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Climate Modelling Dominates Climate Science

May 19, 2016

By Paul Homewood




An interesting study from Pat Michaels and David Wojick:


Computer modeling plays an important role in all of the sciences, but there can be too much of a good thing. A simple semantic analysis indicates that climate science has become dominated by modeling. This is a bad thing.


What we did

We found two pairs of surprising statistics. To do this we first searched the entire literature of science for the last ten years, using Google Scholar, looking for modeling. There are roughly 900,000 peer reviewed journal articles that use at least one of the words model, modeled or modeling. This shows that there is indeed a widespread use of models in science. No surprise in this.

However, when we filter these results to only include items that also use the term climate change, something strange happens. The number of articles is only reduced to roughly 55% of the total.

In other words it looks like climate change science accounts for fully 55% of the modeling done in all of science. This is a tremendous concentration, because climate change science is just a tiny fraction of the whole of science. In the U.S. Federal research budget climate science is just 4% of the whole and not all climate science is about climate change.

In short it looks like less than 4% of the science, the climate change part, is doing about 55% of the modeling done in the whole of science. Again, this is a tremendous concentration, unlike anything else in science.

We next find that when we search just on the term climate change, there are very few more articles than we found before. In fact the number of climate change articles that include one of the three modeling terms is 97% of those that just include climate change. This is further evidence that modeling completely dominates climate change research.

To summarize, it looks like something like 55% of the modeling done in all of science is done in climate change science, even though it is a tiny fraction of the whole of science. Moreover, within climate change science almost all the research (97%) refers to modeling in some way.

This simple analysis could be greatly refined, but given the hugely lopsided magnitude of the results it is unlikely that they would change much.


What it means

Climate science appears to be obsessively focused on modeling. Modeling can be a useful tool, a way of playing with hypotheses to explore their implications or test them against observations. That is how modeling is used in most sciences.

But in climate change science modeling appears to have become an end in itself. In fact it seems to have become virtually the sole point of the research. The modelers’ oft stated goal is to do climate forecasting, along the lines of weather forecasting, at local and regional scales.

Here the problem is that the scientific understanding of climate processes is far from adequate to support any kind of meaningful forecasting. Climate change research should be focused on improving our understanding, not modeling from ignorance. This is especially true when it comes to recent long term natural variability, the attribution problem, which the modelers generally ignore. It seems that the modeling cart has gotten far ahead of the scientific horse.

Climate modeling is not climate science. Moreover, the climate science research that is done appears to be largely focused on improving the models. In doing this it assumes that the models are basically correct, that the basic science is settled. This is far from true.

The models basically assume the hypothesis of human-caused climate change. Natural variability only comes in as a short term influence that is negligible in the long run. But there is abundant evidence that long term natural variability plays a major role climate change. We seem to recall that we have only very recently emerged from the latest Pleistocene glaciation, around 11,000 years ago.

Billions of research dollars are being spent in this single minded process. In the meantime the central scientific question – the proper attribution of climate change to natural versus human factors – is largely being ignored.

  1. Graham permalink
    May 19, 2016 10:08 am

    Hi Paul I’ve been waiting for something from you about a report in yesterday’s Times that a uni has banned the celebratory throwing of mortarboards due to H&S. Apparently they plan to Photoshop them in later. Guess which university?


  2. David Richardson permalink
    May 19, 2016 10:25 am

    They started with an answer and have steadily worked towards refining that answer ever since. To be useful models have to display skill and only a few, out of dozens, at the lower end of alarmism come close to what has happened in the real world over the last 25 years.

    GCMs are very clever models with millions of lines of code and they produce an output that vaguely looks like the Earth, but the Earth’s actual climate is not what they are modelling.

    We know there are a lot of variables that GCMs don’t even begin to handle. On top of that scientists (remember them?) in fields unrelated to climate have discovered that Kirchoff’s Law (hard-wired into GCMs) is not true for CO2, and many other gases, anyway.

  3. May 19, 2016 10:33 am

    When I first came across the IPCC reports I was excited that they could model radiation in the atmosphere to the level where it modelled the real atmosphere. We had struggled for years with simple furnace-radiation!

    Then on reading the convoluted explanations for the Radiation fiddle-factor, I became and remain a “denier”. A mathematical model whose input values are decided by a committee (along with the weightings to apply) has never been science. As Dave Richardson says, the application of Kirchoffs law was a basic fault in this: reasonably suited to rough estimates but no more.

    My view now is that the models are the “Big Brother” of the AGW believers and even when proved wrong cannot possibly be.

  4. It doesn't add up... permalink
    May 19, 2016 11:39 am

    So we have a 97% certainty that climate science findings are computer model based. Do 97% of scientists agree with that?

    • shep permalink
      May 19, 2016 1:33 pm

      We don’t have to ask them, we can simply model their answers.

  5. May 19, 2016 12:34 pm

    Well, well. I just gave a talk to a conservative luncheon group in nearby Fairmont, WV yesterday: “Science Gets A Slap-Shot From Michael Mann’s Hockey Stick”. After defining what science was and was not, I briefly dealt w/ the origin of the “97% of scientists” drivel. I pointed out that climate pronouncements were based on COMPUTER MODELS, which had yet to be right as we did not know enough to put in data which could give reliable predictions. Then I described what Michael E. Mann did/has done/is still doing, why it is not science, the mess at UEA with McIntyre and McKittrick’s plug pulling, and the ramifications to the public and to science. Mark Steyn’s new book: “A Disgrace to the Profession” was a good, yet depressing read. I ended by reading 2 quotes from the scientists: Dr. Atte Korhola and Dr. William Happer.

    I gave them a handout with useful sources and why: NOTALOTOFPEOPLEKNOWTHAT was the first on the list. Also The Daily Signal, Watts Up With That, Christopher Booker/Telegraph and James Delingpole. I listed sources for a description of the 97% hoax and useful books. OH, and sources for the suppression of the U of Cincinnati fracking study.

    I will be forwarding this piece to the man who runs the group. Likely I will be doing a further talk in late summer. It is a huge subject and I just wanted to get them started in understanding of what science IS, what Mann had done and that “science” can no longer be trusted without answering a lot of questions about each and every pronouncement.

  6. May 19, 2016 12:40 pm

    if you don’t have empirical evidence you have to rely on models

  7. Bitter& Twisted permalink
    May 19, 2016 1:37 pm

    It just proves the saying “Garbage in, garbage out”.

  8. TonyM permalink
    May 19, 2016 3:26 pm

    Mathematicians have opined on the use of models as predictors of the future values of model variables. The mathematical models of climate are a complex system of non-linear partial differential equations that relate variables to each other in ways that express rates of change of one variable with respect to another in vary complicated ways. These equations have no known solution that looks like a formula. That is, a computer cannot deal with these equations directly to arrive at a formula that expresses the relationship of the variables so that you only need to plug in the values of a bunch of variables to calculate the value of another variable. Unlike the first equation you learned in algebra, distance=speed x time, there is no known formula, no matter how complex, that can relate temperature or sea level or any other climate variable to all of the other variables. The only way computers work with the calculus equations is by transforming them into approximate equations, that is, equations that can be dealt with using basic arithmetic calculations subject to algorithmic programming.

    The original climate equations are at best gross approximations of climate reality since the interactions of the many variables are not well understood in many cases, are approximated in other cases, or just left out completely. There are hundreds, perhaps thousands of variables that in some way impact climate. To make matters worse, there are apparently built in biases to the equations that help assure a global warming outcome. The computer algorithms require trillions of calculations which at each step generate errors. The errors build up over the trillions of calculations so the end results have an error range so large as to make the results meaningless. Peter Landesman describes the issues in his paper, “The Mathematics Of Global Warming”, available here:

    As Donald Rumsfeld, former US Secretary of Defense said during the Iraq war. “There are things that we know that we know. There are other things that we know that we don’t know, and there are other things that we don’t know that we don’t know”. This is a perfect description of the state of climate science. Unfortunately, climate scientists refuse to accept that description of their knowledge. To place any credence on computer output in this case is anti-science and foolish, and because it is being used to make political policy, it is outright dangerous..

    • Broadlands permalink
      May 20, 2016 5:58 pm

      “Beware of false knowledge; it is more dangerous than ignorance.” George Bernard Shaw

  9. emsnews permalink
    May 19, 2016 3:51 pm

    And the biggest variable thingie in climate change is that yellow star our planet circles! Assuming the sun has the same energy output over time is crazy. It is not a young star, either, but telling people that we are stuck in this hot/cold cycle because the local star is cycling in and out of hot/cold output scares everyone since we have zero control over this.

  10. May 19, 2016 8:48 pm

    Reblogged this on Tallbloke's Talkshop and commented:
    Therein lies the problem – or one of them. How’s that cloud modelling going for instance?

  11. May 19, 2016 9:36 pm

    Reblogged this on WeatherAction News and commented:

    “Climate science appears to be obsessively focused on modeling. Modeling can be a useful tool, a way of playing with hypotheses to explore their implications or test them against observations. That is how modeling is used in most sciences.

    But in climate change science modeling appears to have become an end in itself. In fact it seems to have become virtually the sole point of the research…
    Climate change research should be focused on improving our understanding, not modeling from ignorance”

    If only all the money had been spent on observational research, just as Hubert Lamb lamented.

  12. May 19, 2016 10:04 pm

    A few years ago, I read that the scientists creating computer models did not know how to model the effects of water in the atmosphere, one of the more important variables, but incredibly complex (i.e., water exists in the atmosphere as solid, liquid, and vapor, with substantial heat transfer required to go from one state to another). So, they all leave water out of their models. Further, two computer scientists looked at the details. One estimated it would be 30 years before computational capacity would be adequate to model climate complexity. The other estimated 40 years.

  13. May 19, 2016 10:14 pm

    Reblogged this on TheFlippinTruth.

  14. May 19, 2016 11:16 pm

    Reblogged this on Climatism and commented:
    97% of climate models say that 97% of climate scientists are wrong. Yet we base, literally, trillions of dollars of other people’s (taxpayers) money on climate change policy, schemes and rent-seeking scams (windmills/solar) on overheated, predictive models that do not observe climate reality.

    CMIP5 IPCC climate models still don’t even ‘model’ clouds, the sun or ocean currents (AMO/PDO).

    What possibly could go wrong? /sarc.

    • May 21, 2016 12:37 pm

      A major problem with the cloud modelling is the lack of historical data.

  15. May 20, 2016 4:06 am

    Reblogged this on Climate Collections.

  16. May 20, 2016 12:01 pm

    I spoke with an chemical engineer at WVU who specialized in atmospheric chemistry a couple of decades ago. He said, at that time, that chemical changes occurred so rapidly in the atmosphere it was impossible to follow and document them. I might presume that some progress has been made in instrumentation to capture some of these processes. However, I imagine much of it still holds.

    As for my field of taxonomic botany…..not enough is known about a single species to be able to create a useful model of it: light, temperature, nutrient, soil requirements, interactions with those, and on and on and…… There are “ecologists” who spend their careers behind their computers crunching whatever and literally would not recognize a forest if dropped into one. The knowledgeable ones are those who spend a lifetime in the field observing.

    Plug-in computers are a useful tool to shovel data around rapidly. However, when properly stocked with facts and useful information, the human brain is still the best computer for deduction and observation.

  17. May 20, 2016 9:14 pm

    the map is not the terrain.

  18. Terbreugghen permalink
    May 21, 2016 8:22 pm

    No one has noted that a paper which stated that “climate modelling is bunk” would also be tabulated by the aforementioned study as “relying on modelling.” One wonders how much of the 55% of papers actually DID “rely” on modelling for their conclusions.

  19. Stephen permalink
    June 30, 2016 4:46 pm

    Better late than never I hope…

    A ‘model’ is the basis of science and also our understanding of everyday things. The Laws of Physics are models. Newton’s Law of Gravity was perfect for apples, then it was shown to be invalid with new data and a new model showed a better ‘understanding’ = more valid.

    The key point is that the models need to be assessed properly. I wonder whether the predictions from the original climate models from 25 years ago are valid or not, and those from 20 years ago, etc.

    I’d say that valid models are good, invalid models are bad, and a model that’s not tested with fresh data is a bit like Schroedinger’s Cat – no one knows what it is. But we all build models to understand things, it’s how we got out of the Dark Ages.

    Proper validation makes me wonder if we spend money in the right proportion. Here are some wild guesses for the UK
    – spends about 10M on gathering climate data (not counting all the weather satellites, since they are weather-funded, not climate)
    – about 200M or so on developing/running climate models (incl Met Office and universities)
    – then UK makes multi-billion decisions

    The first two seem out of kilter, the third is way out there, compared to what we invest in climate observations. I’m not sure of my numbers, maybe got it wrong? Also, maybe others think the proportions of the three are fine?

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