Eco Worrier: “CO2 is a pollutant!”
Gaia: “Tell that to the biosphere.”
Biosphere: “More….yum-yum!” (Burp!)
Gore: “Eeeeeeek!”

That fact trumps all the pointless ad hominem attacks directed at Specncer, Monckton, etc., by the folks on the wrong side of the debate.

The impartial Sun and the Earth themselves are falsifying the AGW/CO2 hypothesis.

If/when the planet starts to agree with the AGW crowd, I’ll listen. But as of now, the AGW/CO2 hypothesis has been discredited.

Evan,
If jeez hadn’t put a muzzle on religious discussion on this site, I would straighten some of your thinking out. I doubt you’ll end up in the hot place because you are a rather nice heathen. But if you do, I ‘ll try to bring you some ice water (assuming it doesn’t boil away first).

Here is the link to the discussion of TVMOB’s behavior regarding Arthur Smith’s rebuttal:
http://rabett.blogspot.com/2008/07/biter-bit-arthur-smith-has-had-quite.html

But most importantly, nothing on the Viscount’s aberrant behavior as evidenced from his own postings on SPPI site. Viscount Monckton was sent a private copy of a rebuttal by Arthur Smith as a courtesy, at the same time Smith submitted the rebuttal for publication. The Viscount Monckton of Brenchley then added additional text to the paper, then PUBLISHED it on the SPPI site, with his own rebuttal of the rebuttal. This was before the author could possibly respond to any comments from the organization where he submitted the paper, and was done entirely without the permission of the author!

Meanwhile the editor indicated that the paper they published by The Viscount Monckton of Brenchley was NOT peer reviewed. Normally this would involve three peers reviewing the methods and calculations in the paper, for those not familiar with peer review. The paper had received editing and the scientist who suggested some editing changes said he didn’t conduct a peer review. The Viscount Monckton of Brenchley then accused both the editor who published his paper, and the scientist who suggested some edit changes (who actually tried to constructively help him get the paper published), of being LIARS.

These kind of public attacks and behavior by The Viscount Monckton of Brenchley, is without precedent in the scientific world, as far as I know. Any reputable scientist knows that you can’t publish the work of your colleagues without their permission, especially private courtesy copies. This is essentially stealing the fruits of another person’s work. I would think that those on this site, who believe in free enterprise systems, can appreciate and condemn these actions.

But alas, nothing posted here. If you want to read the followup story to the post here.

“Spencer and Christy had calculation errors in their original analysis, as they also have recognized and fixed with good grace. The error was picked up by other scientists in 2005, some five years after it was originally published. This was a very significant error, removed a quite drastic conflict between the erroneous data on the one hand, and all physical theory and other indicators on the other.”

It’s too bad that Mann, Schmidt, Hansen, Briffa, Jones, Amman, Wahl, Schneider, etc, ad nauseum can’t be as graceful and humble as Spencer and Christy when it comes to data and other errors pointed out by others. Instead they attack those who discover the errors.

http://www.weatheraqestions.com/Spencer_07GRL.pdf

About the last eight years. It seem to me since the oceans “temperature” have remained flat and the air “temperature” have remained flat, while CO2 has increased, incoming and outgoing “heat” should be a “known”. So, it should be easy to calculate the forcing/amplification numbers if forcing/amplification happens ever day, ever week, ever month, ever year. So what’s the problem?

About the “Wind” theory were the gentlemen took the wind speed came up with higer “Temperature” than observed data. It sort of like saying wind chill is warmer than what my thermometer is reading.

But the implication that this is the only way in which Monte Carlo methods are used, or that every other use is merely borrowing terminology from your own preferred application, is going to get you tied up in knots. Your professional experience will give you some very useful insights in your specific areas of expertise. But if you can never see any other field or application except as a some kind of shadow of your own, then your expertise can easily become a handicap.

And the truth is… both the applications we describe are borrowing terminology from gambling! :-)

Anyhow — Good night!

A Monte Carlo method is just a tool of integration. This means you have to have the equations written down in the generator of “events”, where the events are generated according to the phase space weighted by the equations. It is not what climate models describe as doing, i.e. covering the envelope of the solutions. A Monte Carlo comes up with a very specific solution, like any other numerical integration method.

It sounds to me like another hand waving and borrowing terminology way of saying the inputs are randomized.

Anyway this thread is fast disappearing in the past, so as you say, whoever has ears to hear let him hear.

The “video game” method, as you call it, is not being used here as a way to obtain a solution to the equations at all (and hence it is doing something rather different from “perturbation expansion”; they are apples and oranges). It is being used as a way to find a range within which various solutions will lie. The more usual term for this sort of technique is a “monte carlo” method, and it is a perfectly legitimate technique with wide general applicability. It’s computationally very expensive; but for some problems it’s the way to go.

Your various analogies are still not making any direct connection with the problem at issue, except as a convenient way to express what you think climate models must be like, and how you believe the solutions of the equations on which they are based must behave.

You are effectively asserting that the trends obtained by climate models are unreliable because of higher order effects. I am effectively asserting that the higher order effects actually don’t happen to upset the trends. I am content to let our two different perspectives on this stand side by side, for readers of this blogs to see as two incompatible views.

Fortunately, those interested are also able to check out the matter further, with a bit of work, by looking at the actual matter of climate models; rather than drawing conclusions on the basis of looking at particle physics. In brief; this has become merely silly. I’m sure you think the same.

On the other hand, on a positive note. Your mention of Tsonis is a genuinely very interesting and relevant. I suspect your reference might have been Tsonis A.A. et al (2007) A new dynamical mechanism for major climate shifts., Geophysical Research Letters, Vol. 34, L13705, doi:10.1029/2007GL030288. (Or possibly related papers by the same team.) I thank you! It looks really interesting.

I am sorry, but what you are describing is a video game method, not a way of approximating the solutions of multiple coupled differential equations.

The envelope of a wrong solution perturbed in its initial values is more of a wrong solution. “Perturbation expansion solutions” does not mean perturbing initial values.
It means having a valid differential equation system written down and inserting a series expansion of the hypothetical solution and taking its first few terms as the solution of the differential equations to which solution one applies boundary conditions.

What you are describing is equivalent in my field, elementary particle physics, as if one took the parton model, stuck some different initial values , calculated the envelope of these different solutions and said “this is elementary particle scattering”. Whereas the whole panoply of Quantum ChromoDynamics was necessary to be able to expand perturbatively towards a correct solution. The parton model was actually quite good because in reality it was the representation of first order terms( the equivalent of the mean values used in the climate models), which of course exist in the more correct QCD solution; the perturbation expansion of QCD solutions was much more successful in describing the data than the parton model.

In the elementary particle case there are no coupled differential equations to generate chaotic behaviors, higher order terms are expected to be less in effect than lower order ones, in contrast to the meteorological equations tinkered with by the climate models, where higher order terms can be quite important, due to the chaotic nature of the problem ( again I refer to the Tsonis et al paper). Thus first order terms, the mean values used in the boxes for propagating effects in time in meterology christened climatology models, cannot be valid for very many steps.

The difference is that weather prediction is trying to identify a time series, and climate prediction is trying to identify the envelope within which time series are constrained. In both climate and weather models, it is normal to repeat a simulation with small initial perturbations, which in both cases will diverge chaotically. The difference is in what you do with the multiple time series that you obtain.

With weather simulations, you see how far into the future the divergence starts to become significant. You then can have some confidence in using the time series up to that point as a projection of the weather.

With climate simulations, you take all your many diverging time series together, and look at the distribution for those series. Your projection is not a single time series (weather) but is rather the envelope within which the many runs are contained (climate).

The envelope does not diverge chaotically in the same way as weather, but tends to be a well defined and repeatable projection, giving a trend and a natural variance for your weather variables.

That simplifies things somewhat; and there is still plenty of room to improve the “skill” of climate models; and weather models, for that matter. But failing to grasp this distinction is at the heart of invalid objections to climate models based on confused analogies with weather prediction.

Spencer and Christy had calculation errors in their original analysis, as they also have recognized and fixed with good grace. The error was picked up by other scientists in 2005, some five years after it was originally published. This was a very significant error, removed a quite drastic conflict between the erroneous data on the one hand, and all physical theory and other indicators on the other.

Spencer and Christy continue to defend a weaker version of their original claims, and other scientists continue to claim there are still defects in their revised analysis, though not simply straightforward calculation errors. But as I said previously, the rest of the field has pretty much moved on.

The ongoing debate within the scientific world is looking more and more like tidying away the remaining wreckage of a failed idea. This is pretty common in science, by the way. Data and analysis and conclusions that get through peer review don’t get a stamp of infallibility. Peer review is just the initial hurdle for making new ideas available to other researchers, to replicate or falsify the ideas. Spencer and Christy are still in there swinging, which is all part of the process; but the wind shear work I mentioned above is going to be another body blow to their notions on the troposphere, I think.

Now don’t get me wrong; they are all still active and in good standing as part of the scientific community, and their work is being published and considered. But its not having much impact, because it is not standing up well by comparison with the analysis by others, and is becoming increasingly irrelevant. Spencer himself is frankly in some danger of being ignored altogether as a bit of a crank; and not just for his idiosyncratic views on climate. Christy is more credible, and less off the wall in his claims.

I.e. the science is not settled.

In my humble opinion it behooves also all ethical climatologists to come out and say this loud and clear, and stop politicians from misusing their research and plunging the world and the already shaky economy into chaos.

They should say : “I believe I am right and stand fully behind my analysis of the data, but I accept that more research is needed on all fronts”.

Duae Quartunciae (11:15:54) :

“At least; the presumption that they will diverge in the same way is not actually backed up with any argument other than an analogy, the validity of which must be assumed since it is not actually argued.”

Both solve the same differential equations. From what I have read climate models use 20 minute steps in boxes 1kmby150ksby150km. Is it the boxes that are different between meteorology and climate?

20 minute steps to go up to a century are way out of any first order approximation in a perturbative expansion: all those mean values set in the boxes are really that, a first order approximation. Nobody who has worked with coupled differential equations can believe that a first order approximation can be valid after thousands of steps.

Tsonis et al, I do not have the reference at hand but it is in a discussion at CA, studied with neural nets the chaotic system of coupled ocean and air currents. It is interesting that their results also predict a leveling off of the temperatures.

Real life is not a first order approximation.

Observation:
On feedbacks and forcings instead of normal thermodynamics: I feel as if I am being forced to predict an eclipse using the epicycles system. It can be done, but should one do it?

“It’s since been recognized by all concerned that Spencer and Christy of UAH made some significant errors in their analysis.”

“significant errors in their analysis”?
What errors are you refering to? Did he make assumption about the observed data that were not true? Were the calculations in error?

The amplification factor is not something you can measure at a point in time using observations, but a kind of diagnostic of climate sensitivity. Since climate sensitivity cannot be measured directly or inferred from any short term trend, the question of “amplification number for the last 8 years” is ill-formed.

Best estimates of climate sensitivity continue to be somewhere in the range 2 to 4.5 K per CO2 doubling; or (as I prefer to give it in SI units) somewhere in the range 0.5 to 1.2 K/(W/m^2). This is an amplification of somewhere from 1.7 to 3.9

Best estimate is still around about 2.5; but you can see that the uncertainties associated with this are large. The quoted extract you provide is a good statement of the state of play; although 4 is a bit too large for the forcing from doubling of CO2. 3.7 would be better.

You don’t indicate what particular “observations” of Spencer you mean. Names don’t have any weight; and PhDs are dime a dozen. It’s the observations themselves that matter, whatever they are.

Spencer’s most important actual observations are associated with satellite measures. Some of this was taken very seriously indeed a few years ago, when the data seemed to indicate a lack of tropospheric warming. It’s since been recognized by all concerned that Spencer and Christy of UAH made some significant errors in their analysis. Spencer and Christy and a few others continue to argue for a weaker version of their original strong conclusions; but the rest of the field has mostly moved on. Tropospheric warming continues to be actively investigated; and there are right now a couple of new papers coming out dealing with wind shear. This is going to help narrow the uncertainties, and give even more strong support to the reality of tropospheric warming.

As well as observations, Spencer also has his own opinions. They are rather idiosyncratic and have pretty much no impact on other scientists; just as the overwhelming contrary opinion of almost all his professional peers has pretty much no impact on Spencer. Which is fine; it’s healthy to have a couple of mavericks doing real work in the relevant science.

“The sensitivity of the climate system to a forcing is commonly expressed in terms of the global mean temperature change that would be expected after a time sufficiently long for both the atmosphere and ocean to come to equilibrium with the change in climate forcing. If there were no climate feedbacks, the response of Earth’s mean temperature to a forcing of 4 W/m2 (the forcing for a doubled atmospheric CO2) would be an increase of about 1.2 °C (about 2.2 °F). However, the total climate change is affected not only by the immediate direct forcing, but also by climate “feedbacks” that come into play in response to the forcing.”

“As just mentioned, a doubling of the concentration of carbon dioxide (from the pre-Industrial value of 280 parts per million) in the global atmosphere causes a forcing of 4 W/m2. The central value of the climate sensitivity to this change is a global average temperature increase of 3 °C (5.4 °F), but with a range from 1.5 °C to 4.5 °C (2.7 to 8.1 °F) (based on climate system models: see section 4). The central value of 3 °C is an amplification by a factor of 2.5 over the direct effect of 1.2 °C (2.2 °F). Well-documented climate changes during the history of Earth, especially the changes between the last major ice age (20,000 years ago) and the current warm period, imply that the climate sensitivity is near the 3 °C value. However, the true climate sensitivity remains uncertain, in part because it is difficult to model the effect of feedback. In particular, the magnitude and even the sign of the feedback can differ according to the composition, thickness, and altitude of the clouds, and some studies have suggested a lesser climate sensitivity.”

Climate Change Science: An Analysis of Some Key Questions, pp 6-7,
Committee on the Science of Climate Change
National Research Council

But hey… let’s proceed a bit futher. Even if we don’t agree, some of the differences may be clarified.

Weather is certainly chaotic, but that does not mean temperatures in a weather model diverge without limit. It means that after a comparatively short period of time, a weather model has no idea of what weather will be within the given distribution of weather.

At a given location, you can obtain a probabilistic distribution for different weather variables (temperature, wind speed, humidity, precipitation, anything you like), for a given season. If you want to know the weather next year, a weather model is no good to you. The best you can do is the probability distribution as an idea of what to expect.

A climate model is in someways similar to a weather model; but you can think of it also as a way to see how the probability distribution changes. It’s actually aiming at something quite different from a weather model, even though there are similarities in the way they run. What’s really different are the kinds of boundary conditions applied, but that’s getting a bit detailed.

Suffice to say… the temperature trend from a climate model is not actually a prediction for the future temperature; since you still have all the usual chaotic variation of weather, which cannot be predicted years in advance. But what you can predict is the change in distributions. This can be both the changes in the mean and in the variance of the distributions of temperatures; but most particularly the mean. The temperature trend from a climate model is actually telling you how much the mean of the weather distribution is likely to shift; which is quite a different thing from predicting the actual weather itself… the day by day specific temperatures. In general, climate models also suggest changes in the envelope or distribution for other variables, like precipitation or humidity.

Now I am sure you are skeptical about all that; and I do not presume to actually persuade you that climate models are useful. But as an actual argument, the analogy from lack of prediction of weather to lack of prediction of climate is invalid; because they aren’t the same, and perturbations don’t diverge in the same way for the weather itself, and the distribution within which weather varies. At least; the presumption that they will diverge in the same way is not actually backed up with any argument other than an analogy, the validity of which must be assumed since it is not actually argued.

I quite agree that we don’t predict weather months or years in advance.

And by the way. Anyone who works with a climate model is bound to be something of an expert in thermodynamics.

Fred, your question is a bit open ended, and simply presumes the points in dispute. I don’t think we know everything, and I don’t think there are any big issues with models conflicting with data. If you have some more specific issue, I may be happy to comment. Make the point as hard as you like, but not as open ended as you like, since any reply has to fit into a fairly small space! Have mercy.

Peer review; that’s an interesting idea about Earth’s rotation. Unfortunately, it’s not enough to know the rotation speed; you also will need to know moment of inertia; and that varies as well, as mass shifts within the Earth. Since the variations in length of a day are in about the ninth significant figure, you would need to know the change in moment to at least that accuracy as well. But there’s one thing you can estimate! The biggest consistent trend in varying rotation speed is actually a gradual slowdown as angular momentum is transferred to the moon. Most of the energy involved is actually dissipated as tidal drag; which is know is about 3 TeraWatts. Since solar input as a bit over 120 PentaWatts; even tiny variations in the solar constant, or albedo, will swamp changes in tidal drag. So in fact, the role of changes in Earth’s rotational speed is quite negligible in the energy budgets that bear upon climate.