As often happens, Eli Rabett has yet another fascinating post, this time about how Barry Bickmore has solved some of the mysteries created by Christopher Monckton. Monckton responded to the expose of his misdeeds the way he usually does: by threatening Bickmore. But you can read about that from the Rabett (honor among bloggers and all that).

First, Bickmore figures out where Monckton got his “data” for the IPCC projections of CO2 concentration; it seems that Monckton was less than totally forthright about this. Again I’ll let Bickmore tell the story. Second, when Bickmore asked the House of Lords about Monckton’s membership in that august body he received this reply:

“Christopher Monckton is not and has never been a Member of the House of Lords. There is no such thing as a ‘non-voting’ or ‘honorary’ member.”

I noticed something odd in Bickmore’s revelations about Monckton’s manipulation of IPCC CO2 scenarios. Monckton insists that actual CO2 concentration isn’t rising exponentially, that in fact the increase is linear, saying:

I am, of course, familiar with the fact that, over a sufficiently short period (such as a decade of monthly records), a curve that is exponential (such as the IPCC predicts the CO2 concentration curve to be) may appear linear. However, there are numerous standard statistical tests that can be applied monotonic or near-monotonic datasets, such as the CO2 concentration dataset, to establish whether exponentiality is being maintained in reality. The simplest and most direct of these is the one that I applied to the data before daring to draw the conclusion that CO2 concentration change over the past decade has degenerated towards mere linearity. One merely calculates the least-squares linear-regression trend over successively longer periods to see whether the slope of the trend progressively increases (as it must if the curve is genuinely exponential) or whether, instead, it progressively declines towards linearity (as it actually does). One can also calculate the trends over successive periods of, say, ten years, with startpoints separated by one year. On both these tests, the CO2 concentration change has been flattening out appreciably. Nor can this decay from exponentiality towards linearity be attributed solely to the recent worldwide recession: for it had become evident long before the recession began.

For some reason, I don’t feel inclined to take Monckton’s word for it. I guess I’m just a natural-born skeptic.

So let’s “calculate the trends over successive periods of, say, ten years, with startpoints separated by one year” to find out whether or not “the CO2 concentration change has been flattening out appreciably.”

We’ll use CO2 data from Mauna Loa with the seasonal signal removed; the de-seasonalized data look like this:

That certainly doesn’t look linear — and in fact it isn’t. There’s an increase in the growth rate (rather than constant growth rate as for a linear trend) that is definitely statistically significant.

But we’re really interested in whether or not CO2 is increasing fast enough to be called “exponential.” Despite Monckton’s claims about “standard statistical tests” for exponential growth (he talks better than he knows), the simplest way to tell is just to log-transform the data. If the data are growing exponentially, the log-transformed data will be growing linearly. Here’s the logarithm of the CO2 data:

Well well! Over time, the growth of CO2 has NOT been linear, but it also has NOT been exponential. It’s been **faster than exponential** (as the logarithm has grown faster-than-linear, i.e., it has accelerated). And yes, the acceleration of log(CO2) (the faster-than-exponential growth of CO2) is statistically significant. That settles it.

But hey, let’s give Monckton his due, and apply a test he himself recommended. We’ll compute the linear regression slope for 10-year intervals with starttimes spaced 1 year apart. Here’s what we get:

Note that the rate is increasing overall, it’s even increasing recently; the last 10-year interval has a higher growth rate than the 1-year-preceding interval.

We can even compute the linear regression slope for 10-year intervals of the **logarithm** of CO2 concentration, with starttimes spaced 1 year apart:

Once again, the rate is increasing overall, it’s even increasing recently; the last 10-year interval has a higher growth rate than the 1-year-preceding interval. So even recently, the growth of CO2 has been **faster** than exponential.

It’s true that recently, the growth rate isn’t increasing as fast as it used to be, so CO2 concentration isn’t increasing **as much** faster than exponential as it used to be. But it’s still faster than exponential. And of course, we expect some variations because, like all geophysical data, the CO2 concentration shows noise as well as signal.

But the fact remains that Monckton’s claim that CO2 has been increasing linearly rather than exponentially just doesn’t hold water. Most telling is the fact that Monckton claims this linear-rather-than-exponential growth for the time period **2002-2009**. The noise just doesn’t allow such a determination. If Monckton knew what he was talking about, he’d surely know **that**.

One final note: in his regular publications through the SPPI (the “Science” and Public Policy Institute), Monckton often graphs global temperature, which is sometimes referenced as “Source: SPPI global temperature index,” and sometimes referenced as “Data source: SPPI index, compiled from HadCRUt3, NCDC, RSS, and UAH.” But I can’t seem to find the actual data. Anybody know where to get the “SPPI global temperature index” data?

Using an eyeball Mark IV, I would think the “SPPI global temperature index” is closely related to the

Wood for Trees Temperature Index( http://woodfortrees.org/notes#wti ), which is “created from the mean of HADCRUT3VGL, GISTEMP, RSS and UAH, offset by their baseline differences.” Add about 0.4 °C (such that their minimum index is zero) and you’re done. You’ll have to do an overlay, but I’m pretty sure I’ve got it right.http://woodfortrees.org/plot/wti/offset:0.4/from:1980/to:2009

SPPI Temperature Index sounds scientiffy and by adding the 0.4°C they can’t be called on plagiarising a simple idea.

Make an FoI request to the SPPI? :-)

I went over to WoodforTrees and plotted Mauna Loa CO2 plus a linear trend. Even with such a simple tool, and without a proper analysis like Tamino’s, anyone can see that CO2 rise is not linear. WFT CO2

But this raises another question: if anyone wishes to discredit a link between CO2 and global average temperature, wouldn’t it best serve their purpose to exaggerate CO2 rise?

Perhaps an FOI request?

I thought I’d also try a test that Monckton claims to have done.

I have done this here, with 1960 as start year, increasing the period in 10-year steps. Either Monckton is lying or he lacks the competence to do what he claims to have done.

(Note that at WfT, the ‘from’ year is included in the range but the ‘to’ year is not.)

Your curve fitting is a little naive. It is not the CO2 concentration that is exponential with year, but only anthropogenic part. IOW, fit to a+b*10^year, not simply b*10^year. A good fit is:

CO2 = 282.6 + 34.0 * 10^((year-1959)/100)

This puts pre-industrial concentration at 282.6 ppm, and predicts 390 ppm for 2009. It’s pretty good at both ends, whereas simple linear fit gives 154 ppm for 1850, and 382 ppm for 2009.

It’s available upon request from the Monckton Archive of Fabricated Data Sets(MAFD).

“Christopher Monckton is not and has never been a Member of the House of Lords. There is no such thing as a ‘non-voting’ or ‘honorary’ member.”so we can officially call him a liar now?

here is his claim:

Finally, you may wonder why it is that a member of the Upper House of the United Kingdom legislature, wholly unconnected with and unpaid by the corporation that is the victim of your lamentable letter, should take the unusual step of calling upon you as members of the Upper House of the United States legislature either to withdraw what you have written or resign your sinecures.http://www.rawstory.com/news/2006/Moncktons_letter_to_Snowe_Rockefeller_on_1218.html

Sod:

“so can we officially call him a liar now?”

I’d say go for it, because Monckton calls himself that…

http://rabett.blogspot.com/2010/04/monckton-jumps-shark-gets-eaten.html?showComment=1271052260151#c1605506754642246461

I don’t understand what you are doing with the 10 year and 1 year transforms. Why not just do a log transform of all the data and do a regression? Do the same thing for linear.

I would be very cautious about log transforms for data with this apparent amount of curvature. There is a well known issue in physics of abuse of log transforms (“anything can look like a power law”).

Obviously there is a tradeoff of degrees of freedom versus correlation, the more complicated your function is. Personally, given that shape, I would just do quadratic. I bet the difference between quadratic and exponential regressions is pretty darn small.

You might be interested to have a look at Craig Loehle’s recent paper in Atmospheric Environment (if you haven’t already):

http://dx.doi.org/10.1016/j.atmosenv.2010.02.029

Where he argues that the rise in CO2 is not exponential, but that other models fit equally well and give lower estimates for future levels. It doesn’t look quite right to me, for a start he doesn’t de-seasonalise the data, unlike the paper on which he seeks to comment and doesn’t give a reason why, which raises alarm bells for me at least.

Dikran,

I would be very interested to see Tamino examine Loehle’s paper in greater detail.

My alarm bells also went off. Eli Rabett said that he might look at it, but has not yet.

Eli read the paper. It is nonsense, but the nasty is how Loehle misuses the underlying literature to justify his slight of hand.

OK, you got Eli to actually do something.

Bah.

According to Hofmann, Butler, and Tans (2009) the growth rate of CO2 concentrations has been doubling about every 30 years since 1800. At this rate, CO2 concentration will reach 560 ppm (doubling pre-Industral Revolution values) by the year 2050.

Hofman, D., Butler, J., & Tans, P. (2009). A new look at atmospheric carbon dioxide.

Atmospheric Environment,43, 2084-2086.Yes Scott, that is the paper Loehle is commenting on.

My feeling is that he is numerically correct but misses the physics of the situation. CO2 emissions have to do with both population growth and economic production growth, which both behave exponentially due to the ‘compound interest’ effect: people beget more people, production capacity begets more production capacity. The exponential model naively used by Hoffman et al. incorporates this idea, Loehle’s quadratic model does not. Also, I expect that including the ice core CO2 data will solidly refute the quadratic model — obviously, as the t to minus infinite limit must be constant.

As for the “saturating” model, it contains one more free parameter and thus is “cheating”.

I believe one could come up with three-parameter functions that end up well above IPCC 2100. Haven’t tried it, but one could look at a – b ln(1 – ct) for fun…

Another one to try: a – b/(t – c).

Nope, neither of those work.

Loehle?

“We thank Craig Loehle for comments on aspects of the statistical analysis used”.

From MdFC 2009…

Priceless :)

Sod, that’s an interesting link you’ve got there.

From this link I quote Monckton: “An unusual heatwave in France a couple of years ago killed 3,000 old people.” which must refer to the 2003 heat wave which, according to French National Institute of Health, killed 14,802 people (mostly elderly). Rather a large underestimation isn’t it?

The good Viscount Monckton then continues with “Last winter’s cold snap in the UK killed 25,000.” which is an overestimation as the Excess Winter Mortality in the UK was 23,740 deaths for the winter of 2005/2006.

So the first problem is an artificial exageration of the data Monckton’s using, surprisingly.

The second problem is that Monckton implies that the Excess Winter Motality is completely due to “Last winter’s cold snap” which, quite frankly put, is pure bollocks because the EWM is defined as “[..] a measurement of the additional deaths that occur in the winter months.”. Therefore EWM is a yearly reoccuring event, each winter to be precise. Nothing to do with a certain “cold snap”.

Anyway, let’s give some EWM numers for other winters (some numbers I could find quickly at the UKparliament website):

– 2003/2004: 21930

– 2004/2005: 29740

– 2006/2007: 22400

– 2007/2008: 23900

From this we can conclude that the EWM for the winter of 2005/2006 was even below average. Nowhere as spectacular like the French heat wave of 2003.

To claim that all the excess deaths during that winter were due to a “cold snap” and presenting it as a counter argument to the French heat wave is pretty thick.

So, in a very long letter by Monckton to two US senators there are two false claims in just one paragraph containing only 6 sentences.

More EWM stats here. Yes, 05/06 was quite ordinary. Strange he didn’t pick 04/05.

It’s as I have eyeballed above. SPPI TI and WfT TI are pretty close, here’s an overlay.

Apart from a few wiggles they are identical. The Monckton data is from 2009, the WfT data is current, so the difference might be due to the recent change in the UAH data.

I did much the same, but for the past 13 decades. Check the “diffs” column in

http://www.realclimate.org/index.php/archives/2010/03/unforced-variations-3/comment-page-12/#comment-168530

to see the nonlinarities in lnCO2.

Good point, TrueSceptic.

If indeed CO_2 growth has been slower than exponential and the temperature trend is linear, we are in deeper trouble than we thought. Or. more likely, Monckton is even stupider than we thought.

Another one on these lines is the insistence that a small dip in solar output presages an ice age. The fact that it’s continued to warm (if slower) across such a dip fails to register as strong evidence for warming, and has even been reported by some as the end of AGW.

Tamino, could you desist from insulting our simian relatives by equating stupidity with their name?

the “S”SPIT Index?

“Finally, you may wonder why it is that a member of the Upper House of the United Kingdom legislature” (not explicitly saying he is one, just saying, you may wonder, if such a thing were to happen ….)

I got the same response from the House of Lords a year ago. They also said he wasn’t entitled to use the coronet and portcullis logo and were investigating his use of it on his graphics. I note that he appears to have stopped doing so since then.

Monckton used that logo for the debate with Tim Lambert.

In which case he was in breech of copyright:

From the HoL Information office, ” the crowned portcullis is the emblem of the two Houses of Parliament; a House of Commons factsheet describing the history of the emblem is online at http://www.parliament.uk/documents/upload/g09.pdf. Use of the emblem by others is regulated by the two Houses, in accordance with the general principles governing parliamentary copyright.”

Someone should ask them (or FOI if necessary) for the cease-and-desist letter and any related correspondence. It could be good stuff.

Tamino: Anybody know where to get the “SPPI global temperature index” data?

BPL: Sure, I can tell you. But put on rubber gloves before handling it, or at least wipe it off with a tissue first.

BPL,

Bluegrue seems to have found that it is essentially the same as the WTI (Wood For Trees Temperature Index). This is an average of the main data sets so there is nothing wrong with it IMO.

Rick, your equation has five constants–to fit a relationship between two variables. You’re curve-fitting, not doing science. The numbers may not mean anything. Have you done partial-F tests on them?

“But I can’t seem to find the actual data. Anybody know where to get the “SPPI global temperature index” data?”

I’d say you’d probably have to present for Monctons next colonoscopy to see that data.

House of Lords Diner

@Barton Paul Levenson: Not five, actually only 3. I fitted two parameters, but chose 10^(year/100) which means an assumption that the anthr. component increases a factor of 10 every 100 years (which is the same as assuming doubling every 30 years).

I re-did the fit, this time not choosing any of the three. I get:

CO2 = 250 + 2^((year-1683)/45.9)

It’s actually much better fit and implies doubling of anthr. component every 46 years. The curves can be found here.

I screwed up the link to the graph. It’s here.

Actually, we should not fit the baseline CO2. If we look at Schwartz’s graph (slide 7), we see that the baseline CO2 concentration should be 275ppm. We then fit both the rate and the start time. Then the rate turns out to be a doubling time of 33.41 years and the start time is 1780.4. IOW,

CO2 = 275 + 2^((year-1780.4)/33.41)

The graph is here.

Notice we are doing a little better then exponential in the last 10 or so years.

Looked into SPPI TI some more. It’s pretty much in flux somewhat like I suspected. It’s not a WfT-TI rip-off, but close.

From the July 2009 monthly report

“Our global-temperature graphs show changes in real-world temperature at or near the Earth’s surface. Each temperature graph represents the mean of one surface and two satellite datasets: the monthly surface temperature anomalies from the Hadley Center in the UK, and the lower-troposphere anomalies from the satellites of Remote Sensing Systems, Inc., and of the University of Alabama at Huntsville. We do not use the NCDC/GISS datasets.On each graph, the anomalies are zeroed to the least element in the dataset. For clarity, the IPCC’s range of predictions is zeroed to the start-point of the least-squares linear-regression trend on the realworld data.”Yep, SPPI TI is so flexible, that it will change from plot to plot. So “index” is a misnomer, but who’s surprised by that anyway. To render their TI even more useless, they’ve repeatedly changed the basis of their compilation.

2009-01 to 2009-06: HadCRUT3, NCDC, RSS, UAH

2009-07 to 2009-10: HadCRUT3, RSS, UAH

2009-11 to 2010-01: RSS, UAH

They don’t mention, which version of the data sets they use. And yeah, they dropped

“any terrestrial-temperature datasets, because they have become near-universally discredited as unreliable”So, to figure out what to use:

– look up the data sets mentioned in the report on the first temperature plot (search for “RSS”, typically on page 10)

– calculate the unweighted mean of the data sets

– subtract the minimum value in the period you plot

If Monckton is using SPPI TI in a talk, you’re out of luck, it could be anything.

I verified the above reicpe for 12/2009, I got an exact match using these datasets (via WfT)

http://www.ssmi.com/msu/msu_data_description.html

RSS_Monthly_MSU_AMSU_Channel_TLT_Anomalies_Land_and_Ocean_v03_2.txt

http://vortex.nsstc.uah.edu/public/msu/t2lt/

tltglhmam_5.2

It’s Willis’ trick.

http://scienceblogs.com/deltoid/2006/08/climate_fraudit.php

TCO,

Something on this by the Rabett and a paper by Lola.

Dr. Bickmore writes: “Lord Monckton says that he fed his Fantasy CO2 projections into the IPCC’s exponential equations for equilibrium temperature response to CO2 forcing to produce his famous temperature graphs”

I’m curious as to where in the IPCC report these “master” equations are. In his SPPI paper (http://scienceandpublicpolicy.org/images/stories/papers/originals/feb_co2_report.pdf) Monckton goes through all of the parameters to show how much the IPCC “hypes” up the numbers. Ummm… is it just me, or isn’t the IPCC predicted temperature range based on model simulations, and not a single 4-parameter equation?!

What section of the IPCC report is Monckton referring to?

I think extrapolating the quadratic backwards is not a particularly reasonable objection, for various reasons, which I can bore with. If you look only at the period of actual data observed, there is probably not enough difference between the two to shake a stick at, nor really between linear and expo. Of course, expo has an interesting physical interpretation, but then you are a bit circular to say the data shows that. and I would be a little bit leery of jumping on expo bandwagon so fast (lots of natural buildups can have limiting process and even sigmoid curves)

[

Response: From observed data alone, there is no doubt that a linear model is insufficient. The growth of CO2 is certainly faster than linear.]Supply of carbon is finite and eventually the growth will end. But so far the concentration is increasing at what matches exponential and even hyperexponential growth. That is the only point being made.

And I say this not from any AGW standpoint, who honest, is not a denialist who always wants one answer or a refuter, who likes the opposite. Say this, from a modeling semiconductors (and degenerate ones, and metals, &ct) point of view. I can be verrry gratifying to jump to log transforms and for curves with relatively small curvature is not really robust. Would not be considered Phys Rev B proof.

I have no idea what this comment is trying to say.

PolyisTCOandbanned : “I think extrapolating the quadratic backwards is not a particularly reasonable objection, for various reasons, which I can bore with.”

Please do bore us. Are you seriously saying that the fact that the quadratic fit is wildly wrong for years before 1900 is of no relevance? (See Eli Rabbet’s analysis.)

Rick,

The exponential formula you gave above,

CO2 = 275 + 2^((year-1780.4)/33.41),

makes a lot of sense to me, perhaps because I can see where the numbers came from. I’ve been playing with that and notice that I need a rather different start-year and doubling interval to get a good fit to the whole 1850-on range compared with just the 1958-on period, but I suppose that’s to be expected. How did you arrive at your figures?

The exponential formula used by Loehle and modified by Eli,

a + b exp(ct)

a=259.4, b=2.978 x 10-13, and c= 0.01677,

seems more obscure and uses oddly sized factors, although I assume

ais supposed to be the long-term CO2 natural baseline.Can anyone (Eli?) explain why Loehle’s formula takes the form it does? Is it supposed to represent physical constants? Is it explained in the paper, which I don’t have access to?

Update: I see that Loehle claims to use a number of [sig fig](http://rabett.blogspot.com/2010/04/puzzler-where-did-loehle-go-too-far.html?showComment=1271255005867#c4832971764959340044) that is utterly bizarre and that you (I assume it’s the same Rick) have commented appropriately. ;)

Sorry, wrong format. Forgot where I was! Loehle’s digit diarrhoea

@TrueSceptic: C = a + b*exp(ct) is mathematically identical to C = a + 2^((t-t0)/k).

Write b as exp(-c*t0) where t0=-ln(b)/c. Then C = a+exp(c*(t-t0)). Next replace exp(c)=e^c with 2^(1/k). The k you need is k = ln(2)/ln(c).

Yes, I only fitted the Mauna Loa data. Fitting these plus the Law Dome data together, results in a different k and different t0.

Dissing the quadratic fit because the parabola goes up at a certain point is like dissing the linear because, if you extend it back far enough, it goes through zero. It’s a trivial gotcha. When we are talking about a growth regime, we should look at that area. And we have reasonable physical evidence, that the mechanisms have changed. Heck, even the exponential ends up having a paramater to show the initial state (it’s the “A” in the equation).

Certainly, the rate of growth has been faster than linear to date (or the acceleration faster than quadratic). Heck, the rate has been faster than exponential! The fact remains, that during the time of growth, we do not have much total curvature and a linear fit is almost as good (I would have to chekc, but Ibet it is is like 95% correlation versus 98% or whatever). given, two models that both show such good (or similar) agreement, you should be very cautious about saying that one is better than the other. Probably there is some fancy test for looking at this. But it’s not Eli saying a parabola goes up in the back (by the way, he still owes me heat capacity discussion from back in 2006…I like to act dumb, but I can bring it ok on p=chem…actually am not even a jokc…but know enough dunning kruger wise to know that I don’t know it all and can smell same in others…that’s why I said JohnA was an evasive idiot on thermo and Lambert was not all nailed down either, although better.)

If you had the carbon curve and tried to say that carbon was a semiconductor, people would laugh at you. (I think.) And certainly extrapolating far outside the bounds of a given curve, via exponentials is a dangerous thing. For instance population growth, was supposed to be more, but has been tailing off versus old predictions.

[

Response: I think Eli’s point (and I happen to agree with it) is this: the fact that the parabola curves upward so soon in the past is a clear indication that it is NOT a valid model for extrapolation — either into the past or the future.As for IPCC projections of exponential growth up to 2100, I think those are based NOT on extrapolation of curve fits at all — they’re based on emissions scenarios and carbon cycle models. And that’s the way it should be.]For instance in crystallography, if you have one model that shows the best fit of the data, but another that is almost as good…you can’t just go with the best one. need to do some more sophisticated tests.

If you look at Loehle’s first excuse he said he was forecasting the rise, not backcasting. Of course you can make wild guesses, but in general since we don’t have measurements of the future, we want to test our forecast models against the past.

Eli looks forward next to Loehle claiming that the CO2 concentration was much higher in 1940, just as Beck found……

OK, TCO, I’ll bite. How do you get a quadratic increase out of an exponential driver? I can see getting zero trend if the sinks respond proportionally or an exponential trend if they don’t. However, a power law or polynomial doesn’t make much sense given the physics.

Ray:

1. “Given the physics”, you could say that theorize an exponential sans any data at all (of course, you have to ignore limits, more complicated models than simple buildups, etc.)

2. What do you make of the rate of growth, being faster than exponential? Step back for a second from the “what is good for my side” and think about this if it were just conductivity in a complex oxide.

3. I actually think all the kerfluttering about quadratic versus exponential is a little silly. For near term estimates of something like this in a science lab or engineering topic or factory floor, I would probably just use linear for near term estimates, and for far estimates, basically curve extension is not a wise way to roll.

[

Response: Jesus Christ, give it up. Abandon the linear model, it’s demonstrably wrong, your continued insistence on its validity makes you look as idiotic as Monckton.And please GET THE POINT that proper projections of future CO2 levels aren’t based on extrapolation, they’re based on physical models. Those models are surely INFORMED by observed data, but extrapolating curve fits is a fool’s errand.]TCO, the preference for the exponential has nothing to do with any “side”. It has to do with the fact that the driver behind increasing CO2 is rising exponentially. When you have an exponential driver, it is extremely difficult to get a polynomial response. You can get an exponential response. You can get zero response (for a while). You can get an exponential times an exponential modula a constant. But quadratic is tough. Think about how the system would have to respond to give you a quadratic–or indeed linear. It ain’t easy.

Eli:

You’re a lot better than Loehle. He is a silly man.

And Eli is a silly wabett (morphing two cartoon characters and an on-line lagamorph).

. . .”lagomorph”. . . THAT sent me hopping off to the online dictionary.

Well done. . .

I knew all those hours spent studying comparative anatomy would come in handy.

Isn’t he? What about those craze parameters with zillions of sig fig? Does he have the faintest clue

how numbers work?

Tamino: I am not “insisting on the linear model”. Please drop the anger.

[edit]

You have repeated — so often as to be infuriating — that the linear model has validity, that it’s nearly as good or indistinguishable from other models. It’s NOT. If you don’t want an angry response, try to keep your head above the stupid threshold.

TCO:

Quote Tamino accurately, at least. He didn’t say you’re insisting on the linear model, but rather that you’re insisting on the *validity* of the linear model.

Which squares perfectly with this comment of yours:

Unless you’re suggesting you’re in the habit of probably using invalid models …

Now, back to the real point: we’re dumping shit in the sky exponentially, so what physical sink is growing at a rate to take that shit out in such a way that it’s not accumulating exponentially?

Actually faster than that, even, for the past several decades. See the diffs column in

http://www.realclimate.org/index.php/archives/2010/03/unforced-variations-3/comment-page-12/#comment-168530

Tamino, my point is not that it is “valid”, but that there is not enough data to make a meaningful differentiation (and yes I say this knowing that one fits the data better).

[edit]

Yes there is enough data to make a meaningful differentiation. Goodbye.

There is enough data, Craig just chose to ignore it.

Dude, if I’m pissing in your boot, and you tell me that there’s not a good enough fit to this or that model to demonstrate that I am indeed pissing in your boot, as I piss in your boot, I will tell you “your analysis doesn’t matter, I am pissing in your boot”.

We’re dumping CO2 into the atmosphere at an exponentially increasing rate. Pissing in the boot, so to speak. Occam’s razor suggests fitting the accumulation in the atmosphere to correlate to the rate at which we’re pissing in it. Observations tell us that about 1/2 of what we put into the atmosphere goes to sinks, largely the ocean.

You’re playing a version of the VS BS … ignoring what’s known physically in order to argue for some unphysical model.

Bingo! It’s a game denialists love to play. Ignore the evidence. Ignore the science. Then fixate on some tiny, little portion of the globe somewhere that momentarily seems to support their delusion that everything is just fine.

Trip to Knowhere

Who needs spherical elephants when you’ve got Klein-bottle-shaped unicorns?

Not just denialists; industrialists.

Example from the US of delaying formaldehyde health restrictions, for decades now:

http://www.scientificamerican.com/article.cfm?id=vitter-formaldehyde-epa

Spokesman for the guy who accomplished the most recent delay: “we need to get absolutely reliable information to the public about formaldehyde risk as soon as possible” — so they added more delay, because of course “absolutely reliable” is never, quite, attainable in fact.

“…

Congress stalled the formaldehyde risk assessment once before. In 2004, Sen. James Inhofe, R-Okla., persuaded (PDF) the EPA to delay it, even though preliminary findings from a National Cancer Institute study had already linked formaldehyde to leukemia. Inhofe insisted that the EPA wait for a more “robust set of findings” from the institute.

Koch Industries, a large chemical manufacturer and one of Inhofe’s biggest campaign contributors, gave Inhofe $6,000 that year. …”

It’s scary to think how cheaply our safety and future can be bought.

James Inhofe. Imagine that.

We are a new climate denial skeptics goup from Calgary that will take care of this problem:

http://friendsofginandtonic.org/

I am pleased to announce a new Calgary climate denial skeptics group:

http://friendsofginandtonic.org/

I would appreciate if you helped our (=your cause) and spread the word around to colleagues and friends.

Thanks,

Derek L Schweinsgruber (schwein@telus.net)

FoGT Climate Blog: http://friendsofginandtonic.org/page1/page1.html

RSS Feed: feed://friendsofginandtonic.org/page1/files/blog.xml

Facebook: http://www.facebook.com/pages/Friends-of-Gin-Tonic-Climate-Change-Denial-Skepticism/111612652207175?ref=ts

> It’s scary to think how cheaply our safety

> and future can be bought.

Yeah. I’m sure there are MBA programs and papers written on figuring the return (in delaying rulemaking) on investment (in lobbying, donations, bribery and other financial leverage).

It is probably the best investment (fastest and highest profit) available in many industries, compared to things like improving processes, increasing wages and dividends, etc.

Lord Monckton testified before a Congressional Committee again today, and did some fancy footwork to evade the question of whether he had misrepresented himself as a member of Parliament. See some commentary here:

http://bbickmore.wordpress.com/2010/05/06/dance-monckey/

Yes, go visit Barry’s blog and give him some traffic:

http://bbickmore.wordpress.com

He’s doing good work in a state that isn’t the easiest.

And he has earned his honored place in that select circle of those threatened by Monckton with dire fates…

If they confronted this fake lord with the facts, like in this New Republic article by Al Gore, he would wilt!!!!

http://www.tnr.com/article/politics/the-crisis-comes-ashore

Anne // May 8, 2010 at 9:24 pm — I greatly fear not.

Impervious to fact.

Concave up indeed:)

“But I can’t seem to find the actual data. Anybody know where to get the “SPPI global temperature index” data?”

I suggest presenting some FOI requests to SPPI.

A few hundred should do the trick.

Please excuse my off-topic comment!

2 questionable denier talks at GeoCanada 2010 this week.

If you attend the meeting, please visit the session an ask critical questions.

Info here: http://friendsofginandtonic.org/

[

Response: There’s an open thread for off-topic comments.]