95% of Climate Models Agree: The Observations Must be Wrong

News Flash!……
Hidden unmeasurable heat causes snow….and polar vortexes
…film at 11

“If the deep ocean ends up averaging 4.1 deg. C, rather than 4.0 deg. C, it won’t really matter.”
First, I’m a CAGW skeptic, so you’re singing to the choir here a little, but when I see things like the statement above in quotes, I cringe.
REALLY?
How do you know it won’t really matter? Have you done a dynamic energy balance study?
Can you cite a peer reviewed reference that shows we can dump 10^22 joules of energy every year into the ocean and it won’t really matter? It won’t change circulation? It won’t cause long-term adverse effects in the thermohaline cycle?
I would love to see this reference and be convinced, because it would be a great way to defuse the whole CAWG meme.

Can’t remember who did the clip, but they examined the temperature record. They exposed that the warmists had actually tilted the x axis back, so they could show the graph as rising. They did not start at zero ( start point 1978) but below zero to accentuate the rise ( hahaha).
With all that, the temperature was still the same in 2013 as 1978. The debunkers of this graph also noted that 1978 was chosen as the starting point because it was when the world is going to freeze scare started.
Those were among many other faults with the graph.
Point is that anyone that believes in the graphs the models produce is being well and truly suckered.

The politics and Press are shameful.
How many people know England lost 30,000 elderly to last winter’s cold? There is Socialism for you.
How many people know South Dakota lost 20,000 head of cattle and thousands of other farm animals to a snow blizzard storm in October 2013?
How many people know New Zealand and Scotland lost thousands of lambs due to early winter storms?
Now, northern Indian Reservations and I assume non-reservation are short on propane gas. The number of people without electric is now lapsing over to the next storm
The death toll is piling up while the Media including FOX fail to report the lost of life due to a colder period from a sunspot minimum.
Instead of in bracing what is going on the media and the US government is in goose step March right down the road to destruction with the hypothesis of Man-Made Global Warming.
Shameful.
Paul Pierett

Ok, I have to ask. Why does the graph being in 1983, but the label says “(’79-2013)”? Anyone?

David in Texas says:
February 10, 2014 at 12:51 pm
Ok, I have to ask. Why does the graph being in 1983, but the label says “(’79-2013)”? Anyone?
*********************
Perhaps because the graph has 5-year running means? The first such mean from data beginning in 1979 would be 1983.

It is articles like this that make me not completely give up on WUWT
– an actual professional scientist has done some analysis..
Here is another one that you linked to in the weekly round up
– it contains a fairly similar message – the models are over cooked…
http://www.c3headlines.com/2014/01/2013-nasa-hansen-climate-model-prediction-global-warming-reality-those-stubborn-facts.html
Both worthwhile, IMHO…
– unlike, ahem, this drivel..
http://wattsupwiththat.com/2014/02/07/friday-funny-two-guys-with-a-ruler-blow-up-the-white-house-global-warming-video-claims/
– IMO, publishing mindless drivel like this one, which some may find humorous (I don’t because it’s just too moronic for my tastes), dilutes the more intelligent content of WUWT….

richardscourtney says:
February 10, 2014 at 12:30 pm
Friends:
“It seems sensible to copy two posts from the thread discussing the superb article by Roger A. Pielke Sr. It is here.
The first post I here quote was from Roger A. Pielke Sr. in reply to me and says
——————
Roger A. Pielke Sr. says:
February 8, 2014 at 2:40 pm
Hi Richard
Thank you for your follow up. We are in complete agreement, as you wrote, that
Hence, the models are excellent heuristic tools. And they should be used as such.”
The differences between hypotheses and heuristic tools is of great importance. Darwin’s claim that similarity of morphology indicates a common ancestor had to be down graded from a scientific truth to a heuristic tool. Because the vast majority of scientists are common sense realists, the distinction is doubly important. Computer models can serve as heuristic tools only.

I have a book called `Oceans` at home, written in the 70`s, as the closing piece it wistfully talks of the future, where consideration is now being given to sink nuclear thermal power plants into the deep oceans to warm them from their inhospitable cold, to promote and sustain living organsims.
how times change

Larry Ledwick: ‘A quick calibrated eyeball evaluation of that mess of spaghetti seems to me that only about 5 or 6 are even in the running for reasonable approximations of reality’.
No, we need an integral judgement. If you take a multiple choice exam and you answer 95% of the items incorrectly, what would you achieve with the five percent you answered correctly? The judgement is that you failed miserably, like Spencer concluded, and the five percent is only correct by chance. The climate scientists are in the mourning phase of negotiating about a few correct models, missing heat hiding in the deep ocean, and a temperature development which is only a pause, as their hoped future will show. They may get some help from family and friends but this is not our task.

I’ve been hammering exactly the same point on two of yesterday’s and the day before’s threads. Roy’s figure doesn’t do it justice. If one compares to figure 9.8a of the IPCC AR5, one notes that the leftmost part of his graph includes part of the training data, the “reference period” from 1961 to 1990 used to initialize and pretend to validate the CMIP5 models. That is, the models and HADCRUT4 are virtually constrained to come together in 1990, not the starting point in Roy’s graph (which looks like a redrawn variation of AR5’s infamous figure 1.4 from the SPM.
I’ve been reading over chapter 9 of AR5 in some detail, as it deals with the statistical basis for claims of validation and accuracy of model predictions. It is interesting to note that in sections 9.2.2.2 and 9.2.2.3, AR5 openly acknowledges that the Multimodel Ensemble (MME) mean is, well, dubious at best, utterly meaningless at worst. To quote (again) from section 9.2.2.3:
…collections such as the CMIP5 MME cannot be considered a random sample of independent models. This complexity creates challenges for how best to make quantitative inferences of future climate…
To put it bluntly, it doesn’t “create challenges”. The correct statement is that there is no possible basis in the theory of statistical analysis for assigning a meaning to the MME mean! Specific problems that they mention in section 9.2.2 with this mean include:
a) The models in this “ensemble” (it isn’t an ensemble in any sense that is meaningful in statistics, so we must presume that they really intended the term “collection” or “group”) are not independent. This means that even if the model results were in some defensible sense samples drawn from a statistical distribution “of models” the variance and mean cannot be quantitatively analyzed using e.g. the central limit theorem and the error function. Any assignment of “confidence” on the basis of MME mean results is pure voodoo with no defensible basis in statistics.
b) The models in this ensemble do not all contribute the same number of “perturbed parameter” runs from the per model perturbed parameter ensemble (PPE) of outcomes when tiny changes are made to initial conditions and model parameters. These results do constitute a defensible statistical sampling of outcomes — for that one model, per model — to the extent that a valid statistical method for doing a Monte Carlo sampling of the phase space of possible initial conditions is used. The PPE simultaneously tells one how robust the model results are and what the statistical spread of results around the PPE mean is, which in turn can be used in an ordinary hypothesis test to gauge the probability of observing the actual climate given the null hypothesis “this is a perfect model”. Still, when one model only generates 10 PPE runs and another generates 160 and the two PPE means are given equal weight in the meaningless MME super-mean, this is simply a statistical absurdity. One is expected to have 4 times the variance of the other and even the crudest of chi-square methodology would discount the lesser model’s statistical relevance in the final number.
c) Finally, 2.2.3 openly acknowledges that mere model performance is ignored in the construction of the MME mean. That is, the IPCC is perfectly happy to average in obviously failed models that are run far too hot as long as it keeps the MME mean equally high, even though I literally cannot imagine any sort of statistical analysis were such a practice could be justifiable.
This decision is not arbitrary. One has (or should have) direct access to the PPE data, and can directly compare per model the degree to which the actual predictions of the model with perturbed parameters overlaps the observed temperature and interpret this as the probability of the natural occurrence of the observed temperatures if the model were a perfect model and all variation is due to imperfect specification of model parameters and initial conditions. That is, one can perform a perfectly classic hypothesis test using the PPE data, per model to clearly reject failed models (p-values less than 0.05, to call into question model with low p-values (given an “ensemble” of model results, Bonferroni corrections mean that rejection should occur for substantially higher p-values given all of the chances to get an acceptable one and the known/expected overlap in the various model lineages), and to include at most the models that have a reasonable p-value in any sort of collective analysis.
These are the errors they acknowledge. Ones they make no mention of include the fact that all of the models are effectively validated against the reference period, and that the MME mean utterly fails to describe the entire thermal history of the last 155 years in HADCRUT4 as it stands!
This is perfectly obvious from a glance at figure 9.8a in AR5. The black line (actual HADCRUT4 measured/computed surface temperature) lies above the red line MME mean) a grand total of perhaps 25 years out of 155, including the training set! If one just estimates the p-value for this assuming a roughly 5 year autocorrelation time and random discursion in both cases from some sort of shared mean behavior with equal probability of being too high or two low, the p-value for the overall curve is order of 0.0001 or less. Less because there are two clearly visible stretches — from 1900 to 1940 and from 2000 to the present — where the MME mean is always greater than the actual temperature.
The stretch from 1900 to 1940 is especially damning, since in the 20th century the warming visible in HADCRUT4 in 1900 through 1950 exactly matches the warming observed from 1950 through 2000, so much so that only experts sufficiently familiar with HADCRUT4 to be able to pick up specific features such as the 1997-1998 Super ENSO spike at the right of the latter record would ever be able to differentiate them. The MME mean completely smooths over this entire 50 year stretch, effectively demonstrating that it is incapable of correctly describing the actual natural, non-forced warming that occurred over this period!
Even before one looks at the CMIP5 models one at a time, and fails to validate most of them one at a time for a variety of reasons (not just failure to get the global mean surface temperature anywhere near correct, but for failure to get weather patterns, rainfall, drought, tropospheric warming, temperature autocorrelation and variance, and much more right as well) nobody could possibly look at 9.8a in AR5 and then assert a prediction, projection, or prophecy of future climate state of the Earth based on the MME mean with any confidence at all!
If one eliminates the obviously failed models from CMIP5 from playing any role whatsoever in forecasting future warming (because there is no defensible basis for using failed models to make forecasts, is there?), if one takes the not-failed yet models and weights their contribution to mean and variance of the collective model average on the surviving residual models, if one accounts for the fact that the surviving models are all clearly still consistently biased on the warm side and underestimate the role of natural variability when hindcasting the bulk of the 20th century outside of the training/reference interval, there would be little need to add a Box 9.2 to AR5 — basically a set of apologia for “the hiatus”, what they are calling “the pause” because ordinary people know what a pause is but are a bit fuzzy on the meaning of hiatus and neither one is particularly honest as an explicit description of “a period of zero temperature increase from 1997 to the present”.
Although the remaining models would still very likely be wrong, the observed temperature trend wouldn’t be too unlikely given the models and hence it cannot yet be said that the models are probably wrong.
And I promise, the adjusted for statistical sanity CMIP5 MME mean, extrapolated, would drop climate sensitivity by 2100 like a rock, to well under 2 C and possibly to as low as 1 C.
Where is the honesty in all of this? Is not the entire point to educate the poor policy makers in the limits in the statistical confidence of model projections? How can one possibly publish chapter 9, openly acknowledge in one single numbered paragraph that the MME mean is a meaningless quantity that nobody knows how to transform into confidence intervals because it is known to be corrupted by multiple errors that they do not bother to try to accommodate, and then make all sorts of bold statements of high confidence in the SPM?
High confidence based on what, exactly? Somebody’s “expert opinion”? A bogus average of failed models that artificially raise climate sensitivity by as much as 2 C over any sort of sane bound consistent with their own observational data? Or the political needs of the moment, which most definitely do not include acknowledging that they’ve been instrumental in the most colossal scientific blunder in recorded history, one that cost enough money to have ended world poverty three times over, to have treated billions of the world’s poorest people for easily preventable diseases, to have built a system of universal education — I mean, what can one do with a few trillion dollars and the peacetime energies of an entire global civilization, when CAGW is no longer a serious concern?
We may, possibly, soon find out.

Steven Mosher says:
February 10, 2014 at 1:01 pm
” …Blog commenters do not get tell policy makers what information assists them. …”
—-l
That seems to be the preise attitude which resulted in the CAGW meme. Perhaps if policy makers paid more attention to blogs, they would be able to make better decisions?

A cautious policy maker may look at history and argue that he wants to be really safe
http://www.dailymail.co.uk/news/article-1386978/The-Japanese-mayor-laughed-building-huge-sea-wall–village-left-untouched-tsunami.html

FTFA:

But 10-term mayor Wamura never forgot how quickly the sea could turn. Massive earthquake-triggered tsunamis flattened Japan’s northeast coast in 1933 and 1896. In Fudai, the two disasters destroyed hundreds of homes and killed 439 people.
‘When I saw bodies being dug up from the piles of earth, I did not know what to say. I had no words,’ Wamura wrote of the 1933 tsunami in his book about Fudai, ‘A 40-Year Fight Against Poverty.’
Read more: http://www.dailymail.co.uk/news/article-1386978/The-Japanese-mayor-laughed-building-huge-sea-wall–village-left-untouched-tsunami.html#ixzz2sxRsQMIg
Follow us: @MailOnline on Twitter | DailyMail on Facebook

Where are the bodies of those killed by human-caused climate change again? Where is this “history” we should be learning from?

Blog commenters do not get tell policy makers what information assists them.

Inasmuch as lowly “blog commenters” are voters they most certainly get to tell policy makers what the government’s policy should be. Or at least they should, in a democracy.

Yep, spot on.

The Great Helmet Debate
So why do we have people campaigning for mandatory helmet laws if there is scientific evidence that they may be harmful?
Common sense suggests that helmets should save lives. It is reasonable for people to have preconceptions based on common sense. Unfortunately many people, particularly those without scientific backgrounds, become quite distressed when scientific observations challenge their preconceptions. Even more so if those preconceptions are based on common sense.
A scientist, with an open mind, will become curious and start looking for mechanisms to explain the unexpected observations. A non-scientist is more likely to close his mind and assume that the observations must be wrong. People start cherry picking the observations that support their preconceptions and dismissing the observations that challenge their preconceptions. This is scientific fraud and the intellectual equivalent of sticking your fingers in your ears and saying: “La la la”.
Unfortunately there are a lot of people out there who are unwilling to have their preconceptions challenged and prefer to say: “La la la”.

95% of climate models agree that they totally missed predicting real temperatures and are unfit for their intended purpose. A large portion of them never even at their lowest projected temperature limit even touch real world measured temperatures.
Larry, it is worse even than that. In Box 9.2 in AR5, I quote:
However, an analysis of the full suite of CMIP5 historical simulations (augmented for the period 2006–2012 by RCP4.5 simulations, Section 9.3.2) reveals that 111 out of 114 realizations show a GMST trend over 1998–2012 that is higher than the entire HadCRUT4 trend ensemble…
That is, it is 97.4% of CMIP5 simulation and they know it but did not moderate the confidence of their projections or alter their presentation of figure 1.4 in the SPM in any way except to hide this fact from policy makers, who of course are most unlikely to read, or correctly interpret, paragraph 9.2.2.3 or figure 9.8a.
Oh, and the 97.4% is as of 2012 and does not include the last two years of a continued lack of warming. I suspect that we are out there at 99% at this point. As I said, a proper analysis of figure 9.8a already would produce a p-value for the null hypothesis “The MME mean is a meaningful predictive quantity” of practically zero, under no reasonable circumstances higher than 0.01 and IMO (without doing a full, detailed computation) more likely to be around 0.00001 to 0.000001 once one does the integrals for some sort of Kolmogorov-Smirnov test with some reasonable assumption of autocorrelation and unbiased excursion from a correctly represented mean.
Mentally compare the time integrals of for to those of for . K-S tests determine whether it is plausible that these two curves could be samples of some sort of the same underlying process. Obviously, if they were the total integral of the two should be close to zero. There are other methods one might use to compare them, but they are all going to give the same general result. No, the CMIP5 MME mean is not a good representation of HADCRUT4.
Interestingly, the EMIC simulations (figure 9.8b) in AR5 do much better from 1961 to 2005. They still are not convincing as predictors of the future, however, because across the entire span they run too cool across the critical stretch of 20th century warming from 1920 to 1940 where the temperature change most closely mirrors the reference period. They are certainly a lot more convincing than CMIP5, however, perhaps because some of the EMIC models do actually run cooler than observation even as some run warmer. It would be interesting to compare the systematics of this — from a glance a 9.8b it appears that they manage this magic trick by mixing many models that are far too flat and were too warm in the past that crossover to too cool in the present, with models that were too cool in the past and crossover in the reference period to models that are too warm in the present. All of them are too smooth and fail to reproduce the excursions of the actual thermal record qualitatively or quantatively, have terrible autocorrelation times (they all appear to be heavily smoothed over decadal intervals where the actual climate has substantial variations — zigs and zags up and down — over segments of rough 5 years, suggesting that the EMIC fail to capture the local climate dynamics that hold the climate to a semi-stable centroid while also failing to correctly locate the centroid in almost all cases. I cannot tell from the figure if there exist models between the two crossover extremes that are close to “just right” — not too hot, not too cold — but if there are, obviously they should be given the greatest weight in any future projection of the climate.
rgb
[GMST is Global Mean Surface (or Sea) temperature? Earlier, you used GAST. Mod]

Mod, sorry, failed to correctly close a boldface tag again. Please help. I didn’t mean to shout/emphasize the entire latter half of the previous comment.
[But at which point? All should be emphasized! 8<) Mod]

rgbatduke says:
February 10, 2014 at 2:04 pm
…
Larry, it is worse even than that. In Box 9.2 in AR5, I quote:

Thanks for the elaboration!
After reading your comments in the other thread, the light bulb goes on and you know what to look for in that pile of spaghetti. As I commented above, even a naive casual observer can easily see that the model plots in no way correspond to the real world once they understand what they should be looking for.
I wish I had the skills to assist you with that project you outlined, but do, not so have to settle for urging people to make graphs and visual aids that would communicate your line of reasoning effectively and more easily show how awful the models are to the casual non-statistically educated observer. Most people know they smell a dead rat but have no clue where to look for it.
You have certainly helped open a new avenue of discussion with my pro-AGW friends. I recently tried to point out to one of them that the real data is literally falling out the bottom of even the best case lower limit of the envelope defined by that assembly of plots. Unfortunately he is one of those folks that thinks that since the guys who made the plots have phd’s Their judgement is far superior my common sense observation that even a blind rat would find the cheese occasionally. When all the blind rats are heading away from the cheese you know something is wrong.
I submit that your line of attack would also be a good basis for the legal presumption of malicious intent and intentional fraud. It is a bit much to presume that a large number of well educated scholars all are clearly producing nonsense data that violates good statistical practice and common testing methods used in other branches of science to validate models without begining to ponder if it is intentional or they all without exception are profoundly incompetent.
You literally have to be very very lucky to be that bad at projecting future climate, since even a simple persistence model with a bit of noise, out scores their projections by a few orders of magnitude.

Dearest Mod,
Please close after the word warming. That hopefully will do it, although pesky tags are easy to miss.
As for GASTA vs GMST, I’m trying to adhere to the terminology used in AR5 for comparison purposes, although I’m not doing it very well. In AR5 they call it the Global Mean Air Surface Temperature (Anomaly), which would be GMASTA or something equally horrid.
Sigh.
rgb

@ richardscourtney says:
– I just think WUTW should refrain from publishing stuff that confuses people who have difficulty understanding a simple graph and which is made by people who clearly have difficulty understanding a simple graph….
LoL!
If WUTW has a purpose, then surely is it to point out the weaknesses of the AGW argument, which isn’t helped by an ‘any old rubbish’ goes mindset, IMO

WUWT that is…

To summarize this:
1) All models are wrong, but some are useful (George Box)
2) Mistakes were made, but not by me (Carol Tavris and Elliot Aronson’s )
3) The numbers tell the tale

@ richardscourtney
– you’re right
– I apologise profusely for my mistake!
– if I could, I would go back & correct it!

David in Texas says:
February 10, 2014 at 12:51 pm
Ok, I have to ask. Why does the graph being in 1983, but the label says “(’79-2013)”? Anyone?
Larry Ledwick says:
February 10, 2014 at 12:56 pm
Go to Roy’s site and look at the current “Latest Global Temps” chart. Note where the data are for 1979 through 1983. These are a bit below the 1981-2010 line (black, horizontal). The chart at the top of this post sets the horizontal line at the 1979-’83 average and then shows models and temperature from there. Neither the charts nor the intended use thereof are the same. The purpose of the one here is to accompany a critique of climate models. If you just want to look at 34 years of temperature data go to Roy’s site.
I have the chart from Dr. Spencer’s site open. I can take another window and use the top as a straight-edge and put it along the top of the red line (13 month average). The right end of the red line ends about mid-2013. Going back in time, say to mid-2002, there is almost no slope to the temperatures. If you go back more — at mid-1999 then the slope is up, but at mid-1998 to now, the slope is down. Respectively then, we get flat, up, and down for 11, 14, and 15 years (approximately). So, is the temperature going up, down, or sideways?

@Aussie Phil2.33
So WUWT should not publish any of the consensus climate science?
Or just anything that confuses you?

re: Michael Wonders. Using HadCrut4 data; the rate of warming from 1976 to 2007 was +0.019°C/yr. There has been no warming since 2007, likely since 2001 and even since 1997… The rate of the previous ~34yr warming cycle (1911-1945) was 0.014°C/yr. Prior to that GSTA decreased by 0.008°C/yr from 1879 to 1911. Between 1945 and 1976, GSTAs decreased by 0.002°C/yr (not rounded figures to show similarities). Please note the ~30+yr periods of well-documented 60 yr natural cycles, as well as the fact that the warming in the previous warming cycle was only 0.005C/yr more (well within measurement error) than the one prior.
Assuming linear data, applying linear regression through the entire HadCrut4 data set (1850 till now) produces a slope of 0.005C/yr. The IPCC predicts that by 2100 GSTAs are on average 1.2 to 3.5C higher than what they are now, based on different CO2 emission scenarios. That would require unabated/continuous (!!) warming of 0.01-0.04C/yr starting today. The lower warming rate is about the same as what was experienced in the previous two warming periods. Nothing unusual in other terms. But it does also require unabated warming starting today, whereas the data record clearly shows 30+ yr long periods of alternating warming and cooling; of which the Earth is likely now in a cooling period. So the subsequent warming periods need to become even warmer… However, and in addition, these “needed*” warming rates are also 3.0-8.5x higher than the average warming the earth has experienced over the past 160+yrs… Does that make any sense? In all honesty; the numbers really suggest not, and that these predictions don’t make all that much sense. There is neither no president for it, even with CO2 levels now ~120ppm (40+%!!!) higher than in pre-industrial times… Given the now well-document “pause in global warming” since the early 2000s, likely lasting till the late 2020s, early 2030s, based on know natural cycles, these predictions by the IPCC’s computer models become even more erroneous.
* needed in the sense of getting the GSTAs to the by the IPCC predicted levels by 2100

Paul Pierett says: @ February 10, 2014 at 12:42 pm
The politics and Press are shameful.
>>>>>>>>>>>>>
Yes it is.
You forgot these horrors. These people have even less resources that the UK, EU or the USA:
February 10, 2010 – Mongolia: The Disaster You Haven’t Heard Of
tens of thousands of people, and millions of animals, are right now in a daily struggle between life and death, and many have already lost. I’m speaking of our brothers and sisters — two- and four-legged — caught in the most catastrophic winter the country of Mongolia has seen in at least 30 years…. As of this writing, Mongolian and international aid agencies estimate that more than 2 million domestic animals have perished so far in this dzud. Ten to twelve million died in the last disastrous episode ten years ago, and this dzud is regarded as far worse. Some fear that up to 20 million animals — half of Mongolia’s total herd — may succumb before tolerable weather arrives in late May.
And it happened again a short three years later:
February 26, 2013 – Tibetan nomads in Ladakh call out for help, Thousands of livestock perish
And this February in a different part of China
February 9, 2014 China on blizzard alert – Snowstorms kill livestock, disrupt lives
Continuous snowstorms in northwest China’s Xinjiang Uygur Autonomous Region have killed more than 300 livestock
South America is not immune either:
September 2013: More than 25 000 animals killed in southern Peru, Drugs sent to combat illnesses caused by cold and snow – “After their livestock died, I am afraid that people are going to die now if this bitter cold continues,” says reader Argiris Diamantis. “Many people are already sick, 500 kg of medicines is being sent to them.”
October 2013: Chile – One billion dollars damage to fruit crops – Worst cold spell in 80 years hammers Chile fruit crops
October 1 2013: Argentina – 2,200 cattle die in snowstorm

Yep…. you sum it up perfectly…. “global warming” then “climate change” was all about surface warming. To turn around and explain that it has all gone into the oceans is the equivalent of saying, “Sorry, we were wrong about that”.
Such a settled science.

What I see missing from this discussion is the realization that the modelers actually, really, totally, unquestioningly, sincerely (if that latter word attaches to their sort of mindset) believe their models are right and the observations are wrong. For them this is not a sarcastic joke – it’s their true belief, however convoluted and irrational and delusional it may be. Their ideology, which they are forbidden by its first and overarching principle to question, says their models are right, so there is no possibility in their minds of the models being wrong – only that which does not comport with the models is wrong. They will most assuredly cling to this belief no matter how much contrary, physical, upside-the-head evidence comes along to refute it. They’ll still claim global warming is the cause when the next ice age descends upon their heads and scrapes away they ivory towers they inhabit.. And obviously they don’t mind at all that 33,000 people died from hypothermia in 2013 in the UK thanks to the carbon policies they promote.

Chad;
That 33K hypothermia death figure should be used with caution.
I believe that was total UK deaths in the cold months, of which about 5-6K were maybe “excess deaths”.
Further, residents of “temperate” (non-tropical) climes tend to have better coping skills and tolerances for temperature swings. Most hypothermic deaths occur in sub-tropical areas hit with unexpected cold snaps. It’s a lot harder to kill a Brit or German with cold than an Algerian.

Steven Mosher says: at February 10, 2014 at 1:01 pm “Suppose I am a policy maker.”
I am supposing from your comments that you are not.
There is a huge difference between using information you can not be sure of and using information that has been proved wrong . You conflate the two in your lecture.
As for your conclusion – “Blog commenters do not get (to) tell policy makers what information assists them.” – speaking as a policy maker I am open to all suggestions via all forms of communication. Certainly “97% of scientists” and other so called experts do not get a monopoly of access based on their record to date.
Don’t be so dismissive Mr Mosher. One person, one voice.

Ta. Just trying to avoid Gail’s defenestration. 😉

None of the modeling tools can be predictive tools. The Navier Stokes differential equations describe fluid flow with changes in temperature and density. They are nonlinear, chaotic, with sensitive dependence on initial conditions. Because of that, no finite set of past states is sufficient to predict future states. This has been known since Edward Lorenz’s paper “Deterministic Nonperiodic Flow” in 1963. A butterfly can flap his wings in Beijing and change the weather in Omaha. That means you can never predict the weather (or climate) in Omaha until your measurement grid is infinte. When anyone attempts to predict the future from past states, first ask “Is his record of past states finite?” if it is, then he is incompetent or a fraud. See Michael Mann. Lather, rinse repeat.

She has pointed to a post on hotwhopper.com (have to love that blog name! — http://blog.hotwhopper.com/2014/02/roy-spencers-latest-deceit-and-deception.html ) which indicates that the graph is wrong because it “shift[s] the CMIP5 charts up by around 0.3 degrees”.
Which charts are those? I agree that there are several troubling things about the graph above, one of them being a lack of caption or legend, another being that HADCRUT4 is missing features (like the 1997-1998 ENSO peak) that are there, smoothed or not:
http://www.woodfortrees.org/plot/hadcrut4gl/from:1983/to:2014/mean:12
It might be even more smoothed, of course, but only at the expense of the end point(s). However, it indicates 90 CMIP5 model runs as well, where only 36 distinct named models are similarly plotted in AR5. So, are these runs for a single CMIP5 model, runs from several, the independent means of many perturbed parameter runs for 90 different models (if so, which 90, and how many runs contributed to each), how was the mean of the CMIP5 models computed (weighted or not, PPE runs or not etc) and finally, how were they normalized/locked down in the first year on the graph (which is well back into the model reference period and hence is not really when the models were released into the wild).
However, none of these things save CMIP5 from the point he is making, although they are all valid questions for a peer review. At the very least the graph needs to be explained much better, and of course the BEST thing to do would be to analyze each contributing model one at a time relative to e.g. HADCRUT4, and only then (after e.g. 36 such analyses) make statements that are not argumentative but definitive and based on simple statistical analysis concerning the meaning and reliability of the CMIP5 MME mean as displayed in AR5 specifically.
rgb

I appreciate the message, which can’t be repeated enough, that the models of “settled science” have been spectacular failures, but I do have some technical qualms about the graph.
(1) Where’s the big temperature spike in 1998 (from a “super El Nino”, if I have the term correct)? This temperature history does not reflect the 15-20 year “pause”, in part because that is missing. The green data shows about a 10-year pause, and the blue just shows a low slope – no pause at all, really.
(2) The vertical axis is labelled “Departure from 1979-83 Average”. That’s fine, but how come ALL the curves start at zero? Did they ALL – INCLUDING THE REAL-WORLD DATA – just HAPPEN to be equal to that average? It’s just not credible.
(3) The real-world data is in the neighborhood of a handful of model results. Are we to conclude that these 5% of models might possibly be right!?! I kind of doubt it, myself, but you can’t see how those few compare with the data (it’s too “busy”), and there’s no discussion about what is special about their models. Probably, they don’t match very well early on, but from what you can see here you’d have to wait 5 or 10 years to throw away those models, too (assuming they don’t finally get a break from Mother Earth).
I’d really like to see what this would look like if these issues were addressed.

ferdberple says:
February 11, 2014 at 5:43 pm
Isn’t the fact that the models show a spaghetti result an indication of the size of natural variability? Otherwise, what explains the large variability in the model results?

You mean beside the obvious conclusion that they have no clue what they are doing and the models are just churning out random crap that sort of indicates their might be warming in the future sorta maybe we think?

The missing heat is being absorbed by the land not the sea. This has energized the continents and the speed of continental drift has increased dramatically. With carbon dioxide continuing to increase in the atmosphere it won’t be long before the continents are up to motor boat speed.
Eugene WR Gallun

Does anyone know of a procedure that is recognized by the IPCC by which an IPCC climate model or specified group of such models can be invalidated? According to Vincent Gray (“Spinning the Climate”) the IPCC stopped claiming to have validated its models after he complained to the management that these models were insusceptible to being validated.