The Greatest Scientific Fraud Of All Time is the fraud by which our government alters existing U.S. and worldwide temperature data in order to enhance an apparent warming trend, and thereby support a narrative of supposedly dangerous global warming. This is Part XXXIII of this series, which goes back to July 2013. A composite link to all 32 prior posts in this series can be found here.
As has been widely reported and discussed, the arrival of the new Trump 2.0 presidency is bringing disruption and change to many areas of a previously complacent federal bureaucracy. One of the areas where disruption appears to be hitting is an agency called NOAA — the National Oceanic and Atmospheric Administration, which is a part of the Department of Commerce. NOAA is the place where the world and U.S. temperature data are collected and compiled — and altered.
Will the new disruption shed some light upon the systematic alterations of our temperature data? It’s too early to tell, but there is reason to hope.
First up, CBS News reported just yesterday that massive layoffs have hit NOAA. The headline is “Hundreds of NOAA employees laid off in latest cuts to federal workforce.”
Hundreds of staffers at the National Oceanic and Atmospheric Administration, or NOAA, were laid off Thursday. . . . A congressional source told CBS News the layoffs affected 880 NOAA employees. . . . Prior to Thursday’s cuts, NOAA had about 12,000 staffers across the world.
880 staffers out of 12,000 would be about a 7+% cut. But then there’s this:
Former NOAA officials told CBS News earlier this month that current employees had been told to expect budget cuts of 30% and a 50% reduction in staff.
The CBS piece does not give any indication of whether the cuts are reaching the people who compile — and alter — the temperature data.
But is there reason to think that there may be some concern that the temperature alterations may come under scrutiny? Well, there is this February 25 piece from ABC News, headline “Yes, NOAA adjusts its historical weather data: Here’s why.” Suddenly, it is time to admit that the alterations are occurring:
When digging into conspiracies claiming that the federal agency “manipulates” its historical weather data, ABC News chief meteorologist and chief climate correspondent Ginger Zee was able to confirm that it was true — but that the routine, public adjustments to records happen for good reason. . . . NCEI [a branch of NOAA] adjusts weather data to account for factors like instrument changes, station relocation and urbanization, and it does so through peer-reviewed studies that are published through its federal website.
It’s nice to see ABC News catching up to the Manhattan Contrarian in noticing that these adjustments are occurring. But I’m seriously put off by their calling claims that NOAA has been altering data “conspiracies.” Have they checked to see if the adjustments are quantitatively appropriate versus completely made up? Beyond noting that the changes are “peer reviewed,” the answer is no.
Others have checked to see if the adjustments are quantitatively justified, and the results so far have been damning for NOAA. Back in Part XXIX of this series (February 18, 2022), I noted the fundamental problem of NOAA’s adjustments:
NOAA/NCEI make no secret of the fact that they are altering the raw data, and they give what appear to be legitimate reasons for the adjustments (e.g., a given temperature station may have moved to a warmer location); but at the same time they make the details of the alterations completely opaque such that no outsider can directly assess the appropriateness of each adjustment.
My February 2022 piece reported on an article then just out from a group of 17 authors let by Peter O’Neill, Ronan Connolly, Michael Connolly and Willie Soon, published in the journal Atmosphere. My description of this article:
[The authors attempt] to reverse-engineer the adjustments to figure out what NCEI is doing, and particularly whether NCEI is validly identifying station discontinuities, such as moves or instrumentation changes, that might give rise to valid adjustments. The bottom line is that the adjusters make no attempt to tie adjustments to any specific event that would give rise to legitimate homogenization, and that many of the alterations appear ridiculous and completely beyond justification. . . .
The O’Neill, et al., article looks specifically into numerous individual stations, to see if the NOAA/NCEI adjustments correlate to legitimate things like station move, instrumentation changes, or the like. The result:
The more the authors looked, the less they found any relationship at all between valid station discontinuities and temperature adjustments inserted by NCEI’s computer algorithm.
My February 2022 piece goes specifically into several specific sites, where data exist as to specific station moves, but NOAA adjustments do not correspond to those moves. Here is the conclusions from the O’Neill, et al., paper itself:
[T]he results raise serious concerns over the reliability of the homogenized versions of the GHCN dataset, and more broadly over the PHA techniques, which do not appear to have been appreciated until now. As shown in Table 1, the homogenized GHCN datasets have been widely used by the community for studying global temperature trends.
If the NOAA data adjustments cannot be tied to specific metadata like station moves or instrumentation changes, then they are not really scientific “data,” but rather just opinions of people who are interested in promoting the global warming narrative. They are completely unusable for purposes of making public policy.
I’ll await further revelations as the prior NOAA personnel get thrown out.
Tony Heller has been quite vehement on this topic for quite a while.
He has been deficient in basic science at times, as with “CO2 snow in Antarctica”, but he is a very accomplished data engineer.
My take is that the “adjustments” are Noble Cause Corruption, and lacking the ability to read minds, either conscious or unconscious. Shoehorning the records so they fit CO2 levels better is corrupting the database.
Noble Cause Corruption
Yes that is what inspires it! They think that they are helping things along, and that only good can come from pushing the agenda. They want to be a key element of the Team, on the road to a green marxist utopia. It is all crashing down now, and I am glad of it.
The ends justify the means.
We’re saving humanity, so what if a few peons happen to die along the way. /sarc
Typically, the end does justify the means, if the person is rational. If one is out camping, a rational person doesn’t walk a mile to obtain some firewood that is readily available within a few yards of the campfire.
The real problem is when someone espouses using any means to achieve their end, without regard to unintended side effects or cost. It is the proverbial using a sledge hammer to kill a fly. An irrational person concerns themselves only with achieving their end, at any cost. A rational person considers the costs and tries to minimize them.
Yes, inserting “any” is key.
“Whatever it takes.” A socialist mantra in Australian politics. BoM and CSIRO fall into the same Cause.
Year after year, NOAA has had its thrall
Let Justice be done though the Heavens fall.
CO2 gets to solid state at about -79 C at 1 atmosphere and the coldest temperature recorded n Antarctica is about -90 C. I have no idea if CO2 snow ever happened or if we could recognize it should it happen but seems as though the conditions exist for it to happen.
What exactly was he deficient on in this case? Since you used “times” indicating more than one please identify others.
There was insufficient vapor pressure to maintain solid CO2, which is still a trace gas. Dry ice sublimates at normal pressure. It was cold enough, but the level of CO2 is too low.
There is a difference between Climastrologists exaggerating the effects of CO2 and greenhouse gases in general, and there being no real effect. It is a somewhat subtle difference between bupkis and none.
Thanks for the response. So partial pressure too low.
I seem to recall Tony Heller is an electrical engineer by training so the concentration of chemical mixtures at their condensing point doesn’t typically occur in the engineering curriculum until after electrical engineers have left the common course….He can be forgiven…they are however, going to be very good at statistical observations of instrumentation.
When I went to university in the electrical engineering curriculum all engineers had to take inorganic chem, organic chem, and thermodynamics. Has that changed since 1968?
This is an area thoroughly covered in Chemical Engineering curricula.
Yes. I filled a cylinder with liquid CO2 at ~1000 psi and took it down to between -75 and -80C and after a month it was empty due to a small leak which developed from upon cooling (seal leakage). The concentration gradient even at low temperatures provides enough driving force for sublimation.
Did he repeat the claim after being corrected? If not, it’s an understandable mistake for someone not versed in chemistry to make.
Its partial pressure is too low for it to solidify. Another example of this would be large scent molecules that have very high boiling points, but don’t solidify or form droplets at room temperature – they diffuse around and into your nose.
And I like his videos of he, his wife and his dogs roaming around the beautiful Western landscapes. I got turned on to climate emergency skepticism by watching his videos, before coming here.
How is your surgery recovery going, Joseph?
It’s been 3 weeks. The pain has finally almost stopped. My first surgery since I had a tonsillectomy in 1960, so I was freaked out. But it went well. It’s a good reminder of how precious our life is- even though a groin hernia is not a big deal compared to many other health issues. I’ve always been very vigorous- so I needed a reminder. 🙂
I may have to get the same thing done so I was wondering how you did.
It takes a little longer to heal up when you get older. 🙂
And btw, what do you think about Trump promoting the use of American wood products? Trump wants American lumber to supply Americans!
I’m 75. My mother was an RN who got her training in the 1930s. She used to say anyone over 50 should never get surgery. Probably true back in the ’30s. I was confused about the different methods and where to get it done. There is 1. open method 2. laparoscopy 3. robot. The surgeon I had do it does the first. I asked why and he said it has less chance of recurrence though it offers more pain. 🙂 It hurt pretty bad for about 10 days then started improving. I was prescribed oxycodene but didn’t take it. He said after a month I should be able to do anything- it’s now 3 weeks. The first surgeon I went to was only 32 and looked like a high school kid. The second surgeon spent an hour telling me everything that can go wrong- didn’t care for him. The third was highly recommended- a guy in his 60s who also teaches surgery. He’s done thousands. I was more afraid of the anesthesia than the surgery but that was amazing- woke up 100% clearheaded. There are hundreds of videos on YouTube about hernia surgery. It seems it can be put off but I think best to get it over with.
I do like Trump’s ideas about wood production in America. America can easily produce all the wood we need and much, much more. The “clean energy” tree huggers- who don’t want trees cut- already own a home with lots of wood. Wood is a terrific raw material and forests can actually be improved when the work is properly done.
Thanks for all that good information, Joseph. I appreciate it.
“America can easily produce all the wood we need and much, much more”
That’s what Trump was saying. 🙂 He had a list of countries from which the U.S. was importing billions of dollars worth of lumber, and said we should be buying American lumber instead.
Yes, “the cause” was specifically mentioned in the Climategate leaks “the cause” is still corrupting science to this day.
Yes, the Climate Alarmists had/have an agenda.
Promoting an agenda is politics, not science. These anti-CO2 “climate scientists” are really politicians in disguise. They tell us so by talking about “the Cause”.
More than fifteen years ago, I reported that NOAA appeared to homogenize data (USHCN) prior to making any adjustment for UHI. Thus, homogenization would spread the effects of UHI through all data prior to adjustment, and the adjustments themselves would also be suspect. This report garnered no interest.
I agree that adjustments need to be made. The time of day issue was of importance at one time, but how many stations still have changing schedules of reading Tmax and Tmin?
I agree that there are times for adjustment. My problem with adjustments is there are multiple adjustments to the same station.
2006 vs 2025 NOAA “adjustments”.
Its a complete FARCE, tantamount to FRAUD
This link shows these adjustments in an animated form.
USHCN-Final-Raw-TMAX | Real Climate Science
Tmax and Tmin probably never happen at exactly that same time anywhere, and in the case of mechanical thermometers that hold the iron bar at the max and min respectively, one doesn’t even know when it happened. It is conceivable that with a cold front moving past a station, the times of Tmax and Tmin could even be reversed for a particular day.
Fully agreed! I’m in Calgary AB and our main weather station is at our airport. Google Earth history and air photos available online show that the site was clast 1 or 2 until the late 1980s. Data from “extreme weather watch” lists the daily high and low temperature records for every day of the year (because of ties, approximately 400 data points in each set).
Most high temperature records set: 1920s, 1980s. Most low temperature records set: 1890s, 1950s. But not 2010s.
UHI removed the minima, the current City of Calgary heat map (to help save people from dangerously high summer temperatures) shows a daytime temperature 10 degrees C hotter than the now-more-distant farming fields that still surround the city.
For comparison’s sake I looked at Boulder, CO. Highest temperatures 1950’s and 2010s. Lowest temperatures 1930s and 1990s. Almost completely out of phase from 1400 miles north along the Rocky Mountains. I’m considering how to write it up and submit to Anthony as a potential post.
It’s not apparent why the adjustments to temperature data are being done in the first place. Take station moves — why is it assumed that the new microclimate is going to be the same as the old microclimate? If the two microclimates are different then the “old” data can be as legitimate as the new data and vice versa.
The excuse that “we need long data sets” is just illegitimate. If you are combining *temperature* data from multiple stations into one big data set then just start adding the new data into the big aggregate data set. You’ll still have the same number of stations contributing. It’s not going to make any difference in the trend of the aggregate if both the old and new data are legitimate. They don’t need to be “adjusted” to look the same before and after the break point.
This mostly stems from the use of “anomalies” instead of analyzing the raw data. To keep the “anomalies” at a station the same you have to “adjust” either the old data or the new data. The problem is that climate science refuses to come to grips with the use of measurement uncertainty in their protocols. Anomalies inherent both the variances and the propagated measurement uncertainty of the contributing data, both the average base and the current measurement. This measurement uncertainty is almost always greater than the change in the anomaly caused by a station move ore replacement meaning the assumption that the anomalies are 100% accurate is a joke. If the change in the anomaly outpaces the measurement uncertainty of the component data then something is wrong and either the old data or the new data should be thrown out anyway. Any guess at an adjustment just *adds* to the measurement uncertainty because you can’t know what was going on with the old station in the past. If a station move is involved then it might just be that the microclimates at the two different locations are different enough to generate the difference in readings but that would mean that both sets of data are legitimate since they are actually measuring two different things. Adjusting one or the other would actually make the data illegitimate!
Anomalies are meant to somehow “equalize” the contribution to the trends from different stations and locations. And yet the variances of the data contributing to those anomalies are ignored by climate science. If the variances are different then you have to weight the anomalies to provide the same contribution to the total. Yet climate science doesn’t do this. They willy-nilly average northern hemisphere anomalies with southern hemisphere anomalies, each with different variances, while ignoring the requirement for weighting them accordingly.
If I were King I would have climate science just find the statistical descriptors for the combined raw data, including variance, kurtosis, and skew, and be done with it. For a legitimate metric I would have all stations measure the temperature at 0000GMT and 1200GMT and use that for the combined temperature data set. First off, temperature is *not* a legitimate metric for climate so trying to get a daily mid-point temperature is useless for differentiating climates anywhere, including the globe. A common measuring time would still allow tracking trends in the metric. But it would be obvious that the metric doesn’t measure a “global climate”.
When the adjustments required to make data fit the model become unsustainable, they’ll use a similar argument to King Tim’s to start over with a new ramp. Maybe they’ll squeeze a few decades out of it.
Tim, your scary use of facts is going to trigger some of those ‘researchers’.
I believe tmax should be taken at the hottest time of the day. The same with tmin at the coldest time of the day. It’s not too difficult to take data automatically at its peak and minimum. In the old days when data was recorded manually it was obviously easier to pick times but as you watch the temperature change the peak arrives at different times almost daily. Of course the actual area under the curve would be a more accurate average.
Temperature is not a metric for climate. If it was Las Vegas and Miami would have the same climate. Therefore it’s completely arbitrary as to when the observations are done. The trend of the temperatures at 10AM should be the same as the trend of temps at 3PM if climate science is to be believed.
I am *not* saying that knowing Tmax and Tmin is useless. They just aren’t necessary for climate science to generate a Global Average Temperature. You can generate a Global Average temperature using observations from any point in time. They are both useless in defining a Glotal Average Climate however.
I can’t even find anywhere in the climate science literature I’ve read when climate science speaks to the negative feedback that results from a ΔT causing an increase in radiation of T^x (where x is greater than 1 and less than 4). That relationship pushes against any rise in Tmax.
I covered some of this in my report here in the UK.
https://tallbloke.wordpress.com/2025/02/24/norwich-airport-dcnn-3018-mathematical-modelling-madness-my-fact-check/
The net effect of all adjustments actually reduces the apparent worldwide warming trend.
[Hausfather 2017]
The nClimDiv dataset shows less warming as compared to USCRN over their overlap period.
The source code for NOAA’s adjustments can be downloaded here.
[Menne & Williams 2009]
There is no need to wait. We already know how well the adjustments work to correct errors.
[Hausfather et al. 2016]
[Williams et al. 2012]
[Venema et al. 2012]
[Vose et al. 2003]
So, let’s assume that ‘morning’ and ‘afternoon’ readings resulted in opposite ‘carry-over’ biases. Would a long-term trend derived from readings from the former differ from a trend derived from readings of the latter?
It’s possible I’m not understanding your question. I’m going to interpret it as the scenario where two different datasets are analyzed: one with all morning observations and a completely separate one with all afternoon observations. If that wasn’t the intent then my answer won’t apply.
Anyway, the answer is no. The trends computed from both datasets independently will be similar.
The issue is not primarily with the time of observation itself. The issue is when the time of observation changes.
The Vose publication I cited performs the experiment showing how the carry-over bias works by comparing daily observations (which contain the effects of the changes in time of observation) with hourly observations.
Thanks. If the trends are the same, then at most there may be a one-day ‘glitch’ on the day when the changeover in reading methodology occurs. I’m skeptical – why don’t these glitches average out like, say, the so-called random errors generated by GCMs when CMIP composites are created? More importantly, why do comparisons of raw vs. adjusted data consistently show massive cooling (warming) of past (recent) data?
Since the daytime temperature profile is sinusoidal and the nighttime temperature profile is exponential decay it is entirely possible that they could demonstrate different trends.
The mere fact that we know nighttime temps are warming more that daytime temps is a clue to the fact that the trends of each are different.
Certainly the variance of daytime temps is different from the variance of nighttime temps. When they are combined to create a “daily mid-range temp”, the average should be weighted based on their different variances. Does climate science do this? Ans: NO!
Tim, while what you say appears to be the general consensus, unfortunately, even the Tmax and Tmin trends are not constant. See particularly Figure 1 at
http://wattsupwiththat.com/2015/08/11/an-analysis-of-best-data-for-the-question-is-earth-warming-or-cooling/
Never said the trends were constant. They are “different”.
Then O’Neill et.al. is wrong. Explain why, please.
I haven’t combed the paper in depth, but the general conclusion that PHA used in GHCN identifies undocumented shifts is completely true, and is indeed part of the purpose of the PHA. Not all changes in the station network are documented.
What O’Neill fail to do is demonstrate that there is an substantial number of false positives among the breakpoints identified with the PHA or that these false positives impart any significant bias in the results. Menne and Williams actually show precisely the opposite.
I don’t necessarily think it is wrong. What the paper is saying is that there is a seemingly low match rate between PHA identified changepoints and station metadata documented changepoints. But everyone already knows this. It is the primary motivation behind Menne & Williams’ development of PHA in the first place.
The Zeke show, who would rather try to shutdown an honest debate than defend his fiddling of the data. Zeke, who supported the flawed 2015 Karl “pause buster” paper. Zeke, one of many hopelessly biased NOAA employees. Just my opinion, based on experience.
What is flawed about [Karl et al. 2015]?
Apparently nothing. The publisher invites “substantive and scholarly commentary” on the article but no one has so far attempted to provide any.
Maybe jgorline will be the first?
The most significant flaw is that he adjusted high-quality data to match unadjusted low-quality data.
That’s the one where Karl and Peterson refused a congressional subpoena, isn’t it.
NCDC/NCEI’s Karl and Peterson refuse congressional subpoena on flawed ‘pausebuster’ paper – Watts Up With That?
Plenty more here showing just how BOGUS that paper was.
Or you could look here
NOAA/NCDC’s new ‘pause-buster’ paper: a laughable attempt to create warming by adjusting past data – Watts Up With That?
End thread.
Seriously, the contrarians have never substantively addressed any of these rebuttals. More importantly, the contrarians have yet to produce a “non fraudulent” global temperature estimate that avoids any errors they allege others are engaging in, and present their results.
Can any goober in the U.S. with a connection to the internet print out all of the raw data? (Or download it as useable data?)
I have heard that the raw data is no longer available. (I do NOT have the ability to confirm or deny this.)
If the raw data are NOT available, then it is going to be very tough for the contrarians to produce “non fraudulent” results to present as an alternative to the “official” data!
Yes. you can get it here: https://www.ncei.noaa.gov/pub/data/ghcn/v4/
qcu is the unadjusted raw GHCN dataset.
What dou you think of Dave Dibbell’s post below: “ an overall pattern that the result of the adjustments is to have “cooled” the early part of the record and “warmed” the later part.”
The adjustments will either increase the trend or reduce it. In the contiguous US, the adjustments increase the trend. For the globe as a whole, the adjustments reduce the trend, as bdgwx showed above:
The question is not whether the adjustments do something, of course they do, the question is whether what they do is justified. For the contrarians who insist that the adjustments are not justified, it is incumbent on them to show the “right” way to do it, and to present their results.
That graph doesn’t show temperatures.
It shows anomalies.
That’s not raw data. You are comparing one bastardized dataset to another bastardized dataset.
Why is original, written, historic temperature data called “raw”? It should be called what it is: original data.
Raw implies there is something wrong with the data that requires fixing. The data is the data. “Fixing it” is someone’s opinion.
It’s comprised of the original values as recorded at the individual weather stations, with no modifications. Are there other written historical temperature records we could leverage? Can you share the results of your analysis using this other dataset?
Obfuscation.
The fact that neither graph remotely matches any original data from the NH is the real give-away.
ie they are TOTALLY FAKE. (as one expects from the Haus stable)
There is basically no data from southern hemisphere for much of the early part of the fabrications, and next to none for most of the oceans.
You summed it up quite nicely. 🙂
Except that those two graphs are both TOTAL FABRICATION.
Show us where data was collected globally in 1880. !
roflmao.. The ocean data is a total FABRICATION. !
No measurements actual exists for most of the oceans even in 1950. (see chart below)
… and the land has no measurements for a HUGE percentage of the surface.
Where ever you go that nonsense chart from, you need to remove it from you link, as being total garbage.
This figure shows observational coverage for subsurface ocean temperature, not SSTs.
“roflmao.. The ocean data is a total FABRICATION. !
No measurements actual exists for most of the oceans even in 1950.”
So true!
It isn’t just the U. S. It is all over the globe that trends are being increased.
Part of the problem is averaging means that have different variances, part is that temperatures are auto correlated, and part that trends are not stationary. Climate science ignores this with a passion.
This is a pretty specific allegation that needs pretty significant substantiation. I’ve showed above that the net effect of adjustments globally is to reduce the overall trend.
Raw data from USA is available.
Tony Heller compares “before” and “after”
USHCN-Final-Raw-TMAX | Real Climate Science
Your link is a perfect example of the way Climate Alarmists have bastardized the temperature record to make it appear warmer than it really is.
The truth (the original, unmodified temperature data) shows that it was just as warm in the recent past as it is today. It shows NO unprecedented warming as of today. That’s why the Climate Alarmists bastardize the data, because if it was just as warm in the recent past, then that means CO2 has had no visible effect on the Earth’s temperatures and that means the Climate Alarmists are full of BS (Bad Science).
Our Climate Alarmist “chart experts” take the bastardized version of the data and then claim it is original data.
But original, regional temperature data does not have a “hotter and hotter and hotter” temperature profile, so if someone shows you data that does nave such a thing, then you are not looking at original data. That’s all there is to it.
If it has an instrument-era Hockey Stick temperature profile, then it is a bastardized version of the temperature data.
Climate Alarmists hang on to this Hockey Stick chart for dear life because it is the only thing that shows a correlation with CO2 increases. But it was created for that purpose, it is not tracking reality. It’s a BIG LIE. The BIG LIE of Alarmist Climate Science. The only thing keeping it going and it’s all a BIG LIE.
I’ll have to borrow that Tony Heller chart. That’s a good one. 🙂
Here’s a pretty good link, too, that I stumbled across.
https://realclimate.science/2025/02/28/climate-tampering-crisis/copyq-vbrfoc/
It shows how the Temperature Data Mannipulators systematically cooled the past and warmed the present in their efforts to fool people into believing today is the hottest time in human history.
It’s all a BIG LIE.
The issue, IMHO, is one of a fundamental difference in how people perceive ethics.
You, I, and scientists in general take the position that it is unethical or even fraudulent to ignore known errors.
However, many contrarians take the position that it is unethical or even fraudulent to address known errors.
There is also a mix of people who genuinely don’t understand what is being done and people who are not arguing in good faith on the contrarian side that continues to muddle things. I think the folks here would find it all less objectionable if they were just reading primary sources instead of the “helpful” commentary like the head post.
You have been told MULTIPLE TIMES that no one is advocating for ignoring uncertainty. You have been told MULTIPLE TIMES that uncertainty is not error. You have been told MULTIPLE TIMES that you cannot know error if you don’t know the true value.
You address uncertainty by stating what the uncertainty interval is. Something climate science IGNORES totally – from the very beginning step of giving daily mid-range values as stated values only instead of “stated value +/- measurement uncertainty”.
The unstated meme of climate science has always been that “all measurement uncertainty is random, Gaussian, and cancels”. Climate science doesn’t even recognize that systematic uncertainty can exist and can’t be identified using statistical analysis.
Ah, no.
Honesty would be saying –
“we don’t know what this value should be, or what to do about it.
Everyone’s guess is a possibility”
You have absolutely no way of knowing the magnitude of any of these supposedly “known errors”.
Fake Data fraud.
The problem is, the only thing that is known is the original raw data. There may be good reasons to believe that there are errors, but it is a subjective opinion in most cases, and what the correct adjustment is, is similarly a subjective judgement. As Tim Gorman has pointed out, the correct way to conflate data sets with different variance is to weight the data as a function of the variance. Those who don’t do that (apparently all climatologists) are introducing an error that they do nothing about. Unless all data processing is described in detail, no one can verify what researchers have done has improved or degraded the quality of the original data set. It is difficult to get climatologists to state what (and why) the uncertainty is and whether it represents one or two-sigma. It seems that in the situations where an uncertainty range (error bars) is provided, it is usually only 1-sigma, instead of the more common +/-95% of other disciplines. The more the data are adjusted, the greater the introduced uncertainty.
NASA publishes a detailed description of their uncertainty model:
https://pubs.giss.nasa.gov/abs/le07900t.html
along with the complete source code for their GISTEMP analysis:
https://data.giss.nasa.gov/gistemp/sources_v4/
It’s simply untrue to say that they don’t describe all data processing in detail.
I’m not going to pay to read the full article. But the abstract says: “This ensemble characterizes the complex spatial and temporal correlation structure of uncertainty”
Nowhere in the abstract is measurement uncertainty mentioned. That leads me to believe that what the are addressing is spatial and temporal sampling error – which are totally separate from measurement uncertainty.
Try again, you failed with this one.
Just google the name of the paper + “pdf” and you will find abundant free copies, often uploaded by the authors. This works for most scientific papers.
No one needs to do ANYTHING. Climate science needs to change its methods and practices to comply with physical science standards.
All you are doing is whining about people pointing out the failures in the methods and practices of climate science – and trying to shift the burden onto others.
Be the change you want to see in the world. Do the work, publish the results. Greatness awaits.
That is easy to say. The reality is, many people have documented the difficulties they have encountered getting published in high-status peer-reviewed journals. Part of the problem is that the journals are more interested in protecting their reputations than they are in assisting in the discovery of truth. Considering the stiff opposition that Einstein encountered from the physics community, we are all fortunate that it wasn’t as difficult to get published at the beginning of the 20th century as it is today. What chance would a mere patent attorney have of being taken seriously today with a theory that would turn classical physics on its head?
Ignoring that for a moment, not all of us are currently employed academics with access to a departmental budget to support publishing. Then, someone like myself, can anticipate ad hominem criticism complaining that I don’t have a degree in climatology, despite having a background not all that different from Michael Manning. We present out findings here, at not cost, subject to immediate peer review. Those who are working in the field of climatology should welcome alternative viewpoints from peers who disagree with the methodology or conclusions of those living in the academic echo chamber.
These are just excuses. Folks here have been commenting and participating for more than a decade. Any number of those thousands of hours could have been spent putting their money where their mouth is. People outside the contrarian circle like Nick Stokes have done exactly that individually. The Berkeley Earth folks did it. Anthony has obtained funding for his projects. He could do that to fund a global temperature reconstruction. It would put the debate to rest for he and his followers for once and all.
During that time I have had 15 articles, complete with citations and illustrations, published here. What should be important is the quality of the work, not where it is published. There is more to the “debate” than a temperature reconstruction. My considerable experience in science and academia should be respected, not dismissed because it isn’t published in the ‘right’ place.
You e published a global temperature reconstruction here?
You are a living dinosaur. My two sons pointed out to me a long time ago that a lot of publishing of research is done on the internet today in forums just like this one. It reminded me of some of my old days on the “usenet” groups I was involved in. Publishing in academic journals is becoming less and less important every day. The advent of AI is going to make it even less important in the future.
10 years ago I posted here my research on cooling degree-days over a twenty year period from about 50 stations spread over all continents using inland stations to eliminate coastal biases. I spent a lot of money I couldn’t afford to get the data. The result was that a few stations showed growth in cooling degree-days, a few showed a negative trend, and the vast majority showed a stagnant trend. That’s when I began to question the CAGW meme that the earth was getting “hotter and hotter”.
This was valid research, matching what agricultural science has since confirmed – longer growing seasons from rising minimum temps but no loss of harvest totals from higher, i.e. “hotter”, temps.
You are still floundering around, trying to come up with an excuse for not answering the criticisms posted on here of the measurement and analysis protocols of climate science. YOU, yourself, have even claimed that all measurement uncertainty cancels and can be ignored – be it in the climate models or analysis of the actual temperature data.
Peer reviewed research is still the gold standard. But that’s irrelevant, because the contrarians aren’t publishing their “better” temperature reconstructions here, or anywhere else on the internet, much less in the peer reviewed literature. It’s just lame excuse after lame excuse.
Alan I am in the process of reviewing every UK weather station and over here I can access the original readings from every site. I am not sure quite what you mean by rebuttals however my so far limited passes indicate much lower warming rates at know good quality sites.
https://tallbloke.wordpress.com/author/raymsanders1956/
you may wish to read some. I will shortly be appearing on the Tom Nelson podcast with more details.
The algorithms used to identify and address sites-specific biases are applied to sites in the UK when NOAA or NASA or the Met Office are compiling their global temperature estimates. It might be that better quality sites in the UK indicate less warming than poor quality sites, but you have to demonstrate that the measures used to address siting deficiencies in the major temperature products are inadequate.
We have seen many time that when it comes to “climate”, a HUGE percentage of sites in the UK are TOTALLY UNFIT FOR PURPOSE.
But climate scientists think they can “adjust” JUNK DATA to make it real.
That is scientific stupidity and maleficence at its worst
We haven’t seen that. Nor have we seen anyone show that the adjustments don’t perform as expected. Nor have we seen a contrarian publish a global temperature reconstruction using their “good” stations and “better” methodology. Will you be the one to buck the trend?
Like what Anthony Watts has done?
Has Anthony published a global temperature reconstruction?
I don’t think so, but he has made a significant contribution to assessing what a “good” station is and which ones in the US meet the criteria.
It is common practice in science to estimate the uncertainty of a mean, whose mean and standard deviation don’t vary over time, by citing +/- 2-standard deviations of the stationary mean. While not always shown, the estimated uncertainty can be implied by the number of significant figures displayed. NOAA and NASA commonly display tables with temperatures shown to two significant figures (digits) to the right of the decimal point. That implies an uncertainty of about +/- 0.005 degrees. However, the Empirical Rule in statistics suggests that a first-order approximation of the standard deviation for a normal distribution should be about +/- 1/4th of the range. Annual global temperatures are highly skewed. Therefore one could approximate an upper and lower-bound for the standard deviation by reflecting the graph of the frequency distribution about the mode. That is, use the range of the long tail and the short tail and divide each by two to bracket the estimated standard deviation. The point of this is that the Empirical Rule suggests that the standard deviation for annual global temperatures should be a few ten’s of degrees Celsius, but climatologists insist on treating the samples as though they have an uncertainty of about +/- 0.005 degrees C. This is the state of affairs for temperature readings!
Right, but existing temperature reconstructions already deal with the issues Anthony found. The claim being made on the contrarian side is either that these issues are not being adequately dealt with or that the techniques purportedly used to deal with them are invalid. What the contrarians ought to do is just show everyone how to do it the right way. They’ve had more than a decade to do it.
No one has to redo the climate data analysis in order to identify the things that climate science gets wrong in their analysis of the data.
It’s up to climate science to fix their methods and practices. It’s not up to anyone else but climate science.
That is true, although you do have to address what is actually being done, instead of attacking strawmen.
But even this isn’t an ultimately useful exercise, because it doesn’t help us determine the thing we actually care about: how the climate is changing and has changed. The only way you can contribute meaningfully to this question is by adding to the sum of knowledge. So if you think you know how to do a temperature reconstruction the right way, do it. Describe your methodology, publish your results. Be part of the scientific enterprise.
What you are describing is that the variance of the data is a metric for the uncertainty of the average. The problem is that climate science totally ignores the variance of the data. They focus instead on how accurately they can calculate the value of the average, i.e. the standard deviation of the sample means. The standard deviation of the sample means, commonly called the standard error or “uncertainty of the mean”, is *NOT* the uncertainty of the mean. It is a sampling error and does not relate to the uncertainty of the actual data. They get confused because statisticians and mathematicians always work with data that are “stated values” instead of “stated values +/- measurement uncertainty”. So they carry this over to data that comes from measurements and ignore the second half of each data point, the +/- measurement uncertainty, instead focusing on the sampling error as being the uncertainty metric.
The uncertainty estimates for the major global temperature indices are produced using probabilistic techniques, not simple error propagation.
Probabilistic techniques? What are those? Something you pull out of a cloudy crystal ball?
Probability techniques are behind the derivation of how to weight variables with different variances when calculating their average. Where is the use of *that* probability technique in analyzing temperature data sets consisting of variables with different means?
I cited the paper describing the uncertainty quantification for the GISTEMP analysis, clearly you didn’t do your minimum due diligence and actually look at it.
The willful ignorance of the contrarians stymies productive debate more than anything else.
Why does it have to a global temperature reconstruction? That is you method to stifle debate.
As I have shown here, there are many, many locations globally that do not follow the GAT growth. You are now beginning to see more and more folks starting to question what you are peddling. Doom and gloom will turn into the old “crying wolf” syndrome when nothing appears.
Shuttup, LiarJ.
When you have been shown that global temperature is a worthless, made up metric from data that was never intended to be used in that fashion, why would you expect someone to do your work for you?
If you want to be brilliant, show everyone how temperature averages can provide a deterministic “climate” at any given location.
He won’t answer. I’ve asked him at least three times why Las Vegas and Miami have different climates when their temperatures are quite similar – NO ANSWER.
Global temperature is a vital metric that helps us track change in the climate system. It is needed. Scientists have produced their best estimates and published their results. The contrarians reject those results and insist that it could be done better. I’m challenging the contrarians to do it better, because they’ve yet to step up and do more than talk.
“Global temperature is a vital metric that helps us track change in the climate system”
You’ve never answered how Las Vegas and Miami can have the same temperature while having vastly different climates.
How then can temperature be a metric for climate?
Will you *ever* give us the answer to that simple question?
“The contrarians reject those results and insist that it could be done better. I’m challenging the contrarians to do it better, because they’ve yet to step up and do more than talk.”
I’ve done studies of cooling degree-day around the globe where the data is based on integrating the entire daily temperature curve. There appears to be no overall trend in the number of cooling degree-day values for much of the globe.
I suggest you go start investigating agricultural science studies. You will find that they show minimum temps going up resulting in longer growing seasons while the maximum temps are stagnant. That’s one reason for higher harvests of grain crops every year, longer growing seasons mean larger corn cobs with more kernels and stagnant max temperatures mean no increasing damage to the crops from high temperatures.
Hubbard and Lin showed that you can’t do regional adjustments to individual stations because of varying microclimates at each station. They did this almost 20 years ago but climate science (AND YOU) ignore this and just continue with homogenization, infilling, etc.
It’s *been* done better. You just have to go look. There are none so blind as those who will not see.
It isn’t clear if you’re misquoting me because you genuinely don’t understand what I’m saying or because you’re being intentionally disingenuous. Temperature change is a metric for climate change. The thing we are tracking is change in the system.
Well that sounds an awful lot like a change in the climate, identified by examining temperature change.
So cite those temperature reconstructions that do it better.
“Temperature change is a metric for climate change”
If temperature is not a metric for climate then delta-T can’t be a metric for climate change. The same delta-T in Las Vegas as in Miami tells you nothing about how their respective climates have changed.
A change in growing season length signals a change in minimum temps. Are we now going to be told by climate science that higher minimums are going to kill us all? I would note that hardiness zones haven’t changed and hardiness zones are a much better metric for climate than temperature.
You just demonstrated that you are one if those that refuse to see. How do you think ag science found and quantified growing season length? How do you think integrative degree-day calculations are done for HVAC engineering?
If your position is merely that temperature change isn’t the only facet of climate change we should be looking at, I agree, and not one single person has ever said otherwise.
You’re saying you have better global temperature reconstructions than those produced by NASA, Met Office, etc. So cite those specific reconstructions. You’re being weirdly evasive for no reason.
“You’re saying you have better global temperature reconstructions than those produced by NASA”
Ag science and HVAC engineering don’t misuse the temperature data to find averages of intensive properties when identifying growing season length or integrative degree-day values.
Climate science starts off wrong by finding a median value of a skewed distribution and calling it an “average”. And it just gets worse from their.
Ok, so give us the superior temperature reconstruction, show everyone the right way to do it. Provide your methods and present your results.
I’ve already told you how it should be done. I shouldn’t have to do it for you. Go find out how to do integrative degree-days. You’ve got all the data you need.
I’m satisifed with the existing reconstructions, I think they’re quite robust. It’s you who insists you have a better way. I’m happy to accept your concession that you don’t.
In other words garbage-in/garbage-out is ok with you. So be it.
“The net effect of all adjustments actually reduces the apparent worldwide warming trend.”
An argument easily settled in 2 charts. 1) Series unadjusted. 2) Series adjusted.
Exactly! Refer to Hausfather 2017 I cited above.
Referring to ‘the Haus’ for anything is referring to twisted and distorted fabrication by one of the chief NOAA data manipulators.
The minor little problem with the “adjustments” is that they are being done in an unblinded way. And the researchers “know” what is supposed to be the trend.
I’m not sure what you mean. Can you point to the line or section of code in the algorithm that you feel is “unblinded”?
Again this fallacy of comparing ClimDiv, which is deliberately adjusted to match USCRN, to the actual USCRN series.
Any difference is TOTALLY because of the adjustments being carried out.
THEY ARE NOT REAL. !
Anyone can use adjustment parameters to make one series approximately match another.
Tony Heller shows the real adjustments of the US temperature data in this link
USHCN-Final-Raw-TMAX | Real Climate Science
The adjustments to ClimDiv are not arbitrary and are documented in peer reviewed literature. If the adjustments bring the full network in line with the reference network it means the adjustments are doing exactly what they are supposed to do.
Fake Data fraud. and you propagate it.
The data are public property since they were obtained over many years with public money. Massaging the data to satisfy some jumped-up minion’s idea of what the data should be is tantamount to the destruction of the data set. These geezers better have the raw data stored somewhere or else they should be prosecuted for the dereliction of public property and be incarcerated.
The raw data is available in the GHCNd repository and GHCNh repositories for daily and hourly respectively.
It’s important to understand that not all public data is available online yet since digitization efforts are still ongoing.
If it looks like a Hockey Stick chart, it’s not “raw” data.
“Raw” data does not look like a Hockey Stick chart.
The “raw data” climate alarmist talk about here is not really raw data, it’s already been massaged. If “raw” data looks like the instrument-era portion of the Hockey Stick chart, then it’s not raw, original data, it has been adjusted by someone in the past.
Original regional temperature data does not have a Hockey Stick temperature profile, so any chart that does have a Hockey Stick temperature profile, is not derived from original temperature data.
You can’t get a “hotter and hotter and hotter” Hockey Stick temperature profile out of data that does not have a “hotter and hotter and hotter” temperature profile. The original, written, regional temperature records from around the world, that is the only basis for computer-generated charts like the Hockey Stick, does not have a “hotter and hotter and hotter” temperature profile, so if you see a chart that does, it is not original “raw” data.
How do they get that scary Hockey Stick chart profile out of regional data that has no scary Hockey Stick profile, bdgwx? The original, “raw” data does not have a scary Hockey Stick profile. Instead, the original, “raw” data shows a benign, “just as warm as today” temperature profile.
What computer magic trick turns a benign climate, with no unprecedented warming, like what the original “raw” temperature data shows, into a scary climate where today is the “hottest year evah!, like the Hockey Stick shows? Is there a statistical explanation for this?
Tom Abbott,
How much can you adjust data before it’s no longer data? At some point, it’s just input.
The raw data gets processed by an algorithm then input into climate models. Sounds contrascientific to me.
adjusted data is no longer temperature data … its is calculated or proxy data at that point … and in this case fraudulent
Definitely.
Short of replacing a missing reading with an interpolation to achieve a smooth graph, additional processing becomes a poorly documented model composed of unjustified changes and changes of questionable validity.
For the USHCN monthly data, for raw, tob, and FLs, I updated my analysis through 2024 a few weeks ago. This Google Drive folder contains plots of the results expressed as a plot by year from 1895 through 2024. I computed station anomalies, and means of those anomalies, instead of actual mean temperatures, noting that the number of active stations still collecting raw values is not constant. There are 12 plots each for Tmax, Tavg, Tmin. The plots show the effect of the steps of adjustment: tob-raw, FLs-tob, and FLs-raw (i.e. the total adjustment) for each of the twelve months separately.
Source of the files analyzed: https://www.ncei.noaa.gov/pub/data/ushcn/v2.5/
Yes, I know that USHCN is not the official dataset any longer for NOAA’s published graphs for the U.S. But USHCN is a subset of GHCN, and the plots demonstrate an overall pattern that the result of the adjustments is to have “cooled” the early part of the record and “warmed” the later part. So as a generalization, the published record bears the influence of the nature of the adjustments, whether justified or not.
https://drive.google.com/drive/folders/1oIl56KrA7c_ACYs_VqC0LluC2Z5zhFzn?usp=sharing
“raw” = files of values without tob or PHA adjustment.
“tob” = files of “time of observation” adjusted values.
“FLs”= files of finalized values after also applying pairwise homogenization.
Yes. That’s because the time-of-observation changes and station shelter/instrument changes tend to bias the observational record high in the early part of the period and low in the later part of the period.
The USCRN dataset is good test of how the adjustments in USHCN/nClimDiv to correct those biases performed. It turns out that the adjustments worked pretty well, but if anything they are still underestimating the magnitude of the low bias that these errors cause.
It is important to point out that the pattern you noticed only applies to US observations. The pattern is opposite on a global scale where the net effect of all adjustments actually reduces the overall warming trend relative to the raw data.
“The USCRN dataset is good test of how the adjustments in USHCN/nClimDiv to correct those biases performed.”
No. USCRN tells us nothing about the 20th century methods, instruments, records, or trends.
PHA is applied equally to both 20th and 21st century records.
roflmao.. so naive !!.
Then why does this happen.
USHCN-Final-Raw-TMAX | Real Climate Science
If proper urban adjustments were carried out, they would be totally the opposite of the data corruption they carry out..
I said what I said. USCRN means nothing to anything about the 20th century part of this.
I’m pointing out that the overlap period between the two datasets provides a falsification test of PHA because PHA is applied equally throughout the entire period. If it fails in the post-2005 era then you can argue that might fail in the pre-2005 era as well. The 20th century part of this is included in the pre-2005 era.
As I said, whatever USCRN indicates to us, it justifies nothing about what to do with values from 20th century records. You keep wanting to make it mean something as though there is a time machine. It is way too much of a stretch.
How very convenient the “errors” fit the model.
No. The algorithm doesn’t know what the correct temperatures are. The correct temperatures are unknown.
“The correct temperatures are unknown.”
Exactly. This obvious point is important.
The Romans, nearly 2000 years ago, predicted this:
Quis custodiet ipsos custodes?
Verification is everything.
So, who will guard the guards themselves?
“Quis custodiet ipsos custodes? is a Latin phrase found in the Satires (Satire VI, lines 347–348), a work of the 1st–2nd century Roman poet Juvenal. It may be translated as “Who will guard the guards themselves?” or “Who will watch the watchmen?”.”
Gaaaah. I thought it was Shakespeare. He f(&^&$(& stole it! To be fair I only learned it was Shakespeare when I went looking for who Star Trek stole it from.
I remember that episode.
I was fortunate to view the original first season airings (and 2 and 3 also).
Now I am a rocket scientist. I wonder if it had an influence on me?
At Reddit, r/weather, there are many lamentations over any loss of employee positions at NOAA and NWS. But one thing I have noticed about most of the people on r/weather. They do not go to the National Weather Service to look at their local forecasts. They use apps from this place or that place. There are many posts about these apps and which ones are the best. Now if they were so convinced that every single person at NOAA was vital, shouldn’t they go directly to NWS for their forecasts?
I’m sure there is bloat at NOAA and NWS just like there is bloat at every government agency. I’m not concerned about a little tightening of the purse strings. But the only place I go to check my forecast is NWS.
Just an observation.
I dug into this subject a decade ago in long essay ‘When Data Isn’t’ in ebook Blowing Smoke. After lots of examples from around the world, the end conclusion was fairly simple. The surface temperature data is simply not fit for climate purpose. And there has clearly been ‘a thumb on the scales.’
But I also don’t think it is among the most important ‘climate science’ tells. There are at least three fundamental problems with climate models (tropical troposphere hotspot, tuned best hindcast absolute divergence hidden by anomalies, ECS ~2x EBM estimates), the failures of past model based predictions, the intractable problems with scaled renewable solutions, and the unwillingness of China and India to ‘play along’.
Plus peripheral obviously ‘bad science’ like supposing climate endangered polar bears—which do NOT depend on Arctic summer ice since most of their annual caloric intake is during the spring seal whelping season, after which they come ashore.
“The surface temperature data is simply not fit for climate purpose.”
Primarily because temperature is not correlated to climate. If it was Las Vegas and Miami would have the same climate.
Las Vegas and Las Vegas don’t even have the same climate.
How can that be when whatever happens in Vegas stays in Vegas? 🙂
“the routine, public adjustments to records happen for good reason”
plus
“[The authors attempt] to reverse-engineer the adjustments to figure out what NCEI is doing,”
equals a process that is not public.
I understand private corporations holding back their recipes. Public corporations should publish “this is what we did”.
NOAA is the place where science goes to die.
So, apart from the PROBITY, PROVENANCE and PROSECUTION of temperature “data” inputs into “global average temperature” constructs, what’s the problem with the results presented?
Gawd, you people are picky 🙂
(/sarc)
https://nsidc.org/sea-ice-today
Does NOAA manufacture Arctic Sea Ice data also? I think not…..!
Currently heading for record low at Seasonal maximum. Meanwhile, Antarctica not faring much better for seasonal low.
While NOAA data adjustments are certainly not ideal, the fact of the matter is that according to satellite measurements of both temperature and sea ice, there’s a clear warming trend over the past 46 years.
I would agree that NOAA was behind fraud if it wasn’t for the indicators that support the general warming trend (albeit the warming is not as extreme as NOAA would have us believe). By all means challenge their methodology but if the data is supported by other indicators, then at least keep an open mind.
Many years ago, Willis Eschenbach pointed out a major flaw in the Berkeley temperature adjustment method. If they found a discontinuity in a station’s temperature history, they cut it at that point and created two notional stations. It sounds OK until but w realised that as a station ages its paint deteriorates and its temperature reading goes up. Re-painting a station brings it back on trend, but if you split the station record at that point because of the discontinuity then you create an artificial warming trend.
Hi I have been pursuing this issue for some while in the UK with our Met Office. When challenged to prove their peer reviewed conjuring tricks to justify their 103 fake stations they were unable to produce the data used.
https://tallbloke.wordpress.com/2024/09/04/dungeness-wmo-03888-and-the-103-missing-met-stations-mystery/comment-page-1/
Anyone can view my work here
https://tallbloke.wordpress.com/author/raymsanders1956/
I am about half way through analysing each and every UK site. The only response I get from the Met Office are inept fact checks
https://science.feedback.org/review/no-the-uk-met-office-is-not-fabricating-climate-data-contrary-to-a-bloggers-claims/
but I did manage to get the Met Office awarded the Dino award for fake news in 2024!
Very nice Francis. Raw data should never be tampered with. There may be dozens of reasons why those readings may be suspect but they should never be changed rather it should be explained that there may be problems with the reading and why there are questions. Do all the fiddling around to make your case but no alterations to the observed data.
Scenario.
You take some samples of rock to an accredited lab that analyses them for gold. Tiny traces of gold are found. You adjust some of the assays to higher levels, you float a mining company and you accept shareholder money.
For this, you go to jail. Correctly.
Why should climate research have immunity from prosecution for its adjustment?
Geoff S
Because Geoff, climate researchers aren’t in it for money.
(and if you buy that, there’s a green hydrogen prospectus I’d like you to consider 🙂 )
“This process produces methane as a by-product, which is exhaled by the animal (cow breath). Methane is also produced in smaller quantities by the digestive processes of other animals, including humans, but emissions from these sources are insignificant. Livestock contributions are place between 85-95 Tg (1 Tg = 1 trillion metric tons) each year.”
https://gml.noaa.gov/education/info_activities/pdfs/CTA_the_methane_cycle.pdf
Science at its best.
It’s all pointless anyway. “Climate scientists” claim they are measuring “air temperature” (they’re not), at the “surface” (they’re not), and that this somehow shows how hot the “globe” is (it doesn’t).
The Earth has cooled to its present temperature, and continues to do so. Adding CO2 to air does not raise its temperature, and the hottest surface temperatures occur where GHGs are least!
Thermometers react to heat, not CO2, and anthropogenic heat has increased markedly since the Industrial Revolution. Who would deny that increased appropriate heat raises temperatures?
Phooey to “climate scientists” who claim they can measure sea-levels to within the thickness of a human hair (they’re can’t), or who claim they can usefully predict the weather or climate better than a 12 year old who possesses historical data, a straightedge and a pencil (they can’t do that, either).
Great article. It outlines my basic problem with declaring a temperature series as “biased”. Doing so requires explicit evidence that the station has a systematic measurement error. Only a calibration procedure can identify a measurement bias. A measurement bias is not amenable to statistical analysis since it is included in all measurements.
A change in the microclimate of a station such as a new measurement device, new/refurbished enclosure, move, geographical differences (grass, buildings, tree growth, etc.) can cause DIFFERENT measurements to result. This does NOT mean earlier readings are biased, they may be 100% accurate. THEY ARE JUST DIFFERENT.
The GUM recognizes this as the need for repeatable conditions.
This doesn’t mean readings that don’t meet these conditions are inaccurate, they are just different. They can not be used to represent repeatable measurements.
If a station data is missing why does NOAA/NASA feel the need to make up (infill) the missing data.
If a weather station is moved, for what ever reason, that makes it a new station.Why do the go through the effort to match the old station location data….if you move to a new house at a new location you don’t get to keep your old address….
You have to infill missing data, it doesn’t matter if you want to consider a moved station as two stations or not. But it’s also the case that many station moves are undocumented, and only identified by automated computer algorithms that look for unnatural breakpoints by comparing neighboring stations together.