Some Comments on the Lindzen and Choi (2009) Feedback Study
by Roy W. Spencer, Ph. D.
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.
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:
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.
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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thats odd, as NASA’s energy flowchart says that 25% leaves as evaporation.
I guess it all depends on what acounting procedures are used
Before the AGW theory was concocted, it was known that most heat leaves by convection.
“It is common knowledge that nights cool down a lot more when the sky is clear than when there is an overcast. There is a tendency to assume that the overcast absorbs infrared radiation from the surface of the earth holding in heat. it is convection, not radiation, that accounts for the overcast holding in heat.
An overcast means there are stable layers, and convection is not occurring. A clear sky means convection is occurring. Since the earth is warmer near the surface, there is always a lot of convection unless blocked by layering of air masses.”
P Wilson says:
25% of what? The 80 W/m^2 that leaves by evapotranspiration represents ~16% of the energy that leaves the earth’s surface. However, it represents about 23-24% of the energy that is incident from the sun at the top of the atmosphere.
Well, since the AGW theory in some form has been around since at least Arrhenius around 1900, God knows what people thought on overall earth energy balance before it. However, the quote that you give was apparently lifted from this site: http://nov55.com/41r.html which is by some crank who calls himself an “independent scientist” and who doesn’t even believe in relativity ( http://nov55.com/eins.html ). His claims about clear skies indicating convection whereas overcast indicates stability is pretty close to being backwards! (The real truth is probably more complicated than any simplistic statement like this but, as a general rule, clouds are associated with convection and clear skies indicate a relatively stable atmosphere.) Why do you go around trolling for garbage on the internet and then accept it as gospel? Is that what being a “skeptic” means to you?
By the way, now that I have looked around that site a bit, it seems to be where you are getting a lot of your wacky ideas from, P Wilson, isn’t it? Here is where your ideas on the Stefan-Boltzmann Eq. seem to originate from, complete with references to night vision equipment: http://nov55.com/steph.html Why do you fall for this crap?
one has to be a bit cautious with K&T as it’s dated and there seems to be a few things that are problematic. I seem to recall that there was some serious muddling somewhere in it that had values which should have been clear sky only being applied to the clear cloudy averaged condition – or something similar to that. However, there is a lot of good basic info there. Seems that convection (including evaporation) is indicated to be about 100 W/m^2 at the surface. Actually, running an energy balance, one sees that around 100 W/m^2 of nonradiative power is needed at the surface, dropping down to zero around the tropopause for balance in the various levels.
As for sunlight, TOA, it’s about 341w/m^2 but only about 266 W/m^2 ever reach the surface on overall average, excluding incoming absorption.
To try to use radiative theory while ignoring ‘re-radiation’ is something unrelated to anything. The 70% is clear sky transmission which amounts to around 270W/m^2 outgoing radiation. You’ll note that this is way too much for current balance – based upon around 235 W/m^2 average incoming after clouds and atmospheric albedo has removed its portion from the total 341 w/m^2 incoming. You should also note that this is also not nearly enough for a planet with no cloud cover to balance if one had an average of 341 w/m^2 coming in and an albedo of only 0.08 which is the total surface albedo.
Whether it was mentioned or not, your 60% number is referring to the overall average composed of about 40% clear skies and 60% cloudy (with some sort of averaged cloud effect value). Cloudy skies emits power from well into the troposphere where temperature and hence power is somewhat lower – less than 235w/m^2 and the weighted average is what must balance.
This is not related directly to albedo, it’s simply happens to be a similar value. You’ll note it doesn’t actually provide balance but rather provides balance only by the combination of clear and cloudy sky fractions and clear and cloudy sky emissions. Clear sky emissions from the surface, cloudy sky emissions are essentially done from cloud tops.
Thats someone who I corresponded with yes, and who’s ideas I agree with though some that I don’t- at least the ones I learned at University and beyond. However, that was before the AGW theory become popular, or fashionable some 20 years ago. So I don’t think its crap. Most of the results of radiative energy when they are measured conform to the reduced values that certainly not those of the SB equation. Take underfloor heating as a good comparison. It radiates more heat than the earth’s average, and is verified at – in the UK at least – to run at 100 w/m2 maximum. like the earth, the ground level is where radiationmatters the most, although a room is closed to convection.
The same for the production of human heat, which is its own generator. Why are these values measured more than a 1/4 of terrestrial matter which is much cooler?
joel
25% of what?
25% of incoming solar radiation leaves the surface as evaporation according to NASA
http://earthobservatory.nasa.gov/Features/EnergyBalance/page6.php
What I dispute is that incoming radiation has to balance outgoing radiation as though thermal energy were a mechanical fixed constant.
The flowchart on that page shows 17% radiation leaving from the surface and 59% radiation from the atmosphere, yet this one:
http://eosweb.larc.nasa.gov/EDDOCS/images/Erb/components2.gif
shows 51% absorbed by land and oceans. 41% radiation from the surface and 64% from the atmosphere
If earth had to radiate all the heat it received, then 51% should be the radiation that leaves the earth’s surface
… and the flowchart on the graph immediately below the 1st link
http://earthobservatory.nasa.gov/Features/EnergyBalance/page6.php
shows 6% reflected by the surface
whilst this:
http://eosweb.larc.nasa.gov/EDDOCS/radiation_facts.html
shows 4% reflected by the surface.
these equations are made up according to context and are all different scores from the same institution.
these anomalies could all be cleared if the measurement was the actual radiation that earth gives at its average temperature 0f 59F
P Wilson: There are two issues here:
(1) All values are not known or measurable to perfect precision, so yes, there will be some variability in the values. The ability to measure the various radiative fluxes from satellites is getting better all the time. In particular, the link you gave here http://eosweb.larc.nasa.gov/EDDOCS/radiation_facts.html seems to be to an older chart than the one from Kiehl & Trenberth (in fact, I found it on one page dated 1998). I think http://earthobservatory.nasa.gov/Features/EnergyBalance/page6.php is also newer but I am not exactly sure.
(2) You are not correctly reading some of the charts. For example in reference to http://eosweb.larc.nasa.gov/EDDOCS/radiation_facts.html , you say “If earth had to radiate all the heat it received, then 51% should be the radiation that leaves the earth’s surface”. In fact that is what the chart shows. 23% leaves via water vapor, 7% via conduction and rising air, and 21% (net) via radiation (of this 21%, 6% is radiated directly out into space and 15% is “radiation absorbed by the atmosphere” that is subsequently emitted into space). [I know that diagram is a little confusing in regards to the 15% because they didn’t put a directional arrow meaning that it could mistakenly be seen as an extension of the “absorbed by atmosphere” component of the incoming solar energy…but it’s not.] Another thing to note is that this diagram is a bit different in how it breaks things down than the one from Kiehl and Trenberth in that it only shows the NET flow of thermal radiation from earth to atmosphere whereas Kiehl and Trenberth break it down into the amount that the gross amount that the earth emits and then the amount that the atmosphere emits that goes back to the Earth.
Joel,
I cringe when looking at that KT09 paper and those graphs made up from imitating it. They imply accuracies for incoming solar, outgoing solar and albedo are known to 0.1 accuracy. Albedo is known to vary and that affects the outgoing. They have chosen a very low value for albedo as compared with practically all measurement studies, just under 0.3. There is debate on whether the SORCE satellite at 1361 is correct or previous satellites at 1367W/m^2 have the correct value. Averaged, that’s over 1, not 0.1 W/m^2 uncertainty. From this they deduce there’s a 0.9 W/m^2 imbalance in the overall budget, like a rabbit out of the hat. It’s not statistically significant beyond 0 for their uncertainties which must be greater than 1W/m^2 based upon incoming alone and considering the variations in albedo, it’s a lot more than that.
As for their cartoon, the numbers are in the ballpark. I think they are high in incoming absorption and low in cloud albedo. Differences between kt09 and kt97 all seem to be in the same direction – that direction is the same as the promoted view points. The segegration between power flowing from the surface and reradiation is neither relevent nor clear in understanding. Radiative transfer is radiative transfer and emission is simply a part of that. Do you think a photon or sensor exiting the atmosphere cares if it was emitted at 30,000 ft or at the surface or that one could ever distinguish between origin?
F = 1 would imply instability (which they explicitly exclude as nonsensical for “equilibrium” analysis). A positive f < 1 describes a stable system in which equilibrium is IMPLICITLY achieved. Remember, total radiation increases with the temperature of the radiating body, and this is IMPLICITLY part of the UNDERLYING equilibrium equations and models (although not discussed in the paper). Hence, even for a realistic positive "f" (i.e. < 1) the larger resulting total radiation power (for a CO2 doubling increment) results in equilibrium at a higher atmospheric temperature despite a reduction of the net radiative-loss factor.
The "f" in Lindzen/Choi is an equilibrium factor and NOT a dynamic feedback-loop factor.
They do make the point that as f approaches 1 the "relative" (as opposed to "absolute") stability of the system becomes pretty low — intuitively this does seem less realistic for a life-sustaining atmosphere as old as Earth's.
The underlying system dynamics are only crudely discussed in the text of the paper, strictly in terms of the various time responses that are assumed in order to justify the validity of the atmospheric response measurements. These largely seem to rely on earlier work of their (i.e. Lindzen's and Choi's) own earlier work. They have nothing to do with "f", and indeed if "f" were part of a dynamic feedback system then the system would have to include dynamics/delays (described by differential equations with respect to time, since it takes time for energy transfers to take place).
Before reading the paper, I myself was confused by the discussion of a "positive" feedback factor, assuming this referred to an overall dynamic system feedback rather than an equilibrium-equations factor.
The final graph/curve of the paper shows that the IPCC models fall pretty closely to the curve described the simplified equilibrium equation dT= dTo / (1 – f). This implies that the models can be reasonably well approximated or described in this way, although some clearly incorporate small deviations from the simple equation.
Any criticism of the paper's quantitative conclusions will have to rely upon verifiable invalidation of the underlying assumptions and/or the measurement methods (in the form of a published paper). It seems to be implied (by Lindzen/Choi) that the IPCC models, in terms of feedback, are based purely upon theoretical hypotheses and/or model-tuning (the latter being essentially fraudulent if not done transparently).