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August 23, 2023

I may be one of the first scientists in the country to know that predicting long-term temperatures is not possible.

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Almost 50 years ago, while in grad school, I had a contract from an Army research lab to use a state-of-the-art models to predict long-term temperatures.  I quickly realized that the goal of the project, to forecast accurately the temperature long-term, was impossible because small errors in data inputs could result in huge forecasts errors.  Equally important was that errors compounded so quickly that it caused the error ranges to explode.  The results were junk.  As an example, what good is a temperature forecast with an error range of plus or minus one hundred degrees?

I give university speeches to scientists and tell them: if you ever see some data or forecasts, your first question has to be “what’s the error range?”  If you don’t know the error range, the data are almost useless.  It’s not coincidental that the Climate Mafia don’t highlight this problem

So what about modern technology solving these problems?  These error problems are still true today.  It‘s not that the long-term temperature forecasts are wrong; it‘s that they can‘t be right.  All global warming modelers know this, or they are incredibly stupid, or they just lie about it for money or power.

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When the U.N. Intergovernmental Panel on Climate Change made even a pretense of being science-based, they used to admit it.  From the 2001 IPCC Third Assessment Report: “The climate system is a coupled non-linear chaotic system, and therefore the long-term prediction of future climate states is not possible.”

The weather is a coupled, non-linear chaotic system.  Chaos theory says very small changes in inputs can result in totally different outcomes.  This concept is counterintuitive for most people.  We intrinsically think that if you’re a little off at the beginning, you should be a little off at the end.  Try that on a mountain trail next to a cliff.

The Climate Mafia know that this is true, but they still want money and power.  They argue that even though you can‘t make a real temperature forecast, they can create a completely bogus forecasting approach, where they take a bunch of different climate models that don‘t agree (so much for settled science) and combine their outputs.  They then say voilà: we have a correct prediction, and they use pseudo-statistics to get around the error problem.  The way I visualize it is, if you take a bunch (an ensemble sounds more scientific) of wrong answers and then combine them, that is the right answer.  Absurd.

Since your input data are critical to forecasting the Chaotic Future, fully understanding past temperatures is also critical.  The Climate Mafia create the entirely bogus concept of an “average Earth temperature” to create a bogus base data set for their bogus models.  The Warming Scammers like to use a garbage temperature history that starts about 1850.  The Scammers say that their increased temperatures since 1850, just coincidentally at the end of a three-hundred-year cooling cycle, represent the rise of the industrial pollution age.  In 1850, and even in 1950, only a small percentage of world’s population could even be considered close to industrialized.  Look at India, Africa, and China then: almost medieval energy use patterns until really recently.  Humans have been around in their current form for many tens of thousands of years.  To say the weather since the 1850s is representative of anything from a statistical perspective is a joke.

So what kind of temperature data do we have since 1850?

With oceans and ice caps covering over 80+% of the world’s surface, we have virtually no reliable long-term data on any of that, other than the last few decades.  Even then, you are talking about a relatively small number of measuring devises in all those places.  (Do you check the weather a few hundred miles away to know if you need an umbrella?)  How about the temperature trends in deserts, on mountains, in the middle of Africa, South America, Siberia — at sea level, a hundred feet elevation, a thousand feet elevation?  The data are so bad in all of the Southern Hemisphere — half the globe — that there are only a few datasets even close to reliable since the 1850s.  There are almost no real, reliable, and complete long-term data globally, and particularly none reliable enough to create model of a chaotic system entirely dependent on very accurate input data.