Quote of the week – reality is in the eye of the beholder

qotw_croppedOne of the biggest issues facing climate science today is the divergence of reality (observations) from the model output. The draft image from IPCC AR5 (seen below) clearly illustrates this as does the analysis done by Dr. Roy Spencer. WUWT regular Tom Trevor wrote this short paragraph in comments, and it seemed prescient to me, so I thought it was worth elevating to Quote of the Week.

You know when I was a boy I would build models, I wasn’t very good at building models, but I built them anyway so I could play with them afterwards. I would pretend that the models were real ships or planes, but I always knew they weren’t even close to real ships or planes. For some reason these people can’t seem to tell the difference between a climate model and the real climate.

Original comment here

The IPCC AR5 draft models-vs-reality image:
IPCC_AR5_draft_fig1-4_with

Dr. Roy Spencer’s analysis of models-vs-reality:

CMIP5-73-models-vs-obs-20N-20S-MT-5-yr-means1[1]

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81 thoughts on “Quote of the week – reality is in the eye of the beholder

  1. We have become so innured to the weasel words of climate science that we almost don’t read them any more. And when the MSM gets a hold of these speculations, add another layer of biased obfuscation.

    • Wouldn’t it be ironic if Dr Spencer’s simple technique of superimposing the tens of (taxpayer funded) broken models and the real temperature was the visual catalyst for the average citizen to understand this academic & scientific fraud?

    • You people just don’t understand…..one day the temp is going to shoot straight up and meet that line
      just wait and see

      It’s called volatile induced anthropogenic global rectified alarmism…………….VIAGRA

      • Lat,
        I think you’re right on. That is HARD science right there. Unfortunately the warmunists and their believers will soon find they’ve been STIFFED. The only thing going UP are their expectations, which will soon go limp as their house of cards is ERECTED on sand. Their expected CLIMAX is definitely PREMATURE.

        Their VIAGRA problem will soon become:
        FLACCID: Failed Long-term Anthropogenic Climate Change Identification Disorder.

        I am so EXCITED to be here! You have no idea!

        :-)

    • Spencer’s graph illustrates starkly the failure of projected CAGW for this century. If this was any other science they would dial down dramatically projected warming, but they can’t because the IPCC would be shut down and climate change funding would dry up.

  2. Anyone who tuned their Climate model that produced a +1.0 deg C sensitivity lost their funding 20 years ago. That funding selection gave us the GCM failures we have today.

    • We have already experienced the ecotopian model. Horse-drawn plows, horse-drawn carriages, horse-drawn carts (as a child I used to jump in the ice-man’s wagon to filch slivers of ice in the summertime.) Believe me, it is not an idyllic experience. Especially dodging the horse-hockey everywhere.

  3. Only my PE teachers have called me “Spence”. Hope Anthony doesn’t have any exercise planned for my future. Against my religion.

  4. Modern climate scientists like their comforts – they don’t like to leave the office, and when they do, it often ends badly (ship of fools, anyone? :-) ).

  5. I believe the climate modelers have a Mr. Spock fetish. They believe they are on the bridge of the Enterprise and can calculate the how to save the known universe from an out-of-control black hole using a pocket calculator.

    • The interesting thing is that previous researchers “thought” they knew what the herd and the sheepdog did and tried to model it without doing any in depth observation. This led to failure of the models.

      I wonder if they could have asked any ranchers about how they herd cattle….seems about the same concept i.e. Collecting and Driving

      • “King and his group suspect that the sheepdog algorithm would prove tremendously useful for human crowd control.”

        Jawohl.

      • errr, have you seen the latest idiocy wher theyve tried to model sheep herds and replace the dog with a robot?
        I am looking forward greatly to seeing how a robot copes with sheep going over/through fences and being flattened BY a aggro sheep.
        be worth a lot for the laughs:-0

  6. Hate to spoil a bit of the fun here, since agree with the general sentiment. But Dr. Spencer’s comparison is to RCP 8.5, which has elsewhere on this blog ( and elsewhere) been established to be literallyimpossible. The better comparison is to RCP 6.0 (the old SRES A2 is closer to 6.0 than to 4.5). Of course, the change from AR4 was made to obscure the many provably false assumptions in the explicit SRES, covered up by yet more IPCC blathering.
    There is no need to resort to hyperbole to stop CAGW. The wheels are coming off all by themselves. Best that the high road is taken.

    • My understanding is that RCP 8.5 predicts .27C per decade which is right in line with AR4’s 02-.03C per decade prediction. I think the proper comparison is what the IPCC has predicted — not what other (saner) voices have decided as “literally impossible.” ;-)

  7. One of the major reasons why I have no confidence in these types of models is that they almost perfectly hindcast but start to diverge as soon as they forecast. They remind me of models that try to predict the stock market. You can, with sufficient effort, get them to do a reasonable curve fit of the past, but they have no predicative abilities.

      • Perhaps… …if there’s enough of that “renewable”, biofuel, ethanol in it!

        Hurray for yeast! Zymurgically enhanced global-warmistalarmism!

        On second thought, maybe not: they’re a litigious lot, and if we encourage ‘em, they’ll start drinking the actual biofuel, too many will develop bizarre symptoms and cirrhosis, and they’ll sue us…

  8. Climate modelers live in a rectum reality — where keeping your head stuck up your ass is all that matters.

    • There is hope for sufferers of proctocraniosis. One of these can be screwed into the navel:

  9. Engineers build and test models and (mostly) get it right. That is their job, the models can fail but, lessons are learned, the models modified until the desired outcome is achieved. Think of aircraft,vehicles, buildings, bridges. The big difference in climate models is that Co2 is assumed to be major driver, producing the present divergence from reality, and I cannot see that changing in the future.

    There is no connection between quiet,behind the scenes,engineering model generation where accuracy is literally life and death,and these noisy,politically motived grant seekers masquerading as scientists.

    • The bigger difference is that the parameters used in engineering design models are based on materials and situations that have often been thoroughly proven before being programmed into the model. Climate modelers have no idea how many factors impact climate, how the various known cycles that impact climate impact other cycles, not to mention those factors and cycles that are unknown. How can any true scientist (and I am not one) believe these models have any validity whatsoever?

    • In the real world, there is a price to be paid for being wrong.

      In the climanista bizzaro world, there is price to paid for being right (realistic)

  10. Tropical mid-troposphere, compared to a small number of balloon data sets – really? How many data sets were screened to come up with that one?

  11. “For the vast majority of mankind accept appearances as though they were reality, and are more influenced by those things that seem than by those things that are.” Machiavelli~ The Prince

  12. ”Die wichtigkeit oder Bedeutung eines Problems haengt immer auch von subjektiven, bewer tendens Elementen ab” Vollmer Gerhard, Wissenschaftstheorie in Einsatz, Stuttgart 1993

    quick English translation:

    The importance or significance of a problem always depends on subjective, personal evaluativable Tendens elements

  13. If any one of the arrows in this graphic has a greater variability than man made total CO2 energy trapping, then global warming made by man is extremely unlikely… I especially like the magnetospheric driven ionospheric convection…

    Ionosphere-Thermosphere Processes Public Domain NASA

  14. Thanks, an interesting idea.
    “One of the biggest issues facing climate science today is the divergence of reality (observations) from the model output.”
    I think that one of the biggest issues facing popular scientific culture today is the divergence of reality (observations) from the accepted cultist dogma.

  15. BTW Andrew, there’s a current fissure eruption in progress in Iceland, it began around ten minutes after midnight local.

    Red alert is current for aviation

  16. reality? try McKibben. much more, incl how The World Council of Churches, representing 580 million Christians, will try to persuade Pope Francis to get the Vatican to divest from fossil fuels, at the link:

    VIDEO/TRANSCRIPT: 28 Aug: Democracy Now: As Obama Settles on Nonbinding Treaty, “Only a Big Movement” Can Take on Global Warming
    As international climate scientists warn runaway greenhouse gas emissions could cause “severe, pervasive and irreversible impacts,” the Obama administration is abandoning attempts to have Congress agree to a legally binding international climate deal…
    This comes as a new U.N. report warns climate change could become “irreversible” if greenhouse gas emissions go unchecked…
    We speak to 350.org founder Bill McKibben about why his hopes for taking on global warming lie not in President Obama’s approach, but rather in events like the upcoming People’s Climate March in New York City, which could mark the largest rally for climate action ever…
    BILL McKIBBEN: The new U.N. report is more of the same. In a sense, it’s the scientific community, through the Intergovernmental Panel on Climate Change, telling us what they’ve been telling us now for two decades, that global warming is out of control and the biggest threat that human beings have ever faced. They’re using what was described as blunter, more forceful language. At this point, you know, short of self-immolation in Times Square, there’s really not much more that the scientific community could be doing to warn us…
    BILL McKIBBEN:…We need to be doing what the Germans have done. There were days this summer when the Germans were getting 75 percent of their power from solar panels within their borders…

    http://www.democracynow.org/2014/8/28/as_obama_settles_on_non_binding

  17. another contender for “quote of the week”!

    28 Aug: WaPo Letters: How to change the climate on global warming
    From: Elliott Negin, Washington
    (The writer is director of news and commentary for the Union of Concerned Scientists)
    BLAH BLAH
    If The Post is serious about clearing up confusion about global warming, it would follow the lead of the BBC and stop publishing scientifically indefensible statements.

    http://www.washingtonpost.com/opinions/how-to-change-the-climate-on-global-warming/2014/08/28/408d0340-2ca0-11e4-be9e-60cc44c01e7f_story.html

  18. “Two important characteristics of maps [or models] should be noticed. A map [or model] is not the territory it represents, but, if correct, it has a similar structure to the territory, which accounts for its usefulness.” – Alfred Korzybski

    Unfortunately the climate scientists are not good at building models or maps.

    It is an impossible and futile task after all, to model the climate that is. Why?

    Due to the climate systems being systems that generate INTERNAL randomness it will NEVER be possible to predict climate systems.

    The nature of all systems that generate internal randomness is that the only way to see what happens next is to observe them in real time.

    This discovery was made by Stephen Wolfram in his ground breaking book, A New Kind Of Science; see Chapter 2 for the mathematical (and computer science) proof.

    All climate models will always fail due to this newly discovered INTERNAL randomness.

    Then there is the external randomness that comes from Chaos Theory.

    That’s two kinds of randomness, internal randomness and chaos randomness, that mean that it is not possible to come up with an accurate prediction of the Earth’s climate.

    The only way to know what the climate of the Earth is going to do is to observe and measure it as it actually happens in real time.

    As a result climate models will always fail due to first principles of chemistry, physics, computer science, mathematics, and due to the fundamental laws of Nature.

  19. PWL makes a very important point. Why are all the models so much at variance from the real data?
    There must be a common weakness. Is it the boundary values used , or the values for the constants in the radiation equations ? Is it that there is some rule , a combination of chaos and information theory, that says that a model with > n variables and m (m = or <n) boundary values is inherently unstable.?
    There is an opportunity for an enterprising graduate student to make a name for him or herself by analysing exactly why the models are inadequate, rather that just producing more of them.
    As a one time user of Mathematica I have admired Wolfram's work and have been waiting for second hand copies of his book , quoted above , to drop at Amazon or Abebooks to a level that I can afford .

    • mikewaite

      You ask

      PWL makes a very important point. Why are all the models so much at variance from the real data?
      There must be a common weakness. Is it the boundary values used , or the values for the constants in the radiation equations ? Is it that there is some rule , a combination of chaos and information theory, that says that a model with > n variables and m (m = or <n) boundary values is inherently unstable.?

      The short answer is that each climate model is tuned in a unique way so its output matched past variations in global average surface temperature anomaly, and the tuning is achieved by using a high value of climate sensitivity to greenhouse gas concentrations which are expressed as carbon dioxide (CO2) equivalence.

      It seems I need to provide the following explanation yet again.

      None of the models – not one of them – could match the change in mean global temperature over the past century if it did not utilise a unique value of assumed cooling from aerosols. So, inputting actual values of the cooling effect (such as the determination by Penner et al.
      http://www.pnas.org/content/early/2011/07/25/1018526108.full.pdf?with-ds=yes )
      would make every climate model provide a mismatch of the global warming it hindcasts and the observed global warming for the twentieth century.

      This mismatch would occur because all the global climate models and energy balance models are known to provide indications which are based on
      1.
      the assumed degree of forcings resulting from human activity that produce warming
      and
      2.
      the assumed degree of anthropogenic aerosol cooling input to each model as a ‘fiddle factor’ to obtain agreement between past average global temperature and the model’s indications of average global temperature.

      More than a decade ago I published a peer-reviewed paper that showed the UK’s Hadley Centre general circulation model (GCM) could not model climate and only obtained agreement between past average global temperature and the model’s indications of average global temperature by forcing the agreement with an input of assumed anthropogenic aerosol cooling.

      The input of assumed anthropogenic aerosol cooling is needed because the model ‘ran hot’; i.e. it showed an amount and a rate of global warming which was greater than was observed over the twentieth century. This failure of the model was compensated by the input of assumed anthropogenic aerosol cooling.

      And my paper demonstrated that the assumption of aerosol effects being responsible for the model’s failure was incorrect.
      (ref. Courtney RS An assessment of validation experiments conducted on computer models of global climate using the general circulation model of the UK’s Hadley Centre Energy & Environment, Volume 10, Number 5, pp. 491-502, September 1999).

      More recently, in 2007, Kiehle published a paper that assessed 9 GCMs and two energy balance models.
      (ref. Kiehl JT,Twentieth century climate model response and climate sensitivity. GRL vol.. 34, L22710, doi:10.1029/2007GL031383, 2007).

      Kiehl found the same as my paper except that each model he assessed used a different aerosol ‘fix’ from every other model. This is because they all ‘run hot’ but they each ‘run hot’ to a different degree.

      He says in his paper:

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

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

      And, importantly, Kiehl’s paper says:

      These results explain to a large degree why models with such diverse climate sensitivities can all simulate the global anomaly in surface temperature. The magnitude of applied anthropogenic total forcing compensates for the model sensitivity.

      And the “magnitude of applied anthropogenic total forcing” is fixed in each model by the input value of aerosol forcing.

      Kiehl’s Figure 2 can be seen here

      Please note that the Figure is for 9 GCMs and 2 energy balance models, and its title is:

      Figure 2. Total anthropogenic forcing (Wm2) versus aerosol forcing (Wm2) from nine fully coupled climate models and two energy balance models used to simulate the 20th century.

      It shows that
      (a) each model uses a different value for “Total anthropogenic forcing” that is in the range 0.80 W/m^2 to 2.02 W/m^2
      but
      (b) each model is forced to agree with the rate of past warming by using a different value for “Aerosol forcing” that is in the range -1.42 W/m^2 to -0.60 W/m^2.

      In other words the models use values of “Total anthropogenic forcing” that differ by a factor of more than 2.5 and they are ‘adjusted’ by using values of assumed “Aerosol forcing” that differ by a factor of 2.4.

      So, each climate model emulates a different climate system. Hence, at most only one of them emulates the climate system of the real Earth because there is only one Earth. And the fact that they each ‘run hot’ unless fiddled by use of a completely arbitrary ‘aerosol cooling’ strongly suggests that none of them emulates the climate system of the real Earth.

      Richard

      • Thanks for the writeup Richard. This is what I was referring to when I told “Sonic” yesterday that modellers just “turn the knob”. He retorted that they solve equations, not “turn a knob”.

        But, they make assumptions and those assumptions are turning the dial to get the desired result on the backtest while keeping the “run hot” in the forecast going forward. Of course, I don’t know the specifics as well as you.

        What’s you opinion on anthropogenic aerosols? Are they short-lived in the atmosphere and mainly a N Hemis phenomena? Wouldn’t changes in aerosols lead to dichotomy between hemispheres?

      • Mary Brown

        You ask me

        What’s you opinion on anthropogenic aerosols? Are they short-lived in the atmosphere and mainly a N Hemis phenomena? Wouldn’t changes in aerosols lead to dichotomy between hemispheres?

        Anthropogenic aerosols are “short lived” because they are washed out of the air by rain. Typically an anthropogenic aerosol emission will stay in the air for less than a month and, therefore, their concentration in the air is associated with sites of the emissions.

        There is a “dichotomy between hemispheres” as a result of the different ratios of land to ocean in the hemispheres. Hence, global temperature rises by 3.8°C in six months each year (and falls by the same amount in the other six months); see

        I hope this answer is what you wanted

        Richard

    • Three main reasons.
      1) We don’t understand all of the interactions that create weather/climate. As a result lots of assumptions are built into the models.
      2) Even if we did have a full understanding, we don’t have enough computer power to adequately model the weather/climate. As a result lots of parameterizations are introduced that are simpler to calculate but not as accurate.
      3) We don’t have adequate data to feed into the models. This is especially true when attempting to “tune” the models to replicate past climate.

  20. Various “It’s almost too late” warnings are popping up in news outlets based on the latest leaked IPCC reports. No mention of the accuracy of previous predictions just that current predictiosns are freightening. Be scared…I guess.

    Here was a headline from one of an article, “Climate Change Scientists Warn: We’re Almost Too Late”.

    What is interesting is, is there any such thing as a “Climate Change Scientist”? Aren’t they supposed to be “Climate Scientists” not “Climate Change Scientists”? The bias is using “Climate Change Scientists” is painfully obvious, personally I think they should be called “Climate Doom Scientists”.

  21. This might have been foreseeable decades ago when we had ‘social scientists’ fretting about the effect of violent cartoons (Felix the Cat, Daffy Duck, Elmer Fudd, Tom & Jerry, The Coyote and The Roadrunner, etc) on the minds of small children. The children could obviously tell the difference between reality and cartoons, but the Liberal Arts graduates could not. They still cannot. Only now we see the results of the proliferation of people who are unable to properly identify “Reality.”

  22. You should compare apples to apples. Use figure 9.8a from the actual AR5 compared to the draft graph above. They replaced the draft above (which was, of course, laughable — look a the “error bars” on the actual temperature, which somebody just drew in by hand) with a spaghetti graph that obscures just how badly individual models in CMIP5 do against e.g. HADCRUT4 — presented without any error bars whatsoever — to make it very, very difficult to assess the quality of the individual models in CMIP5.

    If they had done this honest, with every model in CMIP5 drawn against HADCRUT4, one at a time people might have been tempted to ask why we believe any of the models when they have enormous per-model temporal variance compared to reality in spite of already being averages over many perturbed parameter ensemble runs — they have the wrong variance, the wrong autocorrelation, the wrong mean as averages, and for a result that is already an average over (say) 100 runs that means that the actual model run variance is approximately ten times larger.

    To put it bluntly, if one compared the model runs from any model to reality one at a time people would laugh the model out of the room. What you are portraying above is the statistical band-aid on a truly spectacular failure, a failure of monumental proportions, where even hiding the failure as they attempt to do with multi-run averages and then averaging the averages and pretending that the variance of the model runs generated in this way is somehow relatable to the central limit theorem is utterly without foundation in the theory of statistics. If one attempted to actual present the tower of Bayesian priors that goes into each step of the formation of these super-averages and then does a posterior probability analysis of those priors, one simply concludes that the priors are almost certainly incorrect. Or in lay terms, that the models are bullshit, useless for predicting anything at all.

    rgb

    • The spaghetti graph is the only one of the two presented here that has any relevance to what the models projected. The IPCC’s is deceptive to say the least.

      Spencer’s spaghetti graph is the one to note. I smile every time I see it!

  23. Forecast vs Observed… So simple yet so dangerous

    I once worked on a forecasting project at taxpayers expense with eight other research groups. Our job was to create a forecast model that would become operational and solve a pesky problem.

    When we got to our conference we suggested that we run everyone’s systems and calculate their skill scores and investigate how they might used in a consensus approach or simply select the best one. The other 8 groups all refused to have their skills scores quantified. Instead they convinced the government funding representatives that our work was not very good and that more research was needed and another round of grant money should be handed out.

    So our perfectly good operationally ready model was ignored and another million dollars was handed around the table. We quit the project and have never sought grant money again. Now we stick to real-world projects where forecast minus observes actually matters.

  24. I’ve said it before and I’ll say it again.

    Reality is clearly faulty. It’s time it scrap the whole thing. I’m pretty sure we don’t need it for anything, but if we do, we’ll have to start from scratch and build a new one.

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