Guest Opinion: Dr. Tim Ball
Early Awareness
Vincent Gray, M.A., Ph.D. is one of the most effective critics of the Intergovernmental Panel on Climate Change (IPCC) through his NZ Climate Truth Newsletter and other publications. He prefaces comments to the New Zealand Climate Science Coalition as follows.
As an Expert Reviewer for the Intergovernmental Panel on Climate Change for eighteen years, that is to say, from the very beginning. I have submitted thousands of comments to all of the Reports. My comments on the Fourth IPCC Report, all 1,898 of them, are to be found at IPCC (2007) and my opinions of the IPCC are in Gray (2008b).
His most recent publication is “The Global Warming Scam and the Climate Change Super Scam” that builds on his very effective first critique, The Greenhouse Delusion: A Critique of “Climate Change 2001”. We now know that the 2001 Report included the hockey stick and Phil Jones global temperature record, two items of evidence essential to the claim of human causes of global warming. In the summary of that book he notes,
· There are huge uncertainties in the model outputs which are recognized and unmeasured. They are so large that adjustment of model parameters can give model results which fit almost any climate, including one with no warming, and one that cools.
· No model has ever successfully predicted any future climate sequence. Despite this, future “projections” for as far ahead as several hundred years have been presented by the IPCC as plausible future trends, based on largely distorted “storylines”, combined with untested models.
· The IPCC have provided a wealth of scientific information on the climate, but have not established a case that increases in carbon dioxide are causing any harmful effects.
On page 58 of the book, he identifies what is one of the most serious limitations of the computer models.
No computer model has ever been validated. An early draft of Climate Change 95 had a Chapter titled “Climate Models – Validation” as a response to my comment that no model has ever been validated. They changed the title to “Climate Model – Evaluation” and changed the word “validation” in the text to “evaluation” no less than describing what might need to be done in order to validate a model.
Without a successful validation procedure, no model should be considered to be capable of providing a plausible prediction of future behaviour of the climate.
What is Validation?
The traditional definition of validation involved running the model backward to recreate a known climate condition. The general term applied was “hindsight forecasting”. There is a major limitation because of the time it takes a computer to recreate the historic conditions. Steve McIntyre at Climateaudit, illustrated the problem:
Caspar Ammann said that GCMs (General Circulation Models) took about 1 day of machine time to cover 25 years. On this basis, it is obviously impossible to model the Pliocene-Pleistocene transition (say the last 2 million years) using a GCM as this would take about 219 years of computer time.
Also, models are unable to simulate current or historic conditions because we don’t have accurate knowledge or measures. The IPCC accede this in Chapter 9 of the 2013 Report.
Although crucial, the evaluation of climate models based on past climate observations has some important limitations. By necessity, it is limited to those variables and phenomena for which observations exist.
Proper validation is “crucial” but seriously limited because we don’t know what was going on historically. Reducing the number of variables circumvents limited computer capacity and lack of data or knowledge of mechanisms.
However, as O’Keefe and Kueter explain:
As a result, very few full-scale GCM projections are made. Modelers have developed a variety of short cut techniques to allow them to generate more results. Since the accuracy of full GCM runs is unknown, it is not possible to estimate what impact the use of these short cuts has on the quality of model outputs.
One problem is that a variable considered inconsequential currently, may be crucial under different conditions. This problem occurred in soil science when certain minerals, called “trace minerals”, were considered of minor importance and omitted from soil fertility calculations. In the 1970s, the objective was increased yields through massive application of fertilizers. By the early 80s, yields declined despite added fertilizer. Apparently, the plants could not take up fertilizer minerals without some trace minerals. In the case of wheat, it was zinc, which was the catalyst for absorption of the major chemical fertilizers.
It is now a given in the climate debate that an issue or a person attacked by anthropogenic global warming (AGW) advocates is dealing with the truth. It proves they know the truth and are deliberately deflecting from it for political objectives. Skepticalscience is a perfect example and their attempt to justify validation of the models begins with an attack on Freeman Dyson’s observation that,
“[Models] are full of fudge factors that are fitted to the existing climate, so the models more or less agree with the observed data. But there is no reason to believe that the same fudge factors would give the right behaviour in a world with different chemistry, for example in a world with increased CO2 in the atmosphere.”
They use “reliability” instead of validation and use the term “hindcasting”, but in a different context.
“If a model can correctly predict trends from a starting point somewhere in the past, we could expect it to predict with reasonable certainty what might happen in the future.”
They claim, using their system that,
Models successfully reproduce temperatures since 1900 globally, by land, in the air and the ocean.
And,
Climate models have to be tested to find out if they work. We can’t wait for 30 years to see if a model is any good or not; models are tested against the past, against what we know happened.
It is 25 years since the first IPCC model predictions (projections) and already the lie is exposed in Figure 1.
Source: University of Alabama’s John Christy presentation to the House Committee on Natural Resources on May 15, 2015.
Figure 1
Fudging To Assure Reliability Masquerading As Validation
Attempts at validation during the 120 years of the instrumental period also proved problematic for the same reasons as for the historical record. A major challenge was the cooling period from 1940 to 1980 because it coincided with the greatest increase in human production of CO2. This contradicted the most basic assumption of the AGW hypothesis that a CO2 increase caused a temperature increase. Freeman Dyson described the practice, generally described as “tweaking”, and discussed in several WUWT articles. It is the practice of covering up and making up evidence designed to maintain the lies that are the computer models.
They sought an explanation in keeping with their philosophy that any anomaly, or now a disruption, is, by default, due to humans. They tweaked the model with human sourced sulfate, a particulate that blocks sunlight and produces cooling. They applied it until the model output matched the temperature curve. The problem was after 1980 warming began again, but sulfate levels continued. Everything they do suffers from the T. H. Huxley truth; “The great tragedy of science, the slaying of a beautiful hypothesis by an ugly fact.”
As Gray explained,
Instead of validation, and the traditional use of mathematical statistics, the models are “evaluated” purely from the opinion of those who have devised them. Such opinions are partisan and biased. They are also nothing more than guesses.
He also points out that in the section titled Model Evaluation of the 2001 Report they write,
We fully recognise that many of the evaluation statements we make contain a degree of subjective scientific perception and may contain much “community” or “personal” knowledge. For example, the very choice of model variables and model processes that are investigated are often based upon the subjective judgment and experience of the modelling community.
The 2013 IPCC Physical Science Basis Report Admits There Is No Validation.
Chapter 9 of the 2013 IPCC Report is titled Evaluation of Climate Models. They claim some improvements in the evaluation, but it is still not validation.
Although crucial, the evaluation of climate models based on past climate observations has some important limitations. By necessity, it is limited to those variables and phenomena for which observations exist.
In many cases, the lack or insufficient quality of long-term observations, be it a specific variable, an important processes, or a particular region (e.g., polar areas, the upper troposphere/lower stratosphere (UTLS), and the deep ocean), remains an impediment. In addition, owing to observational uncertainties and the presence of internal variability, the observational record against which models are assessed is ‘imperfect’. These limitations can be reduced, but not entirely eliminated, through the use of multiple independent observations of the same variable as well as the use of model ensembles.
The approach to model evaluation taken in the chapter reflects the need for climate models to represent the observed behaviour of past climate as a necessary condition to be considered a viable tool for future projections. This does not, however, provide an answer to the much more difficult question of determining how well a model must agree with observations before projections made with it can be deemed reliable. Since the AR4, there are a few examples of emergent constraints where observations are used to constrain multi-model ensemble projections. These examples, which are discussed further in Section 9.8.3, remain part of an area of active and as yet inconclusive research.
Their Conclusion
Climate models of today are, in principle, better than their predecessors. However, every bit of added complexity, while intended to improve some aspect of simulated climate, also introduces new sources of possible error (e.g., via uncertain parameters) and new interactions between model components that may, if only temporarily, degrade a model’s simulation of other aspects of the climate system. Furthermore, despite the progress that has been made, scientific uncertainty regarding the details of many processes remains.
These quotes are from the Physical Basis Science Report, which means the media and Policymakers don’t read them. What they get is a small Box (2.1) on page 56 of the Summary for Policymakers (SPM). It is carefully worded to imply everything is better than it was in AR4. The opening sentence reads,
Improvements in climate models since the IPCC Fourth Assessment Report (AR4) are evident in simulations of continental- scale surface temperature, large-scale precipitation, the monsoon, Arctic sea ice, ocean heat content, some extreme events, the carbon cycle, atmospheric chemistry and aerosols, the effects of stratospheric ozone and the El Niño-Southern Oscillation.
The only thing they concede is that
The simulation of large-scale patterns of precipitation has improved somewhat since the AR4, although models continue to perform less well for precipitation than for surface temperature. Confidence in the representation of processes involving clouds and aerosols remains low.
Ironically, these comments face the same challenge of validation because the reader doesn’t know the starting point. If your model doesn’t work, then “improved somewhat” is meaningless.
All of this confirms the validity of Dr Gray’s comments that validation is mandatory for a climate model and that,
“No computer model has ever been validated.”
And
Without a successful validation procedure, no model should be considered to be capable of providing a plausible prediction of future behaviour of the climate.
The traditional definition of validation involved running the model backward to recreate a known climate condition. The general term applied was “hindsight forecasting”.
Back testing style validation is mostly a waste of time. It can only prove a model is wrong, it can’t prove a model is right. I could probably make a model of pink noise that if I iterate long enough on the random number seed, I can probably find a curve that matches HadCrut, GISS, or (pick your temperature history. Don’t forget the release version!). The model would be perfectly back tested, but completely silly.
In fact, the code is fairly trivial. I’ll just go do it and report back. Might take a couple of hours of compute time…
“With four parameters I can fit an elephant, and with five I can make him wiggle his trunk”. A pink noise generator has piles of parameters in the random number state…
http://en.wikiquote.org/wiki/John_von_Neumann
Peter
Yes. My Lotus 1-2-3 model of the global temperature reproduces the past perfectly (it’s nothing more than a simple table). But it’s useless for predicting future temperature.
Reproducing the past is the old fallacy of “testing on the training data”. The fact that your model can reproduce the training data tells you nothing about whether or not it represents the physical reality correctly.
Reproducing the past is the old fallacy of “testing on the training data”.
One way to address that is to divide the history into two different components, one is the training set and one the test set. Of course, that assumes you have enough data to divide into two. We don’t really have enough data to even use as a training set period. With ocean oscillations at 60 years we need at least 120 and preferably 240* years of reliable data just for the training. So we need 480 years total data. It doesn’t exist. Also the anthropogenic C02 signal is only about 70 years old…
We’ll start to have enough data in the year 2099, with 120 years of satellite temperature records and the entirety under anthropogenic C02 increases. (Argo started in ~2005, so maybe it should be year 2125 if the heat wants to hide in the ocean).
Humans really have a hard time with the “sorry not enough data” outcome. They’d rather just make things up than be resigned to that outcome…
Peter
* Nyquist is 2x the period, however that assumes error free sampling. With errors, you begin to have something useful at 4x. Oscilloscopes for example use 4x-8x. Yes Nyquist is symmetrical – applies to both low frequency side and high frequency side.
In the spirit of demonstrating absurdity by absurd, here’s a back tested pinknoise noise model of the surface temperature as published by GISS.
?dl=0
Hey look, it even captures 60 year cycles in it. It must be right!
Of course this is silly. But it’s just as silly as taking a million lines of code and claiming it can predict the future of climate. In this case I (1) calculated the linear trend and detrended the data, (2) iterated on the 600+ size input state of octave’s random-normal number generator until I found a sufficiently small root mean square error, and (3) added the trend back. It’s actually very few lines of code, but a whole pile of hidden state in the random number generator. But that’s about as opaque and hidden as a million lines of grad student and PhD code. Both have emergent behaviors that can’t be traced to physical processes directly.
Again, what I’m demonstrating is backtesting only verifies that your model isn’t horribly wrong. It does not prove correctness. Most especially if the training and test set are the same. The pinknoise + trend model here isn’t horribly wrong, however it’s not correct. Just like climate models.
I’ll be tickled pink if the temperature actually follows the predicted track!
Peter
source code:
https://www.dropbox.com/sh/qi9h70otb2p9j9h/AABPE2Uf-s8xe8iGGr1BhQULa?dl=0
There is in fact a 1-D climate model that was verified by millions of observations: The 1976 US Standard Atmosphere, which remains the gold standard today. The hundreds of physicists, physical chemists, meteorologists, rocket scientists, etc that worked on this massive effort mathematically proved & verified with millions of observations that the Maxwell/Clausius/Carnot/Feynman gravito-thermal greenhouse effect is absolutely correct, and did not use one single radiative transfer calculation whatsoever, and furthermore, completely removed CO2 from their physical model of the atmosphere.
http://hockeyschtick.blogspot.com/2014/12/why-us-standard-atmosphere-model.html
That model models vertical slice of the atmosphere. It doesn’t model over time. Still, it’s cool that simplified physics sometimes can be applied to complex systems and still have a reasonable outcome. Doesn’t usually happen that way.
I always describe computer-based climate modeling as “Double-precision arithmetic operations against estimated data.” Pretty much meaningless.
One of Vincent Gray’s masterpieces is “The triumph of doublespeak – how UNIPCC fools most of the people all of the time” (26 June 2009, http://nzclimatescience.net/index.php?option=com_content&task=view&id=483&Itemid=32 ). Vincent recognized before anyone else that the IPCC was using the equivocation fallacy for the purpose of deceiving people and that this strategy had been so successful as to have achieved “the triumph of doublespeak” over logic. “Doublespeak” was a synonym for “equivocation.”
What’s up with Figure 1 showing a 5-year running mean of average of 2 satellite datasets from 1979 or 1980 to 2014? One of the two satellite datasets started with December 1978 and the other started with January 1979, assuming the satallite datasets are the main ones of concern to the global warming debate – the TLT ones by UAH and RSS. If the satellite data is not a 5-year running mean, then why does it not show the 1998 spike as being the alltime high, and why does it show a slight warming trend from 1997-onwards that C. M. of B. likes to assert based on the RSS TLT is completely lacking?
I agree, the graphic does not look correct. In the satellite era 1998 was clearly, and by a large margin, the “warmest year ever”, but the graphic with apparently yearly data points does not depict that?
If only coupled systems of nonlinear partial differential equations were easy to be hard.
Easy to be Hard
by Three Dog Night
Before trying to validate computational climate models one has to make them comprehensible, for no incomprehensible complexity can be validated ever.
Just for the taste of it.
It is a peculiar property of Keplerian orbits around a star, that as long as the solar constant is constant indeed (it is not), annual average incoming radiation at ToA (Top of Atmosphere) is exactly the same for the two hemispheres. That’s so, because of Kepler’s Second Law of Planetary Motion (“A line segment joining a planet and the Sun sweeps out equal areas during equal intervals of time”), conservation of angular momentum in a disguise.
It says while the planet proceeds by a small angle along its orbit, the time needed to do that is proportional to the square of its instantaneous distance from the star. At the same time incoming radiation flux is inversely proportional to that quantity. Therefore integrated incoming radiation is strictly proportional to the angular distance travelled.
If there were no precession (it is a negligible effect at first approximation), equinoxes were exactly 180 degrees apart along the orbit, so annual insolation is the same for the two hemispheres. Q.E.D.
In case of Earth clear sky albedo of the Southern Hemisphere is much lower than that of the Northern one (by some 6 W per square meter). That’s because fraction of the surface covered by oceans is higher in the Southern hemisphere (4:5 vs. 2:3) and water is almost black under clear sky conditions, while land surface is not. In spite of this, annual average reflected shortwave radiation is almost the same for the two hemispheres, the difference being less than 0.1 W per square meter (as observed by CERES satellites).
That means absorbed radiation (sunshine) also exhibits a high level of interhemispheric symmetry. That symmetry is brought about by clouds, of which one has a higher fraction of surface covered in the Southern hemisphere, than in the Northern one, and not only that, but it cancels differences in clear sky albedo nicely.
The usual explanation, that this symmetry is brought about by regulation of the positioning of ITCZ (InterTropical Convergence Zone) with its bright cloud band. However, it can’t be the full truth, because in general the ITCZ is located in the Northern hemisphere (5 degrees north of the Equator). Therefore even mid latitudes should be more cloudy in the South.
The mystery is made even deeper by the fact, that there is no such symmetry in OLR (Outgoing Longwave Radiation). The observed asymmetry is an order of magnitude higher, the Northern hemisphere radiates out 1.2 W per square meter more on average. The difference is accounted for by warm surface water transport across the equator.
One can try to understand it in the context of EP (Entropy Production). The vast majority of entropy production occurs in the terrestrial climate system, when incoming shortwave radiation with a high color temperature (5778 K) gets absorbed and thermalized. Compared to this entropy increase associated with both reflected shortwave and conversion of heat to outgoing longwave are small.
Therefore, if there is a sweet spot for rate of entropy production in the climate system, it could explain such a symmetry.
The trouble is it’s utterly incomprehensible what’s actually going on.
There is such a thing as MEPP (Maximum Entropy Production Principle). In climate science this approach was pioneered by Paltridge, but was only applied to internal processes, where rate of entropy production is negligible compared to absorption.
The principle itself is pretty general, and applies to all reproducible nonequilibrium thermodynamic systems (as shown by Dewar, 2003). However, the climate system is not reproducible, that is, microstates belonging to the same macrostate can evolve to different macrostates in a short time due to its chaotic nature.
Indeed, we find rate of entropy production could easily be increased in the climate system by making Earth just a little bit darker, that is, by lowering its albedo. But that does not happen, Earth is not pitch black as seen from the outside, not even close to it.
The only precondition to maximum entropy production listed by Dewar, but missing from the climate system is reproducibility, so chaos must have a profound effect on both albedo and its regulation. Unfortunately theoretical treatment of irreproducible nonequilibrium thermodynamic systems is missing, so there is nothing to say about them on theoretical grounds.
The fact observed interhemispheric symmetry in average rate of entropy production is replicated by no computational climate model is only a minor issue compared to its incomprehensible state.
Albedo is clearly regulated (otherwise it could not be the same for the two hemispheres), its regulation is chaotic (done by clouds, genuinely chaotic and fractal-like objects) and its set point (~30%) has not theoretical explanation whatsoever.
Therefore its dependence on changing atmospheric composition is unknown. Until this issue is resolved, it is both premature and pointless to construct sophisticated computational models.
Don’t overlook the recent article
http://wattsupwiththat.com/2015/07/30/new-study-narrows-the-gap-between-climate-models-and-reality/
1. According to Cowtan, the models compute air temperature and hence output air temperature projections, not land/ocean temperatures, and therefore the output from the corresponds with what the satellites are measuring.
So in any verification test, the validation should be tested against the satellite observations.
2. However, whilst the models output air temperatures, they were not tuned on the basis of satellite data but rather on a mix of land thermometer and ocean surface temperatures.
So apples were used in the input, and pears is what is outputted.
3. Of course, the land/sea thermometer record has been so bastardised and corrupted and polluted by endless adjustments/homogenisation, station drop outs, UHI etc, that very bad apples were used in the input/tuning process.
Given this, and ignoring the problem that we have insufficient knowledge of how the climate works and the problems inherent in non linear chaotic systems, it is no surprise that the output projections is so far from reality that it is essentially simply cr*p.
I’m wrapping up a summary paper on verification and validation in multiphysics simulations. The main conclusion is that we don’t do either well. Even in the simple cases of two monolithic (single physics) components coupled together, we sometimes lose an order of convergence (this means we need finer and finer grids to get accurate results). Without convergence, we can’t do a good job of verifying the models (verifying is ensuring the mathematics are completed sufficiently well and ideally ends with a high confidence estimate of numerical error associated with the discretization and selected solution method). Without good verification, validation is impossible (validation is characterizing the uncertainty in a simulation’s ability to reproduce reality, so without good experimental or observational data, validation is impossible as well). Then, when we want to predict outside of the validation space (beyond where we have data), the uncertainties rapidly increase.
With this as the case for simple models, climate models, if they were to use the same metrics as the rest of the modelling community, would have such large error bars on their simulation results that they would be embarrassed to publish them.
“With this as the case for simple models, climate models, if they were to use the same metrics as the rest of the modelling community, would have such large error bars on their simulation results that they would be embarrassed to publish them.”
Exactly right. Figure 1 here is a representative example of their actual error bars, more details here (2.9 MB pdf). Propagated error bars come to, at least, ±15 C after a projection century.
Despite the “tweaking” and “fine tuning” the computer models are still coming up with nonsense predictions when compared to actual measurements of the temperature. It is now apparent that the warmists have given up on making the models match the temperature and are now adjusting the temperature measurements to match the models.
Hypothesis: The Asymmetry of Evil
The Asymmetry of Evil proposes that Evil is much more powerful than Good, because Evil can be extremely incompetent, swift and devastating in its effect and Good can only partially mitigate the resulting harm with great skill, effort, cost, and time.
The Asymmetry of Evil is described in the following examples:
– Any vandal can destroy a great work of art in an instant, which took a genius years to create.
– Any thug can injure someone in an instant, but our best doctors can only mitigate the harm, and only with effort, cost and time.
– Any thief can steal a cherished possession in an instant, which the victim took years to earn or to create.
– Any liar can blurt a falsehood, but it can take years for an honest person to disprove it.
The Asymmetry of Evil states that any villain can cause great and irreparable harm in an instant, but our best citizens can only mitigate and not fully reverse the harm and can only do so with skill, effort, cost and the passage of time.
The villain cannot create a great work of art, the villain cannot properly raise a child, the villain is not honest in his or her life, but the villain can damage or destroy the great and the good by his or her acts of evil: destruction, violence. theft and deceit.
Such is the Asymmetry of Evil – Evil is more powerful than Good – because Evil can be utterly incompetent, yet can cause great and lasting harm in an instant: but Good can only mitigate, and can never fully repair the harm done by Evil, and can only mitigate with effort and cost, over a much longer time.
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Here’s an example, showing how reparations cost 7 million times the cost of doing the evil…
http://www.nytimes.com/interactive/2011/09/08/us/sept-11-reckoning/cost-graphic.html?_r=0
ONE 9/11 TALLY: $3.3 TRILLION
By SHAN CARTER and AMANDA COX Published: September 8, 2011
Al Qaeda spent roughly half a million dollars to destroy the World Trade Center and cripple the Pentagon. What has been the cost to the United States? In a survey of estimates by The New York Times, the answer is $3.3 trillion, or about $7 million for every dollar Al Qaeda spent planning and executing the attacks. While not all of the costs have been borne by the government — and some are still to come — this total equals one-fifth of the current national debt. All figures are shown in today’s dollars.
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Climate science also provides such examples, where nonsensical hypotheses promoting catastrophic humanmade global warming have been proposed by scoundrels and adopted by imbeciles, and have cost society trillions of dollars of squandered scarce resources. Ethical and learned individuals have spent decades disproving falsehoods that were “cooked up” by scoundrels in scant days or weeks, and yet the falsehoods still linger in the press and the public consciousness.
When the unlimited wealth and power of the State collaborates to do evil, the damage can be enormous and the limited resources of individuals to remediate can easily be overwhelmed by the continued deceit and power of the State.
In other words Anthony, it’s going to be a long and difficult road.
I suggest that natural global cooling, which I believe will commence by about 2020, will put an end to global warming alarmist nonsense.
Regards to all, Allan
Allan,
I offer one comment that might help explain the concept of intentional evil. The money channels are very interesting. Any research funded by public funds must be public. So, to keep the results private and secret the EPA, NOAA, NASA and many other agencies fund grants to the Sierra club, Greenpeace and the other e=green nonprofits are given grants.
Now those grant monies are merged with private funds and then they can require that basic data, math, methods, equipment, and in the end the methods of creating the computer models are proprietary properties and are hidden behind nondisclosure agreements. The only reason to do this is so there can be no peer review and they create a desired result. We have all been scammed by a very deceitful group that wants to use climate to control economies and nations,
Determination of local climate is complex but determination of average global climate, i.e. a single average temperature trajectory for the entire planet is simple.
Proof that CO2 has no effect on climate and identification of the two factors that do cause reported climate change (sunspot number is the only independent variable) are at http://agwunveiled.blogspot.com (new update with 5-year running-average smoothing of measured average global temperature (AGT), the near-perfect explanation of AGT since before 1900; R^2 = 0.97+).
Thanks, Dr. Ball.
Yes, Dr. Gray is correct. A model has to be validated by reality to be considered a model of reality.
“We can’t wait for 30 years to see if a model is any good or not…”
Well after 25 years we see that the model’s predictions were worthless.
Then the authors say, “Climate models of today are, in principle, better than their predecessors.” But upon what do they base this conclusion? It is entirely possible that within 25 years the climate models of today will be just as worthless as the models of 25 years ago.
Sorry guys, unless someone invents a time machine I don’t think GCMs can ever be validated.
Climate Science is like a box of chocolates…… you never know what you are going to get 😉
Dr. Tim Ball, Thx for clearing the view.
Sadly, 21st century climate models remind on
‘The Turk, an 18th-century fake chess-playing machine;’
Regards – Hans
Why are those IPCC scientists blind to reality? Why do they remain silent? Why do they not own up and admit the IPCC’s dangerous man-made global warming hypothesis is up the creek without a paddle? When are they ever going to admit that the IPCC is simply wrong?