The 2013 IPCC AR5 Report: Facts -vs- Fictions

Chad Wozniak says: @ur momisugly October 3, 2013 at 8:28 pm
…. There is more than enough there to prove those periods happened even without any physical science. No amount of pseudoscience can erase this record, Michael Mann’s denials to the contrary notwithstanding. Somehow, I doubt that even he would attempt to go to the libraries preserving this documentation and attempt to destroy it…..
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If Mann did he would run into Tony (ClimateReason) who haunts libraries ferreting out all those records and Tony already has information up on the internet.
http://climatereason.com/LittleIceAgeThermometers/
Besides, destroy a BOOK? in a LIBRARY? You would be lucky to make it out alive!

Janice Moore says: @ur momisugly October 3, 2013 at 8:50 pm
….Aaaand, that may not be too far from the truth; China would LOVE it if the rest of the world’s nations shanghaied their own economies….. guess who will be there to pick up the pieces.
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Lately I have been suggesting to my little customers and their parents they should start studying Mandarin instead of Spanish…

Dr. Easterbrook,
After you have your draft run the gauntlet here at WUWT, it should be submitted to a major publication like the WSJ.
As far as the spring vs winter ice records, seems like the IPCC is cherry picking so both should be pointed out. (The fall/winter NH snow records are also interesting)

Gail the fact that both should be pointed out is fine, but totally irrelevant. Mr Easterbrook used one to debunk the other, different entirely. Run the gauntlet here? lol you all just cheer him on staring fatal schoolboy errors in the face, then make rubbish excuses for them.

Nick Kermode says: @ur momisugly October 3, 2013 at 11:37 pm….
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I should also mention that I think Dr. Robert Brown (Duke Univ.) has the best criticism of the IPCC that I have seen but unfortunately it is not all that good for those without science and math backgrounds.

http://wattsupwiththat.com/2013/10/02/ipcc-climate-a-product-of-lies-damn-lies-and-statistics-built-on-inadequate-data/#comment-1433981
Curiously, I just posted a list of four statistical “sins” committed in AR5 with regard to figure 1.4 (and in AR4′s SPM as well), on CA in the thread Steve just opened on statistical problems with this figure. They are a bit more specific than the general essay above:
* Cherrypicking (presenting single model runs without telling us how those particular model runs were selected or constructed, permitting them to be selected out of an ENSEMBLE of runs from slightly variable initial conditions per model).
* Data Dredging in the worst possible way and concealing the fact in a plateful of spaghetti.
* Applying the concept of the statistical mean of an ensemble of models as if it has any meaning whatsoever, especially when the models individually fail or do very poorly on an ordinary hypothesis test compared to the actual data, once you remove the illusion of the plate of spaghetti and display the results, one model at a time, compared to the actual data.
* Applying the concept of the statistical variance/standard deviation to the results from an enemble of models as if it has any meaning whatsoever. This is the only rational basis for statements such as “95%” confidence or “high confidence” and is based on the central limit theorem, which does not apply to ensembles of model predictions because the general circulation models used in the ensemble are not randomly selected independent and identically distributed samples drawn from a probability distribution of correct general circulation models.
Steve also added the fact that they cherrypicked and shifted the start date for figure comparison to maximize the overlap of their spaghetti with the actual climate data, certainly compared to the earlier AR5 draft. I’d add another feature of 1.4 I pointed out back in the originally leaked draft — the error bars shown on the current temperatures are utterly meaningless — somebody just made them up at plus or minus 0.1C. HADCRUT4 doesn’t even claim to be accurate to better than 0.15C across the contemporary time frame. As usual, the graphic adds an error bar onto individual points that are what they are (conveying a false sense of precision beyond that represented by the data itself) and omits any sort of error estimate or analysis of fluctuations in the individual GCMs represented in the spaghetti. Finally, one should really look AT the fluctuations in the spaghetti — in particular, how does the autocorrelation of the GAST produced by the GCMs compare to the observed climate autocorrelation. Hmmm, not so well, not so well.
I’d add further that performing the most cursory of fits of the form T(t) = a t + b sin(ct) + d to the 165 year HADCRUT4 data, one obtains a result with vastly smaller a (the linear rate of warming), b around 0.2 C, c around 1/70 inverse years, and d set by the need to optimize the slope and oscillation relative to the data. This curve explains almost all of the visible variation in the data remarkably accurately, including the rapid warming of both the early and late 20th century and the “pause/cooling” in the middle and end of the 20th century, as WELL as a similar cycle visible even in the 19th century data with its presumably large uncertainties. In fact, I think I could pretty easily make up a stochastic function such as:
T(t) = a t + b sin(ct) + d + F(t)
where F(t) is trendless, exponentially correlated or power correlated noise and produce temperature curves that are ALMOST indistinguishable from the HADCRUT4 observed 165 year temperature series, and could do even better if I cherrypicked a different start and (say) threw out all data before 1880 or whatever as being too imprecisely known.
If this curve (which is pure numerology — I have no idea what the physical basis of a and d are beyond mumblings about Milankovitch cycles that add up to “I don’t really know”, might GUESS that c is the inverse of the PDO period with b an empirical amplitude, and of course F(t) is noise because, well, the climate system is empirically noisy as all hell in ways we cannot predict or understand) has any meaning at all, it is that making egregious claims about the “unprecedented” nature of the warming trend in the late 20th century is ridiculous — no trend explainable by a four parameter sinusoidal fit is “unprecedented”, and as Dick Lindzen has shown, no audience that is shown early and late 20th century temperature variations at the same scale and asked to pick which one is with and which without CO_2 can do this unless they are enormously familiar with the data and can pick out individual FINGERPRINTS in the data such as the late 20th ENSO/Pinatubo bobble. The curves are qualitatively and quantitatively identical to within this sort of feature specific noise. What isn’t appreciated is that the curve actually extends decently back into the 19th century as well, at least as far as HADCRUT4 is concerned.
Personally, I’d like to see AR5 criticism be a little less general and a little more specific, and think that one can pick figure 1.4 completely to pieces in a way that is most embarrassing to the IPCC. I’d start by simply separating out the contributing GCMs, one at a time, from the spaghetti. Look at the one in orange near the very top! It doesn’t come within MILES of the empirical curve. To hell with it, it fails an INDIVIDUAL hypothesis test regardless of how you massage starting points and so on. Remove it from the contributing ensemble as a falsified model. Look at another. Yes, it dips down as low as the empirical curve — for less than 10% of its overall values — and it has UTTERLY incorrect autocorrelation, showing wild spikes of warming that really ARE unprecedented in the data. To hell with it — remove it from the contributing ensemble.
In the end, you might have a handful of GCMs that survive this winnowing not because they are CORRECT, but because they at least spend SOME significant fraction of their time at or below the actual temperature record and have at least approximately the right autocorrelation and heating/cooling fluctuation range. Better yet, run entire Monte Carlo ensembles of runs per model and see what fraction of the models have EXACTLY the right mean and autocorrelation behavior. If the answer is “less than 5%” chuck the model. Even fewer of the GCMs would make the cut here.
When you are done, don’t present a pile of spaghetti and don’t data dredge a conclusion. The non-data dredged conclusion one SHOULD draw from considering the GCMs as if they WERE iid samples would be “throw them all out”, because one has to apply MUCH MORE STRINGENT STATISTICAL REQUIREMENTS FOR SIGNIFICANCE if you have many jars of jellybeans you are trying to correlate with acne, and commit even more grevious sins that mere data dredging if one PIECES TOGETHER regions where individual models HAPPEN to descend to barely reach the data in order to be able to claim “the models embrace the data”.
Of course, if one does this one ends up with the conclusion that the rate of warming is highly exaggerated in AR4 and AR5 alike by any of the ensembles of GCMs. You might even end up with a rate of warming that is surprisingly close to a, the slope of the linear trend in my numerological fit. That’s order of a half degree per century, completely independent of any presumed cause such as CO_2.
rgb

Pippen Kool says: October 3, 2013 at 7:44 pm
Which means we are warmer now than then? And much quicker? and we are finding things that have been frozen for 6000 years in the siberian arctic or in the alps?
Pippen, you need to read a little more, perhaps:

An ancient forest has thawed from under a melting glacier in Alaska and is now exposed to the world for the first time in more than 1,000 years.
http://www.nbcnews.com/science/still-standing-ancient-alaskan-forest-thaws-melting-glacial-tomb-4B11215106
Canadian researchers from the University of Alberta were exploring the area around Teardrop Glacier in the Canadian Arctic when they found plants that had been frozen for over 400 years during the period known as the Little Ice Age (1550-1850).
Regeneration of Little Ice Age bryophytes emerging from a polar glacier with implications of totipotency in extreme environments
Catherine La Farge, Krista H. Williams, john H. England
Melting glaciers in Western Canada are revealing tree stumps up to 7,000 years old where the region’s rivers of ice have retreated to a historic minimum,
http://www.livescience.com/4702-melting-glacier-reveals-ancient-tree-stumps.html
Die Welte, Nov. 14, 2005
Stone Age trade routes yield spectacular finds on alpine pass – clothes, weapons and devices also from Roman time and the Middle Ages
A glacier near Berne releases finds from the Stone Age – remnants of a forgotten alpine pass…..
……..they discovered a birchbark arrow-quiver. A dating with the archaeological service of the canton Berne showed that the birchbark is nearly 5000 years old.

The leveling off of sea level rise in figure 9 is very significant. It points to a large scale thermal change in the ocean. It also directly contradicts claims of continued OHC increase in the oceans. Instead, there appears to be a downward trend in ocean thermal energy. It could signal an oceanic regime change of long term significance.

Guest essayist Dr. Don J. Easterbrook said,

From the IPCC Report
Warming of the climate system is unequivocal, and since the 1950s, many of the observed changes are unprecedented over decades to millennia.

There just isn’t any nice way to say this—it’s is an outright lie. A vast published literature exists showing that recent warming is not only not unusual, but more intense warming has occurred many times in the past centuries and millennia. As a reviewer of the IPCC report, I called this to their attention, so they cannot have been unaware of it. . . .

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The IPCC intellectuals have done something profoundly worse than just lying with that AR5 statement.
The IPCC intellectuals are openly flaunting their irrational thought systems before those of us with rational thought systems, and they are doing so in the name of our rational thought systems; they are being epistemically irrational in the name of our epistemically rational physical science systems.
An irrational world view with plenty of support within academia, but not outside of academia. That is the weakness which skeptics can exploit, namely few normal rational members of our generally rational culture would accept the irrationality if clearly pointed out to them.
John