On Not Knowing What the Frack You are Talking About

Those of us who wish to break through to the next level of human existence, are going to have to learn to love uncertainty (via Bishop Hill). The science correspondent of The Guardian looks at some answers to the latest Edge question:

Carlo Rovelli, a physicist at the University of Aix-Marseille, emphasised the uselessness of certainty. He said that the idea of something being "scientifically proven" was practically an oxymoron and that the very foundation of science is to keep the door open to doubt.

"A good scientist is never 'certain'. Lack of certainty is precisely what makes conclusions more reliable than the conclusions of those who are certain: because the good scientist will be ready to shift to a different point of view if better elements of evidence, or novel arguments emerge. Therefore certainty is not only something of no use, but is in fact damaging, if we value reliability."

...Neil Gershenfeld, director of the Massachusetts Institute of Technology's Centre for Bits and Atoms wants everyone to know that "truth" is just a model. "The most common misunderstanding about science is that scientists seek and find truth. They don't – they make and test models," he said.

"Building models is very different from proclaiming truths. It's a never-ending process of discovery and refinement, not a war to win or destination to reach. Uncertainty is intrinsic to the process of finding out what you don't know, not a weakness to avoid. Bugs are features – violations of expectations are opportunities to refine them. And decisions are made by evaluating what works better, not by invoking received wisdom." _Guardian_via_BishopHill

The mind naturally hates uncertainty. If we do not know what is to happen, we cannot plan for it, and are at the mercy of "the fates."

The basic idea is simple. It is that to perceive the world is to successfully predict our own sensory states. The brain uses stored knowledge about the structure of the world and the probabilities of one state or event following another to generate a prediction of what the current state is likely to be, given the previous one and this body of knowledge. Mismatches between the prediction and the received signal generate error signals that nuance the prediction or (in more extreme cases) drive learning and plasticity. _AndyClark

Of course it is one thing to embrace uncertainty and accept humility. It is quite another to pretend to embrace uncertainty, then to tell everyone to shut the fuck up because you understand what is going on better than anyone else does. That is what several of the commenters at the Edge site appeared to be doing.
True science does not attempt to limit what models can be tested by which experiment. True science does not limit the nature of models which can be entertained and played with. True science does not play down the massive uncertainty within its boundaries so as to influence public policy on a massive scale -- for its own financial benefit and increase in prestige.

This is what we are dealing with in modern climate science -- and in a wide range of other sciences which have attracted the attention of politically ambitious scientists, politicians, and bureaucratic administrators. The quest to build better models -- falsifiable models which can be ruthlessly tested by experiment -- is being hampered and biased by political and ideological concerns. Even at more "enlightened" websites such as Edge.org you can see the underlying corrupting influence of politics.

That is why science is escaping the bounds that "scientists", science bureaucrats, science journalists, science publishers, politicians, political lobbyists on scientific issues, and other supposed gatekeepers of science are trying to maintain. The tools of experimentation are escaping into the public domain, with all the attendant risks and opportunities which that entails.

Scientists are also politicians, at least within their own little realm. Some of them actually sell themselves to "the dark side" and attempt to practise politics on a grander scale. If a scientist lacks humility in his public pronouncements and policy recommendations, he is not practising true science, but is practising politics.

Until we can quantify the uncertainty in our statements and our predictions, we have little idea of their power or significance. So too in the public sphere. Public policy performed in the absence of understanding quantitative uncertainties, or even understanding the difficulty of obtaining reliable estimates of uncertainties usually means bad public policy. _LawrenceKrauss

All models are metaphors and all metaphors are man-made. The greatest danger in financial modeling and the modeling of all human activities is therefore the age-old sin of idolatry. Financial markets are alive but a metaphor is a limited human work of art, entrancing perhaps, but inanimate. To confuse the model with the world is to embrace a potential future disaster. All metaphors have their limits. _EmanuelDerman PDF

Emanuel Derman Map of Emotions PDF
Humans are not programmed for rationality, except insofar as finding the next meal, or a relatively warm, dry, and safe place to sleep are measures of rationality. The lofty world of grand scientific ideals and integrities is more of a convenient fiction to justify academic and research budgets. The reality is more one of roiling emotions and jostling egos of unbridled ambition and jealousy.

Politicians of all stripes want to project an image of invincibility and absolute certainty. Journalists, speechwriters, and public relations hacks assist them toward that end. Scientists -- by keeping their mouths shut about how little they themselves know -- also assist the politicians (at all levels) toward their petty, venal ends.

One of the happy exceptions among scientists is Freeman Dyson.

My first heresy says that all the fuss about global warming is grossly exaggerated. Here I am opposing the holy brotherhood of climate model experts and the crowd of deluded citizens who believe the numbers predicted by the computer models. Of course, they say, I have no degree in meteorology and I am therefore not qualified to speak. But I have studied the climate models and I know what they can do. The models solve the equations of fluid dynamics, and they do a very good job of describing the fluid motions of the atmosphere and the oceans. They do a very poor job of describing the clouds, the dust, the chemistry and the biology of fields and farms and forests. They do not begin to describe the real world that we live in. The real world is muddy and messy and full of things that we do not yet understand. It is much easier for a scientist to sit in an air-conditioned building and run computer models, than to put on winter clothes and measure what is really happening outside in the swamps and the clouds. That is why the climate model experts end up believing their own models. _Dyson

But if your paycheck depends upon wrangling a grant from a government that is down to writing IOUs, you want to project a degree of certainty and a crucial pivotal nature within your research, which you cannot actually know or prove. You probably don't even believe it yourself, deep down, but bills must be paid, kids must be raised, appearances must be kept up....

The enormous academic, political, and journalistic house of cards which exists to bamboozle the public into believing that the ruling and credentialed class knows what it is doing, cannot last forever. It is already crumbling about the edges and showing deepening cracks in its infrastructure.

What should you do about it? Learn to embrace skepticism toward the proclamations of would-be authority, and adopt a robust system of planning in your personal life. Things do not typically turn out the way we expect. Learn to deal with a healthy measure of uncertainty and unpredictability.

And keep your powder dry, you just may need to use it.

Is it Real, Or is it an Elaborate Computer Simulation?

Tim Hunt, Nobel Prize winner in biology, confronts two students in "systems biology" about the difference between "computer simulated biology" and real experimental biology in this video.

There is a very real gap between hands-on experimentalists and those who make their lives in the world of computer models and simulations. This is as true in biology as it is in climate, or in cognitive science. It is too easy for a computer modeler to believe that he has demonstrated something about the real world, when all he has done is generate a hypothetical output which obeys delineated constraints. As complex as the model may be, the real world is far more so. And the complexity of the model does not necessarily accurately reflect real world conditions.

The hope for the future of science lies with those who are cross-trained in both modeling and experimentation. They may be the only ones who will understand how to illuminate the way forward through an increasingly complex jungle of fractal realities.

Brains Like Ours?

Terrence Sejnowski is a Princeton trained physicist who found his way into neurobiology via a Harvard postdoc.  He is currently the head of the Computational Neurobiology Laboratory at the Salk Institute for Biological Studies in La Jolla.  Sejnowski suggests that humans may begin to create "brains like ours" sooner than most people think.

Last November, IBM researcher Dharmendra Modha announced at a supercomputing conference that his team had written a program that simulated a cat brain. This news took many by surprise, since he had leapfrogged over the mouse brain and beaten other groups to this milestone. For this work, Modha won the prestigious ACM Gordon Bell prize, which is awarded to recognize outstanding achievement in high-performance computing applications.

However, his audacious claim was challenged by Henry Markram, a neuroscientist at the Ecole Polytechnique Fédérale de Lausanne and the leader of the Blue Brain project, who announced in 2009 that: "It is not impossible to build a human brain and we can do it in 10 years.". In an open letter to IBM Chief Technical Officer Bernard Meyerson, Markram accused Modha of “mass deception” and called his paper a “hoax” and a “scam.”

...Unfortunately, the large-scale simulations from both groups at present resemble sleep rhythms or epilepsy far more closely than they resemble cat behavior, since neither has sensory inputs or motor outputs. They are also missing essential subcortical structures, – such as the cerebellum that organizes movements, the amygdala that creates emotional states and the spinal cord that runs the musculature. Nonetheless, from Modha’s model we are learning how to program large-scale parallel architectures to perform simulations that scale up to the large numbers of neurons and synapses in real brains. From Markram’s models, we are learning how to integrate many levels of detail into these models. In his paper, Modha predicts that the largest supercomputer will be able to simulate the basic elements of a human brain in real time by 2019, so apparently he and Markram agree on this date; however, at best these simulations will resemble a baby brain, or perhaps a psychotic one.... _SciAm

And from there, Sejnowski unfortunately veers off to briefly discuss "intelligent communications systems", then quickly ends his article. In other words, Sejnowski doesn't actually tell us anything about when we might expect to create "brains like ours."

Ray Kurzweil predicts human level computing by 2029, but Mitch Kapor is betting that Ray is wrong. Henry Markram's prediction for an artificial human brain by 2019 goes far beyond what the Blue Brain website is willing to predict, or what some of his more sober colleagues at Lausanne are willing to claim. Ben Goertzel and Peter Voss each believe they are on the trail of artificial general intelligence (AGI), and see no reason why they cannot achieve their goal.

Noah Goodman -- a researcher at MIT's Cognitive Science Group -- is quite forthcoming and honest in this interview at Brian Wang's NextBigFuture. Goodman says that "we could achieve human-level AI within 30 or 40 years", but he also admits that it could take longer.

A startling new approach to massively parallel computing comes from Michigan Technological University working with a research team in Japan.

In their work, instead of wiring single molecules/CA cells one-by-one, the researchers directly build a molecular switch assembly where ∼300 molecules continuously exchange information among themselves to generate the solution. This molecular assembly functions similarly to the graph paper of von Neumann, where excess electrons move like colored dots on the surface, driven by the variation of free energy that leads to emergent computing...

...By separating a monolayer from the metal ground with an additional monolayer, the NIMS/MTU team developed a generalized approach to make the assembly sensitive to the encoded problem. The assembly adapts itself automatically for a new problem and redefines the CA rules in a unique way to generate the corresponding solution.

"You could say that we have realized organic monolayers with an IQ" says Bandyopadhyay. "Our monolayer has intelligence."

Furthermore, he points out that this molecular processor heals itself if there is any defect. It achieves this remarkable self-healing property from the self-organizing ability of the molecular monolayer.

"No existing man-made computer has this property, but our brain does: if a neuron dies, another neuron takes over its function" he says.

With such remarkable processors that can replicate natural phenomena at the atomic scale researchers will be able to solve problems that are beyond the power of current computers. Especially ill-defined problems, like the prediction of natural calamities, prediction of diseases, and Artificial Intelligence, will benefit from the huge instantaneous parallelism of these molecular templates.

According to Bandyopadhyay, robots will become much more intelligent and creative than today if his team's molecular computing paradigm is adopted. _Nanowerk

Most intelligent observers of the AI field who have been able to take a step back and view the phenomenon from the perspective of many decades of history, are forced to conclude that a new physical substrate -- other than von Neumann architecture supercomputers -- will be necessary before anything close to a human-level AGI can be built.

Whether using memristors, qubits, molecular monolayers, fuzzy logic - enabled neural nets with genetic algorithmic ability, or physical substrates and architectures not yet envisioned or announced, AGI researchers of the near to intermediate future will eventually make rapid strides toward useful machine intelligence -- once the right architectural substrate is discovered.

In the meantime, cognitive scientists are learning a great deal about how the brain works, and how artificial mechanisms may better emulate brain function. I suspect that both Modha and Markram (along with several other prognosticators including Kurzweil) may have allowed wishful thinking to get the better of them, when making timeline predictions.

In the dramatic history of genetic science, only after the breakthrough of Watson and Crick could molecular biology explode into the present and future. The ongoing history of artificial intelligence is still lacking its "Watson and Crick." Progress can be made, but not the explosive progress that is necessary to approach human level intelligence.

Where Modern (Climate) Science Goes Wrong

As Richard Feynman famously quipped, “Philosophy of science is about as useful to scientists as ornithology is to birds.” _Source

Scientists in real life do not behave like scientists are supposed to. Instead of pursuing the truth dispassionately with open minds, their minds are too often full of pre-conceived beliefs and firmly fixed expectations of results. If the experimental data do not fit the preconceived theory, scientists may just throw out the data instead of the theory. Honest scientists like Feynman have understood this reality for a long time, but now brain science is beginning to put the pieces together as to why scientists frequently behave in a most unscientific manner.

Science is a deeply frustrating pursuit. Although the researchers were mostly using established techniques, more than 50 percent of their data was unexpected. (In some labs, the figure exceeded 75 percent.) “The scientists had these elaborate theories about what was supposed to happen,” Dunbar says. “But the results kept contradicting their theories. It wasn’t uncommon for someone to spend a month on a project and then just discard all their data because the data didn’t make sense.” Perhaps they hoped to see a specific protein but it wasn’t there. Or maybe their DNA sample showed the presence of an aberrant gene. The details always changed, but the story remained the same: The scientists were looking for X, but they found Y.


...There were models that didn’t work and data that couldn’t be replicated and simple studies riddled with anomalies. “These weren’t sloppy people,” Dunbar says. “They were working in some of the finest labs in the world. But experiments rarely tell us what we think they’re going to tell us. That’s the dirty secret of science.”


Dunbar realized that the vast majority of people in the lab followed the same basic strategy. First, they would blame the method. The surprising finding was classified as a mere mistake; perhaps a machine malfunctioned or an enzyme had gone stale. “The scientists were trying to explain away what they didn’t understand,” Dunbar says. “It’s as if they didn’t want to believe it.”


...The reason we’re so resistant to anomalous information — the real reason researchers automatically assume that every unexpected result is a stupid mistake — is rooted in the way the human brain works. Over the past few decades, psychologists have dismantled the myth of objectivity. The fact is, we carefully edit our reality, searching for evidence that confirms what we already believe. Although we pretend we’re empiricists — our views dictated by nothing but the facts — we’re actually blinkered, especially when it comes to information that contradicts our theories. The problem with science, then, isn’t that most experiments fail — it’s that most failures are ignored.


...when Dunbar monitored the subjects in an fMRI machine, he found that showing non-physics majors the correct video triggered a particular pattern of brain activation: There was a squirt of blood to the anterior cingulate cortex, a collar of tissue located in the center of the brain. The ACC is typically associated with the perception of errors and contradictions — neuroscientists often refer to it as part of the “Oh shit!” circuit — so it makes sense that it would be turned on when we watch a video of something that seems wrong.


...there’s another region of the brain that can be activated as we go about editing reality. It’s called the dorsolateral prefrontal cortex, or DLPFC. It’s located just behind the forehead and is one of the last brain areas to develop in young adults. It plays a crucial role in suppressing so-called unwanted representations, getting rid of those thoughts that don’t square with our preconceptions. For scientists, it’s a problem....The DLPFC is constantly censoring the world, erasing facts from our experience. If the ACC is the “Oh shit!” circuit, the DLPFC is the Delete key. When the ACC and DLPFC “turn on together, people aren’t just noticing that something doesn’t look right,” Dunbar says. “They’re also inhibiting that information.”


...Dunbar tells the story of two labs that both ran into the same experimental problem: The proteins they were trying to measure were sticking to a filter, making it impossible to analyze the data. “One of the labs was full of people from different backgrounds,” Dunbar says. “They had biochemists and molecular biologists and geneticists and students in medical school.” The other lab, in contrast, was made up of E. coli experts. “They knew more about E. coli than anyone else, but that was what they knew,” he says. Dunbar watched how each of these labs dealt with their protein problem. The E. coli group took a brute-force approach, spending several weeks methodically testing various fixes. “It was extremely inefficient,” Dunbar says. “They eventually solved it, but they wasted a lot of valuable time.”


The diverse lab, in contrast, mulled the problem at a group meeting. None of the scientists were protein experts, so they began a wide-ranging discussion of possible solutions. At first, the conversation seemed rather useless. But then, as the chemists traded ideas with the biologists and the biologists bounced ideas off the med students, potential answers began to emerge. “After another 10 minutes of talking, the protein problem was solved,” Dunbar says. “They made it look easy.”


...This is why other people [inside and outside the field -- ed.] are so helpful: They shock us out of our cognitive box. “I saw this happen all the time,” Dunbar says. “A scientist would be trying to describe their approach, and they’d be getting a little defensive, and then they’d get this quizzical look on their face. It was like they’d finally understood what was important.” _Wired

Scientists are people too. Their brains want them to find confirmation of their preconceived beliefs. If the data tells them something different, who will help them get out of the rut of their strong (and sometimes personally profitable) belief, to see the larger, more inclusive theory that is just waiting to be found?

If scientists surround themselves with others who believe the same theories as themselves -- as in climate science (see ClimateGate emails and codes) -- there will be few, if any, outsiders around to show the insiders where their brains and beliefs are leading them astray.

Climate scientists too often shut out the voices of outsiders, and sequester themselves together. When data from the outside world proves intractable, they focus inwardly on computer models using input that they can control. They use clever "tricks" to adjust or change what their data was saying. They erect strong castles to withstand what is turning into a prolonged siege from the outside.

Since the science funding agencies and big science publishers are firmly within the cloistered castle of climate catastrophe, it is up to the mainstream media to inform the public of the true complexity of the situation. But the mainstream media has largely crossed the drawbridge into the climate keep, and is shooting out orthodoxy-blessed arrows of true belief.

So it comes down to the new media, and the true outsiders -- knowledgeable, intelligent, and creative persons willing to look into the realities that climate scientists do not care to face. Outsiders who will use the new media to communicate with anyone willing to look for larger and "truer" answers to the conflicting data.

Clouds and Climate: Confirmation and Uncertainty

Researchers of the National Space Institute in the Technical University of Denmark (DTU) have confirmed earlier research, finding an inverse relationship between solar activity and cloud formation in Earth's atmosphere. The finding adds to current controversy over the trustworthiness of modern climate models -- models which fail to take this very important physical phenomenon into account.

"A link between the Sun, cosmic rays, aerosols, and liquid-water clouds appears to exist on a global scale," the report concludes. This research, to which Torsten Bondo and Jacob Svensmark contributed, validates 13 years of discoveries that point to a key role for cosmic rays in climate change. In particular, it connects observable variations in the world's cloudiness to laboratory experiments in Copenhagen showing how cosmic rays help to make the all-important aerosols. _SD

An ongoing climate uncertainty relating to clouds, involves the action of dust particles in the atmosphere: Do dust particles increase cloud formation -- thus contributing to reduced insolation and increased cooling?

A knowledge gap exists in the area of climate research: for decades, scientists have been asking themselves whether, and to what extent man-made aerosols, that is, dust particles suspended in the atmosphere, enlarge the cloud cover and thus curb climate warming. Research has made little or no progress on this issue. Two scientists from the Max Planck Institute for Meteorology in Hamburg (MPI-M) and the American National Oceanic and Atmospheric Administration (NOAA) report in the journal Nature that the interaction between aerosols, clouds and precipitation is strongly dependent on factors that have not been adequately researched up to now. They urge the adoption of a research concept that will close this gap in the knowledge. (Nature, October 1st, 2009) _WUWT

Another interesting recent finding on how clouds could be affecting climate, comes from the University of Leeds:

Scientists at the University of Leeds have proved that acid in the atmosphere breaks down large particles of iron found in dust into small and extremely soluble iron nanoparticles, which are more readily used by plankton.

This is an important finding because lack of iron can be a limiting factor for plankton growth in the ocean - especially in the southern oceans and parts of the eastern Pacific. Addition of such iron nanoparticles would trigger increased absorption of carbon dioxide from the atmosphere. _SD

So here are three different ways in which clouds and cloud associated aerosols may be significantly affecting the climate -- but climate models do not accurately incorporate these important factors when making their $trillion predictions. And yet, Obama and the United Nations are willing to throw the economies of the industrialised world on the rubbish heap in order to placate computer models which are almost certainly wrong in their predictions.

When are climatologists going to stop pretending that they understand the climate? When are they going to admit that their knowledge is far too limited to base the economic future of the developed world upon them? Do not hold your breath. These climatologists have based their reputations and livelihoods upon the soundness and reliability of their methods and projections. For them to do the right thing now would require a level of character and integrity that is simply beyond them, at this point in the process.

Which means that it is up to you to put political pressure on the funding agencies that pay these pretenders -- to bring them to heel. Make them honest for once. Only pressure from the people who pay the piper can possibly slow down this train wreck to a manageable speed.

A Brain Balanced on the Razor's Edge of Chaos

The human brain is made up of 100 billion neurons — live wires that must be kept in delicate balance to stabilize the world’s most magnificent computing organ. Too much excitement and the network will slip into an apoplectic, uncomprehending chaos. Too much inhibition and it will flatline. RockefellerUniversity

One of the problems with past and present efforts to reverse-engineer the brain, is the lack of a deep understanding of how the brain manages to balance its own activity so well -- like a surfer on a big wave. Researchers at Rockefeller University have discovered that some previous conceptions of how the brain operates, may be in error. If so, then correcting the error may bring cognitive scientists a little closer to reverse-engineering a functioning brain.

Marcelo O. Magnasco, head of the Laboratory of Mathematical Physics at The Rockefeller University, and his colleagues developed the model to address how such a massively complex and responsive network such as the brain can balance the opposing forces of excitation and inhibition. His model’s key assumption: Neurons function together in localized groups to preserve stability. “The defining characteristic of our system is that the unit of behavior is not the individual neuron or a local neural circuit but rather groups of neurons that can oscillate in synchrony,” Magnasco says. “The result is that the system is much more tolerant to faults: Individual neurons may or may not fire, individual connections may or may not transmit information to the next neuron, but the system keeps going.”

Magnasco’s model differs from traditional models of neural networks, which assume that each time a neuron fires and stimulates an adjoining neuron, the strength of the connection between the two increases. This is called the Hebbian theory of synaptic plasticity and is the classical model for learning. “But our system is anti-Hebbian,” Magnasco says. “If the connections among any groups of neurons are strongly oscillating together, they are weakened because they threaten homeostasis. Instead of trying to learn, our neurons are trying to forget.” One advantage of this anti-Hebbian model is that it balances a network with a much larger number of degrees of freedom than classical models can accommodate, a flexibility that is likely required by a computer as complex as the brain.

In work published this summer in Physical Review Letters, Magnasco theorizes that the connections that balance excitation and inhibition are continually flirting with instability. He likens the behavior to an indefinitely large number of public address systems tweaked to that critical point at which a flick of the microphone brings on a screech of feedback that then fades to quiet with time.

This model of a balanced neural network is abstract — it does not try to recreate any specific neural function such as learning. But it requires only half of the network connections to establish the homeostatic balance of exhibition and inhibition crucial to all other brain activity. The other half of the network could be used for other functions that may be compatible with more traditional models of neural networks, including Hebbian learning, Magnasco says. _RU

The next time your brain tells you "it's not easy being me", perhaps you should believe it. We all would like better brains, no doubt. But first we need to understand the things that work well vs. the things that could work better, in the brains we already have.

Clearly the emerging view of the dynamic brain goes far beyond a mere mapping of all synaptic connections. Because as we have been learning for the past few decades, synaptic mappings in the cortex change frequently -- sometimes not so subtly. The complex dynamic neural codes that underpin our knowledge, memories, speculations, loves, hates, and dreams, will not be cracked easily or soon.

Mental Models and Metaphors: Creative Thinking

The human mind is built upon models and metaphor. This is true from the pre-natal moment that enough nerve cells come together to co-oscillate, and it is true for the most advanced explorations into the cutting edge of science and technology. Cognitive scientist Nancy J. Nersessian has discovered the importance of model building in science:

Designing, building, and experimenting with physical simulation models are central problem-solving practices in the engineering sciences. Model-based simulation is an epistemic activity that includes exploration, generation and testing of hypotheses, explanation, and inference. This paper argues that to interpret and understand how these simulation models function in creating knowledge and technologies requires construing problem solving as accomplished by a researcher–artifact system. It draws on and further develops the framework of "distributed cognition"... _TopicsCognitiveScience

More from Nersessian:

To develop an understanding of the system under investigation, scientists build real-world models and make predictions with them. The models are tentative at first, but over time they are revised and refined, and can lead the community to novel problem solutions. Models, thus, play a big role in the creative thinking processes of scientists. _SD

This is a very basic understanding of the work of invention and creativity at the borders of science and technology. But what Nersessian is doing is to make the process more explicit, in order to bring creativity to a wider range of activities -- including the classroom.

Model-building and metaphor is basic to the thinking process. But as the domain of thinking and creativity grows more complex -- as at the cutting edge of science and engineering -- the models and metaphors used will grow more intricate.

Einstein made wonderful use of mental models in his pursuit of new science, as did Feynman and other great scientists. As better methods of building physical and computing models are developed, the creative process is augmented by a form of "distributed cognition", as Nersessian terms it. Once the model exits the mind of the scientist and exists in the outer world, other minds can grasp it and tweak it -- making the tool of cognition distributed.

Models are only tools to help discover reality, however. They are not the reality. That is the error that climate scientists too often make: they forsake scientific observation and data confirmation and pursue computer models as if the models were the reality.

Models sit at the crux between data and theory. Without data, theory is mere confabulation. But without theory, data is simply noise. The human mind is always attempting to create order out of chaos, model out of data. This process is unconscious, and begins to occur long before the mind acquires language. It leads to optical illusions, common delusions, and mass confusion when conclusions are jumped to without adequately testing the models ("carbon climate catastrophe").

But since it is how we think, we need to know how to make the most of it -- while also keeping things "real."More: The above image is a computer mapping of shipping traffic near Rotterdam. The human mind is capable of creating similar maps mentally, but as a phenomenon grows more complex, computer mappings and visualisations become more helpful

A map (or complex visualisation) is similar to a model, in that the map takes a large mass of data, and connects the data in such a way that the mind can grasp it more easily -- in all its complexity.

No Sunspots Throw Climate Models Into Disarray

Modern climate models make many erroneous assumptions. Bad assumptions lead to errors in inference, when examining model output. One of the most significant data crimes of modern climate modelers is in ignoring the variability of Sol, our variable star.Fortunately, real scientists (not mere modelers) are beginning to examine the very real climate effects that arise from the solar energy flux when going from solar maximum to solar minimum.

A new study in the journal Science by a team of international of researchers led by the National Center for Atmospheric Research have found that the sunspot cycle has a big effect on the earth's weather. The puzzle has been how fluctuations in the sun's energy of about 0.1 percent over the course of the 11-year sunspot cycle could affect the weather? The press release describing the new study explains:

The team first confirmed a theory that the slight increase in solar energy during the peak production of sunspots is absorbed by stratospheric ozone. The energy warms the air in the stratosphere over the tropics, where sunlight is most intense, while also stimulating the production of additional ozone there that absorbs even more solar energy. Since the stratosphere warms unevenly, with the most pronounced warming occurring at lower latitudes, stratospheric winds are altered and, through a chain of interconnected processes, end up strengthening tropical precipitation.

At the same time, the increased sunlight at solar maximum causes a slight warming of ocean surface waters across the subtropical Pacific, where Sun-blocking clouds are normally scarce. That small amount of extra heat leads to more evaporation, producing additional water vapor. In turn, the moisture is carried by trade winds to the normally rainy areas of the western tropical Pacific, fueling heavier rains and reinforcing the effects of the stratospheric mechanism.

The top-down influence of the stratosphere and the bottom-up influence of the ocean work together to intensify this loop and strengthen the trade winds. As more sunshine hits drier areas, these changes reinforce each other, leading to less clouds in the subtropics, allowing even more sunlight to reach the surface, and producing a positive feedback loop that further magnifies the climate response.

These stratospheric and ocean responses during solar maximum keep the equatorial eastern Pacific even cooler and drier than usual, producing conditions similar to a La Nina event. However, the cooling of about 1-2 degrees Fahrenheit is focused farther east than in a typical La Nina, is only about half as strong, and is associated with different wind patterns in the stratosphere.
_Reason

We are currently experiencing a rather unusual transition from solar cycle 23 to cycle 24. Sunspots have been very slow in appearing, and solar scientists are at a loss to explain why all of their predictions for the current cycle transition have failed.

...something is unusual about the current sunspot cycle. The current solar minimum has been unusually long, and with more than 670 days without sunspots through June 2009, the number of spotless days has not been equaled since 1933 (see http:// users . telenet .be/ j . janssens/ Spotless/ Spotless .html). The solar wind is reported to be in a uniquely low energy state since space measurements began nearly 40 years ago [Fisk and Zhao, 2009].

Why is a lack of sunspot activity interesting? During the period from 1645 to 1715, the Sun entered a period of low activity now known as the Maunder Minimum, when through several 11- year periods the Sun displayed few if any sunspots. Models of the Sun’s irradiance suggest that the solar energy input to the Earth decreased during that time and that this change in solar activity could explain the low temperatures recorded in Europe during the Little Ice Age [Lean et al., 1992]. _Eos_PDF_via_Reason

Real scientists are teaching the climate modelers a lesson: models are not the climate. Climate models are merely hypotheses in mathematics and computer code. They have to be thoroughly tested by scientific observations, before society is massively disrupted to suit the models.

Climate catastrophists wish to skip the normal steps of the scientific method, and pass from hypothesis directly to a massive overhaul of society and economy. I can hear you saying, "but that is sheer folly!!" Yes, I quite agree, but unfortunately, the US government has recently fallen into a quagmire of folly. Escape from the quagmire will not come easily.

Daily solar photos and solar status are available from SpaceWeather.

Update 28 Aug 2009, Further Reading:
How Small Fluctuations in Solar Radiation Lead to Large Climate Effects

The Oceans Really Are Getting Cooler

The Idiots and Their "Collapsing Ice Sheets"

Why the Oceans are such a good measure of Earth's heat content

IPCC Climate Models Not Holding Up Well

Image Source

The image above shows a plot of solar cycle length against global temperature. As many of you know, we are still in solar cycle 23, waiting for solar cycle 24 to begin. As you can see from the graphic above, the longer the solar cycle, the cooler the global climate.
Current data from the Argos ocean monitoring buoys points to an ongoing cooling trend in Earth's heat content--instead of global warming.

The big problem with the Argo findings is that all the major climate computer models postulate that as much as 80-90% of global warming will result from the oceans warming rapidly then releasing their heat into the atmosphere.

But if the oceans aren't warming, then (please whisper) perhaps the models are wrong.

The supercomputer models also can't explain the interaction of clouds and climate. They have no idea whether clouds warm the world more by trapping heat in or cool it by reflecting heat back into space.___NP

A world-class MIT climatologist who possesses perhaps the highest wattage brain in the field, has already suggested that the IPCC climate models have been falsified by the last 10+ years of satellite data.

This is a time of turmoil within all fields of climate science--although you would never know it from the public front as reflected by the popular media. The orthodoxy is circling the wagons, with the unquestioning assistance of global media and national governments, inter-governments, and non-governmental lobbyist organisations.

Under a Stalinist world government, the climate orthodoxy might have a chance of pulling the wool over the global public's eyes long enough to put an irrevocable "climate change economic regime" into place. Under such an economic regime, market mechanisms would be so painfully and dysfunctionally distorted that even in the case of a significant climate cooling--equivalent to a Maunder Minimum--the ability of human science and technology to respond in a timely or effective fashion will have been hamstrung.

Top-level climate scientists are reaping significant rewards from the alarmist message. It is not in their interest to introduce any element of doubt into the scenarios. The same applies to investors and financiers who are neck-deep in climate cap and trade schemes. Even the world's number 1 polluter--China--is demanding massive technology transfers to its state-owned enterprises, as a condition for considering reducing its greenhouse gas output. Not a bad scam, if you can get in on it before it all collapses.

Update 25March08: IPCC on increasingly shaky ground
The study of the multiple drivers of Earth's climate has just barely begun. The premature identification of mainstream media conglomerates with the catastrophist extreme view of climate reveals the political underpinnings of both the media, and the catastrophist wing of climate modeling. Non-catastrophist climate mavericks--who want to study all the mechanisms of climate and climate change--have a tougher time getting financing, tenure, and publication. But that situation is subject to very rapid and radical change, as the oncoming deluge of better data begins to hit the windscreen.

The Map Is Not the Territory

Climate Model Prediction left, vs. actual readings at right (latitude x, altitude y)

It is sometimes easy to confuse the map with the territory. One can easily become lost if he believes the territory must absolutely conform to the map he is reading. It is good to make allowances.

The media and general public do the same thing with climate models--confusing the climate models and the reality. As we have learned from Surface Stations, Climate Science, and Climate Audit, the data that the models are based upon is less than stellar. Garbage in, garbage out.

While many branches of science engage in the study of climate--geologists, ocean scientists, atmospheric scientists, astrophysicists, meteorologists, dendroclimatologists, computer modelers etc--it is the modelers who get the most attention. These are the "scientists" who predict the alarmist futures that are written up in the media and trumpeted by political opportunists such as Al Gore. But as you can see from the graphic above, the model is not the reality.

Even among the computer modelers, the "consensus" is much less than is being presented to the (mostly) gullible public.

The media and policy-makers with their own vested interests, would like for the public to live on a razor's edge of anxiety over climate. This makes their work easier and more profitable. But what is good for the media is not always good for everyone else. Think for yourself.

Hat tip Lubos Motl, by way of Green Watch.