After looking at hundreds of temperature series from different locations across Australia, I’ve come to understand that only cities show the type of warming reported by the IPCC, and other such government-funded institutions. Much of this warming is due to what is known as the Urban Heat Island (UHI) effect: bitumen, tall-buildings, air-conditioners, and fewer and fewer trees, means that urban areas become hotter and hotter.
For example, in a recent study of temperature variability and change for south-east Australia it is evident that maximum temperatures in the cities of Melbourne and Hobart are increasing at a rate of about 0.8 degree Celsius per century; while the rate of increase at the nearby lighthouses is half of this.
While the trend of about 0.4 degree Celsius per century at the lighthouses – as shown in Chart 1 – is arguably an accurate record of temperature change, the Australian Bureau of Meteorology changes this. To be clear, the Bureau changes a perfectly good temperature series from Cape Otway lighthouse by remodeling it so that it has Melbourne’s temperature signal – all through the process of homogenisation.
Government agencies in the USA have done exactly the same thing to temperature records for Colorado. This is all explained in detail in this new video by Monte Naylor:
The video runs for about 40 minutes, and is quite technical.
The conclusions from this study have been summarized by Monte as follows:
(1) The USHCN Fort Collins station temperature record was not recognized by NOAA as having the heat bias from expanding UHI which has been easily identified by other researchers.
(2) NOAA’s homogenization program adjusted the USHCN Boulder station temperature history in a fashion that does not match any of the four other nearby rural/suburban long-term temperature histories. Nor does the NOAA-homogenized Boulder temperature history resemble the average temperature trend found by this study.
(3) NOAA’s homogenization program adjusted the Boulder temperature history to resemble the UHI-contaminated temperature history of the Fort Collins station.
(4) The best estimate of the northern Colorado Front Range temperature trend is obtained by using the TOB-adjusted Group of 5 average which shows a warming temperature trend of 1.7 °F (0.95 °C) from 1900 to 2015. The NOAA temperature trend, about 4 °F over 115 years, is more than twice the best estimate of this study.
(5) About 70% of the warming shown in the Group of 5 average temperature trend occurred before 1932. Temperatures trends of recent decades do not show anomalous warming. Distinct warm temperature events occurred in the 1930’s and 1950’s that were much warmer than those observed since the turn of the 21st century.
(6) The Northern Front Range Group of 5 average temperature trend does not increase in a fashion consistent with increasing atmospheric carbon dioxide.
Monte Naylor’s study is well done, quite precise and easy to follow. Living in Colorado I found it very interesting. I wonder if NOAA will take a look at his study and make the proper adjustments to their temperature record?
Great to see new researchers driving these points home. It is 30 years now since Prof Phil Jones and his team published their hemispheric compilations which cemented UHI trends into “global warming”. In the case of the southern hemisphere 40% of their ~300 stations were from cities greater than 50,000 population. And of course these stations tend to be long term compared to a lot of the data used from rural areas.
Sea surface temperature (SST) records are subject to potential biases due to changing instrumentation and measurement practices. Significant differences exist between commonly used composite SST reconstructions from the National Oceanic and Atmospheric Administration’s Extended Reconstruction Sea Surface Temperature (ERSST), the Hadley Centre SST data set (HadSST3), and the Japanese Meteorological Agency’s Centennial Observation-Based Estimates of SSTs (COBE-SST) from 2003 to the present. The update from ERSST version 3b to version 4 resulted in an increase in the operational SST trend estimate during the last 19 years from 0.07° to 0.12°C per decade, indicating a higher rate of warming in recent years. We show that ERSST version 4 trends generally agree with largely independent, near-global, and instrumentally homogeneous SST measurements from floating buoys, Argo floats, and radiometer-based satellite measurements that have been developed and deployed during the past two decades. We find a large cooling bias in ERSST version 3b and smaller but significant cooling biases in HadSST3 and COBE-SST from 2003 to the present, with respect to most series examined. These results suggest that reported rates of SST warming in recent years have been underestimated in these three data sets.
Science is convincing because it builds on independent assessments, which either confirm or disagree with previous findings. A scientific consensus is established when many independent lines of evidence underpin the same conclusions.
It is important to realize that science is about universal truths, which means that you should get a consistent picture in a comprehensive analysis. The idea of a hiatus was indeed inconsistent with other indicators, such as the global sea level which continued to rise unabated (Watson et al, 2015). And there was no reason to think that changes in the cryosphere and precipitation had ceased either.
More than 70% of earth’s area is oceans, and sea surface temperatures (SSTs) carry a large weight in the global mean surface temperature estimates. Karl et al. (2015) reported a cold bias in recent SSTs due to changing observing network. This bias gave the false appearance of a slow-down in the warming of the oceans, and by taking into account artifacts from a change in the observing network, Karl et al found a more pronounced warming in the recent decade. Hausfather et al. (2017) studied these more closely, and their findings confirmed the NOAA analysis.
Ha ha! 🙂
The Ostrich or the Phoenix? a straight talking presentation by Kevin Anderson UK Tyndall Centre – Choosing between Cognitive Dissonance (aka Hypocrisy) or Imaginative Creativity in a Changing Climate.
(please watch and consider … what he says is 100% correct imho)
Nasa Data conspiracy theories
To illustrate the shenanigans of self-styled “climate skeptics”, take for example the following graph, which has been circulating for a while on climate denier websites. It beautifully illustrates two of the favorite tricks of climate deniers: cherry picking and deceptive trick graphics.
Graham Young says
Not just the Russians spreading fake news it appears. The climate establishment is into it. Thomas, instead of showing how the data presented here is wrong, throws in irrelevant papers as though they somehow rebut the research that has been done. And then there is the gratuitous accusation of cherry picking.
If Jennifer is wrong, he should be able to argue directly against her arguments. If she has cherry picked, he should be able to explain why it is a cherry pick. Instead, what he does is cuts and pastes from somewhere else in an effort to confuse anyone trying to follow the debate, and to look smarter and better-informed than he is.
Someone with real knowledge would address the arguments presented here.
Good job, Jennifer. The intellectual fallacies that conflated CO2 increases to global climate crisis permeate far too much of science. You are hitting home since you have already attracted a true believer to troll echoing conspiracy trash from Gavin. Gavin and gang are too unself-aware and too prideful to consider that their being wrong doesn’t require a conspiracy; just the typical human foibles of bias confirmation, rent seeking and nobel cause corruption- all well documented throughout the climatocracy controlled part of the public aquare. The climate kooks recycle their stuff so quickly now, their distractions are more of red minnows than red herrings.
I just watched an amazing video from a California environmental activist showing how California is actually doing worse with emissions reduction than prior to their “clean energy” laws. And he documents in sad black and white detail just how corrupt the enviros were when they decided to kill off the nuclear energy industry.
The point is there is every reason to believe your work. Human biases in those “saving the world” environmentally control nearly everything they do.
Keep up the good work!
Here is the link to the nuclear TED talk
Which raises the question: Have you considered doing some videos to talk about your work to a potentially larger audience?
Yet again I am deeply puzzled by the material you present. You show a graph that has plot of maximum temperatures for light houses and cities.
I have attempted to replicate your data.
For the city data I have combined the raw unhomogenized data from the following sites , Essendon Airport, Melbourne Airport, Olympic Park, Moorabin and Scoresby and then combined it with the data for Hobart.
For the light-houses, the sites were Cape Otway, Cape Schanck, Gabo Island, Point Hicks and Wilsons Prom. The data has been weighted , of course, with the average temperature at each site before being merged.
Needless to say the resemblance between long term trends the data and your is non-existent. See https://s20.postimg.org/hil6jjyal/Citties_and_Light_Houses.jpg .
The trend using weighted data for the 5 light houses for the period 1910 to 2016 is 1.2 degrees per century while for the city temperatures it is only 0.86 degrees per century.
Before anyone can say “where the heck is the UHI effect”. It is there, but only for later years i.e. for 19810-2016 the city trend is 3 degrees per century while for the light houses it is ‘only’ 2.4 degrees per century.
I know I should now get used to the new credo of post truth and alternate facts, but the data you present appears only to be distantly related to the raw data published on the BOM web site. Have you weighted and/or homogenized the data? If so how?
In the spirt of glasnost I am providing a link to a spreadsheet that shows the raw BOM data for these sites and both the unweighted and weighted averages. The link to my spreadsheet is at
Jennifer if you want to retain some residual credibility, you should likewise provide a link to the data presented in the graph above so that it can be analyzed.
Jennifer your work in “keeping the bastards honest’ at the BOM and CSIRO is important but your material should also be able to withstand the same level of scrutiny.
George Gell says
Thank you for your efforts to get some balance into this argument.
Non scientists like me struggle to weave our way through the claims and counter claims.
I have always thought that the effect of the huge increase in human population and urbanisation over the past 150 years must be the main cause of any temperature increase.
Anyone who, on a hot day, has stepped off a lawn onto concrete, or worse still, tarmac is impacted by the effect of urbanisation. Do it in bare feet and you will get blisters. I know that is not science, but it is fact..
The effect of gases emitted by vastly increased numbers of humans and the vastly increased number of stock used to feed them seems also to be ignored.
To put climate change down to one single factor- increased CO2- just does not make sense to me.
Certainly, if Europeans, 150 years or so ago, had not tapped into the huge energy available from fossil fuels 90% of the present world population would not exist for lack of the production, transport and distribution of the means of feeding and sheltering them.
To that extent fossil fuels may be a cause but the only answer to a reversal of any global warming which may exist would be for that 90% of world population to conveniently die.
Are the climate alarmists and greenies going to volunteer to go to the head of the queue ?
Thank you for the sanity you bring to the subject.
Mick In The Hills says
After the climategate emails scandal, we have every right to distrust everything coming out of the “climate consensus” brigade (of which our BoM was a willing participant).
If they had demonstrated the conservatism that proper science demands in their published claims (and opened their methods to sceptical scrutiny), there would be no need for the blood sport that climate discussions have become today.
So kudos Jennifer for your ongoing work in shining your scientific light into places deliberately kept dark by those who abide by the “why should I give you my data when you’ll only try to find errors in it” philosophy.
Thanks for taking an interest. But you seem to be as committed as the BOM to confusing things. I’m not sure why you have combined the muddle of stations that you have – some with long and incomplete records, others clearly UHI affected. Then you area weight!
Of course one can potentially get any result they want through the application of particular weightings. So, in the first instance best not to apply them. There are no weightings applied in my Chart 1.
For the lighthouses I’ve simply combined the long and complete records from Wilsons Promontory and Cape Otway – making two adjustments to only the Cape Otway record (both pre-1910) associated with equipment changes after I ran the series through QA software. Its all detailed in Chapter 5 – please email me if you would like a copy.
PS. The SE series is here: http://climatelab.com.au/wp-content/uploads/DA-2016-002-SEAUST-TMAX.pdf
Ah Jennifer, what a tangled web you are weaving.
I am particularly amused by your comment “ I’m not sure why you have combined the muddle of stations that you have – some with long and incomplete records, others clearly UHI affected. Then you area weight!”
Yes, you must weight by average temperatures if you are going to get a sensible result when you combine partial records. Do I need to explain why?
Why I was so amused, was that in your previous post ,you presented Vlock’s data without comment. Vlock presumably used exactly this methodology see http://jennifermarohasy.com/wp-content/uploads/2017/01/Screen-Shot-2017-01-06-at-10.10.39-am.png. Vlock combined 289 partially complete and in your words totally muddled , records for the whole of Victoria! Some of these would have been as short as 2 years long!
Possibly the lack of appropriate weighting was most likely the reason Vlock’s data Is nonsense or there may be some other possibility. Who knows? If his data was posted then we might to be able to make some sense of what he did.
You say you combined the data from a total of two light house stations, I gather without weighting, one of which was personally homogenized t(he Cape Otway data). I thought the heinous sin of homogenization was only performed by the perfidious BOM!
This could all be cleared up if you could provide a link to your actual data (pre and post your homogenization) for the Cape Otway light house and also the city data that you used for the figure above. The data in Excel or even a text file would suffice.
The PDF you linked to directly above just has a single column of average
data for S.E. Australia and is fairly useless if you are going to allow someone to audit your work.
I will however take up your kind offer of providing me with a copy of your paper . Does it have the links to the primary data?
Area weighting can be justified if you have series of different lengths and with lots of missing data.
But then, depending on how you apply the weightings, it can be quite easy to manipulate the trend. Such is the nature of the data. The trend can also be easily changed depending on your start date.
And yes, Vlok’s series/reconstruction in my earlier blog post has no area weighting and series of different lengths. I think this blog post/series very nicely demonstrates how sensitive the data is to area weightings.
So, for my SE reconstruction I went to great trouble to find the longest continuous series available. I could then combine them without the need for any fancy statistics, without the need for any area weightings. Indeed, I apply no area weightings in the above Chart 1.
The raw data for Cape Otway can be downloaded directly from the BOM website here: http://www.bom.gov.au/climate/data/
Without any adjustments to raw Cape Otway TMax series the overall trend is one of cooling. But after running the Cape Otway data through I-MR-R/S control charts it is evident that there are discontinuities in the data associated with equipment changes that occurred in July 1898 (new thermometer shed) and March 1908 (Stevenson screen). The methodology I applied to determine the extent of the necessary adjustments is detailed in Atmospheric Research Volume 166, Pages 141-149. And I will send you this journal article, as well as chapter 5.
This issue of area weightings, and series of different lengths, has been concerning me for some time.
It was a focus of my submission to the government’s panel/forum back in January 2015. My submission can be downloaded here: http://jennifermarohasy.com/wp-content/uploads/2014/03/Let-Bob-Baldwin-Re-New-Panel-18-January-1015-F.pdf
I have never received a response to this submission from the BOM or the panel or Mr Baldwin.
Merrick Thomson also picks up on this point in his submission, which is the first that can be accessed from this page… http://jennifermarohasy.com/temperatures/submissions-to-the-panel/
1. Why was the mix of stations changed with the transition to ACORN-SAT, and why was this not explained and declared, particularly given that it has resulted in a large increase in the 2013 annual temperature for Australia. He calculates 0.56 degree Celsius, and
2. What criteria is used to determine whether or not a station becomes part of the national network, and specifically, why was the very hot location of Oodnadatta added to the national network in 2011/12?
Hi Graham. To clarify your misunderstandings about my intentions and so on;
“Not just the Russians spreading fake news it appears. The climate establishment is into it.”
> Big call. The two RC articles have references. Kevin Anderson’s work is easily checked for accuracy and refs. You may dispute them. But I am not here nor appointed to defend them nor the referenced academic and science papers. I am not that presumptuous to imagine I could speak for others.
“Thomas, instead of showing how the data presented here is wrong, throws in irrelevant papers as though they somehow rebut the research that has been done.”
> Well that’s a false assumption and/or a strawman. The info has no relationship to anything said by JM. It was a recent thread, so I shared the info for what it is worth.
“And then there is the gratuitous accusation of cherry picking.”
> Graham, reading that in context via the link will show that it’s merely part of a longer commentary and is fit for purpose. Make of it what you wish, nothing nefarious or gratuitous there at all.
“If Jennifer is wrong, he should be able to argue directly against her arguments.”
> See above.
“If she has cherry picked, he should be able to explain why it is a cherry pick. Instead, ”
> See above.
“what he does is cuts and pastes from somewhere else in an effort to confuse anyone trying to follow the debate,”
> No that;s incorrect.
“and to look smarter and better-informed than he is.”
> No, that’s incorrect too. I am as smart and as informed as I am. I am quite content with that truth.
“Do not compare yourself with others, for always there will be greater and lesser persons than yourself.” Desiderata
“Someone with real knowledge would address the arguments presented here.”
> If they were interested. I am not. What I was interested in was sharing the information and the links on this blog. That’s the beginning and end of it.
This reply is simply to correct the inaccuracies you portrayed here about me and the content, and the intent which were not correct or true. I had assumed this would be obvious and taken at face value without additional comments.
Unfortunately, I was wrong on that point. I still trust the bulk of readers here are quite capable of accepting or rejecting what I posted without any special assistance on my part and coming to their own judgments.
I’ll leave you to your own devices. Best.
Firstly a question. Did your homogenization of the Cape Otway data that accounts for two changes prior to 1910, affect the temperatures after 1910?
I am also a little perplexed by the following-
“But then, depending on how you apply the weightings, it can be quite easy to manipulate the trend. Such is the nature of the data”.
Yes, it is an obvious given, that different types of weightings will inevitably change the average.
However, in the case of the merging of incomplete data sets, weighting by the average temperature for each data set is the only sensible weighting mechanism. I cannot think of any other methodology that would make sense but maybe I am just dumb. If anyone can suggest a better weighting mechanism, I am all ears!
The point regarding cherry picking is of course true with regard to starting points. It also true for selection of sites to be included in an average. I note that you have only 2 sites for your light- house data. I am not sure why you have not used Gabo Island which has a long and almost complete temperature data record similar to the other two sites that you include . Inclusion of Gabo Island data changes the trend significantly upwards.
Thanks for sending me a copy of your papers. Personally I think calling the paper “Southeast Australian Maximum Temperature Trends …: is a bit fanciful as you represent this massive region by just 6 sites. It is a heroic assumption that these 6 sites could be used to represent the remainder of SE Australia including desert regions, mountains etc..
I understand why you did this as these sites have some of the longest and continuous temperature data sets. However I much prefer , the Vlock approach if it was done correctly (i.e appropriately weighted). One of the key maxims of information science is that you should try and maximize the information and only reject data if it is known to be faulty or too imprecise to be useful. This is loosely (very) related to the law of large numbers (see https://en.wikipedia.org/wiki/Law_of_large_numbers)
The more data you have , the closer your estimate will be to the expected value so just using 6 sites and ignoring hundreds of others is inappropriate . Even the Acorn approach of 104 sites to cover Australia is also limited in this sense. The SE is represented by about 20 sites in the Acorn database.
Looking at your publications and the data shown in your top figure above, the issue of pre 1910 data raises its ugly ahead. From your exercise in homogenization, it must have been clear that the pre 1910 maximum temperature data for the lighthouses is unreliable to say the least.
To illustrate this here is the annual curve for the difference between the average pre 1910 (1877-1909) and post 1910 (1910-1942) maximum temperatures for the five light houses (see https://s20.postimg.org/hhfu3vlul/Difference_pre_and_post_1910.jpg ).
The largest difference is for the summer period (over 2 degrees C) and it is minimum for the winter months. When you plot the data against cloud cover for Victoria you get negative correlation with a correlation coefficient R2 of 0.7.
This strongly suggests that the measuring stations were not screened effectively before 1910 and were affected by direct sunlight . This annual signal is not seen for Melbourne where Stevenson screen were installed in the 1870s. It also not present for minimum temperatures for the five light houses.
I just had another look at the data for 43 Victorian sites that include a dozen that have data from the 1890s onward. The average maximum curve exhibits the same phenomenon.
This suggests that statements that seem to pervade parts of the blogosphere, that extreme maxima were hotter in the 1890s in Victoria, Australia etc. are very dubious. It may be true for Melbourne, Sydney etc. where screens were installed relatively early but it is not true when it comes to the remainder of the sites where the screens were installed later.
In light of the above, Jennifer what do you think about the integrity of the pre 1910 temperature data, particularly when it comes to maximum temperatures?
The real question is what about the integrity of the alleged “fixes” for the alleged data problems from early stations?
The other real question is if we are experiencing a climate catastrophe, exactly where is the catastrophe?
A C Osborn says
A very intresting question from MikeR ” what do you think about the integrity of the pre 1910 temperature data, particularly when it comes to maximum temperatures?”
Isn’t it very odd how those questioning Temperature values from simple Thermometers make no reference to anecdotal data which corrobarates those values.
Does he think we have had any modern temperatures where “people & animals died of heat exhaustion and birds fell dead out of the air”
Or perhaps he thinks these diary entries and newspaper articles were made up so that we are all fooled 100-150 years later.
The same thinking applies to the 1930s in the USA, now cooled to less than current temperatures by so called “Scientists”.
I sent you two technical papers, as promised, yesterday. If you had read them, and reflected on my previous comments in this thread, you would NOT be asking the silly questions in your most recent post.
Perhaps you are not really interested in the truth. Perhaps your posts here are intended to confuse people by introducing ideas that are clearly not true. This is a form of ‘fake news’?
Clearly, no adjustments were made to 1910 data. It is disingenuous of you to ask this question.
And I did NOT homogenise Cape Otway.
Homogenisation is a technique applied in climate science where algorithms remodel data relative to other sites. Rather, I made two adjustments to Cape Otway data before 1910 and where metadata indicated an equipment change, and the change corresponded with discontinuities in the data.
Because there is a lot of inter-annual variability in raw climate data, and because even in cities the warming trends is NOT particularly strong relative to the year-on-year variability… start dates and area weightings can be used to manipulate trends.
So, we should be wary of the 1910 start date artificially applied by the Bureau because it generally corresponds with a dip in the record. Furthermore, the Bureau applies a very complex system of area weighting which can not be replicated.
I go to great lengths to ensure my method is totally transparent, and avoids area weightings to the extent possible.
There are no area weightings in Chart 1.
Furthermore, there are also no area weightings applied in Chart 2, by Monte.
The shorter the record, and the more area weightings applied: potentially the more contrived any result.
Your statement “The shorter the record, and the more area weightings applied: potentially the more contrived any result.” may in isolation be true, but merging of data sets in many areas of science is extremely common. As I have said repeatedly, appropriate weighting must be done for this methodology to produce reasonable results. The methodology of merging shorter sequences is proving to a be a useful tool and competitor to meta analysis in a number of fields . You need to read to about it.
You can start here- https://www.hindawi.com/journals/isrn/2014/345106/ (and you only need to read the abstract to get the idea.) The same approach is used here https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4905611/.
Also in different contexts the following http://scrippsco2.ucsd.edu/graphics_gallery/mauna_loa_and_south_pole/merged_ice_core_record and http://ieeexplore.ieee.org/document/5605273/?reload=true .
On the other matter you raised. I apologize for referring to the procedure you used for Cape Otway as homogenization. I was using the term in the more general sense ,in the manner you defined it in your document http://jennifermarohasy.com/wp-content/uploads/2011/08/Changing_Temperature_Data.pdf .On page 1 you state “Homogenization refers to a process of changing the actual temperature records using mathematical algorithms.”
So the periodic rants against the BOM’s homogenization that punctuate your sequence of blogs are only with respect to using the BOM’s use of data from correlated adjacent regions to correct spurious data. You are not referring to break point analysis that is used to identify station moves, equipment changes etc..
I am glad you have clarified this and have adopted a narrower (and as the name suggests probably more appropriate) definition, but in the end , homogenization by any name would still smell the same.
I also need to apologize for not reading the papers you sent me with the necessary diligence due to the intrusion of life into my schedule. I just skimmed them and hopefully over the long weekend I can devote the time required to take a detailed look. My cursory look at the second paper did raise particular concerns about the extremely limited number of sites (sample size N=6) that were used to make general conclusions about SE Australia. I thought the title should have read something like “Southeast Australian Maximum Temperature Trends, 1887-2013: A Speculative Guess”.
I also note with some alarm that you calculate trends (Table 5.2) without including the relevant uncertainties. With such a limited number the uncertainties would most likely be much larger than the trend values themselves. I think you should have at least done a calculation of uncertainties at least using OLS via Excel (you can use the LINEEST function or use the regression tool in the Data Analysis Add in).
I would also have expected the data in Table 5.2 to have been reproduced on a graph. I can understand why it was not, as the absence of error bars would have been embarrassing, for a chapter inside a scientific book that claims it is evidence based. As for the book itself that published this paper, it either has no editorial review, or if it does, the reviewers were asleep at the wheel.
An alternative explanation is that the editors of the book will include anything that fits in with its self- proclaimed agenda “Data Opposing CO2 Emissions as the Primary Source of Global Warming”.
To further emphasize the difficulties associated with such limited amounts of data, I refer back to your treatment of light houses, when you use N=2 for this sub category. If you Increase it to N=3 by the inclusion of Gabo Island , the trend then changes significantly.
This suggests that it may be better to maximize the number of sites even though it may require more work (it is manageable, I managed to do 43 sites in a single afternoon) and merge the data appropriately.
Finally, Jennifer I must thank you for your forbearance in allowing an uncensored contrary viewpoint to be displayed in the comments section of your blog. For that you have my deepest respect.
AC Osborn’s question goes to the heart of the matter.
We know that there is a powerful political and faith-based agenda demonstrated by those *managing* the temperature systems of today: They have the desired result in mind long before they “analyze” the data.
The climate data gatherers from the ages prior to the age of CO2 obsession were simply after good temperature and weather readings. The climate priests and acolytes of today are out to prove their conclusions true.
Time and time again we see post hoc excuses to explain tampering with existing data.
The underlying goal, as the results of the tampering shows, is always to enhance the claims of the cliamte consensus.
That alone is cause to deeply suspect the methods of those “managing” the data.
Quick question: has anyone done a QA test on the old style stations vs. the new by doing a side-by-side comparison?
In other words, recreate the vintage weather station device and set it beside a new style and see what the actual difference may be.
Jennifer, it seems you were on to this a long time ago. I would suggest you are vindicated. https://cliscep.com/2017/02/06/instability-of-ghcn-adjustment-algorithm/#comment-11493