Prof in NYTimes: Failure of climate models vs. reality not important since we use them to predict the future

Thomas Lovejoy writes in the New York Times:

Does the leveling-off of temperatures mean that the climate models used to track them are seriously flawed? Not really. It is important to remember that models are used so that we can understand where the Earth system is headed.

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22 thoughts on “Prof in NYTimes: Failure of climate models vs. reality not important since we use them to predict the future”

  1. An amazing spin of the truth. The models have been the entire basis for climate change forecasts. How can they be used to predict the future when they can’t even get the present right?

  2. “We use them because we don’t want to run the experiment of elevating greenhouse gas concentrations and discovering decades hence that humanity has a first-class disaster on its hands.”

    What’s this “we” stuff?

    He has determined that “elevating greenhouse gas concentrations” will create disaster. Models are for confirming his opinion.

  3. “People never seem to question science when it underlies something easily valued — like iPhones and other handheld devices.”

    Making iPhones is not science. It is called engineering. The difference is that engineering can work by meaningless curve-fitting and it does not see the need to question the underlying science as critical for its success.

    Ironically, the underlying science is questioned most by engineers, not the self-proclaimed and accredited scientists, because engineers face reality every day. Ivor Catt is one example. Another example: we are now using computers that science predicted were not possible just a few years ago.

    Apparently, Thomas Lovejoy doesn’t know what science is. That is apparent even before he proceeds to spin junk about climate.

  4. The professor … has a bit of a point, but he flubs it. In a complex system, fluctuations due to everything going on overtake and mask overall changes in the system. For example, in the economy, if the government starts pumping cash into the economy, it won’t be immediately noticeable. Small rises due to the cash infusion would be indistinguishable from daily ups and downs. This is why you don’t put fast, dynamic control loops on floating bed reactors, because you easily get to the point that you are trying to control fluctuations instead of the actual process.

    However, his point runs head-first into the basic principles of uncertainty. If the effect is too small to be measured, then should it not be too small to meaningfully affect anything?

  5. The NYT must have really dug deep to find this guy. Pity there is no opportunity to rebut him directly; he’s probably not going to check JS for comments.

  6. Typically engineers can’t hide behind “models” which are at odds with reality. They are stuck with the rules of the universe regardless of these can be explained or understood.However useful the ability to change universal constants might be!

  7. That’s the thing, Mark. Engineers don’t need to hide anything and they could care less about the rules of the universe. It is those who claim to know the rules of the universe that are hiding behind piles of bad math.

    For an engineer, the only validation of a model is if it can guide him in the making of useful devices. Here’s the the kind of stuff I’m talking about:

    You need to work hard to find any rules of the universe there. It is simply not possible (yet) to design working things of entirely from first principles. Even if you start with first principles, intending to keep the theory all the way to the end, you end up with a pile of meaningless fudges on top of it, just to make sure it matches reality. But when it does, you can’t take it as a proof of the initial theory. By the time you have a working model, there may be little left of the foundational theory (and you will be lucky if it doesn’t get in your way).

  8. “That is not to say that science doesn’t make mistakes. But we do have a culture of questioning and testing scientific results that fosters self-correction.” Really!! So that’s why sceptics are constantly berated and told “the science is settled” and “there’s no debate”? Tell that one to Mike Mann and see what his answer is.

  9. Nice try, it is unfortunate that in the real world the future inevitably becomes the present. Too bad the Left’s delusion is undone by reality. So when the model predicts X at time Y and it is now time Y but it is not X, the model is wrong. My 11 year old son understands this stuff better than these idiots.

  10. Long time died-in-the-wool green, Lovejoy is on the National Council of WWF US. Lots of other interesting names on this page:

    He is Professor of Environmental Science and Policy at George Mason University, and a former member of the US DIVERSITAS National Committee, where Jane Lubchenco and Paul Ehrlich also hang out. Former President of H. John Heinz III Center for Science, Economics and the Environment ( and on the Council of Conservation International, plus several other NGO type organisations. CV includes World Bank, UN Foundation.

  11. Interesting that he likes to use the word fact.

    – It is a fact that the models predict impending doom in the next 50+ years
    – It is fact that the observational data is quite a bit lower than them models and that this happened within the first two decades of their use.
    – it is a fact that if I had drawn a projection based on warming of the last 130 years my result would have been more accurate than all of the worlds best and most sophisticated climate models.
    – It is a fact that my projection would have been cheaper too 🙂

    Now exactly why should we have confidence in the quality of the science if the best science can do is be wrong?

  12. Reminds me of Freshman Intro to Engineering way back when.
    Things to remember
    1: Your model is wrong
    2: Your diagram is backwards
    3: Your mechanism is missing key points
    4: Your boss has point hair
    5: The primary function of your coworkers is to transport fluid from coffeemaker to urinal
    6: No, it’s not strong enough.
    7: You are an idiot.

    All of these are true until proven otherwsie. Except for the first and last. Your model may be good enough, but it is still wrong, and you are still an idiot.

    If you cannot or will not believe these things, get out now before you get someone killed.

    It’s that last point that really separaes scientists from engineers.

  13. His claim is a non sequitur. If a model does not agree with whatever it intended to model it then that model can’t be used to predict anything.

  14. I am reminded of this quote :-
    “It doesn’t matter how beautiful your theory is, it doesn’t matter how smart you are. If it doesn’t agree with experiment, it is wrong.” – physicist Richard Feynman.
    The period of the experiment has been about the last fifteen years. The real world did not agree with the computer model theory. Therefore the theory is wrong.
    It seems to me that there are some variables that can’t be predicted, such as the Sun having an oddly quiet period. The solar cycle (that has just ended) took 14 years instead of the usual 11 years. It makes me wonder if solar researchers have any input into the IPCC reports. It would be logical to at least consult them, since knowing how much energy is entering a system would seem to be a necessary first step.

  15. Reminds me of Enrico Fermi’s reply to a young post-doc who was excited about his projections; Fermi asked, “did you use a model?” Yes, was the reply to the question. And Fermi replied, with a model I can prove the moon is made of cheese.

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