Why Climate Models Can’t Predict The Future (And Never Have)

Written by Dr Jay Lehr

crystal-ball-earth

Fearmongers prophesy daily the end of the world as we know it if we do not convert our planet to wind and solar energy. Sensible folks realize that we would then be living again the life of the 19th century.

The human-caused global warming scare may well be the best false fear-mongering ever conceived. It has half the world clamoring to be lead to safety from climate change without a shred of physical evidence.

What they do have are mathematical equations considered to be models of the Earth’s climate.

Our government has financed more than one hundred efforts to model our planet for the better part of three decades.

They continue to do so even though none have ever predicted the known past, or after decades of study, accurately predicted what was to happen ten years hence.

If you watched this year’s Indianapolis 500 motor car race you know they predicted 80% chance of rain, but the sun never went behind a cloud.

The problem facing real scientists who study climate with no bias is that the public has no clue what a mathematical model actually is, how they work or what they can and can not do.

So let’s try to simplify the very complex subject of mathematical modeling.

Before we build buildings or airplanes we surely make physical, small scale models and test them against the stress and performances that will be required of them when they are actually built.

When dealing with systems that are totally beyond our control we try and describe them with computer programs or mathematical equations that we hope may give answers to the questions we have about how the system may work today and in the future.

We attempt to understand the variables that affect the operation of the system. Then we alter the variables and see how the outcomes are affected. This is called sensitivity testing, the very best use of mathematical models.

Historically we were never foolish enough to make economic decisions based on predictions calculated from equations we think might control how nature works. Today we are doing just that.

All problems can be viewed as having five stages, observation (seeing physical occurrence), modeling (estimating mathematical relationships), prediction (how the system might work), verification (seeing a correct result) and validation (determining that the result was not a random occurrence).

Perhaps the most active area for mathematical modeling is the economy and the stock market. No one has ever succeeded in getting it right and there are far fewer variables than what occurs in determining the climate of our planet.

For many years, the Wall Street Journal selected five eminent economic analysts to select a stock they were sure would rise in the following month.

Then they had a chimpanzee throw five darts at a wall covered with that day’s stock market results. A month later they determined who did better in choosing the winners, the analysts or the chimpanzees darts.

For many many years, the chimps won so often that they discontinued the contests. I am not saying today’s mathematical modelers would not beat chimps throwing darts at future Earth temperatures, but I will not object if you reach that conclusion.

SEE ALSO: Climate Models Of Incompetence

Consider the following: we do not know all the variables that control our climate, but we are quite sure they are likely in the hundreds.

Just take a quick look at ten obviously important factors for which we have limited understanding:

1- Changes in seasonal solar irradiation;

2- Energy flows between ocean and atmosphere;

3- Energy flow between air and land;

4- The balance between Earth’s water, water vapor, and ice;

5- The impacts of clouds;

6- Understanding the planet’s ice;

7- Mass changes between ice sheets, sea level and glaciers;

8- The ability to factor in hurricanes and tornadoes;

9- The impact of vegetation on temperature;

10- Tectonic movement on ocean bottoms.

Yet, today’s modelers believe they can tell you the planet’s climate decades or even a century in the future and want you to manage your economy accordingly.

Dr. Willie Soon of the Harvard-Smithsonian astrophysics laboratory once calculated that if we could know all the variables affecting climate and plugged them into the world’s largest computer, it would take 40 years for the computer to reach an answer.

It is time to stop placing any credence in the recommendations of today’s climate modelers.

Read more at CFACT

Comments (6)

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    Fonseca-Statter

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    Very well… And thanks for a concise synopsis. I understand the «time/text constraints» but you coul have added «dependence on initial conditions». I confess that when I was doing a doctorate in Complexity Sciences, it took me some time to realize they were talking about the «numbers to be introduced in those equations»… At first I thought they were refering to the historical situations «out there», in the real world… And the issue of accuracy of measurement and recording, immediately came up in our discussions. The other point to consider is how do the inumerable feed-back mechanisms affect the behaviour of so many variables and intervening factors.

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    Chris Marcil

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    It is easy to figure out how the feedbacks work…they don’t exist. The atmosphere is causal, feedbacks are a part of non causal systems. The atmosphere only uses the present input and past inputs, it doesn’t wait for future inputs.

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    tom0mason

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    The entire idea for the climate models comes from the faulty idea that the Scientific Method will give us absolute fact, give us certainty. This is a gross error of hubris on the part of those who believe so, for science does not give us a neat catalogue of known facts about the Universe but instead reveals how little we know and understand. And what we know and understand is fragmentary, approximate, and often inaccurate. Our scientific listing is incomplete, approximate, and has many errors in it.
    So too are the climate models and the very paradigm upon which they sit. These models attempt to take many parameters that we think we know and can fully describe, most of which are coupled in some feedback manner, and interact in many and various ways. (And these couplings, and feedback methods are far from being fully understood.) From all this we understand that the climate as a whole acts as a deterministic but chaotic process. However those who model the climate believe that by statistical averaging some parameters of this chaotic process, they can reveal the probable future outcomes. This is nothing but HUBRIS, for they do not understand the very basic properties of many parameters well enough, or the way they interlink through the climate system.

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    E B nalton

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    In the Gerlich and Tseuchner papers on the falsification of climate science.they mention that the Navier Stokes equations that are required ,are actually unsolvable.Thus the models are useless for a non linear dynamic chaotic system ,and should be scrapped.
    I do not have the maths to argue.Any thoughts?

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    tom0mason

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    It is nice to see the work from honest scientist occasionally gets through …

    From https://www.sciencedirect.com/science/article/pii/S0378437118301766

    Physica C. Essex, A.A. Tsonis, Model falsifiability and climate slow modes,
    https://doi.org/10.1016/j.physa.2018.02.090

    Highlights

    • Climate models do not and cannot employ known physics fully. Thus, they are falsified, a priori.
    • Incomplete physics and the finite representation of computers can induce false instabilities.
    • Eliminating instability can lead to computational overstabilization or false stability.
    • Models on ultra-long timescales are dubiously stable. This is referred to as the “climate state.” Is it real?
    • Decadal variability is understandable in terms of a specific class of nonlinear dynamical systems.

    Abstract

    The most advanced climate models are actually modified meteorological models attempting to capture climate in meteorological terms. This seems a straightforward matter of raw computing power applied to large enough sources of current data. Some believe that models have succeeded in capturing climate in this manner. But have they? This paper outlines difficulties with this picture that derive from the finite representation of our computers, and the fundamental unavailability of future data instead. It suggests that alternative windows onto the multi-decadal timescales are necessary in order to overcome the issues raised for practical problems of prediction.

    So the climate models are not a way to verify the actuality.

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