HadCRUT5 shows 14% more global warming since 1850 than HadCRUT4

By Christopher Monckton of Brenchley

They’re at it again. The old lady of temperature datasets – HadCRUT, the only global dataset to reach back to 1850 – has released its revised monthly global mean surface temperature anomalies for 1850-2020. The earlier dataset (HadCRUT4) showed a least-squares linear-regression trend of 0.91 K on the monthly anomalies from 1850-2020 – only just over half a degree per century equivalent.

This was not enough. Like the endlessly-adjusted GISS, RSS and NCEI datasets, HadCRUT5 hikes the trend – and does so by a startling 14%. The usual method is adopted: depress the earlier temperatures (we know so much better what the temperature was a century and a half ago than the incompetents who actually took the measurements), and elevate the later temperatures with the effect of steepening the trend and increasing the apparent warming.

Of course, elaborate justifications for the alterations are provided. It is beyond my pay-grade to evaluate them. However, it is fascinating that the much-manipulated GISS, HadCRUT, RSS and NCEI datasets are managed by climate fanatics, while the UAH dataset – the only one of the big five to have gone the other way – is managed by climate skeptics.

I know the two skeptics who keep the UAH dataset. They are honorable men, whose sole aim is to show, as best they can, the true rate of global warming. But I do not trust the GISS dataset, which has been repeatedly and reprehensibly tampered with by its keepers. Nor do I trust RSS: when Ted Cruz displayed our graph showing the 18 years and 9 months of the last great Pause in global temperature to the visible discomfiture of the “Democrats” in the Senate I predicted that the keeper of the RSS dataset, who describes skeptics as “climate deniers”, would tamper with it to make the Pause go away. A month or two later he announced that he was going to do just that, and then he did just that. As for HadCRUT, just read the Harry-Read-Me file to see what a hopeless state that is in.

And the NCEI dataset was under the influence of the unlamented Tom Karl for many years. I once testified alongside him in the House of Representatives, where he attempted to maintain that my assertion that there had been nearly a decade of global cooling was unfounded – when his own dataset (as well as all the others) showed precisely that.

HadCRUT5 shows a 1.04 K trend from 1850-2020, or three-fifths of a degree per century equivalent, up 14% from the 0.91 K trend on the HadCRUT4 data:

From the HadCRUT5 trend, one can calculate how much warming would eventually be expected if we were to double the CO2 in the air compared with 2020. One also needs to know the net anthropogenic forcing since 1850 (2.9 W m–2); the planetary energy imbalance caused by the delay in feedback response (0.87 W m–2); the doubled-CO2 radiative forcing (3.52 W m–2 taken as the mean in the CMIP6 models); the anthropogenic fraction of observed warming (70%); the exponential-growth factor allowing for more water vapor in warmer air (7% per degree of direct warming); and the Planck sensitivity parameter (0.3 K W–1 m2).

All of these values are quite recent, because everyone has been scrambling to get the data shipshape for IPCC’s next multi-thousand-page horror story, due out later this year. The calculations are summarized in the table. I selected the seven input parameters using three criteria: they should be up-to-date, midrange, and mainstream: i.e., from sources that the climate fanatics would accept.

The industrial era from 1850-2020 is the base period for calculating the feedback response per degree of reference sensitivity over the period. This turns out to be 0.065. Then one finds the unit feedback response for the 100-to-150-year period from 2020 (415 ppmv CO2) to 830 ppmv CO2 by increasing the unit feedback response to allow for extra water vapor in warmer air.

Finally, one multiplies the 1.053 K reference sensitivity to doubled CO2 by the system-gain factor, which is the unit feedback response plus 1: midrange equilibrium doubled-CO2 sensitivity, known as ECS, turns out to be just 1.1 K. If one were to use the HadCRUT4 warming trend, ECS would be less than 1 K. I had previously guessed that the HadCRUT5 trend would be 1.1 K, which implied 1.2 K ECS.

Compare these small and harmless midrange values with the official CMIP6 predictions: lower bound 2 K; midrange 3.7 K; upper bound 5.7 K; lunatic fringe 10 K.

One can work out how many times greater the unit feedback response after 2020 would be when compared with the unit feedback response from 1850-2020 if these absurdly inflated predictions from the latest generation of models were correct: lower bound 14, midrange 19, upper bound 67, lunatic fringe 130.

These revealing numbers demonstrate how insanely, egregiously exaggerated are the official global-warming predictions. There is no physical basis for assuming that the unit feedback response from 2020 onward will be even 14 times the unit feedback response from 1850-2020. At most it might be about 1.1-1.2 times the earlier unit feedback response. Therefore, even the 2 K lower-bound global warming predicted by the models, which implies X = 14, is way over the top.

This is the most straightforward way of showing that the models’ global-warming predictions are without a shred of legitimacy or credibility. They are elaborate fictions. They suffer from two defects: they are grossly excessive, and they are accordingly ill-constrained.

For, as the graph shows, the ECS response to feedback fractions is rectangular-hyperbolic. The feedback fraction (the fraction of ECS represented by feedback response) implicit in the models’ ludicrous predictions generally exceeds 0.5: but there is absolutely no way that the feedback fraction could be anything like 0.5 in the near-perfectly thermostatic climate. When I first showed this graph to a group of IPCC lead authors, they suddenly stopped the sneering to which they had subjected most of my lecture. Suddenly, the lead sneerer fell silent, and then said: “Have you published this?”

No, I said, for at that time I had not worked out what climatologists had gotten wrong. “Well, you must publish,” he said. “This changes everything.”

So it does. But publication is going to be very difficult, not because we are wrong about this but because we are right. If there is going to be little more than 1 K anthropogenic warming over the next century or so, there is absolutely no need to do anything to prevent it. The flight of major manufacturing industries to China, which profiteers mightly from the climate scam sedulouosly promoted in the West by the fawning front groups that it subsidizes, can and should be reversed.

We are taking steps to compel HM Government to pay attention to the truth that global warming will be no more than a third of current official midrange predictions and that, therefore, no net harm will come from it. Watch this space.

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Nick Schroeder
February 21, 2021 10:05 am

0.91 K is 0.91 C above absolute 0 K.
Probably not what you meant.

Pillage Idiot
Reply to  Nick Schroeder
February 21, 2021 10:19 am

“The earlier dataset (HadCRUT4) showed a least-squares linear-regression trend of 0.91 K on the monthly anomalies from 1850-2020 …”

0.91 K for an anomaly is exactly the same temperature change for 0.91 C for an anomaly.

Or do you think a monthly anomaly of 0.91 C means the global temperature was 0.91 degrees above 0 C?

Pauleta
Reply to  Pillage Idiot
February 21, 2021 11:09 am

It further proves my point that Math is racist.

fred250
Reply to  Nick Schroeder
February 21, 2021 11:39 am

I assume you were very tired when you typed that, Nick !

Its a trend, and yes, the trend is above zero.

And getting bigger with each new HadCrud fabrication.

Last edited 4 months ago by fred250
M Courtney
Reply to  fred250
February 21, 2021 12:31 pm

Well said fred250.
The first comment was clearly an error. We’ve all said something stupid on the internet. Mistakes happen.
It’s worth correcting, not rebuking.

Meab
Reply to  M Courtney
February 21, 2021 1:09 pm

Mistakes happen. However, smart people know that the best way to deal with a mistake is to admit it and move on. By not admitting his mistake, Nick will leave us wondering if he actually knows anything about thermodynamics.

Joel O'Bryan
Reply to  Nick Schroeder
February 21, 2021 1:42 pm

Nick, it would get rather tedious if every time Chris wanted to mention an anomaly temp trend above a baseline he had to write, “+0.91 K trend over the base period 1850-2020.”
An inspection of either of the two graphs and seeing the blue printed text “trend” value and the associated r value clearly puts this value in to context so that a clear discussion can be had.

Giordano Milton
Reply to  Nick Schroeder
February 22, 2021 3:46 am

0.91 K doesn’t necessarily men 0.91 degrees K. I think we understand it means 0.91 K degrees (unless stated as degrees K).

David Walker
Reply to  Giordano Milton
February 22, 2021 3:17 pm

Unlike Centigrade or Fahrenheit, Kelvin doesn’t take the ‘degree’ suffix.

Russ R.
February 21, 2021 10:19 am

Science by revising past measurements is not science. It is propaganda promoting a globalist agenda. This is a cancer on civilized society, and needs to be condemned.

Scissor
Reply to  Russ R.
February 21, 2021 11:04 am

Yes. There used to be a thing called the big lie. Scientists used to be regarded as honest. Today that’s not the case, and it would be difficult to know what this big lie is as there are so many to choose from. It could be AGW.

It’s almost as if it’s a challenge to see who can get away with the biggest whopper.

Dave Fair
Reply to  Scissor
February 21, 2021 3:25 pm

Among many top contenders, my vote is for Kerry.

dodgy geezer
Reply to  Scissor
February 24, 2021 4:21 am

Critical Race Theory is probably leading the pack at the moment. With the whole array of Covid Death predictions following close behind.

I’m afraid that AGW is an ‘also-ran’….

James Schrumpf
Reply to  Russ R.
February 21, 2021 11:33 am

This is like some “re-analysing” Tycho Brahe’s observations to support the Ptolemaic model.

Izaak Walton
Reply to  Russ R.
February 21, 2021 12:28 pm

Russ,
No one is revising past measurements. What people are doing are changing the way they use those measurements to estimate the average temperature.

The fact is that there is no simple direct way to measure the average temperature of the earth. Every method involves taking local measurements made at specific times and place sand then analysing that data to provide an estimate for the global temperature.

Derg
Reply to  Izaak Walton
February 21, 2021 1:12 pm

“The fact is that there is no simple direct way to measure the average temperature of the earth.”

But yet we still try and then tell everyone it’s the warmest year evah 😉

Rory Forbes
Reply to  Izaak Walton
February 21, 2021 1:18 pm

Of course they’re revising old temperatures. They’re doing it because they are “changing the way they use those measurements”. They have enormous political (and for some an economic) incentive for doing so …. rather than some more advanced scientific incentive. Lesson #1 you don’t alter data. Otherwise you lose track of where you’ve been. It’s essentially canceling history. Error bars are what should be used to show measuring discrepancies.

Average temperatures are largely a myth. They have little practical purpose beyond politics. It’s like averaging telephone numbers or street addresses.

Izaak Walton
Reply to  Rory Forbes
February 21, 2021 1:34 pm

Rory,
They are not altering data. The raw data is still available for anyone to use. Feel free to take it, devise your own algorithm to take a set of data from random locations around the world to produce an average global temperature. Or you could take the algorithm used by HadCRUT5 and analyse it to show just why it is flawed.

It is noticeable that Mr. Monckton has completely given up even the pretence of doing science. He knows that HadCRUT5 is wrong even though he admits that the “elaborate justifications” are “beyond his pay grade” or in other words he just doesn’t understand the method used. Claiming that something is wrong even while admitting you don’t understand it is not the way to do science but rather speaks to having preconceived ideas that nothing can change.

Newminster
Reply to  Izaak Walton
February 21, 2021 2:02 pm

The point, Izaak, is that once you’ve done your analysis you’ve done your analysis. You can’t keep going back to the raw figures and twiddling them or “changing the methods used”.

And no, nothing can change that has already happened. The data are what they are. The more you tweak them in a way which gives a result that more closely aligns with your preferred scenario the more you lay yourself open to skepticism, cynicism, and eventually ridicule!

Izaak Walton
Reply to  Newminster
February 21, 2021 2:48 pm

Newminster,
Once you have done your analysis what is to stop other people taking the raw data and doing a different analysis? If you can say why my analysis was wrong or that there is a new improved method then it makes sense to redo the analysis.

Again nobody is “going back to the raw figures and twiddling them “. The raw data is what is always was.

If you want to claim that the new analysis is wrong then you are free to do so. But unless you can provide a sensible reason as to why that is the case
then you don’t have a case that would stand up to scientific scrutiny.

fred250
Reply to  Izaak Walton
February 21, 2021 9:28 pm

“The raw data is what is always was. “

.

But the data used in their calculations is CONTINUALLY BEING ADJUSTED

Are you totally and WILLFULLY BLIND !

The blind monkey has nothing on you.

comment image

Izaak Walton
Reply to  fred250
February 21, 2021 9:40 pm

Fred,
Can you show me where the raw data being used is being adjusted? What they are doing is changing the analysis of the raw data not the data itself.

fred250
Reply to  Izaak Walton
February 21, 2021 11:26 pm

Just look at the FARCE that is GISS et al

SEVERAL ITERATION at many stations, nearly all cooling the past.

Don’t be BLIND all your pathetic ACDS life Izzy.

Makes you look extraordinarily DUMB.

Which are you WILLFULLY IGNORANT

or DELIBERATELY LYING ??

eg

comment image

comment image

jim hogg
Reply to  fred250
February 22, 2021 11:13 am

Izaak’s original point was that the RAW data hasn’t been changed – meaning that it’s still intact and available for others to work on.What’s so problematic about that? It doesn’t contradict Monckton’s post in any way.

And, following your response, attacking something different from his main point, sundry others piled on from the same angle, attacking a point Izaak hadn’t actually made. . I suspect that if you look again, carefully, at what Izaak said in relation to the RAW data, you’ll probably agree with him.

His point about average temperatures was agreed to by Rory. But strangely Rory gets a sack of up votes and Izaak a torrent of downs. Izaak’s crime it would seem is that he dared to criticise Monckton on the logic of condemning the changes while implyinig that he hadn’t analysed the “justificatioins” for them.. That seems to be a logically sound and easily understood point to me. .

Science and its analysis are not a team game – a point that keeps being made on here (though not quite in those terms), but time and time again I see pile ons which are based on ideological positioning, or the poor miscreant’s reputation for dissent. Disagreeing with the herd seems to be the gravest of transgressions on here, paradoxically (in view of the repeated claims about the invalidity of the concept of consensus in science).

We should each switch off the emotions as fully as we can and analyse carefully what is being said (especially those views we disagree with, because we won’t learn anything new from those which are the same as ours) not what we imagine is being said. Festina Lente.

Tim Gorman
Reply to  jim hogg
February 22, 2021 2:19 pm

The issue isn’t the raw data itself, it’s the purpose its being put to. It’s being used to create data where none existed or exists.

It’s like saying the wind is blowing on the east face of the Rockies so it must be blowing the same in Kansas. That’s what they do when they “infill” and “homogenize” the raw data into places where they don’t have data.

Just because it is blowing in Denver doesn’t mean it is blowing in Kansas.

For instance, one of the justifications is that there are 3000 new stations in the Arctic than previously. So go back and add new data from today into yesterday’s data – *cold* data. Which makes the past look colder. Just like adding wind data from Denver today into wind data for Kansas from the turn of the century!

It’s making up data and is *very* unscientific. Lot’s of researchers in the physical sciences and medical science get fired for making up data! But apparently not in climate science.

Again, the right way to do this is analyze the raw data and estimate its uncertainty, including due to lack of measurements. Write down how you developed your uncertainty estimate. Then do the same for the modern data. And compare the two. Don’t create data for the past out of thin air!

What would you say about a nuke operator that made up temp data for the pile during periods when the measuring instrument was malfunctioning and there was no data?

Newminster
Reply to  Izaak Walton
February 22, 2021 3:52 am

I repeat: data are data are data!! The recorded figures for Stations A & B are what they are. You cannot go back and say “we don’t have data for Station C so we’ll pretend we do by adding A and B and dividing by 2” and then going back 10 years later and saying “maybe a bit more weighting towards B because … “ and lo and behold C is a degree warmer than before!

I’ve made the point a dozen times over the years: if you don’t have data then you don’t have data. Live with it! Infilling, extrapolating, smearing does not provide you with data and it opens the door to subjective interpretation — and we know where that leads. In any sphere of human activity!

There is no reason at all to assume that HADCRUT5 is any more accurate than its predecessors. It just fits the current paradigm better.

Tom Abbott
Reply to  Izaak Walton
February 22, 2021 8:19 am

“Can you show me where the raw data being used is being adjusted?”

I can.

http://www.giss.nasa.gov/research/briefs/hansen_07/

The US surface temperature chart (Hansen 1999) on the left of the webpage shows an “unadjusted” view of a regional surface temperatue chart, which shows the Early Twentieth Century to be just as warm as it is today.

All other unadjusted regional surface temperature charts from around the world show the same temperature profile as the US chart, with the ETC warming being equal to the warming today.

The Official Data Manipulators adjust the regional temperature data and come up with a chart like the one on the right on the webpage, the so-called fraudulent, instrument-era, Hockey Stick chart, which changes the temperature profile and cools the ETC into insignificance, which makes things appear as though temperatures have been getting hotter and hotter for decade after decade and we are now at the hottest temperatures in human history.

But it’s all a Big Lie. The computer-generated Global surface temperature chart does not represent reality.

The regional charts are the ones that represent reality. They show us that CO2 is a minor player in the Earth’s atmosphere, so minor, we don’t have to worry about it or regulate it.

If all the regional surface temperature chart are in agreement on the basic temperature profile, then *that* is the temperature profile of the Earth as a whole.

None of these unmodified regional charts resemble the fraudulent Hockey Stick chart. It is all by itself and tells a different story than what the actual data says.

The unmodified, regional surface temperature charts are made up of actual readings taken by human beings at the time, who had no Climate Change bias when they read the thermometers, and the data they collected tell the true story of the Earth’s climate.

There is no climate crisis due to CO2.

Michael Jankowski
Reply to  Izaak Walton
February 22, 2021 9:57 am

“…If you can say why my analysis was wrong or that there is a new improved method then it makes sense to redo the analysis…”

Replacing the prior analysis with a “new improved method” demonstrates that the prior analysis was wrong.

HadCRUT was right until HadCRUT2 said it was wrong and no-good.

HadCRUT2 was right until HadCRUT3 said it was wrong and no-good.

HadCRUT3 was right until HadCRUT4 said it was wrong and no-good.

HadCRUT4 was right until HadCRUT5 said it was wrong and no-good.

Now you’re going to pretend HadCRUT5 is “right” like you did all of the rest. It is only a matter of time before HadCRUT6 emerges to prove HadCRUT5 wrong and send it into the trash with the others.

dodgy geezer
Reply to  Izaak Walton
February 24, 2021 4:24 am

What do you do if you can’t see any justification for the specific corrections made, and no one will explain them to you?

Rory Forbes
Reply to  Izaak Walton
February 21, 2021 2:14 pm

You seem to be oblivious to what’s taking place … what we’re all concerned about. Yes, the raw data IS still available if you know where to look and how to assess it. But that isn’t what is happening. We’re being told that what is being shown publicly is the real data, that temperatures are rising dangerously, when they’re doing nothing of the sort. It’s being used politically, not scientifically. As Russ R. has said … the “corrections” always seem to show the data erred on the low side.

He knows that HadCRUT5 is wrong even though he admits that the “elaborate justifications” are “beyond his pay grade”

He’s being facetious, you ninny. Lord Monckton’s math skills are beyond reproach and at least 5 SDs beyond the frauds at the CRU. He’s alluding to the fact that their rationals are unreasonable. He understands precisely what they are doing … cooking the books as they have been doing since “climategate”, with the blessings of the government. You need to learn some critical thinking skills.

I know you people hate him because he’s a gifted amateur, but Lord Monckton is the scientist. They’re showing the ways they’re not. Before it got political (and profitable) all science was done by gifted amateurs. They have no cross to bear. Remember that … you know – history.

Reply to  Izaak Walton
February 21, 2021 2:30 pm

The binomial probability of four unbiased adjustments that result in the same direction of change is 0.0625. A couple more revisions that result in the same trend in outcome will start making them look pretty silly. There is a limit on the number of times one can make “unbiased” adjustments that have same direction of effect. I would say the age of climate data adjustment has come pretty close the threshold of ridiculousness.

Izaak Walton
Reply to  BCBill
February 21, 2021 3:02 pm

Actually there are plenty of cases in science where unbiased adjustments all go the same way. The speed of light is the most famous one since creationists have tried claiming that the speed of light was slowing and used to be infinite 6000 years ago which surprisingly enough was when they believe the Universe was created. In this case you could ask yourself what is the binomial probability that over 50 measurements of the speed of light would result in the same direction of change?

If you talk to a sociologist they will explain it as being due to the fact that scientists do not want to publish results that are too far from the previously accepted ones and so measured values tend to change slowly and consistently over decades.

Tim Gorman
Reply to  Izaak Walton
February 21, 2021 3:39 pm

You are kidding, right? Equating metaphysical data with actual data?

Rory Forbes
Reply to  Izaak Walton
February 21, 2021 4:32 pm

You’re talking utter balderdash.

The speed of light is the most famous one since creationists have tried claiming that the speed of light was slowing and used to be infinite 6000 years ago which surprisingly enough was when they believe the Universe was created.

Creationists don’t even know what the speed of light is, let alone apply it to actual science. Besides, “YoungEarth Creationists” shouldn’t be confused with the half dozen or more other types, some of which even pretend to use science to prove their beliefs. So your analogy is false … no surprise there. Creationists are using science in exactly the same way the mystics at the CRU are using corrupted data to distort reality for political reasons.

BTW … it’s never wise o spend too much time talking to sociologists. They tend to be very, very confused people. Just because they append “ist” to their description doesn’t make them scientists.

Izaak Walton
Reply to  Rory Forbes
February 21, 2021 7:11 pm

Rory,
Where is the error in what I said? It is a fact that historical measurements of the speed of light were higher than current ones and that the measured value of the speed of light has been declining over several hundred years.

The fact that young earth creationists claim this is real and proof that the earth is 6000 years old is a source of humour and no one in their right mind takes it seriously.

On the other hand once you start treating science as a human endeavour you can explain the decline in the speed of light as being caused by people not wanting to stick their necks out or not wanting to contradict other scientists. And the same issue can be seen in the measurements of other physical quantities as well.

Rory Forbes
Reply to  Izaak Walton
February 21, 2021 8:38 pm

You’re comparing soup to nuts … the physical to the metaphysical, as Tim Gorman pointed out. The only sense I can derive from your analogy is that AGW true believers are like creationists and skeptics are like scientists. You said:

Actually there are plenty of cases in science where unbiased adjustments all go the same way.

In what universe does creationism qualify as part of the world of science? I guess Lord Monckton is just too advanced for your meager grasp of these concepts. That’s a shame. I find his erudition and rich use of language delightful.

Bottom line; there is no justification whatsoever to publish adulterated data, especially when their methods are already questionable (having been already caught out during the climategate scandal). They only avoided prosecution due to the statute of limitations.

Izaak Walton
Reply to  Rory Forbes
February 21, 2021 9:46 pm

Rory,
You are missing the point. It is a historical fact that measurements of the speed of light have consistently decreased over several centuries. No-one doubts this since it is a matter of public record.

Creationist have a particularly ridiculous explaination for this. Historians of science explain it in a different way as human error
and it shows that scientists are falliable and as human as the rest of
us.

And again the bottom line is that there is no adulterated data being published. The data is the same as it always was. The only thing changing is the analysis of the data.

Rory Forbes
Reply to  Izaak Walton
February 21, 2021 11:12 pm

You had no point for me to miss. You lost the plot long ago. You’re just not able to get your mind around it. The speed of light has remained constant. It has never varied. That’s a matter of public record.

And again the bottom line is that there is no adulterated data being published. The data is the same as it always was. The only thing changing is the analysis of the data.

The real “bottom line” is that you’re so muddled by your belief in politically motivated science fantasy, you’re unable to see the wood for the trees, the nose in front of your face, the obvious, the elephant in the room, the fraud. You, my son, can’t see the dead parrot.

Of course they’ve altered the bloody data, you thundering lummox! They have yards of double talk to explain why and how it is done. They’ve cooled the past and warmed the present. Of course they can’t destroy the real data, but they can tell the public how flawed THAT is. Lord Monckton showed it, and provided the evidence.

Tim Gorman
Reply to  Izaak Walton
February 22, 2021 4:49 am

The speed of light never changed. What changed was the uncertainty interval around what was being measured. As better instrumentation and processes developed that uncertainty interval became less and less.

The problem with the “global temperature” today is that the uncertainty has *increased” because of the data manipulation. Frist, a “global temperature” is meaningless to begin with. Secondly, the data shouldn’t be manipulated, the data should be used as is and if there is an uncertainty associated with it then that should be estimated and stated. That’s how *real* science should be done.

Jean Parisot
Reply to  Rory Forbes
February 22, 2021 10:29 am

In what universe does creationism qualify as part of the world of science? ” In the same one as climatology.

Reply to  Izaak Walton
February 22, 2021 12:00 am

I suppose that when something is beyond the range of your instruments then improvements in technology allow your instruments to make improvements always in the same direction. In the case of temperature measurement, the changes in instrumentation error are inconsequential. The rational for the adjustments is to remove random unkown error which is quite a different proposition than improving instrumentation. It is relatively easy to demonstrate why instrumentation error would be in the same direction but another matter entirely to say that various errors related to unknown factors should always come out the same. Also it is worse than a binomial distribution as both the past and recent past could be warmer, colder or the same leading to 9 possible outcomes. Yet in 4 adjustments we only get one outcome, colder past- warmer present. Occam’s razor says biased adjustments.

Michael Jankowski
Reply to  Izaak Walton
February 22, 2021 10:06 am

“…Actually there are plenty of cases in science where unbiased adjustments all go the same way…If you talk to a sociologist they will explain it as being due to the fact that scientists do not want to publish results that are too far from the previously accepted ones and so measured values tend to change slowly and consistently over decades…”

So you’re pointing-out that adjustments all going the same way in temperature records could be due a reluctance by scientists to go against the global warming mantra. Great job.

OweninGA
Reply to  BCBill
February 21, 2021 3:04 pm

Past that point a long time ago! We are now in the ridiculocene climate epoch.

Monckton of Brenchley
Reply to  Izaak Walton
February 21, 2021 3:11 pm

Izaak Walton has misunderstood the head posting. One hopes the misunderstanding was inadvertent. I fairly pointed out that HadCRUT, along with three of the other four major global-temperature datasets, had revised its data so as to show a steeper warming trend. And I fairly pointed out that HadCRUT had advanced various reasons for the alterations. I did not at any point say the alterations were wrong.

I did, however, point out that in my original calculations of equilibrium doubled-CO2 sensitivity I had assumed a HadCRUT5 trend of 1.1 K from 1850-2020, but that once the data had become available I had calculated the trend as 1.04 K. I also explained that the effect of using this slightly lesser figure was to reduce the midrange estimate of ECS from 1.2 to 1.1 K. Far from assuming, as Izaak Walton has incorrectly stated, that the HadCRUT5 data are incorrect, I had assumed – ad argumentum – that they were correct for the purposes of calculating ECS.

For that is how Socratic elenchus works. One accepts, for the sake of argument, everything in the interlocutor’s case except the one point that is demonstrably false. I have, therefore, taken the HadCRUT5 data at face value and used the resulting trend as the basis for calculation. And that, like it or not, is how science is done.

Bellman
Reply to  Monckton of Brenchley
February 21, 2021 3:54 pm

I did not at any point say the alterations were wrong.

You did heavily imply it though.

“This was not enough. Like the endlessly-adjusted GISS, RSS and NCEI datasets, HadCRUT5 hikes the trend – and does so by a startling 14%. The usual method is adopted: depress the earlier temperatures (we know so much better what the temperature was a century and a half ago than the incompetents who actually took the measurements), and elevate the later temperatures with the effect of steepening the trend and increasing the apparent warming.”

Doonman
Reply to  Bellman
February 21, 2021 7:58 pm

14% is not a small amount of change using the same past observed data. Since the “science was settled” before the 14% change, one must now accept that a 14% unknown settlement in the data is withing the bounds of “settled science”.

Be the first to step and say it was. We will wait for you while you do that.

Bellman
Reply to  Doonman
February 22, 2021 4:27 am

14% is not a small amount of change using the same past observed data.

It’s not using the same past observations. There’s about 3000 more land stations being used in the new version. But the main reason for the extra warming is the new grid system, including infilling.

The 14% figure isn’t very useful in any case, based as it is on a linear trend over 170 years of non-linear changes. The overall change is from around 1.6°C total warming in version 4, to 1.7°C warming in version 5. More like a 6% change. Although the last time I had a conversation like this, Monckton was saying you had to look at it as percentage of change from absolute zero.

I’m not sure what you mean by “the science was settled in version 4. Science is never settled, but we are not talking about “the science”, just a particular data set, and nobody said HadCRUT version 4 was settled. It’s been a constant debate as to how accurate it was compared to other data sets that showed slightly more warming.

Bellman
Reply to  Bellman
February 22, 2021 5:32 am

I should have said that the 1.6 – 1.7°C comparison is only for the changes to the land component of HadCRUT.

Bellman
Reply to  Bellman
February 22, 2021 9:22 am

Incidentally, the change from UAH 5.6 to 6.0 was around 19%, resulting in a change in warming of about 0.1°C over 38 years.

fred250
Reply to  Bellman
February 22, 2021 10:38 am

poor bellhop doesn’t understand the difference between justified adjustments due to KNOWN orbital issues..

vs AGENDA driven fabricated “adjustments”.

Bellhop should stick to delivering luggage to the wrong room. !

It’s one competency.

Bellman
Reply to  fred250
February 22, 2021 12:03 pm

But you won’t accept that HadCRUT has changed to deal with KNOWN issues?

Tim Gorman
Reply to  Bellman
February 22, 2021 2:35 pm

What known issues? That not enough stations providing enough cold data existed in the past? So you just make up more cold data? And you call that science? Making up data?

fred250
Reply to  Bellman
February 21, 2021 9:29 pm

THEY ARE WRONG though.

They do not represent REALITY

….. they represent IDEOLOGY.

No science involved.

Lrp
Reply to  Bellman
February 21, 2021 10:18 pm

The implication is self evident; how many times can you be wrong in your science and still ask taxpayers for money? What value do you add to society?

Monckton of Brenchley
Reply to  Bellman
February 21, 2021 11:14 pm

The whining Bellhop is wrong as usual. The head posting, which is worth a read before trying to comment on it, plainly states that HadCRUT gives reasons for its alterations – reasons that were above my pay-grade to comment on. It is also evident in the head posting that, in accordance with the usual processes of Socratic elenchus, I had accepted the upward-revised HadCRUT trend ad argumentum and had based my calculations on it.

Bellhop is wasting his time whining here about whether the tampering with the temperature datasets is justifiable. A large and growing fraction of the imagined global warming of the past 170 years is attributable to ex-post-facto adjustments to the datasets. That in itself raises questions in the mind of any honest and dispassionate observer – as will be seen frequently in the comments in this thread.

And if Bellman is so confident that HadCRUT represents real-world data with respectable accuracy, he may wish to read the Harry Read-Me file and think again.

Meanwhile, wait for the coming update to the New Pause.

Bellman
Reply to  Monckton of Brenchley
February 22, 2021 4:06 am

The whining Bellhop is wrong as usual. The head posting, which is worth a read before trying to comment on it…

I did read it, that’s how I was able to quote the passage from it.Are you really saying that when you talk about them hiking the trend, you weren’t trying to imply wrongdoing?

… plainly states that HadCRUT gives reasons for its alterations – reasons that were above my pay-grade to comment on

Your actual words where “Of course, elaborate justifications for the alterations are provided.”

Bellhop is wasting his time whining here about whether the tampering with the temperature datasets is justifiable.

I wasn’t intending to spend any time debating the latest revisions. It was a quick one sentence observation about your insinuations. But if you insist…

A large and growing fraction of the imagined global warming of the past 170 years is attributable to ex-post-facto adjustments to the datasets.

So which is it? Is the global warming imagined, or do you not have a view on validity of any data set, because it’s above your pay-grade?

I’t pretty easy to see why version 5 shows more warming than 4 – the latest version now infills for missing data, and that includes all the warming in the arctic. You’re free to disagree with this model or not, HadCRUT even provides a non-infilled version for you, but it’s always easier, if you want a conspiracy, to ignore the details and just suggest the data changed for mysterious incomprehensible reasons.

That in itself raises questions in the mind of any honest and dispassionate observer …

An honest and dispassionate observer would want to know what the reasons where for the changes, rather than have them dismissed as “above my pay-grade”.

And if Bellman is so confident that HadCRUT represents real-world data…

I’m not sure what you think my level of confidence is, or what “represents real-world data” means. As I see it, the goal is to use real-world data to estimate as best as possible the global temperature anomaly. I don’t trust any specific data set, which is why it’s useful to have multiple sets available. No estimate can be exactly correct and all can be improved. What I don’t assume is that every time a data set changes, it’s because of malfeasance. I’d prefer to see direct evidence of that, rather than just not liking the results.

I’m not sure what lessons I’m supposed to take from that old read me file.

Tim Gorman
Reply to  Bellman
February 22, 2021 4:57 am

the latest version now infills for missing data, and that includes all the warming in the arctic.”
There’s about 3000 more land stations being used in the new version”

There weren’t 3000 more stations in the past so why did the past get colder? It appears to be from arbitrary addition of more cold temps through the process of “infilling”.

Infilling is creating data that has not been measured. Lots of scientists get fired for making up data to prove their “hypothesis”.

Except for climate scientists I guess.

The correct scientific process would be to use the data as it exists and then estimate an uncertainty interval for it, perhaps because of not enough measuring stations. Then declare your stated value and its uncertainty interval. That’s the only honest way to compare past data with current data. The dishonest way is to make up data!

fred250
Reply to  Bellman
February 22, 2021 10:40 am

“I’m not sure what lessons I’m supposed to take”

.

Your capacity to understand and learn is minimal at best.

We have no expectation of you learning anything, at all, ever.

Bellman
Reply to  fred250
February 22, 2021 4:50 pm

Well here’s the thing, someone asks me to read a document, which apparently will explain to me why the current version of HadCRUT does not represent real-world data, whatever that means. Said document is a long angry message, from some anonymous source from at least 12 years ago. I have no idea how accurate any complaints are and it’s very long and will mean little to me, so have no desire to work through someone else’s private conversations, but maybe there is some part where they explain why HadCRUT isn’t very good, but also suspect that if it did say that someone would just point me to the relevant section.

However, it seems to be a self-defeating argument, as it clearly cannot be revering to the new version of HadCRUT, and so at best it’s explaining why a previous version of HadCRUT needs to be improved, in which case why complain when it’s improved.

Monckton of Brenchley
Reply to  Bellman
February 24, 2021 9:38 am

Don’t whine. I have fairly pointed out that much of the imagined warming in most of the principal datasets is attributable to ex-post-facto adjustments. Like it or not, that is the case, and it is something of an embarrassment to climate science, which is no doubt why Bellhop whines so much about it. I have also pointed out that I do not propose to contest those adjustments. I have also replied to Bellhop that I have used the adjusted linear trend in calculating observationally-derived ECS.

Bellman
Reply to  Monckton of Brenchley
February 24, 2021 10:08 am

You insist you are not contesting the results of HadCRUT5 but then call them imaginary which sounds a lot like you are contesting them. I’m not sure what you are getting at by ex post facto adjustments. This isn’t a matter of law, and it’s difficult to adjust data before you have it. Would you say UAH6 was made ex post facto?

I’m not debating what you think the ECS should be, we’ve been over this before and I expect you are making the same mistakes, but it’s irrelevant to what I’m discussing (or whining if it makes you feel superior).

Monckton of Brenchley
Reply to  Bellman
February 24, 2021 9:34 am

Don’t whine.

Rory Forbes
Reply to  Monckton of Brenchley
February 21, 2021 4:39 pm

It is a real pleasure to watch a master at work …

For that is how Socratic elenchus works. One accepts, for the sake of argument, everything in the interlocutor’s case except the one point that is demonstrably false.

Thank you for your always instructive contributions, not to mention the illustrations of how the Socratic method works … and always with your own brand of sardonic wit.

Monckton of Brenchley
Reply to  Rory Forbes
February 21, 2021 11:20 pm

Mr Forbes is most kind. The academic far-out hard Left are now busy denying and demolishing logic itself. Aeon, one of the growing number of Communist academic websites that permit contributions only from Communists (they don’t admit that, of course, but it is the reality), has a piece saying that Karl Popper’s Logik der Forschung [“The logic of scientific discovery”] had done grave harm to the Party Line by, inter alia, allowing “climate deniers” [a phrase invented and near-exclusively used by Communists] to demonstrate that global warming might not be as grave as the Party Line obliges the faithful to believe.

As CS Lewis’ Merlin once said: Qui verbum Dei contempserunt, eis auferetur etiam verbum hominis.

Rory Forbes
Reply to  Monckton of Brenchley
February 22, 2021 12:03 am

The academic far-out hard Left are now busy denying and demolishing logic itself.

That is sadly becoming evident in so many ways and so many places. Our society is under grave attack and many choose to remain blind to it. Electronic media has facilitated the Left at a greater pace than ever before.

I would despair but for bastions of logic and good will like you who have maintained the fight against great challenges. The Lewis quote is so apt … they’re messing with my beautiful language and it pisses me off.

Thank you for the response … and the hope.

Monckton of Brenchley
Reply to  Rory Forbes
February 24, 2021 9:43 am

It is comments like those from Mr Forbes that make my unpaid work on the climate scam so much more rewarding than the paid disruptions to these threads by increasingly desperate climate Communists.

One of my co-authors and I are now putting some real pressure on the British Government to stop being so sappily compliant with the demands of the climate Communists. It will take a few months, but by the time of the Glasgow propaganda-fest several Ministers are going to be a lot less content to go along with the sheer economic destructiveness that is climate Communism than they are now.

Russ R.
Reply to  Izaak Walton
February 21, 2021 1:41 pm

Odd that these “re-imagining the data exercises” always seem to increase the rate of warming!!!
Downright amazing how that works.
In fact I have $1000.00 to bet that the next revision will also increase the rate of warming.
Any takers?

Joel O'Bryan
Reply to  Izaak Walton
February 21, 2021 2:25 pm

It is quite obvious they revised the past annual anomaly temperatures. Anomalies are what we discussing.

Click on the image below to compare the past in HadCRUT 4.6 with HadCRUT5.0. Look at the years around 1870 in the graphic below. Compare the Red circles of right and left. Then compare the green circles on the right and left.

Very clear they cooled the past between the two versions. Synthetic adjustments to increase the temperature trend. HadCRUT5.0 continues the fraud on science from the Hadley Centre climate dowsers.

HadCRUT46v50.jpg
Izaak Walton
Reply to  Joel O'Bryan
February 21, 2021 3:05 pm

Joel,
No one is claiming that they haven’t revised past anomalies. But an anomaly is not a measurement. It is something you calculate from measurements. There are different ways of doing the calculations which will lead to different anomalies. Especially when you are taking a limited number of measurements made a specific times and places around the globe (and biased towards Europe initially) and extending them to create an average temperature of the entire planet.

Joel O'Bryan
Reply to  Izaak Walton
February 21, 2021 3:12 pm

“No one is claiming that they haven’t revised past anomalies.”

That IS the fraud they are perpetrating on the politicians and the public. Cooling past anomalies, raising more present anomalies to increase the trend is simply synthetic adjustments. Adjustments clearly intended to support the climate deception and keep the (failed) model outputs relevant in the eyes of the duped public.

Tim Gorman
Reply to  Joel O'Bryan
February 21, 2021 4:17 pm

Changing anomalies is EXACTLY the same thing as changing the temperatures themselves! As Nick stated himself, anomalies are calculated from measurements. The only way to change the calculated value is to change one or both of the measurements in the calculation! Nick is hung by his own petard!

Izaak Walton
Reply to  Tim Gorman
February 21, 2021 7:17 pm

Tim,
The graph that Joel put it is a graph of the global temperature anomaly against time. The actual measurements are temperature measurements at specific times and locations around the globe. How you go from a set of nonuniformly distributed (in both time and space) data to a single measurement in non-trivial and there is no single right method.

It should also not come as a surprise that people are constantly refining their methods and producing new results. If you want to object to this then you need to critique their analysis and provide a justification as to why you think it is wrong.

Tim Gorman
Reply to  Izaak Walton
February 22, 2021 5:30 am

If you are using Tmax and Tmin to create a mid-range value for a day and using that to calculate an anomaly then you are *NOT* using a time consistent data set. Tmax and Tmin happen at all different times of the day.

If you *are* taking the measurements at the same time of the day then you are probably missing Tmax and Tmin for a majority of the day because Tmax and Tmin happen at different time each day.

The satellite measurements probably happen at the same time each day and thus suffer from just being a single sample per day which is doubtfully an accurate measurement of the true average temperature each day. That makes it a consistent metric but its usefulness is limited. It won’t really tell you if a specific location is getting warmer or colder over time.

I *have* my method. It is using degree-day values. The integral of the entire temperature profile. It limits the uncertainty in the calculation because it doesn’t suffer from root sum square growth in uncertainty

I dimply don’t understand why today’s climate scientists do not move to this method. We have enough data from many stations to create thirty years of degree-day integrals.

Doing this would allow you to just add the number of degree-day values for each station to get a global total. Then you can track the total day-to-day, month-to-month, and year-to-year and you will have minimized the uncertainty as much as possible.

Graemethecat
Reply to  Izaak Walton
February 22, 2021 7:24 am

The level of dishonesty displayed by Izaak Walton is breathtaking. Full marks to him for sophistry, however.

When past measurements are “adjusted” (debased), it means those adjusting the figures think they know MORE about the temperatures which obtained then than the people who actually measured them. This cannot be so – any additional information has been lost forever, and can never be recovered. In any field other than Climate “Science” this would be called “making data up to fit a narrative”, i.e. fraud.

Graemethecat
Reply to  Izaak Walton
February 22, 2021 7:39 am

Reading Ken Irwin’s post below has suggested to me a good analogy for the practices you are trying to defend. Imagine “adjusting” the historical record of (say) the Norman Conquest so it contradicts contemporary accounts. That’s what you’re attempting to do.

Rory Forbes
Reply to  Izaak Walton
February 21, 2021 4:48 pm

It’s all just playful fun for the guys at the CRU, messing about with numbers, as is their daily pastime … all just a harmless exercise to see where the numbers take them — no malice intended, right? WRONG! The trouble is, they plug those artificial numbers, those facsimiles of reality into their climate models to provide “scientific” evidence of a rubbish conjecture. CO2 does not control the planet’s temperature, and by extension neither do humans.

Izaak Walton
Reply to  Rory Forbes
February 21, 2021 8:17 pm

Rory,
What do you think would happen to the temperatures if all the CO2 were to be suddenly removed from the atmosphere? Without it the earth would very quickly freeze and the temperature would drop to about -18C.

Rory Forbes
Reply to  Izaak Walton
February 21, 2021 8:49 pm

Without it the earth would very quickly freeze and the temperature would drop to about -18C.

Prove it.

Even if CO2 did play some small role in contributing to this planet’s remarkably stable temperatures, that role certainly didn’t continue much beyond 100 ppm concentration. Beyond that point the science suggests its effect is increasingly saturated.

Note: if all the CO2 were removed, it wouldn’t matter what the temperature was. No life would exist to notice.

Another Joe
Reply to  Izaak Walton
February 21, 2021 11:32 pm

No it would not!

fred250
Reply to  Izaak Walton
February 22, 2021 12:06 am

What a load suppository NONSENSE

Hypo-patheticals is all you have left.

Let’s see the scientific evidence for warming by atmospheric CO2, Izzy-a-loser.

1… Do you have any empirical scientific evidence for warming by atmospheric CO2?

2… In what ways has the global climate changed in the last 50 years , that can be scientifically proven to be of human released CO2 causation?

Last edited 4 months ago by fred250
Eric Vieira
Reply to  Izaak Walton
February 22, 2021 4:26 am

You forgot … the water vapor …

Carlo, Monte
Reply to  Izaak Walton
February 21, 2021 5:04 pm

It is something you calculate from measurements.

Which indicates that you have no formal training in uncertainty analysis.

fred250
Reply to  Izaak Walton
February 21, 2021 9:31 pm

BELLIGERENT DENIAL of the ACDS sufferers is getting worse and worse.

REALITY of the data FRAUD is obvious to anyone but a willfully blind monkey.

comment image

fred250
Reply to  Izaak Walton
February 21, 2021 11:30 pm

No one is claiming that they haven’t revised past anomalies.

,

WOW, a tiny figment of TRUTH seeps through the mental ooze. !

They are also using MUCH ALTERED historic data

It is basically FRAUD,

… and you can only be justified by your deep-seated ACDS mental disease.

eg

comment image

comment image

Carlo, Monte
Reply to  Izaak Walton
February 21, 2021 2:40 pm

Izaak has arrived on the scene to explain the latest developments in NewSpeak.

fred250
Reply to  Carlo, Monte
February 21, 2021 11:31 pm

ie LYING his ass off. !!

icisil
Reply to  Izaak Walton
February 21, 2021 3:03 pm

They don’t have to revise past measurements because so little of the temperature record from 1850-1950 is actual measurement data. All they have to do is tweek their computer algorithms to generate whatever numbers they want for all of the places that didn’t have thermometers (which was most of the world). Probably 90-95% of the temperature data for that hundred year period are computer generated. There just wasn’t a sufficient number of thermometers throughout the world during that time.

Last edited 4 months ago by icisil
Joel O'Bryan
Reply to  icisil
February 21, 2021 3:32 pm

So few people in the public understand the large-scale synthetic nature of the climate change claims underpinning this gross scam.

  • The fake treemometers in the paleo-recons of temperatures of past climates to make “unprecedented” claims.
  • The fake synthetic adjustments of modern instrumental record in-order to produce ever growing anomaly trend claims.
  • The fake computer models, hand-tuned statistical noise generators run on supercomputers, to produce future science fiction stories, not unlike a StarWars movie or any other Hollywood CGI animation fiction.

The climate scam is by far the biggest fraud on science ever perpetrated. When it finally comes unraveled as nature will do to man’s folly, the damage to all of science in the eyes of the public will be enormous. And the reputational damage will have been well-earned.

Rory Forbes
Reply to  Joel O'Bryan
February 21, 2021 4:55 pm

That’s exactly right, Joel!

I have been trying for years to explain the difference between a real measured datum vs. an interpolated, synthetic artifact, derived from averaging (hopefully) two measured data points. The trouble is, they often average two synthetic loci to build their 1500 m grid. Averaging temperatures is pointless enough … but averaging twice and thrice averaged artifacts is just silly. As you say, often less than 5% of the so called “data” is measured.

Bill Rocks
Reply to  Joel O'Bryan
February 22, 2021 7:32 am

I do agree that it appears to be that “The climate scam is by far the biggest fraud on science ever perpetrated.” An honorable scientist said something like: extraordinary claims require extraordinary evidence. Biased manipulation of historical temperature measurements is not evidence.

I think back to my university class, History of Science, and put this all into perspective and context. Modern technology and an unfortunate alignment of honorable and dishonorable motives enables massive, nearly global, dissemination and proliferation of questionable and faulty claims, to the detriment of humanity and democratic institutions.

fred250
Reply to  Izaak Walton
February 21, 2021 9:24 pm

ROFLMAO

No izzy, they DO ADJUST the past measurements.

They even show it on their web data.

WAKE UP to the BIG CON in front of your very eyes. !

Izaak Walton
Reply to  fred250
February 21, 2021 9:52 pm

Fred,
Can you point to a single temperature station where the raw data has been adjusted without public acknowledge and without the actual temperatures as entered not being available? Unless you can you are confusing analysis with data
manipulation. The former is acceptable the later isn’t.

Rory Forbes
Reply to  Izaak Walton
February 21, 2021 11:25 pm

Look who’s missing the point now and pretending not to understand what is happening. You’re either very dense or extremely disingenuous. There isn’t a soul on this planet, sufficiently informed of the events leading up to present climate policy, who is NOT aware that it has no basis in science. It is 100% political and economic. CO2 is NOT heating the planet. There is NO climate crisis. The ONLY “consensus” has been to keep fooling the public as long they can. It’s all political theater, like the US election.

fred250
Reply to  Izaak Walton
February 21, 2021 11:33 pm

Great that you ADMIT that they have used MASSIVELY ADJUSTED PAST DATA

At least you aren’t trying to DENY the MASSIVE FRAUD any more.

comment image

comment image

Tim Gorman
Reply to  Izaak Walton
February 22, 2021 5:04 am

How did the anomalies change if temperatures didn’t change? A-B=C doesn’t change unless A or B changes.

fred250
Reply to  Tim Gorman
February 22, 2021 10:41 am

Izzy-a-loser… is not very bright. !

You are asking questions he is clueless about.

Ken Irwin
Reply to  Izaak Walton
February 21, 2021 10:55 pm

Here is an example of physically overwriting the original logbook entry – so yes in some cases they really are changing the past data.

An Australian “adjustment” – by deleting the hottest day ever recorded in Australia in Bourke @ 51.7°C

https://wattsupwiththat.com/2020/07/10/hottest-day-ever-in-australia-confirmed-bourke-51-7c-3rd-january-1909/

The reason given at the time was it was an “observation error” as it did not agree with other stations in the vicinity – this is simply not true as brought to light by an Australian MP Craig Kelly.

Canada’s Climate Change Ministry has produced reports (for policy makers) with 100 years of climate data omitted and replaced with modelled data (which they freely admit).

https://www.therebel.media/100-years-historic-climate-data-deleted-catherine-mckenna-canadian-government-policy-report?fbclid=IwAR0rqZ3U6TWzyGfcieQA4A33bWWEwgJ8mSQ_Zx-4FibDi15HmLEGqU_xxBs

Similarly the Australian bureau has been persistently cooling the past and warming the present and simply refuse to explain why when asked to do so.

https://wattsupwiththat.com/2020/02/06/cooling-the-past-made-easy-for-paul-barry/

If you read the above article and look at the graphics for Wagga Wagga you will see the unadjusted records tell a completely different story – which does not support the alarmist narrative – from the adjusted records which does support the alarmist narrative.

This consistent fiddling with “Historical” temperature records over time led many researchers to note that when quoting historical temperature data it is necessary to record the source and the date of that source as temperature history can and does change !

Whilst in most cases the original data is available to researchers, the problem is they will most easily find or be presented with the adjusted data sets.

The future is certain; it is only the past that is unpredictable … Old Soviet Joke

fred250
Reply to  Ken Irwin
February 22, 2021 10:45 am

Bourke actually had a slight negative trend until they changed to AWS.

comment image
.

That led to a lot more missing data that had to be “in-filled™“…

…. so warming ensured.

comment image

fred250
Reply to  Ken Irwin
February 22, 2021 10:47 am

Yearly maxima were also decreasing until AWS installation.

comment image

Rich Davis
Reply to  Izaak Walton
February 22, 2021 11:29 am

A distinction without a difference, Izaak.

While I’m not aware of anyone attempting to report modified raw data, the obvious commonsense meaning of an “adjustment” is to correct data which is believed to be in error, so that we work with adjusted data for purposes of further valid analysis.

We are expected to believe that early in the record, there was a widespread systematic bias toward reporting temperatures which were overly warm, but then over time the situation has shifted to a bias toward reporting cooler than accurate temperatures.

These adjustments (or choices of new algorithms if you insist on that convoluted obfuscation), have the practical effect of cooling the past and warming the present.

Rory Forbes
Reply to  Rich Davis
February 22, 2021 2:24 pm

They’re doing everything in their power to convince everyone that “the man behind the curtain” isn’t manipulating the plot … and people actually believe them.

dodgy geezer
Reply to  Russ R.
February 24, 2021 4:18 am

Before it can be condemned it needs to be revealed. And you will find that no one in authority will listen to you if you try to tell them…

Oldseadog
February 21, 2021 10:19 am

I do so hope that you can achieve the aim in your last paragraph.
Is there anything any of the rest of us can do to help, or would that just muddy the water for you?

Monckton of Brenchley
Reply to  Oldseadog
February 21, 2021 3:13 pm

In response to OldSeaDog’s kind offer of help, we may at some point need to raise money. But at this preliminary stage no costs arise.

Chris Nisbet
February 21, 2021 10:33 am

I appreciate that it would be a full time job trying to keep up, but is there a site we can visit to see the measured datasets alongside the adjusted datasets?
It seems to me that it would be helpful to have the measured/raw values prominently displayed for all to see.

Monckton of Brenchley
Reply to  Chris Nisbet
February 21, 2021 3:13 pm

Just google HadCRUT and you will soon find your way to the data page.

Nicholas McGinley
February 21, 2021 10:33 am

Wait…so, it’s worse than they thought?
Been a while since they trotted out this old chestnut.

rah
February 21, 2021 10:34 am

Historical revision has always been a primary tool of the left to support whatever their current agenda may be. Don’t like the history? Rewrite it! Don’t like the recorded temperatures from the past? change them. Not enough ocean temperature data for the southern hemisphere to make any scientifically valid conclusions? Make it up from whole cloth.

And the beat goes on and on and on……….

Richard M
Reply to  rah
February 21, 2021 12:26 pm

It’s the only way to go when your science is fake. CO2 does not control the temperature, H2O does. It provides limits on the heating and cooling that occurs each and every day.

Earth’s greenhouse has windows that open. 
A greenhouse prevents solar energy entering through its glass windows from escaping. This keeps the interior of the greenhouse warm. While not exactly like a greenhouse, certain gases in our atmosphere do absorb energy (IR radiation) from the planet’s surface and redirect it back towards the surface. Often referred to as trapped heat, this energy has led to a metaphor where our atmosphere acts like a greenhouse. It is assumed that this redirected energy will warm the Earth. CO2 emitted from human activities is one of those gases.
 
We have seen the Earth’s climate system react to warm temperatures by increasing evaporation and producing clouds. This produces a limit to ocean warming at around 30 C. Essentially, Earth’s greenhouse has blinds that automatically appear to reduce warming.
 
What if Earth’s greenhouse also has windows? What if the windows get opened? Does Earth also have a general limit to warming??
 
The answer to all 3 questions is YES. We open our windows in the evening after a warm day to cool our homes, the Earth’s climate system has the equivalent of windows. These windows open at night and the energy that has been redirected by greenhouse gases is released through these windows every night.
 
The common view within climate science is that greenhouse gases produce a constant forcing on the surface which can only be compensated for by the surface warming. If any greenhouse gas concentration changes then the forcing changes along with it and the Earth will react accordingly.
 
On the other hand, if you have windows allowing energy to escape every night there is no build up of heat and hence the surface will not warm as climate science assumes.
 
This feature of the climate system is due to the interaction of two seemingly unrelated items.
1)      The large difference in heat capacity between the Earth’s surface and its atmosphere.
2)      The way moisture controls Earth’s surface cooling.
 
The high heat capacity of the surface prevents any energy from significantly increasing its temperature. Most of the surface (water = 4.1796) has a heat capacity more than 3000 times greater than the atmosphere (air = 0.00121). As a result, the energy that could raise the temperature of the atmosphere by 1 C will only raise the surface temperature by less than 0.001 C even though the energy levels are equivalent.
 
As a result, the moisture level of the atmosphere, which is tied to its temperature, remain essentially unchanged.
 
At night the sun’s energy is no longer available and the atmosphere quickly starts to cool. As the atmosphere cools the difference in temperature between it and the surface increases. The energy which has been absorbed by the surface, from both daytime heating and greenhouse gases, starts to radiate away (into the atmosphere and then into space). The moisture level of the atmosphere (often represented by the dew point) controls how much surface cooling takes place. When the dew point and the surface temperature are nearly the same, they both radiate energy at the same level. They are both emitting and absorbing the same amount of energy. That keeps their temperatures about the same.
 
This is the way all energy in excess of what the dew point allows is removed. The high heat capacity of the surface prevented greenhouse gas energy from significantly increasing its temperature thus keeping the moisture content of the atmosphere constant. It is this moisture content that determines the equilibrium temperature and that temperature ends up the same as it would have been if there was no added greenhouse energy. The greenhouse energy is lost with the day time heating energy… right out the greenhouse window.
 

Meab
Reply to  Richard M
February 21, 2021 1:29 pm

Don’t get me wrong as I’m not a believer in the climate crisis scam,
But most of the small amount of warming that has been observed is in winter and overnight temperatures. The “window” does open at night, but GHGs (mostly water vapor but also CO2) slow the heat loss at night too. It’s like opening the windows in your house but leaving the sheer curtains closed. The curtains reduce the amount of warm inside air that can move outside. The key here is how small an effect that CO2 has on temperature, the fact that CO2’s effect on temperature is logarithmic, most other CO2 effects are advantageous, and that there are limited amounts of fossil fuels on the planet means that there will be no climate crisis in either the near or long term.

Richard M
Reply to  Meab
February 21, 2021 1:36 pm

I think what you’re saying is the loss of heat is not 100% and I agree with that. I think the amount of loss is the ratio of the heat capacities of the air vs. the surface. If that is 1/1000 then you still end up with .1% of the GHE.

Another way to look at it is to divide the forcing by this ratio. So, if the CO2 doubling has a forcing of 3.7 w/m2 then it becomes 0.0037 w/m2. Not really zero but low enough that is will never be detected.

Tim Gorman
Reply to  Richard M
February 21, 2021 4:02 pm

Consider also that higher lower temps don’t mean higher max temps. The sun’s insolation determines daytime temps and completely overwhelms everything else.

Reply to  Richard M
February 21, 2021 2:01 pm

Your description of the process does not include any “trapped” heat at night by CO2?…..when the night time earth’s surface is sending IR into the atmosphere? The CO2 was “trapping” IR during the day but does not trap at night? Just asking.

Rick C
Reply to  Anti_griff
February 21, 2021 4:00 pm

The difference is that on a clear night surfaces are emitting energy to much colder space at just 3 or 4 K absolute temperature rather than clouds of blue sky with a much higher apparent temperature. Per Stefan-Boltzmann, the rate of energy transfer is proportional to the difference in the 4th powers of the absolute temperatures of the emitting and receiving objects. This is why dew or frost often forms on surfaces where there’s no cover, but not under trees or other overhead covers.

Humidity and CO2 do slow down the night time rate of heat loss but the temperature difference effect is very large. This is why overnight lows in deserts get very cold (no clouds, very little water vapor) and why spring and fall late/early frosts happen on clear calm nights.

Reply to  Rick C
February 21, 2021 4:56 pm

Well, I agree with all that and the night clouds may or may not match daytime cloud cover…I don’t know what the summation of clouds for the entire planet is on average…but CO2 and IR should not interact any differently night or day? It is the same amount of CO2 night/day?

Rick C
Reply to  Anti_griff
February 21, 2021 6:02 pm

I would say that the net effect of clouds and atmospheric water vapor is a net negative feedback to any warming. If it were a positive feedback, the temperature would continue to increase as a result of any warming, whether natural or man made. This would continue until the RH reached 100% everywhere or the earth ran out of liquid water. A negative feedback results in control within narrow limits. Clearly the data shows stability disrupted occasionally by major perturbations such as massive volcanos or asteroid impacts. Obviously there are also many natural processes that cause variability in climate like ocean oscillation and orbital effects but these are very slow and not an issue.

Richard M
Reply to  Anti_griff
February 21, 2021 7:17 pm

You are correct. The GHG forcing is always going on. Rick gave a good answer in that the cooling really kicks in at night. The key here is all those things are happening anyway even without adding any CO2. The difference is small and there is plenty of time at night to remove most of the energy.

The dew point is controlled by the surface temperature and that, on average, sets how much cooling will occur.

Alasdair Fairbairn
Reply to  Anti_griff
February 22, 2021 3:13 am

Pedantically you are right; but as you say the energy involved is insignificant. At night there is no radiation to evaporate the water droplets on their travels up through the atmosphere so the Partial pressure of water increases to reduce the evaporation rate and causes fog or mist to form. This may be seen at dawn until sunrise ‘ Burns off the mist’ as they say.

Wade
Reply to  rah
February 21, 2021 12:42 pm

Take a lesson from China and their campaign against the “Four Olds” — Old Ideas, Old Culture, Old Habits, and Old Customs. These were four things the Chinese communist wanted to destroy.

What do you see in society now? What are teachers indoctrinating into children? Old ideas, culture, habits, and customs are being destroyed because it is “racist” and “sexist”. They are removing statues that have been around for over a hundred years because it is “white supremacist racist” and causing some people emotional grief. Another example: Children, who are considered too young to decide to make certain decisions, are being told they are old enough to decide which gender they wish to be. The old culture says that men are men and women are women.

It is all about power and control. I am convinced that the COVID-19 lockdowns had absolutely nothing to do with our health. I am convinced that politicians would gladly cause 10 million people to die of despair and 8 billion people to become poorer for just 1 centimeter more of power; who knows what they would do for a lot more power? COVID-19 and “climate change” are just a means to an end, and the end always justifies the mean.

Patrick MJD
Reply to  Wade
February 21, 2021 1:06 pm

“Wade

I am convinced that the COVID-19 lockdowns had absolutely nothing to do with our health.”

If you look at the snap 5 day lock down recently enforced on the whole state of Victoria here in Australia for 21 CASES in a hotel and related to hotel workers who tested positive for “COVID-19” then yes, it’s nothing about health. The RT-PCR “test” is not a test for the disease or infectiousness. It is not a test at all but we’re being told by Govn’t it is. Maybe they should ask Mullis?

Graemethecat
Reply to  Wade
February 22, 2021 7:51 am

I’ve spoken to older Chinese people in the West who experienced the Cultural Revolution first hand, and they say today’s hysterical and vengeful SJW’s remind them of Mao’s Red Guards.

Gregory Brou
February 21, 2021 10:39 am

I am not a statistics guy but the technology of temperature measurement changed from mercury and paper to digital in the 70’s. the hand method of recording temperatures would seem to have a built in bias toward lower temperatures. any reference to how this is incorporated would be interesting

Rory Forbes
Reply to  Gregory Brou
February 21, 2021 10:51 am

the hand method of recording temperatures would seem to have a built in bias toward lower temperatures.

Why would you assume that? There is a difference in reading mercury and alcohol thermometers. Alcohol wets the walls making it more likely to read high.

Reply to  Gregory Brou
February 21, 2021 10:53 am

There is somewhat like a certain logic:
Usually, the thermometers are installed at 2 m high.
The average hight of the people reading these thermometers may have been about 1m50 – 1m60, so they read from below looking upward and may have so an bias to higher temperatures due to the perspective, and they have to be downgraded 😀 /sarc

Last edited 4 months ago by Krishna Gans
M Courtney
Reply to  Krishna Gans
February 21, 2021 12:34 pm

Take off the sarc. That makes some sense.

Peter Fraser
Reply to  Krishna Gans
February 21, 2021 12:50 pm

Do you think the “incompetents” who read the mercury or alcohol thermometers of the past were not aware of the error of parallax?

Tim Gorman
Reply to  Peter Fraser
February 21, 2021 3:53 pm

So what? How did they correct for it? And this wasn’t the only source of uncertainty. How accurate was the inside diameter of the tube? How smoth was it? How well was the tube isolated from wind gusts? Was the tube shaded part of the time? Was cloud cover, i.e. shade, recorded with the temperature. Even older thermometers used wine as a medium, how well was the alcohol content controlled between thermometers?

If you ignore the uncertainty of all the collected measurements then you are only fooling yourself, not mother nature.

Reply to  Peter Fraser
February 22, 2021 6:43 am

Not that the persons reading the thermometers are incompetent, but the people evaluating the read values may have suggested their incompetence 😀

Last edited 4 months ago by Krishna Gans
Tim Gorman
Reply to  Krishna Gans
February 21, 2021 2:53 pm

This all has to do with uncertainty. The readings are stated value +/- uncertainty. The readings by different people can be covered by the uncertainty interval.

Propagation of uncertainty is a known process. You don’t need to fiddle with the data.

The problem is that the so-called climate scientists want to claim there is no uncertainty in anything so then they don’t have to deal with it, not even in their models.

So they adjust the data to get the model outcomes they want and claim there is no uncertainty at all in their model outputs. Everything cancels somehow!

fred250
Reply to  Gregory Brou
February 21, 2021 9:35 pm

“the hand method of recording temperatures would seem to have a built in bias toward lower temperatures”

.

That is arrant nonsense, un-backed by any actual science.

What is backed by science is that new ASW small screens are MUCH more liable to short period high temperatures.

Add fake adjustments and UHI smeared all over the place, and you realise that..

…. the surface temperature FABRICATIONS are basically WORLTHLESS and MEANINGLESS as any measure of actual “climate”…

except, of course, as ACDS PROPAGANDA.

Redge
February 21, 2021 10:43 am

“Who controls the past controls the future: who controls the present controls the past.”

Nicholas McGinley
February 21, 2021 10:45 am

In other news, it begins to look like the pause may be back at some point in the near future, eh?

UAH_AUH.jpg
Monckton of Brenchley
Reply to  Nicholas McGinley
February 21, 2021 3:15 pm

Yes, the Pause is already 5 years 6 months on the UAH dataset shown by Mr McGinley. I shall update the New Pause data monthly until the New Pause stops.

Bellman
Reply to  Monckton of Brenchley
February 21, 2021 5:00 pm

Maybe you should try the pause using HadCRUT5.As of December 2020, it shows the pause starts in May 2015. Slightly longer than the UAH pause.

Monckton of Brenchley
Reply to  Bellman
February 21, 2021 11:24 pm

Bellman is perhaps unaware that HadCRUT5 is not at present being updated monthly. In due course it will be, and at that time we shall have a look at the trend.

Bellman
Reply to  Bellman
March 3, 2021 6:22 am

With February being 0.2°C in UAH, the start of the “New Pause” remains at August 2015. So headline will be New Pause extends by one month to 5 years 7 months.

Mark BLR
Reply to  Nicholas McGinley
February 22, 2021 4:02 am

Off-topic, and definitely “for math-nerds only”, but …

I also noticed that with the new “standard” climate Reference Period — 30 years with the format “xxx1 to yyy0” — of “1991-2020” UAH had a zero-line very close to the average of the “Pause” from 2001 to 2014 (UAH turned out to be the closest to zero, as well as the only negative value, see table below).

Out of curiosity I did a “quick and dirty” set of calculations using the annual anomalies for the main datasets, and found the following “2001-2014 averages” when using “RP = 1991-2020” (all numbers in “Celsius degrees” …) :

HadCRUT4 : 0.026
Cowtan & Way (= “Kriged HadCRUT4”) : 0.033
HadCRUT5 (Non-infilled, = “Updated HadCRUT4”) : 0.016
HadCRUT5 (Infilled, = “Updated Cowtan & Way”) : 0.025
BEST (Air) : 0.026
BEST (Water) : 0.024
GISS : 0.024
NCEI (ex-NCDC) : 0.016
UAH (V6) : -0.010
RSS (V4) : 0.033

More analysis should probably be done by people (much) more competent than me using monthly anomalies, but to a first approximation [ “-0.01 to +0.033C°” is roughly equal to “0C°” … ] when using “RP = 1991-2020” with all the major datasets it looks like we will be able to say immediately whether (or not …) the latest individual monthly numbers have “regressed to the mean [ of the 2001-2014 Pause ]” as they come out, just by asking “Are they less than or greater than zero ?”.

Richard M
February 21, 2021 10:46 am

Not really too concerned with this data. It’s filled with researcher bias. Only two data sets are meaningful. HadSST and UAH. The rest are unnecessary. So, I’m more worried about HadSST4 and whether they are fiddling with it.

This graph demonstrates why.

https://woodfortrees.org/plot/uah6/from:1979/to/plot/uah6/from:1979/to/trend/plot/hadsst3gl/from:1979/to/offset:-0.35/plot/hadsst3gl/from:1979/to/offset:-0.35/trend

DMacKenzie
Reply to  Richard M
February 21, 2021 1:35 pm

Since early 1970’s, long term temp changes are mostly the result of the AMO. UAH shows the shorter ENSO fluctuations on top of the longer AMO trend.

Dave Fair
Reply to  Richard M
February 21, 2021 3:43 pm

And they both have trends of a little over 1C/Century during a period of rapid CO2 increases. Alarming?

John Dilks
Reply to  Dave Fair
February 21, 2021 5:45 pm

No.

Andrew Wilkins
Reply to  Dave Fair
February 22, 2021 4:22 am

No.

Richard M
Reply to  Dave Fair
February 22, 2021 4:36 am

The trends are dominated by natural climate influences (AMO, PDO, ENSO). This can be seen if you take HadSST3 back to 1940. The trend drops to .07 C / decade.

We know on top of that that warming was already in process since the17th century. Hence, most of that is probably due to what is often referred to as the millennial cycle.

Lots of opinions on what has caused this cycle but one real possibility is ocean salinity changes.

Carlo, Monte
February 21, 2021 10:48 am

Now the climate goblin-doomsters are proclaiming that carbon dioxide no only controls atmospheric temperature, but also the behavior and position of the jet stream.

Rory Forbes
Reply to  Carlo, Monte
February 21, 2021 1:27 pm

Hey … get with the program. CO2 is the miracle compound. It serves whatever function the pundits, luminaries and crackerjack scientists deem is necessary to promote the cause. If they need its many skills to control the Jet Stream this week it cannot be denied.

Richard M
Reply to  Carlo, Monte
February 21, 2021 1:57 pm

Their models are nonsense so they can do anything. GHGs don’t control the temperature of our planet, water in its 3 forms is the thermostat.

Rory Forbes
February 21, 2021 10:55 am

Ah … further advancement towards the ‘Hockey Stick Project’. They’ll get there soon. When did raw data stop being sacrosanct?

Scissor
February 21, 2021 10:55 am

At this rate, it’l be up to the cold of 1816 in no time.

Ron Long
February 21, 2021 10:58 am

Good posting by Lord Monckton as usual. The blind faith (OK, not faith but adoption for hidden agenda) of the left/marxist/globalists/fund hounds in the unrestrained power of CO2 in the atmosphere is truly amazing. The whole issue of forcing and/or feedback is so chaotic that no current scientific study can produce a forecast for the future. Meanwhile, the earth is greening, crops have greater yields, Whoopi Goldberg is on her way to gigantism, and maybe, just maybe, us humans have pushed the onset of the next glacial cycle of the Ice Age we currently live in by a day or two.

CO2isLife
February 21, 2021 10:58 am

The Only warming you will find in Temperature Data is measuring the Urban Heat Island and Water Vapor, it is not measuring the impact of CO2 on temperatures. Here are over 400 Stations that show no warming over extended periods of time. I literally have trouble finding stations that show an uptrend in warming.

WUWT, you should start a Station Watch Page where people could post Stations that show no warming. I’ve been shocked at how easy it is to find them. SImply look for weather stations that haven’t been impacted by urban development and/or are desert locations.

Steveston (49.1333N, 123.1833W) ID:CA001107710 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=CA001107710&ds=14&dt=1 Maiduguri (11.8500N, 13.0830E) ID:NIM00065082 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=NIM00065082&ds=14&dt=1 Zanzibar (6.222S, 39.2250E) ID:TZM00063870 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=TZM00063870&dt=1&ds=15 Laghouat (33.7997N, 2.8900E) ID:AGE00147719 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=AGE00147719&dt=1&ds=15 Luqa (35.8500N, 14.4831E) ID:MT000016597 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=MT000016597&dt=1&ds=15 Ponta Delgada (37.7410N, 25.698W) ID:POM00008512 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=POM00008512&dt=1&ds=15 Wauseon Wtp (41.5183N, 84.1453W) ID:USC00338822 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00338822&dt=1&ds=15 Valentia Observatory (51.9394N, 10.2219W) ID:EI000003953 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=EI000003953&dt=1&ds=15 Dombaas (62.0830N, 9.1170E) ID:NOM00001233 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=NOM00001233&dt=1&ds=15 Okecie (52.1660N, 20.9670E) ID:PLM00012375 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=PLM00012375&dt=1&ds=15 Vilnius (54.6331N, 25.1000E) ID:LH000026730 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=LH000026730&dt=1&ds=15 Vardo (70.3670N, 31.1000E) ID:NO000098550 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=NO000098550&dt=1&ds=15 Port Blair (11.6670N, 92.7170E) ID:IN099999901 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=IN099999901&dt=1&ds=15 Nagpur Sonegaon (21.1000N, 79.0500E) ID:IN012141800 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=IN012141800&dt=1&ds=15 Indore (22.7170N, 75.8000E) ID:IN011170400 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=IN011170400&dt=1&ds=15 Enisejsk (58.4500N, 92.1500E) ID:RSM00029263 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=RSM00029263&dt=1&ds=15 Vladivostok (43.8000N, 131.9331E) ID:RSM00031960 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=RSM00031960&dt=1&ds=15 Nikolaevsk Na Amure (53.1500N, 140.7164E) ID:RSM00031369 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=RSM00031369&dt=1&ds=15 Nemuro (43.3330N, 145.5830E) ID:JA000047420 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=JA000047420&dt=1&ds=15 York (31.8997S, 116.7650E) ID:ASN00010311 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00010311&dt=1&ds=15 Albany (35.0289S, 117.8808E) ID:ASN00009500 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00009500&dt=1&ds=15 Adelaide West Terrace (34.9254S, 138.5869E) ID:ASN00023000 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00023000&dt=1&ds=15 Yamba Pilot Station (29.4333S, 153.3633E) ID:ASN00058012 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00058012&dt=1&ds=15 Wilsons Promontory Lighthouse (39.1297S, 146.4244E) ID:ASN00085096 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00085096&dt=1&ds=15 Mount Gambier Post Office (37.8333S, 140.7833E) ID:ASN00026020 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00026020&dt=1&ds=15 Cape Otway Lighthouse (38.8556S, 143.5128E) ID:ASN00090015 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00090015&dt=1&ds=15 Lencois (12.567S, 41.383W) ID:BR047571250 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=BR047571250&dt=1&ds=15 Eagle (64.7856N, 141.2036W) ID:USC00502607 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00502607&dt=1&ds=15 Orland (39.7458N, 122.1997W) ID:USC00046506 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00046506&dt=1&ds=15 Bahia Blanca Aero (38.733S, 62.167W) ID:AR000877500 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=AR000877500&dt=1&ds=15 Punta Arenas (53.0S, 70.967W) ID:CI000085934 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=CI000085934&dt=1&ds=15 Brazzaville (4.25S, 15.2500E) ID:CF000004450 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=CF000004450&dt=1&ds=15 Durban Intl (29.97S, 30.9510E) ID:SFM00068588 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=SFM00068588&dt=1&ds=15 Port Elizabeth Intl (33.985S, 25.6170E) ID:SFM00068842 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=SFM00068842&dt=1&ds=15 Sandakan (5.9000N, 118.0670E) ID:MY000096491 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=MY000096491&dt=1&ds=15 Aparri (18.3670N, 121.6330E) ID:RP000098232 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=RP000098232&dt=1&ds=15 Darwin Airport (12.4239S, 130.8925E) ID:ASN00014015 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00014015&dt=1&ds=15 Palmerville (16.0008S, 144.0758E) ID:ASN00028004 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00028004&dt=1&ds=15 Coonabarabran Namoi Street (31.2712S, 149.2714E) ID:ASN00064008 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00064008&dt=1&ds=15 Newcastle Nobbys Signal Stati (32.9185S, 151.7985E) ID:ASN00061055 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00061055&dt=1&ds=15 Moruya Heads Pilot Station (35.9093S, 150.1532E) ID:ASN00069018 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00069018&dt=1&ds=15 Omeo (37.1017S, 147.6008E) ID:ASN00083090 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00083090&dt=1&ds=15 Gabo Island Lighthouse (37.5679S, 149.9158E) ID:ASN00084016 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00084016&dt=1&ds=15 Echucaaerodrome (36.1647S, 144.7642E) ID:ASN00080015 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00080015&dt=1&ds=15 Maryborough (37.056S, 143.7320E) ID:ASN00088043 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00088043&dt=1&ds=15 Longerenong (36.6722S, 142.2991E) ID:ASN00079028 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00079028&dt=1&ds=15 Christchurch Intl (43.489S, 172.5320E) ID:NZM00093780 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=NZM00093780&dt=1&ds=15 Hokitika Aerodrome (42.717S, 170.9830E) ID:NZ000936150 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=NZ000936150&dt=1&ds=15 Auckland Aero Aws (37.0S, 174.8000E) ID:NZM00093110 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=NZM00093110&dt=1&ds=15 St Paul Island Ap (57.1553N, 170.2222W) ID:USW00025713 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00025713&dt=1&ds=15 Nome Muni Ap (64.5111N, 165.44W) ID:USW00026617 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00026617&dt=1&ds=15 Kodiak Ap (57.7511N, 152.4856W) ID:USW00025501 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00025501&dt=1&ds=15 Dawson A (64.0500N, 139.1333W) ID:CA002100402 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=CA002100402&dt=1&ds=15 Atlin (59.5667N, 133.7W) ID:CA001200560 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=CA001200560&dt=1&ds=15 Juneau Intl Ap (58.3567N, 134.5639W) ID:USW00025309 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00025309&dt=1&ds=15 Skagway (59.4547N, 135.3136W) ID:USC00508525 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00508525&dt=1&ds=15 Hay River A (60.8333N, 115.7833W) ID:CA002202400 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=CA002202400&dt=1&ds=15 Prince Albert A (53.2167N, 105.6667W) ID:CA004056240 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=CA004056240&dt=1&ds=15 Kamloops A (50.7000N, 120.45W) ID:CA001163780 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=CA001163780&dt=1&ds=15 Banff (51.1833N, 115.5667W) ID:CA003050520 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=CA003050520&dt=1&ds=15 Mina (38.3844N, 118.1056W) ID:USC00265168 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00265168&dt=1&ds=15 Merced Muni Ap (37.2847N, 120.5128W) ID:USW00023257 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00023257&dt=1&ds=15 So Entr Yosemite Np (37.5122N, 119.6331W) ID:USC00048380 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00048380&ds=15&dt=1 Santa Maria (34.9500N, 120.4333W) ID:USC00047940 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00047940&ds=15&dt=1 Maricopa (35.0833N, 119.3833W) ID:USC00045338 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00045338&ds=15&dt=1 Ojai (34.4478N, 119.2275W) ID:USC00046399 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00046399&ds=15&dt=1 Death Valley (36.4622N, 116.8669W) ID:USC00042319 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00042319&ds=14&dt=1 Rio Grande City (26.3769N, 98.8117W) ID:USC00417622 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00417622&dt=1&ds=15 Beeville 5 Ne (28.4575N, 97.7061W) ID:USC00410639 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00410639&dt=1&ds=15 Carlsbad (32.3478N, 104.2225W) ID:USC00291469 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00291469&dt=1&ds=15 Burnet (30.7586N, 98.2339W) ID:USC00411250 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00411250&dt=1&ds=15 Mtn Park (32.9539N, 105.8225W) ID:USC00295960 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00295960&dt=1&ds=15 Williams (35.2414N, 112.1928W) ID:USC00029359 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00029359&dt=1&ds=15 Needles Ap (34.7675N, 114.6189W) ID:USW00023179 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00023179&dt=1&ds=15 Loa (38.4058N, 111.6433W) ID:USC00425148 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00425148&dt=1&ds=15 Priest River Exp Stn (48.3511N, 116.8353W) ID:USC00107386 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00107386&dt=1&ds=15 Republic (48.6469N, 118.7314W) ID:USC00456974 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00456974&dt=1&ds=15 Rangely 1E (40.0892N, 108.7722W) ID:USC00056832 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00056832&dt=1&ds=15 Lovelock (40.1906N, 118.4767W) ID:USC00264698 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00264698&dt=1&ds=15 Pendleton (45.6906N, 118.8528W) ID:USW00024155 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00024155&dt=1&ds=15 Nevada City (39.2467N, 121.0008W) ID:USC00046136 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00046136&dt=1&ds=15 Culbertson (48.1503N, 104.5089W) ID:USC00242122 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00242122&dt=1&ds=15 Indian Head Cda (50.5500N, 103.65W) ID:CA004013480 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=CA004013480&dt=1&ds=15 Sherman (33.7033N, 96.6419W) ID:USC00418274 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00418274&dt=1&ds=15 Ballinger 2 Nw (31.7414N, 99.9764W) ID:USC00410493 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00410493&dt=1&ds=15 Ocala (29.1639N, 82.0778W) ID:USC00086414 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00086414&dt=1&ds=15 Akron 4 E (40.1550N, 103.1417W) ID:USC00050109 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00050109&dt=1&ds=15 Yates Ctr (37.8786N, 95.7292W) ID:USC00149080 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00149080&dt=1&ds=15 Alfred (42.2497N, 77.7583W) ID:USC00300085 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00300085&dt=1&ds=15 Georgetown (6.8000N, 58.15W) ID:GYM00081001 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=GYM00081001&dt=1&ds=15 Casa Blancala Habana (23.1670N, 82.35W) ID:CUM00078325 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=CUM00078325&dt=1&ds=15 Ft Kent (47.2386N, 68.6136W) ID:USC00172878 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00172878&dt=1&ds=15 Moosonee (51.2833N, 80.6W) ID:CA006075420 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=CA006075420&dt=1&ds=15 Jackman (45.6275N, 70.2583W) ID:USC00174086 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00174086&dt=1&ds=15 Columbia Rgnl Ap (38.8169N, 92.2183W) ID:USW00003945 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00003945&dt=1&ds=15 Srinagar (34.0830N, 74.8330E) ID:IN008010200 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=IN008010200&dt=1&ds=15 Olekminsk (60.4000N, 120.4167E) ID:RSM00024944 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=RSM00024944&dt=1&ds=15 Turkestan (43.2700N, 68.2200E) ID:KZ000038198 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=KZ000038198&dt=1&ds=15 Shimla (31.1000N, 77.1670E) ID:IN007101600 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=IN007101600&dt=1&ds=15 Silvio Pettirossi Intl (25.24S, 57.519W) ID:PAM00086218 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=PAM00086218&dt=1&ds=15 El Golea (30.5667N, 2.8667E) ID:AG000060590 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=AG000060590&dt=1&ds=15 Salamanca Aeropuerto (40.9592N, 5.4981W) ID:SP000008202 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=SP000008202&dt=1&ds=15 Kahler Asten Wst (51.1817N, 8.4900E) ID:GME00111457 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=GME00111457&dt=1&ds=15 Coloso (18.3808N, 67.1569W) ID:RQC00662801 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=RQC00662801&dt=1&ds=15 Nassau Airport New (25.0500N, 77.467W) ID:BF000078073 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=BF000078073&dt=1&ds=15 Tarpon Spgs Sewage Pl (28.1522N, 82.7539W) ID:USC00088824 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00088824&dt=1&ds=15 Cape Hatteras Ap (35.2325N, 75.6219W) ID:USW00093729 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00093729&dt=1&ds=15 Hamburg (40.5511N, 75.9914W) ID:USC00363632 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00363632&dt=1&ds=15 Charlottetown A (46.2833N, 63.1167W) ID:CA008300301 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=CA008300301&dt=1&ds=15 Saint Johnsbury (44.4200N, 72.0194W) ID:USC00437054 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00437054&dt=1&ds=15 Lake Placid 2 S (44.2489N, 73.985W) ID:USC00304555 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00304555&dt=1&ds=15 Elmira (42.0997N, 76.8358W) ID:USC00302610 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00302610&dt=1&ds=15 Franklin (41.4003N, 79.8306W) ID:USC00363028 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00363028&dt=1&ds=15 Sparta (43.9364N, 90.8164W) ID:USC00477997 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00477997&dt=1&ds=15 La Harpe (40.5839N, 90.9686W) ID:USC00114823 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00114823&dt=1&ds=15 Ashley (46.0406N, 99.3742W) ID:USC00320382 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00320382&dt=1&ds=15 Tooele (40.5353N, 112.3217W) ID:USC00428771 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00428771&dt=1&ds=15 Lander Hunt Fld Ap (42.8153N, 108.7261W) ID:USW00024021 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00024021&dt=1&ds=15 Green River (41.5167N, 109.4703W) ID:USC00484065 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00484065&dt=1&ds=15 Kennebec (43.9072N, 99.8628W) ID:USC00394516 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00394516&dt=1&ds=15 Cooperstown (42.7167N, 74.9267W) ID:USC00301752 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00301752&dt=1&ds=15 Marshall (39.1342N, 93.2225W) ID:USW00013991 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00013991&dt=1&ds=15 Imperial (40.5208N, 101.655W) ID:USC00254110 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00254110&dt=1&ds=15 Milan 1 Nw (45.1219N, 95.9269W) ID:USC00215400 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00215400&dt=1&ds=15 Grundy Ctr (42.3647N, 92.7594W) ID:USC00133487 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00133487&dt=1&ds=15 Laramie Rgnl Ap (41.3119N, 105.6747W) ID:USW00024022 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00024022&dt=1&ds=15 Curtis 3Nne (40.6742N, 100.4936W) ID:USC00252100 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00252100&dt=1&ds=15 Laketown (41.8250N, 111.3208W) ID:USC00424856 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00424856&dt=1&ds=15 Springview (42.8222N, 99.7467W) ID:USC00258090 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00258090&dt=1&ds=15 Culbertson (40.2333N, 100.8292W) ID:USC00252065 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00252065&dt=1&ds=15 Deseret (39.2872N, 112.6519W) ID:USC00422101 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00422101&dt=1&ds=15 Lamoni (40.6233N, 93.9508W) ID:USC00134585 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00134585&dt=1&ds=15 Vestmannaeyjar (63.4000N, 20.2831W) ID:IC000004048 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=IC000004048&dt=1&ds=15 Akureyri (65.6800N, 18.0794W) ID:IC000004063 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=IC000004063&dt=1&ds=15 Maliye Karmakuly (72.3794N, 52.7300E) ID:RSM00020744 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=RSM00020744&dt=1&ds=15 Torshavn (62.0170N, 6.767W) ID:DAM00006011 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=DAM00006011&dt=1&ds=15 Oestersund (63.1831N, 14.4831E) ID:SWE00100026 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=SWE00100026&dt=1&ds=15 Karlstad (59.3500N, 13.4667E) ID:SW000024180 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=SW000024180&dt=1&ds=15 Linkoeping (58.4000N, 15.5331E) ID:SW000008525 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=SW000008525&dt=1&ds=15 Torungen Fyr (58.3831N, 8.7917E) ID:NO000001465 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=NO000001465&dt=1&ds=15 Oksoey Fyr (58.0667N, 8.0506E) ID:NOE00105483 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=NOE00105483&ds=15&dt=1 Brockport (43.2000N, 77.9333W) ID:USC00300937 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00300937&dt=1&ds=15 Pana (39.3686N, 89.0867W) ID:USC00116579 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00116579&dt=1&ds=15 Susanville 2Sw (40.4167N, 120.6631W) ID:USC00048702 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00048702&dt=1&ds=15 Choteau (47.8206N, 112.1919W) ID:USC00241737 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00241737&dt=1&ds=15 North Platte Rgnl Ap (41.1214N, 100.6694W) ID:USW00024023 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00024023&dt=1&ds=15 Billings Wtp (45.7717N, 108.4811W) ID:USC00240802 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00240802&dt=1&ds=15 White Hall 1 E (39.4411N, 90.3789W) ID:USC00119241 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00119241&dt=1&ds=15 Helena Montana (46.7186N, 112.0017W) ID:USR0000MHEL https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USR0000MHEL&dt=1&ds=15 Miles City F Wiley Fld (46.4267N, 105.8825W) ID:USW00024037 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00024037&dt=1&ds=15 Ipswich (45.4478N, 99.0383W) ID:USC00394206 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00394206&dt=1&ds=15 Wilbur (47.7681N, 118.7239W) ID:USC00459238 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00459238&dt=1&ds=15 Wamsutter (41.6717N, 107.9786W) ID:USC00489459 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00489459&dt=1&ds=15 Elko Rgnl Ap (40.8289N, 115.7886W) ID:USW00024121 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00024121&dt=1&ds=15 Cascade Locks (45.6778N, 121.8736W) ID:USC00351407 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00351407&dt=1&ds=15 Canon City (38.4600N, 105.2256W) ID:USC00051294 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00051294&dt=1&ds=15 Missoula Intl Ap (46.9208N, 114.0925W) ID:USW00024153 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00024153&dt=1&ds=15 Pipestone (44.0139N, 96.3258W) ID:USC00216565 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00216565&dt=1&ds=15 Ketchum Rs (43.6842N, 114.3603W) ID:USC00104845 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00104845&dt=1&ds=15 Ely Yelland Fld Ap (39.2953N, 114.8467W) ID:USW00023154 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00023154&dt=1&ds=15 Faulkton 1 Nw (45.0364N, 99.1342W) ID:USC00392927 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00392927&dt=1&ds=15 Albia 3 Nne (41.0656N, 92.7867W) ID:USC00130112 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00130112&dt=1&ds=15 Medford (45.1308N, 90.3439W) ID:USC00475255 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00475255&dt=1&ds=15 Minonk (40.9125N, 89.0339W) ID:USC00115712 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00115712&dt=1&ds=15 Chicago Midway Ap (41.7861N, 87.7522W) ID:USW00014819 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00014819&dt=1&ds=15 Crawfordsville 6 Se (40.0028N, 86.8011W) ID:USC00121873 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00121873&dt=1&ds=15 Clarinda (40.7244N, 95.0192W) ID:USC00131533 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00131533&dt=1&ds=15 Melilla (35.2778N, 2.9553W) ID:SP000060338 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=SP000060338&dt=1&ds=15 Dublin Phoenix Park (53.3639N, 6.3192W) ID:EI000003969 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=EI000003969&dt=1&ds=15 Hanty Mansijsk (61.0167N, 69.1167E) ID:RSM00023933 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=RSM00023933&dt=1&ds=15 Biser (58.5167N, 58.8500E) ID:RSM00028138 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=RSM00028138&dt=1&ds=15 Gyzylarbat (38.9800N, 56.2800E) ID:TX000038763 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=TX000038763&dt=1&ds=15 Lahore City (31.5500N, 74.3330E) ID:PK000041640 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=PK000041640&dt=1&ds=15 Hyderabad Airport (25.3830N, 68.4170E) ID:PKM00041764 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=PKM00041764&dt=1&ds=15 Mukteswar Kumaon (29.4667N, 79.6500E) ID:IN023420800 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=IN023420800&dt=1&ds=15 La Estanzuela Eele (34.45S, 57.85W) ID:UY000086562 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=UY000086562&ds=14&dt=1 Yuma (40.1236N, 102.7217W) ID:USC00059295 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00059295&ds=14&dt=1 Waialua 847 (21.5750N, 158.1203W) ID:USC00519195 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00519195&ds=15&dt=1 Asmara (15.2830N, 38.9170E) ID:ER000063021 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ER000063021&dt=1&ds=14 Malkal (9.5500N, 31.6500E) ID:SU000062840 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=SU000062840&dt=1&ds=14 Gulu (2.8200N, 32.3300E) ID:UGXLT448852 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=UGXLT448852&dt=1&ds=14 El Fasher (13.6170N, 25.3330E) ID:SU000062760 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=SU000062760&dt=1&ds=14 Wau (7.7000N, 28.0170E) ID:SU000062880 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=SU000062880&dt=1&ds=14 Fort Portal (0.6700N, 30.3000E) ID:UGXLT766407 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=UGXLT766407&dt=1&ds=14 Zinder (13.8000N, 9.0000E) ID:NG000001090 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=NG000001090&dt=1&ds=14 Kayes Dag Dag (14.4820N, 11.44W) ID:MLM00061257 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=MLM00061257&dt=1&ds=14 Mbarara (0.62S, 30.6500E) ID:UGXLT101295 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=UGXLT101295&dt=1&ds=14 S Tome (0.3833N, 6.7167E) ID:TPXLT533006 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=TPXLT533006&dt=1&ds=14 Fort Lamy (12.2800N, 12.4800E) ID:NIXLT944649 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=NIXLT944649&dt=1&ds=14 Maracaibo (10.5500N, 4.77W) ID:UVXLT362943 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=UVXLT362943&dt=1&ds=14 Sokotonigisoko (13.0000N, 5.3000E) ID:NIM00065010 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=NIM00065010&dt=1&ds=14 Bobo Dioulasso (11.1600N, 4.331W) ID:UVM00065510 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=UVM00065510&dt=1&ds=14 Jinja (0.4500N, 33.1830E) ID:UGXLT843949 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=UGXLT843949&dt=1&ds=14 Entebbe Airpo (0.0500N, 32.4500E) ID:UGXLT430579 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=UGXLT430579&dt=1&ds=14 Tabora Airport (5.083S, 32.8330E) ID:TZ000063832 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=TZ000063832&dt=1&ds=14 Pemba (5.07S, 39.7200E) ID:TZXLT051591 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=TZXLT051591&dt=1&ds=14 Zomba (15.38S, 35.3000E) ID:MIXLT389630 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=MIXLT389630&dt=1&ds=14 Quelimane (17.883S, 36.8830E) ID:MZ000067283 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=MZ000067283&dt=1&ds=14 Gwelo (19.43S, 29.7500E) ID:ZIXLT622116 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ZIXLT622116&dt=1&ds=14 Okiep Northern Cape (29.6S, 17.8700E) ID:SFXLT220486 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=SFXLT220486&dt=1&ds=14 Beira (19.8S, 34.9000E) ID:MZ000067297 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=MZ000067297&dt=1&ds=14 Harare Kutsaga (17.917S, 31.1330E) ID:ZI000067775 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ZI000067775&dt=1&ds=14 Livingstone (17.817S, 25.8170E) ID:ZA000067743 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ZA000067743&dt=1&ds=14 Bulawayo Goetz Obs (20.15S, 28.6170E) ID:ZI000067964 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ZI000067964&dt=1&ds=14 Ihosy (22.25S, 43.9200E) ID:MAXLT339911 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=MAXLT339911&dt=1&ds=14 Fianarantsoa (21.45S, 44.7800E) ID:MAXLT429888 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=MAXLT429888&dt=1&ds=14 Antananarivoville (18.867S, 47.5000E) ID:MAM00067085 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=MAM00067085&dt=1&ds=14 Pamplemousses (20.1S, 57.6000E) ID:MPXLT384158 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=MPXLT384158&dt=1&ds=14 Cocos Island Aero (12.183S, 96.8330E) ID:CK000096996 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=CK000096996&dt=1&ds=14 Hamelin Pool (26.4008S, 114.1667E) ID:ASN00006025 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00006025&dt=1&ds=14 Nabawa (28.5008S, 114.7897E) ID:ASN00008028 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00008028&dt=1&ds=14 Marshalltown (42.0647N, 92.9244W) ID:USC00135198 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00135198&dt=1&ds=14 Fremantle (32.055S, 115.7500E) ID:ASN00009017 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00009017&dt=1&ds=14 Northam (31.6508S, 116.6586E) ID:ASN00010111 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00010111&dt=1&ds=14 Collie (33.36S, 116.1467E) ID:ASN00009628 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00009628&dt=1&ds=14 Katanning (33.6856S, 117.6064E) ID:ASN00010916 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00010916&dt=1&ds=14 Esperance Post Office (33.85S, 121.8833E) ID:ASN00009541 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00009541&dt=1&ds=14 Balladonia (32.4569S, 123.8653E) ID:ASN00011017 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00011017&dt=1&ds=14 Laverton (28.6306S, 122.4072E) ID:ASN00012045 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00012045&dt=1&ds=14 Southern Cross (31.2319S, 119.3281E) ID:ASN00012074 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00012074&dt=1&ds=14 Cue (27.4233S, 117.8994E) ID:ASN00007017 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00007017&dt=1&ds=14 Wiluna (26.5914S, 120.2250E) ID:ASN00013012 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00013012&dt=1&ds=14 Murgoo (27.3636S, 116.4261E) ID:ASN00007064 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00007064&dt=1&ds=14 Yalgoo (28.3392S, 116.6828E) ID:ASN00007091 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=ASN00007091&dt=1&ds=14 Coleman 3 Wnw (39.3500N, 76.1333W) ID:USC00181980 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00181980&ds=14&dt=1 Cet Central England (52.4200N, 1.83W) ID:UK000000000 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=UK000000000&ds=14&dt=1 West Point (41.3906N, 73.9608W) ID:USC00309292 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00309292&dt=1&ds=14 Mount Hope (40.9833N, 73.8667W) ID:USC00305540 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00305540&ds=14&dt=1 Elizabeth (40.6667N, 74.2333W) ID:USC00282644 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00282644&dt=1&ds=14 New York Wb City (40.7000N, 74.0167W) ID:USC00305816 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00305816&ds=14&dt=1 Kodaikanal (10.2333N, 77.4667E) ID:IN020081000 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=IN020081000&dt=1&ds=14 Fort Cochin (9.9670N, 76.2330E) ID:IN010033100 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=IN010033100&dt=1&ds=14 Mannar (8.9700N, 79.9200E) ID:CEM00043413 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=CEM00043413&dt=1&ds=14 Gallesri Lanka (6.0000N, 80.2000E) ID:CEXLT267392 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=CEXLT267392&dt=1&ds=14 Cuttack (20.4670N, 85.9330E) ID:IN017042600 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=IN017042600&dt=1&ds=14 Raipur (21.2170N, 81.6670E) ID:IN011291000 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=IN011291000&dt=1&ds=14 Darbhanga (26.1667N, 85.9000E) ID:IN004031400 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=IN004031400&dt=1&ds=14 Mymensingh (24.7500N, 90.4500E) ID:BGXLT840267 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=BGXLT840267&dt=1&ds=14 Patna (25.6000N, 85.1000E) ID:IN004102500 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=IN004102500&dt=1&ds=14 Cherra Poonjee (25.2500N, 91.7300E) ID:INXLT243961 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=INXLT243961&dt=1&ds=14 Allahabad (25.4410N, 81.7350E) ID:INM00042475 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=INM00042475&dt=1&ds=14 Khushab (32.3000N, 72.3500E) ID:PKXLT403174 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=PKXLT403174&dt=1&ds=14 Peshawar Intl (33.9940N, 71.5150E) ID:PKM00041530 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=PKM00041530&dt=1&ds=14 Multan Intl (30.2030N, 71.4190E) ID:PKM00041675 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=PKM00041675&dt=1&ds=14 Hindu Muslim Bagh (30.7500N, 67.8700E) ID:PKXLT059176 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=PKXLT059176&dt=1&ds=14 Sibi (29.5500N, 67.8800E) ID:PKXLT024011 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=PKXLT024011&dt=1&ds=14 Narayanjan (23.6200N, 90.5000E) ID:BGXLT435877 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=BGXLT435877&dt=1&ds=14 Dinajpur (25.6500N, 88.6800E) ID:BGXLT792072 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=BGXLT792072&dt=1&ds=14 Srimangal (24.3000N, 91.7300E) ID:BGXLT440631 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=BGXLT440631&dt=1&ds=14 Ya’An (29.9800N, 103.0000E) ID:CHXLT781875 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=CHXLT781875&dt=1&ds=14 Pabna (24.0200N, 89.2300E) ID:BGXLT428269 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=BGXLT428269&dt=1&ds=14 Mandalay (21.9830N, 96.1000E) ID:BMM00048042 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=BMM00048042&dt=1&ds=14 Iskanderkul (39.1000N, 68.3800E) ID:TI000038718 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=TI000038718&dt=1&ds=14 Buzaubaj (41.7500N, 62.4670E) ID:UZM00038403 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=UZM00038403&dt=1&ds=14 Kochkorka (42.2000N, 75.7000E) ID:KGXLT480001 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=KGXLT480001&dt=1&ds=14 Tangivoruh (39.8500N, 70.5500E) ID:KGXLT472364 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=KGXLT472364&dt=1&ds=14 Pendzhikent (39.5000N, 67.6000E) ID:TI000038705 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=TI000038705&dt=1&ds=14 Ciili (44.1670N, 66.7500E) ID:KZ000038069 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=KZ000038069&dt=1&ds=14 Baityk (42.7000N, 74.5000E) ID:KGXLT973718 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=KGXLT973718&dt=1&ds=14 Kokpekty (48.7500N, 82.3670E) ID:KZ000036535 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=KZ000036535&dt=1&ds=14 Magnitogorsk (53.3500N, 59.0830E) ID:RSM00028838 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=RSM00028838&dt=1&ds=14 Kara Tjurek (50.0000N, 86.4200E) ID:RSM00036442 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=RSM00036442&dt=1&ds=14 Rodino (52.5000N, 80.2000E) ID:RSM00036020 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=RSM00036020&dt=1&ds=14 Nakanno (62.8800N, 108.4300E) ID:RSM00024713 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=RSM00024713&dt=1&ds=14 Jiuquan (39.7670N, 98.4830E) ID:CHM00052533 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=CHM00052533&dt=1&ds=14 Lampasas (31.0717N, 98.1847W) ID:USC00415018 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00415018&ds=14&dt=1 Blanco (30.1061N, 98.4286W) ID:USC00410832 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00410832&ds=14&dt=1 Boerne (29.7986N, 98.7353W) ID:USC00410902 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00410902&ds=14&dt=1 San Marcos (29.8833N, 97.9494W) ID:USC00417983 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00417983&ds=14&dt=1 Uvalde (29.2167N, 99.7667W) ID:USC00419265 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00419265&ds=14&dt=1 Llano (30.7425N, 98.6542W) ID:USC00415272 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00415272&ds=14&dt=1 Luling (29.6756N, 97.6578W) ID:USC00415429 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00415429&ds=14&dt=1 Hondo (29.3364N, 99.1383W) ID:USC00414254 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00414254&ds=14&dt=1 Junction 4Ssw (30.4453N, 99.8045W) ID:USC00414670 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00414670&ds=14&dt=1 La Pryor (28.9831N, 99.8686W) ID:USC00414920 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00414920&ds=14&dt=1 Whiteriver 1 Sw (33.8214N, 109.9839W) ID:USC00029271 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00029271&dt=1&ds=14 Holbrook (34.9094N, 110.1544W) ID:USC00024089 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00024089&ds=14&dt=1 Saint Johns (34.5172N, 109.4028W) ID:USC00027435 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00027435&ds=14&dt=1 Petrified Forest Np (34.7994N, 109.885W) ID:USC00026468 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00026468&ds=14&dt=1 Keams Canyon (35.8111N, 110.1917W) ID:USC00024586 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00024586&ds=14&dt=1 Bluff (37.2825N, 109.5575W) ID:USC00420788 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00420788&ds=14&dt=1 Silverton (37.8089N, 107.6633W) ID:USC00057656 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00057656&ds=14&dt=1 Monticello 2E (37.8736N, 109.3075W) ID:USC00425805 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00425805&ds=14&dt=1 Delta (38.7531N, 108.0783W) ID:USC00052192 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00052192&ds=14&dt=1 Caliente (37.6128N, 114.5264W) ID:USC00261358 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00261358&ds=14&dt=1 Huntington Lake (37.2275N, 119.2206W) ID:USC00044176 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00044176&ds=14&dt=1 Lemon Cove (36.3817N, 119.0264W) ID:USC00044890 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00044890&ds=14&dt=1 Ash Mtn (36.4914N, 118.8253W) ID:USC00040343 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00040343&ds=14&dt=1 Villa De Aldama (28.8300N, 105.18W) ID:MX000008157 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=MX000008157&dt=1&ds=14 Las Burras (28.5300N, 105.42W) ID:MX000008092 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=MX000008092&ds=14&dt=1 Ciudad Delicias (28.2000N, 105.43W) ID:MX000008044 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=MX000008044&ds=14&dt=1 Meoqui (28.2700N, 105.48W) ID:MX000008102 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=MX000008102&dt=1&ds=14 La Boquilla P (27.5500N, 105.4W) ID:MX000008085 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=MX000008085&dt=1&ds=14 Pecos (31.4167N, 103.5W) ID:USC00416892 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00416892&dt=1&ds=14 Battle Mountain 4Se (40.6117N, 116.8917W) ID:USW00024119 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00024119&dt=1&ds=14 Visalia (36.3278N, 119.2994W) ID:USC00049367 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00049367&dt=1&ds=14 Prescott (34.5706N, 112.4322W) ID:USC00026796 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00026796&dt=1&ds=14 Red Bluff Muni Ap (40.1519N, 122.2536W) ID:USW00024216 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00024216&dt=1&ds=14 Portland Intl Ap (45.5908N, 122.6003W) ID:USW00024229 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00024229&dt=1&ds=14 Walla Walla Rgnl Ap (46.0947N, 118.2869W) ID:USW00024160 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00024160&dt=1&ds=14 Boise Air Terminal (43.5667N, 116.2406W) ID:USW00024131 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00024131&dt=1&ds=14 Bismarck (46.7708N, 100.7603W) ID:USW00024011 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00024011&dt=1&ds=14 Dodge City (37.7608N, 99.9683W) ID:USW00013985 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00013985&dt=1&ds=14 Shreveport (32.4506N, 93.8411W) ID:USW00013957 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00013957&dt=1&ds=14 Little Rock (34.8364N, 92.2619W) ID:USW00003952 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00003952&dt=1&ds=14 Mobile (30.6794N, 88.2397W) ID:USW00013894 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00013894&dt=1&ds=14 Pensacola Rgnl Ap (30.4781N, 87.1869W) ID:USW00013899 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00013899&dt=1&ds=14 Portsmouth Sciotoville (38.7569N, 82.8872W) ID:USC00336781 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00336781&dt=1&ds=14 Ironton (38.5333N, 82.6833W) ID:USC00333971 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00333971&ds=14&dt=1 Ashland (38.4536N, 82.6131W) ID:USC00150254 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00150254&ds=14&dt=1 Winfield Locks (38.5278N, 81.9153W) ID:USC00469683 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00469683&ds=14&dt=1 Charleston 1 (38.3500N, 81.65W) ID:USC00461575 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00461575&ds=14&dt=1 Jackson 3 Nw (39.0800N, 82.7078W) ID:USC00334004 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00334004&ds=14&dt=1 Parkersburg (39.2811N, 81.5572W) ID:USW00013867 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00013867&ds=14&dt=1 Creston (38.9628N, 81.2728W) ID:USC00462054 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00462054&ds=14&dt=1 Spencer (38.8008N, 81.3583W) ID:USC00468384 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00468384&ds=14&dt=1 Cairo 3 S (39.1667N, 81.1667W) ID:USC00461328 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00461328&ds=14&dt=1 Glenville (38.9339N, 80.8325W) ID:USC00463544 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00463544&ds=14&dt=1 Bens Run 1 Sse (39.4667N, 81.1W) ID:USC00460687 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00460687&ds=14&dt=1 Weston (39.0439N, 80.4725W) ID:USC00469436 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00469436&ds=14&dt=1 Rome (34.2453N, 85.1514W) ID:USC00097600 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00097600&dt=1&ds=14 Tallapoosa 2 N (33.7667N, 85.3W) ID:USC00098547 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00098547&ds=14&dt=1 Valley Head (34.5686N, 85.6064W) ID:USC00018469 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00018469&ds=14&dt=1 Dalton (34.7700N, 84.8872W) ID:USC00092493 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00092493&ds=14&dt=1 Chattanooga Lovell Ap (35.0311N, 85.2014W) ID:USW00013882 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00013882&ds=14&dt=1 Atlanta Nas (33.8667N, 84.3W) ID:USW00093830 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00093830&ds=14&dt=1 Scottsboro (34.6736N, 86.0536W) ID:USC00017304 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00017304&ds=14&dt=1 Copperhill (34.9939N, 84.3758W) ID:USC00402024 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00402024&ds=14&dt=1 Dahlonega (34.5328N, 83.99W) ID:USC00092475 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00092475&ds=14&dt=1 La Grange 1N (33.0536N, 85.0317W) ID:USC00094949 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00094949&ds=14&dt=1 Camp Hill 2Nw (32.8236N, 85.6561W) ID:USC00011324 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00011324&ds=14&dt=1 Clanton (32.8203N, 86.6522W) ID:USC00011694 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00011694&ds=14&dt=1 Union Springs 9 S (32.0142N, 85.7464W) ID:USC00018438 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00018438&ds=14&dt=1 Napoleon (41.3939N, 84.1144W) ID:USC00335669 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00335669&ds=14&dt=1 Defiance (41.2783N, 84.3847W) ID:USC00332098 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00332098&ds=14&dt=1 Bowling Green Wwtp (41.3831N, 83.6111W) ID:USC00330862 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00330862&ds=14&dt=1 Paulding (41.1247N, 84.5919W) ID:USC00336465 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00336465&ds=14&dt=1 Findlay Wpcc (41.0461N, 83.6622W) ID:USC00332791 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00332791&ds=14&dt=1 Toledo Blade (41.6500N, 83.5333W) ID:USC00338366 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00338366&ds=14&dt=1 Toledo Wb Ap (41.5667N, 83.4667W) ID:USW00014849 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00014849&ds=14&dt=1 Findlay Ap (41.0136N, 83.6686W) ID:USW00014825 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00014825&ds=14&dt=1 Adrian 2 Nne (41.9164N, 84.0158W) ID:USC00200032 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00200032&ds=14&dt=1 Van Wert 1 S (40.8494N, 84.5808W) ID:USC00338609 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00338609&ds=14&dt=1 Olney 2S (38.7003N, 88.0817W) ID:USC00116446 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00116446&dt=1&ds=14 Mt Carmel 4 Nw (38.4500N, 87.7833W) ID:USC00115893 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00115893&ds=14&dt=1 Fairfield Radio Wfiw (38.3806N, 88.3264W) ID:USC00112931 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00112931&ds=14&dt=1 Flora 5 Nw (38.7103N, 88.5758W) ID:USC00113109 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00113109&ds=14&dt=1 Vincennes 5 Ne (38.7386N, 87.4878W) ID:USC00129113 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00129113&ds=14&dt=1 Princeton 1 W (38.3567N, 87.5906W) ID:USC00127125 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00127125&ds=14&dt=1 Effingham 3Sw (39.1181N, 88.6244W) ID:USC00112687 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00112687&ds=14&dt=1 Washington 1 W (38.6489N, 87.1989W) ID:USC00129253 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00129253&ds=14&dt=1 Mt Vernon 3 Ne (38.3483N, 88.8533W) ID:USC00115943 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00115943&ds=14&dt=1 Mcleansboro (38.0844N, 88.5425W) ID:USC00115515 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00115515&ds=14&dt=1 Charleston (39.4761N, 88.1653W) ID:USC00111436 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00111436&ds=14&dt=1 Evansville Regional Ap (38.0442N, 87.5206W) ID:USW00093817 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00093817&ds=14&dt=1 Minden (40.5156N, 98.9514W) ID:USC00255565 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00255565&dt=1&ds=14 Kearney 4 Ne (40.7258N, 99.0133W) ID:USC00254335 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00254335&ds=14&dt=1 Holdrege (40.4517N, 99.3803W) ID:USC00253910 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00253910&ds=14&dt=1 Franklin (40.1000N, 98.9667W) ID:USC00253035 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00253035&ds=14&dt=1 Hastings 4N (40.6472N, 98.3836W) ID:USC00253660 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00253660&ds=14&dt=1 Ravenna (41.0319N, 98.9214W) ID:USC00257040 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00257040&ds=14&dt=1 Alma (40.1000N, 99.3667W) ID:USC00250145 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00250145&ds=14&dt=1 Red Cloud (40.0978N, 98.5197W) ID:USC00257070 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00257070&ds=14&dt=1 Grand Island Ap (40.9611N, 98.3136W) ID:USW00014935 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00014935&ds=14&dt=1 Loup City (41.2808N, 98.9681W) ID:USC00254985 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00254985&ds=14&dt=1 Clay Ctr 6 Ese (40.5033N, 97.9372W) ID:USC00251680 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00251680&ds=14&dt=1 Fayette (42.8503N, 91.8158W) ID:USC00132864 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00132864&dt=1&ds=14 Elkader 6 Ssw (42.7753N, 91.4536W) ID:USC00132603 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00132603&ds=14&dt=1 Independence (42.5069N, 91.9014W) ID:USC00134052 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00134052&ds=14&dt=1 New Hampton (43.0453N, 92.3122W) ID:USC00135952 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00135952&ds=14&dt=1 University (34.3725N, 89.5308W) ID:USC00229079 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00229079&dt=1&ds=14 Batesville 2 Sw (34.3061N, 89.9806W) ID:USC00220488 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00220488&ds=14&dt=1 Holly Springs 4 N (34.8219N, 89.4347W) ID:USC00224173 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00224173&ds=14&dt=1 Pontotoc (34.2486N, 88.9975W) ID:USC00227106 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00227106&ds=14&dt=1 Pontotoc Exp Stn (34.1381N, 88.9983W) ID:USC00227111 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00227111&ds=14&dt=1 Hernando (34.8039N, 90.0103W) ID:USC00223975 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00223975&ds=14&dt=1 Moscow (35.0711N, 89.4117W) ID:USC00406274 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00406274&ds=14&dt=1 Memphis Intl Ap (35.0564N, 89.9864W) ID:USW00013893 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00013893&ds=14&dt=1 Booneville (34.6344N, 88.5622W) ID:USC00220955 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00220955&ds=14&dt=1 Clarksdale (34.1864N, 90.5572W) ID:USC00221707 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00221707&ds=14&dt=1 Helena (34.5211N, 90.59W) ID:USC00033242 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00033242&ds=14&dt=1 Corinth 7 Sw (34.8792N, 88.6178W) ID:USC00221962 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00221962&ds=14&dt=1 Aberdeen (33.8300N, 88.5214W) ID:USC00220021 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00220021&ds=14&dt=1 Rushville (39.6042N, 85.4528W) ID:USC00127646 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00127646&dt=1&ds=14 Greensburg (39.3475N, 85.4892W) ID:USC00123547 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00123547&ds=14&dt=1 Shelbyville Sewage Plt (39.5283N, 85.7914W) ID:USC00127999 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00127999&ds=14&dt=1 Greenfield (39.7858N, 85.7611W) ID:USC00123527 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00123527&ds=14&dt=1 Cambridge City 3 N (39.8667N, 85.1833W) ID:USC00121229 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00121229&ds=14&dt=1 Anderson Sewage Plt (40.1117N, 85.7164W) ID:USC00120177 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00120177&ds=14&dt=1 Columbus (39.1661N, 85.9228W) ID:USC00121747 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00121747&ds=14&dt=1 La Crosse Muni Ap (43.8789N, 91.2528W) ID:USW00014920 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00014920&dt=1&ds=14 Trempealeau Dam 6 (43.9994N, 91.4378W) ID:USC00478589 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00478589&ds=14&dt=1 Winona (44.0422N, 91.6364W) ID:USC00219067 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00219067&ds=14&dt=1 Viroqua (43.5594N, 90.8761W) ID:USC00478827 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00478827&ds=14&dt=1 Blair (44.2906N, 91.23W) ID:USC00470882 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00470882&ds=14&dt=1 Gays Mills (43.3144N, 90.8486W) ID:USC00473022 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00473022&ds=14&dt=1 Hatfield (44.4169N, 90.7314W) ID:USC00473471 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00473471&ds=14&dt=1 Alma Dam 4 (44.3272N, 91.9194W) ID:USC00470124 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00470124&ds=14&dt=1 Decorah (43.3042N, 91.7953W) ID:USC00132110 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00132110&ds=14&dt=1 Mather 3 Nw (44.1747N, 90.3483W) ID:USC00475164 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00475164&ds=14&dt=1 Gibson Dam (47.6017N, 112.7536W) ID:USC00243489 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00243489&ds=14&dt=1 West Point (41.8450N, 96.7142W) ID:USC00259200 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00259200&ds=14&dt=1 West Point (32.8789N, 85.1808W) ID:USC00099291 https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00099291&ds=14&dt=1

Eric Harpham
Reply to  CO2isLife
February 21, 2021 11:23 am

All of these stations are being closed down tomorrow. sarc. off

Gyan1
Reply to  CO2isLife
February 21, 2021 11:48 am

Scaffetta 2021- https://link.springer.com/article/10.1007/s00382-021-05626-x

 Between 25-45% of the warming from 1940-’60 to 2000-’20 appears to be artificial, or non-climatic.

fred250
Reply to  CO2isLife
February 21, 2021 11:58 am

?? I checked your links for Okecis, Vilnius, Varda (3 in a row at random start)

And they all show warning . ???

DMacKenzie
Reply to  fred250
February 21, 2021 1:40 pm

the four i checked at random too, except 1….CisL should make his own free website and link to that, instead of spamming WUWT readers…..

CO2isLife
Reply to  DMacKenzie
February 21, 2021 2:53 pm

An uptrend is defined as a series of higher highs and higher lows. Almost every chart posted will show that recent temperatures are at or below levels reached at the beginning of last century.

This is what an uptrend looks like. Clearly temperatures are headed higher. Just because the most recent level is at a high doesn’t mean there is an up-trend. CO2 can’t cause a sudden increase in temperatures, it can only cause a gradual increase.

Here is what an uptrend looks like:comment image

fred250
Reply to  CO2isLife
February 21, 2021 9:42 pm

trying to pretend this doesn’t have an upward trend, is really pretty silly. !!

comment image

Andrew Wilkins
Reply to  fred250
February 22, 2021 4:36 am

That graph appears to show that evil seeh-oh-toos only started warming the earth in 1980. That doesn’t fit the narrative, does it?

CO2isLife
Reply to  Andrew Wilkins
February 22, 2021 6:15 am

Did CO2 int increase between 1880 and 2000? The only warming over 140 years occurs post 2000. Did the laws of physics cease to exist between 1880 and 2000? Clearly something other than CO2 caused the warming post 2000. Maybe they built a road by the thermometer, or moved it to an airport run way? hy would you claim that a graphic with temperatures in 2000 being below the level in 1880 is an “uptrend?”

CO2isLife
Reply to  fred250
February 22, 2021 9:35 am

Did CO2 cause the cooling between 1930 and 1990? The only warming is clearly an outlier. If that was true warming, why don’t all the other graphics spike post 2000? Are the physics unique to this location? More importantly, between 1910 and 2000 the GISS Global Temperature Graphic shows a full 1 degree warming, the graphic you posted shows that area COOLING. What kind of sellted science allows you to choose locations to decide if it warmed or not?

CO2isLife
Reply to  DMacKenzie
February 21, 2021 3:18 pm

If I can find just one station there is a problem, the laws of physics don’t cease to exist in one location. None of those charts show an uptrend in warming. They may be at a peak, but that will be due to a recent spike in temperatures, which can’t be due to CO2. NASA shows temperatures to have an uptrend, ie a series of higher highs and higher lows. None of the charts I posted has a clearly defined uptrend. This is what an uptrend looks like:comment image

CO2isLife
Reply to  fred250
February 21, 2021 3:02 pm

Okecie shows sudden warming post 2005, it doesn’t what an uptrend. The level in 2015 is the same level as 1882.

Vilnius shows sudden warming post 2005 and no warming uptrend, just a spike. The level in 2017 is below the level in 1882.

Vardo shows sudden warming post 2000, but in 2000 the temperatures are below the level of 1882. CO2 can’t cause a sudden increase in temperatures. In each of the 3 charts you listed there is no warming between 1882 and at least 2000, or 118 years when CO2 increased from around 300 to 400.

CO2isLife
Reply to  CO2isLife
February 21, 2021 3:05 pm

This is what an uptrend looks like:comment image

None of the charts you identified show a trend anything like that.Trends are a series of higher highs and higher lows. Not a flat range for 118 years and then a spilke. CO2 didn’t spike post 2005.

Tim Gorman
Reply to  fred250
February 21, 2021 3:12 pm

I charted the monthly cooling degree-day data for Vilnius for the past twenty years. It shows a slight increase. 0.02x + 23.6.

That’s almost non-existent. It’s less than one heating degree-day per month over 20 years if my math is right.

I checked Columbus and got a trend line of 0.12x + 84 over twenty years. Again, that’s not a huge trend.

Last edited 4 months ago by Tim Gorman
CO2isLife
Reply to  Tim Gorman
February 21, 2021 3:44 pm

Regression analysis isn’t appropriate because of the huge volatility and the R-Square of the regression is basically 0.00. Placing a upper and lower range range is a better approach to define a normal range. Also record the T-score of the value and chart it over a time series. If it has an uptrend the early data will represent the left tale and the current data will be the right tail. Every chart I posed has the t-score all over the place in relationship to time.

Tim Gorman
Reply to  CO2isLife
February 21, 2021 4:24 pm

Oh, I don’t necessarily disagree with you. Temperature data is non-stationary. Trying to combine non-stationary data to do statistical analyses is fraught with all kinds of problems.

That’s why I tend to use degree-day values which are integrals of the entire temperature profile during a day. And then add those together to get a sum per month rather than using some kind of meaningless mid-range value per day and taking an average of those over a month. You lose all concept of time in your reslt when you do that.

Dave Fair
Reply to  CO2isLife
February 21, 2021 3:47 pm

Christ! Couldn’t you just put all that data into an attachment such that I wouldn’t have to spend my day scrolling the computer screen?

CO2isLife
Reply to  Dave Fair
February 22, 2021 9:28 am

I would but it doesn’t allow for attachments.

Jim Gorman
Reply to  CO2isLife
February 21, 2021 4:11 pm

Now, try to find offsetting stations that have enough warming to give you 1,2, or 3 degrees of warming (that aren’t UHI affected) that will give you an average warming of somewhere around 1 degree.

You can’t!

Nicholas McGinley
Reply to  CO2isLife
February 21, 2021 5:54 pm

For a minute there, I thought you were gonna post the long version.

February 21, 2021 11:02 am

If the HADCRUT5 data are correct, then previous versions may be incorrect. And the conclusions/findings from any study, particularly the quantitative ones, that used the previous versions are suspect. [I say “may be” because error bands may overlap.] The suspect conclusions/findings includes any global temperature trends as well as models that were developed/derived/verified using input data from pre-HADCRUT5. I would ask all journals, papers, reports, policies, etc. based on crummy data to put an asterisk next to those papers, etc., indicating they need to be verified and/or withdrawn otherwise. Retraction Watch should also be notified.

Clyde Spencer
Reply to  Indur Goklany
February 21, 2021 1:26 pm

How many times can an organization be wrong before people start to doubt everything they do?

Monckton of Brenchley
Reply to  Indur Goklany
February 21, 2021 11:27 pm

I am delighted that Indur Goklany has made the excellent point that different versions of the same dataset cannot all be correct.

Dr Goklany has recently published an excellent paper for the Global Warming Policy Foundation showing just how many of the gruesome predictions of the climate Communists have not come to pass. It’s well worth a read.

Pauleta
February 21, 2021 11:08 am

Math is racist. I don’t believe in any of this.

February will the hottes month on record in the past two decades.

Science is settled, we just need to deconolize it.

Last edited 4 months ago by Pauleta
Latitude
February 21, 2021 11:09 am

the temperature they tell us today….will not be the temperature they told us tomorrow

….science is evolving….LOL

fretslider
February 21, 2021 11:13 am

Result!

John Robertson
February 21, 2021 11:14 am

Does it matter?
I call 2021 the International Year of Lying.
Under the “Medical Emergency” declared over the Dread Covid,we have no individual rights to resist direct attack by our government morons.
Now these very same “Helpers” are imposing their “Solutions” to Anthropogenic Global Warming,of the Catastrophic kind..

Nothing our fearless helpers,have told us for the last 12 months has been effective or beneficial to the citizen.
In fact as I read it; Government,our parasitic overload,has declared war on the citizen..Seeking to deny or remove every human right we thought we had,to “protect us”.
From what?
We don’t know,except these treacherous,power hungry fools, keep insisting they are saving us from a virus which kills less 1%.
As they imprison all.

So this years rewrite of climate history,is just another lie,from serial liars and abusers.
Now next they will have to burn the libraries and museums,as this “correction” of the data throw written records even further from the narrative.
Now according to our progressive comrades,those people of the past lacked their supernatural ability to know what the daily weather should be.

What was that soviet snark?
“We know the present and future,but the past is unknowable”?

I think the phoney wars are over.CAGW was just a test case.
Government has lied to us for 4 decades ,about the weather,shaping a narrative to herd the gullible and trusting into surrendering ever more wealth and control to the State.

Now these clueless and useless bureaus,believe they rule.
And we have always been at war with Oceana.

It is almost an act of madness to discuss the science,when science is not really at play.
Climatology is pure politics.

Perhaps Lord Monckton is privy to this information;Did the CRU ever produce the “original data” promised in 2010?

My apologies if I am too cynical.
There is a limit to my tolerance for the parasitic overload,as the economies collapse worldwide,these first world problems will be swept aside by the by those age old realities,the 4 horsemen riding through your living room tend to adjust your focus.

garboard
Reply to  John Robertson
February 21, 2021 12:23 pm

1% of the earths population is about 60,000,000 people . lets hope covid kills a lot less than 1%

ATheoK
Reply to  garboard
February 21, 2021 6:40 pm

Earth’s population growth rate is greater than 1%.

Current cumulative total COVID deaths is 2,477,819. Far far below 1% of the total population.

Right now, COVID’s cumulative death rate includes pneumonia, influenza, bronchitis and portions of a multitude of other death causes.

Monckton of Brenchley
Reply to  ATheoK
February 21, 2021 11:30 pm

The Chinese virus is a disease chiefly of the elderly. Because of various degrees of lockdown, the death rate from other respiratory diseases has been very small in the past year.

ResourceGuy
February 21, 2021 11:15 am

Airbrush science goes with authoritarianism because it can.

Bruce Cobb
February 21, 2021 11:27 am

“HadCrud5 – now with 14% more warming. Tastes great, less filling!”

JCM
February 21, 2021 11:30 am

Funny how the change in total trend on the HadCRUT5 (+0.13K; increase 0.91K to 1.04K) is outside the error range commonly cited on HadCRUT4 (+ and – 0.12K) . It’s safe to say that most published error bars are hog-wash.

From AR15 sr15 ch1 “Accordingly, warming from pre- industrial levels to the decade 2006–2015 is assessed to be 0.87°C (likely between 0.75°C and 0.99°C).” https://www.ipcc.ch/sr15/chapter/chapter-1/

Maybe an honest assessment would list the trend as indistinguishable from zero.

Reply to  JCM
February 21, 2021 12:53 pm

“Funny how the change in total trend on the HadCRUT5 (+0.13K; increase 0.91K to 1.04K) is outside the error range commonly cited on HadCRUT4 (+ and – 0.12K) .
A little matter of units here. The change in trend is 0.13K/century, the uncertainty of HADCRUT4 quoted is presumably for a monthly average, and is in degrees kelvin only. For any kind of data, the uncertainty of a trend depends very much on the trend period.

JCM
Reply to  Nick Stokes
February 21, 2021 2:14 pm

You are mistaken. The precision of the HadCRUT4 warming since pre-industrial was listed as + and – 0.12K in IPCC documentation. This is presumably the 95% OLS residuals on the raw data. Presumably this is without any proper consideration of all error sources and poor analytical judgement on the scale of analysis for century scale changes. HadCRUT5 has adjusted the slope of the trend to reflect a value outside this range, perhaps somewhere in the 99% range of the old residuals, or over the top.

JCM
Reply to  JCM
February 21, 2021 2:33 pm

There is no reason to believe error bars attempted on HadCRUT5 will be any more honest. Plus, one should surely add an additional error source from the infilling.

Reply to  JCM
February 21, 2021 3:42 pm

Again, you have to pay attention to units, and compare like with like. One is a trend, the other is a value. You just can’t compare the numerical values.

Tim Gorman
Reply to  Nick Stokes
February 21, 2021 4:11 pm

Huh? If a trend line is .1x + b then exactly what is the trend line telling you but a value?

Reply to  Tim Gorman
February 21, 2021 4:59 pm

It is not a value of temperature. It is a trend in °C/century, or °C/decade. Different units. Cripes, this stuff is elementary.

Tim Gorman
Reply to  Nick Stokes
February 22, 2021 8:00 am

and C is not a temperature?

JCM
Reply to  Nick Stokes
February 21, 2021 5:40 pm

If it helps you, the range at either end of the regression line has increased by 0.13K calculated for the same period.

Publications of HadCRUT4 tend to suggest with 95% confidence that the extent of the possible range is plus and minus 0.12K.

To calculate the change in temperature for a change in time using a linear regression line multiply the slope by the time period.

Carlo, Monte
Reply to  Nick Stokes
February 21, 2021 2:49 pm

The GUM is not your friend.

Jim Gorman
Reply to  Nick Stokes
February 21, 2021 4:17 pm

If one month has that kind of error, every month is just as likely to have the same amount of error and logic dictates that annual values will carry the same or more. Why don’t we ever see variance quoted with these averages. To find an average, you need a population. It should be easy to calculate the variance from that mean and population! Then we can easily calculate the total variance when combining different populations with different variances.

Reply to  Jim Gorman
February 21, 2021 4:57 pm

“Why don’t we ever see variance quoted with these averages. ” 

Why don’t you ever look to see:
https://www.metoffice.gov.uk/hadobs/hadcrut5/#:~:text=HadCRUT5%20is%20a%20gridded%20dataset,a%201961%2D1990%20reference%20period.&text=The%20dataset%20is%20a%20collaborative,the%20University%20of%20East%20Anglia.

In fact, they are far more thorough
“Both forms of the dataset are presented as an ensemble of 200 dataset realisations that sample the distribution of uncertainty.”

All the graphs on that page have uncertainty bands shown.

Carlo, Monte
Reply to  Nick Stokes
February 21, 2021 5:12 pm

Examination of chapter 9 of the IPCC 5th try glaringly indicates the authors had no clues about what uncertainty and error propagation are.

Dave Fair
Reply to  JCM
February 21, 2021 4:04 pm

Since we have no accurate idea as to the global temperatures way-back, why don’t we just say we think that it has warmed a little less than 1C since the end of the Little Ice Age? Then look at ARGO and satellites/radiosondes to see what has been happening with our best measurements. We could then say that during peak CO2 forcing periods, it looks like the world is warming at a rate of a little over 1C/Century.

Then we could say that a collection our best (CMIP6) climate models indicate that, in the worst case, it might warm 2-5.7C by the end of the 21st Century. Of course, we’d have to admit that our “best” UN IPCC climate models overpredict observed warming; in the aggregate, they predict tropospheric hot spots that don’t exist and their ‘physics’ have a range of average global temperatures of 3C. And it gets uglier from there.

Alan Welch
February 21, 2021 11:32 am

I’ve shown this graph before. It shows that quadratic fitting and long term (1000 year cycle) fit readings equally well. The sinusoidal shows a large temperature range (nearly +/- 3 degrees) but I am not claiming it as indicative of the future any more than the quadratic but I feel there is a message in it some where. Also shown are the 20year moving average indicating 60 year decadal changes.

https://drive.google.com/file/d/14tMkBslz2MLKSRQQ97XXX6ydSlpjuCNS/view?usp=sharing

Loydo
Reply to  Alan Welch
February 21, 2021 9:16 pm

“I feel there is a message in it some where…”


https://drive.google.com/file/d/14tMkBslz2MLKSRQQ97XXX6ydSlpjuCNS/view

Yes, the message is acceleration, CO2 conc. is also accelerating…
comment image

Last edited 4 months ago by Loydo
fred250
Reply to  Loydo
February 21, 2021 9:47 pm

GREAT NEWS THAT CO2 continues to increase, Isn’t it Loy-dodo.

Thing is, the planet is stating to COOL.

Try not to let your deep-seated ACDS mental illness make you become TOO DESPERATE.

And guess what, loy-dodo….

…. there is ABSOLUTELY NOTHING your mindless little tantrums can do about it. 🙂

China , India, and many other countries will continue to produce PLENTY of LUVLY, LIFE-SUPPORTING CO2 emissions

Last edited 4 months ago by fred250
Monckton of Brenchley
Reply to  Loydo
February 21, 2021 11:35 pm

All those trillions wasted, and all that has happened is that Western high-energy-density businesses have been forced to close, transferring the jobs to Loydo’s ideological soulmates in Communist China, whose emissions per unit of production are far greater than in the more efficient West. Result: the anthropogenic greenhouse-gas forcing continues to increase in a more or less straight line.

fred250
February 21, 2021 11:44 am

“I know the two skeptics who keep the UAH dataset.”

.

Roy is actually more of a partial lukewarmer.

But I agree that UAH is run by honest people who are not inflicted with ACDS (Anti-CO2 Derangement Syndrome)

DMacKenzie
February 21, 2021 11:48 am

Back of napkin calculation….something, anything (not necessarily CO2) raises the surface temperature say 1 degree….. Stephan Boltzmann equation says that corresponds to 3.7 watts more Q would be emitted from surface….
Look at another important equation, Dalton’s law. 1 degree warmer ocean surface means 7% more water molecules in the air immediately above the ocean…..say the moist air rises, and dry air falls to replace it, then we will eventually get 3.5 % more clouds somewhere, some kilometers and days away by advection, or that afternoon via convection in the form of thunderstorms…lets say cloud albedo is a low .5, so these clouds will reflect 500 W/sq.M for a couple of hours for a daily average of about 40 watts heat reflected. A strong negative 40 watt feedback compared to the 3.7 watt feedback…yes I am comparing apples and oranges, nonetheless fruit.

CO2 is a bit player since its calculated effect is only few watts per doubling, compared to our cloud that only had a 2 hour life…… thus CLOUDS control the temperature of the planet….the vapour pressure of the water that covers 70% of the planet controls the planets temperature….via additional cloud formation.

It is interesting that before clouds to form, the additional water vapour back-radiation causes the temperature to stay a bit higher…we call the combination of humidity and temperature “muggy”. Then, oversimplifying and being somewhat facetious, it starts to rain from the clouds, Ph.D students pack up their equipment and go home. Then they calculate that the increased cloudiness from their instrument readings resulted in a positive cloud feedback, them having missed the rainy low temperature part of the day, or maybe deciding that raindrops had invalidated their net radiometer readings….

Reply to  DMacKenzie
February 21, 2021 5:30 pm

All that…. plus the daily cooling effect of dew point from sunset through near sunrise makes this entire scenario “chaotic” and virtually impossible to model or predict – especially on a global scale.

The above list of like a thousand T stations with little or no uptrend not withstanding… does nothing to explain why GISS continues to show near-hockey-stick uptrends.

We will never get better at this until we ALL agree on some key standard metrics:

  1. No more anomalies to undefined baseline zero actual T value. I use 14.0°C.
  2. No more mixing °K and °C in the same data graphs. Some are mixed up above.
  3. No more confusing heat island T with rural T values. Remove stations in urban zones.
  4. No more altering past and present observed T’s when “updating” databases!
  5. Reduce databases down to about two: UAH and http://www.temperature.global examples.
  6. The above two (satellite atm and rural land + air-over-sea) do not record urban heat.

There… fixed it for ya’s *(^_^)*

Monckton of Brenchley
Reply to  DMacKenzie
February 21, 2021 11:39 pm

Actually, the current best estimate of the CO2 radiative forcing is 3.52 Watts per square meter per CO2 doubling. And one cannot derive that value from the Stefan-Boltzmann equation. Instead, one uses the SB equation to derive the Planck sensitivity parameter, which is the first derivative of that equation: i.e., 288 K surface temperature divided by four times the albedo-adjusted top-of-atmosphere flux density 241 Watts per square meter, or 0.3 Kelvin per Watt per square meter. The product of the Planck parameter and the CO2 forcing gives the reference or pre-feedback sensitivity to doubled CO2: i.e., about 1.053 K.

DMacKenzie
Reply to  Monckton of Brenchley
February 22, 2021 7:28 am

…agreed, “apples and oranges, nonetheless fruit” is my attempt to give the reader a sense of scope within a short comment without writing something as long as an IPPC report. If I wrote about wide band and narrow band water vapor and CO2 absorption coefficients versus altitude, the average reader would “tune out” and end up not exposed to the main message at all. BTW, great ECS chart in your article….

bonbon
February 21, 2021 12:09 pm

The good LMB just cannot deal with the Great Reset green Prince Charles right in his own backyard and blames ”China” . See Xi’s Davos , and in fact Russia´s Putin address – they are not playing along with jolly ol´ Britain.
Petitioning HM Gov, well what a farce! Look at BoJo’s betrayal of Brexit!
Problem is in all this the Ami’s think it is a TX problem.
Nuts!

Monckton of Brenchley
Reply to  bonbon
February 21, 2021 3:22 pm

In response to bonbon, I have spoken to Prince Charles and have indicated to him that the science behind the global warming storyline is dubious, though transiently fashionable. However, he wishes to adopt a partisan political stance on this question, as on many others. Whether his abandoning the iron impartiality of our present Sovereign (whom God preserve for as many decades as possible) will protect or endanger the monarchy is more than I can say: but I fear his partisanship will endanger it.

And of course I am not “petitioning HM Government”: I am compelling it to respond.

CO2isLife
February 21, 2021 12:12 pm

To demonstrate just how dishonest NASA GISS is, this is what they show as the Global Temperature Graph. It shows a clear uptrend.
https://data.giss.nasa.gov/gistemp/graphs_v4/graph_data/Global_Mean_Estimates_based_on_Land_and_Ocean_Data/graph.html

The problem is, it is extremely hard to find any stations that show any warming at all that isn’t due to the Urban Heat Island Effect or Water Vapor.
https://wattsupwiththat.com/2021/02/21/hadcrut5-shows-14-more-global-warming-since-1850-than-hadcrut4/#comment-3190000

To perpetuate the myth NASA GISS had to work hard to find a station that shows warming. The station they found to highlight is literally synonymous with data corrupted by the Urban Heat Island Effect. NYC Central Park. Here is the graphic they use.comment image

That is how NASA presents their argument, with a station known to be corrupted by the UHI Effect.

Here is NYC Central Park:
New York Cntrl Pk Twr (40.7789N, 73.9692W) ID:USW00094728
https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USW00094728&ds=14&dt=1

Here is basically the same location, but not impacted by the UHI Effect, West Point.
West Point (32.8789N, 85.1808W) ID:USC00099291
https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00099291&ds=14&dt=1

NASA literally deliberately chooses corrupt data to manufacture warming. If they simply chose locations sheltered from the UHI and WV they will discover there is no warming.

Columbus (39.1661N, 85.9228W) ID:USC00121747
https://data.giss.nasa.gov/cgi-bin/gistemp/stdata_show_v4.cgi?id=USC00121747&ds=14&dt=1

CO2isLife
Reply to  CO2isLife
February 21, 2021 3:12 pm

How NASA GISS shows temperatures:comment image

The Station they choose to demonstrate this is corrupted with the Urban Heat Island Effect:comment image

This is West Point, which is basically the same location (60 miles North) but shielded from the UHI Effect:comment image

NASA deliberately chooses a corrupted data set to make their case for warming.

DHR
February 21, 2021 12:29 pm

“Then one finds the unit feedback response for the 100-to-150-year period from 2020 (415 ppmv CO2) to 830 ppmv CO2 by increasing the unit feedback response to allow for extra water vapor in warmer air.”

But water vapor in air has not been increasing, at least according to the data presented by climate4y.com.

Care to comment my Lord?

DHR
Reply to  DHR
February 21, 2021 12:30 pm

climate4you.com

Monckton of Brenchley
Reply to  DHR
February 21, 2021 3:26 pm

Yes, indeed. The source cited by DHR is Professor Ole Humlum’s excellent monthly climate data update. Specifically, he cites the updated NOAA specific humidity dataset (Kalnay et al. 1996). The data are presented at three pressure altitudes – the near-surface boundary layer; the lower troposphere above the boundary layer; and the mid-troposphere. In the lower troposphere there is no trend in specific humidity; in the mid-troposphere, specific humidity has been declining, directly contrary to the predictions of all the climate models. However, in the boundary layer, where we live and move and have our being, the specific humidity is increasing at the rate of 7% per degree of atmospheric warming – which is precisely the rate of increase that one would expect given the Clausius-Clapeyron relation, one of the very few proven results in the slippery subject that is climatology.

Tim Gorman
Reply to  DHR
February 21, 2021 3:43 pm

Anyone that says you can increase temperature without also increasing water vapor just isn’t thinking straight. If that assertion was true you would never get water condensed on the lid of a heated pot!

Rob_Dawg
February 21, 2021 12:30 pm

Here’s an idea. Climate insurance pools. No payouts for ±5% prediction outcomes. Then $1t per percent falling outside those bounds. Publishing authors to purchase this insurance from their grant money and backed by their organizations and personal assets.

Peta of Newark
Reply to  Rob_Dawg
February 21, 2021 1:54 pm

A nice idea for a Properly Functioning Situation but that is the last thing that’s going on here.
Quite totally dysfunctional.

These ‘Scientists’ are behaving like spoilt brat children, racing around the playground shouting fire fire fire. When someone asks ‘Where, I see no fire’ they go racing off somewhere else to point, laugh and hurl insults like ‘Haha, how can you be so stupid’

Then they change the rules of the game, move the goal posts and adjust the data basically, and race around again shouting fire fire fire.

There has to be some control exerted over by, in a normal situation, their parents or teachers but, The Parent in this situation is all our very own Government(s)

Hence why the ‘publishing author‘ bit and ‘insurance‘ wont gain any traction:
Government effectively is the publishing author and the insurance provider.
The brats are thus perfectly safe to carry on doing what they’re doing because left leaning Governments don’t have the guts to slap these brats down.

Now we see where Mr Trump ‘went wrong’ even tho he didn’t do any wrong.
The brats could see what was coming viz: A Slap Down or Swamp Draining and every effort was made to avert that happening.

Despite Biden adding one halfpenny per litre to price of petrol every day since taking office, Trump Derangement Syndrome is still raging- in the BBC, Grauniad and most of, haha, Polite Society here in the UK

Last edited 4 months ago by Peta of Newark
ResourceGuy
February 21, 2021 12:45 pm

Stalin would have been so proud.

February 21, 2021 1:03 pm

“The usual method is adopted: depress the earlier temperatures (we know so much better what the temperature was a century and a half ago than the incompetents who actually took the measurements), and elevate the later temperatures with the effect of steepening the trend and increasing the apparent warming.”

As usual, no evidence cited. HADCRUT does not generally adjust station readings, but accepts them as reported by the Met offices. The actual reason for the change is the long overdue use of correct averaging methodology. Till HAD5, they made no attempt to properly estimate the temperature of missing grid cells. They just omitted them from the average, which has the effect of assigning to them the average of the cells that have data. As Cowtan and Way pointed out back in 2013, this leads to a major bias. The gaps are more frequent in the Arctic, and this is a region that has been warming rapidly. Assigning average behaviour to those missing cells artificially dilutes that warming. Taking account of regional behaviour is the right thing to do, and it took them another 7 years to do it. 

Derg
Reply to  Nick Stokes
February 21, 2021 1:15 pm

Settled science indeed 😉

Clyde Spencer
Reply to  Nick Stokes
February 21, 2021 1:38 pm

Stokes
And infilling with some procedure, using nearby stations, replaces missing data, which shouldn’t be used for calculating averages, with extra weight given to the good stations. Weighted, areal interpolations were developed for a parameter, that while varying with distance, do not vary with time. Done properly, the uncertainty should increase when infilling with temperatures that vary with time as a result of moving air masses and abrupt boundaries as at a cold front.

Tim Gorman
Reply to  Clyde Spencer
February 21, 2021 3:57 pm

You took the words right out of my mouth. Using temperatures north of a river valley to infill temperatures south of the same valley can be off by 1C or even more. Regularly! Meaning you the average is biased high!

The answer is more measuring stations, not more infilling.

Weekly_rise
Reply to  Tim Gorman
February 22, 2021 1:53 pm

You can’t go back in time and install more measuring stations, so that is not a realistic solution.

Also, the HadCRUT temperature analysis is done using anomaly fields, not absolute temperatures, so influence of siting is not significant and covariance extends across much larger regions.

Tim Gorman
Reply to  Weekly_rise
February 22, 2021 2:51 pm
  1. I told you the scientific way of handling this. You use the data you have. You calculate an uncertainty estimate for the analysis and show how you estimated it. Then that analysis can be compared to an analysis of newer, more populated data sets with their own uncertainty analysis. YOU DON’T MAKE UP DATA!
  2. As you have been told over and over, anomalies destroy any possible weighting. A 0.1C anomaly in Miami is far different than a 0.1C anomaly in Point Barrow.
  3. As you have been told repeatedly, temperatures on one side of a geographical feature can be quite different than temperatures on the other side. E.g. north and south of the Kansas River valley. Thus infilling data from a station on one side into a grid on the other side distorts the data set irrevocably.
  4. Exactly what is the covariance between two single, independent temperature measurements from two different sites? My guess is that you don’t have a clue!
Weekly_rise
Reply to  Tim Gorman
February 22, 2021 3:42 pm

1. Interpolation can be a perfectly valid component of that analysis. There are no newer datasets containing more stations than exist – none of the groups compiling these records can time travel, so far as I’m aware. All are working with the same station networks.
2. You’ve said that to me before, but it didn’t make sense then and doesn’t make sense now.
3. That’s why the anomalies are used. The absolute temperature will be quite different but the anomaly is not likely to be.
4. The covariance is the climate.

Last edited 3 months ago by Weekly_rise
Carlo, Monte
Reply to  Weekly_rise
February 22, 2021 4:14 pm

Contrary to the common claims, subtracting a baseline value does NOT reduce uncertainty.

Weekly_rise
Reply to  Carlo, Monte
February 22, 2021 4:53 pm

I believe the benefit of using anomalies is that the anomaly represents a much broader geographic area than does the absolute temperature and it allows you to work with a station network whose composition changes through time, not so much that it reduces uncertainty.

Carlo, Monte
Reply to  Weekly_rise
February 22, 2021 5:02 pm

Averaging Pt. Barrow and Kuala Lumpur then subtracting Cleveland does not give you the “climate”.

Regardless, if you don‘t have a handle on the true measurement uncertainty, you are just fooling yourself.

Tim Gorman
Reply to  Weekly_rise
February 23, 2021 9:26 am

How in Pete’s name does using anomalies represent a much broader geographical area than using absolute temps? Once again you make a wild claim and have no math to back it up. It’s always just religious dogma with you!

If you take 30 stations in a state, take their measurements at 3PM in the afternoon and get an average, and then delete ten of the stations and take an average, will the resulting average change? If so, why? If you add ten more stations and take an average, will their average change? If so, why?

Why would using anomalies from a baseline being subtracted from the temperatures change any more or less than the change in the absolute average?

What happens with the uncertainty in each situation?

Weekly_rise
Reply to  Tim Gorman
February 23, 2021 1:23 pm

The anomaly represents a broader geographic area than the absolute temperature because the anomaly removes the influence of the specific point location where the measurement was taken. A hot summer in a mountainous region will likely be hotter both at high altitudes and in the mountain valleys, but the two places will have very different temperatures.

Also importantly, the anomaly normalizes the temperature value, allowing records to be combined from a network whose composition is changing through time. It would be a big problem averaging together a record from a mountain and a record from a low valley if the mountain record is half the length of the valley record, unless we use the anomaly. It would introduce a spurious trend.

We can make a really simple example to illustrate this. I created two fake temperature series for a station on a mountainside (StationA) and a station in a valley (StationB). Each station has the same long term trend of 0.1 degrees per year baked in, and each has some random noise thrown on top. They differ by a 7 degree difference in the mean annual temperature, and by the fact that StationA came online in 2005, while B came online in 1990:

comment image

If we simply average the absolute values together, we will get a spurious cooling trend introduced into the series because the composition of the records differs:

comment image

Whereas if we average the anomalies, the trend reflects the underlying common signal between both records:

comment image

Weekly_rise
Reply to  Weekly_rise
February 23, 2021 2:52 pm

I realized too late after posting that the Average Anomaly graph above is only showing the anomaly for Station A. Here is a corrected series of graphs with the correct Average Anomaly (the numbers are generated randomly each iteration, so they’re similar but not identical).

Tim Gorman
Reply to  Weekly_rise
February 23, 2021 3:26 pm

Do you *really* understand what you did here?

You imposed a rising trend on top of a series with no trend! And you are trying to claim that is is a proper scientific process?

This is what arises when you try to superimpose uncorrelated data on top of each other.

And why *shouldn’t* you get an overall cooling trend if one data series is much less than the other? What you are trying to say is that averaging temperatures in Minnesota with temperatures in Kansas shouldn’t show cooling if Minnesota is cooling and Kansas isn’t!

You’ve just proven the fallacy of trying to come up with a global temperature!

BTW, just exactly how did you calculate the anomalies? Did you use a common base? Or did you just calculate the differences between the values in each set? If you used a common base then why? Is the base in each location the same? If you just subtracted the values then you didn’t actually create an anomaly, just a difference!

Show me the data sets. Visually the blue data doesn’t seem to actually show an increasing trend, the trend looks to be zero!

If these *are* uncorrelated data sets then tell me exactly what you think you have shown!

And if these are uncorrelated data sets then why wouldn’t taking a data set with larger numbers and subtracting a data set with smaller number result in an increasing trend. Jeesh, that’s just basic aritthmetic, it’s not even statisitcs!

I *really* don’t know what you think you have shown here. My belief that you know any statistics at all gets less and less with every message you post.

Last edited 3 months ago by Tim Gorman
Weekly_rise
Reply to  Tim Gorman
February 23, 2021 5:46 pm

Tim, I appreciate that these might be new concepts for you. I’ll try to explain them more carefully.

The anomaly is computed by taking the differences between a series and a reference baseline mean. For both series, the same baseline period from 2005-2020 was used to compute the anomaly. For a series, take the mean of the series from 2005-2020. The anomaly for a given year is then the difference between the temperature value and this mean.

You should not get a cooling trend in the mean when the underlying signal for both series is a warming trend – the trend for the absolute values is spurious – we know that it does not represent reality because these are artificial series that we’ve baked a warming trend into. The cooling trend is occurring because the series with a cooler mean temperature doesn’t start until 2005. Using the anomaly prevents this issue because both series are normalized.

Also note in that updated graphics I’ve plotted the trend lines, so you can see that both series have a positive trend, so we’d expect their average to have a positive trend as well.

Last edited 3 months ago by Weekly_rise
Tim Gorman
Reply to  Weekly_rise
February 24, 2021 8:09 am
  1. So where is your baseline that you used to calculate the anomalies? I don’t see it anywhere. Obviously you made it up as well as the data series. Why are you unwilling to show it?
  2. Using a *global* baseline to calculate anomalies still propagates the uncertainty of the global baseline into the anomalies. Why do you show no uncertainty intervals with your data series and the baseline? Talk about being being a new concept. So what is the uncertainty associated with each data point? I can’t even make out the vertical scale on your anomaly graph. It appears to have markings 1, 1, 3, and 5 of the positive side and 1, 5, and 7 on the negative side. Your anomaly for 1992/1993 is about a -7, meaning your baseline had to be about a positive 34. With a baseline of 34 With a baseline of 34, your anomaly for 2015 should have been highly negative (34-34) = 0 and (34-28) = -6. The average of the two is -6 but you are showing about a +1, I can’t really tell because of the crazy vertical index on your graph. I simply can’t tell how you came up with your average anomaly graph. You might want to check your math again!
  3. Again, why wouldn’t you get a cooling trend when averaging a hot data series with a cold data series? You are trying to find an AVERAGE CLIMATE. If you add in a cold data series then why wouldn’t that change the overall trend? There is so much wrong with your analysis. First, these are not stationery data series. Therefore doing a linear regression can be confusing, especially after combining two independent non-correlated data sets. You should try calculating the first differences and see what that shows. That tends to move a non-stationery series into a psuedo-stationery series. That’s also a problem with *all* of the AGW advocates.
  4. Anomalies do *not* normalize anything. Anomalies only hide data. There is simply no doubt that the climate for the blue data series is *much* colder than for the yellow series. How do the anomalies tell you this?
  5. I simply do not know why you keep claiming a data set with a positive trend when added to another data set with a positive trend should give you a positive trend. You are combining data sets that are non-congruent! If you take the 2nd half of a long data set and subtract a significant value from all the data then it will *always* give you a negative trend overall in the whole data set. If you look at your average values from 2005 on there *is* a positive trend. You’ve just highlighted part of the problem with current temperature data sets. Most of them are non-congruent, the data sets making all this up start and stop at different intervals, different measurement techniques are used at different times, uncertainty is not applied anywhere in any data set (all temps are assumed to be 100% accurate) and then the data is bastardized by trying to use infills and homgenazition to create what someon’s opinion says it should all show. That’s about as unscientific as it can possibly get. Each temperature set should be analyzed separately, including its uncertainty, and then *compared* to other data sets. If the compared trends match then you can use the matching trends for current analysis of temperatures. If they don’t match then you have a problem. You can’t mask that problem with hand-waving magic.
Weekly_rise
Reply to  Tim Gorman
February 24, 2021 10:30 am
  1. Here is a version with the baselines plotted, along with the calculated slope of the trends. I’m not sure how I can safely and anonymously share the excel workbook with you, but happy to do that if you point me to a way to do it. In the meantime, here is a screenshot of the workbook, showing how the baseline is calculated for Station A.
  2. The baseline is not global, it is computed for each series using a regional climatology (in this case, the baseline is simply the mean of each station series over the 2006-2020 reference period).
  3. We aren’t trying to find average climate (which is not a meaningful quantity), we are trying to find the average change in the climate between both stations. The average trend is the thing we want to get, and the trend is positive for both stations. Notice in the image above, the average anomaly trend is close to the value of the two trends, but the trend for the average of the absolute temperatures is not even close. It is an artifact of the averaging, and doesn’t reflect the change that is occurring at both sites through time.
  4. The fact that one station is in a colder place is not relevant information for us. We want to know how the temperatures are changing through time (we want to know “is station A getting warmer? Not, “is station A in a warm place?”
Last edited 3 months ago by Weekly_rise
Tim Gorman
Reply to  Weekly_rise
February 25, 2021 8:21 am
  1. I know to subtract.
  2. How did you use a baseline of 2006-2020 for dates earlier than 2006? This is the problem of using non-time consistent data.
  3. You can’t find the “average” change using a mid-range value. All you find is the average mid-range value or the average mid-range anomaly. Nor can you find anything for a point in-between stations. You have no data for the points in-between therefore you miss terrain, altitude, and humidity differences for the mid-points. Trying to interpolate between stations at altitude 1000ft and 5000ft is bound to be wrong. You have no idea of the altitude or pressure slope. This is why even regional averages are so uncertain!
  4. So the fact that D*eath Valley and Kansas City have vastly different altitudes, pressures, and humidity means nothing? The temperature anomalies will tell you everything you need to know about what is changing? ROFL! The average global temperature, even if calculated from anomalies, has HUGE uncertainties, uncertainty intervals wider than the values of the anomalies you are calculating. Remember, the uncertainty interval travels with the anomaly. If its uncertainty is +/- 0.5C then how do you distinguish a difference in the anomaly of less than +/- 0.5C? Especially if you are using a baseline that is a yearly average?
Weekly_rise
Reply to  Tim Gorman
February 25, 2021 12:18 pm
  1. The baseline is an average, not a difference. Recommend you click through the links provided above. The discussion will not be productive if you don’t even look at the materials you demanded and that I’ve provided for you.
  2. The baseline is a reference value. Given that the trend of this series is upward, we’d expect that values prior to the start of the base period would be negative, values after will tend to be positive.
  3. Those reasons are why we use the anomaly rather than the absolute temperature. You’re halfway to having a breakthrough in understanding, just need to push a bit more.
  4. It means nothing to the anomaly, which only measures how different temperature for a given year is in a given location relative to the mean temperature of that location during base period. The uncertainty in the mean is reduced by averaging (by the square root of the number of stations), which means that the uncertainty in the global mean is significantly smaller than the mean uncertainty (standard deviation) of the individual stations.
Last edited 3 months ago by Weekly_rise
Tim Gorman
Reply to  Weekly_rise
February 25, 2021 5:31 pm
  1. if the baseline is an average then what is its uncertainty? if it is calculated from temperatures with uncertainty then the baseline has uncertainty also. And guess what the total uncertainty of a-b is? sqrt(u_a^^2 + u_b^^2). The uncertainty goes UP when you form an anomaly by subtraction, just like it does by addition.
  2. I have no idea what you are talking about. When you take a data set that is trending upward over time and then you basically subtract values from the last half of the data set you are quite likely to turn an upward trend into a downward trend.
  3. See 1. The uncertainty of your anomaly GROWS when you do the subtraction. So you wind up with a result that is more uncertain that what you started with! So why wouldn’t you just use the less uncertain absolute temperature? It only makes sense if you want to ignore uncertainty, which is what most climate scientists seem wont to do.
  4. Again, your anomaly has a wider uncertainty interval than the absolute temperatures. Why would any competent scientist want to move from a more certain data set to a less certain data set?

There is no mean for independent measurements other than the value itself. Each measurement represents a population of size 1.

What is the mean of (70)? What is the mean of (20)? Their covariance is zero so they *are* independent (you *do* know how to calculate covariance, right?).

You cannot simply take independent data points that are not correlated (cov = 0) and put them in a combined data set and call them a random variable with a mean. Why is that so hard to understand?

Weekly_rise