From NOT A LOTOF PEOPLE KNOW THAT
By Paul Homewood
Nice summary by Richard Lindzen:
We are generally told that the following defines ‘climate’…
Figure 1 Global average temperature anomaly.
Actually, we are not looking at ‘average temperature’. Averaging Mt. Everest and the Dead Sea makes no sense. Instead, we average what is called the temperature anomaly. We average the deviations from a 30-year mean. The figure shows an increase of a bit more than 1°C over 175 years. We are told by international bureaucrats that when this reaches 1.5°C, we are doomed. In all fairness, even the science report of the UN’s IPCC (i.e. the WG1 report) and the US National Assessments never make this claim. The political claims are simply meant to frighten the public into compliance with absurd policies. It remains a puzzle to me why the public should be frightened of a warming that is smaller than the temperature change we normally experience between breakfast and lunch.
My puzzlement becomes clearer when one includes the data points in Figure 1, as shown in Figure 2. This was first noted by Stanley Grotch, and updated by John Christy and I).
Figure 2 Temperature anomalies at individual stations as well as the mean.
We see that the data points are spread pretty densely over a range of about 16°C – over an order of magnitude greater than the range of the mean. The change in Figure 1 looks big simply because the data points are left out and the scale is expanded by over an order of magnitude.
What exactly does this say about climate? In point of fact, the Earth has dozens of different climate regimes. This is shown in Figure 3 showing the Koppen climate classification for the period 1901-2010. Each of these represents different interactions with their environments. Are we really supposed to think that each of these regimes responds in lock-step with the global mean temperature anomaly? On the contrary, Figure 2 tells us that at any given time, there are almost as many stations cooling as are warming.
Figure 3 Koppen climate classification.
Of course, the notion that global average temperature anomaly constitutes ‘climate’ is attractive due to its simplicity.
Unfortunately, that doesn’t mean that it is correct.
https://www.netzerowatch.com/all-news/what-is-climate?mc_cid=6084154d58&mc_eid=4961da7cb1
I was particularly intrigued with Figure 2:
I know it’s only eyeballing, but if you back to 1980, there appears to be little increase in the positive anomalies, but there is a noticeable reduction in negative ones. It is something I have often seen in UK data, that average temperature rise is being driven by less extreme cold weather, rather than more extreme heat.
The figure shows an increase of a bit more than 1°C over 175 years.
Lindzen once noted that the “alarming” temperature increase was less than a person standing (say, in his kitchen) might feel between his feet and his head.
A more thorough explication of the insanity:
https://www.thegwpf.org/content/uploads/2016/04/Lindzen.pdf
What is Climate?
It is, like weather, a local phenomenon. The climate in the Midwest is not the same as climate in the Keys. The climate in Seattle is not the same as the climate in Maine.
The data points in climate change are, for example, changes in growing season over years. The data points are centuries. 1000 years has 10 data points.
Climate changes in cycles unless disturbed by some non-cyclic event like volcanoes, magnetic pole shifts, coronal mass ejections, and asteroid impacts.
“Climate Change(TM)” is the belief that there is a catastrophe in our future unless we do something (expensive) right now.
“Doing something” has become blaming the very stuff of all life, plants and animals – like us – included. More carbon dioxide is a good thing in my opinion.
The World Meteorological Organization changed the definition of “climate” to be only 30 years. That means it is always changing.
“It is something I have often seen in UK data, that average temperature rise is being driven by less extreme cold weather, rather than more extreme heat.”
Less extreme cold for Seattle, USA depicted on plot in a recent Cliff Mass blog post:
https://cliffmass.blogspot.com/2024/01/some-of-coldest-air-in-years-is-moving.html?m=1
What is “climate”.
I scanned this from my 1972 World Book Encyclopedia.
I went back to the scan and it pretty small even after I clicked on it.
Hope this one is better.
The WMO change the definition of “climate” to be only 30 years now.
Nice! What science, as opposed to hubris driven ideology, looks like.
Very nice.
Having mapped the ‘climate zones’ per se now is the time for the real work. It is time to map the anomalies in each of the time zones. A big task but I know you are up for it. Homogenising has its own sameness of results.
In an earlier comment I mentioned a large sheet with 128 grapks about Astralian heatwaves. Here is that link, now I am back before the old PC.
Please give it time to load.
Geoff S
https://www.geoffstuff.com/eightheatwave2022.xlsx
“It is something I have often seen in UK data, that average temperature rise is being driven by less extreme cold weather, rather than more extreme heat.”
But you can’t push civilization back to the 1850s on a slogan like “global milding”.
Years back Japan cleaned America’s clock by applying a little known statisticians work to manufacturing.
The Japanese used Deming’s work to calculate how much the dimensions of a part will vary naturally, and how much is abnormal.
Climate Science to this date cannot tell how much Climate varies naturally And how much is abnormal.
Quality Control! That’s a whole engineering classification.
If you replace gridding with the sum of sums, you can transform chaotic data like climate into the standard or normal distribution (central limit theorem). From this Edward Demming used 3 sigma (standard deviations) as the expected deviation that occurs naturally (99.7%))
The 0.3% that occurs outside of 3 sigma he called abnormal. This simple metric resulted in a huge boost to Japanese quality control, as they could now measure quality.
Climate science has no such measure. They do not know if climate change is natural or a result of human activity because they have no measure of natural variability
Indeed, climate science tosses all the variance of the numbers they play with into the garbage disposer.
They don’t even know if average/variance apply – i.e. they just assume a Gaussian distribution of temperatures on a global basis – no proof needed.
Figure 2 is preferred over figure 1 because it shows added information. Not only does it show the average, it shows the variability
With a bit of work statistically, you can calculate natural variability of climate from fig 2.
Exactly, and it isn’t milli-Kelvins, it is ~5 Kelvins. Can’t scare anyone if they show this.
When I first saw Figure 3, I thought “great, now I can see how much the average temperature has been changing by region”. But, unfortunately, it only classifies regions over a ~100-year average of some kind. Can anyone point to a similar map showing average temperature change in each region over some period of time?
schmoe, if memory serves, you can create trend maps at the KNMI Climate Explorer:
https://climexp.knmi.nl/start.cgi
It’ll take you a while to sift through the temperature datasets to find the one you want, and to figure out how to create trend maps, selecting variables like time period and resolution.
I used that feature of the KNMI Climate Explorer a couple of times many years ago when I was blogging.
Regards,
Bob
An interesting test would be to duplicate Figure 2 with a Monte Carlo simulation.
This could be sliced into 150 year segments to find if a 1.5 C average global temperature change is possible due simply to chance, and what the odds were.
Hopefully climate science has somewhere performed this extremely simple statistical exercise.
My experience with Monte Carlo analysis is that it requires a functional relationship between inputs and outputs. The input factors can then be randomly combined, within boundaries for each, to determine what happens to the output.
I keep seeing mentions of using Monte Carlo simulations to match to an observational data set. This isn’t really a Monte Carlo analysis as I know it. It’s a method of eliciting a “best fit” trend line using randomly generated values. That doesn’t really identify statistical significance of anything.
Statistical significance, as I know it, applies to testing a hypothesis, i.e. a functional relationship. It also requires an assumption that the functional relationship generates a Gaussian distribution of values so that an average/variance set of parameters actually describes the distribution.
Neither of these exist with temperature. CGM’s are not a functional relationship, they are a data matching algorithm. No one knows if the temperature data elements are actually a Gaussian distribution, in fact they are probably *not* Gaussian because of combining Northern hemisphere data with Southern hemisphere data. The data is far more likely to be multi-modal.