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Systematic error in global temperature measurement – Rising with that?


Moritz Busing

Some time ago, I came across a curiosity about thermography publications of the past 30 years:

When you turn an anomalous temperature curve into an absolute temperature curve, the past has gotten colder.

The decade 1880-1890 in recent publications was 0.3°C (0.5°F) colder than in publications 15 years ago and 0.5C (0.9°F) colder than previous publications publication 30 years ago. The weather station’s database of land surface temperatures for this time period hasn’t changed much over the past 30 years, but the methods of analysis have changed.

So I went down the rabbit hole and tried to understand how people analyze data from thousands of weather stations in various locations with time-varying technology. Here, I found a systematic error in one of the most important analytical procedures: homogenization

Homogenization involves removing step-by-step errors and trends in the data series as a result of non-climate-related sources. For example, relocating a weather station from a mountaintop to a valley can cause permanent disparities in temperature measurements. In addition, the use of a new thermometer or a new type of thermometer housing can permanently change the measured temperature. These changes result in step-by-step interrupts in the data series. Other changes, such as urbanization, that lead to non-climate-related trend changes in the data series are also permanent. These permanent errors are corrected by increasing or decreasing all past data in increments so that the temperature curve becomes continuous (This process is not trivial and I will not detail it here) .

Here I found the error:

Not all non-climate changes are permanent.

In particular, the aging effects of the paint or plastic of the weather station housing are eliminated when the housing is repainted or replaced. But after the effects of aging have been removed, the new paint or plastic begins to age again. A study by a team at the Istituto Nazionale die Ricerca Metrologica in Turin, Italy Comparative analysis of the effects of solar irradiance screen aging on temperature measurements using weather stations confirm that this aging effect is real.

This alone wouldn’t be a big deal. The aging effect reaches only 0.1-0.2°C (0.18-0.36°F) a negligible difference and is truly undetectable by homogenization algorithms. Homogeneous algorithms cannot detect such a small warming trend due to aging, nor can they detect small increments of disruption from innovation. However, when other sources of larger increment errors (relocation, new gauges) coincide with repainting, replacement or at least cleaning of the housing, a system failure occurs when these small steps are added up each time.

While the aging effect is too small to detect in individual weather stations due to noisy data, it is still large enough to detect changes in temperature trends in a statistical analysis of thousands of time stations. details. So, I analyzed the homogenized datasets from the National Center for Environmental Information (NCEI) against the heterogeneous datasets. Here I was actually able to identify and quantify the effects of aging.

On average, the weather station data has a step break every 19 years. As a result, there have been about 7 “corrections” of weather station data on average over the past 140 years. Even a small aging effect of 0.1°C would then result in a misrecorded global warming of about 0.7°C!

This is just a rough estimate, so I looked at the GISTEMP global land surface temperature calculation from the Goddard Institute for Space Research (GISS). They have done a great job making their methods transparent and making all their tools available for download online, so that anyone can reproduce their results. I corrected the aging effect in the homogenous dataset and ran this corrected dataset using the GISTEMP team’s tool. The result is a reduction in temperature variation between the decades 1880-1890 and 2010-2020 from 1.43°C to 0.83°C CI (95%) [0.46°C; 1.19°C].

This result also shows a better agreement with satellite data provided by the University of Alabama at Huntsville (UAH):

I gathered all the sources and wrote an article about my findings: Systematic error correction in global temperature analysis related to aging effects. I tried to publish this article in four different peer-reviewed journals, but it was always rejected with boxed replies (“…our readers wouldn’t care…”) even before when it is evaluated by a colleague.

My methods are very careful and I have made some conservative assumptions. In the paper, I also quantify a less conservative analysis, resulting in only 0.41°C of global warming over 140 years.

Another interesting finding is that the corrected temperature curve is better suited to the CO2 concentration. The R² values ​​(the statistic that determines how well one data set predicts another) of the temperature curves obtained and the base 2 logarithm of CO2 (the change in temperature per rise) double CO2) as follows:

– GISTEMP: up to 92%

– Corrected conservative average: up to 73%

– Estimate adjusted mean without conservation: up to 36%

This means that a smaller part of global warming is caused by CO2. So, for the conservation case, up to 73% of global warming of 0.83°C, i.e. 0.61°C at most, is caused by CO2. For the less conservative case, only 36% of global warming of 0.41°C, i.e. 0.15°C at most, is caused by CO2.

These temperature data curves are the basic input to many other studies and the calibration target of many climate models. This will revolutionize climate science, if my findings are confirmed.


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