I cannot think of a single example in which an old theory has been discarded without a replacement in place.
Dec 5, 2015 at 7:08 PM Entropic man

Ah, yet another EM Fallacy™. This time the fallacy of "I cannot think of a single example, which proves that none exists".

Well, forgetting about theories that were believed in their time but perhaps never really could qualify as science in today's sense (Vitalism, phlogiston as a substance, psychoanalysis as psychological science and so on).

Just off the top of my head here are a few theories that were known to be wrong *prior* to a correct replacement theory appearing:

■ Classical dynamics for gases was known to be wrong (inconsistent measured specific heat values) in the mid 19th century decades before the current theory appeared.

■ Rayleigh–Jeans black body radiation theory was known to be incorrect (ultraviolet catastrophe) prior to Planck.

■ Classic theory of photoelectron emission was known to be wrong well before Einstein's quantum explanation.

■ Methods for calculating the total mass of the universe today are known to be wrong (give contradictory results for unknown reasons). The correct theory has yet to appear. "dark matter" whatever that might be has been hypothesised.

Mike Jackson - Why should I believe a word they say?

Yes. I suspect that, for you and me, even if "the science" were not so obviously flaky (homogenised data, original data 'lost in an office move' (© Phil Jones), models "validated" by comparison with other unvalidated models, 'proof by ignorance',..) the behaviour of its proponents would have left us profoundly sceptical.

Even without seeing evidence of unacceptable behaviour, I find that being subjected to a hard sell convinces me completely that the product has a serious problem - at very least that it is grossly overpriced.

But, look at it from the point of view of EM. (Who, it has to be said, is probably one of the most clear thinking CAGW believers that you are likely to come across.)

EM will, I know, find it hard to understand why the behaviour of the stars of climate science make people like you and me say "Why should I believe a word they say?"

EM has said that he finds surprising that, given a clear and convincing but purely technical explanation, someone will remain unconvinced purely for non-technical reasons. In the past, he says that he assumed that a technical explanation would suffice to convince and it was hard for him to come to terms with the fact that it did not.

EM has a fascination with prophesies of catastrophe - he is not really happy unless he is scared to death of something awful that will befall us all. I think he has mentioned, in addition to catastrophic global warming, mass starvation and catastrophic overpopulation as being things for which he fears the worst.

I have pointed out many times, including once or twice in this thread, EM has a tendency for something to enter his mind and if it agrees with his view of things, it becomes for him reality, without further need for evidence. (See previously in this thread for his views on the mental inflexibility of engineers for just one example.)

Although EM will happily believe a thing without evidence, he spends huge amounts of time and effort studying the data and the formulas searching for confirmation of his fears. If he'll believe something without evidence, then *with* something that appears to be evidence, any remaining doubt is removed, and he can enjoy his fears to the hilt.

EM does not find it hard to discover what he sees as evidence. He has a touching faith in simple mathematical formulas - eg the holy logarithm formula of climate science for radiative forcing, that you can compute meaningful confidence limits on climate data ( even though the data points are clearly not independent, not known to be normally distributed, and come from nonstationary time series). From starting points such as these, he reinforces his beliefs and he is perplexed why the things that he finds convincing leave apparently intelligent people such as you and me completely unconvinced.

I assume that his ramblings about the mental inflexibility of engineers is his attempt to rationalise this apparent paradox.

Come on EM, we are all gagging to answer but, since you clearly have a unique view of what "an engineering approach" is, it's hard to answer with that vital piece of information missing.

So, as I asked, what do you mean by "an engineering approach"?

OK EM. I got tired of waiting so I did my own research to understand what you think about engineers.

Poor understanding of scientific method is something I would expect of an engineer.
Engineering courses teach old science. Orbital mechanics and optics go back to Newton.
Electricity and materials are 19th century science. Electronics is almost a century old.
The equations are written in stone, the doubts and uncertainties long resolved.
What engineers are not good at is new science. You hide from anything not yet settled. You reject anything whose equations are still provisional. You qualil before data uncertain enough to require statistical analysis.
(Copyright EM)

EM – the profundity of your ignorance and your pride in parading it is inspiring to behold.

Anyway, I looked at the first question. 1. Do clouds intensify climate change?

Some time back, I learned that when asked a technical question by someone whose understanding was – shall we say - limited, it was often best to find out if they were really asking the question they needed to ask.

For example, if asked “How can we get a datacomms link between A and B with enough bandwidth to do X and Y?”, after asking a few questions about the problem, I'd probably come back and say something like: “Do you think it really makes sense to be splitting a key business process between two different teams, in different locations, reporting to different managers and working to different priorities? I don't think your problem is really a technical one at all.”

It is clear what the questioner here has in mind: Is warming due to increased atmospheric CO2 increased* by the presence of clouds? It is obviously a question asked by somebody who has uncritically bought into the idea that the notion of “feedbacks” makes sense and can be used to analyse climate effects.

Clouds are an intrinsic part of climate. Clouds are there as the result of other aspects of climate and other aspects of climate are affected by the clouds, where they are and what they are up to. I think that the question as asked is probably not a well-posed question and the fact that it still bewilders climate science perhaps confirms that.

I think a bit of linear dynamic system theory modelling (where all variables, in general, depend on all other variables) would probably give, at very least, some useful insights, and perhaps even some results that could be compared with real observations. You could probably use Kalman filtering methods to derive parameters for the model from multichannel observed data.

I'd have a go at it myself in the next hour or so, except that I have some hydraulic brake cylinders that I really want to get connected up this evening.

______________________________________________________________________________
* Note the unscientific way that the result is assumed even in the question. As a scientific question it would have been better neutrally posed as "Do clouds intensify or reduce climate change?". Just one little illustration of the bias built into 'climate science' as it searches for confirmation of the CAGW hypothesis.

EM - I was not sure if you actually *read* what I said (as opposed to copy-pasting it). If you did read it, then, from what you say, I think that you may not have fully taken in what I said - perhaps I was too succinct and made assumptions about shared terminology. Here is a bit more about what I was on about.

I don't know you are familiar with the state-space formulation for representing continuous time lumped parameter linear dynamic systems. In brief, such system can be represented by a 1st order vector differential equation:

dx(t)/dt = A x(t) + B u(t) + v(t)

y(t) = C x(t)

where x(t) is an N-element state vector, A is an N × N transition matrix, u(t) is a vector of input variables,
y(t) is a vector of observed variables, v(t) is a noise vector.

To construct a model of a system consists essentially of constructing the matrices A, B and C. There are huge advantages in having a system model in such a form as all sorts of things (optimal control, estimation, prediction, model dimensionality reduction,...) can be computed simply as matrix manipulations.

Obviously, anything to do with climate is not actually linear, lumped parameter, but then nor is anything else in the real world.

If you know the internal details of the system you can construct the matrices by directly analysing it to get its differential equations and transforming them into state-space form. But if you don't understand the internal functioning of the system (as seems to be the case with clouds and climate), you can use statistical methods for estimating the matrices from whatever observed variables are available to you. There is a *huge* literature on doing this. Essentially you are treating the system as a black box whose internal mechanisms are unknown and have to be deduced from whatever is observable.

To me it seems as if it could be a promising approach for modelling the function of clouds in the climate system where the actual mechanisms are obviously not understood. I have never ever come across anything in climate science that uses state-space modelling - a bit of a mystery as, for fifty years or so, it has become the standard formulation for modelling dynamic systems. So there you are - a possible approach to modelling and understanding the interaction of clouds with the climate system based on what is today standard control engineering theory but which seems to have bypassed climate science.

But as BYIJ just pointed out, engineering is just science - it is not something inferior (as you seem to think) nor, (as you pretend we have claimed), is it something superior.

Except that, it has to be said, many 'climate scientists' do come across as not being all that bright, but that is a separate question altogether.