A few models wandered over the pause…

CMIP5-90-models-global-Tsfc-vs-obs-thru-2013[1]

Dana Nuccitelli has written a defence of climate models, in which he appears to claim that a few models randomly replicating the pause should be considered evidence that climate modelling is producing valid results.

According to The Guardian;

… There’s also no evidence that our expectations of future global warming are inaccurate. For example, a paper published in Nature Climate Change last week by a team from the University of New South Wales led by Matthew England showed that climate models that accurately captured the surface warming slowdown (dark red & blue in the figure below) project essentially the same amount of warming by the end of the century as those that didn’t (lighter red & blue).

There’s also been substantial climate research examining the causes behind the short-term surface warming slowdown. Essentially it boils down to a combination of natural variability storing more heat in the deep oceans, and an increase in volcanic activity combined with a decrease in solar activity. These are all temporary effects that won’t last. In fact, we may already be at the cusp of an acceleration in surface warming, with 2014 being a record-hot year and 2015 on pace to break the record yet again.

Read more: http://www.theguardian.com/environment/climate-consensus-97-per-cent/2015/may/06/pause-needed-in-global-warming-optimism-new-research-shows

The problem I’ve got with this line of reasoning, can best be illustrated with an analogy.

Say your uncle came to you and said “I’ve got an infallible horse betting system. Every time I plug in the results of previous races, plug in last year’s racing data, it gets most of the winners right, which proves the system works.”.

Would you:

  • Bet your life savings on the next race?
  • Wait and see whether the model produced good predictions, when applied to future races?
  • Humour the old fool and make him a nice mug of chocolate?

Anyone with an ounce of common sense would go for option b) or c). We instinctively intuit that it is much easier to fit a model to the past, than to produce genuinely skilful predictions. If your uncle was a professor of mathematics or statistics, someone with some kind of credibility in the numbers game, you might not dismiss his claim out of hand – occasionally skilled people really do find a way to beat the system. But you would surely want to see whether the model could demonstrate real predictive skill.

What if a few months later, your uncle came back to you and said:

“I know my model didn’t pick the winners of the last few months races. But you see, the model doesn’t actually predict exactly which horse will win each race – it produces a lot of predictions and assigns a probability to each prediction. I work out which horse to pick, by kind of averaging the different predictions. The good news though is one of the hundreds of model runs *did* predict the right horses, in the last 4 races – which proves the model is fundamentally sound. According to my calculations, all the models end up predicting the same outcome – that if we stick with the programme, we will end up getting rich”.

I don’t know about you, but at this point I would definitely be tending towards option c).

194 thoughts on “A few models wandered over the pause…

  1. Option c, or I would ask him to lend me the money for the bets with the promise to pay it back once the gains are in.

    • Yes, this reminds me of all the spam I get where I have to pay a few thousand dollars to get my millions of dollars…and there is always a “legal” catch that they just can’t deduct the few thousands from my millions.

      • There’s another scam involving an inverse pyramid scheme where you send out a power-of-two number of letters (say 1024) – half of which say the stock market will be up at some future date, and half of which say it will be down. To the 512 people who received the letters with the correct prediction you send 256 letters saying the stock market will be up at another future date, and 256 letters to the other half saying it will be down. …etc. until you’re down to say 32 people who have received 5 correct predictions in a row, and you concentrate on pressuring them to buy your ‘secret’ method of predicting the stock market.

      • Here are the leading paragraphs from The Texas Sharpshooter Fallacy Wikipedia article:
        The Texas sharpshooter fallacy is an informal fallacy which is committed when differences in data are ignored, but similarities are stressed. From this reasoning a false conclusion is inferred. This fallacy is the philosophical/rhetorical application of the multiple comparisons problem (in statistics) and apophenia (in cognitive psychology). It is related to the clustering illusion, which refers to the tendency in human cognition to interpret patterns where none actually exist.
        The name comes from a joke about a Texan who fires some gunshots at the side of a barn, then paints a target centered on the biggest cluster of hits and claims to be a sharpshooter.

      • Those scams are deliberately worded to fool only those suffering from dementia or alzheimers..
        I read Levitt and Dubner’s recent book, “Think Like a Freak”. , how one person was trying to devse a program to flood those con artist’ websites with spam= planting ‘weed” emails in their garden of the gullible.
        After reading that, I sometomes use one of my “gmail” junk accounts to reply to those spams, helping sew “weeds” in their gardens. I always ask them to deduct the fee from the balance, due me

      • noaaprogrammer,
        There is a short cut to this pyramid scheme now. Step 1, find someone scared of AGW. Step 2, sell them anything. Step 3, profit.

    • Great idea Ben. Maybe we should get the money for the sustainable energy projects from Algore and friends.

    • Back in the early 1990s, I set out to do battle with Reno Nevada casinos. My tactic was to use a round robin system, the NFL football over/under and point spreads would be utilized for making the picks. I decided on going with the six team parley ticket, also known as the suckers bet. It payed 52 to one. I made the choice of using 8 picks to fill in the 28 different tickets that comprised all possibilities of 8 picks into a 6 spot ticket. The beauty of the system was that I no longer had to be 100% correct to have a winning day. As long as I could pick 6 out of 8, then I had 1 winning ticket paying $52 for every $28 spent on the total group. I did very well for 6 years. Although, I could never hit that elusive 100%.
      I would have never considered embarking on this gamble in the first place, except that I found that I could consistently pick 2 out of 3 correct every weekend from a group of selected picks. I typically would make between 13 and 16 four star choices for the main list. Then I would draw my 8 choices for each package played. My best weekends were 15/16, 14/15, or 13/14 correct picks, around 10 times in the 6 years.

  2. a team from the University of New South Wales led by Matthew England showed that climate models that accurately captured the surface warming slowdown (dark red & blue in the figure below) project essentially the same amount of warming by the end of the century as those that didn’t (lighter red & blue).
    Which translates as ‘the models are useless at predicting what will happen’.

    • Actually, Nuccitelli’s rantings translate to “3% of the models got it right”.
      I feel for the fool.

      • But you really should give him credit for the effort. With results like that, most would have given up already.

      • Does that translate to a 3% Consensus on The models/science is right/indisputable.
        Nuccitelli’s silly fool rule!

      • “3% of the models got it right”
        That proves that a 97% consensus can be wrong!
        How nice of Dana Nuccitelli to point that out.

  3. The model’s usefulness for making predictions is so weak that they IPCC does not endorse it.
    From PCC AR5:

    Frequently Asked Questions
    FAQ 1.1 | If Understanding of the Climate System Has Increased, Why Hasn’t the Range of Temperature
    Projections Been Reduced?

    The models used to calculate the IPCC’s temperature projections agree on the direction of future global change, but the projected size of those changes cannot be precisely predicted. Future greenhouse gas (GHG) emission rates could take any one of many possible trajectories, and some underlying physical processes are not yet completely understood, making them difficult to model. Those uncertainties, combined with natural year-to-year climate variability, produce an ‘uncertainty range’ in temperature projections.

    The fact that Dana does endorse it is evidence that he has nothing else. And that he’s a fruit loop way out there away from the scientific mainstream.
    The whole scare lives and dies with the models.
    RIP.

    • The direction is definitely up, but the uncertainties are such that it could be up by no amount, or possibly even up by a negative amount.

      • My prediction for the S&P trade tomorrow…It will be up, down or sideways. Take that to the bank.

      • Yes, I’m afraid it does Eric, he’s luverly.
        “Being a connoisseur of strange, exotic and fabled creatures, I like Dana immensely because he’s a pure prat, a thinking man’s prat as well as a mustachioed pipe-smoking woman’s prat, a complete thoroughbred prat prancing around like a ballet dancer doing seemingly impossible grand jetés through the mediasphere, clad in nothing more than green hose and a beautiful miniature yellow codpiece with silvery tinsely bits carefully streaming back out between his legs like the tail of a flatulence-propelled Halley’s comet.
        He flies through the air, right arm and fist stuck out defiantly like superman and steely eyes firmly fixed on the coming green state of bliss. And all of that in casual hush puppy shoes with invisible high riser heels. Visually, he’s mind-blowing, gorgeous and ultimately jaw-droppingly beautiful. I want to have his babies.”
        https://thepointman.wordpress.com/2013/12/20/climate-prat-of-2013-we-have-a-winnah/
        Pointy

  4. Are actual scientists falling for this scam?
    I mean, come on… You don’t have to study thermodynamics, even basic physics, not to be a numerical modeler, nor to be a computer scientist…
    Do universities secretly perform unneeded brain surgery on students now? Who performed the first common-senserectomy and didn’t get the Ig Nobel?

    • It is surprising how many scientists will suspend scientific principles when it comes to religion, it is not surprising that they would when it comes to ideology as well.
      There was a talk by Neil deGrasse Tyson addressing why Americans were so scientifically illiterate, for example a large majority of the public believe the Adam and Eve story as factual. I can’t remember the exact survey number but the percentage of scientists deferring to religion was a much smaller minority (maybe 15%) , but the point was if a PHD scientist finds the need to defer to religion or ideology even if only in selected areas, how can we expect the public to not do the same?
      In the end religion and ideology are part of the human experience, driving the innate need to belong to a group or tribe. The challenge for modern societies is preventing religion or ideology becoming a cancer on the society.

      • It is surprising how many scientists will suspend scientific principles when it comes to religion, it is not surprising that they would when it comes to ideology as well.
        Don’t forget the motivators of pay, status, perks, benefits, recognition, tenure, political tribe signalling, job security…
        Doesn’t NDT (being part of the Klimate Kult) ridicule non-believers?

      • Wars have been fought over whether they were Braeburn or Fuji apples in the Garden of Eden.

      • Anything coming from Tyson is immediately suspect, he’s a glory hound who has no trouble lying if it advances the cause.
        The only survey I’ve ever seen regarding evolution and scientists asked about the belief that God played a hand in evolution. Which is a far cry from believing that the earth is 5000 years old and all animals were created as is.

      • ALX Religion comes in many forms. I’ve requested examples of the experiments that proved DNA could occur naturally and you failed to respond. When you can disprove Sir Fred Hoyle you can convince me that evolution is not a religion.

      • Max,
        RNA occurs naturally. It self-assembles from its constituent parts, for example, in ice or when catalyzed by PAHs, which abound in the universe. RNA not only replicates itself, but catalyzes peptide formation, from which polypeptides, ie proteins, bond together. From the simple, dual-function RNA World of early life developed DNA World, in which DNA took over from RNA the genetic information storage and replication function, while RNA specialized in the metabolic functions. Retroviruses still use RNA in this original, replicative way, although they hijack the metabolism of cells in order to reproduce.
        Evolution is not a religion but a repeatedly observed, scientific fact explained by a body of theory, just like the germ theory of disease, the atomic theory of matter and the theory of universal gravitation, among other well established but constantly improved theories.
        Existing species giving rise to new species by a variety of evolutionary processes both in the wild and in labs is so frequently observed as to be trivial. The evolution of new genera is witnessed less often, but still repeatedly. Instances of observed evolution of higher taxa is naturally less frequent, but can be inferred throughout the history of life from many observations including those not available in 1858.

      • I might add, apropos of Sir Fred’s Panspermia Hypothesis, that some recent origin of life work has produced chemical evidence that life on earth might have gotten started on Mars. That would be a very soft version of panspermia.
        http://www.newscientist.com/article/dn24120-primordial-broth-of-life-was-a-dry-martian-cupasoup.html#.VVFJw_BultA
        Abiogenesis of course is a separate study from evolution, which is about what happens after cellular life already exists, not so much about how it gets going. It’s chemical evolution as opposed to biological evolution, although processes akin both to selection and stochastic evolution probably occur in the development of pre- and peribiochemistry, too.
        IMO where and when life arose are less important questions than how. There has also been some recent good work done on what might have preceded the well supported by evidence RNA World.

    • “Are actual scientists falling for this scam?”
      No. Its pretty clear from the comments on The Guardian site under all these kinds of posts, that nobody on there who supports this ‘thinking’ is any kind of actual scientist.
      Idiots, yes, scientists, no

  5. Mods
    My post has vanished. Please let me knowif it is not in the ‘bin’.
    Richard

  6. “some underlying physical processes are not yet completely understood,” I think “we don’t have much of a clue about what drives climate!” Would be a better expression, certainly more truthful.
    Reminds me of the BBC’s “No one can explain what effect the power of the Sun has on our climate, but whatever it is, it has already been overtaken by manmade global warming!” We don’t know what effect element A has upon element B, what whatever it is, it’s been overpowered by element C! If that isn’t the most stupid statement ever made I don’t know what is!

  7. Stealing billions from taxpayers under the pretext of spreading the bets is not something I would give anybody a cup of hot chocolate. A punch in the nose, perhaps.

  8. Essentially it boils down to a combination of natural variability storing more heat in the deep oceans, and an increase in volcanic activity combined with a decrease in solar activity. — Dana

    But wait, all along the meme has been that natural variation, volcanic activity, and the sun have been factored out as irrelevant. Now that Dana finally admits other factors are relevant, is this a sign of Dana’s faith based global warming beliefs beginning to waiver? Heaven forbid! Natural variation, and other non-human CO2 activity is only “temporarily” affecting global temperature. Well that’s good news, the faithful require mans CO2 to wrest back control from an unpredictable nature.

    • I tried to post that. Hold the front page and surprise of surprises, it got moderated. They *really* don’t like it when such an obvious truth is pointed out to them.

    • “Essentially it boils down to a combination of natural variability storing more heat in the deep oceans, and an increase in volcanic activity combined with a decrease in solar activity.”
      ++++++++++++++++++++++++++++==
      Why do they not publish details of exactly what the specific forcing’s of those two models closest to reality actually used? I have yet to see this information. The likely reason is that the details of those forcing scenarios in no way match what the earth is actually doing, and showing this would further discredit the already failed models.
      We know from Bob T detailed posts that the actual very small recorded rise in ocean T is below, not above what the IPCC expected. We know the surface SST, the only part the affects the atmosphere, has been hiding nothing, but in fact is very high, hiding nothing. We have been ENSO neutral for much of the pause, especially the last few years, although the AMO is starting to turn this is only the beginning of a balance to the warm AMO that drove much of the multiple decade warming. Volcanism has certainly been relatively flat during the pause.

      • Eric W, I question the first graphic. Are the model runs for the surface or the troposphere? The troposphere runs should be higher then the surface. The observations shown are for both the surface and the troposphere? Also the graph does not show the current UAH chart.
        Thanks

  9. It’s not really a fair criticism.
    If 20 different people produced betting models and 19 were rubbish, you can’t conclude the 1 who got it right has no skill.
    Now they could be getting it right by chance, but it isn’t like the same person got 19/20 wrong. A person got their model right.

    • Christoph Dollis
      There is no reason to suppose that any model got it right in a manner which suggests they will get it right in future.
      Similarly, if you spray water from a shower rose and some drops contact the wall there is no reason to say which future drops will hit the wall.
      My post that has vanished explains this. I will try to post it again and see if it appears this time.
      Richard

      • So by your way of doing science, if some large number of people get it wrong and some small number of people get it right, one can’t say anything about that except, “Hey, big bucket! A few drops don’t matter.”
        You realize this utterly invalidates all skeptical, minority positions on everything in one fell swoop?
        (Your position doesn’t work.)

      • Christoph Dollis
        Science does work. It has given humanity many benefits.
        You failed to understand my first explanation so I will try again.
        A model needs to make a series of accurate predictions of the future before it has demonstrated any predictive skill.
        This is because
        1.
        The are an infinite number of ways to model the one pattern of past global temperature changes observed to have happened.
        2.
        And there are an infinite number of possible patterns of future global temperature and an infinite number of ways to model each of them.
        3.
        But only one pattern of future global temperature will occur.
        4.
        Therefore, there is an infinite number of possibilities that a model which matched the pattern of past global temperature changes will not match the pattern of future global temperature which has yet to occur.
        Richard

      • Christoph Dollis
        You ask me to “Define “get it right” “. I have.
        You used the phrase “A person got their model right” which is in the past tense.
        I discussed the ways a model model matches the patterns of past and future global temperature. A model may match the past pattern (i.e. got that “right”) but – as I explained – there are an infinite number of possibilities that the same model will not match the future pattern (i.e. will get that “wrong”).
        Richard

    • Christopher,
      I am sure you receive several more scientifically worded responses but here in Texas we would simply say, “Even a blind pig finds and acorn now and then”.

    • If 20 different people produced betting models and 19 were rubbish, you can’t conclude the 1 who got it right has no skill.
      ======
      then why doesn’t the IPCC and climate modelling community drop the 19 models that got it wrong? you can certainly conclude they 19 have no skill.

      • Now that they have the ‘right’ model, it won’t be too difficult to replicate. This could be a breakthrough. (Even though the science is settled, it would be comforting to have it confirmed before we allocate the next trillion.)

      • That was my thought. Of the two models that got it “right”, then document the inputs and coded processes
        and move forward. Drop the others. Of course bad luck for the keepers of the models dropped. They will be finding ways for ask for more research money or “Would you like fries with that?”.

      • Since pretty much Anthony, I, and every skeptic argues that CO2 is a weak greenhouse gas, easily dwarfed by natural factors, but may have led to some small warming, that most of the models exaggerate … I can’t dismiss out of hand that the few models which match the observations fairly well might have been skillfully made.
        Or, as I allowed for, it could just be chance.
        But we shouldn’t find ourselves in the position of dismissing models that match our own general position based on the observations just because.

    • If they can’t explain why the one got it right and the other 19 got it wrong, then the odds are the one that got it right was just coincidence.

      • Your problem is making the one group “they”. You’re not allowing for the possibility that these scientists may vary greatly in skill and understanding.
        So most of the scientists in question might be producing rubbish, but not necessarily all.

      • Christoph, this modeling exercise does show, if nothing else, just how far from “settled” that climate science really is. Going along with your possibility, if perhaps one or two scientists are not producing rubbish, and the rest are, then a person of intellectual honesty would own up to this fact and declare (1) this science is indeed very far from settled, and (2) we will discard the 88 models shown defective to this point, and continue to follow the 2 that have not yet disqualified themselves. Along these lines, they would acknowledge that future warming predictions may be incorrect, and that we must continue to observe these 2 remaining models until either the actual temperature begins a steep uptrend, at which point models may gain back some credibility, or until the 2 remaining models have failed to show skill, at which point the models have zero credibility and governments quit funding their development.
        One other point – if the 2 low trending models do indeed end up at approx. the same temperature by 2100, the only way this could happen is if they enter a steep uptrend at some point where they close the gap on the average of the models. I have not seen a chart with the models projected out to 2100. It would seem more likely that those trending low now would continue the shallower trend, thus ending at a far lower temperature by 2100. What would cause them to initially trend less steeply than the ensemble average, and later change to trending more steeply, to “catch up” so to speak. This would seem unlikely to me. If there is some rational behind this thinking, it would seem that would be the ultimate test of skill. See how well actual temps follow as the model turns to a very steep uptrend. If they actually did follow it steeply upward for several years, that would be an indication that the models do have skill. Until that time is reached, there is no merit to the claim that the science is settled.

  10. “Say your uncle came to you and said “I’ve got an infallible horse betting system. Every time I plug in the results of previous races, plug in last year’s racing data, it gets most of the winners right, which proves the system works.”.”
    A better analogy would be that you have a loaded dice which tends to come up “6”, but not always because that would be too suspicious. The dice comes up with 1s and 2s for 18 tries. Should you stop betting on 6?

    • If it came up ‘6’ only slightly better than raw chance, then I wouldn’t bet much.
      Regardless, in this situation, the “models” are coming up with the “right” answer way less than pure probability would predict.

  11. This is a variant on “The Broken Clock Fallacy”. Take 90 broken clocks. You can expect at least one of them to be within a few minutes of the exact time. However, you do not know which one. Furthermore, the average of the times shown on the broken clocks is likely to be a long way from the actual time. (Should also specify that the clocks are running, but not keeping accurate time.)

    • Walt D.
      No, this is an example of the Texas Sharpshooter fallacy. My post(s) that have vanished explain this with a link to an accurate wicki explanation of the fallacy.
      Richard

    • The The Broken Clock Fallacy is about stopped clocks….not clocks running fast or slow.The idea is that a broken clock is right twice a day (the US at least).

  12. Definition of an Economist – Someone who tries to explain why what they predicted yesterday would happen tomorrow did not happen today.

    • There’s also a little predicting what’s gonna happen next week after a failed prediction that it would happen last week:
      “In fact, we may already be at the cusp of an acceleration in surface warming…”
      Or, “our stock is likely undervalued and on the cusp of an acceleration in price…”
      But I really like this: “These are all temporary effects that won’t last.” Yup, with prose like that there is absolutely no chance that the climate change issue is suffering from a communication problem. Alas I can’t read the rest of the article ‘cos I’m still on my Guardian boycott.

    • Don’t knock economists. They correctly predicted nine of the last three global recessions.
      (source unknown)

  13. There should be another course in denial 101 – which aims to teach :
    •How to recognise the social and psychological drivers of natural climate variation denial
    •How to better understand that natural climate cycles beyond anthropogenic control do occur, and gather the evidence of how they function by the collection of validated raw datasets. This will allow realistic mitigation controls to be implemented.
    •How to identify the techniques and fallacies that some climate researchers employ to ‘homogenise’ historic climate records
    •How to effectively debunk climate misinformation and alarmism created by modelling techniques that consistently trend away from current observations
    Works both ways – doesn’t it?

      • Just download the online course, search for “anthropogenic” and “man-made”, and replace with “natural”, and you have a course in Natural Climate Change Denial – the “denial” process is the same regardless of which side of the climate change causation (anthropogenic or natural) one is aligned to.
        Those occupying the middle lukewarmer ground are being labelled simplistically and incorrectly as “deniers of climate change” when in reality they have an open mind on the subject.

  14. This is the attempt at a repost that I promised to Christoph Dollis.
    Friends:
    I again remind of the following.
    The selection of models that seem to fit after the event is an example of the Texas Sharpshooter Fallacy .
    Such post hoc selection indicates nothing about ability to forecast the future but it is tempting to think it does, and fr@udsters use the temptation to mislead their ‘marks’.
    I again explain how they do this.
    A set of, say 4, different investment plans is generated.
    Each investment plan is sent to, say 4000, random people.
    At a later date one (or more) of the plans has provided a very good return.
    Those who were sent the ‘successful’ plan are now sent a report of its ‘success’ together with another investment plan. These new investment plans are another 4 different investment plans so 4 groups each of 1000 people each obtains one of these second plans.
    Again, at a later date one (or more) of the second plans has provided a very good return.
    Those who were sent the ‘successful’ second plan are now sent a report of its ‘success’ together with a third investment plan. These new investment plans are another 4 different investment plans so 4 groups each of 250 people each obtain one of them.
    Yet again, at a later date one (or more) of the third plans has provided a very good return.
    Those who were sent the ‘successful’ third plan are now sent a report of its ‘success’ together with an offer to invest $10,000 in the next investment plan which uses the astonishingly accurate prediction method that has apparently been successful three times without fail.
    If 100 of the 250 targeted people invest then the fr@udsters gain an income of $1,000,000.
    This is, in fact, the same ploy as is used when the ‘best’ climate models are selected after the event.
    Richard

    • Thank you Richard. Have you seen this one?
      In a 1984 speech, Buffett asked his listeners to imagine that all 215 million Americans pair off and bet a dollar on the outcome of a coin toss. The one who calls the toss incorrectly is eliminated and pays his dollar to the one who was correct.
      The next day, the winners pair off and play the same game with each other, each now betting $2. Losers are eliminated and that day’s winners end up with $4. The game continues with a new toss at doubled stakes each day After twenty tosses, 215 people will be left in the game. Each will have over a million dollars.
      According to Buffett, some of these people will write books on their methods: How I Turned a Dollar into a Million in Twenty Days Working Thirty Seconds a Morning. Some will badger ivory-tower economists who say it can’t be done: “If it can’t be done, why are there 215 of us?” “Then some business school professor will probably be rude enough to bring up the fact that if 215 million orangutans had en­gaged in a similar exercise, the result would be the same-215 ego­tistical orangutans with 20 straight winning flips.”

    • I certainly agree that we can’t say these models which seem to perform are skilled, whereas we can say that most of these models are not skilled. However, I don’t think we can rule out that some of the models that seem to match observations are skilled, nor do I think it ought to bother us if one or more are skilled over time. Indeed, that would be great.

      • None of the models is skilled. They’re all based on faulty and inadequate assumptions. It’s just that some of them make less wildly erroneous presumptions. The two that diverge the least from observations (not that HadCRU is actually real data) use the lowest, ie less preposterous, estimate for Equilibrium Climate Sensitivity (ECS), ie 2.1 degrees C per doubling on CO2 level.
        In fact, negative and positive feedback effects probably roughly cancel each other out globally, for an ECS closer to 1.0 than 2.0 degrees, let alone the allegedly “canonical” 3.0 or 4.5 and falling.

  15. My view is that the “best” that can be said is that some models (3%?) out of many models showed some correlation with “the pause”.
    The problem is that this fact (that some very few models correlate with the pause) is being used as an implied argument to say that all the models are somehow “right” and that things are heating up exactly as “the models” (all of them) have suggested all along.
    Of course this is rubbish. I’d fire anyone who persisted in presenting such rubbish logic to me.
    Unfortunately, this “argument” will be seen as perhaps having some sort of validity by many journalists and others.

  16. No model got it right. Those few that are now crossing over the actual observations were simply too low in tha 1990s and are in the process of catching up with their even more erroneous fellows.

    • “No model got it right. Those few that are now crossing over the actual observations were simply too low in tha 1990s and are in the process of catching up with their even more erroneous fellows.”
      Indeed, it seems that climate models show considerable skill in predicting events which have already happened.
      On future trends – who knows?

  17. “First they tell you that you’re wrong, and they can prove it.
    Then they tell you you’re right, but it’s not important.
    Then they tell you it’s important, but they’ve known it for years.”
    -CF Kettering
    We see phase 2 in action. When do the Nutti warministas move on to phase 3?

  18. Reblogged this on Centinel2012 and commented:
    It is my understand that all these projections are based on economic projections first and then CO2 second assuming a CO2 release based on those projections. So this is really not a CO2 driven system but an economic driven system. That is what gives so many different outcomes. I’m not even sure this is science it seems to be more a study of social system.

  19. “There’s also no evidence that our expectations of future global warming are inaccurate.” Isn’t this a reversal of the burden of proof. He’s effectively expecting skeptics to have to prove the null hypothesis. He should be reminded that the burden of proof lies with those with the hypothesis. It’s up to him to prove accuracy.

    • They’ve always done that, the warmist’s obsession with consensus is another way they try to usurp that status quo position and shift the burden of proof to the other side.

    • The difference between the climate models and the Texas Sharpshooter is that some bullets actually hit the side of the barn!
      /Sarc

  20. This is the apocalyptic-al hysteria of the climate alarmist.
    When religious groups were predicting the end of the world most of the intelligent world laughed at them in the face. These alarmists are as bad as those religious fanatics (or perhaps worse) and all I can do is laugh in their faces.

  21. Whether intentional or not, Nuccitelli’s rant has elements of the “I agree with you, but you’re wrong” con gambit. The con artists intent is to make you shake your head “yes,” and then they use the comeback, “but I have more information.” It’s often used in selling shares in “speculative” ventures to unsophisticated victims.

  22. I have a model of climate sensitivity. If calibrated to results known to the IPCC’s creation in 1990, it fits the last 25 years well (out of sample). It predicts the pause. So it hindcasts well and fits well out of sample as well as getting the general shape accurate.
    It predicts 0.9C of warming over the 21st century at current emissions.
    On evidence currently available, I am the greatest climatologist on earth.

  23. Here are the two highest and two lowest models from IPCC AR4.
    The average of UAH and RSS satellite temps is, right now, LOWER than even the lowest model. Hadcrut4 is creeping up toward the average (now that Hadcrut4 is also incorporating the extra adjustments added to surface records).
    But it makes no sense to cherrypick out the two low sensitivity models which seem to have a lot of RANDOM WALK VARIABILITY and then say the models are accurate. (Note these two models have only 2.2C per doubling built into their assumptions, the lowest that IPCC AR4 would accept. Why this would be an “assumption” is strange enough on its own)…
    … Without talking about how the two highest sensitivity models are so far off right now, it is ridiculous. (Even when they had historic data up until 2004 to use in their hindcast, they were still miles off at that point).
    2 high and 2 low models versus Hadcrut4 and UAH-RSS average. All on the same baseline so they are comparable.
    http://s12.postimg.org/bqjdgnffh/IPCC_2_high_low_vs_H4_UAH_RSS_Apr2015.png

    • Model runs assuming a more realistic CS of 1.2 degrees C per 2xCO2 would presumably be below “observations”, which means not that CS is higher but that the observations include man-made “warming” via unwarranted adjustments.

    • That’s the thing. The reason that this graph was cut from the final report was that it showed the actual range of the prediction, which is so wide that even the scientifically illiterate can say “isn’t that predicting almost nothing”

  24. Barn door style guesswork can be right on occasion.
    If when asked to name the card, I answer with the name of every card I can claim 100% accuracy rate . I do not even need models to do it . The trouble comes when I cannot answer with the name of every card .

  25. This scam has been used for years in the Mutual Funds industry. Create 20 different funds with 20 different mixes of stocks. In a years time some will have done well, others will have done poorly. Promote the hell out of the one or two funds that did well as proof of your superior ability to pick stock winners and at the same time quietly drop the funds that did poorly while creating 20 new funds.
    A similar strategy is used by con artists. They generate all possible future outcomes on a horse race or some other betting event and email these out randomly and wait. Most people will receive failed predictions, but some will receive winning predictions, as proof that the con artist’s betting system works. All you need do is invest your life savings to get in on the system.
    Unfortunately for climate modelers we can see the other emails the con artists is sending out, so we know the system doesn’t work any better than chance. A dart board or pair of dice could predict the future with similar accuracy.

  26. The Australian slang for the kind of guy that plays this game at the racetrack is an “urger.”
    “Urger” is defined in the Dictionary of Racing Slang by Ned Wallish as “a racecourse con man who will urge an unsuspecting punter to back a horse after telling him a most impressive story. If the horse should win, the urger is always present when the punter collects to obtain or demand a portion of the winnings.”
    Also look up “tout” which is a slightly more sophisticated ploy but still involving pretending to know something that you don’t.

  27. “There’s also no evidence that our expectations of future global warming are inaccurate. ”
    So he’s saying there’s no evidence showing that future predictions, a.k.a. “Stuff that HASN’T HAPPENED YET”, is wrong?
    No much of a cheese shop then, is it?
    Amazing that these people continue on, without some of the friends and associates saying “Ok…Dana?…what you just said was pretty stupid. Really.”

    • “…No(t) much of a cheese shop then, is it?”
      LOL
      Palin – “But it’s so clean!”
      Cleese – “Certainly uncontaminated by cheese”
      Palin – “but you haven’t asked about Limburger”
      Cleese – “is it worth it”
      Palin – “could be…”
      Love any references to Python (Monty)

  28. well fine…..take them for their word
    Throw all the other models out…and starting right now we only go by the models that got the ‘slowdown’

  29. You are being far to magnanimous when you suggest that an economist, in making a range of predictions, actually finds that a the subsequent observed data fits within his/her range of predictions.
    Though implicit in your comment, and rightfully so, is that economic “science,” along with climate “science,” both have near perfect records of producing predictions that are totally wrong.Coin flipping would at least have a 50% chance of producing a correct prediction.

  30. ‘ … a team from the University of New South Wales … showed that climate models that accurately captured the surface warming slowdown … project essentially the same amount of warming by the end of the century as those that didn’t … .
    ‘There’s also been substantial climate research examining the causes behind the short-term surface warming slowdown. Essentially it boils down to a combination of natural variability storing more heat in the deep oceans, and an increase in volcanic activity combined with a decrease in solar activity.’
    Ok, hold on here just a second. Let’s go to the second paragraph first. So, there’s been a substantial amount of research examining the slowdown. Was this research done before the model ensembles? If so why do a significant majority of those models not show the slowdown? Why do only the outlier models show it? Were those specific modelers privy to this substantial research, and the other modelers not privy to it? Or, was it more likely that nothing other than chance contributed to the outlier models sort of catching the slowdown? The author here is conflating two different things. And, wants to have his cake and eat it too. The two paragraphs are not aligned but they’re claimed to be. The panicked research into the slowdown followed its appearance in the records. It did not precede it. As such, any model showing the slowdown could not have benefited from that research. Try as one might, the two cannot be married. Claiming so is not science. It might be law. It might be public relations. It might be advocacy. It might be propaganda. But it is not science.

  31. A significant statement was made: “Essentially it boils down to a combination of natural variability storing more heat in the deep oceans, and an increase in volcanic activity combined with a decrease in solar activity.” These other things a affecting the Global Temperature: NOT JUST CO2.
    Solar EUV varies much more than TSI, but is in sync with TSI. Why not use it to track the “decrease in solar activity”?? Simply, less Solar EUV, less Solar energy as indicated by the 10.7cm Flux.
    http://www.spaceweather.ca/solarflux/sx-6-mavg-eng.php
    As per the deep oceans, let’s look at the Atlantic first. The AO has changed from the warming to the cooling phase:
    http://weather.gc.ca/saisons/animation_e.html?id=month&bc=sea
    is a great link to watch ocean changes [also a great animation].
    Remember, unless driven by outside forces, heat usually rises therefore the ocean surfaces should be warm. The Solar energy warms them first. Now, if the oceans show cold, that cold probably goes deep.
    The Pacific is a special case due to the Trade Winds at the Equator. The Solar warmed water moves from east to west. When is reaches the west, the currents drive the warmed water deep 200 – 300 meters. It travels back to the east. BUT, the warm water was Solar heated and Solar driven by the Trade Winds. Less Solar energy gives less Pacific warmth. Note the time delay before last years warm water will rise in the east.
    Volcanic activity??? Where?? Hidden under the Oceans???
    What about the Global Sea Ice at almost 1,000,000 sq. km. above average. Doesn’t that say anything?

  32. So, 2 models aren’t as wrong as the other 88 are, so that means the models are a success?!
    Further, that statement should read “2 models aren’t as wrong, yet…”
    The really nutty part about this is that there will be many who accept Nuccitelli’s assertion.
    I predict Nuccitelli will predict that 97% of climate scientists agree with him.

  33. As far as I am aware, climate models are based on the IPCC belief system that CO2 causes global warming through the magic of back-radiation. Back-radiation is another way of saying infra-red radiation from infra-red active gases such as CO2 and other “greenhouse” gases in the atmosphere. As there has always been CO2 in the atmosphere since the very first life forms evolved on Earth, there has always been “back-radiation” since then. Most importantly it is still with us today. The absorption and emission of IR does not stop or even pause. It is always happening. Thus the stable global temperature for the whole of this century in spite of a near 10% increase in CO2 concentration proves beyond any doubt whatsoever that the IPCC belief is false.
    IR is electromagnetic radiation so it travels at the speed of light. That means that it takes an IR photon a mere 37 microseconds to pass through the troposphere. Which means that any IR interaction with a greenhouse gas is going to be almost instantaneous. There cannot be any 15 plus years wait before back-radiation occurs. Just a few seconds of no warming after the addition of more greenhouse gas is sufficient to prove that back-radiation does not cause global warming. However we have waited 15 plus years and still cannot agree. It would seem that the modern human race is seriously lacking in its former powers of deduction. Clearly that is another catastrophe bought on by man-made CO2 so we must all shut down our power stations and go back to living as the Neanderthals did.

    • So that would indicate that “all” the other research that tries to explain the “pause” is flat out erroneus, but it is still included in the discussion for some reason.

  34. I left a couple of comments at Nuccitelli’s Guardian post about the Nature-piece that seems to be the central pillar. The very short summary is: It’s bunk! A no-result wich is totally expected.
    I’ll post my analysis and conclusions here too (and there will be some repitition, sorry)
    _________________________________________________________________________________
    Well, at least I got a good laugh ….
    Allmost all the links given were to SkS or Guardian or some other activist blog. So I checked the Nature-reference. It was a piece by M.England et al (so called ‘climate scientists’) and discussed the pause, trying to downplay its relevance, and to fend off suspicions that models run thoo warm, and their simulations imply sensitivities are a tad too high.
    The way they go about this is quite something else …
    But first one needs to know that all these climate models nothing but code, instructions based on the models (assumptions of the modelers) built into them. Running them will give you what these assumptions amount to. And as we know those models, all of them, produce squiggly lines trending upwards. And there is some noise built into them too, depending on how they are ‘initialized’ these squiggles look different, occur at different times, and make the whole thing look a little more like the temperature record. But letting them run, simulating the 21:st century, each model with trend as it is essentially instructed to do, and show some superimposed noise (usually interpreted as ‘natural variability’).
    So here is what England et al did, in order demonstrate that they still give:

    Robust warming projections despite the recent hiatus

    (Yes, that’s their title!)
    They took many of such simulations using different GCM-models, and looked at those ‘realizations’ (model runs) that happened to display a warming 14-year slowdown between 1995 and 2015. (As ‘slowdown’ was defined temp.trend from ‘cold’ 2000 to ‘warm’ 2013). They even (mis-)labelled this as ‘capture a slowdown’. Whereas it merely was those runs who just happened to be a bit slower around then.
    OK, thereafter they compared the where these runs ended, compared to the rest of the runs (that didn’t ‘capture’ any slowdown there)
    And voilá, the 2100 simulated temperatures for both these groups came out almost on top of each other! Their claimed ‘conclusion’ is astounding:

    We have shown that here that there is no significant shift in projected end-of-century global warming .. This suggests that the recent surface warming slowdown is associated with variability not influencing long term climate change .. In short, the drivers of the recent hiatus do not alter the century-scale warming

    To summarize: Essentially, they have a bunch of trending straight lines, with some noise added. And notice that those lines where the noise seemed to counter the trend at around 2000, pretty much ended in the same ballpark as those where such noise occurred at other times, or wasn’t that prevalent at all!
    Wow!
    That’s climate science™ by climate scientists™. And unfortunately, it is not at all unrepresentative for quite a lot I’ve seen. Not only is thus tosh not any (real) science at all, but what they claim to show is sheer nonsense. And not only that, it reflects very poorly on them that they put such silly stuff in writing and even send it to Nature.
    To be fair, Nature doesn’t label this science, its Opinion and Commentary. But as we know, the activists don’t care about such details. Dana tried to present it as a ‘paper’ in Nature, and interjected ‘accurately’ before ‘capture’. Well, its printed, in Nature, it’s a reference, it will be marketed and used as such as it adds to those 10.000:s of ‘papers’ making up this ‘consensus which are said to prove something.
    Well, this one does. But nothing flattering.

    • I picked up the the topic and added some more points.
      ________________________________________________________________________________
      Ok, a quick recap first:
      Further down I posted a summary of what that Nature-piece (by M.England et al) mentioned above amounted to. And wondered if anybody would respond or address the content and/or my summary and analysis (in a meaningful way)
      Here I am going to expand a little on my summary. But first let me re-iterate that this short Nature-piece is well written and easy to follow for almost anybody. Because the ‘analysis’ they actually perform is very simple. And the ‘data’ they used is not the least contested: It is the output by the CMIP5 GCM models, and that those churn out such projections is not contested either.
      (The question whether or not such models are useful, e.g. for predicting the future, or if they overestimate the sensitivity to CO2 is contentious, however.)
      But here only their output is analyzed, and that analysis is straight forward. Below, I will describe what that ‘analysis’ does, and later on I’ll explain why the authors’ conclusions don’t hold up.
      Simply put, a GCM tries to model the climate system with its components, and particularly how it responds to increased ‘forcing’ as eg by increased GHG-levels. And albeit quite complex (involving many parts, their function, interactions etc within the system) their output is close to a proportional response to external forcing, with a slight time delay.
      On top of that there is all else that goes on in the system, mostly weather and other internal variations (interactions among various components not considered ‘forcings’)
      The result (for a steadily increasing GHG-forcing) is essentially a straight line with superimposed noise (weather and other internal phenomena). The noise is not considered to be of any predictive value, although it is sometimes said to represent (the magnitude of) possible internal variations and to calculate the probabilities of certain outcomes, eg. heatwaves etc.
      It is also worth noting that the various GCM:s among them produce a range of such scenarios, and depending on how they are ‘initialized’ the internal variations might come out quite differently for any particular GCM. Therefore, these simulations (as with the CMIP-project) are often presented as ‘ensembles’ from both many models, and many realizations of each one of them. The (averaged) result is then deemed to represent some best (modelled) estimate, and the scatter around it to be representative of inter-model (and other) uncertainty including internal variation. As is implicitly done every time when the hiatus is deferred to ‘just natural variability’ btw, and also in this Nature-piece.
      Anyway, for practical purposes (and steadily risning GHG:s) these realizations can be seen as an upwards trending line (representing the model-determined sensitivity) with superimposed noise (representing weather and other variability)
      With me so far? OK!
      What the authors have done here is merely to look at all those (differing) simulations, making up the CMIP5-ensemble, and sorting them into two piles:
      1) One with those that did not display any hiatus/slowing down between 1995 and 2015, defined as 14 years somewhere within that time span with average trend les than 0.096C/decade (a value taken from HadCRUT4 for 2000-2013) and which is less than half of the all-model-ensemble during the same time (~0.23C/decade). This would be by far the largest pile
      2) The other pile contained the remainder, those simulations that happened to show such a 14-year slowdown somewhere between 1995 and 2015.
      Note that is no physically motivated difference between the (simulations among the two) piles. One only happened to have some particular (counter-trend) variations in a small time-window, whereas the rest had such elsewhere or not at all.
      Ergo my (previous post) summary:

      Essentially, they have a bunch of trending straight lines, with some noise added. And notice that those lines where the noise seemed to counter the trend at around 2000, pretty much ended in the same ballpark as those where such noise occurred at other times, or wasn’t that prevalent at all!

      They essentially noted (quote correctly I might add) that those lines which had some particular noise-feature at some time, overall looked the same as the rest with their noise (but lacking that particular feature)
      And so far, I have absolutely no quarrel with what they present. I only say that this is trivial, even bordering completely meaningless. So no. There is no beef at all here. This comparison says absolutely nothing about neither the hiatus, or nor if the models can make any useful predictions (natural variability or not).
      Because there is no physics at all involved in this comparison, only simulations (all based on the same physical assumptions) by many models and runs, divided into those who (by pure chance) happen to fit better around ~now, and those who don’t. And yes, the ‘pure chance’ is the main argument from those who want to maintain that what we see is still within the model prediction ranges. Their core argument!
      And we can even take this ‘analysis’ a bit further, by noting that at each simulation time-step the calculated state is used to ‘initialize’ the next time-step and thus the rest of the simulations:
      This means that those two piles of realizations are ‘initialized’ (around ~2010 and for the rest of the run to 2100) at a temperature difference of 0.18C (on the average: (0.23-0.096)*1.4decades), which also seems to be the difference between the two thick lines after the ‘hiatus’-sorting. As I said, this is almost self-evident, trivial stuff. But apparently worthy publication in Nature (OK Nature-ClimateChange, but still).
      But the real beef I have is not this trivial content-free comparison. It is with what the authors claim to have shown. Particularly:

      We have shown that here that there is no significant shift in projected end-of-century global warming .. This suggests that the recent surface warming slowdown is associated with variability not influencinglng term climate change .. In short, the drivers of the recent hiatus do not alter the century-scale warming

      This (bolded) is pure hogwash. It does nothing of the kind! Has nothing to do with any (real) science. It is a circular argument of the worst kind, essentially their ‘study’ tries to imply:
      If the models were correct now, what they show in 2100 would also be correct!’
      (With a necessary addendum: Meaning almost anything within a 3C-range). While what they are saying only is ‘If we look at all the simulations and squint, they still look the same’.
      And what they say about any ‘drivers of the recent hiatus’ is nothing but an expression of blind or hopeful faith in the same unvalidated models, and that all there is to earth’s climate is accurately captured there …
      It is nothing but yet another one of these wordy wobbly model based persuasion-attempts, this time not even knowing what … Well, at least Nature didn’t publish this as a ‘paper’

      • And I needed to make a correction: They dont divide the model runs into two piles. They compared one sub-set of them with the the whole bunch (including the sub-set)

      • Jonas N
        When this came up on an earlier thread that discussed this paper I made the additional point that the criteria for selection of the pattern that represented the pause was very weak i.e. it isn’t just the trend being flatish that is causing people to take note, it is being flatish and consistently below the actuals. see early comment http://wattsupwiththat.com/2015/04/23/climate-modeler-matthew-england-still-ignoring-reality-claims-ipcc-models-will-eventually-win/#comment-1915547
        Had the trend been flatish and reverting down to the actuals or even starting higher and cross over there would be much less room for comment. But England et al have taken them all and so as you note it isn’t surprising that they form a reasonably representative sample of the other model runs when it comes to 2100 (although I note England finds it breaks down with more stringent trends through having only a limited number of models in his sample).
        I was also churlish enough to suggest England et al would have known this (because it is obvious once you conceive of the experiment), and had deliberately concealed what would have happened with a more stringent test.
        So not only is the inference in the conclusion unsupported by the study, it almost certain had the test been more appropriate the study would have failed, and the authors would have known that.
        The Texas sharpshooter failed to even hit the barn wall.

  35. For years, con men have sold sports picks and the like by sending out large numbers of teasers with random predictions for the first few games. Some people receive ones that have all the right picks and to the suckers, it looks like it must be an infallible system.

  36. This one really hits home for me.
    I did this modeling for dog racing once many years ago. All in Excel.
    For data I imported 30 days of racing, 14 races a day, 8 dogs a race, and the data for each dogs last 6 races. The spreadsheet parsed the daily race form from text to excel.
    For variables, as I recall there were close to 20+ for each dog (6 race records for each) … place out of gate, place at first turn, place at finish, season and last 6 races win, place, show record, race time, moving up or down in grade, falls, post position, weight of dog, etc.
    For results I used every paramutual bet, 2-8 ticket box quinella, win, place show, 3-8 ticket box trifecta and the forms expert picks to see where I could get the most winning results if I played that one paramutual ticket for all possible races.
    I then proceeded to tweak every variable to get the best results for all ticket combinations. It looked like i got to a 70% winning percentage. I thought this was greatest achievement of a lifetime. I worked on it for months.
    Until I went to the race track with the days winning picks in hand. And a second time, and a third time. Never won. I would have been better just throwing down an eight sided dice and going with that.
    But it was a wonderful experience and I often think about it when we talk about climate modeling and hind casting. I just wished they would have learned as fast as I had.

    • I just wished they would have learned as fast as I had

      Had it been their own money, they would have. That’s the key to the entire ‘secret’ …

  37. The guardian article contains actual lies. Not a mistake or opinion but the intentional publication of known falsehoods.
    They ‘should’ feel ashamed of themselves. I guess shame doesn’t pay as well as lying – or bring you fame.

    • Jeff, could you be more specific please?
      (Further down, I posted some comments why I think the M.England et al Nature-piece is essentially vacous nonsense, and that the claims about the actual observed hiatus in it are false. But you seem to be talking about something else?)

  38. We instinctively intuit that it is much easier to fit a model to the past, than to produce genuinely skilful predictions.
    The time at which the model was conceived doesn’t make a difference, does it? “The model was conceived before the data came in, not afterward” is not evidence in favor of the model. I think what your “instinct” is getting at is that creating the model after the data has come in gives the modeler the chance to make the model a mere list of special cases catering to each data point, a list which has no predictive power because it reflects no general principles which can be applied to new cases. (For example, “My model is that the northernmost team wins the super bowl, except in the case of the Dolphins winning the Super Bowl in 1973 and the Dolphins winning it in 1974, and… (etc.).”) Before the data comes in, he doesn’t have such an opportunity, so it is unlikely that his model fits the data merely by being a list of special cases. It is more likely to reflect general principles which can be used to predict accurately (for example, “My model is that the team with a solid offensive coaching staff, a big, veteran defensive line, and the fastest wide receivers will win the super bowl.”)
    So, it doesn’t matter when a model was hatched. You have to look under the hood to see if it merely restates the data or gives general principles which are not specific to the data. So, if your uncle says, “Say, I just now figured out a general model which predicts all the past super bowl results without merely restating those results,” and you look at the model and he’s right about this, then he really has something and off to the betting shop you would go. Were he to say, “Actually, I hatched the model long ago before any super bowls took place,” this wouldn’t make his theory any more worthy of belief. If wouldn’t make you justified in betting even more money on its future results.

    • “… [if] you look at the model and he’s right about this…”
      Of course, there’s the rub. If you already know enough to know that he’s right, his model has pretty much accomplished nothing–you should have been betting for years. If you don’t know what the right answer is, the model might add knowledge or not. The only way to have confidence in what knowledge the model adds is to test it on the future. Especially in a context like football betting, where any easy answers have been sucked out of the “market” already by other bettors.
      Climate modelers actually have it somewhat easier than football modelers. The predictions of other climate modelers should not have any effect on global temperatures (unless there is some feedback from predictions to data adjustments). The prediction of other football modelers affects the market itself.

  39. They don’t get, or admit, that the basis of the models, that nearly all the warming from ~1978-1998 was caused by humans, which the alarmists in the 1990s freely said and admitted and expected to continue, is incorrect.
    Some,or most of it, was natural, and therefore the horses aren’t now winning as previously expected.
    The sooner they own up to this, the better for science.

  40. What has happened unfortunately, is what happens often with political discussions, when 2 groups disagree.
    One side will focus on proving that they are right, while the other side tries to prove the opposite is right. Very little weight is given to the opposite side. If 50% of the new information favors one side and 50% favors the other side, both sides, will use that to strengthen their side and try to weaken the other side as their cognitive bias’s drive their belief system.
    With time, both sides can actually get farther apart, even as more meaningful information is discovered that should be dialed into the understanding of the realm in which they disagree.
    Sound familiar?
    Considering the site where I’m posting this, we all know which side has it all wrong.
    Betcha this same post could go up at Skeptical Science and they would think the exact same thing but that WUWT has it all wrong.
    My biggest problem on this relates to an element that there can be no disagreement on. On something that those who claim otherwise are blatant frauds and/or scientifically blind.
    Sun +H2O +CO2 + Minerals = O2 + Sugars(food)
    Either you agree that the big increase in CO2 has resulted in a massive increase in the vegetative health of the planet, with big increases in crop yields/world food production or you are a fraud and anything else you state about the effects of CO2 are not credible either.

    • “One side will focus on proving that they are right, while the other side tries to prove the opposite is right. ”
      No. One side tries to prove they are right, the other side tries to prove that the others are evil demons who should be burned at the stake. Who needs arguments when you can demonize the opposition and GET AWAY WITH IT?

  41. Yogi Berra — ‘It’s tough to make predictions, especially about the future.’ Just in case this has not already been posted.

  42. Haven’t you heard the model/algorithm/formula: coincidence = causation?
    (The belief is widespread. For example, the Director of Planning for the fiefdom of Saanich BC claimed that use of self-selecting respondents is a valid survey method because sometimes the results matched a professionally done survey.
    I even had to try to educate the Association of Professional Engineers and Geoscientists on that.)

  43. How can any respected scientist publish such non sense? What’s worse is that the people who believe in CAGW are smug in any report that supports CAGW whether it would stand up to scientific inquiry.
    Here it is May 11, 2015 and we had a rare and exciting event, 8 inches of snow!!! Oh, if only I had believed in CAGW this wouldn’t have happened!!! Global warming causes cold weather in May, just like last year, the warmest year on record. Maybe they’ll have an ice cream truck out there telling us how warm it is!!

  44. Thanks, Eric.
    Some would argue that there must be something correct in the GCMs for a couple of them to forecast a pause. Is this a robust argument?
    Or is this a typewriting monkeys experiment were you pick at a couple of correct one-syllable words out of hundreds of reams of paper.

    • more observation is the key. If someone writes down 10 different possibilities, which tell you the next 5 coin tosses you will make, and one of them is right, have they perfected a theory of coin tosses? Or did they just get lucky, with one of their guesses? You could resolve this, by asking them to repeat the trick, this time with just one guess based on the previously successful methodology.

      • Yes. In inductive modeling, picking the right model (or ensemble) out of many is a well-known problem. Yours is usually the correct solution. Many ignore it at their peril. If climate modelers (and their funding agencies) were required to put 10% of the income each year into climate futures, all of this nonsense would go away.

  45. The warming is in the pipeline. No, really. Stop laughing. Just you wait and see. It’ll come out of the oceans and there will be a volcano slowdown, then you’ll be sorry.
    It’s just like Linus waiting in the pumpkin patch for the “Great Pumpkin” to arrive.

  46. According to The Guardian… There’s also no evidence that our expectations of future global warming are inaccurate.
    Except for 18+ years of NO GLOBAL WARMING!!
    *Sheesh*

  47. There is little point in following the IPCC models and forecasting climate trends ahead linearly when the climate is clearly controlled by natural orbital cycles and cycles in solar activity – most importantly on a time scale of human interest the millennial solar activity cycle?
    It is of interest that the trends in the new UAH v6 satellite temperature time series are now much closer to the RSS satellite data,. In particular they confirm the RSS global cooling trend since 2003 when the natural millennial solar activity driven temperature cycle peaked.
    see
    http://www.woodfortrees.org/plot/rss/from:1980.1/plot/rss/from:1980.1/to:2003.6/trend/plot/rss/from:2003.6/trend
    It is the satellite data sets which should be used in climate discussions because the land and sea based data sets have been altered and manipulated so much over the years in order to make them conform better with the model based CAGW agenda.
    The IPCC climate models are built without regard to the natural 60 and more importantly this 1000 year periodicity so obvious in the temperature record. This approach is simply a scientific disaster and lacks even average commonsense .It is exactly like taking the temperature trend from say Feb – July and projecting it ahead linearly for 20 years or so. The models are back tuned for less than 100 years when the relevant time scale is millennial. This is scientific malfeasance on a grand scale.
    The temperature projections of the IPCC – Met office models and all the impact studies which derive from them have no solid foundation in empirical science being derived from inherently useless and specifically structurally flawed models. They provide no basis for the discussion of future climate trends and represent an enormous waste of time and money. As a foundation for Governmental climate and energy policy their forecasts are already seen to be grossly in error and are therefore worse than useless.
    A new forecasting paradigm urgently needs to be adopted and publicized ahead of the Paris meeting.
    For forecasts of the timing and extent of the coming cooling based on the natural solar activity cycles – most importantly the millennial cycle – and using the neutron count and 10Be record as the most useful proxy for solar activity check my blog-post at
    http://climatesense-norpag.blogspot.com/2014/07/climate-forecasting-methods-and-cooling.html

    • an interesting exercise would be to run a GCM with the equilibrium climate sensitivity set to zero, just to see how close to null effect the software can run; my assumption is chaos will take over and it will spiral out of control.

  48. I don’t think we can dismiss the bottom two models on the basis of criticisms of all the models. To dismiss them, one must dismiss them (or not) based on their own merits. So a couple of questions:
    1. Did these two models capture the 1998 super El Nino? It is hard to tell from the spaghetti graph, but it appears that they did not.
    2. What is the fundamental difference between these two models and the rest of them? If these models incorporate aspects of (for example) AMO, PDO or other factors as examples, a different question is raised. Did they in fact model these processes correctly while the other models did not? In other words, if the specific differences between these two models and the others can be isolated and shown to be right then they may have some merit. If they did something different however, got it wrong, but by getting it wrong they got the “right” answer, then they are right for the wrong reasons and unlikely to produce future results of any value.
    In brief, I don’t think it wise to condemn these two models with a more in depth analysis of what is fundamentally different about them (if anything).

    • IANAS…but, I agree with this sentiment.
      The difficulty I have is that the argument appears to be that since these 2 models got it right, the rest (or, their average) are accurate. I’d really like to see the full model spread and where these go all the way to 2100. I get the feeling it’s throwing as much sugar honey ice tea up there as possible and claiming success even when major events are missed. Richard mentioned the TSS Fallacy showing this quite well.
      Good luck getting the pro side to show the actual causation since that would likely mean they would have to show that it’s not really catastrophic. We can wait for the claim “See! I…errr…WE were correct!” every time the satellite data crosses or trends along one of the models.

  49. I go back to Christopher Essex’s lecture and Freeman Dyson’s interview.
    The important question is not whether the models are wrong, but whether it is even possible to model the Earth’s Climate system well enough to make any meaningful projections in the first place.
    So far, Norman Page appears to be proposing a feasible and falsifiable model. We can,after all, predict the orbits of the Sun and Planets accurately.
    If I said I could predict all of the scores in next seasons Barclay’s Premier League would you believe me? (I will add the qualification plus or minus 5 goals). At the end of next season I could point to all the scores that I got right and claim that the ones that I got wrong are within the bounds of accuracy. If we get another result like Manchester United 8 Arsenal 2 then I could claim that this is like “El Nino”.

    • You could ‘predict’ every game would have a 5 – 5 score line. With your margin of error, your prosepects of success would be quite good since I cannot recall a 10 – nil thrashing, and such a score line is unlikely to occur.

  50. davidmhoffer
    You say

    In brief, I don’t think it wise to condemn these two models with a more in depth analysis of what is fundamentally different about them (if anything).

    Yes. That is true.
    And it is also true that
    It is foolish to accept these two models without a more in depth analysis that determines what is fundamentally different about them (if anything).
    But that is what Dana Nuccitelli has suggested and is being discussed in this thread.
    Richard

    • Richard,
      To put my objection more bluntly, the article would be better served by exploring the differences between those two models and the rest so that the readership can consider them and comment. I would think that with the contacts at the disposal of this forum, getting in touch with those specific modelers and asking them to elaborate would be of more value.

  51. He seems to have omitted Lord Monckton’s pocket calculator model that outperforms all of the above models.

  52. Friends
    In this thread I have repeatedly commended the wicki explanation of the Texas Sharshooter fallacy.
    In light of how the thread has developed, I copy this example from the link.

    A Swedish study in 1992 tried to determine whether or not power lines caused some kind of poor health effects. The researchers surveyed everyone living within 300 meters of high-voltage power lines over a 25-year period and looked for statistically significant increases in rates of over 800 ailments. The study found that the incidence of childhood leukemia was four times higher among those that lived closest to the power lines, and it spurred calls to action by the Swedish government. The problem with the conclusion, however, was that the number of potential ailments, i.e. over 800, was so large that it created a high probability that at least one ailment would exhibit the appearance of a statistically significant difference by chance alone. Subsequent studies failed to show any links between power lines and childhood leukemia, neither in causation nor even in correlation

    Richard

    • Richard
      The problem with this study occurs prior to the sharpshooter pulling his/her trick, as I noted above the side of the barn wasn’t even hit.

    • I remember a great TV presentation of this or similar (Nightline??) in which they showed the real error with the study was that they picked the highest risk factor rate and presented it as the average when the actual average was near 1.2 or some such meaningless risk. There were areas that had a zero and could, by their logic, show that power lines were protecting children.
      Long live the advocate!

  53. Somehow, the idea that heat can be ‘stored’ in the deep ocean persists. Water in the deep ocean is already at its maximum density die to the extreme pressures. Heating this water would cause it to expand, reducing its density. That MUST result in convection. Heat applied to deep waters by submarine vulcanism causes convection – visibly. I don’t know how the ‘excess heat’ is supposed to get to the deep waters without being absorbed by the not-so-deep waters along the way.
    Such overlying of warm fluids with denser, colder fluids is called an ‘inversion layer’ and is unstable. When such occurs in the atmosphere, convection occurs immediately, resulting in strong updrafts, convection ‘cells’, and thunderstorms, often with tornadic rotation and high flow rates.
    Gravity pulls the denser material down more strongly than the less dense material.
    “In the end, gravity always wins.”

  54. “There’s also no evidence that our expectations of future global warming are inaccurate.”
    Try wrapping your head around that statement!

    • I had the exact same thought. And then there’s this: “These are all temporary effects that won’t last.” Apparently, these guys can predict future volcanic activity.

  55. In what year were these model runs performed? are these model runs from 10 years ago? 20 years ago?
    Or 5 years ago?
    It makes a huge difference.
    Also, note that the range of final values of all the different models seems to be getting wider and wider. So how can it be that they all give the same result at some point in the future?

  56. Similar to a well known scam by unscrupulous stock brokers.
    Mail out 5 different and highly speculative stock picks, each to 1/5th of your prospective clients.
    When one of the picks comes true, you contact the 20% of the people who you sent that pick to, convince them you’re a genius stock picker, and get their money to invest. You forget about the other 4/5th of the clients.
    This is even worse… we have no warming for 18+ years and even though the merchants of doom have been claiming horrific increases in temperature, when a couple of “non-warming” models match the non-warming data they’re suddenly geniuses?
    Dare I say – Bwahahahahahahahahahahahahahaha!

  57. I have been fond of quoting a system that I was “introduced” to some years ago which involved backing either the winners or placed horses in certain selected races on their next outing. Five years of results were included in the system as “proof” and the win ratio (if I remember right) was around 70%!
    Needless to say the system fell down in the very first season afterwards, the most blatant example of how bad it was being a valuable race in September where no fewer than six of the nine runners qualified. None of them won!
    I hadn’t realised at the time that there were “scientists” in the world who were just as reliable in their prognostications as crooked racing tipsters.

  58. I think a problem with the dog race system analogy is that it’s designed to predict a single winner from a range of runners.
    The CMIP models aren’t like that. They represent a range of possible outcomes over time. Perhaps a more appropriate analogy would be to imagine a continuous series of golf drives along a very long fairway. With each drive, the ball has to make the fairway. It can hit the semi rough up to 10% of the time, but if it hits the real rough, you’re out.
    In this analogy, the fairway represents the 5-95% range of the model projections. The semi-rough represents the upper and lower 5% of the range. The rough is outside the range. Each spot at which the ball lands represents the global annual surface temperature.
    If temperatures stray outside the model range, the model range is wrong. If temperatures stray into the upper or lower 5% of the range for more than 10% of the period of projection, the model range is also wrong. As it stands, temperatures are on the low side of the 5-95% range and have strayed into the semi-rough a few times.
    The ‘ball’ is still on the fairway though, and the models are not yet down and out: http://www.climate-lab-book.ac.uk/wp-content/uploads/fig-nearterm_all_UPDATE_2014.png

    • David R
      Your link is very misleading. Its graph assumes all models are equally worthy and there is no reason for that assumption.
      Richard

    • Yes, but the CMIP models are not the set of all possible projections, they’re just the projections we have at the moment. There are much larger sets of other possible projections that do include the current temperature set but don’t include any warming. And there’s no particular reason to think the CMIP projections are more reliable than the set of non-CMIP projections.
      You can’t just claim the model is reliable until its falsified, that is the OPPOSITE of science. In fact, you cannot claim the model is reliable until every prediction has been judged against observation.

  59. In continuing the racehorse analogy I find the system more akin to the con man who in a field of 10 runners hands out tips to 10 gullible punters who after the race is run 9 punters think he doesn’t know what he’s talking about and the one who got the winner thinks he has a foolproof system. If you back the field it is virtually certain you’ ll back the winner. However as you go forward each prediction is still independent of the past. My guess is that if you have two out of 100 models that have been right for ten years then the probability is that if there are two that get the next 10 years right it won’t the same 2 .

  60. All evidence indicates that the causes of the surface warming slowdown are temporary, that the Earth is still accumulating heat at an exceptionally rapid rate, and that surface warming will soon accelerate.

    What utter bollocks. They have no evidence, because there isn’t any.
    Climate Liars just making stuff up. Disgusting.

  61. “For example, a paper published in Nature Climate Change last week by a team from the University of New South Wales led by Matthew England showed that climate models that accurately captured the surface warming slowdown (dark red & blue in the figure below) project essentially the same amount of warming by the end of the century as those that didn’t (lighter red & blue).”
    This isn’t a new argument. The problem is that it ignores the most likely possibility, which is that all the models are wrong. Their only answer to this is “but we can hindcast!” Well, great, but you can’t forecast. At all.

  62. Earth’s temperature simply won’t continue to increase forever.
    It can’t and it won’t. And I don’t see any of the models incorporating that unavoidable fact.

  63. I am having some difficulty reconciling the main plot here with that the “18 year no warming” plot
    Are they produced from the same data set?

  64. Three observations.
    1) Dana writes: “There’s also no evidence that our expectations of future global warming are inaccurate.”
    By the same token, there’s also no evidence that Dana’s expectations of future global warming are accurate. Absence of evidence is not evidence of absence. The current lack of warming provides evidence that Dana’s expectations of future global warming are not accurate.
    2) There is a basic earth science tenet of multiple working hypotheses (T. C. Chamberlin, reprinted here http://www.whoi.edu/cms/files/chamberlin65sci_72744.pdf ) I find it amazing that “climate scientists” will bend over backwards to find multiple working hypotheses to explain the pause; but never, ever consider the possibility of an alternative hypothesis for the prior warming.
    And Dana’s statements regarding volcanoes and solar activity are just asinine: “These are all temporary effects that won’t last. In fact, we may already be at the cusp of an acceleration in surface warming, with 2014 being a record-hot year and 2015 on pace to break the record yet again.”
    How does he know that these are the real reasons for the pause or that they are temporary and won’t last? Just because Ben Santer wrote a paper that put forth an hypothesis involving volcanoes, solar activity, and heat storage in deep oceans does not mean that the hypothesis is correct. No real increase in volcanic activity is noted. Solar activity for Cycle 24 has apparently peaked and is expected to continue declining as Cycle 24 comes to an end. Cycle 25 has been predicted to be of lower intensity than 24. Dana’s “fact” that we “may” be on the cusp is not a fact, rather it is rampant speculation.
    3) Even with hindcasting, less than 3% of the climate model projections predicted the pause (Meehl et al., 2014). What great odds; a casino would love odds like this. Dana compounds the problem by invoking the gambler’s fallacy. The roulette wheel came up red 18 times therefore it’s more probable that it will come up black on the next spin. Casinos love idjits like him.

  65. We can speculate on the reason that the coolest, tiny fraction of global climate models matched pretty good with reality. Maybe they got the ENSO right or something else.
    In weather/climate model(projection) ensembles like this, when you have a couple of the ensembles that go off on their own, with a solution much different than the ensemble average, they are referred to as “outliers”.
    If you have, 90 ensemble members for instance and only 2 or 4 got it right and those were only the ones considered to be the biggest outliers, what does that mean?
    It could mean several things, some that I can’t think of because I really don’t know exactly what the physics were in the correct/cool outliers that caused them to be accurate.
    Was it chance?
    Only another decade of observations will help to clear that one up.
    Did those ensembles have equations that amplified H2O(positive feedback) less?
    I doubt that or you would think the problem would be solved and we know what to adjust in the other 86 or 88.
    Does that mean that global climate models can project accurately?
    4 out of 88 means just the opposite to me. However, I think there is use/value in global climate models if used properly. This means, with time, we should be tweaking parameters/equations and weighting in models as they are compared with observations. By time, that doesn’t mean waiting for the observations to catch up with the model projections, even as they their steeper trend causes them to get farther and farther away. The later is how they are currently being used.
    I realize that weather models and climate models are very different. However, the lesson to be learned by climate modelers that meteorologists learn hundreds of times in their career, is to let your ego go. You spend a great deal of time making a forecast based on your best source for prediction………atmospheric models. You can analyze those models better than anybody and know they accurately represent the physics of the atmosphere and your forecast is based entirely on what should happen.
    However, when the forecast starts to bust, the sooner you acknowledge it, the sooner you can make the adjustment. I really get where these climate scientists/modelers are coming from. There is no meteorologist, especially if money, pride or a million viewers watching that doesn’t “hope” while watching the actual weather in their forecast pan out.
    When it is going the wrong way, we have all looked at the pattern and thought “I know I analyzed this right” or “It’s just taking a bit longer than expected but I know this or that will happen as predicted”
    We often wait a bit longer than if we did not have any “skin in the game” to update/adjust because the absolute WORST thing to happen, is if you changed the forecast and then, your first one verified.
    The same game is gong on here. With this being climate vs weather, the time frame is just much longer and since no climate scientist can have the experience of having learned the lesson dozens of times before, they are learning it for the first time in most cases(climate models have not been around that long either)……….which means they are still “hoping” for the climate to react in the way that they know it should based on their very accurately analyzed interpretation.
    I get that they accurately analyzed the climate and data and models. They understand the known physics better than anyone. However, when a projection is going the wrong way, justifying wrong things in the old projection by dialing in new things that are just temporary “speed bumps” will not work.
    If you are so convinced that your climate models have really captured all the physics, then good luck, reading this was a waste of time.
    There is an old joke(that is not true, I don’t think) that I’ve heard several times before. Climatologists are just meteorologists that couldn’t forecast. I’m sure that a meteorologist came up with that one.
    However, climate scientists, if you don’t start adjusting your climate models, then this will be no joke……….there will be some truth to it!

    • The models that have done less poorly assume the lowest ECS, ie 2.1 degree C per doubling of CO2 concentration.
      From IPCC Chapter 9, “Evaluation of Climate Models”:
      9.7.1 Equilibrium Climate Sensitivity, Idealized Radiative
      Forcing, and Transient Climate Response in the
      Coupled Model Intercomparison Project Phase 5
      Ensemble
      Equilibrium climate sensitivity (ECS) is the equilibrium change in global
      and annual mean surface air temperature after doubling the atmospheric
      concentration of CO2 relative to pre-industrial levels. In the AR4,
      the range in equilibrium climate sensitivity of the CMIP3 models was
      2.1°C to 4.4°C, and the single largest contributor to this spread was
      differences among modelled cloud feedbacks. These assessments carry
      over to the CMIP5 ensemble without any substantial change (Table
      9.5).
      See Fig 9.42 on Page 817 for the ECS assumed in each model.
      They might also assume a lower rate of CO2 level growth, but I didn’t check that parameter.

  66. “Matthew England showed that climate models that accurately captured the surface warming slowdown [3% of the models] (dark red & blue in the figure below) project essentially the same amount of warming by the end of the century as those that didn’t (lighter red & blue).”
    Soooo….the results of the models have low precision — and 3% accuracy — over two decades time because of unforeseen natural variability but in 100 years the models have high precision and by then the 3% accuracy rate will be near 100%? Congratulations Global Warmistas, you’ve reached Cuckoo Level 50!

    • Nutty and England are just plain wrong. The coolest and so far least wrong model predicts only 0.5 degree C of future warming, while the hottest forecasts 1.6 degrees. Hardly “essentially the same”, but even 1.6 degrees C warmer would not be a catastrophe.

      • Correction: Not further warming but warming above the reference period. UAH has us already 0.3 degrees warmer than that, so the coolest model foresees only 0.2 degrees more for the projected period.
        It appears that the super El Ninos of the 1980s and ’90s are mainly responsible for the steps up in GASTA.

  67. A few models wandered over the pause… and into a bar.
    One said “Is it just me, or is it hot in here”?
    Barkeep said, “simmer down, take a load off, and have a cold one”.
    The other two grumbled, “I bet he’ll crank up the heat when we’re not looking”.

  68. Which one of the 90 models is based on the settled science?
    If the science is truly settled, why are there 90 models, and not just one model?
    In any case, which of the models (colour runs) is said to have got matters right?
    Whilst it is difficult to read the plot, I cannot see any model run that has tracked HadCrut 4, or UAH trosphere. I certainly would like to see the identification of this, and then we can see how the projections of the said run compare to real life observation between now and 2020, and that would probably confirm that even the identified model runs are by then departing from reality.

  69. How much does Dana get paid per wishful thinking or misleading word? It all adds up.

  70. ‘Essentially it boils down to a combination of natural variability storing more heat in the deep oceans, and an increase in volcanic activity combined with a decrease in solar activity. These are all
    temporary effects that won’t last.
    After vomitting he called for the room service and some sanitation works.
    @mod: you can stand this?
    Hans

  71. “….we may already be at the cusp of an acceleration in surface warming, with 2014 being a record-hot year… ”
    As I recall, NASA’s data showed a record of – drum roll please – 2 hundredths of a degree, far smaller than the measurement accuracy. And they stated that the probability of a record was 38% In other words, even NASA had to admit that it was most likely not a record at all.
    Talking about computer models, yesterday I was thinking about the claimed “proof” based on computer models, for example in David Attenborough’s 2006 program.
    The first suspicious thing is that the data ends in 2000, so six years of data and computer predictions had gone missing. Why? Could it be that the pause was already apparent, and it was just too convenient. I’ve (approximately) updated the graph to 2015.
    https://drive.google.com/file/d/0B-r4EiZCGxdrLXJnQ2l5TjdoVXM/view?usp=sharing
    The “proof” doesn’t look so good now, does it?
    Chris

  72. @ Mike Maguire May 11, 2015 at 7:46am “My biggest problem on this relates to an element that there can be no disagreement on. On something that those who claim otherwise are blatant frauds and/or scientifically blind. Sun +H2O +CO2 + Minerals = O2 + Sugars(food)
    Either you agree that the big increase in CO2 has resulted in a massive increase in the vegetative health of the planet, with big increases in crop yields/world food production or you are a fraud and anything else you state about the effects of CO2 are not credible either.”
    +1 and the Global NDVI values show just that. Turns out scientists have known this for at least a couple of decades.
    http://www.rationaloptimist.com/blog/the-greening-of-the-planet.aspx
    and here:
    http://nipccreport.org/articles/2012/jun/26jun2012a1.html
    “In discussing the current state of knowledge in this area, De Jong et al. write that “over the last few decades of the 20th century, terrestrial ecosystems acted as net carbon sinks, as evidenced by ecosystem process models and satellite vegetation data (Myneni et al., 1997; Schimel et al., 2001; Zhou et al., 2001).” And they say that “the easing of climatic constraints on plant growth as a result of increased CO2 concentrations and higher temperatures is a likely explanation for this effect (Nemani et al., 2003).” Thus, it can readily be appreciated that the twin evils of the world’s climate alarmists – rising atmospheric CO2 concentrations and global warming – have actually been what has fueled the last quarter-century’s greening of the earth.”
    also here:
    http://www.lifescientist.com.au/content/life-sciences/news/unexpected-increase-in-global-vegetation-and-carbon-capture-379540886
    Hardly unexpected as the first link points out viz Keeling et al.
    There is of course the obligatory AGW doom.
    “It’s important to recognise that global warming would be happening faster if some of our CO2 emissions were not captured by this vegetation growth.”
    Dr Canadell warned that “about 50% of emissions from human activities stay in the atmosphere even after the other half is removed by terrestrial vegetation and oceans. The only way to stabilise the climate system is to reduce global fossil fuel emissions to zero.”
    But surely the climate system is never stable, that’s the point and 50% of what’s left after vegetative processing isn’t that alarming given that an average meeting room can reach 1200ppm/v with minimal affects split each way between yawning from CO2 elevation and the meeting itself. I now have to wonder if plant growth acceleration will at some point reach a net increase in CO2 processing leading to a decrease in observable man made CO2 and the cycle starts again. Oh darn it I feel another model coming on.
    I had always though the susseration in my garden was due to the wind, perhaps it’s the vegetation applauding in anticipation of the buffet to come. I shall never again look at my petunias the same way.

  73. There is a post by one of the co-authors of the paper, Matthew England, on the un-skeptical site with the doublespeak name Skeptical Science which says:
    http://www.skepticalscience.com/climate-hiatus-doesnt-take-heat-off-global-warming.html
    “Until now, however, no evaluation has been made of the possible consequences for long-term projections. Specifically, if the variability controlling the current hiatus is linked to longer-term sequestration of heat into the deep ocean, this might require us to recalibrate future projections.
    With this in mind, we decided to test whether 21st century warming projections are altered in any way when considering only simulations that capture a slowdown in global surface warming, as observed since 2001.”
    So he thinks that models that weren’t dealing with long-term ocean sequestration of heat, but somehow accidentally predicted the pause, have relevance to claims about future warming if the ocean were involved in a way that wasn’t modelled? Wow is that absurd.

    • They keep saying that you cannot have “infinite growth in a finite world” (probably meaning: economic growth, material growth, energy consumption growth, whatever physical parameter growth), but they sure can have infinite bovine excrement arguments growth.
      This there-is-no-pause-and-we-understand-its-cause circus is now actually funny.

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