Why climate engineers beat the climate academics

The academic way: if one theory won't reach, just use two.

The academic way: if one theory won’t reach, just use two.

There is no doubt that skeptics have a proven track record on predicting the inability of climate academics to predict the climate. After nearly 18years without warming which none of the academics predicted (even after it started), they are looking increasingly sheepish and trying to talk about anything but their proven inability to predict the climate.

However, whilst us “climate engineers” have been vindicated, there is still the question: “why?” Why is it that people from a general engineering/science background like us skeptics could have known that the academics would get it wrong?

Of course, the obvious answer is: “because they are academics”. But … how do I put this … I’d rather like a more academic answer.

In my previous post I highlighted yet another shot in the foot comment from The EndOfPhysics:

People who are insisting on validation of models, or precise confirmation of certain quantities (like the ECS for example). It’s as if they think science should be more like engineering and don’t realise that science is about trying to understand the world around us, not control or use it. You can’t just deliver a scientific result on demand, you can only do as well as is possible given the tools/knowledge available at that time.

Strangely he does add to the sum of human knowledge – but not quite in the way he hopes. Because he confirms some key points.

  1. Academics don’t worry if models are not validated (much to the disgust of skeptics)
  2. That asking things to be validated is the type of thing that engineers ask for
  3. “Understanding the world”, is what he sees as important (not boring validation of models to see if they have any utility at predicting anything.)
  4. “Deliver”, “demand”, “at that time” … describe the difference between academics who believe they’ve got all the time in the Universe to (eventually) come up with the right answer – and engineers who had to come up the (best) “right answer” yesterday.
  5. “Control or use” … again this highlights how academics don’t feel comfortable having their theories used in real life.

Here are some more thoughts:

Academia/Scientists Engineering/Doctors
Focus Look to understand a class of problems in some general way To deal with a specific problem on a specific system
Complexity The “quirks” of individual systems are glossed over in order to find the simple universalities. The “quirks” of individual systems make them each far more complex than the theory suggests.
“Understanding” Prime focus Often helpful, but can be replaced by monitoring of problems
Decisions Not a focus Prime focus
How to deal with lack of information. Ask for a grant to get more information Assess whether information is necessary given project time & costs.
People Issues Largely assumed to be irrelevant In real situations, people are part of the system and their behaviour has to be understood together with physics, chemistry, etc.
Predictions A nice way to finish a paper.No one really expects them to be right. A estimation which has direct impacts on safety and profits.Get it wrong – you may lose your job.
Utility Theories not for “control or use” Ideas, theories, concept must have practical utility.
Economics We don’t talk about vulgar subjects like that. Yes the economy is key and unless it improves I doubt my company will be in business with all this foreign competition from low-cost energy.

In particularly the “simple” versus “complex” mindsets of academics and engineers must be important. Engineers assume that a real system is going to be far more complex than they can hope to understand in full. So, we are used to the idea of not knowing everything and we are trained by skills and culture to make the best  decisions (although far from ideal) in these circumstances when evidence is lacking.

In contrast the academics focus is in “understanding” and so not only do they waste huge amounts of effort trying to understand the climate, but they simply lack the skills to make good decisions with a system where they don’t have full understanding.

So, to an engineer “natural variability”, isn’t so much a description of a physical phenomenon as that it is “natural” there will be “variability” whose cause is unknown in almost all systems dealt with by engineers.

For an engineer, all systems have far more variability than we can measure. That’s because we deal with problems as a whole – the science as in physical or chemical properties, the economics, the human, and even politics. All these are part of the bread and butter issues that engineers deal with.

In contrast, academics see almost all these as being “extrinsic” to the system. To the engineer, the “system” is the whole issue. To the academic, the “system” is the basic “science” once all the “irrelevant” externalities are removed.

So academics invent a lot of ways to IGNORE VARIABILITY. They pretend it doesn’t exist, that it is extrinsic to the system under investigation. in contrast engineers have to deal with the actual system and the actual scientific implication on people and profits.

Engineers cannot draw an abstract line around what someone deems to be the “science”, and pretend the rest doesn’t matter.

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5 Responses to Why climate engineers beat the climate academics

  1. Terrific posting. You given the most eloquent description of the societal problem we face.

  2. Pingback: “Climate Engineer” – New Term | Musings on Interesting Things

  3. David L. Hagen says:

    See Aerospace engineer Rutan on climate
    The perspective of a person used to evaluating voluminous data where life and death depend on his decisions is far more valuable to me than one whose future depends on alarming politicians to fork over grants. e.gl,
    An Engineer’s Critique of Global Warming “Science”
    PS On “Predictions” you note: “Get it wrong – you may lose your job.”
    Change to: “People may die, your company fail, and you lose your job”

  4. David Cage says:

    You say :-
    After nearly 18years without warming which none of the academics predicted (even after it started), they are looking increasingly sheepish and trying to talk about anything but their proven inability to predict the climate.

    This is not actually true. I actually knew a group who did. It was based on the way they knew some engineering signal analysts working on noise removal from data in modem design and listened to them but all of them failed to get grants and moved into engineering which is how I got to know them.

    • scottishsceptic says:

      Thanks, I can remember where I was standing when I realised that if I had personally recorded the global temperature signal on one of my instruments as part of my work and I had been asked “is it correlated with this other rising signal” … I would have had to say “I could not possibly say – there’s far too little signal to draw any real conclusion”.

      And then I realised that I had been applying a very different standard to the “big” temperature signal to any other “ordinary” signals. I tried to explain it in my submission to the Climategate inquiry. However, how do you explain something that you really just learn on the job by looking at lots and lots of signals?

      But it was interesting seeing my own perception change as I started to view it as a signal and not as “science”. I think the difference is because I had been taught in science to look at signals in an entirely different way to that which I did as an experienced engineer.

      After studying it, I think the big difference is that in the real world, real world signals are full of 1/f noise. However, in most areas where science works, the signal is a relationship that is “static”. So, the “signal” is a long term relationships and so almost all noise is higher in frequency like white noise. So, in physics the philosophy was “if you average it enough, the noise will disappear”, whereas in the real world of engineering, if you average a signal often, the signal disappears leaving only the noise.

      When you play around with short duration real world signals with multiple frequencies, you learn that averaging can only go so far and eventually it’s a question of judgement. But in science, the experiments will keep going until they have enough data, and/or they only work in areas where averaging is a useful technique.

      The other huge cultural difference, is that in “science” the aim is to “understand” something and anyone who doesn’t understand a system is considered to be … morally corrupt would be a good way to put it.

      In contrast, in engineering (and it also applies to GP doctors), the aim is to “keep something working”. So those real world scientists called engineers, focus on identifying and fixing problems — and if understanding is not necessary — then they aren’t fixated on trying to understand what doesn’t help fix the problem.

      So, engineers tend to look at the world and ask “is it broken” … so they look for symptoms of problems try to assess the validity of available data and look if there are any real and worrying trends.

      In contrast, those from a “science” background are fixated on “understanding” the climate. Then once they have even a minuscule understanding of the climate (akin to the Met Office understanding of the weather on a day one year ahead). They use their minuscule knowledge to make massive predictions of WHAT WILL HAPPEN, [… if … their models are correct].

      And they justify this along the lines of “there is no one who understands the climate better …. therefore we must have the best predictions of what will happen … therefore the politicians must listen to us and only us”.

      So the basic methodology is
      In “Science”

      PROBLEM -> (attempt) UNDERSTANDING -> MODEL -> PREDICTION
      -> APPLY MORAL STANDARD OF “WHAT SHOULD BE DONE”
      -> IF MORALLY “REQUIRED”: DEMAND POLITICAL ACTION

      In Engineering

      PROBLEM -> LOOK AT SYMPTOMS -> ASSESS TRENDS AND DANGER THRESHOLD
      -> GIVE POLITICIANS THE TOOLS TO MAKE THE DECISION ABOUT WHETHER ACTION IS NEEDED AND WHETHER IT IS COST EFFECTIVE

      And those last bit of why action is needed is also highly illuminating. In engineering, the engineer will tell the client the technical reasons why action is needed, perhaps give a best prediction of what might happen if action is taken or not, the cost of action … but ultimately, it’s up to the client to decide whether they take action.

      In “science”, their works produces a model. That should be the end of it … because then it should be up to others [climate consultants?] to use those models to advise government.

      However, when the political “eco” wing of academia saw that governments were not acting as their model suggested they should, they invented organisations to lobby on behalf of “science” to try to force government to act. So, perversely, the supposed “dispassionate” scientists, found themselves working as MORALLY driven political lobbyists. Which is why I call them “science”.

      In other words, I have some sympathy for the academics who thought they should act to ensure their work that appeared [to them] to be showing a problem was acted on by government. However, by doing so, they became morally biased about their own work, they then corrupted the system to prevent those who did not share their views getting money and so the whole of academic science became corrupted by confirmation bias as society lost the benefit of having a dispassionate group of real SCIENTISTS who could advise us.

      Fortunately, a few altruistic engineers chose to give their own time and resources to stop the worst excesses until the climate itself started proving the academic scientists had been wrong.

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