Moritz Büsing
Some time ago I stumbled upon a curiosity in temperature measurement publications of the last 30 years:
When you turn the temperature anomaly curves into absolute temperature curves then the past has been getting colder.
The decade 1880 to 1890 is in recent publications 0.3°C (0.5°F) colder than it was in publications from 15 years ago, and 0.5C (0.9°F) colder than it was in publications 30 years ago. The weather station database for land surface temperatures of this time period has not changed much in the last 30 years, but the analysis methods have changed.
Therefore, I went down the rabbit hole and tried to understand how one analyzes the data from thousands of weather stations at many different locations with changing technologies over time. Here I found a systematic error in one of the most important analysis processes: homogenization
Homogenization consists of removing stepwise breaks and trends in the data series that result from non-climate related sources. For example, relocating a weather station from the top of a mountain to the valley can cause a permanent offset in temperature measurements. Also using a new type of thermometer or a new type of housing of the thermometer can permanently change the measured temperatures. These changes lead to stepwise breaks in the data series. Other changes, such as urbanization, lead to non-climate related trend changes in the data series that are also permanent. These permanent errors are corrected by increasing or decreasing all the past data at a stepwise break such that the temperature curve becomes continuous (This process is not trivial, and I will not elaborate on it here).
Here I discovered the error:
Not all non-climate related changes are permanent.
Especially the ageing effects of the paint or plastic of a weather station housings are removed, when the housing is repainted or replaced. But after the aging effects have been removed, the new paint or plastic starts to age again. A study by a team at the Istituto Nazionale die Ricerca Metrologica in Turin, Italy Comparative analysis of the influence of solar radiation screen ageing on temperature measurements by means of weather stations confirms that this ageing effect is real.
This alone would not be a big issue. The ageing effect only reaches 0.1-0.2°C (0.18-0.36°F) difference which would be negligible, and indeed undetectable by the homogenization algorithms. The homogenization algorithms can neither detect such a small warming trend from aging nor the tiny downward stepwise break from renewal. However, when other sources for larger stepwise breaks (change in location, new instrumentation) coincide with repainting, replacing or at least cleaning of the housing, then a systematic error occurs where these small steps are added up each time.
While the aging effect is too small to detect in individual weather stations due to the noisy data, it is still large enough to detect in the changes of temperature trends in a statistical analysis of thousands of weather stations. So, I analyzed the homogenized data sets from the National Centers of Environmental Information (NCEI) in comparison with the non-homogenized data sets. Here I was indeed able to identify and quantify the ageing effects.
On average, a stepwise break is corrected once in every 19 years of weather station data. Therefore, there are on average roughly 7 “corrections” of the weather station data of the last 140 years. Even a small aging effect of 0.1°C would then lead to roughly 0.7°C of erroneously recorded global warming!
This is only a rough estimate, so I looked at the global land surface temperature calculation GISTEMP from the Goddard Institute for Space Studies (GISS). They did a really good job in making their methods transparent and offered all their tools for download online, so everybody can reproduce their results. I corrected the aging effect in the homogenized data set and ran this corrected data set with the tool from the GISTEMP team. The result is a reduction of the temperature change 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 fit with satellite data provided by the University of Alabama in Huntsville (UAH):
I collected all the sources and wrote a paper about my findings: Correction of Systematic Error in Global Temperature Analysis Related to Aging Effects. I tried to publish this paper in four different peer reviewed journals, but it was always rejected with canned answers (“…our readers would not be interested…”) even before it was reviewed by a peer.
My methods were very careful and I made several conservative assumptions. In the paper I also quantify a less conservative analysis, which leads to only 0.41°C global warming within 140 years.
One more interesting finding is, that the corrected temperature curve is a worse fit with the CO2 concentrations. The R² values (statistical number identifying how much one data set predicts another data set) of the resulting temperature curves and the base 2 logarithm of CO2 (temperature change per doubling of CO2) are the following:
– GISTEMP: up to 92%
– Corrected conservative mean: up to 73%
– Estimate of the corrected mean without conservatisms: up to 36%
This means, that a smaller fraction of global warming is caused by CO2. So, for the conservative case up to 73% of 0.83°C global warming, i.e. at most 0.61°C, are caused by CO2. For the less conservative case only up to 36% of 0.41°C global warming, i.e. at most 0.15°C, are caused by CO2.
These temperature data curves are the basic input data for many other studies and are the calibration targets of many climate models. This will revolutionize climate science, if my findings are confirmed.
I read journals of a Scottish gardener who nearly two centuries ago made notes about temperatures and rainfall. I have wondered about the accuracy of his thermometer measurements. Perhaps a knowledgeable readers can comment about these old devices and whether they were in fact perhaps more accurate than we would care to admit.
I vote for the old gardener’s measurements being MORE accurate.
His life, and the lives of his family, were dependent on good data. In the current era, the meal ticket for the “scientists” is dependent upon getting data that supports the narrative.
It is almost certain that the gardener had no hidden agenda, and recorded the data to the best of his, and his instruments ability. Any errors were just as likely to be in either direction, so “corrections” or “homogenisation” are completely unwarranted. In a valid “homogenisation” process, the “corrections” overwhelmingly being in one direction would alert any unbiased processor of the data that their algorithm was flawed.
If I may, sir; are you concerned with the old gent’s accuracy, or his precision? Whereas precision has somewhat increased for thermometers; going from “just over nine” to 9,3 is somewhat better, but 9,28 has little added value in the real world. Accuracy is an observation issue. Provided the gent knew enough to prevent parallax mistakes, his observations will be as accurate as his machine’s manufacturing tolerance.
I am not aware of any old thermometers being held up as proof that our forebears lacked craftsmanship, so I assume those thermometers were made and calibrated quite well indeed. Our Scottish gent, may he have lived long, probably did not live long enough to be bothered by issues such as mercury dissolving into the glass etc.
They made thermometers with 0.01°F increments. I think that they appreciated, even then, that recording to such a precise value for the local weather was silly. The temperature of the bulb would have been measured very precisely and accurately. Local weather? Not so much.
There is an WUWT post from many moons ago on how pruning of nearby trees will lead to an obvious discontinuity and warmer measurements at the site. The gradual growth over many years will gradually cool measurements but without an obvious discontinuity to show a correction needs to be made.
To be clear, measurement precision is the ability to consistently reproduce a measurement. Measuments can be precise but inaccurate.
I recall the advent of digital instruments and colleagues recording measurements to decimal points beyond the mfr’s stated accuracy just because the extra digits were displayed. I managed a metrology organization for several years and was never surprised to learn my “customers” did not understand how to use their instrumentation correctly.They also did not understand the concept of calibration and measurement uncertainty.
Did he homogenize, pasteurize, infill and adjust the numbers?
If not. that’s a good start!
Two centuries ago, someone would have used boiling water to identify 212°F and ice water to identify the 32°F temperature markings at sea level. These are often etched into the thermometer itself.
Markings to track individual temperature would then be applied. Thermometers would be attached firmly to the temperature scale markings. Certifiers can adjust the thermometer’s position to correct misalignment.
Temperatures read correctly are quite accurate. Temperatures read from differing positions can be off by several degrees.
Simple test. Predict whether they will correlate with other records. Test
What if the other records are wrong?
…or share a systematic error based on using a similar (non-orthogonal) physical indicator?
If all records are “homogenized” using the same faulty method, then the odds are they will all match. Fail.
If you still have access to the data please report it. –AGF
(“…our readers would not be interested…”)
If verifiable, it’s probably very true that most alarmists wouldn’t be interested in seeing this come to light.
Reality is misinformation to them…
This appears to be good work, and confirms what most of us sceptics understand about the irregularities in the homogenization process employed by most of the ground temperature datasets. Obviously the largest adjustments are required for the UHIE, what we didn’t understand was that the largest negative movements were in the older data that should have the least adjustment as the effect was less, so the process appeared obscure and/or dodgy.
That’s why most of us preferred to use the UAH satellite data for general comparisons of the modern data, and the least adjusted datasets for historical comparisons..
Nope read the paper. Fundamental errors
Please name these errors
You’ve been hit by a drive-by generic character attack from The Masher
It’s not acceptable to leave fundamental errors uncorrected.
They become your errors of you see them and leave them.
Please clarify.
I notice you can’t actually list these errors. Again.
(“…our readers would not be interested…”) Well the readers at this site ARE!
Thanks.
When the “corrections” are a very large percentage of the observed effect, doubting how robust the findings are seems in order.
The “corrections” are actually several times larger than the signal claimed.
Moritz, you say that, if your findings are confirmed, they will revolutionize climate science. If only that were true. As the journal editors already told you, their readers aren’t interested.
The reviewers actually pointed out the errors. It’s sad yall can’t see them. Bad skeptic
Such as?
You have to trust in mosher, he’s a self declared expert.
“I tried to publish this paper in four different peer reviewed journals, but it was always rejected with canned answers (“…our readers would not be interested…”) even before it was reviewed by a peer.”
For an English major your comprehension is poor. So, are you calling Moritz a liar ?
“This means, that a smaller fraction of global warming is caused by CO2.
“…if my findings are confirmed…”
You can expect a midnight visit from Dr. Mann and chums.
“In the draft plan, we’re looking at recycling 20 percent of our garbage by 2010. – Michael E. Mann
Presumably we should get ready for hothouse CMIP7 and an even greater level of garbage recycling
The journals likely branded you as a crank or possibly a denier and they have a “rule” to not give you a platform. Concerning your continuing uptrend recalibration concept, since the ice point of water is ultimately used to calibration thermometers, that should eventually correct such errors. Nonetheless, its statistical findings seem important and its publication would allow frank validity analysis by others.
Very good work. Part of what you describe was covered by Zhang et al ‘Effect of data homogenization’ in Theo. Appl.Climatol.115:365-373 (2014). It is called the problem of ‘breakpoint’ Menne stitching.
Unsurprising you cannot get published. Warmunists don’t want to learn they are wrong. We have known that since Climategate.
Thank you, i will read that
The Climate Howlers have been predicting CAGW for over 50 years.
Wrong for 50 years so far.
The historical temperature data seems unrelated to their predictions of doom.
They reject UAH, so obviously don’t accept any contrary data.
Leftist scientists and politicians do not debate.
It’s their way or you are a denier unworthy of debate.
uah does not post its raw data or adjustment algorithms.
they adjust willy nilly and dont provide code or imtermediate data.
Have you taken a hit in the head ? … the silliness of your comments exceeds your reputation.
And the IPCC refuses to publish the perfect global average surface temperature that the earth should maintain in its summary for policy makers.
UAH Methodology:
https://www.drroyspencer.com/2015/04/version-6-0-of-the-uah-temperature-dataset-released-new-lt-trend-0-11-cdecade/
Data:
The new Version 6 files are located here:
Lower Troposphere: http://vortex.nsstc.uah.edu/data/msu/v6.0beta/tlt
Mid-Troposphere: http://vortex.nsstc.uah.edu/data/msu/v6.0beta/tmt
Tropopause: http://vortex.nsstc.uah.edu/data/msu/v6.0beta/ttp
Lower Stratosphere: http://vortex.nsstc.uah.edu/data/msu/v6.0beta/tls
See also:
https://wattsupwiththat.com/2022/08/02/uah-global-temperature-update-for-july-2022-0-36-deg-c/
Mosher is correct. UAH does not publish the materials required to replicate their results. It is a closed source dataset. Contrast this with GISS which provides all materials including source code and raw data. You can download all the materials and have it running on your own PC in a matter of minutes.
Name one satellite data set that is not closed source?
But I would too like to have an open source.
Great insight. Thanks. With each adjustment, the past keeps getting colder and colder and more and more correlated to CO2. Interesting that UAH typically tracks the other data very well… and then the others (GISS, HADCRUT, RSS) make substantial “adjustments” and then they start tracking the UAH again until the next adjustment.
I’m waiting for the Dust Bowl to turn into the Snow Bowl, after adjustments.
It’s just as hard to “predict” the past temperature as it is to predict the future temperature — the past temperature keeps changing !
LOL! You have been on today. About the 3rd time you have gotten a laugh out of me.
I am a proud graduate of the Academy of Lame Jokes
Voted most likely to bomb in a comedy club.
My wife, however, compares me with the famous comedian Rodney Dangerfield: “Do other people tell you that you look like Rodney Dangerfield?”
Hopefully number 4?
After 25 years of climate science and energy reading, I just have to laugh at the junk science. And comments by The Grifter. And comments by The Masher. And comments by The Stroker.
Well they can’t adjust the physical data. It is known, for example, that the coal port at Spitsbergen (Svalbard) which was only readily accessible for 3 months of the year in the years before 1920 was accessible for over 7 months of the year by the late 1930s
Uah is severely adjusted
How do you know UAH is “severely adjusted”? You just said minutes above that “uah does not post its raw data or adjustment algorithms”
I thought the UAH was calibrated with independent radiosonde observations. Are those “severely adjusted” too? Sounds like a DOJ investigation is needed to me. You should file an affidavit.
Excellent work. A similar effect occurs when measuring outdoor particles (PM2.5 or PM10). Vast amounts of air flow through the fans and particles gradually collect on the fan blades. So the instruments are cleaned regularly. However, the loss of particles to the fans begins again, and is greatest immediately after cleaning, The number of particles collected on the filters is thus reduced, leading to an underestimate of the particle concentrations. The error gradually decreases and is smallest immediately before the next cleaning. Back in the 1980s, this was explained informally to the EPA laboratory responsible for quality assurance of the data. The lab chose not to investigate the size of this error, which , although small, was statistically significant in the test data base. This gravimetric method is still described as the “gold standard”.
About 15 years ago I looked at the order in which temperature corrections in the USHCN were done and noticed that the homogenization step was done before correction for UHI effect. Thus, presuming both were done correctly UHI would have been spread into the entire set of stations leading to a warming bias in the resulting data. I suppose I should look at all that work again and see if it is still as before. I also noticed that I had uploaded a DVI file to the website rather than a PDF — something I ought to correct. But until I do, and for those who can read a DVI, the link is here. (Puzzling — some applications think the file is DVI and some that it is PDF???)
On a related note, I notice “metadata” about changing climate, such as newspaper and magazine articles about weather of the current time. The monthly weather review is a good source of such data. One of the striking things about this sort of data from the 1930s is the preponderance of stories about how warm/hot it had become in the U.S. during that decade compared to those from before. Yet, if you look at the corrected temperatures from our climate scientists you are left with no explanation at all about why people thought it was hot in the 1930s. It is a terrible disconnect that can only be rectified by either people imagining the heat or the scientists having balled-up the correction process. As I have noticed scientists being subject to groupthink since I first began taking real science courses in college, I vote for the latter explanation.
UHI effect.
I wrote an article on the NASA-GISS UHI adjustments a few years ago. They accounted for -0.05 degrees C. less warming over a century — just enough to say they adjusted for UHI.
The key problem was that there were almost as many adjustments up (increased warming from UHI) as there were adjustments down (decreased warming from UHI) for a tiny net change. I pronounced the UHI adjustments to be science fraud,
Sorry I pronounced your work as fraud
No one cares what you say Mosher
A fraud calling other people frauds. Now that is funny.
Yea, the 1930s wasn’t hot at all,The Grapes of Wrath is just a story!
Homogenized data does not spread. Myth
It does for homogenizations of trends
You complain, inaccurately, about UAH not showing their work.
On the other hand you actually seem to believe that just whinning that others are wrong constitutes a complete refutation.
“On average, a stepwise break is corrected once in every 19 years of weather station data. Therefore, there are on average roughly 7 “corrections” of the weather station data of the last 140 years. Even a small aging effect of 0.1°C would then lead to roughly 0.7°C of erroneously recorded global warming!”
Only if each homogenisation correction coincided exactly (to the month) with a cleaning event (extremely improbable). Otherwise the cleaning would go as unnoticed as the small accumulation of aging.
The real test of the effect of homogenisation, with possible errors, is to calculate the temperature using unhomogenised data. It makes very little difference, Certainly nothing like what is shown here.
Yes, there is an unknown probability of coincidence between these events. I adressed this in the linked preprint.
The 0.1 degree value is an average. Lopardo et al. Found a much higher aging effect in field experiments. Therefore, I estimate a true effect of roughly 0.23 degrees and a probability of coincidence of 40%. But this is just an estimate and I have no good data on this.
Worse you have errors in start times
The inital start of operation of a weather station is not counted as a homogenization step. Therefore the delay between installation and recording is irrelevant
Why you would assume that station moves, equipment changes and other steps wouldn’t be highly likely to include cleaning is baffling. That would be an obvious time to do cleaning.
Nick automatically assumes whatever is needed to prove his case.
Moritz, I’m not entirely following the argument.
Are you saying that the stepwise breaks do not have to coincide exactly with a cleaning episode for this effect to occur?
The cause of large stepwise breaks (e.g. Relocation, new instruments, new station) often occurs at the same time as repainting, replacing or at least cleaning of the weather station housing. The accuracy needs to be within the same month, because that is the Resolution of the homogenization process.
As I mentioned in an other comment, there is an unknown probability of coincidence between these events. I adressed this in the linked preprint.
The 0.1 degree value is an average. Lopardo et al. Found a much higher aging effect in field experiments. Therefore, I estimate a true effect of roughly 0.23 degrees and a probability of coincidence of 40%. But this is just an estimate and I have no good data on this
.1 average?. For all locations. For all sunlight conditions. For all types of paint or plastic?.This is the problem with discrete adjustment. You also miss the variance. And you compound the error and uncertainty. This is why pha and other homogenization approaches don’t do these discrete fixes. You confused a lot in this approach
Yes, the aging functions were detemined for the average effect on thousands of weather stations after stepwise homogenizations. These thousands of weather stations were in all kinds of climates and had all the technologies of their time period.
I absolutely did not miss the variance. The opposite is the case amd I used a very conservative approach maximizing the variance.
Read the paper and stop spreading lies!
Mosher — defender of government bureaucrats until his dying day !
Once every 19 years starting when. Stations open years and months before there records start.
Starting with the recordings
When you turn the temperature anomaly curves into absolute temperature curves then the past has been getting colder.
nope.
” The ageing effect only reaches 0.1-0.2°C (0.18-0.36°F) difference which would be negligible, and indeed undetectable by the homogenization algorithms.”
nope. this is a testable statement. wheres the test. the code and data is available
if you didnt do this test i would have rejected your paper as a reviewer.
as for the effect on global temps you seem to have forgotten the basics
sst is 70% of the total. errors in land record have a 30% weight
The authors of the papers documenting the homogenization algorithms stated this undetectability below 0.2 degrees themselves. This is also visible in the bimodal distribution of the sizes of homogenization steps.
My paper is only about land surface temperatures.
Also this is an abbreviated article. I have linked the full preprint.
The other “nope” statements are not impressive without data
Detectability is a setting in the algorithm. It’s open R code.
Which one of the many algorithms are you talking about? I used the same data as the GISS team which is already homogenized.
There is no point in rerunning the homogenization algorithm with changed parameters. If I changed the baseline, then the comparison would be meaningless.
you’re a reviewer?
we are doomed !
“sst is 70% of the total. errors in land record have a 30% weight”. And we had almost no idea what sst were before 2004. Let’s face it, the signal/noise in “global temperature” is tiny. A lot more error and possible error and unknown error than anyone will admit.
Almost no idea?. Uncertainty is not total ignorance. I know it was boiling or frozen
Aren’t we a bit pedantic Mr. Mosher?
Hmm… boiling or frozen. How about 70 vs 70.5 degrees? Or even 70 vs 71?
Averaging water and air Temps is useless … Infilling data Into cells missing data is a horrid tool … the models “global” temp record is useless … not fit for purpose
how about UAH after 1979?
It’s not useless. Your fired
He is fired? By you?
Seriously, what is wrong with you?
How typical of an alarmist. Someone disagrees with you. Fire them.
Please demonstrate, if you are able, why he is wrong.
Just how do several hundred measurement stations mostly in the Eastern N. America and Central Europe can be an accurate gauge for the entire Earth’s temperature.
Lord,
useless/useful depends on perspective; not fit for purpose depends on perspective.
Some people make a living, directly and indirectly, by taking advantage of scheme … it is useful to them.
If you were one of the scumbags making a living off of the scheme, you wouldn’t be good at it and as such … you are fired.
‘This means, that a smaller fraction of global warming is caused by CO2’
OR, that a smaller fraction of CO2 increase is caused by global warming as, for instance, CO2 comes out of water bodies.
Nature is a net CO2 absorber The tiny atmospheric CO2 increase from warming oceanss is included in the net CO2 absorption by nature
Well, that is one theory!
If manmade CO2 emissions are enough to increase atmospheric CO2 by about 200ppm, and the actual atmospheric increase is 135 ppm (415ppm now versus 280ppm 1800s), then 100% of the CO2 increase is manmade and nature is a net CO2 absorber. That is not a theory. That is a fact. The important question is not how the CO2 got into the atmosphere — the important question is what does that extra CO2 do?
That is logical. But all logic starts with a premise. How is that premise derived? “If manmade CO2 emissions are enough to increase atmospheric CO2 by about 200ppm”. How is that premise derived? Is it really a fact?
Those statements are not mutually exclusive. Maybe it is both?
This article makes some good points about measurement tools but misses most of the problems with the global average temperature statistic.
The first problem is the lack of Southern Hemisphere measurements before 1920 which must require a huge amount of infilling (guessing)
The second problem is repeated changes in sea surface temperature measurement methodology with no attempt to compare the methodologies in one place at the same time.
The third problem comes after there are enough measurements for a decent number, but the 1940 to 1975 global cooling as reported in 1975, has been “revised away” and is nearly a flat trend now. That’s quite an adjustment.
The satellite data since 1979 has the potential for accuracy with far less infilling than surface numbers. I trust the scientists more than government bureaucrat scientists. They are volunteers with no government funding. Trusting the scientists is as important as the data quality.
In my opinion, the global average temperature statistic is not useful before 1979, and since 1979 reflects moderate global warming. But the predictions for future global warming (CAGW) seem unrelated even to the 1979 to 2022 trend. So even if the GAST measurements were perfectly accurate after 1979, we’d still be hearing predictions of climate doom.
No one knows the global warming in the past 140 years — I have no idea why the author is guessing, and he fails to explain his guess in detail. There was warming since the cold Maunder Minimum 1690s — we know that from local climate reconstructions. Maybe +2 degrees C.
No one knows the exact effect of CO2 on global warming — I have np idea why the author is guessing, and he fails to explain his explain his guess in detail.
If this article is similar in quality to his study, it’s obvious why the study was not published.
Obviously, this article is not complete. The preprint should be more complete. I also published all the code and result files of my analysis which will give you the most complete picture. There was no “guess”
But I concede, that there may be many other things wrong with weighted weather station averaging and homogenization. The whole idea of analyzing and manipulating temperature anomalies is patently absurd, because many physical properties of our environment are linked to distinct absolute temperatures. Especially the freezing point of water, and the partial pressure of water at higher temperatures.
I addressed only one aspect in my study. This one aspect is enough to invalidate climate hysteria.
The gap between your paragraphs on weather stations, and the concluding remarks on 140 years of climate change, and the exact effect of CO2 is a leap of faith. A huge gap. That is a fact. And you are guessing about those two issues. Period.
While absolute temperatures coincide with thermometers that people use and understand, the anomalies can be converted into absolute temperatures which are less deceptive on a chart.
Climate hysteria was not created with facts, data and logic, so can not be refuted with facts data and logic. CAGW is a religious belief based on faith. As an atheist, it seems like other religions to me, but without the good intentions.
There is no CAGW. There has never been CAGW. And CAGW predictions seem unrelated to (faster than) even to the cherry- picked global warming from 1975 to 2022. The Climate Howlers do need some amount of global warming since 1975 to promote their CAGW predictions. They completely ignore global cooling from 1940 to 1975, except to revise away that inconvenient cooling when CO2 was rising.
Bottom line: The Climate Howlers don’t care about temperatures before 1975. They do want to show global warming after 1975, and they have that. But they have predicted warming 2x to 3x faster than 1975 to 2022 for the past 50+ years and their wrong predictions do not seem to matter.
So they keep predicting rapid dangerous global warming (CAGW) while ignoring almost 8 billion first-hand witnesses to some, or all, of the actual mild, harmless global warming in the past 47 years.
If people ignore their own pleasant experience with global warming, have no idea the CAGW predictions were consistently wrong, and still believe current CAGW predictions of a coming global warming crisis, this is a religion. Not science. We need a lot of cold weather to change minds.
The recent energy problems in the EU may change some minds on Nut Zero. Nut Zero is the panic response to the CAGW belief. An infeasible project to solve a non-existant CAGW problem. Only leftists could support such claptrap (scientific term).
Keep trying to refute historical temperature raw data, adjustments and infilling). But remember that what really needs to be refuted are the predictions of a coming global warming crisis — refuting a CAGW belief is not easy.
There is no difference between anomalies and absolute t. Next up why trends don’t change from Fahrenheit to kelvin
You loose a lot of information about trends if you don’t take latent heat into consideration.
I just realized sea surface temperatures were not covered. There were plenty of changes to land surface instruments too. Large Stevenson boxes, small boxes, slower moving mercury thermometers, fast moving electrical instruments, changes in vegetation around the weather stations, moving weather stations to airports and economic growth in the vicinity of weather stations (which could affect rural stations more than urban stations).
And then there is the lack of trust of the people who compile the temperature data. They were hired because they believe in CAGW, their organization predicts CAGW, and they are paid by that organization — how could they not be biased when infilling and making adjustments? Those who collect, infill and adjust data should not be making predictions of the future temperature — predictions that they want to come true — that’s a large conflict of interest.
Repeating the lie about southern data doesn’t make it true.
Infilling is prediction. Testable
Infilling is guessing that can never be verified.
There is no testing
You are lying.
The people doing the infilling have predicted CAGW
Therefore, they are biased to show more warming, trying to make their predictions look better..
There were very few Southern Hemisphere measurements before 1920.
In fact. pre-1990 temperatures were often called Northern Hemisphere only.
The charts at the link below will show Mosher is lying … again:
Honest global warming chart Blog: Land surface weather station distribution in history (elonionbloggle.blogspot.com)
Richard Greene said: “Infilling is guessing that can never be verified. There is no testing”
Data denial?
Jackknife resampling?
Those aren’t ringing a bell?
Don’t worry about that. Not only will your findings not be confirmed, they won’t be published. The status quo may continue.
Insights to MMTS contamination, renewal and NOAA’s failure to recognize error bounds from their temperature stations.
Supression of the truth – the continuing tragedy of the environmental movement.
Always wrong wild guesses of a coming climate crisis = even worse
The issue of time-varying non-climatic influences was pointed out by James Hansen in 2001, here is his figure illustrating the impact:
Link to original paper: https://pubs.giss.nasa.gov/abs/ha02300a.html
One solution to identifying and fixing the problem is to use high quality reference series, Berkeley Earth provides them in regions such as Australia where there is a high density of stations in GHCN.
USCRN is supposed to be a well sited weather station network.
But how can we know the USCN averages numbers NOAA tells the public are accurate?
NOAA has many other weather stations with poor siting, that seem to provide similar numbers to USCRN, after many adjustments. That is suspicious. Maybe all of their US average temperature averages are wrong? Including USCRN.
This solution may help with the UHI sawtooth, but not with the aging sawtooth.
A high quality Reference Series helps with all sawtooths, it allows adjustments to be determined only via periods of stability.
Only if this ficticious reference does not age
It can be constructed with omission of periods of time-varying non-climatic influence.
Interesting. How would you identify such a period?
Just by looking at plots of [RAW – REF], a procedure that could be easily automated.
I think you underestimate the noise in the signal
Only in regions of low station density, or poor quality data, no problems in most of USA, Southern Canada, Australia and Europe.
It is interesting to compare the temperature anomaly values in the NOAA datasets published before and after August 16, 2021 (old and new version). For brevity, only a part of the values that do not coincide are given.
Year Temperature anomaly o C Year Temperature anomaly, o C
old version new version old version new version
1883 -0.36 -0.11 1951 -0.39 0.11
1892 -0.39 -0.14 1969 -0.08 0.10
1905 -0.64 -0.22 1973 0.41 0.16
1912 -0.13 -0.35 1983 0.49 0.30
1929 -0.57 -0.33 1995 0.74 0.42
1936 -0.31 -0.05 2004 0.74 0.47
1946 0.14 -0.07 2016 1.26 0.92
The numbers are whatever NOAA says they are and don’t you forget it
And they keep changing — magic numbers!
This is an excellent effort and there is no excuse not to publish it and let public review and testing of the conclusions take their course. You deserve great credit for bringing this to public attention.
Cumulative errors in calculation, observation, homogenization, adjustments, and conclusions.
The precise number is not that important to me. What is important is that this analysis indicates there is yet another reason to question how much temperatures have increased since 1850 and how much is attributable to CO2.
The list of uncertainties continues to grow. Coverage of land pre 1900 was 12% in the SH. Millions of square miles of ocean pre 1900 had no regular SST direct measurements. Questions are continuing to be raised about even recent temperature measurements.
Millions of square miles of wetlands have been destroyed and massive deforestation has occurred in the last 200 years probably affecting temperatures. Questions about the effects of UHI are still being debated.
We are coming out of the LIA and the tidal gauge data don’t look much different from when we just started out of the LIA.
Instead of increasing our knowledge of how much temperatures are increasing and the role of CO2, we are backtracking and the doubts just keep mounting.
we are coming out of a ‘LIA but
IT HASNT WARMED??????
IT WAS COLDER IN THE PAST, BUT ITS NOT WARMER NOW!!!
WE DONT KNOW EXACTLY HOW MUCH WARMER, THEREFORE IT WASNT COLDER BEFORE
Now you are just spamming
Mosher is just trying to be his cutie self. He knows what I was talking about. He is upset he bet on the wrong horse years ago and the facade is collapsing before his very eyes.
Nobody knows or will ever know the exact proportion of warming from CO2. They will go on for generations with a big question mark. The honest scientists will admit that. The rest will be blowing smoke.
Mosh, like Nick, will say and do whatever is necessary to protect their paychecks.
I’m going out on a limb and guessing that you actually think you are being funny,
Since your statement doesn’t look even slightly like any argument that’s actually been made, the only thing you manage to look like is stupid.
Prof. Lindzen: “Misrepresentation, exaggeration, cherry picking, or outright lying pretty much covers all the so-called evidence marshalled in support of the theory of imminent catastrophic global warming caused by fossil fuels and CO2″.
Climate Science is Awash with Manipulated Data, Which Provides No Reliable Scientific Evidence to Support the Proposed [‘The Enhancement and Standardization of Climate-Related Disclosures for Investors’] Rule.
Climate Science is Awash with Manipulated Data, Which Provides No Reliable Scientific Evidence to Support the Proposed [Enhancement of Civilization]
Fixed!
Having your work published on WUWT gives it a claim to priority. In due course this site will be the go to source for knowledge on climate change. Most journals claiming authority on climate change have been reduced to propaganda vehicles. Your experience is proof of this.
RSS and UAH are tropospheric measurements and lag the surface temperature up to a month.
NOAA/NCEP Reynolds sea surface temperature uses satellite observation to interpolate measurements taken at buoys. It appears to be the most reliable temperature record but only covers water surface.
There are other systematic errors with surface stations. Most are located in developed areas. The change to electronic instruments has removed thermal lag from the measurement and corrections for that do not replicate earlier instruments.
And correlation is not causation. The reason for temperature increase in the Northern Hemisphere is the same reason temperature is reducing in the Southern Ocean – orbital changes, primarily driven by the precession cycle.
If CO2 played any role in the warming and there was next to no natural variation then the Southern Ocean could not have a long-term cooling trend.
Homogenized milk is not milk.
Homogenized data is not data. It is modeling.
Infilling isn’t data. Infilling is modeling.
Adjusted data is not data. Adjusted data is modeling.
The Earth’s climate is not univariate. It is multivariate. I suspect cloud fraction has a greater influence on perceived temperature than a trace gas. Cloud modeling is an oxymoron.
And so on……
At the end of last year, I noticed that Moritz Büsing had floated a draft paper and commented on how hard it was to interest a publisher. We then had some emails that I hope have helped to us seeing this article on WUWT.
In real life, Moritz is in helicopter engineering. He is not a greenhorn with raw views about measurements. He has investigated and publicized here a problem that earlier had little more than a passing mention in the literature. Some of the topic was related to the early work on ageing Stevenson screens by Anthony Watts. Moritz especially noted the ways that the error was more than a one-off in a typical time series and was able to be propagated with increasing effect.
I am surprised by people like Mosh and Nick taking a dismissive, antagonistic approach. What is wrong with acknowledgement that there is a genuine source of error, then constructively trying to get its correction into the general methodology for improving the historical record?
Geoff S
Geoff said: “What is wrong with acknowledgement that there is a genuine source of error, then constructively trying to get its correction into the general methodology for improving the historical record?”
Nothing. I’ve been trying to convince the WUWT audience of this for awhile, but the overwhelming response I get here is that acknowledgement of errors and especially trying to correct them is fraudulent even to the point of being criminal. Maybe it would be a good article idea for you or someone else well respected by the WUWT audience.
Moritz Busing said: “On average, a stepwise break is corrected once in every 19 years of weather station data. Therefore, there are on average roughly 7 “corrections” of the weather station data of the last 140 years.”
Did these cylindrical radiation shield stations go into service 140 years ago?
Moritz Busing said: “This result also shows a better fit with satellite data provided by the University of Alabama in Huntsville (UAH):”
What does the comparison look like with ERA5 and other datasets?
Moritz Busing said: “The ageing effect only reaches 0.1-0.2°C (0.18-0.36°F) difference which would be negligible, and indeed undetectable by the homogenization algorithms. The homogenization algorithms can neither detect such a small warming trend from aging nor the tiny downward stepwise break from renewal.”
GISTEMP uses the GHCN-M repository which uses pairwise homogenization. The code can be found here. Were you able to demonstrate that the PHA was unable to detect to the aging drift and cleaning changepoint?
Answer: Irrelevant
2. What does the comparison look like with ERA5 and other datasets?
Answer: Unnecessary to Möritz Büsing’s conclusions.
3. … Were you able to demonstrate that the PHA was unable to detect …
Answer: Büsing has presented a robust prima facie case. The burden of proof is on GISS to prove their code DID detect … .
If the cylindrical radiation shields did not exist 140 years ago then how could there be 7 “corrections” based on a 19 year recurrence interval?
If Moritz Busing wants legitimize his analysis based off comparisons then why not compare with other datasets. ERA5 was my recommendation because it doesn’t do homogenization. It does 4D-VAR. But I’d like to see other datasets in the mix as well.
GISS does not perform homogenization. But that is moot because I’m not so much concerned with who does it as compared to whether it is right or not. If Moritz Busing thinks it is wrong then he needs to demonstrate that. And if wants to be really convincing he needs to show how to fix it like what everyone else is expected to do.
I think none of the weather stations, maybe an insignificantly small number, have operated for the full 140 years. That was not my point. The data-base consists of an highly overlapping chain of first hundreds , later thousands of concurrent weather station measurements. One can choose any number of non-overlapping chains in this data-base and on average each of these chains would have roughly 7 stepwise homogenizations.
I compared with the GISTemp data set, because I used the exact same input data provided by NOAA as my baseline and I used the weighted averaging algorithm provided by GISS for analyzing the “corrected” input data. This shows purely the effect of the my “correction”
If you are interested in comparing with other data sets, then feel free to do so. There a plenty of comparisons between GISTemp, HADCRUT, BEST, etc. compiled by other people. There are also comparisons with ERA5, but be warned, ERA5 is the unholy mix of climate prediction models and empirical data analysis. There is no way to validate the climate models it uses with empirical evidence due to a circular dependence between input and output.
Your last paragraph just shows that you did not read the paper.
Do journals operate under any kind of regulation? What kind of regulations must they abide by? Do scientific journals operate under stricter regulations? I would think that if a journal claimed to be scientific it would need to show reason to not publish studies that question the popular consensus. That seems outdated, ignorant, dangerous and I don’t understand how it can be legal to block an opposing view.
No, there is no regulation. They are completely free to publish, or not publish whatever they want. They are completely free in choosing the “peers” for peer review. They are also free to charge any amount they want.
But to be honest: Despite being unable to publishing my work in an official way, I would be even more worried about the course of scientific development, if there were government control of which content may, or may not be published.
Sydney 1.5C CAGW solved for a century and a half-
Dark roofs to be banned in NSW, planning minister says | Urban planning | The Guardian
Do the Sydney uni numpties really know what they’re challenging here with their 2.4C light rooves?
All men and women worldwide who are concerned about the climate of the earth should wear white coats and hats. Until they do, their concern about the climate is overwhelmed by fashion.
Even better would be tinfoil hats!
Dear Mr. Büsing,
Three things are very clear:
1. You have done careful, honest, data analysis and made a powerful case refuting the conjecture of the AGW scammers.
2. Your being attacked vehemently and repeatedly by known data fiddlers and unprincipled AGW promoters here merely shows that you are over the target: they know they won’t convince anyone who is informed; they are here to shore up any of their base (party affiliation varies depending on which country they are from) who might be convinced by your evidence to vote against policies/people promoting bogus data products, solar, wind, electric vehicles, and other junk.
3. The above attacks are SO poorly executed that they are doing much to SUPPORT YOUR CASE (heh — I “yelled” to mock a certain data scammer) 🙂 and indicate strongly that AGW is a big fat lie.
In short: Well done!
Sincerely and with admiration,
Janice Moore
The well know “we have more cold steps than warm and we dont know why, but we adjust them out anyway” which you can read in NOAAs publications.