Guest Post by Willis Eschenbach
After I published my previous post, “An Observational Estimate of Climate Sensitivity“, a number of people objected that I was just looking at the average annual cycle. On a time scale of decades, they said, things are very different, and the climate sensitivity is much larger. So I decided to repeat my analysis without using the annual averages that I used in my last post. Figure 1 shows that result for the Northern Hemisphere (NH) and the Southern Hemisphere (SH):
Figure 1. Temperatures calculated using solely the variations in solar input (net solar energy after albedo reflections). The observations are so well matched by the calculations that you cannot see the lines showing the observations, because they are hidden by the lines showing the calculations. The two hemispheres have different time constants (tau) and climate sensitivities (lambda). For the NH, the time constant is 1.9 months, and the climate sensitivity is 0.30°C for a doubling of CO2. The corresponding figures for the SH are 2.4 months and 0.14°C for a doubling of CO2.
I did this using the same lagged model as in my previous post, but applied to the actual data rather than the averages. Please see that post and the associated spreadsheet for the calculation details. Now, there are a number of interesting things about this graph.
First, despite the nay-sayers, the climate sensitivities I used in my previous post do an excellent job of calculating the temperature changes over a decade and a half. Over the period of record the NH temperature rose by 0.4°C, and the model calculated that quite exactly. In the SH, there was almost no rise at all, and the model calculated that very accurately as well.
Second, the sun plus the albedo were all that were necessary to make these calculations. I did not use aerosols, volcanic forcing, methane, CO2, black carbon, aerosol indirect effect, land use, snow and ice albedo, or any of the other things that the modelers claim to rule the temperature. Sunlight and albedo seem to be necessary and sufficient variables to explain the temperature changes over that time period.
Third, the greenhouse gases are generally considered to be “well-mixed”, so a variety of explanations have been put forward to explain the differences in hemispherical temperature trends … when in fact, the albedo and the sun explain the different trends very well.
Fourth, there is no statistically significant trend in the residuals (calculated minus observations) for either the NH or the SH.
Fifth, I have been saying for many years now that the climate responds to disturbances and changes in the forcing by counteracting them. For example, I have held that the effect of volcanoes on the climate is wildly overestimated in the climate models, because the albedo changes to balance things back out.
We are fortunate in that this dataset encompasses one of the largest volcanic eruptions in modern times, that of Pinatubo … can you pick it out in the record shown in Figure 1? I can’t, and I say that the reason is that the clouds respond immediately to such a disturbance in a thermostatic fashion.
Sixth, if there were actually a longer time constant (tau), or a larger climate sensitivity (lambda) over decade-long periods, then it would show up in the NH residuals but not the SH residuals. This is because there is a trend in the NH and basically no trend in the SH. But the calculations using the given time constants and sensitivities were able to capture both hemispheres very accurately. The RMS error of the residuals is only a couple tenths of a degree.
OK, folks, there it is, tear it apart … but please remember that this is science, and that the game is to attack the science, not the person doing the science.
Also, note that it is meaningless to say my results are a “joke” or are “nonsense”. The results fit the observations extremely well. If you don’t like that, well, you need to find, identify, and point out the errors in my data, my logic, or my mathematics.
All the best,
w.
PS—I’ve been told many times, as though it settled the argument, that nobody has ever produced a model that explains the temperature rise without including anthropogenic contributions from CO2 and the like … well, the model above explains a 0.5°C/decade rise in the ’80s and ’90s, the very rise people are worried about, without any anthropogenic contribution at all.
[UPDATE: My thanks to Stephen Rasey who alertly noted below that my calculation of the trend was being thrown off slightly by end-point effects. I have corrected the graphic and related references to the trend. It makes no difference to the calculations or my conclusions. -w.]
[UPDATE: My thanks to Paul_K, who pointed out that my formula was slightly wrong. I was using
∆T(k) = λ ∆F(k)/τ + ∆T(k-1) * exp(-1 / τ)
∆T(k) = λ ∆F(k)(1 – exp(-1/ τ)) + ∆T(k-1) * exp(-1 / τ)
The result of the error is that I have underestimated the sensitivity slightly, while everything else remains the same. Instead of the sensitivities for the SH and the NH being 0.04°C per W/m2 and 0.08°C per W/m2 respectively, the correct sensitivities should have been 0.05°C per W/m2 and 0.10°C per W/m2.
-w.]
ferd berple says:
If no CO2 was required in this model, then this implies the CO2 sensitivity is 0 C,
=========
Or that the effects of CO2 are fully contained within the changes in albedo. That CO2 cannot change temperature except by also changing the albedo, which provides a predictive value for the effects of CO2. However, this doesn’t work for me because it implies that nothing else changes albedo in the meanwhile without our knowledge.
The use of 10 years of data given the method used by Willis doesn`t negate the criticism leveled at his previous post. What he has modeled is the annual variation in temperature of each hemisphere.
First of all, The procedure he used fits high frequency data, with the high amplitude of oscillation, about 20 and 10C peak to peak, using a very simple model, over a period of 10 years, to find a parameter that drives a global temperature trend which measurements show is 0.1 – .2C. The residuals in his fit are much higher than that.
Second using a single lag time for the model is no way to estimate the driver of a long term global temperature trend which takes decades or centuries to reach equilibrium, while short term annual oscillations dominate the data that you are using. Whether one uses a single year or 10 years of data makes no difference to that argument. The only thing you will find is the relationship between the forcing variables and the high frequency oscillations they are causing.
This post is one of the clearest examples of quackery and pseudo science I have seen on this web site..
@ur momisugly Eric Adler
This post is one of the clearest examples of quackery and pseudo science I have seen on this web site..
REPLY: Well then, you have no further reason to comment here. As I say in my policy page, comment as if you were in my home. Since I take exception to your remarks, and because you’ve been banned before, and I relented against my better judgment, I’m motioning you off the sofa, and showing you the door. – Anthony
“””””…………from graeme W………….Playing Devil’s Advocate, CO2 can affect the albedo, so it’s included. The theory is that an increase in temperature due to CO2 will result in increased water vapour being held in the atmosphere, which can manifest as clouds, altering the albedo……….”””””
Well graeme, Willis is saying that there is no evidence that CO2 DOES affect the Temperature; and the WATER VAPOR which you say changes the albedo, is certainly capable of changing the Temperature much more than CO2, so you are using a circular argument. Water vapor increase due to more evaporation leads to more clouds; that’s an observational fact; see Wentz et al SCIENCE for july 7 2007. It’s also a logical necessity, since global precipitation must equal global evaporation, or else the oceans would end up overhead. Both WENTZ (actual real earth observations) and the GCMs predict precip = evap, and both increase 7% for a one deg C rise in global Temperature (observed and predicted; excuse me, projected.
And by the way, there is experimental evidence that there is a high correlation between the existence of precipitation; rain, hail, sleet, snow, frogs, etc and the concurrent existence of clouds, aka water vapor condensations. And yes Wentz et al did also find experimentally that total atmospheric water vapor also increases by 7% for a one deg C rise in global Temperature. The GCMs on the other hand, crash and burn on that score predicting as little as 1/7th of the actual observed amount; well I guess that was a projection aas well.
Willis convinced me long ago that he has a handle on the subject, but the more comments I read by George E. Smith, the more convinced I am that George, like Willis, knows more about climate sensitivity than the entire climate alarmist crowd – doubled and squared. Willis and George know the straight skinny. Their critics… not so much. Really. Not so much.
“””””…..Hans Erren says:
June 2, 2012 at 2:58 pm
Richard, 14 years is way too short to satisfy the Nyquist theorem for a centenial signal response……”””””
Now there’s a startling revelation. The Nyquist Theorem says that a band limited signal (containing NO signal components at frequencies higher than B), can be completely represented and recovered from a sequence of instantaneous samples of the continuous function, taken at time intervals separated by no more than 1/2B.
So Willis has the temerity to calculate values of expected TSI from real known earth orbital parameters, which plot remarkably like a near sinusoidal waveform, and use that for a 14 year comparison with actual known Temperature data, and apparently that violates Nyquist since there is evidently present a much lower climate signal frequency of one cycle per century.
Willis, why don’t you change your analysis, and only take one sample per millenium, and see if that will cure the centennial signal Nyquist problem, that Hans has brought to our attention !
And Willis, is there some way, you can remove the sun variation from your analysis, and see if you can explain the climate data, with just albedo variations. What are we going to do without CO2 to control the people. Leif is always telling us that the sun doesn’t affect the climate, so get rid of it Willis. I think you are on a roll this time Willis !
joeldshore says: June 2, 2012 at 2:58 pm
Wrong again, Joel, the charts are not accrued by concordance with a belief system, they are the only ones I have seen. No picking at all, just the only ones to come along. You certainly spend a lot of energy constructing fantasies.
:Hans Erren:
I rise from bed to find your silly comment at June 2, 2012 at 4:39 pm and the rebuttal of it by George E. Smith June 2, 2012 at 9:56 pm.
Please note I had pointed out that you had evaded my points and asked you to address them. You repeated the silly evasion. George (not me) explained why your evasion is silly.
I repeat, please address my points if you can.
Richard
ferd berple:
re. your posts at June 2, 2012 at 6:07 pm and June 2, 2012 at 6:27 pm.
These matters are explained in my post at June 2, 2012 at 12:03 pm which you say they are answering.
Except that you make a logical error when you say
“this doesn’t work for me because it implies that nothing else changes albedo in the meanwhile without our knowledge.”
No, it does not imply that. It only implies that all responses – both known and unknown – affect albedo.
Richard
Bart says:
June 2, 2012 at 10:33 am
I’m not clear what your argument is here, Bart, perhaps my lack. You say I have “not provided evidence showing that the CO2 contribution to albedo change is negligible’ … what does the CO2 contribute to albedo? The only connection from CO2 to clouds I can think of is that (if you believe the standard story)
CO2 –> increased temperature –> increased moisture –> increased clouds
The problem (if I understand your argument) is that the increased clouds decrease the temperature … so I’m not sure what your claim is.
Finally, I don’t think in terms of bunkers, but of what I can do and show. I’ve shown that the net sun (solar minus albedo reflections) is enough information, in and of itself, to very accurately emulate both the annual and decadal changes in the global surface temperature. I think that is an interesting and significant result, and as always, YMMV …
Thanks,
w.
W.;
Yes; as regards CO2, the implication of your findings is that IIF* CO2 can affect albedo, it has a role in determining temperature.
Not otherwise.
*If and only if
… and using global data, http://virakkraft.com/Sun-Temp-Global.png
Sensitivity annual scale: 4C/12W, or close to 1 C per CO2 doubling.
Steve Keohane says:
June 2, 2012 at 7:30 am
You showed me a graph of the RH% going down. From what study or website did that graph come from?
I am interested how these scientists came to the opposite conclusion than the ones I cited.
Everyone can fabricate a graph without giving a source for verification.
Mr. Eschenbach says at June 1, 2012 at 6:04 pm
I am going to refer to one of your statements in this forum again: “Me, I think that the dominant feedback is clouds and thunderstorms, and they are strongly negative.”
I responded to you on June 2, 2012 at 7:23 am and I would like to see some sources for your claims you make in your piece (see my comment on June 2 at 7:23)
One of your quotes in the original piece: “but please remember that this is science, and that the game is to attack the science”.
If you want to conduct science you also need to put some sources with your claims or else it is not scientific. You cannot make claims like: “Sunlight and albedo seem to be necessary and sufficient variables to explain the temperature changes over that time period” or “and I say that the reason is that the clouds respond immediately to such a disturbance in a thermostatic fashion” without producing some scientific evidence for it.
We know that the Total Observed Greenhouse Effect (TOGE) is ~ 33°C from the Total Greenhouse Effect (TGE) 33+45=78°C. The graph I delivered by Roy Spencer’s Climate Confusion proves that. (See my comment on June 2 at 7:23 for sources). I don’t know exactly why I have to put sources for that, because it’s textbook climate science that every student in Earth Sciences knows.
33°C is 42% of the TGE which means that clouds cause a 58% negative feedback.
Now explain to me please: If clouds and thunderstorms are strongly negative feedbacks then what greenhouse gas(es) are causing 42% of the TGE?
It can’t be water vapor, because water vapor and thus clouds are strongly negative according to you. They will cause more cooling than warming. So it must be CO2 and CH4 or is there some other mysterious gas I am not aware of that floats somewhere in the atmosphere to cause that 42% of the TGE.
If CO2 and CH4 are responsible for the TOGE it simply means that climate is extremely sensitive to CO2 and CH4 increase.
You can come up with all kinds of beautiful mathematical concepts to try to prove that climate is not sensitive to greenhouse gas forcing, especially CO2 and water vapor, and that the observed warming from 1984-1998 is caused by the sun and albedo change (without explaining really what the cause for the albedo changes are and without producing some scientific sources to back up your claims), but you forget to explain the basic concept of the Greenhouse Effect causing a warm and habitable planet. For without it we would be freezing out here.
Willis, you should know better. Attacking the warmarxist’s sacred (and absolutely necessary for CAGW) H2O-feedback amplification strikes a nerve & gets them rattled.
Your analysis jives w/all the other empirical (not model) analyses. CO2 effects are not amplified, they are diminished.
joeldshore says:June 2, 2012 at 2:58 pm
Steve Keohane says:
Sorry to disrupt your fantasy, I did not cherry pick anything. Here is an updated version of that graph, I simply stored these two graphs when I came across them in 2008 and 2012, I did not generate them. http://i48.tinypic.com/2qlfnzn.jpg
I understood them to be US gov’t data.
In other words, you came across some data that agrees with what you want to believe. So, despite the fact that it disagrees with lots of data better suited for the purpose of looking at long-term trends and you have no idea where it is from or what the caveats and issues associated with it are, you conclude things from it that are in contradiction both with what the scientific community has concluded and basic physical principles.
If that is not a cherry pick, I don’t know what is!
Go complain to NOAA about contradicting your personal concept of scientific community.
http://www.esrl.noaa.gov/psd/cgi-bin/data/timeseries/timeseries1.pl
“all responses – both known and unknown – affect albedo”
Yes, but I’d word trhat differently.
All factors – both known and unknown – that change the energy content of the troposphere (mostly latent heat energy from changes in the amount of evaporation) will result in a shift of the climate zones which then affects albedo by changing the length of the lines of air mass mixing between polar and equatorial air masses.
Zonal jets gives shorter for reduced global cloudiness (and albedo). Meridional jets gives longer for increased global cloudiness (and albedo).
So, if CO2 or the sun or ocean cycles or anything else (known or unknown) ADD energy to the troposphere then there is a poleward shift which lets more sun into the oceans due to reduced cloudiness BUT the poleward shift also involves a more intense convective overturning in and around the ITCZ and faster, more intense cyclogenesis and decay along the shorter and more poleward jetstream tracks which transfers the extra energy faster to space so that system energy content remains much the same.
So,
i) All forcings affect the air circulation pattern via a change in the speed or size of the water cycle.
ii) The change in the air circulation pattern affects albedo.
iii) The effect of the change in the speed or size of the water cycle is to offset both the initial forcing AND any change in solar input to the oceans that results from the change in albedo.
Poleward shifting negates system warming by increasing evaporation but the troposphere warms as the energy throughput increases. Ocean energy content remains stable subject only to internal ocean cycles.
Equatorward shifting negates system cooling by reducing evaporation but the troposphere cools as the energy throughput decreases.Ocean energy content remains stable subject only to internal ocean cycles.
Ocean energy content being set by atmospheric pressure plus TSI at top of atmosphere but that is another story.
Willis,
In my post above I showed a small program, which I believe is equivalent to yours but has the advantage that it makes explicit the dependence of tau on output (radiation / convection) coupling and heat capacity.
In the process of this I have noticed a subtlety which you probably also noticed in your model but did not discuss.
I could not get the amplitude of the temperature variation to match the model without using unreasonably different values for the global heat capacity. This raises a point that I have seen often mentioned on blogs but never considered seriously. One cannot just average temperatures. Thermal energy is the meaningful quantity to average in this kind of problem. Also there is the problem of trying to get two terms of opposite phase (nh and sh temps) to cancel when the accuracy is not that great in either one. The firs issue is the more interesting one in my mind.
So I redid the calculation by weighting the temperature values according to the empirically adjusted heat capacities. When this is done the global mean temperature shows a much smaller variation than that shown in your spread sheet. I believe this low variation corresponds better to the real situation.
I won’t bother the forum with another post of rough draft code. I will post it if anyone is interested.
Willis,
I’m sorry but on reading it I see that in the post above I did not make it clear that ihe issue I am addressing is that my and (I think) your simple model does not seem to work when applied to to the global data.
In the post above I am talking about combining nh and sh in the model to obtain a global result.
I thought that I would try this one more time. What you have, Willis, is a plain linear vector autoregressive model for deltat and deltaf. The cross lag of deltat with deltaf is 0, and the lag of deltat with deltat is 1.
****
Bart says:
June 2, 2012 at 10:33 am
I agree with others that you have made a reasonable case that albedo is the response variable which has the greatest control over temperature, but you have not provided evidence showing that CO2 contribution to albedo change is negligible.
****
Huh? Albedo is the fraction of TSI reflected by the atmosphere/surface combo. CO2 doesn’t reflect anything. Yeah, it changes the spectral-emission properties of the tropopause, but that’s a different issue.
Steve Keohane says:
A discussion of the issues in this particular reanalysis in regards to the long term trends in humidity is given here: http://geotest.tamu.edu/userfiles/216/Dessler10.pdf
Not every piece of data (or re-analyzed data) that you can find is trustworthy for every possible purpose. Perhaps NOAA should be more careful to discuss the limitations of their data here, or perhaps those who use it for various purposes need to investigate whether or not it is reliable for that particular purpose.
People who want to prove a certain point rather than do science can almost always find data that is scientifically ill-suited for the purpose that they want to use it for, but is well-suited for their real purpose, which is not to get a scientifically-correct result but rather a result that is agrees with what they want to believe.
Steve Keohane says:
Fine…So, you personally didn’t cherrypick the data. You just visited places where those who presented the graphs cherrypicked them. So, you are blissfully unaware of scientific data that goes against what you want to believe because you hang out in places that present only cherrypicked data to support your preconceptions.
joeldshore says:
June 3, 2012 at 8:57 am
“People who want to prove a certain point rather than do science can almost always find data that is scientifically ill-suited for the purpose that they want to use it for, but is well-suited for their real purpose, which is not to get a scientifically-correct result but rather a result that is agrees with what they want to believe.”
You mean like Roscoe P. Coal Train?
http://wattsupwiththat.com/2012/06/03/shocker-the-hansengiss-team-paper-that-says-we-argue-that-rapid-warming-in-recent-decades-has-been-driven-mainly-by-non-co2-greenhouse-gases/
“joeldshore says:
June 3, 2012 at 8:57 am
“People who want to prove a certain point rather than do science can almost always find data that is scientifically ill-suited for the purpose that they want to use it for, but is well-suited for their real purpose, which is not to get a scientifically-correct result but rather a result that is agrees with what they want to believe.”
”
Wow! That’s a really great description of mikie mann and the ‘TEAM’.
Stephen Wilde:
Your post at June 3, 2012 at 7:41 am provides a complex but plausible and – importantly – falsifiable hypothesis based on Willis’ findings. It is a clear example of the importance of the need for proper understanding of Willis work which I stated at June 2, 2012 at 12:03 pm saying;
“Willis’ analysis is VERY important. So, understanding what his analysis does and what it does not do are also important.”
Richard