NOTE: This post is important, so I’m going to sticky it at the top for quite a while. I’ve created a page for all Spencer and Braswell/Dessler related posts, since they are becoming numerous and popular to free up the top post sections of WUWT.
UPDATE: Dr. Spencer writes: I have been contacted by Andy Dessler, who is now examining my calculations, and we are working to resolve a remaining difference there. Also, apparently his paper has not been officially published, and so he says he will change the galley proofs as a result of my blog post; here is his message:
“I’m happy to change the introductory paragraph of my paper when I get the galley proofs to better represent your views. My apologies for any misunderstanding. Also, I’ll be changing the sentence “over the decades or centuries relevant for long-term climate change, on the other hand, clouds can indeed cause significant warming” to make it clear that I’m talking about cloud feedbacks doing the action here, not cloud forcing.”
[Dessler may need to make other changes, it appears Steve McIntyre has found some flaws related to how the CERES data was combined: http://climateaudit.org/2011/09/08/more-on-dessler-2010/
As I said before in my first post on Dessler’s paper, it remains to be seen if “haste makes waste”. It appears it does. -Anthony]
Update #2 (Sept. 8, 2011): Spencer adds: I have made several updates as a result of correspondence with Dessler, which will appear underlined, below. I will leave it to the reader to decide whether it was our Remote Sensing paper that should not have passed peer review (as Trenberth has alleged), or Dessler’s paper meant to refute our paper.
by Roy W. Spencer, Ph. D.
While we have had only one day to examine Andy Dessler’s new paper in GRL, I do have some initial reaction and calculations to share. At this point, it looks quite likely we will be responding to it with our own journal submission… although I doubt we will get the fast-track, red carpet treatment he got.
There are a few positive things in this new paper which make me feel like we are at least beginning to talk the same language in this debate (part of The Good). But, I believe I can already demonstrate some of The Bad, for example, showing Dessler is off by about a factor of 10 in one of his central calculations.
Finally, Dessler must be called out on The Ugly things he put in the paper.
(which he has now agreed to change).
1. THE GOOD
Estimating the Errors in Climate Feedback Diagnosis from Satellite Data
We are pleased that Dessler now accepts that there is at least the *potential* of a problem in diagnosing radiative feedbacks in the climate system *if* non-feedback cloud variations were to cause temperature variations. It looks like he understands the simple-forcing-feedback equation we used to address the issue (some quibbles over the equation terms aside), as well as the ratio we introduced to estimate the level of contamination of feedback estimates. This is indeed progress.
He adds a new way to estimate that ratio, and gets a number which — if accurate — would indeed suggest little contamination of feedback estimates from satellite data. This is very useful, because we can now talk about numbers and how good various estimates are, rather than responding to hand waving arguments over whether “clouds cause El Nino” or other red herrings. I have what I believe to be good evidence that his calculation, though, is off by a factor of 10 or so. More on that under THE BAD, below.
Comparisons of Satellite Measurements to Climate Models
Figure 2 in his paper, we believe, helps make our point for us: there is a substantial difference between the satellite measurements and the climate models. He tries to minimize the discrepancy by putting 2-sigma error bounds on the plots and claiming the satellite data are not necessarily inconsistent with the models.
But this is NOT the same as saying the satellite data SUPPORT the models. After all, the IPCC’s best estimate projections of future warming from a doubling of CO2 (3 deg. C) is almost exactly the average of all of the models sensitivities! So, when the satellite observations do depart substantially from the average behavior of the models, this raises an obvious red flag.
Massive changes in the global economy based upon energy policy are not going to happen, if the best the modelers can do is claim that our observations of the climate system are not necessarily inconsistent with the models.
(BTW, a plot of all of the models, which so many people have been clamoring for, will be provided in The Ugly, below.)
2. THE BAD
The Energy Budget Estimate of How Much Clouds Cause Temperature Change
While I believe he gets a “bad” number, this is the most interesting and most useful part of Dessler’s paper. He basically uses the terms in the forcing-feedback equation we use (which is based upon basic energy budget considerations) to claim that the energy required to cause changes in the global-average ocean mixed layer temperature are far too large to be caused by variations in the radiative input into the ocean brought about by cloud variations (my wording).
He gets a ratio of about 20:1 for non-radiatively forced (i.e. non-cloud) temperature changes versus radiatively (mostly cloud) forced variations. If that 20:1 number is indeed good, then we would have to agree this is strong evidence against our view that a significant part of temperature variations are radiatively forced. (It looks like Andy will be revising this downward, although it’s not clear by how much because his paper is ambiguous about how he computed and then combined the radiative terms in the equation, below.)
But the numbers he uses to do this, however, are quite suspect. Dessler uses NONE of the 3 most direct estimates that most researchers would use for the various terms. (A clarification on this appears below) Why? I know we won’t be so crass as to claim in our next peer-reviewed publication (as he did in his, see The Ugly, below) that he picked certain datasets because they best supported his hypothesis.
The following graphic shows the relevant equation, and the numbers he should have used since they are the best and most direct observational estimates we have of the pertinent quantities. I invite the more technically inclined to examine this. For those geeks with calculators following along at home, you can run the numbers yourself:
Here I went ahead and used Dessler’s assumed 100 meter depth for the ocean mixed layer, rather than the 25 meter depth we used in our last paper. (It now appears that Dessler will be using a 700 m depth, a number which was not mentioned in his preprint. I invite you to read his preprint and decide whether he is now changing from 100 m to 700 m as a result of issues I have raised here. It really is not obvious from his paper what he used).
Using the above equation, if I assumed a feedback parameter λ=3 Watts per sq. meter per degree, that 20:1 ratio Dessler gets becomes 2.2:1. If I use a feedback parameter of λ=6, then the ratio becomes 1.7:1. This is basically an order of magnitude difference from his calculation.
Again I ask: why did Dessler choose to NOT use the 3 most obvious and best sources of data to evaluate the terms in the above equation?:
(1) Levitus for observed changes in the ocean mixed layer temperature; (it now appears he will be using a number consistent with the Levitus 0-700 m layer).
(2) CERES Net radiative flux for the total of the 2 radiative terms in the above equation, and (this looks like it could be a minor source of difference, except it appears he put all of his Rcld variability in the radiative forcing term, which he claims helps our position, but running the numbers will reveal the opposite is true since his Rcld actually contains both forcing and feedback components which partially offset each other.)
(3): HadSST for sea surface temperature variations. (this will likely be the smallest source of difference)
The Use of AMIP Models to Claim our Lag Correlations Were Spurious
I will admit, this was pretty clever…but at this early stage I believe it is a red herring.
Dessler’s Fig. 1 shows lag correlation coefficients that, I admit, do look kind of like the ones we got from satellite (and CMIP climate model) data. The claim is that since the AMIP model runs do not allow clouds to cause surface temperature changes, this means the lag correlation structures we published are not evidence of clouds causing temperature change.
Following are the first two objections which immediately come to my mind:
1) Imagine (I’m again talking mostly to you geeks out there) a time series of temperature represented by a sine wave, and then a lagged feedback response represented by another sine wave. If you then calculate regression coefficients between those 2 time series at different time leads and lags (try this in Excel if you want), you will indeed get a lag correlation structure we see in the satellite data.
But look at what Dessler has done: he has used models which DO NOT ALLOW cloud changes to affect temperature, in order to support his case that cloud changes do not affect temperature! While I will have to think about this some more, it smacks of circular reasoning. He could have more easily demonstrated it with my 2 sine waves example.
Assuming there is causation in only one direction to produce evidence there is causation in only one direction seems, at best, a little weak.
2) In the process, though, what does his Fig. 1 show that is significant to feedback diagnosis, if we accept that all of the radiative variations are, as Dessler claims, feedback-induced? Exactly what the new paper by Lindzen and Choi (2011) explores: that there is some evidence of a lagged response of radiative feedback to a temperature change.
And, if this is the case, then why isn’t Dr. Dessler doing his regression-based estimates of feedback at the time lag or maximum response? Steve McIntyre, who I have provided the data to for him to explore, is also examining this as one of several statistical issues. So, Dessler’s Fig. 1 actually raises a critical issue in feedback diagnosis he has yet to address.
3. THE UGLY
(MOST, IF NOT ALL, OF THESE OBJECTIONS WILL BE ADDRESSED IN DESSLER’S UPDATE OF HIS PAPER BEFORE PUBLICATION)
The new paper contains a few statements which the reviewers should not have allowed to be published because they either completely misrepresent our position, or accuse us of cherry picking (which is easy to disprove).
Misrepresentation of Our Position
Quoting Dessler’s paper, from the Introduction:
“Introduction
The usual way to think about clouds in the climate system is that they are a feedback… …In recent papers, Lindzen and Choi [2011] and Spencer and Braswell [2011] have argued that reality is reversed: clouds are the cause of, and not a feedback on, changes in surface temperature. If this claim is correct, then significant revisions to climate science may be required.”
But we have never claimed anything like “clouds are the cause of, and not a feedback on, changes in surface temperature”! We claim causation works in BOTH directions, not just one direction (feedback) as he claims. Dr. Dessler knows this very well, and I would like to know
1) what he was trying to accomplish by such a blatant misrepresentation of our position, and
2) how did all of the peer reviewers of the paper, who (if they are competent) should be familiar with our work, allow such a statement to stand?
Cherry picking of the Climate Models We Used for Comparison
This claim has been floating around the blogosphere ever since our paper was published. To quote Dessler:
“SB11 analyzed 14 models, but they plotted only six models and the particular observational data set that provided maximum support for their hypothesis. “
How is picking the 3 most sensitive models AND the 3 least sensitive models going to “provide maximum support for (our) hypothesis”? If I had picked ONLY the 3 most sensitive, or ONLY the 3 least sensitive, that might be cherry picking…depending upon what was being demonstrated. And where is the evidence those 6 models produce the best support for our hypothesis?
I would have had to run hundreds of combinations of the 14 models to accomplish that. Is that what Dr. Dessler is accusing us of?
Instead, the point was to show that the full range of climate sensitivities represented by the least and most sensitive of the 14 models show average behavior that is inconsistent with the observations. Remember, the IPCC’s best estimate of 3 deg. C warming is almost exactly the warming produced by averaging the full range of its models’ sensitivities together. The satellite data depart substantially from that. I think inspection of Dessler’s Fig. 2 supports my point.
But, since so many people are wondering about the 8 models I left out, here are all 14 of the models’ separate results, in their full, individual glory:
I STILL claim there is a large discrepancy between the satellite observations and the behavior of the models.
CONCLUSION
These are my comments and views after having only 1 day since we received the new paper. It will take weeks, at a minimum, to further explore all of the issues raised by Dessler (2011).
Based upon the evidence above, I would say we are indeed going to respond with a journal submission to answer Dessler’s claims. I hope that GRL will offer us as rapid a turnaround as Dessler got in the peer review process. Feel free to take bets on that. 
And, to end on a little lighter note, we were quite surprised to see this statement in Dessler’s paper in the Conclusions (italics are mine):
“These calculations show that clouds did not cause significant climate change over the last decade (over the decades or centuries relevant for long-term climate change, on the other hand, clouds can indeed cause significant warming).”
Long term climate change can be caused by clouds??! Well, maybe Andy is finally seeing the light! 
(Nope. It turns out he meant ” *RADIATIVE FEEDBACK DUE TO* clouds can indeed cause significant warming”. An obvious, minor typo. My bad.)
Gras albert suggested:
“* by the way, might I suggest that an appropriate collective noun for such an eminent group of climate scientists might be a ‘cloud’.”
Rejected.
‘Cloud’ as collective noun applies exclusively to Border Collies, as in:
‘A cloud of Border Collies was seen racing up The Downs’.
Those scientists have absolutely nothing in common with Border Collies, and should not be honoured by a comparison with those highly intelligent canines.
😉
Hey Dr Roy
Thank you I missed DayDay’s DirtyHarryreadmefile.
Sorry but this cloud stuff is over my head (sorry again) but given all the concrete created UHI how does this effect cloud generation ? For instance when we sailed down the coast I used to notice particular cloud build up over Sydney (which I put down to UHI). I’ve noticed cloud does follow the coastline, heat I suppose, but this was different weather, rain wind – but we just turned around and sailed out of it to blue skies.
Chop down all the trees and cover the planet in concrete but it’s manmade CO2 that’s causing climate change ??
As usual, thanks to all the posters, mods and Anthony – an island of sense. (Even the trolls – the debate is what skepticsism is all about – and reasoned comments get posted here.)
Chrs JJ.
(OK, I hope Anthony posts mine under the general reader tag. )
Jeremy says:
September 7, 2011 at 12:56 pm
Using the above equation, if I assumed a feedback parameter λ=3 Watts per sq. meter per degree, that 20:1 ratio Dessler gets becomes 2.2:1. If I use a feedback parameter of λ=6, then the ratio becomes 1.7:1. This is basically an order of magnitude difference from his calculation.
^^^ You have 1 equation and 3 unknowns. It is not clear here how you’re solving for that 20:1 unknown. Also, where was the number 3 arrived at?
Jeremy – that’s not right . Look at the equation again:
First, consider it as a = b + c.
The left hand term, a, has a value of 2.30 and b has a value of 0.56 -> so withn the uncertainty of those two numbers we have a value for c (S in the full equation) of 2.30 – 0.56 = 1,74 (with some unspecified uncertainty. S isn’t otherwise measured directly, but this is an indirect result given yuo accept the two other numbers.
Rewriting the equation, then, it becomes 2.30 = b + c d -0.56, where we know the value of d (0.078), so it’s an equation in two unknowns. The trick now is to pick a reasonable value of c (lambda in the original equation) to determine the ratio, b/d, that results (that ratio, if I’ve been careful along the way, is S/N in the original equation). We’re not completely in the dark on that. But you can put any number you want in for c and you will get an answer for 0.56/b..
The “problem” I’m having is that I can easily reproduce Anthony’s S/N value os 2.2 and 1.7 for values of lambda, respectively, of 3 and 6. But what I can’t do is generate a physically meaningful number for lambda that gives a result of S/N = 20. Well, one that I believe, anyways. The number I get for lambda that results in that ratio is -6. In otherwords, you have to assume and believe that clouds are a strong positive feedback for global warming.
I don’t buy that.
Doesn that help, Jeremy?
Anthony, did I ge tthat right?
Thanks.
Hi Mosher, always nice to see your comments, I’d like to say something about you statement:
“One issue is that the models which show the best correlation are actually form the middle of the pack with ECRs in the range of 3.4 and TCRs in the range of 1.6-2.2”
At first I was assuming that upper and lower sensitivity might be like upper and lower hight bounds which would bracket all the values. Then I thought about the sensitivity of a tracking algorithm. There both high and low sensity setting will not follow the target well. The high sensitivity reacts too wildly to changes and the low sensitivity reacts too slugishly. The best perfomers are in the mid range, not too over or under reactive.
Ed
The units for the specific heat capacity (Cp) are J/kg*K.
Cp is a mass function so I am at a loss as to how you get W/m^2 on the left side of the equation shown.
Even if you wanted to go with C=Q/dT or J/K you end up with J only no m^2.
Here are some of the issues as I see them:
Saying it is natural variability is not an explanation. What are the physical processes?
Where did the heat go? We know there is a build up of ocean heat prior to El Nino, and a
discharge (and sfc T warming) during late stages of El Nino, but is the observing system
sufficient to track it? Quite aside from the changes in the ocean, we know there are major
changes in the storm tracks and teleconnections with ENSO, and there is a LOT more rain on
land during La Nina (more drought in El Nino), so how does the albedo change overall
(changes in cloud)? At the very least the extra rain on land means a lot more heat goes
into evaporation rather than raising temperatures, and so that keeps land temps down: and
should generate cloud. But the resulting evaporative cooling means the heat goes into
atmosphere and should be radiated to space: so we should be able to track it with CERES
data. The CERES data are unfortunately wonting and so too are the cloud data.
– Kevin Trenberth –
http://yourvoicematters.org/cru/mail/1255523796.txt
It’s a travesty Kevin!
If Andy Dessler thinks cloud feedback over the last decade was 0.5W/m^2 then when that is added to the co2 forcing and the lack of volcanos until this year, what does he think has caused temperature to stall?
As Kevin astutely points out;
“Saying it is natural variability is not an explanation. What are the physical processes?”
??
Brian H says…
“Dessler’s Paper should be henceforth referred to as The Dessler Flail.”
I have been calling it ‘Dessler’s Folly’, as he has completely jumped the shark this time, staying his pen would have served him better.
davidmhoffer says:
September 7, 2011 at 9:31 pm
The physicist and the climatologist are arguing at cross purposes. The physicist is discussing energy but the climatologist is discussing temperature. What is more, the climatologist is only discussing the temperature of one small element of the planet, i.e. the air temperature near the surface of the planet under several miles of atmosphere.
Hypothetical:
Two planets in the same star system in the same orbit. One has air of pure nitrogen and no water or other gases, the other has liquid water seas and air composed of oxygen, nitrogen and greenhouse gases (water vapour, methane, CO2 etc).
Both have the same energy budget, 1365 watts in, 1365 out. Both in approximate steady state.
The one with the thick and complex atmosphere with the water vapour etc has a much higher surface air temperature than the one with a thin atmosphere.
Even more hypothetical:
A planet made of pure aluminium and no atmosphere would have almost the same temperature at the poles as at the equator (efficient energy transfer within the system)
A planet made of pure silicon and no atmosphere would have colder poles than equator (poor energy transfer within the system.
Both these situations are possible with the same energy budget. The argument is a red herring. It is quite possible for two systems with the same energy budget to have differing temperatures in various parts of the system depending on composition.
Energy is NOT temperature!! Physics 101 (GCSE to us here in blighty).
I am no warmist and am a close follower of Roy Spencer’s work. I have discussed exactly these issues with him (and others) on his blog and he is as frustrated with this kind of nonsense as me.
@tallbloke: and there is a LOT more rain on land during La Nina (more drought in El Nino)
Really BOTH, dear Tallbloke: There is a lot (more than a lot I would say) rain on the 1+2 El Niño region (along the north coast of Peru: Latitude 0 to -5)) during the El Niño years, while there is drought on the southern andes region, around lake Titicaca.
As a general rule, if we look at the detailes (following the Devil´s advice) we get lost in them. We should seek, as you say, the causes: Saying it is natural variability is not an explanation. What are the physical processes?
KR says:
September 7, 2011 at 11:25 am
Dr. Spencer
“At the very least you should have explained in your paper why you did not show the other eight model results you ran.”
All the models are so far off that they are laughable. Talking about better or worse models here is grading garbage.
“Without some mechanism, some reasons why, we have no reason to believe that variations plus or minus from temperature driven humidity and cloud cover will persist in imbalance long enough (10′s of years) to affect climate.To be quite blunt, without such a physical mechanism overriding the water vapor cycle, your assertions of clouds as the forcing driving the ENSO are “Just So Stories”.”
The fact that a Warmista is willing to talk about physical mechanisms is an amazing development. All Warmista have carefully avoided talk about physical mechanisms for years. I know because I have done my very best to get them to talk about physical mechanisms. None have responded. The reason they do not respond is because talk of physical mechanisms brings in its train talk of scientific method and no Warmista is going to talk about scientific method for the simple reason that none practice it and none understand it.
The first physical mechanism that should be described is the ENSO mechanism. No one has described it. No one has done the empirical research to create reasonably confirmed physical hypotheses which describe the natural regularities that make up ENSO. Sure, there is some hand waving about cold water welling up off the coast of Peru, but no one actually has created reasonably well confirmed physical hypotheses about the actual source of that water, the actual mechanism that drives the up welling water. The same is true for each and every natural regularity which together make up ENSO. (Our scientific understanding of this matter is as bad as our scientific understanding of the behavior of hurricanes or the conditions that give birth to hurricanes. As everyone here knows, it is darn hard to give up the idea that our science of hurricanes is a mature science. It will be equally hard for even the non-biased person to recognize that climate science remains in its birthing stage.)
Spencer’s work has always pushed us toward recognizing the importance of physical mechanisms. His present thesis amounts to the claim that there is no known physical mechanism which would permit us to assign a magnitude to the actions of clouds as a positive “forcing or feedback” or as a negative “forcing or feedback.” (I use “forcing or feedback” because in this discussion the Warmista’s hopeless confusion about “forcing” versus “feedback” has become manifest. Yes, Warmista, I know that you can define them by fiat, as you always do, but that is Arguing in a Circle.) To the extent that Warmista believe that they have physical hypotheses which describe some natural regularity that is a positive feedback or forcing for CO2 concentrations, they are deluded. No one has done the empirical research, the leg work, that is needed to describe the natural regularities that make up ENSO, so any mechanism that Warmista presents is assumed but not discovered and for that reason has no empirical evidence to support it. Heck, modelers treat ENSO as statistical noise. There is not one among them who has ever tried to do the leg work necessary to describe ENSO as a physical mechanism.
What we should take away from Spencer’s work is that the uncertainty associated with claims about forcings or feedbacks is extremely high. The models, each and every one of them, are no better than parodies of physical theory. As physical theories, no model has got off the ground, every model has been born falsified, and no model has achieved reasonable confirmation even for a subsection of the model. All of this is as it should be because no one has done the empirical research necessary to describe the natural regularities that we wish to understand, ENSO first among them.
As with most good science, Spencer’s work is a critical work. It helps us understand what we do not know. The claim that Spencer has not identified a mechanism is a fallacious argument of immense grandeur because it contains an immense number of sub-fallacies. For present purposes, let me just remind readers of the most fundamental principle of science: Science is the critical enterprise par excellence, most good scientific work is criticism, and the idea that you must present a hypothesis to criticize a hypothesis is a mistake worthy of toddlers.
The Wagner-Dessler farce shows that the Warmista have been drawn into debate with Critics for the first time and that they are desperate and terrified. How Critics (sceptics, if you wish) of “mainstream climate science” (MSC) brought this about is unknown to me. Anthony can probably explain the dynamics of this Critical achievement. Of course, Roy Spencer has been and is the scientific leader of the Critical Dynamic. It is truly time for celebration.
TLM says:
September 8, 2011 at 6:27 am
davidmhoffer says:
September 7, 2011 at 9:31 pm
“Energy is NOT temperature!! Physics 101 (GCSE to us here in blighty).”
——————————————————————————————————-
Dang TLM…. that was humor….. look it up……..while we’re at physics 101…..don’t you think it behooves you to explain to others how temps are related to energy? If you believe people are confused about it then explain and expound. (BTW, I don’t believe David is confused.) This is how we learn here. We exchange thoughts and ideas……so anyway, without further ado…..Ahem,……..
Temperatures are a measurement of heat.
From wiki—–
Heat —– In physics and thermodynamics, heat is energy transferred from one body, region, or thermodynamic system to another due to thermal contact when the systems are at different temperatures. It is also often described as one of the fundamental processes of energy transfer between physical entities. In this description, it is an energy transfer to a body in any other way than due to work.
See? Nice and easy. True, we can delve much deeper in an explanation, but that’s a good starter. So, while temps aren’t energy, we see that temps are an expression of energy transfer. sigh……
This plus the rest of this issue/discussion is making me question whether or not we can even model ENSO reliably without an understanding of clouds. This would make comparison of observations of cloud effects to models that “reliably model ENSO” a devilishly tricky job. It seems reasonable to me that cloud cover could be at least as important as oceanic circulation, if not more so.
I looked at it again, I missed the value assigned to what was in the parentheses. It appeared to be 3 unknowns to me for some reason.
TLM says: September 8, 2011 at 6:27 am:
“Two planets in the same star system in the same orbit. One has air of pure nitrogen and no water or other gases, the other has liquid water seas and air composed of oxygen, nitrogen and greenhouse gases (water vapour, methane, CO2 etc). Both have the same energy budget, 1365 watts in, 1365 out. Both in approximate steady state. The one with the thick and complex atmosphere with the water vapour etc has a much higher surface air temperature than the one with a thin atmosphere.”
An intersting point. However, if the “thick and complex” atmosphere only had 400 ppmv CO2 (like Earth) then the densites of the planet’s atmospheres would be very roughly equal, and the only significant difference in surface temperatures could be attributable to clouds – perhaps leading to a lower surface temperature? I think that the temperature of the Venusian atmosphere (96.5% CO2) at 1 bar (and above the cloud tops) is exactly what you would expect it to be from a radiative physics perspective.
Theo Goodwin
“KR says:
September 7, 2011 at 11:25 am
Dr. Spencer
“At the very least you should have explained in your paper why you did not show the other eight model results you ran.”
All the models are so far off that they are laughable. Talking about better or worse models here is grading garbage.”
It seems you have not actually read the papers, Theo. Spencer presented the six models (of fourteen examined) with the worst fits to the observational data – and his entire paper concerned models versus observations, making the models very relevant. It this is “grading garbage”, then you’ve really just insulted Spencer’s work.
The three with the best fits to observed data, GFDL-CM2., ECHAM5/MPI-OM, and MRI-CGCM2.3.2, are known to match ENSO events pretty well. They also have climate sensitivities of +3.4 C, +3.4 C, and +3.2 C, respectively. Hence the data Spencer generated (but did not present) actually support models with a 3.2-3.4 C sensitivity. This rather contradicts his claims of low sensitivities…
As to models, quoting statistician George Box: “Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful.” (http://en.wikiquote.org/wiki/George_E._P._Box)
The internet has changed media profoundly. Few people watch TV news anymore and even fewer read print, electing to get information from the `net instead. I can’t imagine why science is clinging to this peer-review system of submitting papers to scientific journals when what is patently evident from reading this site, is that each and every idea, theory and treatise could simply be posted to a blog such as this and receive absolute critical review within hours from some of the best and most knowledgeable minds on the planet. Peer-review needs to come into the 21st century.
TLM;
Are you seriously being critical of a joke because I didn’t use precise terminology regarding the science involved as part of the story line? Did you completely miss the punch line? Would have using words like “steady state temperature as a consequence of deterministic canges in positive retention of energy flux” really have improved the joke?
Hey, so these two camels walk into a bar and order a beer-
TLM: HOLD IT! HOLD IT! Camels can’t talk, this make no sense at all….
I should think the real travesty is that we are heating the rest of the universe. In so doing, we’re setting the worst kind of bad example for other civilizations. The Guardian warned us about this kind of thing. Did we listen? No – o – o – o…
http://www.guardian.co.uk/science/2011/aug/18/aliens-destroy-humanity-protect-civilisations?CMP=twt_gu
Jeremy – I reread my response and hope I didn’t come off as pedantic. It dawned on me though, and Anthony alludes to this in his write up, that a more “reasonable” answer for S/N and lambda if (as Anthony suggests) you don’t use the most widely accepted values for the knowns Anthony has detailed, but some other number. As with Anthony, I don’t understand the justification for doing so and would love to know how it could have gotten through “peer” review.
KR says:
September 8, 2011 at 8:55 am
“It seems you have not actually read the papers, Theo. Spencer presented the six models (of fourteen examined) with the worst fits to the observational data – and his entire paper concerned models versus observations, making the models very relevant. It this is “grading garbage”, then you’ve really just insulted Spencer’s work.”
It seems to me that we are in a Monty Python skit and I am saying “The bird is dead, it was dead when you sold it to me, and it is attached to the perch with a nail.”
Are you seriously suggesting that the purpose of Spencer’s paper is to defend models? Spencer writes:
“Instead, the point was to show that the full range of climate sensitivities represented by the least and most sensitive of the 14 models show average behavior that is inconsistent with the observations. Remember, the IPCC’s best estimate of 3 deg. C warming is almost exactly the warming produced by averaging the full range of its models’ sensitivities together. The satellite data depart substantially from that. I think inspection of Dessler’s Fig. 2 supports my point.”
What part of that paragraph do you not understand?
You write and quote:
As to models, quoting statistician George Box: “Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful.”
The answer is they are wrong if the whole lot of them and the average of all of them show behavior that is inconsistent with the observations. In science, you cannot have a model that is inconsistent with substantial recorded observations and be right.
By the way, what happened to your talk of physical hypotheses? Are you, like all other Warmista, going to drop that discussion before it gets to observable evidence for the physical hypotheses and scientific method?
“The three with the best fits to observed data, GFDL-CM2., ECHAM5/MPI-OM, and MRI-CGCM2.3.2, are known to match ENSO events pretty well. They also have climate sensitivities of +3.4 C, +3.4 C, and +3.2 C, respectively. Hence the data Spencer generated (but did not present) actually support models with a 3.2-3.4 C sensitivity. This rather contradicts his claims of low sensitivities…”
The scientific question/comment to your first assertion, “…are known to match ENSO events pretty well” is please prove it and demonstrate what you mean by “pretty well”. If, as you say, those model runs were closest to observations, it dooes not logically follow that they (the observations) support them, all the models may be bad and unsupported by the observations, especially if the observations all show lower senstivity.
Theo Goodwin says:
September 8, 2011 at 6:50 am
Theo Goodwin says:
September 8, 2011 at 6:56 am
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Very well said.
Sincerely,
davidmhoffer says:
September 8, 2011 at 9:32 am
Here’s precision for you:
An infinite number of people walk into a bar. The first person says “I’ll have a glass of beer, the person after me will have half a glass, and so on for everyone.” The bartender says “OK, two glasses of beer.”
@KR,
Just because the models and satellite observational readings overlap across their two standard deviation bounds does NOT MAKE THEM statistically significantly similar. They would need to be well within one sigma variance, and even then, you still must satisfy the correlation tests. If we want to check if the variance of the data sets are statistically similar, we must do a CONOVA, with an ANOVA/F-test for the means. Do you see that in the paper? It’s possible I just missed it.
If we wish to test the significance of any correlation between the data (they are independent sets and should not be tested this way, just tested across the mean and variance, but I’ll humor this idea) A correlation test will yield an R value. Do you see an R value that is statistically significant, that is an R value of 0.381 or higher?
Again, how are the models significantly similar to the observed data? Where are the tests showing significance? I honestly may have missed them, but from what I have seen on what tests have been done, there is NO statistical significance (i.e. p value below 0.05 and r value above 0.381. Realize the R^2 is NOT a test of statistical significance, but a test of how well the correlated variances fit).
Only if there is statistical significance between observations and the models can we say the models reflect reality with any confidence. If there is no significance, then we reject the models as flagrantly wrong. It’s as simple as that. Even the best ENSO models do not appear even remotely significant. And it does not matter that their deviations overlap with observations (confidence interval does not mean significance what so ever), not without a CONOVA analysis telling us if that actually means something.
KR says:
September 8, 2011 at 8:55 am
Theo Goodwin
“KR says:
September 7, 2011 at 11:25 am
….paraphrasing———- “Dr. Spencer, why didn’t you show all of the models you looked at?”
Dr. Spencer:..“….the point was to show that the full range of climate sensitivities represented by the least and most sensitive of the 14 models show average behavior that is inconsistent with the observations. Remember, the IPCC’s best estimate of 3 deg. C warming is almost exactly the warming produced by averaging the full range of its models’ sensitivities together. The satellite data depart substantially from that. I think inspection of Dessler’s Fig. 2 supports my point.”
Then KR says: (again paraphrasing) “But Dr. Spencer you really should explain why you didn’t include all of the models.”
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KR, obsess on minutia much? Asked, answered. Explained and shown. Recall the graphic above.
http://www.drroyspencer.com/wp-content/uploads/AR4-models-vs-CERES-lag-regression-Net-flux.png
Tell me KR which one of those models do you think George Box would deem useful? Which do you believe adequately explains our missing energy? And most importantly, which one do you think should have been included in the pretty picture that may have altered Dr. Spencer’s conclusions or the reviewers perception?
KR, it doesn’t matter how often you mention this, it doesn’t change the fact that no one has been able to refute his conclusions. KR, is it that you believe so much in the models that you’re angry reality has shown them to be insufficient? Or is it that Dr. Spencer’s is questioning the orthodoxy that’s got your knickers in a wad? Or is it something else?
I’ve really have to ask you something, why don’t you spend this time and energy and investigate why Dessler would intentionally misrepresent Dr. Spencer’s position on cloud feedback/forcing and how that bit of intentional deception managed to get through the reviewers?