I’m designing an experiment to illustrate how ‘homogenization’ creates a false climate trend – suggestions welcome

Those of you that have been with WUWT for a few years know that I often like to do hands-on experiments to illustrate and counter some of the most ridiculous climate change claims made on both sides of the aisle. On the alarmist side, you may remember this one:

Al Gore and Bill Nye FAIL at doing a simple CO2 experiment

Replicating Al Gore’s Climate 101 video experiment (from the 24 hour Gore-a-thon) shows that his “high school physics” could never work as advertised

Unfortunately, YouTube has switched off the video, but I’m going to try getting it posted elsewhere such as on Rumble. The graphs of temperature measurements and other images are still there.

Despite the fact that I proved beyond a shadow of a doubt that the experiment was not only fatally flawed, but actually FAKED, they are still using it as propaganda today on Al Gore’s web page.

They never took it down. Schmucks.

So along those lines, like Willis often does, I’ve been thinking about the recent paper published in Atmosphere by some of our brothers-in-arms (Willie Soon, The Connallys, etc) in climate skepticism,

Evaluation of the Homogenization Adjustments Applied to European Temperature Records in the Global Historical Climatology Network Dataset

Abstract

The widely used Global Historical Climatology Network (GHCN) monthly temperature dataset is available in two formats—non-homogenized and homogenized. Since 2011, this homogenized dataset has been updated almost daily by applying the “Pairwise Homogenization Algorithm” (PHA) to the non-homogenized datasets. Previous studies found that the PHA can perform well at correcting synthetic time series when certain artificial biases are introduced. However, its performance with real world data has been less well studied. Therefore, the homogenized GHCN datasets (Version 3 and 4) were downloaded almost daily over a 10-year period (2011–2021) yielding 3689 different updates to the datasets. The different breakpoints identified were analyzed for a set of stations from 24 European countries for which station history metadata were available. A remarkable inconsistency in the identified breakpoints (and hence adjustments applied) was revealed. Of the adjustments applied for GHCN Version 4, 64% (61% for Version 3) were identified on less than 25% of runs, while only 16% of the adjustments (21% for Version 3) were identified consistently for more than 75% of the runs. The consistency of PHA adjustments improved when the breakpoints corresponded to documented station history metadata events. However, only 19% of the breakpoints (18% for Version 3) were associated with a documented event within 1 year, and 67% (69% for Version 3) were not associated with any documented event. Therefore, while the PHA remains a useful tool in the community’s homogenization toolbox, many of the PHA adjustments applied to the homogenized GHCN dataset may have been spurious. Using station metadata to assess the reliability of PHA adjustments might potentially help to identify some of these spurious adjustments.

In a nutshell, they conclude that the homogenization process introduces artificial biases to the long-term temperature record. This is something I surmised over 10 years ago with the USHCN, and published at AGU 2015 with this graph, showing how the final product of an homogenized data is so much warmer than stations that have not been encraoched upon by urbanization and artificials urfaces such as asphalt, concrete, and buildings. By my analysis, almost 90% of the entire USHCN network is out of compliance with siting, and thus suffers from spurious effects of nearby heat sources and sinks.

In the new paper, here is a relevant papragraph that speaks to the graph I published in 2015 at AGU:

As a result, the more breakpoints are adjusted for each record, the more the trends of that record will tend to converge towards the trends of its neighbors. Initially, this might appear desirable since the trends of the homogenized records will be more homogeneous (arguably one of the main goals of “homogenization”), and therefore some have objected to this criticism [41]. However, if multiple neighbors are systemically affected by similar long-term non-climatic biases, then the homogenized trends will tend to converge towards the averages of the station network (including systemic biases), rather than towards the true climatic trends of the region.

The key phrase is “multiple neighbors, i.e. nearby stations.

Back on August 1, 2009, I created an analogy to this issue with homgenization by using bowls of dirty water. If the cleanest water (a good station, properly sited) is homgenized with nearby stations that have varying degrees of turbidity due to dirt in the water, with 5 being the worst, homgenization effectively mixes the clean and dirty water, and you end up with a data point for the station labeled “?” that is some level of turbidity, but not clear. Basically a data blend of clean and dirty data, resulting in muddy water, or muddled data.

In homgenization the data is weighted against the nearby neighbors within a radius. And so a station the might start out as a “1” data wise, might end up getting polluted with the data of nearby stations and end up as as new value, say weighted at “2.5”.

In the map below, applying a homogenization smoothing, weighting  stations by distance nearby the stations with question marks, what would you imagine the values (of turbidity) of them would be? And, how close would these two values be for the east coast station in question and the west coast station in question? Each would be closer to a smoothed center average value based on the neighboring stations.

Of course, this isn’t the actual method, just a visual analogy. But it is essentially what this new paper says is happening to the temperature data.

And, it just isn’t me and this new paper saying this, back in 2012 I reported on another paper that is saying the same thing.

New paper blames about half of global warming on weather station data homogenization

Authors Steirou and Koutsoyiannis, after taking homogenization errors into account find global warming over the past century was only about one-half [0.42°C] of that claimed by the IPCC [0.7-0.8°C].

Here’s the part I really like:  of 67% of the weather stations examined, questionable adjustments were made to raw data that resulted in:

“increased positive trends, decreased negative trends, or changed negative trends to positive,” whereas “the expected proportions would be 1/2 (50%).”

And…

“homogenization practices used until today are mainly statistical, not well justified by experiments, and are rarely supported by metadata. It can be argued that they often lead to false results: natural features of hydroclimatic times series are regarded as errors and are adjusted.”

So, from my viewpoint, it is pretty clear that homgenization is adding a spurious climate warming where there actually isn’t a true climate signal. Instead, it is picking up the urbanization effect which leads to warming of the average temperature, and adding it to the climate signal.

 Steve McIntyre concurs in a post, writing:

Finally, when reference information from nearby stations was used, artifacts at neighbor stations tend to cause adjustment errors: the “bad neighbor” problem. In this case, after adjustment, climate signals became more similar at nearby stations even when the average bias over the whole network was not reduced.

So, I want to design an experiment to simulate and illustrate the “bad neighbor” problem with weather stations and create a video for it.

I’m thinking of the following:

1. Use the turbidity analogy in some way, perhaps using red and blue food coloring rather than a suspended particulate, which will settle out. This is purely for visualization.
2. Using actual temperature, by creating temperature controlled vials of water at varying temperature.
3. Mixing the contents of the vials, and measuing the resultant turbidy/color change and the resultant temperature of the mix.
   

The trick is how to create individual temperature controlled vials of water and maintain that temperature. Some lab equipment, some tubing and some pumps will be needed.

Again purely for visual effect, I may create a map of the USA or the world, place the vials within it, and use that to visualize the results and measure the results.

I welcome a discussion of ideas on how to do this accurately and convincingly.