Spencer on Lindzen and Choi climate feedback paper

Some Comments on the Lindzen and Choi (2009) Feedback Study

by Roy W. Spencer, Ph. D.

http://asd-www.larc.nasa.gov/erbe/erbssat.gif

The ERBE satellite

I keep getting requests to comment on the recent GRL paper by Lindzen and Choi (2009), who computed how satellite-measured net (solar + infrared) radiation in the tropics varied with surface temperature changes over the 15 year period of record of the Earth Radiation Budget Satellite (ERBS, 1985-1999).

The ERBS satellite carried the Earth Radiation Budget Experiment (ERBE) which provided our first decadal-time scale record of quasi-global changes in absorbed solar and emitted infrared energy. Such measurements are critical to our understanding of feedbacks in the climate system, and thus to any estimates of how the climate system responds to anthropogenic greenhouse gas emissions.

The authors showed that satellite-observed radiation loss by the Earth increased dramatically with warming, often in excess of 6 Watts per sq. meter per degree (6 W m-2 K-1). In stark contrast, all of the computerized climate models they examined did just the opposite, with the atmosphere trapping more radiation with warming rather than releasing more.

The implication of their results was clear: most if not all climate models that predict global warming are far too sensitive, and thus produce far too much warming and associated climate change in response to humanity’s carbon dioxide emissions.

A GOOD METHODOLOGY: FOCUS ON THE LARGEST TEMPERATURE CHANGES

One thing I liked about the authors’ analysis is that they examined only those time periods with the largest temperature changes – whether warming or cooling. There is a good reason why one can expect a more accurate estimate of feedback by just focusing on those large temperature changes, rather than blindly treating all time periods equally. The reason is that feedback is the radiation change RESULTING FROM a temperature change. If there is a radiation change, but no temperature change, then the radiation change obviously cannot be due to feedback. Instead, it would be from some internal variation in cloudiness not caused by feedback.

But it also turns out that a non-feedback radiation change causes a time-lagged temperature change which completely obscures the resulting feedback. In other words, it is not possible to measure the feedback in response to a radiatively induced temperature change that can not be accurately quantified (e.g., from chaotic cloud variations in the system). This is the subject of several of my previous blog postings, and is addressed in detail in our new JGR paper — now in review — entitled, “On the Diagnosis of Radiative Feedbacks in the Presence of Unknown Radiative Forcing”, by Spencer and Braswell).

WHAT DO THE AMIP CLIMATE MODEL RESULTS MEAN?

Now for my main concern. Lindzen and Choi examined the AMIP (Atmospheric Model Intercomparison Project) climate model runs, where the sea surface temperatures (SSTs) were specified, and the model atmosphere was then allowed to respond to the specified surface temperature changes. Energy is not conserved in such model experiments since any atmospheric radiative feedback which develops (e.g. a change in vapor or clouds) is not allowed to then feed-back upon the surface temperature, which is what happens in the real world.

Now, this seems like it might actually be a GOOD thing for estimating feedbacks, since (as just mentioned) most feedbacks are the atmospheric response to surface forcing, not the surface response to atmospheric forcing. But the results I have been getting from the fully coupled ocean-atmosphere (CMIP) model runs that the IPCC depends upon for their global warming predictions do NOT show what Lindzen and Choi found in the AMIP model runs. While the authors found decreases in radiation loss with short-term temperature increases, I find that the CMIP models exhibit an INCREASE in radiative loss with short term warming.

In fact, a radiation increase MUST exist for the climate system to be stable, at least in the long term. Even though some of the CMIP models produce a lot of global warming, all of them are still stable in this regard, with net increases in lost radiation with warming (NOTE: If analyzing the transient CMIP runs where CO2 is increased over long periods of time, one must first remove that radiative forcing in order to see the increase in radiative loss).

So, while I tend to agree with the Lindzen and Choi position that the real climate system is much less sensitive than the IPCC climate models suggest, it is not clear to me that their results actually demonstrate this.

ANOTHER VIEW OF THE ERBE DATA

Since I have been doing similar computations with the CERES satellite data, I decided to do my own analysis of the re-calibrated ERBE data that Lindzen and Choi analyzed. Unfortunately, the ERBE data are rather dicey to analyze because the ERBE satellite orbit repeatedly drifted in and out of the day-night (diurnal) cycle. As a result, the ERBE Team advises that one should only analyze 36-day intervals (or some multiple of 36 days) for data over the deep tropics, while 72-day averages are necessary for the full latitudinal extent of the satellite data (60N to 60S latitude).

Lindzen and Choi instead did some multi-month averaging in an apparent effort to get around this ‘aliasing’ problem, but my analysis suggests that the only way around the problem it is to do just what the ERBE Team recommends: deal with 36 day averages (or even multiples of that) for the tropics; 72 day averages for the 60N to 60S latitude band. So it is not clear to me whether the multi-month averaging actually removed the aliased signal from the satellite data. I tried multi-month averaging, too, but got very noisy results.

Next, since they were dealing with multi-month averages, Lindzen and Choi could use available monthly sea surface temperature datasets. But I needed 36-day averages. So, since we have daily tropospheric temperatures from the MSU/AMSU data, I used our (UAH) lower tropospheric temperatures (LT) instead of surface temperatures. Unfortunately, this further complicates any direct comparisons that might be made between my computations (shown below) and those of Lindzen and Choi.

Finally, rather than picking specific periods where the temperature changes were particularly large, like Lindzen and Choi did, I computed results from ALL time periods, but then sorted the results from the largest temperature changes to the smallest. This allows me to compute and plot cumulative average regression slopes from the largest to the smallest temperature changes, so we can see how the diagnosed feedbacks vary as we add more time intervals with progressively weaker temperature changes.

RESULTS

For the 20N-20S latitude band (same as that analyzed by Lindzen and Choi), and at 36-day averaging time, the following figure shows the diagnosed feedback parameters (linear regression slopes) tend to be in the range of 2 to 4 W m-2 K-1, which is considerably smaller than what Lindzen and Choi found, which were often greater than 6 W m-2 K-1. As mentioned above, the corresponding climate model computations they made had the opposite sign, but as I have pointed out, the CMIP models do not, and the real climate system cannot have a net negative feedback parameter and still be stable.

ERBE-vs-UAH-LT-36-day-tropics

But since the Lindzen and Choi results were for changes on time scales longer than 36 days, next I computed similar statistics for 108-day averages. Once again we see feedback diagnoses in the range of 2 to 4 W m-2 K-1:

ERBE-vs-UAH-LT-108-day-tropics

Finally, I extended the time averaging to 180 days (five 36-day periods), which is probably closest to the time averaging that Lindzen and Choi employed. But rather than getting closer to the higher feedback parameter values they found, the result is instead somewhat lower, around 2 W m-2 K-1.

ERBE-vs-UAH-LT-180-day-tropics

In all of these figures, running (not independent) averages were computed, always separated by the next average by 36 days.

By way of comparison, the IPCC CMIP (coupled ocean-atmosphere) models show long-term feedbacks generally in the range of 1 to 2 W m-2 K-1. So, my ERBE results are not that different from the models. BUT..it should be remembered that: (1) the satellite results here (and those of Lindzen and Choi) are for just the tropics, while the model feedbacks are for global averages; and (2) it has not yet been demonstrated that short-term feedbacks in the real climate system (or in the models) are substantially the same as the long-term feedbacks.

WHAT DOES ALL THIS MEAN?

It is not clear to me just what the Lindzen and Choi results mean in the context of long-term feedbacks (and thus climate sensitivity). I’ve been sitting on the above analysis for weeks since (1) I am not completely comfortable with their averaging of the satellite data, (2) I get such different results for feedback parameters than they got; and (3) it is not clear whether their analysis of AMIP model output really does relate to feedbacks in those models, especially since my analysis (as yet unpublished) of the more realistic CMIP models gives very different results.

Of course, since the above analysis is not peer-reviewed and published, it might be worth no more than what you paid for it. But I predict that Lindzen and Choi will eventually be challenged by other researchers who will do their own analysis of the ERBE data, possibly like that I have outlined above, and then publish conclusions that are quite divergent from the authors’ conclusions.

In any event, I don’t think the question of exactly what feedbacks are exhibited by the ERBE satellite is anywhere close to being settled.

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Evan Jones

Cheez, that ERBE sat. looks like something out of Jules Verne.

Erik Anderson

Does anybody here have a good understanding of how this study compares with the similar (and earlier) study of Ferenc Miskolczi?
see: http://www.youtube.com/watch?v=Ykgg9m-7FK4

theduke

Good. Those scientists who might generally be described as skeptics are auditing each other. I hope Drs. Lindzen and Choi respond.
Vincit omnia veritas.

Gene Nemetz

(1) I am not completely comfortable with their averaging of the satellite data, (2) I get such different results for feedback parameters than they got
I know it’s a big thing to expect, but I hope to see both Richard Lindzen and Yong-Sang Choi here to first give their reasons, and then discuss them with Roy Spencer.
I would expect it would be a congenial interchange.

Back2Bat

If I had only known I would need feedback and control system knowledge so desperately these days I would have paid a lot more attention. Who knew the world would go insane?
Kudos to those who did pay attention.

Kevin Kilty

Dr. Spencer,
You said
“the real climate system cannot have a net negative feedback parameter and still be stable”
I think you meant net positive feedback.

Neville

On the Beck show Monckton said that the Lindzen paper proved the ipcc models to be in error, this seems to muddy the waters still further.
I thought the same as K. Kitty above that Roy Spencer meant net positive feedback, anyone else?

Jerry Haney

I too hope that Dr’s Lindzen and Choi will discuss their findings with Dr. Spencer on this blog. I would pay for admission to read the thoughts of these honest and brilliant scientists on this important subject.

Jerry Haney

Another thought I have, why not run their discussion, if they agree, on a thread that does require a fee to read. Split the proceeds between this blog and the 3 scientists. I would pay at least $20 for a seat in the audience.

timetochooseagain

He meant in the sense that the slope corresponds inversely to the feedback, so the parameter (slope) has the opposite sign.
A system which responds to warming by slowing infrared cooling further is essentially the climatological equivalent of a divide by zero error. The “no feedback” situation is in fact one in which the normal behavior between radiation and temperature occurs, namely that hotter bodies emit more IR. In more typical terms, this would in fact be called a “negative” feedback. See here:
http://www.drroyspencer.com/2009/04/when-is-positive-feedback-really-negative-feedback/

Neville

Sorry I meant K. Kilty not Kitty.


The authors showed that satellite-observed radiation loss by the Earth increased dramatically with warming, often in excess of 6 Watts per sq. meter per degree (6 W m-2 K-1).
In stark contrast, all of the computerized climate models they examined did just the opposite, with the atmosphere trapping more radiation with warming rather than releasing more.

Gee, maybe that atmospheric window straight into space (and even through light cloud cover!) at 10 um (and +- a couple um) and coinciding with the LWIR peak from avg 288K earth and total radiation ~ T_to_the_fourth_power has something to do with it.
Naw …. just a lucky coincidence …
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evanmjones (19:27:51) :
Cheez, that ERBE sat. looks like something out of Jules Verne.

Has all the appearance of an intake manifold for an in-line four-cylinder engine …
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Terry

RE evanmjones (19:27:51) :
“Cheez, that ERBE sat. looks like something out of Jules Verne.”
I reckon those two big plates on the top are actually the FLUX CAPACITOR.

And, related and therefore on topic –
If you know where to look for the DFW Metrplex (Dallas Ft Worth and mid-citied et al) they can be seen to be WARMER in this three hour sequence of images this evening (after sunset, from 7:15 ish till 10:15 ish PM local) owing to the UHI effect on this very still (wind-wise) evening:
Centered on Texas – LWIR (Long Wave Infra-Red) Satellite Image
I think that Oklahoma City metro area can also be seen ‘warmer’ than surroundings.
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Oliver Ramsay

theduke said: vincit omnia veritas.
If it does, it takes its sweet time. Meanwhile, vincit prope omnis veritatem.

nvw

Thanks for taking the time to post your analysis and the interesting points you raise. I look forward to a reasoned debate – both you and Lindzen have shown how it can be done at this site.

bugs

I predict that McIntyre will find the Lindzen and Choi paper one of the most quietly disturbing papers he has ever read.

anna v

There have been reports in the peer review literature that the models violate the second law of thermodynamics, maybe the disagreement is part of this? After all numerical simulations have to be violating lots of things and brought into line at the boundaries of the grids used.
More to the point,
In my mind, the importance of the Lidzen paper is not if it really is measuring the sensitivity to whatever forcings. It is if they have used the models in the same way as they have used the data when making the crucial scatter plots.
If so, then the models are falsified, no matter if what is displayed is the true sensitivity . The scatter plots show a drastic difference between models and data. These might only be the AMIP models and these are then falsified. Somebody said that the AMIP models are embedded in the CMIP models but I was not able to find the reference.
Secondly, if the difference is between CMIP and AMIP model runs, I would like to see the same scatter plot of the CMIP runs as the crucial ones in the Lidzen presentation.

kuhnkat

bugs,
your predictions are usually wrong. I feel better already!!
HAHAHAHAHAHAHA

Rob

Interesting to see that these two ‘skeptic’ scientists that so often agreed in the past now start to divert in opinion. What I find especially stunning about Dr. Spencer’s report is that he disputes all 3 main conclusions that Lindzen drew in his so widely publicised GRL paper. Spencer essentially states that :
(1) The ERBE data shows no significant climate feedback (as opposed to Lindzen who still concludes a strong negative feedback factor).
(2) The AMIP models that Lindzen used are not suitable for comparison with ERBE data, redering Lindzen’s famous (“infamous”?) ERBE-model figure useless
(3) The short term analysis done makes conclusions on any climate sensitivity factor questionable.
Note that here is the blog that first revealed the difference in results for at least (1) and (2) :
http://motls.blogspot.com/2009/11/spencer-on-lindzen-choi.html
There should be much more to to come on this, since especially the difference in feedback factor that the two scientists find from essentially the same data is not at all adequately explained.

anna v

Found the reference. So AMIP is included as an integral part in CMIP.
http://www-pcmdi.llnl.gov/projects/model_intercomparison.php
“AMIP – Atmospheric Model Intercomparison Project
AMIP is a standard experimental protocol for global atmospheric general circulation models (AGCMs). It provides a community-based infrastructure in support of climate model diagnosis, validation, intercomparison, documentation and data access. This framework enables a diverse community of scientists to analyze AGCMs in a systematic fashion, a process which serves to facilitate model improvement. Virtually the entire international climate modeling community has participated in this project since its inception in 1990.
CMIP – Coupled Model Intercomparison Project
The Coupled Model Intercomparison Project (CMIP) studies output from coupled ocean-atmosphere general circulation models that also include interactive sea ice. These models allow the simulated climate to adjust to changes in climate forcing, such as increasing atmospheric carbon dioxide. CMIP began in 1995 by collecting output from model “control runs” in which climate forcing is held constant. Later versions of CMIP have collected output from an idealized scenario of global warming, with atmospheric CO2 increasing at the rate of 1% per year until it doubles at about Year 70. CMIP output is available for study by approved diagnostic sub-projects. The WCRP CMIP3 multi-model dataset archived at PCMDI, which includes realistic scenarios for both past and present climate forcing, is the latest and most ambitious phase of the project. The research based on this dataset has provided much of the new material underlying the IPCC 4th Assessment Report.”
http://www-pcmdi.llnl.gov/projects/amip/index.php
“AMIP is now an integral part of CMIP
AMIP-style simulations are routinely performed at many climate and NWP centers during model development in order to evaluate atmospheric model performance and identify errors. The systematic intercomparison of atmospheric model components is currently being coordinated under the Coupled Model Intercomparison Project (CMIP), which includes AMIP simulations as an integral part.”

anna v

Let me phrase it differently:
I sit on the moon and every time there is a DeltaSST on the earth I look at the DeltaFlux , or vice versa, I make a point on the scatter plot. The scatter plot does not have cause and effect, because it does not record time.
I run a computer model ( or it has been run and stored in the system linked above) and look from the same place on the moon and every time there is a DeltaSST on the earth I look at the DeltaFlux , or vice versa, I make a point on the scatter plot.
Lindzen’s plots show a drastic difference between data and models for AMIP runs.
AMIPS are to go back to the drawing board.
Now we see that AMIPs are an “integral part of CMIPs”. What can we conclude?

If I understood it well, coupled model (CMIP) says with increasing surface temperature, there is more IR being emitted out. Lindzen AMIP model shows the opposite – that with the rising surface temperature, the atmosphere (probably because of thickening with water vapor as a secondary positive feedback) blocks the outgoing radiation much more, that it even decreases in time.
Dr Spencer also says that CMIP´s calculated increase of outgoing radiation is not that far from observed reality, while the outgoing IR is a bit higher.

Drs. Spencer and Lindzen & Choi (L&C) may both be right.
L&C may have chosen the AMIP models, rather than the CMIP models examined by Spencer, because they attempt to portray only the radiative response of the atmosphere to changes in surface temperatures, without regard for the additional feedbacks and other complexities introduced with the fully coupled (CMIP) models.
According to Lumo on The Reference Frame, Spencer said he had “examined the AMIP run for the CNRM CM3 model, and it, too, shows a negative slope between temperature and radiative flux, just as Lindzen and Choi found. So, the problem might NOT be neglect of the sigma*T^4 effect, which [he] believe[s] is already contained in the AMIP model fields.”
http://motls.blogspot.com/2009/11/spencer-on-lindzen-choi.html
The AMIP models are “integral components” of the CMIP models. So perhaps the CMIPs include some factors which offset the impossible negative slopes of their AMIP components, yielding the required net negative feedback, but one which is smaller than it should be.
There still remain questions about the proper averaging intervals and the difference in the temperature datasets used (Spencer used LT, L&C used SST).

Just to be sure that people don’t oversimplify this debate and don’t consider Spencer or myself traitors. 😉
Lindzen and Choi are using some results of RealClimate’s Ray Pierrehumbert concerning the tropics – he also seems to claim that for zero feedback, the change in the energy flows per degree of warming is close to zero over there.
Richard had to deal with some difficult non-climate stuff but chances are that he will try to convince us in a more detailed text rechecking their points to appear later.

Stephen Wilde

I think I see a confusion here as regards the definition of sensitivity.
A sensitive system can either:
i) Respond quickly and negatively to a forcing so as to cancel or reduce the effect of that forcing.
ii) Respond quickly and positively to a forcing so as to show a large reaction to the forcing.
Both are high sensitivity but one results in long term stability whereas the other results in instability.
I think we have a sensitive climate system as per i) above. Warming proponents think we have a sensitive climate system as per ii) above.
Many are interpreting i) as an example of an insensitive system because it is essentially stable but in fact it is no less sensitive than ii).

Rhys Jaggar

Dr Spencer
I would say in a situation like this, where your results and those of Prof. Lindzen are apparently different, you can either both be fascinated as to why or you can mouth off about each other in public!
To me, it appears that the issue the two of you are addressing is critical in determining how a system returns to equilibrium and hence what the longer-term implications might be.
It also highlights the multiplicity of data sets being collected, the multiplicity of computer models being used and the incomplete understanding of how they all might be brought together.
What I will say is this:
I would make a hunch that given small deviations from equilibrium, the system will tend, usually, to return to equilibrium.
The key question which arises is what causes the deviations to continue to larger magnitudes and what conditions must exist for a new stable state to develop at temperatures considerably different from the older one.
On the understanding of those issues will enlightenment come concerning the nature of 20th/21st century ‘warming’.
A prelude to a long-term warmer steady state?
A prelude to a long-term descent into the next ice age?
Or yet another oscillation cycle around the equilibrium of the past 10,000 years??
The Copenhagen suggestion implies they believe no. 1, although how they have ruled out No. 2 is unclear to me.
Your results might suggest No. 3 is closer to the mark…..
Have the politicians done the calculations on the decision-tree, since it appears to me that No. 3 costs little but No. 1 costs much.
I’d have thought that if No. 3 were 66% plus, then ‘DO NOTHING’ would be irrrefutable.
Does anyone have any evidence-based statistics to point to what the scenario likelihoods are now?

Peter

anna v,
You have taken the keystrokes out of my fingers. I too thought coupled models used AMIP as part of the coupling, Lindzen and Choi have demonstrated that all the IPCC AMIP models are wrong, so what do we have? One wrong model coupled with another obviously wrong model, albeit in the opposite direction, reducing the “wrongness” of the model vs observations comparison. Very confidence inspiring.
It would interesting to see what would happen if the modeling community came clean. “Our models have the physics wrong for everything, but when we jumble them up and take the average, we thing they give us the right answer.”

This appears to be an appropriate thread to throw on a link to Trenberth and Fasullo (2009) “Global warming due to increasing absorbed solar radiation.”
http://www.cgd.ucar.edu/cas/Trenberth/2009GL037527.pdf
Abstract:
“Global climate models used in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) are examined for the top-of-atmosphere radiation changes as carbon dioxide and other greenhouse gases build up from 1950 to 2100. There is an increase in net radiation absorbed, but not in ways commonly assumed. While there is a large increase in the greenhouse effect from increasing greenhouse gases and water vapor (as a feedback), this is offset to a large degree by a decreasing greenhouse effect from reducing cloud cover and increasing radiative emissions from higher temperatures. Instead the main warming from an energy budget standpoint comes from increases in absorbed solar radiation that stem directly from the decreasing cloud amounts. These findings underscore the need to ascertain the credibility of the model changes, especially insofar as changes in clouds are concerned.”
Interesting.

blcjr

Well, the GRL is gray literature, so it is not really of the same quality as the truly peer reviewed literature.
Seriously, that is what one prominent AGW scientist said in response to my using a methodology that was published in GRL.

I want to make it clear that I have a great deal of respect for Dick Lindzen. He, more than anyone else, has helped me understand the major problems with AGW theory. In fact, I brag on him all the time. 🙂
Dick is a big boy and can take criticism. In this business you need to separate your emotions from the scientific results. The results are what they are, and we are all just trying to better understand what those results are telling us (if anything) about the way the climate system works.

Bill Illis

There has be a better way to analyze this data so we can tell what is really going on.
Unrelated to what Spencer and Lindzen are reviewing, let’s look at the ERBE data over two specific periods shows us – the 1997-98 El Nino and the 1991 Pinatubo Eruption.
In the 1997-98 El Nino, the ERBE data shows OutGoing Long-Wave radiation (OLR) increased to 6 watts/metre^2 for a 0.6C short-term increase in Sea Surface Temperatures (SSTs). [the models do not have this type of ENSO effect built in but it is obviously very important – 6 watts is a large number).
or 0.1C per watt/metre^2.
In the 1991 Pinatubo Eruption, 5 watts/metre^2 of Solar Irradiance was reflected by volcanic particles and temperatures at the surface decreased in the short-term by 0.5C. (Then there appears to be a long-lasting period when less Solar radiation is reflected than normal – the same pattern shown in the Stratosphere temperatures and should then be shown in a rise in surface temperatures after 1993.)
or 0.1C per watt/metre^2 again.
These are close to the values predicted by the Stefan-Boltzmann equations. But the climate models are using 0.75C per watt/metre^2.
I think we can use the ERBE data in other ways to see if the models are accurate.

Bill Illis

Correction, the climate models are using 0.3C per watt/metre^2 in the short-term but are using 0.75C per watt/metre^2 as a long-term equilibrium solution. Even in the short-term response, the models are 3 times too high.

Vincent

Very strange: AMIP is wrong, but CMIP is right. CMIP uses AMIP so CMIP should be wrong. Therefore CMIP is correcting the errors of AMIP. Is that about it?

Vincent

As someone mentioned, Monckton made a definate reference to this paper in his presentation, with a prediction that history would show this was when the AGW hypothesis was falsified. I wondered at the time if Monckton was being a bit premature; issues of this complexity take years to resolve. Now, it seems that the good Lord may have just painted a big bullseye on his forehead for any warmist to take a shot at. Never try and politicise science.

Ivan

Dr Spencer,
I have one question for you – how your earlier theory that the real climate feedback in the models is artificially “inflated” by the non feedback radiative forcing produced by chaotic fluctuations of the climate system fits into this newest theory in which sensitivity is assumed to be 1.6 – 2 deg C, and potentially even higher? You are now pretty much in agreement with IPCC.

Richard M

Bob Tisdale, very interesting indeed. If I read this right we have a warmist contending that all the models are WRONG!
This essentially validates those skeptics that have been saying the climate is not well understood and making policy decisions based on this level of knowledge is silly.
As for the conclusions, probably wrong but at least they are willing to admit things don’t work the way they thought they did. The door continues to open. I’d like to see an article on this paper. Hopefully, Dr. Spencer could add his insight as well.

UPDATED: Back-dated image sequence (the other link above show near-live imagery )
If you know where to look for the DFW Metrplex (Dallas Ft Worth and mid-citied et al) they can be seen to be WARMER in this three hour sequence of images this evening (after sunset, from 7:15 ish till 10:15 ish PM local) owing to the UHI effect on this very still (wind-wise) evening:
Centered on Texas – LWIR (Long Wave Infra-Red) Satellite Image
I think that Oklahoma City metro area can also be seen ‘warmer’ than surroundings.
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WatasC

Taking a step back, this is a testament to the complexity of the overall system, and our growing understanding of how feedback systems with hundreds or thousands of players are actually chaotic: one cannot accurately predict an end-state by knowing the beginning state.
To wit:
AGW proponents claim that not only is there correlation, but there is also causality.
AGW skeptics tend to claim that while there may be correlation, there is no causality.
And now we are gettting to the point where we realize that not only is there no *simple* causality, but there are also no simple corrrelations. Feedback systems are highly complex, and often chaotic. We are far from having enough data to drill down even a strong correlation between any pieces of earth data (the solar link may well be more identifiable, since there is probably no real feedback from the earth to the sun).
Finding causality between only a few pieces of a highly complex feedback system is a fool’s errand.
It would not surprise me at all that in the end, we will discover that accurate predictive modeling is out of the question; we can no more simulate *and predict* the global climate than we can the location and velocity of a single electron in a hydrogen atom. And if you think about it, it is not a farfetched analogy.

David L. Hagen

Stephen Wilde
Different groups use different terminology. “Feedback” as used by climatologists can appear opposite to common understandings.
To understand Spencer’s discussion see: Global Warming 101

BUT…everything this else in the climate system probably WON’T stay the same! For instance, clouds, water vapor, and precipition systems can all be expected to respond to the warming tendency in some way, which could either amplify or reduce the manmade warming. These other changes are called “feedbacks,” and the sum of all the feedbacks in the climate system determines what is called ‘climate sensitivity’. Negative feedbacks (low climate sensitivity) would mean that manmade global warming might not even be measurable, lost in the noise of natural climate variability. But if feedbacks are sufficiently positive (high climate sensitivity), then manmade global warming could be catastrophic.

See also discussion in:
Satellite and Climate Model Evidence Against Substantial Manmade Climate Change (supercedes “Has the Climate Sensitivity Holy Grail Been Found?”) by Roy W. Spencer, Ph.D. December 27, 2008 (last modified December 29, 2008)

2. FEEDBACKS IN THE IPCC CLIMATE MODELS
Feedbacks are not explicitly input into climate models. They are instead the net result of all the different physical processes contained in the models…especially those related to clouds and water vapor. Feedbacks are diagnosed from model output in much the same way as they are diagnosed from satellite measurements of the Earth: by comparing (1) global average temperature variations to (2) global average variations in the radiative balance of the Earth (variations in the approximate balance between absorbed sunlight and emitted infrared radiation averaged over the whole Earth).
. . .
Translated into a global warming estimate, a feedback of 6 W m-2 K-1 would correspond to a rather trivial 0.6 deg. C of warming in response to a doubling of atmospheric CO2.
A couple of the SST plots on the upper left in Fig. 4, however, have very different slopes…but as averaging times get longer, the line slopes also end up corresponding to negative feedback (3.7 W m-2 K-1 in Fig. 4g translates to about 1 deg. C of warming for a doubling of atmospheric CO2).
. . .
The resulting picture that emerges is of an IN-sensitive climate system, dominated by negative feedback. And it appears that the reason why most climate models are instead VERY sensitive is due to the illusion of a sensitive climate system that can arise when one is not careful about the physical interpretation of how clouds operate in terms of cause and effect (forcing and feedback).

Pamela Gray

Wonder what the jet stream was doing during the study period. Since it circles the globe in both hemispheres, picks up (or not) evaporated moisture from the oceans and carries atmospheric conditions over land and sea, any cyclical patterns to it would have huge affects all around the globe.

Roy W. Spencer

AMIP was meant to intercompare different models’ atmospheric processes…it was not necessarily meant to provide realistic simulations of all aspects of the climate system.
So, theoretically an AMIP model could have had perfect atmospheric physics, but since there was no interactive ocean model in the AMIP runs, it would not necessarily behave realistically when compared to real measurements.

KevinUK

Roy Spencer (05:27:56)
Roy, are you aware that Lord Monckton has claimed in his recent interview on the Fox News Glenn Beck show that Lindzen and Choi’s ERBE results prove that the climate models have got climate sensitivity wrong by a factor of SIX!
Thank you for conducring some ‘real science’ and for practicing the scientific method and for casting some doubt on this claim as this is what science should be all about namely that one should attempt always to falsify not confirm a previous result when conducting science. If in attempting to falsify a claim you fail and the claim stands up then the claim has some credence until of course the next attempt at falsification proves successful.
In this case your work appears to concluded that Lindzen and Choi are not comparing apples (ERBE data) with apples (CMIP) but rather apples (ERBE data)with oranges (AMIP). Having said that though and having researched the history of coupled ocean-atmosphere climate models somewhat I’m not convinced that thay have any utility other than as ‘sensitivity study tools’. Their value even as ‘sensitivity study tools’ is however strongly determined both the data fed into them and the parameterised equations coded into them and the numerically methods they use. The validity of all three of these facets of the models in predicting future climate are questionable in my opinion. In my opinion data like ERBE’s (provided it hasn’t be adjusted out of all recognition like the instrumental temperature record) will always trump modelling.
KevinUK

PJB

In my experimental experience, all “black-box” type scenarios are subject to analytical interpretation. The only way to verify hypotheses is to verify predictive values to actual. I realize that in the present case, that is difficult if not impossible. Time is definitely not on our side.
Kudos for the calculations and the analysis. Since you cannot “design” the experiment, interpretation is key and the stakes are…..to say the least…global.

Richard M (07:02:12) : I believe it could be argued that the paper indicates that the interpretation of the models has been wrong. Regardless, it’s an interesting paper.

anna v

Roy W. Spencer (08:24:03) :
AMIP was meant to intercompare different models’ atmospheric processes…it was not necessarily meant to provide realistic simulations of all aspects of the climate system.
So, theoretically an AMIP model could have had perfect atmospheric physics, but since there was no interactive ocean model in the AMIP runs, it would not necessarily behave realistically when compared to real measurements.

Fair enough. In this case it would be good to have a scatter plot of the corresponding CMIP values, with the same choices as the choises for the data in the figure on page 46 of prof.inzen’s presentation. Are you saying the model points would follow the same trend?

I am getting increasingly irritated at the use of the term “the authors found”, when referring to observing the output of models. While grammatically correct, it leaves the reader with the impression that someone made an observation of the real world, and not some Playstation version of nature. I would rather see something like “the models suggest”.
Besides, it’s been my experience that a person “finds” what they are searching for, whether it be Indian arrowheads or hockey sticks.

kim

Bob Tisdale @ 4:45:58
So with the cooling phase now, are there more clouds? That would make the increasing CO2 no longer correlated so not causative. Unless the effect changed, and I wouldn’t put it past the ability of this climate system to be that changable. Perish the thought, which is immortal.
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DJ Meredith

One thing I’d like to point out, or, at least share as a personal observation…..we have here people in essentially the same camp tugging in different directions over the same data.
What I think is extraordinary is that this is something I don’t see in the pro-AGW camps. Why is this so important?
Pro-AGW’ers never seem to question each other’s work, never contradict each other, and in so doing, never really allow the truth.