not change the time-averaged (over 64 years) energy stored. The time-integral of the PDO index is very closely proportional to this effective ocean turnover oscillation.

Again, since this is a deterministic engineering analysis and not a statistical analysis, the concept of degrees-of-freedom does not apply. I have no idea whether your undergrad statistics professor would have also failed to understand this type of analysis.

As to the “extension of this line beyond the present”, the line shown beyond the present is a lower-limit trend (which is the expected trend if there are no sunspots). It assumes that the factors that have controlled the average global temperature since 1895 continue. My statement (that you quoted) stands.

I really don’t know why apparently no one considered the time-integral of sunspots before. Sunspot count or a time factor have been considered with poor correlation. Perhaps the scientists that did that work were not as familiar with the first law of thermodynamics as Mechanical Engineers are. If they knew what the second law is they would know that it is not a pertinent factor in this discussion.

As this research shows, increased greenhouse gasses have no significant effect on average global temperature. The quiet sun is allowing the planet to cool. The good news is that because of the huge thermal capacitance of the oceans (the top 3.5 meters of the oceans is equivalent in thermal capacitance to the entire atmosphere) the planet will cool slowly. But we may eventually face crop failure and famine from shortened growing season.

]]>he continues the model prediction 30 years into the future by assuming that in the future sun spot activity will equal zero.

“The extension of this line beyond the present assumes that future sunspot count is zero. Future temperature anomalies depend on future sunspot counts and future PDO behavior neither of which can be confidently predicted.”

So he admits his 30 years of “predictions” are worthless because they are based on entirely bogus assumptions. Then why would include 30 years of erroneous predictions on his plot? Why not just end the plot at 2009?

perhaps just an oversight? but then why write about it in the description?

perhaps he wanted to give the false impression that his model predicts future temperature decreases when it in fact predicts perpetual temperature increase?

We can’t read his mind, so we can not know for sure, but the whole thing smells fishy enough to be highly suspect.

]]>I just read over that link of yours and FYI, that analysis is total bunk. This of course does not support the AGW case, but that analysis employs some awfully sloppy math and doesn’t “prove” anything at all.

top 2 reasons:

1) The model assumes that temperature increase is proportional to the time integral of the sun spot count. This is by definition always a positive integer.

Lets leave aside for the moment the highly suspect assumption that the temperature increase would be related to the time integral of the signal. The integral of a series of positive integers will perpetually increase and tend towards infinity over time. (since it by definition must always have a positive derivative)

What this essentially means is that the model predicts that the earth’s temperature will tend towards infinity over time. Not only is this assertion ridiculous, it violates the first two laws of thermodynamics. This flaw alone is egregious enough discredit the entire analysis.

2) He uses at least 5 constants to “fit” the model. It is important to note that these constants are not independently analytically determined, they are optimized to fit the empirical data. The author readily admits that he does this “to get a value close to the temperature anomalies.”

This means he essentially used an optimized model with 5 degrees of freedom to minimize the error between the model’s response and the empirical data. This makes the model EXTREMELY weak. Given a model with five degrees of freedom, I could likely show a stronger correlation between earth’s temperature and the average American’s credit card debt, or the win percentage of the Boston Red Socks. In other words, the model is meaningless.

Like I said at the beginning, this does not support AGW. But it does mean that you shouldn’t go touting that “study” around to anyone with any experience in mathematically modeling complex systems. And for the love of God, don’t invoke some kind of vast conspiracy to explain why that garbage doesn’t get published. My Undergrad statistics professor would have flunked me for turning in an analysis like that.

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