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Circular Arguments

Posted: Sun Aug 03, 2014 6:12 pm
by MSimon
http://www.nature.com/climate/2009/0902 ... 7391a.html

As an example, in the mid-1990s I was discussing the phase problem with members of a modelling group and learned that their model had Earth in a circular orbit with no precession. This was astonishing. First, we are trying to measure the effects of CO2 to high accuracy — say 0.01 °C, in a system in which annual temperature extremes routinely exceed plusminus 50 °C. Second, on an ice-age timescale, the effects of precession are immense, strong enough to be used as a clock. Third, we have known that the orbit is elliptical since Johannes Kepler in the seventeenth century, and about precession since Hipparchus (around 150 bc). The duration of the instrumental temperature record is now 1% or 2% of the 26,000-year precession cycle: when trying to measure small effects it is unwise to ignore large ones.

One should also note the contrast between the enormous computational resources used by the models and the relatively meagre effort required to analyse real data. Thus, work of the type done by Stine et al. is to be applauded. Ignoring the time required to assemble the data and write the programs, it probably took no more than a few seconds of computer time to show effects that were not predicted by any of the models. As Richard Feynman commented, "It doesn't matter how beautiful your theory is, it doesn't matter how smart you are. If it doesn't agree with experiment, it's wrong."
So the climate system which has been known chaotic since Edward Lorenz in 1972, does not include all known perturbations to the system. It is to laugh. I wonder if this has been fixed in the latest models? What ice ages and the Milankovitch cycles tell us is that small but long lasting changes to the Earth system change things. The Earth is an integrator with internal feedbacks. The feedbacks include clouds (which are modeled poorly - according to the modelers) and ice/snow albedo.

And yet according to the modelers the models are definitive. It is to laugh.

I now await Tom's proof that the models work. Keep the faith Tom. Some one has to.

Re: Circular Arguments

Posted: Sun Aug 03, 2014 7:22 pm
by JoeP
This is exactly what I was getting at in one of my comments in these threads. The complexity of the system is such that the models we have are insufficient. Interdependence between sets of variables and feedback strengths are in many ways unknown.

Years ago I did some work writing genetic algorithms that searched for good solutions out of huge solutions spaces involving dozens of variables. I also did some experiments in mapping optimized routing code -- again using GAs. This gave me some little perspective as to what a proper climate model would require...that kind of system at the scales required are probably not currently sufficient as of now IMO.

Since the warming has flat lined despite the predictions, obviously the models are too simplistic or tuned in such a way to reflect the experimenter's biases. I suppose changing the name from GW to Climate Change is the real answer to that. Spin. How transparent is that?

There very well may be warming going into the future, but it is obvious there are problems with the predictors -- a little bit of humility and less hubris would suit -- on all sides of the issue, but mainly the so called 97%-ers. LOL.

Even if there is more warming, the side effects and the damage of abandoning current energy sources will be large. I have yet to see a serious cost-benefit done on the side of those that want severe limitations. This without the hysteria and political "Green Marxism" that the GW movement seems to have morphed into.

Re: Circular Arguments

Posted: Sun Aug 03, 2014 8:25 pm
by tomclarke
JoeP wrote:This is exactly what I was getting at in one of my comments in these threads. The complexity of the system is such that the models we have are insufficient. Interdependence between sets of variables and feedback strengths are in many ways unknown.

Years ago I did some work writing genetic algorithms that searched for good solutions out of huge solutions spaces involving dozens of variables. I also did some experiments in mapping optimized routing code -- again using GAs. This gave me some little perspective as to what a proper climate model would require...that kind of system at the scales required are probably not currently sufficient as of now IMO.
I too have used stochastic learning algorithms to find patterns in data (including GAs). There are all the issues of over-fitting etc. In fact if you look at the climate denier arguments that are clearly wrong about half are over-fitting to some imagined fictitious cycle, the other half are just not understanding the physics.

HOWEVER - climate models are not the same as stochastic models. They are dynamical.

What that means is that, at ;least in principle, they don't need to learn anything at all. They can be an accurate physical simulation will all parameters tied down to the physics from first principles or experimental observations on specific elements quite different from the overal gungy climate.

This is the thing that lots of critics don't understand.

Now, even with this, climate models are very complex, and it is worth checking that all parametrized relationships are properly validated by physics or observations independent of the global temperature.

This means, in the case of CO2, that we do not need to be able to model all the chaos in the climate (actually it is clear that models are getting quite good at doing this) but suppose there was unknown forcing X - would it stop models from giving accurate estimates of the effect of CO2?

No it would not. The forcing from CO2 can be tied down easily from theory and satellite IR spectrum observations which agree. The feedbacks, which are the difficult bit, are the same for CO2 as for anything else. So all we need is a known forcing - or several different known forcings - and we can over both historical and paleo temperature time sequences look to work out the value of ECS from the correlation between temperature and forcing.

That is just one parameter - climate sensitivity - from a lot of data. Even with massive noise its doable. Now actually the time sequences come on many different time scales, years, decades, centuries. To include all of these you need to know time constants which are complex, though partly constrained by possible physics mechanisms. So maybe 3 or 4 parameters including these.

It is very doable. The errors come from uncertainties in forcings, and uncertainties in global temperatures, and finite amount of data which means that internal variation and unknown forcings cannot be eliminated completely.
Since the warming has flat lined despite the predictions, obviously the models are too simplistic or tuned in such a way to reflect the experimenter's biases. I suppose changing the name from GW to Climate Change is the real answer to that. Spin. How transparent is that?
The projections are not predictive over more than 6 months. You get a lot of internal variation. In teh 1990s temperatures were going up faster than models expected. Recently they have been going up slower (but not flatlining unless you cherry-pick both end-points and temperature datasets).

So every year with lower temperature is some evidence for lower ECS, but it is folded in with lots of years and with estimates of climate sensitivity independent of the direct one as above. And the last 17 years, when looked at with the overall temperature graph and the model run variability, look expected. Finally, we can explain between 50% and 100% of the last 17 years relative slowdown on known internal variation that is not predictaively modelled (it is averaged in the models):
(1) variable TSI
(2) variable ENSO phase.
There very well may be warming going into the future, but it is obvious there are problems with the predictors -- a little bit of humility and less hubris would suit -- on all sides of the issue, but mainly the so called 97%-ers. LOL.
I think you are getting caught up on the politics. the science is saying that AGW exists but the magnitide of it is variable over a 3:1 range and not known. The political campaign against this is full of misinformation and obviously wrong science. I sympathise with the climate scientists who become defensive amd political themselves given such a campaign. Personally I would hate it.

I think the scientists who get politicised are wrong. I think the scientists who argue that presenting the science in its true uncertainty publicly will not work are right. Politicians seem to have to use spin - special ways of saying things - to win elections. Personally I hate that and won't do it. I think no scientist should do it. But I can understand why some do because people are just not good at making decisions based on probabilty PDFs where the systematic uncertainty in the problem introduces meta-PDFs!

But on the internet the lies and misrepresentations from the denier side far far outweigh the political spin from the climate scientists. The IPCC reports do a good job of summarising the science and while I can cavil at the edges overall I think they have got both the expected climate sensitivity and the uncertainties about right.

From what people say here I'm not convinced they even know what the IPCC reports say about the uncertainties.

The IPCC summary for policy makers I would personally ignore. It is vetted by governments which means it will have spin.
Even if there is more warming, the side effects and the damage of abandoning current energy sources will be large. I have yet to see a serious cost-benefit done on the side of those that want severe limitations. This without the hysteria and political "Green Marxism" that the GW movement seems to have morphed into.
The best I have seen is a report commissioned by the UK government. It is serious cost/benefit - but as with all such things there are uncertainties and someone wanting to argue could probably get different answers.
http://en.wikipedia.org/wiki/Stern_Review

My (uninformed) opinion so far is that the cost of measures taken thus far, though real, is very small as fraction of GDP especially because taxes on energy to fund carbon neutral energy only cost the economy for the distortion they introduce, which will always be less than the total tax/subsidy - money spent on renewables circulates round the economy. They have had the desired effect of massively boosting solar - which will soon be competitive (maybe already is) for non-base-load applications. I'm less sure about whether other renewables are going to be competitive.

Electric cars are more difficult with another 15 years at least of technological innovation before being competitive - but arguably we want them anyway when you factor in the costs of PM2 pollution - that is very difficult to get rid of from IC engines. One serendipity is that intelligent charging of electric cars can provide distributed energy storage if car ownership and renewable power get ramped up together.

And, of course, sensible people are pro-nuclear for base load - modern nuclear power stations - even conventional ones - are pretty good. Alas it does not look like public opinion goes that way anywhere except France and China.

Re: Circular Arguments

Posted: Sun Aug 03, 2014 8:43 pm
by tomclarke
To address these points:

climate models are used for a 50-100 years timescale, over which the effects of precession are negligible. Paleo studies include orbital forcings where timescale makes this appropriate.

No-one is trying to measure the effects of CO2 to 0.01C. A +/-30% estimate of TCR means 0.05C/decade. No-one seriously tries to use less than 40 years data to estimate TCR so that would be a temperature accuracy of 0.2C or 0.1C at each end. 10X worse than is claimed here. Also it is worth noting that we don't need this absolute accuracy, but this relative accuracy over the time period considered.

This guy believes that orbital forcings are significant over the instrumental record. But they have been considered and could easily be included if needed. He does not back up his assertion with numbers. Am I supposed to go to the work myself of proving that climate scientists are not idiots by looking up these numbers? Anyone who wants me to do this please say. I will then do it - but I will as recompense feel justified in calling however asks for this an prize idiot when (as will be the case) my figures show this is not a real problem.

Climate scientists use both models and real data to estimate ECS. I agree that complex climate models, while great at forecasting weather, are a sharp tool that is expensive and easy to misuse. Recent work uses a lot of EMICS, so I guess the c;limate science community agrees with this guy here:
http://www.ipcc.ch/publications_and_dat ... 8-8-3.html

This guy clearly has a bee in his bonnet. No climate scientist says that models are definitive. The IPCC report (which climate deniers I note don't seem to read in its entirety) has Ch 8 devoted to evaluating climate models making quite clear that though they have many strengths they also all have weaknesses - different models different types of weakness.

BTW don't you think the best example of Feynman's point is the observational satellite IR spectrometry evidence for CO2 as GHG which completely knocks on the head stupid theories about CO2 not being a significant GHG?


MSimon wrote:
http://www.nature.com/climate/2009/0902 ... 7391a.html

As an example, in the mid-1990s I was discussing the phase problem with members of a modelling group and learned that their model had Earth in a circular orbit with no precession. This was astonishing. First, we are trying to measure the effects of CO2 to high accuracy — say 0.01 °C, in a system in which annual temperature extremes routinely exceed plusminus 50 °C. Second, on an ice-age timescale, the effects of precession are immense, strong enough to be used as a clock. Third, we have known that the orbit is elliptical since Johannes Kepler in the seventeenth century, and about precession since Hipparchus (around 150 bc). The duration of the instrumental temperature record is now 1% or 2% of the 26,000-year precession cycle: when trying to measure small effects it is unwise to ignore large ones.

One should also note the contrast between the enormous computational resources used by the models and the relatively meagre effort required to analyse real data. Thus, work of the type done by Stine et al. is to be applauded. Ignoring the time required to assemble the data and write the programs, it probably took no more than a few seconds of computer time to show effects that were not predicted by any of the models. As Richard Feynman commented, "It doesn't matter how beautiful your theory is, it doesn't matter how smart you are. If it doesn't agree with experiment, it's wrong."
So the climate system which has been known chaotic since Edward Lorenz in 1972, does not include all known perturbations to the system. It is to laugh. I wonder if this has been fixed in the latest models? What ice ages and the Milankovitch cycles tell us is that small but long lasting changes to the Earth system change things. The Earth is an integrator with internal feedbacks. The feedbacks include clouds (which are modeled poorly - according to the modelers) and ice/snow albedo.

And yet according to the modelers the models are definitive. It is to laugh.

I now await Tom's proof that the models work. Keep the faith Tom. Some one has to.

Re: Circular Arguments

Posted: Mon Aug 04, 2014 2:04 am
by MSimon
What that means is that, at ;least in principle, they don't need to learn anything at all. They can be an accurate physical simulation will all parameters tied down to the physics from first principles or experimental observations on specific elements quite different from the overal gungy climate.
But Tom. You know that the models are in fact not physical. That they are physical is a flat out lie. They are in fact fitted. The modelers themselves admit that their understanding of clouds is poor. They don't even know for sure the proper sign of their cloud parameter. And then there is the resolution problem. And the initial value problem in a chaotic system.

All you have left is faith. I'm no fan of your religion. Sorry 'bout that.

Re: Circular Arguments

Posted: Mon Aug 04, 2014 2:40 pm
by tomclarke
MSimon wrote:
What that means is that, at ;least in principle, they don't need to learn anything at all. They can be an accurate physical simulation will all parameters tied down to the physics from first principles or experimental observations on specific elements quite different from the overal gungy climate.
But Tom. You know that the models are in fact not physical. That they are physical is a flat out lie. They are in fact fitted. The modelers themselves admit that their understanding of clouds is poor. They don't even know for sure the proper sign of their cloud parameter. And then there is the resolution problem. And the initial value problem in a chaotic system.

All you have left is faith. I'm no fan of your religion. Sorry 'bout that.
See other threads as well for more general comment on the religion stuff.

I do not know what you are saying. I'm going to give four possibles:
(1) direct solution of atmosphere physics PDEs
(2) parameters, or functional forms, that come directly from other non-atmosphere physics observations or theory
(3) parameters or functional forms that come from averages of simulations done at a different scale, or to make best fit on simulations done at different scale.
(4) parameters that are arbitrary, unrelated to physics, and adjusted to make the model fit the output data.

Your comment only applies to class (4). However its true there can be more subtle curve fitting where classes of model that coincidentally fit output data are preferred, so I don't dismiss the possibility of models over-fitting. except that they are cross-validated in many different ways - not just on the historic temp dataset.

So, which parameters are like this? You claim that everything is like this. I claim almost nothing.

As for the initial value problem - please explain.

As for the resolution problem - yes models are not perfect. But they do darned good weather forecasts up to +5 days which is pretty good on a chaotic system, so they must get some things right! Remember when 24 hour weather forecasts were no good?

Re: Circular Arguments

Posted: Mon Aug 04, 2014 3:03 pm
by MSimon
But Tom,

If you fix the value for CO2 sensitivity then the rest of the values are arbitrary. Which is why the curve fitting exercise is necessary.

And please - if you don't know the sign of the cloud parameter and clouds are VERY significant... You have to curve fit.

The models are not based on first principles like F=ma (at low speeds). There are bunches of parameters whose exact value is unknown. And you know what even small changes in coefficients does in a chaotic system.

I thought I explained how the curve fitting is done.

If you don't even know the sign of the cloud parameter.... And you think on that basis I should give the models credence?

Let us say I have a pedal in my auto design and I tell the company lawyers that "this pedal is VERY important, but I can't tell you if the car speeds up or slows down when you press on it" you think the company lawyers are going to say - "Sure, put it in. We will let our insurance company cover the risk." Is that going to fly?

As I have said repeatedly - your faith is amazing. But it doesn't pass the engineering test.

Re: Circular Arguments

Posted: Mon Aug 04, 2014 3:44 pm
by tomclarke
MSimon wrote:But Tom,

If you fix the value for CO2 sensitivity then the rest of the values are arbitrary. Which is why the curve fitting exercise is necessary.

And please - if you don't know the sign of the cloud parameter and clouds are VERY significant... You have to curve fit.

The models are not based on first principles like F=ma (at low speeds). There are bunches of parameters whose exact value is unknown. And you know what even small changes in coefficients does in a chaotic system.

I thought I explained how the curve fitting is done.

If you don't even know the sign of the cloud parameter.... And you think on that basis I should give the models credence?

Let us say I have a pedal in my auto design and I tell the company lawyers that "this pedal is VERY important, but I can't tell you if the car speeds up or slows down when you press on it" you think the company lawyers are going to say - "Sure, put it in. We will let our insurance company cover the risk." Is that going to fly?

As I have said repeatedly - your faith is amazing. But it doesn't pass the engineering test.
As I've said repeatedly - I don't go on faith.

Which is why your vague assertions also don't move me much. If I thought you'd checked all the details, and had faith in your judgement, then maybe they would.

Now - you say the models are not based on first principles. That is I believe incorrect. Note my 1,2,3,4 classification above. The models use numerical approximations to 1 with a whole load of 2 and 3 mixed in.

"there are bunches of parameters whose exact value is unknown" applies to every single experimental constant in physics.

In this case, the issue is whether the physics that allows the parameter(s) to be estimated is independent of the (global temperature) results, or whether it is fitted to them. It is not about how good is the parameter (and I admit some of these parameters don't do a great job - the models are not perfect). I'm claiming none of your bunches of parameters are fitted to global temperature, CO2, or anything related.

For example, clouds parameters will be related to much smaller scale models which deal with the atmosphere and model cloud formation, movement, etc. The parameters for "how much cloud" are taken as best fit from averages based on these models run many many times.

Now that is not necessarily right - I agree. In fact it is sort of bound to be wrong. Clouds, everyone admits, are very very difficult to model. But it's rightness is checkable against detailed cloud observations, and there is no way that its tuning is related to global temperature, CO2, or any of the model outputs. Maybe many different functional forms are tried to this to find best fit. BUT IT WILL BE BEST FIT TO LOCAL CLOUD DATA - not best fit to the model global temp outputs.

I'd like you to show that you understand the concept here, and then we can move on to your next point.