Nearly a decade ago, when I first started looking into climate science, I began to suspect the modelers were using what I call a “plug” variable.  I have decades of experience in market and economic modeling, and so I am all too familiar with the temptation to use one variable to “tune” a model, to make it match history more precisely by plugging in whatever number is necessary to make the model arrive at the expected answer.

When I looked at historic temperature and CO2 levels, it was impossible for me to see how they could be in any way consistent with the high climate sensitivities that were coming out of the IPCC models.  Even if all past warming were attributed to CO2  (a heroic acertion in and of itself) the temperature increases we have seen in the past imply a climate sensitivity closer to 1 rather than 3 or 5 or even 10  (I show this analysis in more depth in this video).

My skepticism was increased when several skeptics pointed out a problem that should have been obvious.  The ten or twelve IPCC climate models all had very different climate sensitivities — how, if they have different climate sensitivities, do they all nearly exactly model past temperatures?  If each embodies a correct model of the climate, and each has a different climate sensitivity, only one (at most) should replicate observed data.  But they all do.  It is like someone saying she has ten clocks all showing a different time but asserting that all are correct (or worse, as the IPCC does, claiming that the average must be the right time).

The answer to this paradox came in a 2007 study by climate modeler Jeffrey Kiehl.  To understand his findings, we need to understand a bit of background on aerosols.  Aerosols are man-made pollutants, mainly combustion products, that are thought to have the effect of cooling the Earth’s climate.

What Kiehl demonstrated was that these aerosols are likely the answer to my old question about how models with high sensitivities are able to accurately model historic temperatures.  When simulating history, scientists add aerosols to their high-sensitivity models in sufficient quantities to cool them to match historic temperatures.  Then, since such aerosols are much easier to eliminate as combustion products than is CO2, they assume these aerosols go away in the future, allowing their models to produce enormous amounts of future warming.

Specifically, when he looked at the climate models used by the IPCC, Kiehl found they all used very different assumptions for aerosol cooling and, most significantly, he found that each of these varying assumptions were exactly what was required to combine with that model’s unique sensitivity assumptions to reproduce historical temperatures.  In my terminology, aerosol cooling was the plug variable.

So now we can turn to Kaufman, summarized in this article and with full text here.  In the context of the Kiehl study discussed above, Kaufman is absolutely nothing new.

Kaufmann et al declare that aerosol cooling is “consistent with” warming from manmade greenhouse gases.

In other words, there is some value that can be assigned to aerosol cooling that offsets high temperature sensitives to rising CO2 concentrations enough to mathematically spit out temperatures sortof kindof similar to those over the last decade.  But so what?  All Kaufman did is, like every other climate modeler, find some value for aerosols that plugged temperatures to the right values.

Let’s consider an analogy.  A big Juan Uribe fan (plays 3B for the SF Giants baseball team) might argue that the 2010 Giants World Series run could largely be explained by Uribe’s performance.  They could build a model, and find out that the Giants 2010 win totals were entirely consistent with Uribe batting .650 for the season.

What’s the problem with this logic?  After all, if Uribe hit .650, he really would likely have been the main driver of the team’s success.  The problem is that we know what Uribe hit, and he batted under .250 last year.  When real facts exist, you can’t just plug in whatever numbers you want to make your argument work.

But in climate, we are not sure what exactly the cooling effect of aerosols are.  For related coal particulate emissions, scientists are so unsure of their effects they don’t even know the sign (ie are they net warming or cooling).  And even if they had a good handle on the effects of aerosol concentrations, no one agrees on the actual numbers for aerosol concentrations or production.

And for all the light and noise around Kaufman, the researchers did just about nothing to advance the ball on any of these topics.  All they did was find a number that worked, that made the models spit out the answer they wanted, and then argue in retrospect that the number was reasonable, though without any evidence.

Beyond this, their conclusions make almost no sense.  First, unlike CO2, aerosols are very short lived in the atmosphere – a matter of days rather than decades.  Because of this, they are poorly mixed, and so aerosol concentrations are spotty and generally can be found to the east (downwind) of large industrial complexes (see sample map here).

Which leads to a couple of questions.  First, if significant aerosol concentrations only cover, say, 10% of the globe, doesn’t that mean that to get a  0.5 degree cooling effect for the whole Earth, there must be a 5 degree cooling effect in the affected area.   Second, if this is so (and it seems unreasonably large), why have we never observed this cooling effect in the regions with high concentrations of manmade aerosols.  I understand the effect can be complicated by changes in cloud formation and such, but that is just further reasons we should be studying the natural phenomenon and not generating computer models to spit out arbitrary results with no basis in observational data.

Judith Currey does not find the study very convincing, and points to this study by Remer et al in 2008 that showed no change in atmospheric aerosol depths through the heart of the period of supposed increases in aerosol cooling.

So the whole basis for the study is flawed – its based on the affect of increasing aerosol concentrations that actually are not increasing.  Just because China is producing more does not apparently mean there is more in the atmosphere – it may be reductions in other areas like the US and Europe are offsetting Chinese emissions or that nature has mechanisms for absorbing and eliminating the increased emissions.

By the way, here was Curry’s response, in part:

This paper points out that global coal consumption (primarily from China) has increased significantly, although the dataset referred to shows an increase only since 2004-2007 (the period 1985-2003 was pretty stable).  The authors argue that the sulfates associated with this coal consumption have been sufficient to counter the greenhouse gas warming during the period 1998-2008, which is similar to the mechanism that has been invoked  to explain the cooling during the period 1940-1970.

I don’t find this explanation to be convincing because the increase in sulfates occurs only since 2004 (the solar signal is too small to make much difference).  Further, translating regional sulfate emission into global forcing isnt really appropriate, since atmospheric sulfate has too short of an atmospheric lifetime (owing to cloud and rain processes) to influence the global radiation balance.

Curry offers the alternative explanation of natural variability offsetting Co2 warming, which I think is partly true.  Though Occam’s Razor has to force folks at some point to finally question whether high (3+) temperature sensitivities to CO2 make any sense.  Seriously, isn’t all this work on aerosols roughly equivalent to trying to plug in yet more epicycles to make the Ptolemaic model of the universe continue to work?

Postscript: I will agree that there is one very important affect of the ramp-up of Chinese coal-burning that began around 2004 — the melting of Arctic Ice.  I strongly believe that the increased summer melts of Arctic ice are in part a result of black carbon from Asia coal burning landing on the ice and reducing its albedo (and greatly accelerating melt rates).   Look here when Arctic sea ice extent really dropped off, it was after 2003.    Northern Polar temperatures have been fairly stable in the 2000′s (the real run-up happened in the 1990′s).   The delays could be just inertia in the ocean heating system, but Arctic ice melting sure seems to correlate better with black carbon from China than it does with temperature.

I don’t think there is anything we could do with a bigger bang for the buck than to reduce particulate emissions from Asian coal.  This is FAR easier than CO2 emissions reductions — its something we have done in the US for nearly 40 years.

As a reminder, I adhere to what I call the weak anthropogenic theory of global warming — that the Earth’s sensitivity to CO2, net of all feedback effects, is 1C per doubling of CO2 concentrations or less, and that while man may therefore be contributing to global warming with his CO2 (not to mention his land use and other practices) the net effect falls far short of catastrophic.

While in the media, alarmists want to imply that the their conclusions about climate sensitivity are based on a century of observation, but this is not entirely true.  Certainly we have over a century of temperature measurements, but only a small part of this history is consistent with the strong anthropogenic theory.  In fact, I observed in my video is that the entire IPCC case for a high climate sensitivity to CO2 is based on just 20 years of history, from about 1978 to 1998.

Here are the global temperatures in the Hadley CRUT3 data base, which is the primary data from which the IPCC worked (hat tip Junk Science Global Warming at a Glance)  click to enlarge

Everything depends on how one counts it, but during the period of man-made CO2 creation, there are really just two warming periods, if we consider the time from 1910 to 1930 just a return to the mean.

• 1930-1952, where temperatures spiked about a half a degree and ended 0.2-0.3 higher than the past trend
• 1978-1998, where temperatures rose about a half a degree, and have remained at that level since

Given that man-made CO2 output did not really begin in earnest until after 1950 (see the blue curve of atmospheric CO2 levels on the chart), even few alarmists will attribute the runup in temperatures from 1930-1952 (a period of time including the 1930′s Dust Bowl) to anthropogenic CO2.  This means that the only real upward change in temperatures that could potentially be blamed on man-made CO2 occurred from 1978-1998.

This is a very limited amount of time to make sweeping statements about climate change causation, particularly given the still infant-level knowledge of climate science.  As a result, since 1970, skeptics and alarmists have roughly equal periods of time where they can make their point about temperature causation (e.g. 20 years of rising CO2 and flat temperatures vs. 20 years of rising CO2 and rising temperatures).

This means that in the last 40 years, both skeptics and alarmists must depend on other climate drivers to make their case  (e.g. skeptics must point to other natural factors for the run-up in 1978-1998, while alarmists must find natural effects that offset or delayed warming in the decade either side of this period).  To some extent, this situation slightly favors skeptics, as skeptics have always been open to natural effects driving climate while alarmists have consistently tried to downplay natural forcing changes.

I won’t repeat all the charts, but starting around chart 48 of this powerpoint deck (also in the video linked above) I present some alternate factors what may have contributed, along with greenhouse gases, to the 1978-1998 warming (including two of the strongest solar cycles of the century and a PDO warm period nearly exactly matching these two decades).

Postscript: Even if the entire 0.7C or so temperature increase in the whole of the 20th century is attributed to manmade CO2, this still implies a climate sensitivity FAR below what the IPCC and other alarmists use in their models.   Given about 44% of a doubling since the industrial revolution began in CO2 concentrations, this would translate into a temperature sensitivity of 1.3C  (not a linear extrapolation, the relationship is logarithmic).

This is why alarmists must argue that not only has all the warming we have seen been due to CO2 ( heroic assumption in and of itself) but that there are additional effects masking or hiding the true magnitude of past warming.  Without these twin, largely unproven assumptions, current IPCC “consensus” numbers for climate sensitivity would be absurdly high.  Again, I address this in more depth in my video.

the CEA took a conventional Keynesian-style macroeconomic model and used those set of equations to estimate the effect the stimulus should have had.  Essentially, the model offers an estimate of the policy’s effect, conditional on the model being a correct description of the world.  But notice that this exercise is not really a measurement based on what actually occurred.  Rather, the exercise is premised on the belief that the model is true, so no matter how bad the economy got, the inference is that it would have been even worse without the stimulus.  Why?  Because that is what the model says.  The validity of the model itself is never questioned.

Does this sound like climate science or what?  The same models that are used to predict future temperature increases are used to decide how much of past warming was dues to Co2 and how much was due to natural effects.  Here is the retrospective IPCC chart which assigns more than 100% of post-1950 warming to CO2 (since the blue “natural forcings” is shown to go down, see more here)

Here is the stimulus version, showing flat employment, but positing that the stimulus created jobs because employment “would have gone down without it” (sound familiar?)

This kind of retrospective look at causality has the look of science but in fact is nothing of the sort, and can be not much more than guesses laundered to look like facts.

But this may in fact be worse than guessing.  In both cases, these graphs are drawn by folks who think they know the answer (in the first case that CO2 caused all warming, in the second that the stimulus created millions of jobs).  Since in both cases the lower “without” case (either without CO2 or without stimulus) is horrendously, almost impossible to derive and totally impossible to measure, there is good reason to believe it is merely a plug, fixed in value to get the answer they want.  But if I plugged it just on the back of an envelope, everyone would call me out for it, so I plug it in an arcane model where numerous inputs can be tweaked to get different results, to avoid this kind of unwanted scrutiny.

Readers of climate sites will also recognize this criticism of Obama’s self-serving stimulus analysis

Moreover, the fact that other organizations simulating similar models come to similar conclusions is no evidence about the validity of the model’s simulations.  It only tells you the CEA staff did not commit egregious programming errors when running their computer simulations.

Sounds like the logic behind the hockey stick spaghetti graphs, no?

Again, there almost certainly is a warming trend since 1850, and some of that trend is probably due to manmade CO2, but sensitivities in most forecasts that get attention in the media are way too high.  A tenth of a degree C per decade over the next 100 years from manmade CO2 seems a reasonable planning number.

Spencer also looks at the global numbers here.

Edward Long took a selection of US measurement points from the NCDC master list and chose 48 rural and 48 urban locations (one for each of the lower-48 states).  While I would like to see a test to ensure no cherry-picking went on, his results are pretty telling:

More at Anthony Watt, who has this chart from the study:

The Reference Frame has more analysis as well.

If this data is representative of the whole data set, we see two phenomena that should not be news to readers of this site:

• Inclusion of biased urban data points may be contributing as much as 5/6 of the warming signal in the test period
• The homogenization and adjustment process, which is supposed to statistically correct for biases, seems to be correcting the wrong way, increasing clean sites to matched biased ones rather than vice versa  (something I discussed years ago here)

The homogenization process has always bothered me.  It is probably the best we can do if we don’t know which of two conflicting measurements are likely to be biased, but it makes no sense in this case, as we have a fair amount of confidence the rural location is likely better than the urban.

Let’s say you had two compasses to help you find north, but the compasses are reading incorrectly.  After some investigation, you find that one of the compasses is located next to a strong magnet, which you have good reason to believe is strongly biasing that compass’s readings.  In response, would you

1. Average the results of the two compasses and use this mean to guide you, or
2. Ignore the output of the poorly sited compass and rely solely on the other unbiased compass?

Most of us would quite rationally choose #2.

Most climate data bases go with approach #1.

Let’s remind everyone why this matters:  We are not going to eliminate past warming.  The Earth was at one of its coldest periods in 5000 years through about 1800 and it has gotten warmer since.   The reason it matter is twofold:

• The main argument for anthropogenic causes of warming is that the rise of late (particularly 1978 – 1998)  has been so steep and swift that it couldn’t be anything else.  This was always an absurd argument, because we have at least two periods in the last 150 years prior to most of our fossil fuel combustion where temperature rises were as fast and steep as 1978-1998.  But if temperatures did not rise as much as we thought, this argument is further gutted.
• High sensitivity climate models have always had trouble back-casting history.  Models that predict 5C of warming with a doubling have a difficult time replicating past warming of 0.6C for 40% of a doubling.  If the 0.6C is really 0.3C, then someone might actually raise their hand and observe that the emperor has not clothes – ie, that based on history, high sensitivity models make no sense.

“Well, if it is but a myth, and the global scientific community is involved in some sort of conspiracy, why is it then that around the globe sea levels are more than six inches higher than they were 100 years ago?

“This isn’t an opinion – it is a fact.”

He added: “And, ladies and gentlemen please be in no doubt that the evidence of long-term and potentially irreversible changes to our world is utterly overwhelming.”

Here is the deal with sea levels.  Yes, they were rising in 2009.  And they were rising in 2000.  And they were rising in 1950.  And they were rising in 1900.  And they were rising in 1850.   In fact, sea levels have been rising (due to thermal expansion of water and perhaps some melting land ice**) since the end of the little ice age  (and longer, see WUWT)

In fact, I would argue that this extended sea level rise helps disprove, rather than prove, the strong anthropogenic hypothesis.   The influence of manmade CO2 had to be small from 1850 to 1900 or even 1950.  Therefore, for the 1950-2000 sea level rise to be due to man, it means the natural warming had to stop at the exact same moment that anthropogenic effects took over.  Occam’s Razor says a better answer is that the end of the little ice age around 1800 has led to a general recovery of temperatures ever since.  We see the exact same pattern in glaciers melting

So many people are obsessed over whether or not current temperatures are the highest in the last 100o years or not, they forget that the temperatures in the little ice age were in fact lower than at any time in perhaps the last 5000 years.  It was very cold.

Postscript: By the way, I love the carbon footprint for me, but not for thee angle of the Prince Charles story:

Charles spoke after arriving in Manchester by Royal Train pulled by a coal-fired steam locomotive, named the Tornado, which was rebuilt from a 1948 design.

** Footnote: We know glaciers around the world have retreated since 1850, as shown above, but 90% of the world’s land ice is in Antarctica and we don’t fully understand what has happened there.  Some climatologists believe that warming weather actually increases the ice pack in Antarctica because it never will cause much melting but it increases  snowfall.

The paper was based on a test of a widely-used climate model on the mid-Pleiocene warm period, 3 million years ago, when the Earth warmed in response to natural processes. Cores drilled from ocean sediment provide some evidence for atmospheric carbon levels and temperature at the time.

The team found that at that era, although CO2 levels were close to today’s 388 parts per million by volume, global temperature was 3 C° (5.5 F°) warmer than today. The paper assumes – without evidence – that the difference can only be fully explained by the long-term loss of ice sheets and changes in vegetation that caused the Earth’s surface to absorb more solar radiation. One of the authors said that today’s CO2 concentration of 388 ppmv might already be too high to prevent more than 2 C° (3.5 F°) of warming compared with pre-industrial times – the limit agreed as an aspiration by the recent Copenhagen accord.

The authors are concluding that there is therefore another 3C of warming we should see over time due to our current CO2 levels that has just not showed up yet because slow-response-time feedbacks like ice melting / albedo changes haven’t fully come into play.

I presume you see the problem.  This conclusion can only be drawn if either

1.  We know the value of every other climate forcing that was in play 3 million years ago, and know them to be identical to their values today, such that the only changed variable in the temperature system between then and now is CO2.  Of course, this is absurd — we can’t possibly know all the other forcings from 3 million years ago (we argue about what they are today) and there is a very low probability they were all of the same value as today to set up a nice controlled experiment.  – OR -

2.  We assume that the only major driver of climate, the one that dominates and makes all others irrelevant, is CO2.  This is not only not proven, it is not even reasonably true.

These guys, as is so often the case in climate, are assuming their conclusion.  “If we assume that CO2 is the primary driver of climate, then sensitivity of the climate to CO2 is high.”  Duh.

June 4, 2003 Briffa to Cook 1054748574
On June 4, 2003, Briffa, apparently acting as editor (presumably for Holocene), contacted his friend Ed Cook of Lamont-Doherty in the U.S. who was acting as a reviewer telling him that “confidentially” he needed a “hard and if required extensive case for rejecting”, in the process advising Cook of the identity and recommendation of the other reviewer. There are obviously many issues involved in the following as an editor instruction:

From: Keith Briffa
To: Edward Cook
Subject: Re: Review- confidential REALLY URGENT
Date: Wed Jun 4 13:42:54 2003

I am really sorry but I have to nag about that review – Confidentially I now need a hard and if required extensive case for rejecting - to support Dave Stahle’s and really as soon as you can. Please
Keith

Cook to Briffa, June 4, 2003
In a reply the same day, Cook told Briffa about a review for Journal of Agricultural, Biological, and Environmental Sciences of a paper which, if not rejected, could “really do some damage”. Cook goes on to say that it is an “ugly” paper to review because it is “rather mathematical” and it “won’t be easy to dismiss out of hand as the math appears to be correct theoretically”. Here is the complete email:

Hi Keith,
Okay, today. Promise! Now something to ask from you. Actually somewhat important too. I got a paper to review (submitted to the Journal of Agricultural, Biological, and Environmental Sciences), written by a Korean guy and someone from Berkeley, that claims that the method of reconstruction that we use in dendroclimatology (reverse regression) is wrong, biased, lousy, horrible, etc. They use your Tornetrask recon as the main whipping boy. I have a file that you gave me in 1993 that comes from your 1992 paper. Below is part of that file. Is this the right one? Also, is it possible to resurrect the column headings? I would like to play with it in an effort to refute their claims. If published as is, this paper could really do some damage. It is also an ugly paper to review because it is rather mathematical, with a lot of Box-Jenkins stuff in it. It won’t be easy to dismiss out of hand as the math appears to be correct theoretically, but it suffers from the classic problem of pointing out theoretical deficiencies, without showing that their improved inverse regression method is actually better in a practical sense. So they do lots of monte carlo stuff that shows the superiority of their method and the deficiencies of our way of doing things, but NEVER actually show how their method would change the Tornetrask reconstruction from what you produced. Your assistance here is greatly appreciated. Otherwise, I will let Tornetrask sink into the melting permafrost of northern Sweden (just kidding of course).
Cheers,
Ed

A couple of observations

1. For guys who supposedly represent the consensus science of tens of thousands of scientists, these guys sure have a bunker mentality
2. I would love an explanation of how math can have theoretical deficiencies but be better in a practical sense.  In the practical sense of … giving the answer one wants?
3. The general whitewash answer to all the FOIA obstructionism is that these are scientists doing important work not to be bothered by nutcases trying to waste their time.  But here is exactly the hypocrisy:  The email author says that some third party’s study is deficient because he can’t demonstrate how his mathematical approach might change the answer the hockey team is getting.  But no third party can do this because the hockey team won’t release the data needed for replication.  This kind of data – to check the mathematical methodologies behind the hockey stick regressions – is exactly what Steve McIntyre et al have been trying to get.  Ed Cook is explaining here, effectively, why release of this data is indeed important
4. At the very same time these guys are saying to the world not to listen to critics because they are not peer-reviewed, they are working as hard as they can back-channel to keep their critics out of peer-reviewed literature they control.
5. For years I have said that one problem with the hockey team is not just that the team is insular, but he reviewers of their work are the same guys doing the work.  And now we see that these same guys are asked to review the critics of their work.

Only 25 months after a similar chart was shown on this site (and here).

Note: Box, et al in 2009 claim to have found from 1-1.5C of warming since around 1900 when this chart leaves off.  It is very, very , very dangerous to splice data sets together, but one probably has to add a degree or so to the tail of the chart to bring it up to date, putting current warming about at the Medieval level but below earlier Holocene temperatures.