The environmental extremists want us to believe that every global warming prediction is 100% correct. But computer models can err and easily draw wrong conclusions. The author has personally developed and directed the development of, several computer models. It is very easy for a computer model to be wrong. Actually, it is rather amazing that they ever make any correct predictions. So many different errors can creep into a model and cause it to predict erroneous results.
Secondarily, the average computer modeler comes to model development with a particular bent — he or she wants to see a particular result. With that in mind, this author has jokingly said that he should offer his modeling skills to the highest bidder: “Tell me what you want to model, and what you want it to predict, and I will build you a model.” That would be unethical, of course, but anyone I’ve ever met who was developing a computer model wanted it to predict a particular result. If it showed that result, the modeler could quit and call the model complete. If it didn’t show that result, the modeler continued working to develop it further. Even if a particular result is not a conscious goal, subconsciously, most modelers are looking for a certain result. So in addition to all the possible errors that can affect model results, there is always the modeler’s natural bent that must be considered. How ethical is the modeler or the modeling team? Would they intentionally slant a model to produce the results they want? We would like to think most would not intentionally slant a model to the desired result.
One must wonder about this — particularly in the global warming debate because all sorts of unseemly unethical tricks are being used to declare predicted results to be absolute truth and to discourage others from questioning those results. “The debate is over. Consensus has been achieved!” Science doesn’t work by consensus — and the debate is hardly ever over. “The Hollywood elite support the results!” Who cares what Hollywood thinks? “How dare you suggest these results are not accurate?” Well… some people actually know something about models and the model development process. They understand all the possible pitfalls of model development. “How dare you disagree with us?” We disagree for many reasons that have not been included in the debate. We disagree because the debate never occurred. If the intelligentsia is willing to play debating games and wanting to stifle discussion when they think their side is in the lead, one must look carefully at all details and question all results.
A computer model is a computer program that has been designed to simulate a particular function and to make predictions of its expected behavior. For example, the author used computer models to predict the viscous behavior of fluids and suspensions in industrial systems. The software used to render computer generated movies must perfectly simulate the visualizations shown. For example, complex algorithms show reflections on shiny objects to simulate the way light bounces from sources to the viewer’s eye. When the original models and algorithms correctly predicted light reflections, they began to be used to generate movies. The following list includes many of the pitfalls that can unintentionally hinder the success of computer models:
First, models are simplifications of real phenomena. The modeler(s) must determine the proper mathematics to simulate each phenomenon of interest. One usually selects the simplest mathematical algorithm that will perform the task at hand. If one selects incorrectly, the results may be in error. For example, some phenomena appear to have a linear behavior. But the linear behavior may change to non-linear behavior under certain extreme conditions. If that is not known in advance, the model may be asked to predict values in the ‘extreme conditions’ territory and errors will result. This happens easily.
For example, the fluid viscosity of a suspension (powder mixed in a fluid) starts as a linear function of the concentration of powders added to the fluid. When the concentration of powder is small, the function is linear. But as the concentration of powder increases, the viscosity behaves in a non-linear manner. The initial linear function is rather simple to program into a model, but the non-linear behavior is complex to accurately model. It is easy to make programming mistakes and utilize the wrong mathematics. This is closely related to the first pitfall above. If you think you know how a particular phenomenon behaves, but you use the wrong equation, the model will predict erroneous values.
Some phenomena are simply difficult to model. Sometimes, the results of a particular set of phenomena are not known. One must then perform a complex calculation each time those phenomena must be used. Rather than use the resulting mathematical equation to simulate a function, it may be necessary to simulate the actual underlying phenomena to arrive at the results. This may force a model within a model which adds complexity to the whole calculation.
For example, rather than using a simple mathematical equation to simulate how clouds affect sunlight, it may be necessary to model the behavior of individual raindrops in sunlight, and then model the behavior of the bazillions of raindrops that form a cloud to determine how an individual cloud will behave in sunlight. Until one builds up to simulating a whole sky full of clouds, the model can take on enormous proportions and the calculation times can be extremely long. Having gone through such an exercise, one must then determine if the equations and algorithms at each step in this process were modeled accurately.
The memory capacity of a computer and speeds of computation can be limited. This was more of a problem 20-30 years ago, but sizes and speeds can still be limiting. In early computers used by this author, you could program anything you wished — as long as it could fit into a 64,000-byte program (which is quite small as computer programs go.) Program sizes were limited and sizes of memory locations were also limited. Computers have grown over the years where most programs can now be so large, a programmer doesn’t need to be concerned with size limitations or with memory capacity. But sometimes, these still need to be taken into account.
When computation times can grow exponentially with certain simulations, one still needs to determine how long a particular computation will take. If computation times for a particular phenomenon double with each new iteration, capacities can quickly outgrow the available memory and allowed computational times. And models will reach those points within one or two iterations. If it takes one full day, for example, to perform one iteration of a simulation, and the calculation time doubles with each new iteration, how long is the modeler willing to wait to complete the simulation? See — this will build quickly — one day, two days, 4 days, a week, two weeks, a month, two months, four months, eight months, 1 1/3 years, etc. Again — how long is the modeler willing to wait?
How many raindrops are needed to form a cloud? How many individually must be simulated to adequately model the behavior of a cloud? How many in combination are needed to simulate the interaction of light with a cloud? If these types of simulations define a model, we’re talking huge numbers of droplets, huge memory requirements, and extremely long computing times. Even if this process started with an iteration taking a fraction of a second, it doesn’t take many doubles to reach a full day where the list in the previous paragraph began.
In some cases, the mathematical ability of a modeler can limit the complexity of the model. Some phenomena are extremely difficult to simulate mathematically. If the modeler cannot perform a calculation by hand, then they cannot insert that calculation into a computer so it can perform it. Some models require advanced calculus or other higher mathematics to solve a problem quickly. If that level of math is beyond the capabilities of the modeler, a less elegant, longer method of calculation may be required. If that is not possible, it may be necessary to postpone finishing the model until the appropriate algorithms become available.
The fighter jet with its wings canted forward comes to mind. This is a fundamentally unstable configuration for an airplane. Its natural tendency is to flip over and fly backward. It needed two technological advancements before they could design and test such a plane. (1) It needed a controller that could make fast adjustments to its control surfaces so it could fly. They needed to wait until fast computers were available to control the plane. Pilots were simply not quick enough to do this. (2) It needed to wait until light, stiff composite materials were available to make the wings. Stresses on the wings of such an airplane are incredibly high and for years, they simply did not have materials that could handle the stresses and still be light enough for use in a fighter jet. They had a great idea, but they needed to wait for the technology to catch up.
Computer modelers can have great ideas, too, but if they can not code the sufficiently complex mathematics, they may have to wait. An important phenomenon can be overlooked. When problems randomly occur in an industrial process setting, it usually means one or more important phenomena have not been taken into account in the control schemes. Process engineers do their best to include ALL important phenomena in their control algorithms, but most processes still suffer from random, unpredictable, problems. Most of these are blamed on Murphy, but most occur because important control phenomena have been overlooked. In a particular plant control process, we thought we had taken all possible factors into account, yet an occasional batch of raw materials simply didn’t follow expectations and caused enormous problems. When searching for an answer, we learned that a particular characteristic of the batch materials was responsible. In maybe 95% of all batches, this variable was not a problem, but in 5% of the batches, that particular characteristic was extreme, and lots of problems occurred.
This same behavior happens in computer models. For example, according to the ‘big boys’ in the global warming debate, the earth is not heating due to solar radiation variations from the sun. So what if a computer modeler forgets to include solar radiation in the earth’s temperature calculation because the sun has no effect on it. The results will be erroneous because the sun does affect earth’s temperature.
There are lots of reasons why a modeler can overlook an important phenomenon. Sometimes, one phenomenon is simply not known to have an effect on another. When calculating earth’s temperature, must one take into account the area of paved parking lots?… auto emissions?… the height of downtown buildings?… etc. It is fairly easy to miss necessary phenomena simply because they are not deemed to be important enough for inclusion.
Are the mathematics of phenomena a constant with time?… or do they change? This is a question that affects computer models that are supposed to cover long time frames (like the global warming models). Do atmospheric gasses absorb radiant energy today the same way they did thousands of years ago and the same way they will thousands of years in the future? Lots of other phenomena should be questioned in this same way. Uniformitarian principles suggest that everything happens today as they happened in the distant past and as they will happen in the distant future. There are problems, though. According to evidence, earth’s magnetic field not only changed several times in the past, but it supposedly switched polarities several times (i.e., the north became south, and south became north.) If a phenomenon is dependent on the earth’s magnetic field, how does one handle that in a computer model?
Darwinian evolution and uniformitarianism are closely related. Both theories say that changes occurred very slowly over eons of time and all phenomena behaved similarly throughout those eons. True? False? It depends because creationists who believe in a young earth are grouped with catastrophists who believe that the earth was formed by a series of catastrophic — not by gradual changes over eons. Even in this case, unless known to be otherwise, one still must assume that all phenomena occurred in the past and will occur in the future, as they occur today. But in this case, the models may only be dealing with thousands of years, rather than millions or billions of years. This question still needs to be taken into account. When computer models are developed, are they checked against good data?… and are the results published for all to see? The author developed several computer models that applied to ceramic process systems. Those results were all published in the technical ceramics literature because they were only relevant to a small part of the technical community. But each model had to be proven against real phenomena. Each model had to be demonstrated to determine if it accurately simulated the real phenomena. When no prior data were available to make the demonstration, the author had to perform experiments to demonstrate that the computer’s predictions were correct. In some cases, real results were well known, or data was already available to demonstrate a behavior. The models were then used to explain why the behavior occurred. In those cases, extra tests did not need to be run because the results were well known. The reasons why the results occurred were the answers sought by the computer models. And then, depending on the nature of the models, results were published in appropriate journals. In the case of global climate models, the results appear to be buried in the technical literature, and we are left to see the media’s and the politicians’ explanations that dire events are soon upon us! If the models are that important that they are going to affect our economy and our lives, results that demonstrate the veracity of the models should be published in the open literature for all to see. If today’s mass media believes these models are so accurate that Washington is going to alter our behaviors in response, then we should not need to dig to find the articles that show us the models and prove the accuracy of the results.
According to some, we have been collecting excellent satellite temperature data since 2002. Our best computer models should be tested against those satellite data to demonstrate the models can accurately predict 2010 weather behavior. Those results should then be published in the open literature for all to see. We should not need to take the words of politicians, environmental extremists, or the intelligentsia that we are in jeopardy of dire consequences from global warming. They should be willing to show these important results to all of us. The fact that they are not willing to do so lends credibility to the idea that global warming is nothing but a hoax — perpetrated to allow the redistribution of wealth from the “haves” like the US and Europe to the “have nots” like third world countries.
If results are going to be published broadly, are we going to also see good, logical answers to our questions? If global warming is causing the extremely violent hurricanes of the last several years (note — we haven’t had any to the author’s knowledge), are the modellers going to make reasonable explanations for such predictions, or must we continue to hear only from the politicians and extremists, “Well, of course, global warming is to blame!” That is no explanation and computer modelers must have more substantial, logical answers for such claims than that. An “of course it is responsible” answer is insufficient for us to believe that all heat waves, cold waves, hurricanes, tornadoes, snow storms, etc., are the result of global warming. If modelers believe this to be true, they must have better answers than just, “of course.”
Can a computer model successfully predict climate events 10 to 50 years from now? Professor Cotton, a Professor of Atmospheric Science at Colorado State University, [Cotton, W.R., Colorado State University, “Is climate really predictable on 10-50 year time table?”, 20 Jul 2010, Powerpoint presentation] concluded that it is not possible to do this. According to Cotton, there are too many unpredictable phenomena that affect our weather to possibly make accurate predictions over that time frame. Has any one of the other computer modelers asked and answered this question before they began their computer modeling quests? Apparently, such thinking and questioning were insufficient to stop other modelers from attempting to develop such models.
According to the Bible, God controls the wind and the rain. This means God controls the weather and the climate. If He wants it to rain, snow, hail, or drought at some particular location on the earth — He can make it so! Have computer modelers taken this into account in their models? This author has seen at least two managers who exerted their control over their processes in such a way that they each became in input variable into the successful control of their processes. The engineers who were responsible for those processes had to try to take their manager’s decisions into account as they attempted to successfully control the processes. This made it awkwardly difficult to control the processes because the managers’ decisions were unpredictable. If God is actually in control of the wind and rain, in particular, and the weather, in general, how can a modeler take that into account in a model that predicts climate 50 – 100 years from now? The Bible says, “For who hath known the mind of the Lord?” [Rom 11:34] Man certainly doesn’t! So how can a computer model account for God’s decisions? It can’t! It is simply impossible!
There are lots of potential problems that computer modelers must face in the development of climate change models. Some are within their control. Some are fully outside and beyond their control. Some apply specifically to global climate change models, while most apply to all computer models. There are enough potential pitfalls to the accurate development of such models that this author believes we should be seeing the detailed descriptions, results, and proofs of veracity in the open literature.
If the environmentalists truly believe we are facing dire consequences in the near future, all of these details, answers, and results should be out there where all can see. If they have nothing to hide, and they sincerely believe their results, that should be the case. But the underhanded arguments and sneaky methods (“The debate is over!”) used to suggest there is more to these computer model results than meets the eye. When Phil Jones, the former director of the University of East Anglia’s Climatic Research Unit [Petre, Jonathan, UK Daily Mail: “Climategate U-turn as Scientist at Centre of Row Admits: There has Been No Global Warming Since 1995,” 11 Aug 2010] recently admitted that “there has been no ‘statistically significant’ warming over the past 15 years,” one begins to wonder what kind of shenanigans the politicians are trying to pull.
Computer models are very useful to help us understand all sorts of phenomena. Lots of models have been developed and are used to explain lots of different phenomena. Those who wish to model global climate change over the next 50 – 100 years should have a great interest in the proof, testing, and use of their models. That the modelers are being quite and allowing the extremists, politicians, and intelligentsia to defend the results of their models suggests the something underhanded is up!
Dennis Dinger is a Christian who is a Professor Emeritus of Ceramic and Materials Engineering at Clemson University. In 2008, he curtailed his ceramics career when he was disabled by a form of blood cancer called Multiple Myeloma. In 2010, the cancer was in complete remission. Over the past three decades, he has directed many applied ceramic engineering research projects; he has been an active researcher and private consultant, and he is an author of several ceramic engineering textbooks as well as several Christian books.
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