From Maurizio Morabito:

The issue of model falsifiability has already been a topic on the NYT’s “Tierney Lab”, daring to ask this past January questions such as “Are there any indicators in the next 1, 5 or 10 years that would be inconsistent with the consensus view on climate change?” and “Are there any sorts of weather trends or events that would be inconsistent [with global warming}?“.

And what did Gavin Schmidt reply on RealClimate? No, and no:

this subject appears to have been raised from the expectation that some short term weather event over the next few years will definitively prove that either anthropogenic global warming is a problem or it isn’t. As the above discussion should have made clear this is not the right question to ask. Instead, the question should be, are there analyses that will be made over the next few years that will improve the evaluation of climate models?

No “short-term weather event over the next few years” could ever disprove that “anthropogenic global warming“. And observations (events) and their analyses, in the RealClimate world, are only interesting to “improve the models“.

Convinient.  Convinient, that is, if you are after a particular answer rather than the correct answer.

The next alarmist study that considers possible benefits of global warming along with its downsides will be the first.  Many of us have observed that, historically, abundance has always been associated with warmer periods and famine with cooler periods.  To this end, note this:  (via Tom Nelson)

Rain, wind and cold weather have Eastern Iowa farmers stuck and waiting to start the planting season.

Many farmers tell TV9 they’re ready to go but the weather this year simply won’t cooperate.

In 2007, many Eastern Iowa farmers began planting corn by the middle of April. This year, it’ll take several weeks of sun and much warmer temperatures to even think about working in soggy fields. And getting a later start can present some problems.

On a number of occasions, I have emphasized that the key scientific hypothesis that drives catastrophic warming forecasts is not greenhouse gas theory, but is the theory that the climate is dominated by strong positive feedbacks:

The catastrophe comes, not from a mere 1 degree of warming, but from the multiplication for this warming 3,4,5 times or more by hypothesized positive feedback effects in the climate.   Greenhouse gas theory gives us warming numbers we might not even be able to find amidst the natural variations of our climate;  it is the theory of strong positive climate feedback that gives us the apocalypse.

So when I read the interview with Jennifer Marohasy, I was focused less on the discussion of how world temperatures seemed sort of flat over the last 10 years  (I have little patience with climate alarmists focusing on short periods of time to "prove" a long term climate trend, so I try not to fall in the same trap).  What was really interesting to me was this:

The [NASA Aqua] satellite was only launched in 2002 and it enabled the collection of data, not just on temperature but also on cloud formation and water vapour. What all the climate models suggest is that, when you’ve got warming from additional carbon dioxide, this will result in increased water vapour, so you’re going to get a positive feedback. That’s what the models have been indicating. What this great data from the NASA Aqua satellite … (is) actually showing is just the opposite, that with a little bit of warming, weather processes are compensating, so they’re actually limiting the greenhouse effect and you’re getting a negative rather than a positive feedback."

Up to this point, climate scientists who argued for strong positive feedback have relied mainly on numbers from hundreds of thousands of years ago, of which our understanding is quite imperfect.  I have long argued that more recent, higher quality data over the last 50-100 years seems to point to feedback that is at best zero and probably negative [also see video here and here].  Now we have better data from the satellite NASA launched in part to test the strong positive feedback hypothesis that in fact feedback may be negative.  This means that instead of multiplying a climate sensitivity of 1 (from CO2 alone) to numbers of 3 or more with feedback, as the IPCC argued, a climate sensitivity of 1 from CO2 may actually be reduced to a net sensitivity well less than 1.  This would imply warming from CO2 over the next century of less than 1C, an amount likely lost in the noise of natural variations and hardly catastrophic.

Marohasy: "That’s right … These findings actually aren’t being disputed by the meteorological community. They’re having trouble digesting the findings, they’re acknowledging the findings, they’re acknowledging that the data from NASA’s Aqua satellite is not how the models predict, and I think they’re about to recognise that the models really do need to be overhauled and that when they are overhauled they will probably show greatly reduced future warming projected as a consequence of carbon dioxide."

I am going to be out enjoying some snow skiing this week, but I will leave you with a thought that was a prominent part of this video: 

The catastrophe that Al Gore and others prophesy as a result of greenhouse gasses is actually not, even by their admission, a direct result of greenhouse gas emissions.  Even the IPCC believes that warming directly resulting from manmade CO2 emissions is on the order of 1 degree C for a doubling of CO2 levels in the atmosphere (and many think it to be less). 

The catastrophe comes, not from a mere 1 degree of warming, but from the multiplication for this warming 3,4,5 times or more by hypothesized positive feedback effects in the climate.   Greenhouse gas theory gives us warming numbers we might not even be able to find amidst the natural variations of our climate;  it is the theory of strong positive climate feedback that gives us the apocalypse.

So, In a large sense, the proposition that we face environmental Armageddon due to CO2 rests not on greenhouse gas theory, which is pretty well understood, but on the theory that our climate system is dominated by strong positive feedbacks.  This theory of positive feedback is almost never discussed publicly, in part because it is far shakier and less understood than greenhouse gas theory.  In fact, it is very probable that we have the sign, much less the magnitude, of major feedback effects wrong.  But if we are considering legislation to gut our economies in order to avoid a hypothesized climate catastrophe, we should be spending a lot more time putting scrutiny on this theory of positive feedback, rather than just greenhouse gas theory.

Tom Nelson quotes an email from S. Fred Singer that states my position well:

I believe a fair statement is that the GH [greenhouse] effect of CO2 etc must exist (after all, CO2 is a GH gas and is increasing) but we cannot detect it in the record of temp patterns.

So we must conclude that its contribution to climate change is swamped by natural changes.

Why do models suggest a much larger effect? Because they all incorporate a positive feedback from WV [water vapor], which in actuality is more likely to be negative. Empirical evidence is beginning to support this explanation.

This is interesting, but yet to be reproduced by others:

"Runaway greenhouse theories contradict energy balance equations," Miskolczi states.  Just as the theory of relativity sets an upper limit on velocity, his theory sets an upper limit on the greenhouse effect, a limit which prevents it from warming the Earth more than a certain amount.

How did modern researchers make such a mistake? They relied upon equations derived over 80 years ago, equations which left off one term from the final solution.

Miskolczi’s story reads like a book. Looking at a series of differential equations for the greenhouse effect, he noticed the solution — originally done in 1922 by Arthur Milne, but still used by climate researchers today — ignored boundary conditions by assuming an "infinitely thick" atmosphere. Similar assumptions are common when solving differential equations; they simplify the calculations and often result in a result that still very closely matches reality. But not always.

So Miskolczi re-derived the solution, this time using the proper boundary conditions for an atmosphere that is not infinite. His result included a new term, which acts as a negative feedback to counter the positive forcing. At low levels, the new term means a small difference … but as greenhouse gases rise, the negative feedback predominates, forcing values back down.

My scientific intuition has always rebelled at the thought of runaway positive feedback.

By the way, James Hansen has claimed that he is being censored at NASA by the Bush Administration, and that the government should not interfere with scientists work.  So how did he react to this work?

NASA refused to release the results.  Miskolczi believes their motivation is simple.  "Money", he tells DailyTech.  Research that contradicts the view of an impending crisis jeopardizes funding, not only for his own atmosphere-monitoring project, but all climate-change research.  Currently, funding for climate research tops $5 billion per year.

Miskolczi resigned in protest, stating in his resignation letter, "Unfortunately my working relationship with my NASA supervisors eroded to a level that I am not able to tolerate.  My idea of the freedom of science cannot coexist with the recent NASA practice of handling new climate change related scientific results."

I argued a while back that Hansen should do the same if he thought he was being censored.  Certainly you do not have to convince this libertarian of the contradiction between a government agency and the concept of free scientific inquiry.

After releasing my first climate video, which ran over 50 minutes, I had a lot of feedback that I should aim for shorter, more focused videos.  This is my first such effort, setting for myself the artificial limit of 10 minutes, which is the YouTube limit on video length.

While the science of how CO2 and other greenhouse gases cause warming is fairly well understood, this core process only results in limited, nuisance levels of global warming. Catastrophic warming forecasts depend on added elements, particularly the assumption that the climate is dominated by strong positive feedbacks, where the science is MUCH weaker. This video explores these issues and explains why most catastrophic warming forecasts are probably greatly exaggerated.

You can also access the YouTube video here, or you can access the same version on Google video here.

If you have the bandwidth, you can download a much higher quality version by right-clicking either of the links below:

• 640 x 480 Windows media version, 86MB
• 320 x 240 Windows media version, 31MB
• Quicktime 640 x 480 version, 245MB

I am not sure why the quicktime version is so porky.  In addition, the sound is not great in the quicktime version, so use the windows media wmv files if you can.  I will try to reprocess it tonight.  All of these files for download are much more readable than the YouTube version (memo to self:  use larger font next time!)

This is a companion video to the longer and more comprehensive climate skeptic video "What is Normal — a Critique of Catastrophic Man-Made Global Warming Theory."

Roger Pielke Jr has gone back to the first IPCC assessment to see how the IPCC is doing on its long-range temperature forecasting.  He had to dign back into his own records, because the IPCC seems to be taking its past reports offline, perhaps in part to avoid just this kind of scrutiny.  Here is what he finds:

The colored lines are various measures of world temperature.  Only the GISS, which maintains a surface temerpature rollup that is by far the highest of any source, manages to eek into the forecast band at the end of the period.  The two satellite measures (RSS and UAH) seldom even touch the forecast band except in the exceptional El Nino year of 1998.  Pielke comments:

On the graph you will also see the now familiar temperature records from two satellite and two surface analyses. It seems pretty clear that the IPCC in 1990 over-forecast temperature increases, and this is confirmed by the most recent IPCC report (Figure TS.26), so it is not surprising.

Which is fascinating, for this reason:  In essence, the IPCC is saying that we know that past forecasts based on a 1.5, much less a 2.5, climate sensitivity have proven to be too high, so in our most recent report we are going to base our forecast on … a 3.0+!!

Previously, I have argued that climate models can duplicate history only because they are fudged.  I understand this phenomenon all too well, because I have been guilty of it many times.  I have built economic and market models for consulting clients that seem to make sense, yet did not backcast history very well, at least until I had inserted a few "factors" into them.

Climate modelers have sworn for years that they are not doing this.  But Steve McIntyre finds this in the IPCC 4th Assessment:

The strong emphasis placed on the realism of the simulated base state provided a rationale for introducing ‘flux adjustments’ or ‘flux corrections’ (Manabe and Stouffer, 1988; Sausen et al., 1988) in early simulations. These were essentially empirical corrections that could not be justified on physical principles, and that consisted of arbitrary additions of surface fluxes of heat and salinity in order to prevent the drift of the simulated climate away from a realistic state.

Boy, that is some real semantic goodness there.  We are not putting in fudge factors, we are putting in "empirical corrections that could not be justified on physical principles" that were "arbitrary additions" to the numbers.  LOL.

But the IPCC only finally admits this because they claim to have corrected it, at least in some of the models:

By the time of the TAR, however, the situation had evolved, and about half the coupled GCMs assessed in the TAR did not employ flux adjustments. That report noted that ‘some non-flux adjusted models are now able to maintain stable climatologies of comparable quality to flux-adjusted models’

Let’s just walk on past the obvious question of how they define "comparable quality" or why scientists are comfortable when multiple models using different methodologies, several of which are known to be wrong, come up with nearly the same exact answer.  Let’s instead be suspicious that the problem of fudging has not gone away, but likely has just had its name changed again, as climate scientists are likely tuning the models but with tools other than changes to flux values.  But climate models have hundreds of other variables that can be fudged, and, remembering this priceless quote…

"I remember my friend Johnny von Neumann used to say, ‘with four parameters I can fit an elephant and with five I can make him wiggle his trunk.’" A meeting with Enrico Fermi, Nature 427, 297; 2004.

We should be suspicious.  But we don’t just have to rely on our suspicions, because the IPCC TAR goes on to essentially confirm my fears:

(1.5.3) The design of the coupled model simulations is also strongly linked with the methods chosen for model initialisation. In flux adjusted models, the initial ocean state is necessarily the result of preliminary and typically thousand-year-long simulations to bring the ocean model into equilibrium. Non-flux-adjusted models often employ a simpler procedure based on ocean observations, such as those compiled by Levitus et al. (1994), although some spin-up phase is even then necessary. One argument brought forward is that non-adjusted models made use of ad hoc tuning of radiative parameters (i.e., an implicit flux adjustment).

Update:  In another post, McIntyre points to just one of the millions of variables in these models and shows how small changes in assumptions make huge differences in the model outcomes.  The following is taken directly from the IPCC 4th assessment:

The strong effect of cloud processes on climate model sensitivities to greenhouse gases was emphasized further through a now-classic set of General Circulation Model (GCM) experiments, carried out by Senior and Mitchell (1993). They produced global average surface temperature changes (due to doubled atmospheric CO2 concentration) ranging from 1.9°C to 5.4°C, simply by altering the way that cloud radiative properties were treated in the model. It is somewhat unsettling that the results of a complex climate model can be so drastically altered by substituting one reasonable cloud parameterization for another, thereby approximately replicating the overall intermodel range of sensitivities.

I can never make this point too often:  When considering the scientific basis for climate action, the issue is not the warming caused directly by CO2.  Most scientists, even the catastrophists, agree that this is on the order of magnitude of 1C per doubling of CO2 from 280ppm pre-industrial to 560ppm (to be reached sometime late this century).  The catastrophe comes entirely from assumptions of positive feedback which multiplies what would be nuisance level warming to catastrophic levels.

My simple analysis shows positive feedbacks appear to be really small or non-existent, at least over the last 120 years.  Other studies show higher feedbacks, but Roy Spencer has published a new study showing that these studies are over-estimating feedback.

And the fundamental issue can be demonstrated with this simple example: When we analyze interannual variations in, say, surface temperature and clouds, and we diagnose what we believe to be a positive feedback (say, low cloud coverage decreasing with increasing surface temperature), we are implicitly assuming that the surface temperature change caused the cloud change — and not the other way around.

This issue is critical because, to the extent that non-feedback sources of cloud variability cause surface temperature change, it will always look like a positive feedback using the conventional diagnostic approach. It is even possible to diagnose a positive feedback when, in fact, a negative feedback really exists.

I hope you can see from this that the separation of cause from effect in the climate system is absolutely critical. The widespread use of seasonally-averaged or yearly-averaged quantities for climate model validation is NOT sufficient to validate model feedbacks! This is because the time averaging actually destroys most, if not all, evidence (e.g. time lags) of what caused the observed relationship in the first place. Since both feedbacks and non-feedback forcings will typically be intermingled in real climate data, it is not a trivial effort to determine the relative sizes of each.

While we used the example of random daily low cloud variations over the ocean in our simple model (which were then combined with specified negative or positive cloud feedbacks), the same issue can be raised about any kind of feedback.

Notice that the potential positive bias in model feedbacks can, in some sense, be attributed to a lack of model “complexity” compared to the real climate system. By “complexity” here I mean cloud variability which is not simply the result of a cloud feedback on surface temperature. This lack of complexity in the model then requires the model to have positive feedback built into it (explicitly or implicitly) in order for the model to agree with what looks like positive feedback in the observations.

Also note that the non-feedback cloud variability can even be caused by…(gasp)…the cloud feedback itself!

Let’s say there is a weak negative cloud feedback in nature. But superimposed upon this feedback is noise. For instance, warm SST pulses cause corresponding increases in low cloud coverage, but superimposed upon those cloud pulses are random cloud noise. That cloud noise will then cause some amount of SST variability that then looks like positive cloud feedback, even though the real cloud feedback is negative.

I don’t think I can over-emphasize the potential importance of this issue. It has been largely ignored — although Bill Rossow has been preaching on this same issue for years, but phrasing it in terms of the potential nonlinearity of, and interactions between, feedbacks. Similarly, Stephen’s 2005 J. Climate review paper on cloud feedbacks spent quite a bit of time emphasizing the problems with conventional cloud feedback diagnosis.

James Annan, more or less a supporter of catastrophic man-made global warming theory, explains how typical climate sensitivities (of the order of magnitude of 3 or more) used by catastrophists are derived (in an email to Steve McIntyre)  As a reminder, climate sensitivity is the amount of temperature rise we would expect on earth from a doubling of CO2 from pre-industrial 280ppm to 560ppm.

If you want to look at things in the framework of feedback analysis, there’s a pretty clear explanation in the supplementary information to Roe and Baker’s recent Science paper. Briefly, if we have a blackbody sensitivity S0 (~1C) when everything else apart from CO2 is held fixed, then we can write the true sensitivity S as

where the f_i are the individual feedback factors arising from the other processes. If f_1 for water vapour is 0.5, then it only takes a further factor of 0.17 for clouds (f_2, say) to reach the canonical S=3C value. Of course to some extent this may look like an artefact of the way the equation is written, but it’s also a rather natural way for scientists to think about things and explains how even a modest uncertainty in individual feedbacks can cause a large uncertainty in the overall climate sensitivity.

This is the same classic feedback formula I discussed in this prior article on feedback.  And Dr. Annan basically explains the origins of the 3C sensitivity the same way I have explained it to readers in the past:  Sensitivity from CO2 alone is about 1C (that is S0) and feedback effects from things like water vapour and clouds triples this to three.  The assumption is that the climate has very strong positive feedback.

Note the implications.  Without any feedback, or feedback that was negative, we would not expect the world to heat up much more than a degree with a doubling of CO2, of which we have already seen perhaps half.  This means we would only experience another half degree of warming in the next century or so.  But with feedbacks, this half degree of future warming is increased to 2.5 or 3.0 or more degrees.  Essentially assumptions about feedback are what separates trivial nuisance levels of warming from forecasts that are catastrophic. 

Given this, it is instructive to see what Mr. Annan has to say in the same email about our knowledge of these feedbacks:

The real wild card is in the behaviour of clouds, which have a number of strong effects (both on albedo and LW trapping) and could in theory cause a large further amplification or suppression of AGW-induced warming. High thin clouds trap a lot of LW (especially at night when their albedo has no effect) and low clouds increase albedo. We really don’t know from first principles which effect is likely to dominate, we do know from first principles that these effects could be large, given our current state of knowledge. GCMs don’t do clouds very well but they do mostly (all?) suggest some further amplification from these effects. That’s really all that can be done from first principles.

In other words, scientists don’t even know the SIGN of the most important feedback, ie clouds.  Of course, in a rush to build the most alarming model, they all seem to rush to the assumption that it is positive.  So, yes, if the feedback is a really high positive number (something that is very unlikely in natural, long-term stable physical processes) then we get a climate catastrophe.  Of course if it is small or negative, we don’t get one at all. 

My Annan points to studies he claims shows climate sensitivity net of feedbacks in the past to be in the 2-3C range.  Note that these are studies of climate changes tens or hundreds of thousands of years ago, as recorded imperfectly in ice and other proxies.  The best data we have is of course for the last 120 years when we have measured temperature with thermometers rather than ice crystals, and the evidence of this data points to a sensitivity of at most about 1C net of feedbacks.

So to summarize:

• Climate sensitivity is the temperature increase we might expect with a doubling of CO2 to 560 ppm from a pre-industrial 280ppm
• Nearly every forecast you have ever seen assumes the effect of CO2 alone is about a 1C warming from this doubling.  Clearly, though, you have seen higher forecasts.  All of the "extra" warming in these forecasts come from positive feedback.  So a sensitivity of 3C would be made up of 1C from CO2 directly that is tripled by positive feedbacks.  A sensitivity of 6 or 8 still starts with the same 1C but has even higher feedbacks
• Most thoughtful climate scientists will admit that we don’t know what these feedbacks are — in so many words, modelers are essentially guessing.  Climate scientists don’t even know the sign (positive or negative) much less the magnitude.  In most physical sciences, upon meeting such an unknown system that has been long-term stable, scientists will assume neutral to negative feedback.  Climate scientists are the exception — almost all their models assume strong positive feedback.
• Climate scientists point to studies of ice cores and such that serve as proxies for climate hundreds of thousands of years ago to justify positive feedbacks.  But for the period of history we have the best data, ie the last 120 years, actual CO2 and measured temperature changes imply a sensitivity net of feedbacks closer to 1C, about what a reasonable person would assume from a stable process not dominated by positive feedbacks.

Climate sensitivity to CO2 is typically defined as the amount of warming that would be caused by CO2 levels rising from pre-industrial 280ppm to a doubled concentration at 460ppm.  Via Ron Bailey, here is what Hadley presented at Bali today:

Hadley climate models project that if atmospheric concentrations of GHG were stabilized at 430 ppm, we run a 63 percent chance that the earth’s eventual average temperature would exceed 2 degrees Celsius greater than pre-industrial temperatures and 10 percent chance they would rise higher than 3 degrees Celsius. At 450 ppm, the chances rise to 77 percent and 18 percent respectively. And if concentrations climb to 550 ppm, the chances that average temperatures would exceed 2 degrees Celsius are 99 percent and are 69 percent for surpassing 3 degrees Celsius.

I encourage you to check out this post wherein I struggle, based on empirical data, to get a sensitivity higher than 1.2, and even that is only achieved by assuming that all 20th century warming is from CO2, which is unlikely.  A video of the same analysis is below:

However, maybe this is good news, since many climate variables in 2007, including hurricane numbers and global temperatures, came out in the bottom 1 percentile of predicted outcomes from climate models.

When catastrophist climate models were first run against history, they did not even come close to matching.  Over the last several years, after a lot of time under the hood, climate models have been tweaked and forced to match historic warming observations pretty closely.  A prominent catastrophist and climate modeller finally asks the logical question:

One curious aspect of this result is that it is also well known [Houghton et al., 2001] that the same models that agree in simulating the anomaly in surface air temperature differ significantly in their predicted climate sensitivity. The cited range in climate sensitivity from a wide collection of models is usually 1.5 to 4.5 deg C for a doubling of CO2, where most global climate models used for climate change studies vary by at least a factor of two in equilibrium sensitivity.

The question is: if climate models differ by a factor of 2 to 3 in their climate sensitivity, how can they all simulate the global temperature record with a reasonable degree of accuracy. Kerr [2007] and S. E. Schwartz et al. (Quantifying climate change–too rosy a picture?, available at www.nature.com/reports/climatechange, 2007) recently pointed out the importance of understanding the answer to this question. Indeed, Kerr [2007] referred to the present work and the current paper provides the ‘‘widely circulated analysis’’ referred to by Kerr [2007]. This report investigates the most probable explanation for such an agreement. It uses published results from a wide variety of model simulations to understand this apparent paradox between model climate responses for the 20th century, but diverse climate model sensitivity.

One wonders how it took so long for supposedly trained climate scientists right in the middle of the modelling action to ask an obvious question that skeptics have been asking for years (though this particular guy will probably have his climate decoder ring confiscated for brining this up).  The answer seems to be that rather than using observational data, modellers simply make man-made forcing a plug figure, meaning that they set the man-made historic forcing number to whatever number it takes to make the output match history. 

Gee, who would have guessed?  Well, actually, I did, though I guessed the wrong plug figure.  I did, however, guess that one of the key numbers was a plug for all the models to match history so well:

I am willing to make a bet based on my long, long history of modeling (computers, not fashion).  My guess is that the blue band, representing climate without man-made effects, was not based on any real science but was instead a plug.  In other words, they took their models and actual temperatures and then said "what would the climate without man have to look like for our models to be correct."  There are at least four reasons I strongly suspect this to be true:

1. Every computer modeler in history has tried this trick to make their models of the future seem more credible.  I don’t think the climate guys are immune.
2. There is no way their models, with our current state of knowledge about the climate, match reality that well. 
3. The first time they ran their models vs. history, they did not match at all.  This current close match is the result of a bunch of tweaking that has little impact on the model’s predictive ability but forces it to match history better.  For example, early runs had the forecast run right up from the 1940 peak to temperatures way above what we see today.
4. The blue line totally ignores any of our other understandings about the changing climate, including the changing intensity of the sun.  It is conveniently exactly what is necessary to make the pink line match history.  In fact, against all evidence, note the blue band falls over the century.  This is because the models were pushing the temperature up faster than we have seen it rise historically, so the modelers needed a negative plug to make the numbers look nice.

Here is one other reason I know the models to be wrong:  The climate sensitivities quoted above of 1.5 to 4.5 degrees C are unsupportable by history.  In fact, this analysis shows pretty clearly that 1.2 is about the most one can derive for sensitivity from our past 120 years of experience, and even that makes the unreasonable assumption that all warming for the past century was due to CO2.

(cross-posted from Coyote Blog)

Kevin Drum links to a blog called Three-Toed Sloth in a post about why our climate future may be even worse than the absurdly cataclysmic forecasts we are getting today in the media.  Three-Toed Sloth advertises itself as "Slow Takes from the Canopy of the Reality-Based Community."  His post is an absolutely fabulous example how one can write an article where most every line is literally true, but the conclusion can still be dead wrong because one tiny assumption at the beginning of the analysis was incorrect  (In this case, "incorrect" may be generous, since the author seems well-versed in the analysis of chaotic systems.  A better word might be "purposely fudged to make a political point.")

He begins with this:

The climate system contains a lot of feedback loops.  This means that the ultimate response to any perturbation or forcing (say, pumping 20 million years of accumulated fossil fuels into the air) depends not just on the initial reaction, but also how much of that gets fed back into the system, which leads to more change, and so on.  Suppose, just for the sake of things being tractable, that the feedback is linear, and the fraction fed back is f.  Then the total impact of a perturbation I is

J + Jf + Jf² + Jf³ + …

The infinite series of tail-biting feedback terms is in fact a geometric series, and so can be summed up if f is less than 1:

J/(1-f)

So far, so good.  The math here is entirely correct.  He goes on to make this point, arguing that if we are uncertain about  f, in other words, if there is a distribution of possible f‘s, then the range of the total system gain 1/(1-f) is likely higher than our intuition might first tell us:

If we knew the value of the feedback f, we could predict the response to perturbations just by multiplying them by 1/(1-f) — call this G for "gain".  What happens, Roe and Baker ask, if we do not know the feedback exactly?  Suppose, for example, that our measurements are corrupted by noise — or even, with something like the climate, that f is itself stochastically fluctuating.  The distribution of values for f might be symmetric and reasonably well-peaked around a typical value, but what about the distribution for G?  Well, it’s nothing of the kind.  Increasing f just a little increases G by a lot, so starting with a symmetric, not-too-spread distribution of f gives us a skewed distribution for G with a heavy right tail.

Again all true, with one small unstated proviso I will come back to.  He concludes:

In short: the fact that we will probably never be able to precisely predict the response of the climate system to large forcings is so far from being a reason for complacency it’s not even funny.

Actually, I can think of two unstated facts that undermine this analysis.  The first is that most catastrophic climate forecasts you see utilize gains in the 3x-5x range, or sometimes higher (but seldom lower).  This implies they are using an f of between .67 and .80.  These are already very high numbers for any natural process.  If catastrophist climate scientists are already assuming numbers at the high end of the range, then the point about uncertainties skewing the gain disproportionately higher are moot.  In fact, we might tend to actually draw the reverse conclusion, that the saw cuts both ways.  His analysis also implies that small overstatements of f when the forecasts are already skewed to the high side will lead to very large overstatements of Gain.

But here is the real elephant in the room:  For the vast, vast majority of natural processes, f is less than zero.  The author has blithely accepted the currently unproven assumption that the net feedback in the climate system is positive.  He never even hints at the possibility that that f might be a negative feedback rather than positive, despite the fact that almost all natural processes are dominated by negative rather than positive feedback.  Assuming without evidence that a random natural process one encounters is dominated by negative feedback is roughly equivalent to assuming the random person you just met on the street is a billionaire.  It is not totally out of the question, but it is very, very unlikely.

When one plugs an f in the equation above that is negative, say -0.3, then the gain actually becomes less than one, in this case about 0.77.  In a negative feedback regime, the system response is actually less than the initial perturbation because forces exist in the system to damp the initial input.

The author is trying to argue that uncertainty about the degree of feedback in the climate system and therefore the sensitivity of the system to CO2 changes does not change the likelihood of the coming "catastrophe."  Except that he fails to mention that we are so uncertain about the feedback that we don’t even know its sign.  Feedback, or f, could be positive or negative as far as we know.  Values could range anywhere from -1 to 1.  We don’t have good evidence as to where the exact number lies, except to observe from the relative stability of past temperatures over a long time frame that the number probably is not in the high positive end of this range.  Data from climate response over the last 120 years seems to point to a number close to zero or slightly negative, in which case the author’s entire post is irrelevant.   In fact, it turns out that the climate scientists who make the news are all clustered around the least likely guesses for f, ie values greater than 0.6.

Incredibly, while refusing to even mention the Occam’s Razor solution that f is negative, the author seriously entertains the notion that f might be one or greater.  For such values, the gain shoots to infinity and the system goes wildly unstable  (nuclear fission, for example, is an f>1 process).  In an f>1 world, lightly tapping the accelerator in our car would send us quickly racing up to the speed of light.  This is an ABSURD assumption for a system like climate that is long-term stable over tens of millions of years.  A positive feedback f>=1 would have sent us to a Venus-like heat or Mars-like frigidity eons ago.

A summary of why recent historical empirical data implies low or negative feedback is here.  You can learn more on these topics in my climate video and my climate book.  To save you the search, the section of my movie explaining feedbacks, with a nifty live demonstration from my kitchen, is in the first three and a half minutes of the clip below:

At the core of my climate video is a reality check on catastrophic warming forecasts, which demonstrates, as summarized in this post, that warming over the next century can’t be much more than a degree if one takes history as a guide.  The Reference Frame has a nice summary:

Well, we will probably surpass 560 ppm of CO2. Even if you believe that the greenhouse effect is responsible for all long-term warming, we have already realized something like 1/2 (40-75%, depending on the details of your calculation) of the greenhouse effect attributed to the CO2 doubling from 280 ppm to 560 ppm. It has led to 0.6°C of warming. It is not a hard calculation that the other half is thus expected to lead to an additional 0.6°C of warming between today and 2100.

Other derivations based on data that I consider rationally justified lead to numbers between 0.3°C and 1.4°C for the warming between 2000 and 2100. Clearly, one needs to know some science here. Laymen who are just interested in this debate but don’t study the numbers by technical methods are likely to offer nothing else than random guesses and prejudices, regardless of their "ideological" affiliation in the climate debate.

Anthony Watt has a pointer to a nice presentation in four parts on YouTube by Bob Carter made at a public forum in Australia.  He walks through some of the skeptics’ issues with catastrophic man-made global warming theory.

What caught my attention, though, were the pictures Mr. Carter shows in his presentation about about 1:30 into part 4.  Because I took the pictures he shows, down at the University of Arizona, as part of Mr. Watts project to document temperature measurement stations.  Kind of cool to see someone I don’t know in a country I have (sadly) never visited using a small bit of my work.  Part 4 is below, but you can find links to all four parts here.

Coming soon, my own home-brewed video effort on global warming and climate.  Right now it runs about 45 minutes, and I’m in the editing stages, mainly trying to make the narration match what is on the screen.

Cross-posted from Coyote Blog

It turns out to be quite easy to do a simple but fairly robust reality check of global warming forecasts, even without knowing what a "Watt" or a "forcing" is.   Our approach will be entirely empirical, based on the last 100 years of climate history.  I am sensitive that we skeptics not fall into the 9/11 Truther syndrome of arguing against a coherent theory from isolated anomalies.  To this end, my approach here is holistic and not anomaly driven.  What we will find is that, extrapolating from history, it is almost impossible to get warming numbers as high as those quoted by global warming alarmists.

Climate Sensitivity

The one simple concept you need to understand is "climate sensitivity."  As used in most global warming literature, climate sensitivity is the amount of global warming that results from a doubling in atmospheric CO2 concentrations.   Usually, when this number is presented, it refers to the warming from a doubling of CO2 concentrations since the beginning of the industrial revolution.  The pre-industrial concentration is generally accepted as 280ppm (0.028% of the atmosphere) and the number today is about 380ppm, so a doubling would be to 560ppm.

As a useful, though not required, first step before we begin, I encourage you to read the RealClimate simple "proof" for laymen that the climate sensitivity is 3ºC, meaning the world will warm 3 degrees C with a doubling of CO2 concentrations from their pre-industrial level.  Don’t worry if you don’t understand the whole description, we are going to do it a different, and I think more compelling, way (climate scientists are a bit like the Wizard of Oz — they are afraid if they make things too simple someone might doubt they are a real wizard).  3ºC is a common number for sensitivity used by global warming hawks, though it is actually at the low end of the range that the UN IPCC arrived at in their fourth report.  The IPCC (4th report, page 798) said that the expected value is between 3ºC and 4ºC and that there was a greater chance the sensitivity was larger than 6ºC than that it was 1.5ºC or less.  I will show you why I think it is extraordinarily unlikely that the number is greater even than 1.5ºC.

Our Approach

We are going to derive the sensitivity (actually a reasonable range for sensitivity) for ourselves in three steps.  First, we will do it a simple way.  Then, we will do it a slightly harder but more accurate way.  And third, we will see what we would have to assume to get a number anywhere near 3ºC.  Our approach will be entirely empirical, using past changes in CO2 and temperature to estimate sensitivity.  After all, we have measured CO2 going up by about 100 ppm.  That is about 36% of the way towards a doubling from 280 to 560.  And, we have measured temperatures — and though there are a lot of biases in these temperature measurements, these measurements certainly are better than our guesses, say, of temperatures in the last ice age.  Did you notice something odd, by the way, in the RealClimate derivation?  They never mentioned measured sensitivities in the last 100 years — they jumped all the way back to the last ice age.  I wonder if there is a reason for that?

A First Approximation

OK, let’s do the obvious.  If we have experienced 36% of a doubling, then we should be able to take the historic temperature rise from CO2 for the same period and multiply it by 2.8 (that’s just reciprocal of 36%) and derive the temperature increase we would expect for a full doubling.

The problem is that we don’t know the historic temperature rise solely form CO2.  But we do know how to bound it.  The IPCC and most global warming hawks place the warming since 1900 at about 0.6ºC.  Since no one attributes warming before 1900 to man-made CO2  (it did warm, but this is attributed to natural cyclical recovery from the little ice age) then the maximum historic man-made warming is 0.6ºC.  In fact, all of that warming is probably not from CO2.  Some probably is from continued cyclical warming out of the little ice age.  Some, I believe strongly, is due to still uncorrected biases, particularly of urban heat islands, in surface temperature data. 

But let’s for a moment attribute, unrealistically, all of this 0.6ºC to man-made CO2 (this is in fact what the IPCC does in their report).   This should place an upper bound on the sensitivity number.  Taking 0.6ºC times 2.8 yields an estimated  climate sensitivity of  1.7ºC.  Oops.  This is about half of the RealClimate number or the IPCC number! And if we take a more realistic number for man-made historic warming as 0.4ºC, then we get a sensitivity of 1.1ºC.  Wow, that’s a lot lower! We must be missing something important!  It turns out that we are, in this simple analysis, missing something important.  But taking it into account is going to push our sensitivity number even lower.

A Better Approximation

What we are missing is that the relation between CO2 concentration and warming is not linear, as implied in our first approximation.  It is a diminishing return.  This means that the first 50 ppm rise in CO2 concentrations causes more warming than the next 50 ppm, etc.  This effect has often been compared to painting a window.  The first coat of paint blocks out a lot of light, but the window is still translucent.  The next coat blocks out more light, but not as much as the first.  Eventually, subsequent coats have no effect because all the light is already blocked.  CO2 has a similar effect on warming.  It only absorbs certain wavelengths of radiation returning to space from earth.  Once the absorption of those wavelengths is saturated, extra CO2 will do almost nothing. (update:  By the way, this is not some skeptic’s fantasy — everyone in climate accepts this fact).

So what does this mean in English?  Well, in our first approximation, we assumed that 36% of a CO2 doubling would yield 36% of the temperature we would get in a doubling.  But in reality, since the relationship is a diminishing return, the first 36% of a CO2 doubling will yield MORE than 36% of the temperature increase you get for a doubling.  The temperature increase is front-loaded, and diminishes going forward.   An illustration is below, with the linear extrapolation in red and the more realistic decreasing exponential extrapolation in blue.

The exact shape and equation of this curve is not really known, but we can establish a reasonable range of potential values.  For any reasonable shapes of this curve, 36% of a CO2 doubling (where we are today) equates to from 43% to 63% of the final temperature increase over a doubling.  This would imply that a multiplier between 2.3 and 1.6 for temperature extrapolation  (vs. 2.8 derived above for the straight linear extrapolation above) or a climate sensitivity of 1.4ºC to 1.0ºC if man-made historic warming was 0.6ºC and a range of 0.9ºC to 0.6ºC for a man-made historic warming of 0.4ºC.  I tend to use the middle of this range, with a multiplier of about 1.9 and a man-made historic warming of 0.5ºC to give a expected sensitivity of 0.95ºC, which we can round to 1ºC. 

This is why you will often hear skeptics cite numbers closer to 1ºC rather than 3ºC for the climate sensitivity.   Any reasonable analysis of actual climate experience over the last 100 years yields a sensitivity much closer to 1ºC than 3ºC.  Most studies conducted before the current infatuation with showing cataclysmic warming forecasts came up with this same 1ºC, and peer-reviewed work is still coming up with this same number. 

So what does this mean for the future?  Well, to predict actual temperature increases from this sensitivity, we would have to first create a CO2 production forecast and, you guessed it, global warming hawks have exaggerated that as well.  The IPCC says we will hit the full doubling to 560ppm around 2065 (Al Gore, incredibly, says we will hit it in the next two decades).  This means that with about 0.5C behind us, and a 3 sensitivity, we can expect 2.5C more warming in the next 60 years.  Multiply that times exaggerated negative effects of warming, and you get instant crisis.

However, since actual CO2 production is already below IPCC forecasts, we might take a more reasonable date of 2080-2100 for a doubling to 560.  And, combining this with our derived sensitivity of 1ºC (rather than RealClimate’s 3ºC) we will get 0.5C more warming in the next 75-100 years.  This is about the magnitude of warming we experienced in the last century, and most of us did not even notice.

I know you are scratching you head and wondering what trick I pulled to get numbers so much less than the scientific "consensus."  But there is no trick, all my numbers are empirical and right out of the IPCC reports.  In fact, due to measurement biases and other climate effects that drive warming, I actually think the historic warming from CO2 and thus the sensitivity is even lower, but I didn’t want to confuse the message. 

So what are climate change hawks assuming that I have not included?  Well, it turns out they add on two things, neither of which has much empirical evidence behind it.  It is in fact the climate hawks, not the skeptics, that need to argue for a couple of anomalies to try to make their case.

Is Climate Dominated by Positive Feedback?

Many climate scientists argue that there are positive feedbacks in the climate system that tend to magnify and amplify the warming from CO2.  For example, a positive feedback might be that hotter climate melts sea ice and glaciers, which reduces the reflectiveness of the earth’s surface, which causes more sunlight to be absorbed, which warms things further.  A negative feedback might be that warmer climate vaporizes more water which forms more clouds which blocks sunlight and cools the earth. 

Climate scientists who are strong proponents of catastrophic man-made warming theory assume that the climate is dominated by positive feedbacks.  In fact, my reading of the IPCC report says that the climate "consensus" is that net feedback in the climate system is positive and tends to add 2 more degrees of temperature for every one added from CO2.  You might be thinking – aha – I see how they got a sensitivity of 3ºC:  Your 1ºC plus 2ºC in feedback equals 3ºC. 

But there is a problem with that.  In fact, there are three problems with this.  Here they are:

1. We came up with our 1ºC sensitivity empirically.  In other words, we observed a 100ppm past CO2 increase leading to 0.5ºC measured temperature increase which implies 1ºC sensitivity.  But since this is empirical, rather than developed from some set of forcings and computer models, then it should already be net of all feedbacks.  If there are positive feedbacks in the system, then they have been operating and should be part of that 1ºC.
2. There is no good scientific evidence that there is a large net positive feedback loop in climate, or even that the feedback is net positive at all.  There are various studies, hypotheses, models, etc., but no proof at all.  In fact, you can guess this from our empirical data.  History implies that there can’t be any large positive feedbacks in the system or else we would have observed higher temperatures historically.  In fact, we can go back in to the distant historical record (in fact, Al Gore showed the chart I am thinking of in An Inconvenient Truth) and find that temperatures have never run away or exhibited any sort of tipping point effect.
3. The notion that a system like climate, which has been reasonably stable for millions of years, is dominated by positive feedback should offend the intuition of any scientist.  Nature is dominated in large part by negative feedback processes.  Positive feedback processes are highly unstable, and tend to run away to a distant endpoint.  Nuclear fission, for example, is a positive feedback process

Do aerosols and dimming imply a higher sensitivity?

Finally, the last argument that climate hawks would employ is that anthropogenic effects, specifically emission of SO2 aerosols and carbon black, have been reflecting sunlight and offsetting the global warming effect.  But, they caution, once we eliminate these pollutants, which we have done in the West (only to be offset in China and Asia) temperatures will no longer be suppressed and we will see the full extent of warming.

First, again, no one really has any clue the magnitude of this effect, or even if it is an effect at all.  Second, its reach will tend to be localized over industrial areas (since their presence in the atmosphere is relatively short-lived), whereas CO2 acts worldwide.  If these aerosols and carbon black are concentrated say over 20% of the land surface of the world, this means they are only affecting the temperature over 5% of the total earth’ s surface.  So its hard to argue they are that significant.

However, let’s say for a moment this effect does exist.  How large would it have to be to argue that a 3.0ºC climate sensitivity is justified by historical data?  Well, taking 3.0ºC and dividing by our derived extrapolation multiplier of 1.9, we get required historic warming due to man’s efforts of 1.6ºC.  This means that even if all past 0.6ºC of warming is due to man (a stretch), then aerosols must be suppressing a full 1ºC of warming.   I can’t say this is impossible, but it is highly unlikely and certainly absolutely no empirical evidence exists to support any number like this. Particularly since dimming effects probably are localized, you would need as much as 20ºC suppression in these local areas to get a 1ºC global effect.  Not very likely.

Why the number might even be less

Remember that when we calculated sensitivity, we needed the historical warming due to man’s CO2.  A simple equation for arriving at this number is:

Warming due to Man’s CO2 = Total Historic Measured Warming – Measurement Biases – Warming from other Sources + Warming suppressed by Aerosols

This is why most skeptics care if surface temperature measurements are biased upwards or if the sun is increasing in intensity.  Global warming advocates scoff and say that these effects don’t undermine greenhouse gas theory.  And they don’t.  I accept greenhouse gases cause some warming.  BUT, the more surface temperature measurements are biased upwards and the more warming is being driven by non-anthropogenic sources, the less that is being caused by man.  And, as you have seen in this post, the less warming caused by man historically means less that we will see in the future.  And while global warming hawks want to paint skeptics as "deniers", we skeptics want to argue the much more interesting question "Yes, but how much is the world warming, and does this amount of warming really justify the costs of abatement, which are enormous."

As always, you can find my Layman’s Guide to Skepticism about Man-made Global Warming here.  It is available for free in HTML or pdf download, or you can order the printed book that I sell at cost.  My other recent posts about climate are here.