Reality Check: No, we didn’t just have “the hottest week in 100,000 years”

Guest Post by Kit Knightly

The buzz in the Climate Change news is that the five hottest days in the last 100,000 years all happened last week, according to the World Meteorological Organization.

You can read an article about it from Forbes:

The Fourth of July was the hottest day on Earth in as many as 125,000 years—breaking a record set the day before—as the return of the El Niño weather pattern collides with soaring temperatures at the start of summer, researchers say.

Or, if you prefer, you can read Climate alarmists rending their garments on Twitter:

The 7 hottest days on Earth in the last 100,000+ years all happened in the last week:

July 6 ~ 17.23°C / 63.01°F
July 7 ~ 17.20°C / 62.96°F
July 4 ~ 17.18°C / 62.93°F
July 5 ~ 17.18°C / 62.92°F
July 8 ~ 17.17°C / 62.90°F
July 10 ~ 17.12°C / 62.81°F
July 9 ~ 17.11°C / 62.79°F

— Prof. Eliot Jacobson (@EliotJacobson) July 11, 2023

Now, first off let’s be clear – we haven’t had the “7 the hottest days”  in the last 100,000 years since July 4…

…or, more accurately, there is absolutely no way for anyone to reliably know if we have or not.

Actually think about what they’re saying when they make this claim.

They are claiming that they know, for a fact, the global average temperature to two decimal points over the last 36 million days.

Couple of things to bear in mind here before we go any further.

1 – Humans have only had the ability to accurately measure the temperature of anything for maybe four-hundred years.

2 – Official “global temperature” records only began in 1880.

3 – Beyond  that point we only have partial, local and pretty inaccurate readings back to the mid-17th century.

That’s 400 years, give or take.

So, how do climatologists get the data for the other 99,600 years?

Well – they  guess.

Sorry, they “model”, using tree ring data and ice core samples.

NASA claims by comparing modern tree rings from known weather systems they can figure out the weather patterns that created tree rings in the past.

This is not scientific, it is interpretive.

A tree ring represents a growth cycle, that is all. The factors which affect that growth – specific to the individual tree, the local area or on a global level – are far too complicated for them to have any kind of predictive value.

Disease, volcanic activity, competition from other trees, rainfall, solar activity, parasitic insect or fungal infection…we can’t accurately account for any of these factors, and they all impact tree growth.

In short, all a tree ring can tell you is the length of a growth cycle. Everything else is extrapolation and modelling based on nothing but an a priori assumption of causation.

But really, that’s a secondary issue. There is something more important I want to talk about: The very idea of “average global temperature”.

“Average global temperature” – an entirely meaningless statistic

Using numbers and measures to bamboozle the public and control mass-opinion is not a new practice. In fact a shocking amount of propaganda is entirely predicated upon most people’s inability to actually understand statistics. (This inability is actively encouraged by the media and education system, but that’s another story).

This use of statistics probably reached its zenith with the Covid “pandemic”, but no narrative is more deeply steeped in it, or more heavily reliant on it, than Climate Change.

The appeal of using statistics in this manner is it removes the need to overtly lie.

– You can report nothing but real numbers and yet still totally mislead people.

– You can publish nothing but facts, whilst completely disregarding – or even disguising – the truth.

Damned lies and statistics, you know.

Averages – specifically mean averages – are wonderful for this.

For those who don’t know an “average” or “mean” is calculated by adding all the values within a set of numbers together and then dividing the total by the number of members in the group.

For example, if 5 friends go to dinner together and their total bill is £85, then the average each man spent on food would be 85/5, or £17.

This kind of average is excellent at creating deceptive statistics because they can be highly useful or completely misleading totally depending on context, and very few people understand that.

A good example of this problem is “average life expectancy”. I experienced this first hand when studying history in college.

Some people in my class read that life expectancy was 40 years old for men in Victorian London, and they genuinely thought that meant men were literally aging faster, going grey and getting dementia in their late 30s.

Which is completely wrong by the way.

The truth is most Victorian era males who made it to adulthood generally lived a reasonably normal lifespan, as people have been known to live from time immemorial (“three score years and ten”, according to the psalms).

However, Victorian England had a very high infant mortality, and the number of children dying before reaching 1 year old considerably lowers the average age of death.

So, the statistics appear to suggest most people died at 40, but the truth is that very few died at 40,  but many died at around the age of 1 and many others died around the age of 70.

An “average” can be at the same time completely true and yet not at all representative of reality.

“Global average temperature” is the perfect example of this. It produces a number that people can be made to find scary because it lacks all context or any real-world application. It is literally meaningless.

Now, this is not just the rant of a journalist with an A level in statistics. Many scientists and academics over the years have said that the very idea of a “global temperature” is meaningless.

Such as in this paper, “Does a Global Temperature Exist?”, published in the June 2006 edition of the Journal of Non-Equilibrium Thermodynamics, which argues [emphasis added]:

There is no global temperature. The reasons lie in the properties of the equation of state governing local thermodynamic equilibrium, and the implications cannot be avoided by substituting statistics for physics. Since temperature is an intensive variable, the total temperature is meaningless in terms of the system being measured, and hence any one simple average has no necessary meaning.

As an antidote to the academic language, I’ll demonstrate with an example:

First of all, the globe is pretty huge, the scale alone can blind people. Let’s reduce it down – let’s say it’s just my kitchen. We’ll figure out the “average temperature” of my kitchen.

Second of all, we should realise that the name itself is misleading. When they talk about “average global temperature”, they obviously don’t mean they have measured literally everywhere on Earth. They really mean the “average surface-level temperature from a series of weather stations on land and weather buoys at sea.”

These temperature readings form the set we use to make our mean average. Over the globe there are thousands and thousands of these, in our kitchen we’ll just use four: One in the fridge, one in the stove and one at each end of the kitchen table.

We wake up, and the first thing we do is take the temperature at our “kitchen monitoring stations”. They are as follows: 6^oC in the fridge, 19^oC at both ends of the table and 17^oC in the stove. Our average temperature reading: 15.25 degrees.

This data suggests the kitchen is comfortably the coldest room in the house, and is actually much colder even than average room temperature, or the average Summer day.

Later, after cooking dinner, we take the temperatures again: 6^oC in the fridge, 21^oC at both ends of the table and 176^oC in the stove. Our average temperature is now 56^oC.

This is alarming data, don’t you think? Firstly, according to this data it is no longer medically safe to even go into my kitchen, and more worryingly if this rate of increase continues my house will burst into flames by midnight tomorrow.

Certainly it’s the hottest kitchen since records began (that would be this morning). And judging by old photographs of the people who lived in this house before me, it doesn’t look like the kitchen was ever this swelteringly hot before.

Hopefully everyone reading this gets the point.

Four measuring stations across an entire room is very few, and fully 50% of them experience local extremes of temperature that a) don’t apply to the vast majority of the room and b) massively impact the final outcome.

Neither of those “average temperatures” – high or low – is even close to representative of the actual ambient temperature of the kitchen, nor is it likely to have any impact on the real lives of the real people who use that kitchen.

They do not reflect reality, and have no application to the real world.

This next bit  may shock you – but the world is vastly bigger and more complex than my kitchen. Much of it has no weather station coverage at all, much of it is subject to infinitely complex local weather systems you can’t possibly account for.

A statistic is only as demonstrative as it is thorough, only as useful as it is representative of reality. You cannot create a useful “average” over a huge range of data without taking into account the local differences in systems.

The “average global height” of an adult human is 66 inches (168cm). Based on that, a 5 foot 8 inch Dutch man would be said to be “taller than average”…when he’s actually comfortably shorter than most Dutch men.

Reporting “average temperature”, from different sources over a varied and constantly changing globe, is pointless. Especially if you don’t know the context of the recorded data or the multivariate local factors that contribute to it (for example many weather monitoring stations are at airports, which are always hotter than surrounding areas, there’s also the question of “urban heat islands” and how weather stations are not evenly distributed over the world etc).

The “too long, didn’t read” version: They have no idea if last week was “the hottest week ever”, they can’t possibly know the “average global temperature” today let alone 125,000 years ago, and even if they could it would be data so vague as to be meaningless.

…of course, all of that is assuming they’re not just making them up. Which I supposed they could easily be doing.

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