By Christopher Monckton of Brenchley
As the third successive year of la Niña settles into its stride, the New Pause has lengthened by another month (and very nearly by two months). There has been no trend in the UAH global mean lower-troposphere temperature anomalies since September 2014: 8 years 5 months and counting.
As always, the New Pause is not a prediction: it is a measurement. It represents the farthest back one can go using the world’s most reliable global mean temperature dataset without finding a warming trend.
The sheer frequency and length of these Pauses provide a graphic demonstration, readily understandable to all, that It’s Not Worse Than We Thought – that global warming is slow, small, harmless and, on the evidence to date at any rate, strongly net-beneficial.
Again as always, here is the full UAH monthly-anomalies dataset since it began in December 1978. The uptrend remains steady at 0.134 K decade–1.
The gentle warming of recent decades, during which nearly all of our influence on global temperature has arisen, is a very long way below what was originally predicted – and still is predicted.
In IPCC (1990), on the business-as-usual Scenario A emissions scenario that is far closer to outturn than B, C or D, predicted warming to 2100 was 0.3 [0.2, 0.5] K decade–1, implying 3 [2, 5] K ECS, just as IPCC (2021) predicts. Yet in the 33 years since 1990 the real-world warming rate has been only 0.137 K decade–1, showing practically no acceleration compared with the 0.134 K decade–1 over the whole 44-year period since 1978.
IPCC’s midrange decadal-warming prediction was thus excessive by 0.16 [0.06, 0.36] K decade–1, or 120% [50%, 260%].
Why, then, the mounting hysteria – in Western nations only – about the imagined and (so far, at any rate) imaginary threat of global warming rapid enough to be catastrophic?
How long is the cooling trend, 6-7 thousand years?
Make that 50 million years. Ahh, the Early Eocene days were warm days.
You don’t get weather like that any more. The planet is going to the dogs.
Indeed, our grandchildren will not know what the ‘climate change’ was.
Vuk,
For Australia, the Monckton method shows a negative temperature trend of 10 years 9 months, starting in March 2012.
If I wanted to spin a story, I could assert that no Aussie school child under 11 years old has felt any warming effect while being taught that global warming is an existential crisis.
Wake up, educators.
Geoff S
http://www.geoffstuff.com/uahfeb2023.jpg
The US 48 state average temperature trend using NOAAs USCRN weather station system has been relatively flat since 2005.
Global Temperature: | Watts Up With That?
USCRN affects 330 million people. While the global average temperature affects no one, because no one actually lives in the global average temperature!
We could say no US 48 state resident has experienced more than a tiny amount of global warming since 2005 — and that’s 18 years. That beats your 10 years and 9 months, using an official government temperature organization’s own numbers too!
RG said: “The US 48 state average temperature trend using NOAAs USCRN weather station system has been relatively flat since 2005.”
The trend is +0.58 F/decade (+0.32 C/decade). I invite you download the data and see for yourself.
RG said: “That beats your 10 years and 9 months, using an official government temperature organization’s own numbers too!”
Using the Monckton Method the USCRN pause is 0 months. That is a lot less than 10 years and 9 months.
I wrote that the US average temperature trend was relatively flat. I did not write that it was flat.
The trend appears to be flat since 2011. Thats from an eyeball view of the chart at the link below. I revise my claim to 11 years, rather than 18 years, and that still beats the Australians.
Those 11 years of flat temperatures included the largest 11-year increase of global CO2 emissions in the history of of the planet. And you can store your statistics where the sun don’t shine, bedofwax.
Global Temperature: | Watts Up With That?
Ignoring NOAA, here in Michigan there has been slight warming in the winters since the 1970s. I noticed mainly because we lived in the same home since1987 and four miles south in an apartment for 10 years before that. If we had moved 20 miles north in those years, the I might not have noticed.
I don’t need any government scientists to tell me how much warming I have personally experienced where I live.
Especially people from NOAA; who I do not trust. They have two different weather station systems with very different weather station siting. But somehow, magically, they both produce almost the same adjusted data. That is not by chance — hat is by science fraud, in my opinion.
What is the range of C/decade that would make something relatively flat?
relatively flat? maybe you head is relatively pointed
Here comes Masher the fool with brilliant not funny in any way put-down
USCRN affects 330 million people. While the global average temperature affects no one, because no one actually lives in the global average temperature!
you win stupidest argument ever.
You would be an expert on stupid, Masher.
The average temperature is not a real temperature, it is a statistic. A statistic is not an actual temperature.
I tried to explain this simply so even a 12year-old child could understand, Go out and find a 12 year-old child to explain it to you.
RG said: “The average temperature is not a real temperature, it is a statistic.”
The Tmax and Tmin you see reported for each station…both 1-minute averages. Do you think they aren’t real?
Because Tavg IS a statistic. What is the variance of that distribution. It is supposed to represent the midpoint of Tmin and Tmax.
Daytime temps resemble a sine curve, yet nighttime temps are an exponential decay. Sometime in late afternoon, the sun’s insolation energy is less than the earth’s radiation and the decay begins. Do you think Tavg is a true average temp or is it simply a statistic describing the midpoint between max and min.
Tmin and Tmax are averages too. And you’ve already said an average of an intensive property isn’t real. I already know your position on the matter because you’ve made it abundantly clear. I’m asking how deep Richard Greene’s conviction goes.
Again, you have no idea why the measurements are averaged. You are not a physical scientist and nothing in statistics will tell you why a 1 minute average is used.
Do you want to know why? My guess is that you really don’t care!
So you think the average temperature is Tmax and Tmin?
ROFL!!
Why do you think the “average” over 1 minute is used? My guess is that you have not one single clue as to why!
https://www.climate.gov/news-features/understanding-climate/climate-change-global-temperature
Earth’s temperature has risen by an average of 0.14° Fahrenheit (0.08° Celsius) per decade since 1880, or about 2° F in total.
The rate of warming since 1981 is more than twice as fast: 0.32° F (0.18° C) per decade.
2022 was the sixth-warmest year on record based on NOAA’s temperature data.
The 2022 surface temperature was 1.55 °F (0.86 °Celsius) warmer than the 20th-century average of 57.0 °F (13.9 °C) and 1.90 ˚F (1.06 ˚C) warmer than the pre-industrial period (1880-1900).
The 10 warmest years in the historical record have all occurred since 2010
UAH temperatures dropped below the zero line in January. If you ignore “trends”, and just trace your finger from the far right to the far left of the UAH graph, you discover our mean temperatures world-wide were the same as they were in May of 1980. Yes, that was the top of a peak and current temperatures are down at the bottom of the dip, but still, they are the same. In essence, we are back where we were, and we have spent trillions making much ado about nothing.
If the argument is that selecting a top of a local peak as the start is valid then surely the reverse is valid too. That gives us (-0.04 C – -0.67 C) / 461 months * 120 months/decade = +0.15 C/decade.
Bring back the dinosaurs!
Those were the days.
We get deer in our yard almost every day
Up to 14 at one time. After 36 years of watching them eat everything green, we could use some new entertainment. Dinosaurs would be exciting.
I have 40-50 at a time at my bird feeders. It is not cheap feeding them. Just before the cold comes and then afterwards when the ground is snow covered, they really hit it hard!
I have an 8 lb, squirrel proof feeder and two seed block cages I keep stocked. When the weather gets hard they will empty that feeder in two days and the seed blocks will be gone in 1 day.
I also cast feed on the ground for the ground feeders like the morning doves and the cow birds.
LIke I said, it ain’t cheap, and costs me about $30.00 a week to do it, but it is worth every dime.
Good job
We have a four-cake suet feeder and one thistle feeder. We also have a heated bird bath about 10 feet from our living room window. Entertainment for our indoor cat too.
We once had five deer lined up to drink some water from the bird bath. Two males got in a fight to see who would drink first. The males always go before the females.
I once helped break up a summer fight between two male deer whose antlers were locked together. They were tearing up the yard. Afterwards, I realized that was risky and I should have stayed away.
I forgot to mention two hummingbird feeders in warmer weather that the wife fills with freshly made sugar water every few days.
We’ve also had ground hogs, skunks, raccoons, opossum, rabbits, squirrels and hawks live in a nest next door — we used to have a lot of chipmunks but they apparently make good hawk food.
I love the animals but always chase pesky kids off my lawn. I think old guys are supposed to do that. It’s in the Constitution. … I could live without the skunks too. … Killed the moles with poison.
I had a kangaroo in my driveway once, and another on a walk near home late January. I live in an urban area.
No kangaroos here in Michigan. But have had one coyote, two foxes and many Jehovah’s Witnesses visiting in past decades.
We have white tails come through every once in a while. We’re a stopping place for a Raccoon family as they make their nightly rounds. Red fox, skunks, etc come through every once in a while.
The critters not welcome are coyotes and moles. Coyotes because they will go after the dog when we let her out on her lead. And moles, well the reason is obvious.
Had a Pileated Woodpeck stop buy in January. And then a red tail hawk came by trying to use our bird feeders as a hawk feeder. He was a young one and soon moved off to look for a better hunting ground.
Since I am coming and going at all hours for my job, I have to watch for deer on the road coming to my house. the road crosses two creeks and it is along that bottom land that the deer like to travel.
Dinosaur watching is a popular activity, with life lists.
My two seed block cages hang from a double shepherds hook outside a dining room window. As Sherry and I have our coffee we can watch them.
I believe the recent 2.8 million years have been the coldest of the last 200 million years including today. Maybe that’s the reason we call it an Ice Age. We are so lucky to live in an interglacial moment when places like Canada or Greta’s homeland are not under a mile of ice.
https://www.climate.gov/news-features/understanding-climate/climate-change-global-temperature
Earth’s temperature has risen by an average of 0.14° Fahrenheit (0.08° Celsius) per decade since 1880, or about 2° F in total.
The rate of warming since 1981 is more than twice as fast: 0.32° F (0.18° C) per decade.
2022 was the sixth-warmest year on record based on NOAA’s temperature data.
The 2022 surface temperature was 1.55 °F (0.86 °Celsius) warmer than the 20th-century average of 57.0 °F (13.9 °C) and 1.90 ˚F (1.06 ˚C) warmer than the pre-industrial period (1880-1900).
The 10 warmest years in the historical record have all occurred since 2010
It’s a modern millenarian apocalyptic secular movement. They flourish particularly around the turn of every millennium.
The human mind is too complex and has quirks that evolution hasn’t been able to iron out.
Ja. It is also what I said….
https://breadonthewater.co.za/2021/03/04/the-1000-year-eddy-cycle/
The primitive human brain is hard wired to respond to fear with heightened attention. It then looks for correlations to confirm there is a threat creating a false impression through narrow focus. Those who desire control over others know this can be used to increase their power.
The evolutionary escape is to understand the limitations and projections of the conditioned mind and examine how cause and effect brings everything into being.
It is easy to see through the fear mongering when you understand its purpose is to manipulate and control you.
I’ve noticed this for several decades now. Man always needs something to worry about. When I grew up, it was the cold war and nuclear annihilation. For a short period, it was terrorism. Big brother has tried to make global warming front of mind.
Now that the world is starting to cool again (my study of the matter is that the temperature varies from warmer to colder in multidecade cycles, moderated by our huge heat sink also known as the oceans).
Look back in history and you will always find something that man was told to fear.
As for global warming, I like to point to the earth’s warming at the end of the Younger Dryas period (~10C temp rise in a decade) when people spew the BS that the world’s temperature has never risen this fast before. The followup question is – do you think that rise was due to early man discovering how to burn coal for warmth?
I would not argue a “never before”, never is just too big a statement. However: How sure are you that you know how warm the Younger Dryas period was?
How do we know how warm the Younger Dryas period was? Warm??? Mate, it was perishing cold. Young Mr Dryas wrote it all down, and it’s now carefully reported in Wikipedia. They cite all the original Mr D manuscripts.
Or maybe it was Young Ms Dryas? That bit got lost.
How sure are you that you know how warm the Younger Dryas period was?
even when its cold the question “how warm was it ? means
what was the temperature.
Oops that was meant to be a reply to Steven Mosher’s reply to me.
The fastest warming ever is a primary lie the media sows to scare the uninformed. I don’t think that was zzz’s claim.
There are more than a few scientific papers on this subject. I’ve read papers dated from the late 90s to present time acknowledging this rapid heating.
As you know, NOAA is pushing the CAGW agenda, yet here’s a link from them on the Younger Dryas period. https://www.ncei.noaa.gov/sites/default/files/2021-11/3%20The%20Younger%20Dryas%20-FINAL%20NOV%20%281%29.pdf
Note this in paragraph one: The end of the Younger Dryas, about 11,500 years ago, was particularly abrupt. In Greenland, temperatures rose 10°C (18°F) in a decade (Alley 2000).
If you dig through other papers on the subject, you’ll find that this temperature increase has been noted worldwide in that time period.
So unless one says that this rapid temperature increase was due to man, my point here is that the climate can change significantly in short timespans due to natural causes.
“ the climate can change significantly in short timespans due to natural causes.” Common Sense. Not Taught. Not ‘Learned’.
Everytime I see the word “unprecedented” in the press, as they lament so called warming caused events, I ask myself a similar question.
“the BS that the world’s temperature has never risen this fast before.”
That claim came from taking the average increase in temperature over thousands of years from proxies and comparing it to the modern high resolution instrumental record. This is scientific fraud. The two data sets are like comparing apples to frogs.
Exactly right. This ”fast rate of temperature rise” would have happened countless millions of times. Scientists are to blame for not speaking out.
Hmmm. I see your point. Or, I’d see your point IF the modern instrumental record was unaltered. But it’s been altered so many times that it is no longer very accurate. Dr James Hansen not only altered the entire historical temperature database but he also did not save the unaltered data. And then there was the Australia BOM who altered their historical data with Acorn 2.0. https://wattsupwiththat.com/2019/02/22/changes-to-darwins-climate-history-are-not-logical/
I don’t have much faith in the modern historical temperature record, as it’s been heavily altered by people with a bias toward global warming.
My best bet for accuracy would be satellite. There can still be questions of bias in the tabulation. With millions (I assume) data points the tiniest rounding errors could add up significantly, and even with that resolution the data only covers a small fraction of the planet. If we are can panic over 1 or 2 degrees, the planet has much bigger threats to offer, that can make the pain even more exquisite.
Turkey.
Creating general fear is a premier political power play. Political power is always in demand by someone, so creating fear to further that power is always in play somewhere.
“When I grew up, it was the cold war and nuclear annihilation”.
Me too, in the 1960s. But I stopped worrying when we were told by our teachers that we would be safe hiding under our desks during a nuclear attack.
I got in a verbal fight with a grade school teacher when were asked to participate in an under the desk nuclear attack exercise. I had new dark color pants on and didn’t want to be laying on a dirty floor. The start of my career as a juvenile delinquent.
I doubt if hiding under a desk will save children from climate change.
Nice to remember that teachers were always so intelligent, even before the era of leftist brainwashing in schools.
LOL, yes. Same here on the nuke drills, except I wasn’t smart enough to say that hiding under a desk wasn’t going to save anyone from a nuke blast.
But I lived in an area that would have been a primary target – the Washington DC suburbs inside the beltway.
It didn’t matter where you lived. Someone would invent a reason that something near you would be a target.
Nah… the ‘nukers’ would aim for something of use.
At primary school, I had more fear & dread about Sister Mary Constanza and her cane than I did about a nuclear conflagration.
What if they wore a mask as well?
today people fear a green bogeyman that will force them to drive EVs and they fear not having copper,
just read the posts here for loads of fear mongering
Got yer battery car yet, mosh?
no car.
mosh: i think man will go to the moon
wuwt: oh ya, wheres your rocket.
i come here to see how stupid arguments can get, and im never disappointed by you guys
Hypocrite.
Steven Mosher said: “i come here to see how stupid arguments can get, and im never disappointed by you guys”
Here are some of my favorite arguments people have tried to convince me of.
~ The law of conservation of energy holds only after a period of time has elapsed.
~ The law of conservation of mass does not apply to the carbon budget.
~ It is not valid to perform arithmetic operation on intensive properties like temperature.
~ Ocean water below the surface does not emit radiation.
~ The Stefan-Boltzmann Law only applies if a body is in equilibrium with its surroundings.
~ Quantum Mechanics is completely deterministic.
~ If you utilize statistical inference then you aren’t doing science.
~ If you make predictions then you aren’t doing science.
~ Kirchoff’s Law prohibits polyatomic gas species from impeding the transmission of energy.
~ A sum (Σ[x]) is the same thing as an average (Σ[x]/n)
~ A quotient (/) is the same thing as a sum (+).
~ Computer algebra systems like MATLAB and Mathematica output the wrong answers when given the equations from the GUM.
~ The NIST uncertainty machine does not compute uncertainty correctly.
~ Category 4 Hurricane Ian was not even a hurricane because this one really small wind observation hundreds of kilometers from the radius of maximum was less than hurricane force.
And the list goes on and on…
But I don’t come here to see absurd arguments. I come here to learn first and because I still (perhaps naively) think that I can convince people of scientific truths using the consilience of evidence.
My point was not that we should fear not having copper. Sorry if I was unclear.
It was that copper will become much more expensive in the future as the ores being mined become progressively less rich in copper. And as a result, it is foolish to design an energy system dependent on copper.
Regards,
w.
Copper has been expensive enough that thieves will risk getting fried to steal it. Been going on for a long time now.
People under the influence of powerful drugs aren’t exactly thinking clearly. They steal the copper wire, or try to, because they are addicted to Chinese Fentanyl. Stealing energized wire could be done, but it requires specialized training, expensive equipment and huge <<redacted>>.
My point was not that we should fear not having coal. Sorry if I was unclear.
It was that coal will become much more expensive in the future as the ores being mined become progressively less rich And as a result, it is foolish to design an energy system dependent on coal.
Regards,
Mosh, first, we have ~130 years of proven coal reserves and about 20 years of proven copper reserves. Please tell us which one will increase in price faster?
Second, we are being forced by governmental edicts and subsidies to shift to a copper based energy system. If it were a good idea the shift would occur by itself.
But I suspect you know all of that, and are just trying to stir the pot …
My best regards to you, get well, stay well,
w.
Coal is just something to burn to heat water to make steam to use as a working fluid for the steam turbines which drive the generators,
There are alternative heat sources.
An engineer friend from Uni used to consult on converting coal fired boilers to gas when coal was relatively expensive, and converting gas to coal when gas was relatively expensive.
NO! What we fear is overbearing government working for their own and other agendas that have a negative effect on the liberty and welfare of their citizens, and the bone heads that support those totalitarians.
The Green Bogeyman is real. That’s how we got in this screwed-up position in the first place.
NetZero, the Green/Authoritarian Delusion.
real? what screwed up position
Look around you.
Good Grief!
Yes, politics, especially left wing, as usual. H.L. Mencken said:
The whole aim of practical politics is to keep the populace alarmed — and hence clamorous to be led to safety — by menacing it with an endless series of hobgoblins, all of them imaginary.
Ah, last century we had ‘flappers’ …This century they are flapping like a big girls blouse…
Leftism (anyone who wants more government power) uses fear to create a demand for more “government powers”, This has been a strategy for many centuries. Not just once in a while — at all times.
And this is not secular. Religions use tall tales to create fear (in the opinion of this long-term atheist) of God and hell to control people. Also, the claim of heaven. It’s all nonsense to me, just like the fear of the future climate.
I see little difference between people who fear God and hell, versus other people who fear climate change. Unproven fears are irrational. At least the religions have some good commandments. The Climate Howlers don’t even have that. They could not care less about actual air, water and land pollution in Asia, for one example. Instead, they falsely define the staff of life, CO2, as pollution. The climate change secular religion is of no value to mankind.
Honest Climate Science and Energy
In addition, The Golden Rule is one ‘commandment’, and is one which covers any and all. It appears to be as difficult to apply that ONE to one’s life, as to apply any few of the various.
Yes!
Thank you
As of this month we have cooled 0.7C the last 7 years. Hard to keep the existential crisis narrative going with this data but that won’t stop the media from pushing the irrational fear mongering incessantly.
The trend is at 0.24 C / decade since 2016. If this continued the cooling would get hard to ignore.
The trend here is +6 deg C PER DAY this weekend. Now that’s really hard to ignore.
That trend is because of a rare triple La Nina. The next El Nino will change the slope dramatically.
From what I’ve heard based off the amount of warm water volume, the upcoming El Niño will present a 2009/2010 like situation.
We will see. I expect the trend to increase over the next few months due to this La Nina. I’m hoping the next ENSO phase is neutral as that will give us a better feeling for where we are. Of course, the big change will occur when the AMO goes negative. Coming soon to a planet near you.
I don’t think that we will see cooling in the long run until the SSTS in the oceans drop. And that isn’t happening yet.
That would be a good thing.
At the last AMO transition there’s was a reduction in cloudiness. If the reverse happens in the coming AMO transition, this should cause ocean cooling.
Any cooling is counter to the claims of CO₂ addled.
They already choke when CO₂ levels continue to increase while temperatures dive.
That’s when they start claiming CO₂ causes every kind of weather; cold, hot, rainy, arid, stormy, windy, calm, jada jada jada.
Within a few years, they’re likely to flip the narrative back to global cooling. If you look through the decades with news articles, you’ll find this alternating narrative of ‘the world’s on fire’ to ‘an ice age is coming’ is a common theme that runs for a few decades and then flips to the other fear.
I had a link to a Canadian article about this, but lost it a few years ago. The article went back to the early 1900s where it was cooling, then warming, then cooling, now warming.
The cooling and warming predictions pre-1970s were usually from individual scientist crackpots. The 1970s cooling warnings were bigger, but still a small minority of all scientists. The current global warming warnings are a 59% majority, by a libertarian survey last year — 59% believe in imaginary CAGW. And at least 99.9% believe in real AGW of some amount, no matter how small. But climate change means scary CAGW, not harmless AGW.
Richard, that’s likely due to the lack of climatologists and grant money in prior to the 1980s. Now, the amount of money spent on the subject dwarves previous decades. And that grant money delivers the study results desired by government.
Does CO2 warm the earth? Maybe. My opinion is that it does warm the earth but its impact is de minimis. There are simply too many variables to isolate the impact of a single input into the world’s climate. I think that the earth is warmer today than when I grew up in the 60s, but I attribute that to natural temperature variation on a multidecade heating/cooling cycle for the earth.
We can definitively say that CO2 levels have risen annually since it was first regularly measured (1958), yet the world’s temperature has fluctuated in that time. The correlation is not as strong as it’s hyped to be.
”And at least 99.9% believe in real AGW ”
What is this ”believe” nonsense. They either know or they don’t know.
They know CO2 is a greenhouse gas
They know air pollution blocks sunlight
They know a warmer troposphere holds more water vapor
They know human adjustments to raw temperature data, and infilling, could account for a significant portion of the claimed global warming in the past 150 years.
What else do scientists need to know to believe in AGW?
RG said: “They know human adjustments to raw temperature data, and infilling, could account for a significant portion of the claimed global warming in the past 150 years.”
It’s the opposite. Adjustments reduce the amount of warming relative to the raw data over the last 150 years. Hausfather provides a brief summary of how the adjustments affect the long term trend.
bgwxyz reiterates his approval of fraudulent data mannipulations.
BedOfWax is a liar or a fool, or both/
The 1940 to 1975 period had added warming because the global cooling with CO2 rising reported in 1975 was inconvenient for the CO2 is evil narrative that fools like BedOfWax believe in. That was science fraud, and you know it.
In the US the mid-1930s were warmer than even 1998 with that huge El Nino heat release, but not anymore.
Zeke H. is a deceiver.
His “infamous” argument that climate models are accurate used TCS and RCP 4.5, which the IPCC never publicizes, rather than the popular ECS and RCP 8.5 which the IPCC does publicize, and very likely over predict global warming.
Zeke H. Sleight of hand that you warmunists.loved.
Anyone wo thinks there was a real; global average temperature before the 1940s, with so few Southern Hemisphere measurements, is a liar. Pre-1900 is mainly infilling, not data. Still a lot of infilling today — you have no idea how much because you don’t care.
The chart you presented is bogus — it completely ignores the huge data changes for the 1040 to 1975 period based on what was reported in 1975 versus was reported today.
Honest Climate Science and Energy: NOAA US average temperature from 1920 to 2020, Raw Data vs. Adjusted Data presented to the public (science fraud)
Honest Climate Science and Energy: Pre-1980 global average temperature “revisions” from 2000 to 2017 (science fraud)
Honest Climate Science and Energy: Click on READ MORE and watch US climate history get changed to better support the CO2 is evil narrative
Honest Climate Science and Energy: Global Average Temperature History Keeps Changing — National Geographic in 1976 versus NASA-GISS in 2022
Honest Climate Science and Energy: Watch climate history change to better support the false CO2 is evil — it’s magic science fraud
The graph includes all adjustments. This is probably the source of confusion. If you are used to getting your information from contrarian bloggers then you probably were only aware of the adjustment that bump up the temperature anomalies later in the period and had no idea about the more significant bump up earlier in the period. I also recommend reading about the details of the adjustments. They are important because there are subtle implications when deciding which of the seemingly equivalent approaches of bumping up before the changepoint or nudging them down after the changepoint to correct the changepoint bias.
BTW…all of the adjustments and infilling in the traditional surface datasets are done in UAH as well. In fact, UAH not only performs the same adjustments, but they do so more aggressively and then perform other adjustments that the surface datasets don’t even have to worry about. Most people are not aware of this.
More fraud, par for the course for CAGW kooks.
bgwxyz: “UAH does data mannipulations, so it must be ok!”
Do you expect to be taken seriously after making statements like this?
“contrarian bloggers” — ah, poor baby
“They are important because there are subtle implications when deciding which of the seemingly equivalent approaches of bumping up before the changepoint or nudging them down after the changepoint to correct the changepoint bias.“
No, it is all unscientific fraud, and you are a disgrace to the profession.
karlomonte said: “bgwxyz: “UAH does data mannipulations, so it must be ok!””
Can you post a link to the post in which those exact words you have in double quotes appear?
Its a paraphrase, you clown.
The global warming religion posits that warming since 1950 is primarily anthropogenic and that it is somehow remarkably different from all warming periods in the past. The sharp pre-anthropogenic warming of ~1910-1945, and the overall warming trend from 1880-1945 show this to be evidently untrue. So Hausfather cooks up a reason to warm the entire period of 1880-1940, thereby vastly reducing the warming that the new Climate Faith holds cannot have been possible prior to 1950.
The fact that this reduces the amount of warming relative to the raw data over the last 150 years is not the relevant issue. What is relevant is that Hausfather’s adjustment largely erases the inconvenient “pre-anthropogenic” portion of the overall warming, thereby making the climate religion look less ridiculous. This should be rather obvious.
“The cooling and warming predictions pre-1970s were usually from individual scientist crackpots. The 1970s cooling warnings were bigger, but still a small minority of all scientists.”
I wouldn’t say that. The climate scientists were reporting on actual cooling. It cooled significantly from the 1940’s to the late 1970’s by about 2.0C (according to the U.S.temperature chart). No crackpottery there.
Now claiming the world was going into a new Ice Age might be a little bit much, but the climate scientists of the era did have a reason to note the cooling that was taking place at the time.
I was there. I saw all these claims about human-caused global cooling. At first, I thought maybe the climate scientists claiming humans were causing the cooling might have something, so I waited for them to present some evidence proving their case. And I waited, and I waited, and I waited and I waited. And I’m still waiting to see some proof of their claims.
So, when the human-caused global warming crew showed up claiming humans were causing the Earth to warm, I was naturally skeptical from my earlier experiences with these unsubstantiated climate claims, and to this day have not seen one bit of evidence proving humans are causing the Earth’s climate to change. Either way, cold or hot.
I wish I had the internet back in the 1970’s. I would have blistered the ears of all those charlatan climate scientists.
WUWT is like Heaven to me. I get to say just what I think about modern day climate science. 🙂
The reason for that uptick in hysteria is precisely because the facts are beginning to give the lie to climate alarmism, hence it must be restated at each and every opportunity.
Disagree
Hysteria must escalate because it loses its ability to scare people if it remains the same decade after decade.
This is the “Worst than we thought” propaganda strategy.
The actual temperature is not changing enough for many people to notice where they live. People rarely know “the facts”. They usually “know” what they are told by government authorities (who can’t be trusted, but they usually are trusted).
So the climate propaganda must escalate to be effective in creating fear. And creating fear gives leftists in power the opportunity to expand leftist government powers. Which they do. Never letting a crisis go to waste — whether a real Covid crisis, or a fake climate crisis.
.
Isn’t it 102 months?
https://woodfortrees.org/plot/uah6/plot/uah6/last:102/trend
I make it 102 months is you count the start month August 2014 to the end of Jan 2023.
Slope is -2E-05x in Excel notation.
Geoff S
Thanks, Christopher, for continuing to research and present it.
Regards,
Bob
Mike, how very kind and polite you are. The real thanks should go to Roy Spencer and John Christy, who have kept the UAH dataset honest when all others have tampered with theirs.
Which Christy and Spencer do VOLUNTARILY without payment!
Therefore, no financial conflicts of interest are possible.
No Christopher. My thanks for the post go to you. As you will recall, I used to prepare graphs of climate-and-weather-related data and discuss them in blog posts that were cross posted here at WUWT, so I know how much work goes into what you prepared above and into responding to comments.
Regards,
Bob
PS: I’ve been called lots of things, but this is the first time I’ve been called Mike.
I predict much whining.
You mean our biggest threat is all this global whining?
I think you are onto something important here.
The Climate Howlers do Global Whining
I like that and will use it.
As a blog editor, if I read something good, I steal it.
And please note how I am now confirmed as a psychic prophet…
There are a couple of problems with this type of analysis. One is that it does not adjust the statistics for autocorrelation.
The Hurst exponent of the UAH MSU dataset is 0.82. This means that the “effective N”, the number of data points for statistical purposes, is only 9 …
Now, this doesn’t remove the statistical significance of the trend in the full dataset. Here’s that calculation.
With a p-value of 6.30e-5, the trend is obviously statistically significant.
However, properly adjusting the analysis for autocorrelation means that shorter sections of the dataset cannot be said to have a statistically significant trend. Here, for example, is the same analysis for the most recent half of the UAH dataset. There, the effective N drops to a mere three data points.
With a p-value of 0.128, we cannot say that there is a statistically significant trend in the latter half of the MSU data.
As a result, I fear that the analysis of Lord Monckton isn’t valid.
Finally, it should not be a surprise that there are periods of increase and decrease in temperature data. Here, for example, is a breakpoint analysis of fractional Gaussian noise (FGN) with a Hurst exponent of 0.8, with an underlying trend added to the FGN.
Note the similarity to a natural temperature dataset. However, this is just random fractional Gaussian noise plus a linear trend. We know for a fact that there is an underlying increasing trend throughout the data … but despite that, there’s a decreasing section from 1980 to 2000 … is this a significant “pause”?
w.
I was thinking the same thing. e.g. How likely is it to find periods with a valid trend in a volatile, heavily censored dataset? We need a satellite and a time machine to gather enough data to support anything.
nope. satellite data is heavily censored and adjusted. 100 or so locations
will get you a good dataset.
Shen, S. S. P., 2006: Statistical procedures for estimating and detecting climate changes, Advances in Atmospheric Sciences 23, 61-68
You aren’t going to get it. In fact, UAH is one of the most heavily adjusted datasets in existence. And their infilling technique interpolates missing values (and there are a lot) up to 4160 km away spatially and 2 days temporally. Compare that with GISTEMP which only interpolates using data from 1200 km away spatially with no temporal infilling.
Year / Version / Effect / Description / Citation
Adjustment 1: 1992 : A : unknown effect : simple bias correction : Spencer & Christy 1992
Adjustment 2: 1994 : B : -0.03 C/decade : linear diurnal drift : Christy et al. 1995
Adjustment 3: 1997 : C : +0.03 C/decade : removal of residual annual cycle related to hot target variations : Christy et al. 1998
Adjustment 4: 1998 : D : +0.10 C/decade : orbital decay : Christy et al. 2000
Adjustment 5: 1998 : D : -0.07 C/decade : removal of dependence on time variations of hot target temperature : Christy et al. 2000
Adjustment 6: 2003 : 5.0 : +0.008 C/decade : non-linear diurnal drift : Christy et al. 2003
Adjustment 7: 2004 : 5.1 : -0.004 C/decade : data criteria acceptance : Karl et al. 2006
Adjustment 8: 2005 : 5.2 : +0.035 C/decade : diurnal drift : Spencer et al. 2006
Adjustment 9: 2017 : 6.0 : -0.03 C/decade : new method : Spencer et al. 2017 [open]
That is 0.307 C/decade worth of adjustments jumping from version to version netting out to +0.039 C/decade. And that does not include the unknown magnitude of adjustments in the initial version.
“As a result, I fear that the analysis of Lord Monckton isn’t valid.”
I have that fear too. The analysis won’t stand up to any kind of uncertainty analysis, Hurst or otherwise.
I show trends with uncertainty calculated using a Quenouille correction for autocorrelation. This is equivalent to an Ar(1) model. For UAH V6 fron August 2014 to January 2023, I do indeed get a slightly negative trend, -0.027°C/Century. But the 95% confidence intervals are from -2.838 to 2.783°C/Century. That is, you can’t rule out an underlying trend of 2.783°C/Century, which is higher than predicted by the IPCC. That is because of the short interval on which the trend is calculated.
Is the 30 year interval climate “science” uses too SHORT?
Of course it is.
Any interval would, by necessity, be over 10,000 years since that is back to the beginning of this interglacial period. So we are looking at the “climate” and temperature trends of an interglacial, and MUST include that ENTRE period if we wish to determine if there are any changes in trends caused by CO2.
Without that entire period, we cannot eliminate natural variation as the cause of the warming of the recent years. Heck, climate “scientists” have NO explanation of the very warm 1930s, or how it got so cold in the LIA, or so warm in the Roman optimum or Medieval Warm Period.
All of climate “science” is a fraud if the “average” of the models can not be used to hind cast those periods back to the year 0. Heck again, the models can’t even hind cast to the year 1900, 50 years after the end of the LIA.
It is all a very scary and very expensive fraud that is costing the poorest or the world a much better life. Since WWII, there have been skirmishes and a cold war, but no MAJOR conflicts involving the majority of the world’s nations and ALL of society should have been rising on the rising tide of excess production of houses, household items, food production, clean water and improved sanitary waste systems and LOWER “energy” expenses along with greater “energy” availability.
60 years of very good, overall, worldwide conditions wasted for the poor of Asia and Africa, especially, but ALL worldwide poor generally especially over the last 30+ years.
Nick, please defend you position of promoting the climate hysteria agenda in relation to the effect on the poor.
Thank you in advance,
Drake
In theory adding CO2 to the atmosphere all else being equal would increase temperature.
Climate variations prior to around 1880 when the CO2 concentration started to increase are irrelevant to the current climate that is the product of the effect of CO2 forcing and ongoing natural fluctuations acting on various time scales at times enhancing the warming and at times countering it.
As you say the inevitable economic advancement of world’s poor is all that matters in the end.
An excellent comment, Drake.
We live in the best climate in 5,000 years, based on climate proxies, and should be celebrating our current climate. Living during a warming trend in an interglacial is about as good as the climate gets for humans and animals on our planet. The C2 plants would prefer two to three times ambient CO2, but 420ppm is a good start — the best CO2 level for C3 plants in millions of years.
That a trend is not significant doesn’t mean it is not real. I called the attention to the change in Arctic sea ice in 2015 and was told the same. Now the new trend is 15 years old, and the “experts” are scratching their heads.
“That a trend is not significant doesn’t mean it is not real”
Indeed so. But it is a question of what you can infer from it. Clearly here we are asked to infer that it isn’t really warming. But what the insignificance says is that it really could be warming at quite a high rate, with overlying random factors (weather) leading to a chance low result.
The chance for any one instance is low (2.5% at the CI limit). But it becomes much higher if you select from a range of possible periods on the basis of trend. And that is exactly what Lord M’s selection procedure does.
No, I don’t “select from a range of possible periods on the basis of trend”. I calculate the longest period, working back from the present, that exhibits a zero trend or less. By that definition, which is made explicit in the head posting, there is only one possible period.
Since I draw no conclusion from the zero trend except that it provides a general indication that there is not a lot of warming going on, it is fascinating how upset the climate fanatics are about these simple graphs.
If it were indeed true that “it could be warming at quite a high rate”, then it would also be true that it could be cooling at just as high a rate. With so absurdly large an error margin, one wipes out the global warming problem altogether. For Mr Stokes is, in effect, admitting that climate scientists have no idea how much it is warming or cooling, or even which effect is predominant. If so, there is no legitimate basis whatsoever for doing anything at all to cripple the economies of the hated West in the specious name of Saving the Planet.
Stokes is just a propagandist shill for the IPCC, has no compunctions against posting anything that he knows to be false, to keep the hockey stick alive.
“No, I don’t “select from a range of possible periods on the basis of trend”. I calculate the longest period, working back from the present, that exhibits a zero trend or less”
The second sentence describes exactly the process of ““select from a range of possible periods on the basis of trend””
“With so absurdly large an error margin, one wipes out the global warming problem altogether.”
The error margin is just a consequence of your decision, against much advice, to try to make inferences from short term trends. That difficulty is present in any kind of data, and says nothing about global warming.
Climate scientists look at much longer time periods where the trend is indisputably positive.
Nick,
How many days of daily data do you recommend to define a short term trend? For making inferences? Change happens forever over a range of many orders of magnitude of time. You are selecting “short term” to suit your argument in the sense of “You are wrong because you used too short a short term”. Does not compute. Geoff S
Geoff,
As I showed with diagram, you can calculate the error bars, and they diminish as the term gets longer. You can make inferences accordingly.
Mr Stokes is, as usual, on a losing wicket here. I do not “select from a range of possible periods on the basis of trend”. I take a single trend – zero – and then simply enquire how far back one can go in the most trustworthy of the global-temperature datasets and find a zero trend. It is a matter of measurement by the satellites and calculation by me, using the method which, whether Mr Stokes likes it or not, is the method most often used in climatology to derive the direction of travel of a stochastic temperature dataset. If he wishes to argue that climatology should not use the least-squares linear-regression trend, then his argument is with climatology and not with me.
Mr Stokes is at his most pompous when he is most clearly aware that he is in the wrong and is not going to get away with it. For a start, we are not going to take “advice” from a paid agent of a foreign power.
Secondly, if Mr Stokes would take some lessons in elementary statistics he would realize that a zero trend has a correlation coefficient of zero (all one has to do is look at the diagram in the head posting, which is worth a read). What that means is that at any moment the trend might diverge in one direction or another, since the fact that it is zero gives little indication of what may happen next.
Thirdly, it is self-evident that the longer the period of data the narrower the uncertainty interval.
Fourthly, the effect of the uncertainty interval is to lengthen, not to shorten, the period over which it is uncertain whether there has been any global warming or not. The 101 months shown in the head posting is thus a minimum value.
Finally, I do not draw any “inference” from the fact that there has been no global warming for almost eight and a half years. I merely report the fact, explain how I derived it, compare it with the full dataset precisely to avoid the nonsense allegation of cherry-picking, and point out that the longer the zero trend becomes, and the more frequent such long trendless periods are, the clearer it becomes, and the more visible it becomes, that the rate of global warming over the past 33 years since IPCC (1990) is well below half what was then confidently predicted.
To all but those with a sullen vested interest in the Party Line, such observations are of more than passing interest.
“For Mr Stokes is, in effect, admitting that climate scientists have no idea how much it is warming or cooling, or even which effect is predominant.”
You put this in a previous post in the thread. You nailed it!
“point out that the longer the zero trend becomes, and the more frequent such long trendless periods are, the clearer it becomes, and the more visible it becomes, that the rate of global warming over the past 33 years since IPCC (1990) is well below half what was then confidently predicted.
I like the way you state this. It’s not that CO2 doesn’t have any impact on temperature but that it is far less than predicted – which also means that other control knobs can mask CO2 contributions rather easily. Tying all temperature increase to CO2 is just plain wrong. It then becomes a matter of trying to identify the entire range of contributions and when+how they impact temperature.
It is obvious when the Party Line has been shown to be bankrupt when all they have is to blindly press the downvote buttons, as is the case with your carefully crafted and succinct comments here.
If that were actually your selection criteria you would have to stop when you got to January 2022 because your 0 or negative trend would flip positive:
https://woodfortrees.org/plot/uah6/from:2022/plot/uah6/from:2022/trend
So it seems that there’s more to your selection than starting at the present day and seeing how far back you can get a flat or negative trend.
But as Nick shows it is all silliness, since you never actually consider the confidence intervals of your trends.
My thoughts entirely.
We do not use these numbers to predict or hindcast.
The main reason why I am now making an Australian subset each month is to keep alive the possibility that a cusp is under way. Caution is urged in case it foreshadows a temperature downturn.
I have also started an informal comment along the lines that if I wanted to spin a weather story, I could claim that children under 11 here have not felt global warming, despite CO2 increases.
Geoff S
I just look at when our gently warming globe first achieved January’s UAH temperature.and that’s 1988. So no warming for over 30 years
I don’t think we are being asked to infer anything except the obvious, that the surface is not warming faster over time, as it should according to a popular hypothesis.
The existence of multi-year periods of cooling is obvious and expected, but they should become less frequent and shorter if the warming accelerates as expected given the CO2 acceleration. The observation is contrary to the expectation, and Lord Monckton reminds us every month about it. Considering that we are reminded every single day how awful climate change is and how guilty we are, I don’t find his lordship’s reminders out of order.
I am most grateful to Mr Vinos for his kind comment. It is indeed interesting that the rapid decadal rate of warming so confidently predicted in Charney (1978), IPCC (1990) and, most recently, in IPCC (2021) is not coming to pass.
JV said: “he existence of multi-year periods of cooling is obvious and expected, but they should become less frequent and shorter if the warming accelerates as expected given the CO2 acceleration.”
I’ve not heard that hypothesis before. When I get time I’ll look at the CMIP model data and see if the frequency of pauses increases or decreases with time. There is so many little pet projects like these on my plate already though…
I did test the hypothesis that the expectation is for a decrease in the frequency of pauses using the CMIP5 data from the KNMI Explorer. There was no change in the frequency of pauses from 1979 to 2100.
The percent of time we expect to be in a pause depends on the length of the pause. For a 5 yr pause it is 30%. For a 10yr pause it is 13%. And for a 101 month pause like what is occurring now it is 18%.
What does this tell us? Despite Monckton assertion to the contrary it is not unexpected or even notable that we find ourselves in a pause lasting 101 months.
I encourage everyone to download the data from the KNMI Explorer and see for yourself.
And now he tells how he thinks the CHIMPS garbage-in-garbage out “climate” models are somehow valid.
Your need to apply statistics to obtain a looong trend from what appears to me to be a control system with short term excursions is unexplainable. Have you ever done control charts to see if these excursions are out of control?
When Nick the Stroker agrees with Willie E., I know I am living in bizarro land. That is impossible. I’m going back to sleep and hope this agreement has disappeared by the time I wake up. A Willie E. versus Nick the Stroker argument is a highlight of this website.
That is, you can’t rule out an underlying trend of minus 2.838C per century either. But the hypothesis that the trend is not statistically different from zero cannot be rejected. Thus your analysis, using a better technique more appropriate to the data, supports Monckton’s conclusions. The width of the confidence intervals is being driven by the extreme heteroscedascity in the data around the El Nino spike.
What is perhaps rather more revealing is to evaluate how much trend there is across the whole dataset in an AR1 model. When you do that the confidence intervals narrow considerably, but the estimate for the trend drops to an unexciting third of a degree per century.
Don’t panic.
“but the estimate for the trend drops to an unexciting third of a degree per century”
Really? I get 1.33°C/Century, in agreement with Roy Spencer’s calculation. And it is a bit more exciting down here where we live.
UAHV6:
Temperature Anomaly trend
Jan 1979 to Jan 2023
Rate: 1.332°C/Century;
CI from 1.013 to 1.651;
t-statistic 8.180;
RSSV4: 2.112°C/Century;
GISS: 1.868°C/Century;
HADCRUT 5: 1.887°C/Century;
(all except UAH to Dec 2022)
Mr Stokes is perhaps unaware that RSS, which showed a sudden uptick in its warming rate just a month after one of the Pause graphs here was debated in the U.S. Senate by Senator Ted Cruz, uses a defective satellite dataset that is now out of date. Without it, RSS would show much the same warming as UAH.
As to HadCRUT5, Dr Spencer has recently written at his excellent blog about the substantial influence of the urban heat-island effect on this and other terrestrial datasets.
Hmm. Tony Heller predicted that the RSS would suddenly change to showing more warming a couple months before it actually did.
Collusion Is Independence | Real Science (wordpress.com)
And then recently showed that what they have done is move their reported temperatures up to the top limit of the error bars in order to be in closer agreement with NASA data.
Adjusting Good Data To Make It Match Bad Data | Real Climate Science
Nick,
For monthly UAH Australia, from the 2016 high to the 2022 low I get a trend of nearly MINUS 30 C per Century equivalent over 6.5 years, in a time of CO2 induced catastrophic global warming, but I only quote this number to show absurdity of data torture.
Geoff S
Did you use an AR1 model for that, or just OLS? If I use OLS I get results that agree with yours. But the data are clearly autocorrelated, so OLS in NOT appropriate. Try again.
AR1 vs OLS makes very little difference. I get 1.33 with OLS, 1.30 with AR1. Here is my R working:
There is very little need to hunt for autocorrelation in the global temperature record, though it is a relevant consideration in the regional records thanks to seasonality. If the uncertainty in the UAH datea is indeed +/- 0.28 K/decade, as Mr Stokes suggests, then there subsists no basis whatsoever for the sedulously-peddled conclusion that we must Do Something to Save The Planet, because we have no idea whether the globe is warming rapidly or cooling rapidly.
I prefer to base my analyses on the real-world data and the uncertainty intervals issued by the keepers of the datasets. Those intervals are considerably narrower than Mr Stokes’ interval. If, therefore, Mr Stokes thinks the uncertainty interval is greater by an order of magnitude than the keepers of the datasets say it, then he should take up the matter with official climatology and not with me.
Professor Jones at the University of East Anglia, who used to keep the HadCRUT record, was happy to recommend the simple least-squares linear-regression trend as the best way to get an idea of the direction of travel in global temperature datasets. I do not claim anything more than that the least-squares trend has been zero for 101 months. I do not base any prediction on this fact: I merely report it, as well as the 44-year trend on the entire UAH dataset, for context.
The truth is that the world is not warming anything like as fast as had been, and still is, predicted. No amount of fluff and bluster will conceal that fact.
And there is absolutely no reason to try cramming battery cars down the throats of the entire world population.
“There is very little need to hunt for autocorrelation in the global temperature record”
The residuals are autocorrelated, and you absolutely have to allow for that in the confidence intervals. To show what is going on here, I plot the trends for each interval ending at present (Jan 2023). The start of the interval is shown on the x axis. Near 2023, the intervals are short, and the trend is wildly variable. As you go back in time (longer periods) the trend settles to a generally positive value (1.3 at 1979). Between the short term gyrations and the stabler long term, it crosses the axis one last time at August 2014. That is the point Lord M calls the pause.
I have plotted the confidence intervals in color. Blue is the OLS 95% CI, calculated as if there were no autocorrelation. It diminishes as you go back in time, and the trend stabilises. But there is autocorrelation, so OLS exaggerates the confidence. I have plotted the Ar(1) CI’s in red. They are more than double the breadth. The most recent year in which you can be 95% confident that the trend is positive is about 2011.
Mr Stokes digs himself further and further into a hole of his own making. Consider the entire UAH dataset, and compare the observed 0.134 K/decade warming rate since December 1978 or the observed 0.137 K/decade warming rate since January 1990 with the predicted 0.3 K/decade warming rate in IPCC (1990, 2021).
One of two conclusions follows. First, that the real-world rate of warming is indeed well below half what was originally predicted and is still predicted, strongly suggesting at least one systemic error in the models, in which event the expenditure of trillions, bankrupting the West, will achieve nothing.
Secondly, that the uncertainties in measurement of global temperature are so large that we are incapable of drawing any conclusion at all about whether or at what rate the planet is warming or cooling, in which event there is no empirical method by which the rapid-warming hypothesis that Mr Stokes and his paymasters so cherish may be verified, in which event it is merely a speculation that has no place in science.
Furthermore, I performed a detailed autocorrelation analysis on the datasets a few years ago, and found that there is very little of it in the global datasets, though it becomes noticeable in the regional datasets.
Stochasticity and heteroskedasticity are of more significance than autocorrelation in the global datasets. And, as I have pointed out to Mr Stokes before, it is official climatology that likes to use the least-squares linear-regression trend as the simplest way to get an idea of the direction of travel of the global-temperature datasets. If he wishes to quarrel with that custom, let him take up the cudgels with official climatology and not with me.
And once again, all they can do is push the downvote button…
CMoB said: “First, that the real-world rate of warming is indeed well below half what was originally predicted”
You can say that as many times are you want (and undoubtedly will), but it won’t make it any less wrong than any of the other times you’ve said it over last decade. As we’ve repeatedly shown you with the actual diagrams and text of the IPCC FAR their prediction was actually pretty close.
CMoB said: “Secondly, that the uncertainties in measurement of global temperature are so large that we are incapable of drawing any conclusion at all about whether or at what rate the planet is warming or cooling”
Christy et al. 2003 disagrees with you. They say that with just 24 years of data the trend is statistically significant. We now have 43 years of data. And as you can see with Nick’s plot above the more data you have the lower the uncertainty of the trend becomes.
Christy’s conclusion is totally out of line with signal analysis. There are oscillations much longer than that. There are drifting phases that combine to cause differing outputs. Orbital changes. Much longer times are needed to know what is going on. Millennia at least. Twenty or 30 years is somebody’s excuse for poor science.
The last para, along with your plot, is instructive to rookies like me. I.e., I now know why the expected values from my “raw” evaluations are the same as yours, but your confidence intervals are (somewhat) larger. I wish that your tool showed standard errors, or am I missing that? Yes, I can back calc them….
It shows the t-value. But sorry, no, you’ll have to back-calc. I think it is 1/4 (actually 1/3.92) of the CI range.
No problem. I use opencalc and solver to back into these all the time.
You missed part of the conclusion. Probably due to your bias.
You can’t rule out a “-2.838 °C/Century” underlying trend either. That is certainly lower than the IPCC prediction. Ain’t statistics a bi**h?
“Probably due to your bias.”
The only “bias” is in your faux claim that he “missed part of the conclusion”. The chance of the lower value was not only noted, but was quantified.
FYI, your bias also made you miss his larger point. That, unlike evaluations of data with enough physical/statistical rigor to use, the whole “pause” evaluation is bogus, due to the data spread.
Funny how that “spread” only applies to the pause and not the trend. It’s like how the uncertainty of the mean in surface temps data is ignored also. Funny how when I brought that up in another thread, NIST was said to be wrong.
“Funny how that “spread” only applies to the pause and not the trend.”
It does. It’s just that the relative spreads are night and day. The UAH 6 trend, corrected for autocorrelation, for the last 40 years is 1.4 degC/century, with a standard error of 0.2 degC/century. The chance that it is positive is 99.99999999998%. OTOH, the comparable trend, again corrected for autocorrelation, for the last 101 months, is -0.228 degC/century*, with a standard error of 1.43 degC/century. The chance that it is positive is 43.6%.
https://mojim.com/usy129026x6x51.htm
What a terrific tool. I can see why the fora here objects to it. It channels the “48 Hours” fear of a “”****** with a Badge and A Gun”.
FMI, do you have a link to the Quenouille correction for autocorrelation? I can find an evaluation of it, but not how you correct for it.
No TDS today, blob? Did you finally get treated?
“FMI, do you have a link to the Quenouille correction for autocorrelation? “
I wrote about the general methods here. There are links to some earlier posts, and also to a quite informative post on Climate Audit. Basically you work out the lag 1 autocorrelation r, and then multiply the OLS σ by sqrt((1+r)/(1-r)) to get the expanded CI.
In that post I also worked out Q-type corrections for higher order Ar().
Thanks, I’ll read it all tomorrow.
That was super informative. I have this technique in my workflow now. Well…the AR(1) v correction anyway. The ARMA v correction is a lot harder so I’ll punt for now.
Hey, Willis, it’s a “pause”, just like the ones I get with my investments. I just know that they will go up if I sell them.
When I retired age 51 in 2005, my computer model said my net worth would be over $1 million by 2023. My actual net worth has been going up, up, up, as predicted, and today is $129. I must have programmed my computer wrong.
Funny, my investments go down when I buy them.
Fair enough. From the results of your model, what is the physical meaning of the line’s intercept at -27.446997?
My simpleton version:
101 months is short term global average weather trend data mining, not a long term 30 years or more global climate trend
The short term trend is temporarily flat.
That fact predicts nothing
It does show the expected warming effects of the largest 101 month rise of manmade CO2 in history were completely offset by net cooling effects of all other climate change variables.
That is evidence CO2 is not the climate control knob, as the IPCC has claimed since 1988.
The past 101 month trend is not likely to have any predictive ability for the next 101 month trend.
I’m not qualified at the statistics level to agree with or dispute Willis’s discussion above——but I do understand the scientific process well, and the politics of “climate change” even better.
Lord M’s contribution re the lengthening “pause” is highly relevant in the context of the general worldwide climate change discussion.
It is asserted that we must approach Zero Carbon soon or something terrible will happen. It is asserted that rising carbon dioxide levels are a powerful driver of climate, indeed talked about as if the only important driver.
CO2 levels continue to rise steadily. If global temps are not rising hand in hand, something other than CO2 must also be in play. Solar cycles, ocean currents, thunderstorms ….something.
But admitting something might work in a cooling direction such that continuously rising CO2 is not followed by continuously rising temperatures means that when temperatures are rising, maybe that other factor is the reason are rising and not CO2.
This is politically important.
CO2 was rising from 1950-1980, too. But temperatures were not, indeed maybe even falling.
CO2 was minimal in 1910-1940, but temps were rising.
This gross non-correlation is both scientifically and politically important.
The CO2-climate change argument is scientifically interesting. Exacting statistics are important for this.
But the ongoing disaster is political: the ongoing destruction of our economy in the war against carbon/based energy.
Lord Moncton’s “lengthening pause” will become scientifically interesting if it extends enough years to reach statistical significance as per Willis’s analysis.
But it is of immediate politically significance. There is a palpable apocalyptic hysteria surrounding the question of climate change. Any analysis based on data that tends to quiet the hysteria is important.
So, yes, Lord M’s observation is immediately important. And time will tell if scientifically important, in addition to politically important.
Kwinterkorn is right. These monthly Pause columns are clear and simple. They are a great deal easier to understand than the more complex statistical methods which, if used, would merely confirm that global temperature might be rising or falling at a rate of almost 0.3 K/decade (the midrange being zero, which is exactly what my monthly graphs show).
Precisely because these graphs are easy to understand, they are very widely influential. And that is why there is so much screaming about them by real and faux skeptics here each month.
kwinterkorn said: “CO2 levels continue to rise steadily. If global temps are not rising hand in hand, something other than CO2 must also be in play.”
Yep. Scientists have long known that CO2 is not the only thing that modulates Ein and Eout of the UAH TLT layer of the atmosphere. And it has little contribution to the variability of the energy flows especially on shorter timescales like months or years.
BTW…climate models predict a lot of these extended pauses. I encourage you to go the KNMI Climate Explorer and download the data and see for yourself just how prevalent pauses like these are predicted to be.
Yet you delight in posting your zettajoules hockey stick chart over and over.
of garbage.
Fixed it for you.
“BTW…climate models predict “
No, climate models make projections, they do *NOT* make predictions. Prediction allows for changing conditions in the future, projections assume no changing conditions in the future, a projections just assumes that what has happened in the past determines what happens tomorrow.
Predictions allows for cyclical processes to produce different outcomes. Projections don’t allow for cyclical process to provide different outcomes, the linear trend line will just continue forever.
Willis Eschenbach raises the interesting question of autocorrelation. I had a good look at this some years ago and did a straightforward analysis, comparing regional with global temperature records and looking for autocorrelation. Unsurprisingly, there was plenty of seasonally-driven autocorrelation in the regional datasets, but nothing like enough to worry about in the global datasets.
Interestingly, heteroskedasticity is really of considerably more significance than autocorrelation in the global temperature datasets. For this reason, it is not particularly useful to analyze the 2-sigma uncertainty interval: it keeps changing over time. Stochasticity is also important, given the strong and unpredictable el Nino/la Nina peaks and troughs.that lead to frequent and often sharp departures from the least-squares linear-regression trend.
For these and other reasons, Professor Jones at East Anglia, with whom I discussed this some years ago, is on record as having said that the least-squares linear-regression trend is the best way of getting a general idea of what is happening to global temperatures.
Willis says that there is no statistically-significant trend in the most recent half of the UAH dataset. Quite so: but that conclusion reinforces the argument in the head posting a fortiori. The head posting finds a zero trend over the past eight years five months: but there has been no statistically significant trend for something like 22 years. And yet we are all supposed to panic about global warming and blame every transient extreme-weather event on the West’s sins of emission.
It is important not to overthink these things. When I show the slide showing no warming trend for many years, audiences get the point at once. That is why the usual suspects spend so much of their time trying to challenge what is, at root, a very simple exercise, which I began to do many years ago because nobody else was doing it.
The ineluctable fact remains that there has been no global warming to speak of for the best part of a decade, and that the longer-run trend over the entire 44 years of the UAH dataset is well below half the originally-predicted midrange value.
Unfortunately, some of the piece I wrote was truncated without notice or explanation, so it looks as though the scientific discussion on this and related points that are of great interest to readers here will have to take place elsewhere from now on, which is a shame.
“The ineluctable fact remains that there has been no global warming to speak of for the best part of a decade, ”
The rate of warming over the last ten years according to UAH has been 0.16°C / decade. Faster than the overall rate.
Given that 85% of that period is on pause might give some indication of why just looking at the pause in isolation is misleading.
Bellman should follow Monckton’s Rule: read the head posting before commenting on it. The graph showing the entire UAH trend since December 1978 is also in the head posting. And “the best part of a decade” does not mean “a decade”: it means “most of a decade”.
The arguments against the conclusion that global warming is not occurring at anything like the originally-predicted or currently-predicted midrange decadal rate are becoming feebler and feebler.
Hear, Hear!
lordmoncktonmailcom should also follow that rule. I’m not sure what point he thinks I got wrong. I’ve been pointing out on the UAH comments section that Monckton shows the linear trend over the whole of UAH series. I keep being told that’s the wrong thing to do, and a meaningless value, but I defend Monckton’s right to do it.
I never claimed that a decade was the same as the best part of a decade. I specifically said it was around 85% of the decade. My point was just to demonstrate how much of a difference there is depending on how carefully you select your start dates. Start in August 2014 and there is zero trend. Start less than two years earlier and there is a faster rate of warming. This is a good indication of insignificant the pause is so far. It’s done less than zero to reduce the overall rate of warming.
And this is not an argument against the idea that UAH shows less warming than predicted. It’s simply an argument about how misleading focusing on the length of “the pause” is.
Bellman fails to take account of the fact that the reason for the frequency of these long Pauses is the failure of global temperature change to approach even half the midrange prediction. The previous Pause was 18 years 9 months. This Pause is already 8 years 5 months (or well over 9 years on most other datasets). Cherry-picking, as Bellman here does, to take improper advantage of a short-term el Nino spike, is inappropriate and anti-scientific.
As I keep pointing out, the correlation between length of pause and rate of warming is slim. The length of these so called pauses depends a lot on the strength of the spikes and troughs along the way.
Take RSS for example. A much faster rate of warming, but it’s pause is just as long as UAH’s. Monckton himself says that other data sets now have pauses over 9 years long, but other data sets also show more warming.
As always he accepts that it is cherry picking to use short term el Niño spikes to show a short term accelerated warming trend, but will reject the idea that starting a trend just before a major spike in order to show no warming is also cherry picking.
And in the case of the trend over the last decade, it isn’t the 2016 spike that causes the rate of warming, that happened befor the midway point, so would be expected to reduce the rate of warming. Just as all those la Niñas at the end. No, the reason there is a warming trend over the last ten years but not eight years, is because those last eight years have all been substantially warmer than the years before them.
Short, long, and cherry-picking is in the eye of the beholder. Climate is considerably longer than 100 years even. I haven’t seen any refinement of the climate zones lately so those folks must not have gotten the message.
Your trend forecasts the past correctly, right? If not, then it must be cherry picked too.
“because those last eight years have all been substantially warmer than the years before them.”
So what? If warming is supposed to follow CO2 in the atmosphere then it obviously isn’t doing so. We have yet to see a comprehensive explanation for why based on physical science.
All you have to offer is that based on your 40 year linear regression the earth is going to turn into a cinder because the warming will never stop.
“We have yet to see a comprehensive explanation for why based on physical science.”
Do you keep unseeing graphs like this. The only explanation needed is that ENSO is doing it usual thing.
“All you have to offer is that based on your 40 year linear regression the earth is going to turn into a cinder because the warming will never stop.”
Not a word of that is anything I claim or believe.
TG said: “So what? If warming is supposed to follow CO2″
It’s not supposed to follow CO2 and only CO2. Remember, the temperature change is given by ΔT = ΔE/(c * m) and the law of conservation of energy says ΔE = Σ[Ein_x, 1, n] – Σ[Eout_x, 1, n]. CO2 is only one many of the Ein_x and Eout_x terms.
I know…you don’t fully accept the law of conservation energy. That doesn’t make it any less true.
“””””From this law follows that it is impossible to construct a device that operates on a cycle and whose sole effect is the transfer of heat from a cooler body to a hotter body. I”””””
https://www.thermal-engineering.org/what-is-second-law-of-thermodynamics-definition/
You need to reconcile your conservation of energy with the second law of thermodynamics. In other words, cold CO2 warming the hotter surface of the earth!
JG said: “You need to reconcile your conservation of energy with the second law of thermodynamics.”
No I don’t. Both the 1LOT and 2LOT are indisputable laws of physics that no one seriously challenges except a handful of contrarians on the WUWT blog.
JG said: ” In other words, cold CO2 warming the hotter surface of the earth!”
No that’s not how it works. The surface does not warm because there is a net energy transfer from the colder atmosphere to the warmer surface. It warms because there is a net energy transfer from outside the climate system to the inside of the climate system.
So you agree that CO2 DOES NOT warm the surface?
You must also then agree that all the radiation diagrams that show the surface radiating not only from the sun’s energy but also from “back radiation” are screwy?
No. I don’t agree. CO2 like any thermal barrier/insulator causes warming. It just doesn’t do it via a positive net transfer of energy. Instead, it does it by decreasing Eout. This is not unlike the door on your oven. There is no net transfer of energy from the door to the inside. Nevertheless when you close the door the inside gets warmer. The door caused the warming. And it did so by reducing Eout.
Do you really think that an oven door, especially with glass, stops radiation and when you open the door the radiation is freely emitted causing cooling?
I’m sorry, but conduction/convection is the primary phenomena with an oven, not radiation. It is why “greenhouse effect” is a terrible misnomer.
Secondly, if 0.1 W/m^2 we’re “trapped” each day for the last 10,000 years, what would the earth’s temperature now be?
The earth has different ways to shed energy, predominantly water vapor.
Where does he get this nonsense from? It has to be an external source.
I didn’t say an oven door with glass stops radiation. I said closing an oven door reduces the Eout from the oven. Based on your incredulity I’m assuming you do not agree that opening the door on your oven will result in ΔEout > 0 and thus will cause the inside to cool and closing the door will result in ΔEout < 0 and thus will cause the inside to warm?
You probably don’t know it, but the conduction/convection from inside the oven perfectly describes adiabatic cooling which also happens on the earth via the lapse rate.
Crack the door and put your hand over it. Is that radiative heat you feel?
First, it doesn’t matter what form the energy is. It can be either conduction, convection, or radiation. Placing a thermal barrier around a system reduces Eout all the same. It’s no different than the oven door, fiberglass insulation in your home, or a CO2 layer.
Second, yes. You are definitely feeling radiant heat in addition to that which is convected toward your hand and which is imparted onto the skin via conduction. Oven doors impede the transmission of radiant energy too ya know.
Third, I guess I don’t even other choice but to accept that you don’t think a thermal barrier, like an oven door or the fiberglass insulation in your home or whatever reduces Eout of the system. I’ll just make note it in my ever expanding list of absurd arguments and move on.
Insulation doesn’t reduce Eout if you include time in the equation.
Heat loss is a rate, not a scalar total. The insulation may reduce the rate but it won’t stop the oven from cooling off, it just takes longer.
As for the Earth, the warmer it gets, the more heat it radiates thus increasing its heat loss. Somehow that always gets lost in the push for the CAGW meme.
“It’s not supposed to follow CO2 and only CO2.”
Then why don’t we have an RCP for orbital variation? For meridional heat transport? An RCP for global food harvests which are a direct measure of temperature *and* CO2? Why don’t we have any RCP except for GHG gases?
Exactly what *are* the other “terms”? And why don’t we have an RCP for each of them so all the models are working from the same physics base?
It’s all based on using part of a sinusoid to define the slope of the entire sinusoid – an impossibility. If the impact of CO2 is “x” today then it should have been “x” in the past – when we’ve seen both warming and cooling in a cyclical, sinusoidal manner.
Bellman denies it but he *is* trying to use his linear regression line as a predictor of our planet’s future.
If one looks at the planet’s temperature profile over time then the issue is only where the maximum natural variation might wind up before heading back down. Using the linear regression line over the past forty years just does the same thing the climate alarmists do – claim the earth is going to turn into a cinder.
Your pauses indicate that there *are* cyclical processes at work. It is those cyclical processes, and their combinatorial outcomes that need to be identified. Not just models based on the projected growth of one or a few GHG gases.
“Bellman denies it but he *is* trying to use his linear regression line as a predictor of our planet’s future”
You can lie as much as you like. It doesn’t make you look smart.
“Your pauses indicate that there *are* cyclical processes at work.”
If Lord Monckton wants to make that claim then he should do it himself and present the evidence. He explicitly says the pause is not meant to be predicting the future and that it may well start going up in the future. You have this odd idea that whilst it’s wrong to look at linear trends as suggesting what will happen in the future (I agree to a large extent), it’s perfectly fine to find some ambiguous cycle pattern and insist that will be repeated forever.
Yep. CoM is *not* in the business of predicting. He’s only in the business of showing CO2 can’t be the driver of warming if, when it increases, it doesn’t cause warming.
What it shows is that the correlation between CO2 and temperature rise is most likely spurious. Just like the rise in postal rates correlating with temperature rise!
How do you know temperatures are not rising in line with CO2 if you don’t know how much temperatures are rising?
*I* don’t know. But you and the climate alarmists seem to think you do!
So why keep claiming the pause proves there is no connection between CO2 and temperature?
The point of the “pause” is not that it is significant statistically in the overall trend, but that it is a refutation of CO2 emissions driving temperatures. The futile expense of trying to eliminate CO2 emissions is not justified if there is evidence that it is not the “control knob”.
Additionally, the ECS is all whacky if it doesn’t follow a functional relationship with CO2 and temperature.
Too much attention is being focused on a very small increase in temperature. Many here have fallen into the trap of calling anomalies “TEMPERATURE”. They are not temperature, they are at best a first derivative of temperature. 1/15 is about a 6% increase. You can achieve this kind of change by moving north or south just a few miles or meters. It is not a catastrophic change in the earth.
When I look at the attached graph, I see a control system that has a baseline that the negative AND positive excursions always return to. Anomalies should be changed to show a sign of the slope from the previous value. That would let folks determine warming or cooling in a proper fashion. In other words, temps go up and temps go down. There is not an inexorable trend one way or the other.
Trending temperature versus time creates a time series that needs proper treatment. Even a reduction in diurnal temperatures can provide change in the mean that would indicate an increasing trend yet say nothing about what is changing.
“The point of the “pause” is not that it is significant statistically in the overall trend, but that it is a refutation of CO2 emissions driving temperatures.”
A claim not even Lord Monckton makes. I keep showing you why that claim doesn’t work. You can’t just look at the pause in isolation. You have to see that the pause was part of a larger amount of warming. The result is that the pause has just increased the correlation between CO2 levels and temperatures.
Besides. How can you claim UAH data can refute anything at the same time as claiming its annual uncertainty is ±7°C? How you you make any claims about the true rate of warming given that level of uncertainty?
Bellman confirms that, if the uncertainty in the global temperature record is anything like as great as Mr Stokes imagines it is, then official climatology is incapable of telling us that that there is a warming rate at all, let alone whether it is likely to prove catastrophic unless the hated West is forced into an economic shutdown, with the last few major industries going to Communist China, India and Russia, whose emissions per unit of production are greater than ours, adding to global warming.
” then official climatology is incapable of telling us that that there is a warming rate at all”
Not true. Anyone can confirm that the UAH data shows a statistically significant warming over the past 44 years. It’s only when you insist on looking at short term trends that the uncertainty becomes too large to read anything into the trend.
To get some idea of how useful a 101 month trend is in indicating the overall rate of warming, here is a graph of all such trends starting in each month.
Note there have been times in the last few years when the 101 month warming trend was 0.4 – 0.5°C / decade.
Anyone looking at the location of American glacial moraines can confirm that they show a statistically significant warming over the last 10,000 years. 100% of them no longer have ice behind them. But no one can connect that evidence to CO2 which was constant at 280 ppm for millions of years until Man started burning oil.
He never understands that he is his own worst enemy.
He wants us to believe that the best-fit measurement, i.e. the residuals, is the uncertainty while ignoring the actual uncertainty of the measurement data points. He substitutes a measurement of line fit for uncertainty.
It’s why he doesn’t believe that Possolo’s method of finding the uncertainty of a dataset of temperatures can give an uncertainty of +/- 7C.
I still believe that the current temperature databases are not fit for purpose when it comes to climate. With today’s modern measurement devices we should be using enthalpy (i.e. taking into account humidity) as a metric or we should be using degree-day integrals of the entire temperature curve for multiple locations and combining those into heating and cooling metrics.
This is the real core issue, the GAT is not climate, which is why I call them trendologists—they instead study trends (don’t forget that the big climate models spit out GAT). They study and study every little 10-20 mK wiggle in the UAH data, with no comprehension these numbers are orders of magnitude smaller than the real world temperature measurement uncertainties. Even going to the trouble of trying to predict (or rather post-dict) the next month’s UAH result with ad hoc curve fitting “models”.
“He wants us to believe that the best-fit measurement, i.e. the residuals, is the uncertainty”
More gibberish. In what way is “the best-fit measurement” the “residuals”. The trend is the best fit, the residuals are the errors of that fit. And neither of these are the uncertainty of the trend.
“while ignoring the actual uncertainty of the measurement data points”
If you don’t think a 101 month trend is meaningful just say so. I agree, that’s the whole point of my graph. But this nothing to do with the uncertainty of the UAH monthly values.
“He substitutes a measurement of line fit for uncertainty.”
No. Still know idea if you think there is any meaning in your words. If you mean I’m using the standard equations for calculating the uncertainty ion a linear regression, rather than some measure based on the UAH measurement uncertainty, then you still need to ask why I’m wrong to do that, but it’s fine for lordmoncktonmailcom to ignore all uncertainty when calculating his pause trend.
We’ve been other this before, and I still don’t think you’ve answered the question. If I say the uncertainty in a trend is say ±0.5°C / decade, do you think the real uncertainty should be bigger or smaller than that?
Continued.
“It’s why he doesn’t believe that Possolo’s method of finding the uncertainty of a dataset of temperatures can give an uncertainty of +/- 7C.”
The reason I don’t believe it is because it’s patently nuts. And you and Jim have done nothing to explain why this is based on whatever you think “Possolo’s method” is. But go ahead. Explain to Monckton why his pause and claims that UAH only shows a fraction of the predicted warming is nonsense, because it’s based on data that only has an uncertainty of ±7°C for the annual data.
“I still believe that the current temperature databases are not fit for purpose when it comes to climate.”
And yet you will happily claim that a 101 month trend based on this not fit for purpose data, can somehow prove that CO2 is not causing warming.
Bellman appears terrified of the fact that global warming, as measured, is rising so much more slowly than the midrange prediction. His suggestion (copied from his soulmate Stokes) that the uncertainty in the data could be as large as +/- 7 C carries with it the implication that climatology is not able to tell us whether the planet is warming or cooling. Therefore, climatology is not able to tell us that there is a climate “emergency”.
Mr Stokes is, in effect, making the same point as Professor Frank, in his 2019 paper. He says that the uncertainty in a single one of the thousands of initial conditions informing the general-circulation models indicates that any global-warming prediction of less than +/- 12 C is mere guesswork. Since it is now the view of the climate extremist faction here that global temperature has similarly large uncertainties in its measurement, there is no point in trashing the economies of the hated West for the sake of Saving The Planet from a “threat” that can neither be predicted nor measured.
“His suggestion (copied from his soulmate Stokes) that the uncertainty in the data could be as large as +/- 7 C”
Lord Monckton needs to read what he’s replying to more carefully. I am not the one claiming the uncertainty is ±7°C. In fact I’m saying it’s patently nuts. The only people making such a claim are the Gorman brothers.
You deny every source used to statistically address temperature. I have both shown references and now computations.
You have done neither at any point. Your only response is denial.
Even Nick Stokes has said in the past that anomalies are determined station by station and month by month. That makes these uncertainties very relevant.
You have a computer, and can use the internet. Show us your calculations, in detail, for uncertainty when using NWS/NOAA published uncertainties. Then show us references that support your calculations.
I have no intention of doing a proper uncertainty analysis of any global data set. As I’ve said many times any real analysis is complicated, has to to take into account multiple sources of uncertainty, and is still only going to be an estimate. I’m happy to accept any reasonable analysis done by an expert, but also to accept that this may still be underestimating the true uncertainty.
But doesn’t mean I can’t point out that impossibly large uncertainties are impossibly large, and point out where you are going wrong in your assumptions.
We’ve been through so many different iterations of your claims it’s difficult to keep up, and it seems you are prepared any argument as long as it leads to impossibly large uncertainties.
First you were claiming that when calculating the uncertainty of the average you ignored the fact that the sum is divided by the number of measurements – insisting that therefore the uncertainty of the average was the same as the uncertainty of the sum, and the larger your sample size was the more uncertain the average would be.
Then you kept insisting that the SEM was not in anyway the uncertainty of the average, and kept rejecting my point that random sampling had more to do with the uncertainty than any uncertainty in the measurement.
Then you started insisting that the mean wasn’t actually a measurand and so couldn’t have a measurement uncertainty.
And now, we have a complete reversal and you are insisting the uncertainty of the global mean has to be calculated as the SEM of the stated values. Which would be fine if you didn’t start applying it to all the wrong places.
But you never explain why, if the uncertainties are as large as you say, you are also believe that it’s possible to detect a pause of a few years, and claim that this can prove CO2 has no effect on temperatures.
So, the Gorman’s latest argument which they think is dictated by NIST is to look at the 12 monthly temperature values at one station for one year, and calculate the standard deviation of those 12 months and divide by √12, giving a 95% confidence interval of around ±7°C. Then they claim that this must also be the uncertainty of a global average made up of hundreds of different stations, and must also be the same when taking the anomaly values.
Calculating the SEM like this for a single station would be fine, if you are doing what the SEM is intended for and taking a random sample of monthly values.
What that means is each month has been randomly selected from the year. Imagine having a 12 sided die and the monthly temperature on each side. You roll the die 12 times, add up all the values and divide by 12. This gives you an estimate of the average value of all the sides. But it it is only a rough estimate with a 95% interval of ±7, because it is random. Some times you might throw the value for January 3 times and never roll the values for August and September. Other times you might get twice as many summer values than winter values. It’s random and the large uncertainty reflects that.
But your 12 months on monthly averages are not randomly selected, they are all of the 12 months and only the 12 months. Take the die example above, and change it to a pack of 12 playing cards each representing a different month. Take your sample of 12 but don’t replace any card you take, and calculate the average. There is no randomness in this, there were only 12 cards in the deck and you selected all 12 cards. The average you get will be exactly the same as the average value of the cards, there is no ±7 confidence interval.
Ans that is why it would be inappropriate to calculate the SEM of those 12 cards, and why it makes no sense to use the SEM to estimate the uncertainty of your annual station average.
That doesn’t mean there is no uncertainty, just that any uncertainty comes from the uncertainty of your monthly values. This uncertainty may come from measurement errors, or from missing daily data, as in the case of the NIST example. If you took the NIST example as a rough estimate of the uncertainty of each monthly value, around ±2 (this is assuming qround a third of the daily values are missing), then the annual uncertainty would be 2 / √12, around ±0.6°C.
Then of course you aren’t basing the global average on one station. If you base it on the annual averages of say 1000 stations, then using the SEM at this point makes sense (in reality you do not have 1000 random stations which is why it’s more complicated.) But if you did then take the standard deviation of those 1000 annual values, divide by √1000, and that give you the SEM for global average. Multiply by 1.96 and you have your 95% uncertainty interval.
But for some reason this is when Gorman insists you cannot calculate the SEM in this way, and simply assumes the global average will have the same uncertainty of any individual station.
Finally, we are not interested in temperatures, but in anomalies. But the Gormans think at this point it’s just a question of subtracting the annual 30 year average temperature from the current annual temperature and hence the uncertainties add.
But this is not how anomalies are calculated. As Jiom says above they are calculated month by month station by station. The main effect this has on the global average is that the standard deviation of anomalies of all the different stations is much less than that for the absolute temperatures. Smaller standard deviation means smaller standard error of the mean, hence using anomalies reduces uncertainty.
WOW, an EPIC bellcurvewhinerman RANT!
Four PARTS!
Encore!
“The main effect this has on the global average is that the standard deviation of anomalies of all the different stations is much less than that for the absolute temperatures.”
Nope. standard deviations of anomalies should be the same or greater as the standard deviations of the anomaly components.
The baseline *IS* an absolute temperature. The temperature being used to calculate the anomaly *IS* an absolute temperature. Their variances should add thus the variance of the anomaly will be the sum of the component variances used to calculate the anomaly.
Variance add whether you are subtracting or adding.
“Nope. standard deviations of anomalies should be the same or greater as the standard deviations of the anomaly components.”
They obviously are not which you could easily verify for yourself.
“Their variances should add thus the variance of the anomaly will be the sum of the component variances used to calculate the anomaly.”
True if you are only talking about one location at one consistent part of the year, say a month. I’m talking about the standard deviation caused by seasons and locations. Your method is to look at all the monthly temperatures and treat the seasonal variation as the uncertainty in monthly values. But it’s not surprising if it’s always a lot hotter ion summer than it is in winter. Subtracting the average monthly temperature removes the seasonal variation, hence the standard deviation between monthly values is much smaller.
The same when averaging stations across the globe.
“They obviously are not which you could easily verify for yourself.”
You don’t even know what root-sum-square actually is.
It is the square root of the sum of the variances.
u_total^2 = u_x^2 + u_y^2
u_x is the standard deviation and u_x^2 is the variance. Same for u_y.
u_total is the standard deviation (typically considered to be the uncertainty) and u_total is the square root of the sum of the variances.
When you add the average baseline, a random variable, with the monthly average temperature, a random variable, the variances of the baseline and the annual average ADD!
“True if you are only talking about one location at one consistent part of the year, say a month.”
Bull Malarky! The baseline is supposed to be location by location just like the part of the year being considered. The difference of the baseline and the monthly average is the anomaly and the variance of the anomaly is the sum of the variances of the baseline and the monthly average.
Thus each anomaly for each location will carry forward a variance that is the sum of the component parts.
“Your method is to look at all the monthly temperatures and treat the seasonal variation as the uncertainty in monthly values.”
More Bull Crap! That is *NOT* what I did in the spreadsheet I gave you. It was *ALL* based on the month of August in 5 serial years! There was nothing about seasonal variation in there at all!
“But it’s not surprising if it’s always a lot hotter ion summer than it is in winter.”
So what? I’m now working on Jan for 2018-2022. Guess what? Initial calculations show Jan temps have higher variance than Aug temps even though the absolute values in Jan are smaller!
“Subtracting the average monthly temperature removes the seasonal variation,”
it does *NOT* remove the variance differences in Jan and Aug! It is the variances that indicate the certainty level of the anomalies. Aug temps look like they are going to have smaller variances (i.e. smaller standard deviation => uncertainty).
If Jan temps have a higher variance than Aug temps then how is that accounted for when averaging Jan anomalies with Aug anomalies?
As usual, you are blowing all this out your backside with absolutely no understanding of what you are talking about.
More patronising ad hominems. Stop telling me I don’t understand something before going on to explain exactly what I say it is.
Then you might be able to address the points I make rather than the ones I don’t.
The point isn’t about whether the uncertainty of an anomaly is bigger than of an absolute value. The point is that the standard deviation, and hence SEM of absolute seasonal monthly values will be larger than the same values expressed as anomalies. As your latest scheme is to use the SEM of monthly values over the year as the uncertainty, that makes a big difference.
“That is *NOT* what I did in the spreadsheet I gave you.”
No but it’s what Jim does when he claimed the annual uncertainty is ±7°C. It’s what asked you to justify when you started talking about monthly uncertainties much smaller than that.
You can complain all you want but you never show a calculation.
Missing days in the NIST only comes into play when determining the factor used to expand the standard uncertainty. The missing days may even expand the variance, you have no way to know. Your assumption that the uncertainty is larger because of the missing days is unwarranted.
I would also point out what NIST says about the uncertainty.
“””””The {εi} capture three sources of uncertainty: natural variability of temperature from day to day, variability attributable to differences in the time of day when the thermometer was read, and the components of uncertainty associated with the calibration of the thermometer and with reading the scale inscribed on the thermometer.”””””
{εi} is the uncertainty by the way. Also notice “natural variability of temperature from day to day”, does that remind you of VARIANCE? In case you missed it, in the example SD =4.1 and the standard uncertainty, u(τ) = 4.1 / √22 = 0.872 which is then expanded!
You don’t divide ONE monthly uncertainty by √12, you must add the 12 variances together and divide by twelve to get the AVERAGE uncertainty. If you add the twelve means then you also add the variances.
You also forget that the SD of the temps have already been divided by √n to get standard uncertainty which is then expanded. You DON’T divide uncertainty by the √12 AGAIN to get anything.
An uncertainty u(τ) of ±2 implies a variance u(τ)^2 of ±4. If you divide by 12 to find an annual mean, then divide the annual sum of variances by 12. In other words 48/12=4. So the u(τ)^2 = 4 and u(τ) = √4 = 2. You get what you started with.
Why am I having to to teach an expert? You appear to have no concept of random variables or uncertainty!
You are a troll.
+42
“He’a a troll, Tim” — Pat Frank
“You can complain all you want but you never show a calculation.”
I suggested how you could work out the uncertainty for an individual station. Use the NIST example and propagate the monthly uncertainties for the year, using the standard rule for adding in quadrature, i.e. using the GUM equation 10. And I explained how you would calculate the standard error of the mean of 1000 random stations.
“Missing days in the NIST only comes into play when determining the factor used to expand the standard uncertainty.”
No. It’s implicit in determining the SEM by dividing by root 22, where 22 is your sample size. Yes the small sample size also requires using a student distribution, but it’s the root N aspect that really determines the size of the uncertainty.
(Note, I’m not sure if I degree that this is the best way of determining the monthly uncertainty, the text says there are different approaches. But it’s good enough for this argument.)
“I would also point out what NIST says about the uncertainty.”
One day you might figure out that this is what I’ve been trying to tell you the past two years. Any measurement has an error which comes from multiple sources. That’s why you can “use the stated” values without worrying about the measurement uncertainties. It’s already accounted for in the stated value.
“{εi} is the uncertainty by the way.”
No, they are the errors. ERROR IS NOT UNCERTAINTY.
“Also notice “natural variability of temperature from day to day”, does that remind you of VARIANCE?”
That’s how the variance is calculated. The average of the squares of the errors. I really never no what point you think I need to understand when you say things like this.
“In case you missed it, in the example SD =4.1 and the standard uncertainty, u(τ) = 4.1 / √22 = 0.872 which is then expanded!”
Again, why do you think I’ve missed something here.
“You don’t divide ONE monthly uncertainty by √12, you must add the 12 variances together and divide by twelve to get the AVERAGE uncertainty.”
What you do is add the monthly uncertainties in quadrature, the square root of the sum of the squares, to get the uncertainty of the sum, and then divide that by 12. If all the uncertainties are the same this reduces to divide the individual uncertainty by √12.
If you don’t get that, why do you think you can divide the daily standard deviation by √22. It’s the same argument.
“You also forget that the SD of the temps have already been divided by √n to get standard uncertainty which is then expanded.”
I didn’t forget that. That’s how the monthly uncertainty is determined. You should then be able to figure out how to use the standard rules for propagating uncertainty to get the uncertainty of the average of 12 monthly values.
My calculation was a bit simplified as I was using 95% interval rather than the standard uncertainty. The standard uncertainty is 0.872°C, so the standard uncertainty of the 12 monthly values(if they all had the same uncertainty) would be 0.872 / √12 = 0.252°C. Using the student-t distribution with 11 degrees of freedom gives a coverage factor of 2.201 for a 95% uncertainty interval of 0.252 * 2.201 ~= ±0.55°C.
“An uncertainty u(τ) of ±2 implies a variance u(τ)^2 of ±4.”
As I said above, that’s not the standard uncertainty. The standard deviation of the daily values is 0.872, the variance is 0.760.
“If you divide by 12 to find an annual mean, then divide the annual sum of variances by 12.”
And you still don’t get how random variables are combined. If you add 12 values you simply add the variances. But if you scale any variable you have to scale the variance by the square of the scaling factor. Hence the uncertainty of the mean is the sum of the variances divided by 144. Then take the square root and you get the root of the sum of the squares of the standard deviations divided by 12. Which if all standard deviations are the same gives that standard deviation divided by √12.
God why do I bother?
“””””But if you scale any variable you have to scale the variance by the square of the scaling factor. Hence the uncertainty of the mean is the sum of the variances divided by 144.”””””
Why do you think GUM EQ 10 has a term (∂f/∂xi)? That is a weighting factor to be used when you have a functional relationship where different terms have more or less weight in the function. Since there is no complicated function when averaging temperatures all values have equal weight and the partial differential is “1” for each term.
Now why do you think additional scaling is necessary when summing variances of temperatures? As I pointed out, each month is already scaled by the days in that month.
This is important and you need to explain better why it is necessary to further adjust by showing a reference that shows why adding variances requires more scaling than what GUM EQ 10 does.
None of my references preclude adding variances of random variables directly without some kind of scaling, especially when the the variance has already been scaled.
It might help for you to do a dimensional analysis and see what dividing by months^2 does to the values. Ask yourself what the dimension of a monthly variance is. Then determine what it becomes when you divide each term by “months^2”.
“Why do you think GUM EQ 10 has a term (∂f/∂xi)?”
It’s the same thing.
“That is a weighting factor to be used when you have a functional relationship where different terms have more or less weight in the function.”
The weighting is the partial differential for the term. If you are dividing a term in a sum by 1/12 the partial derivative is 1/12. You have to square this to get the weight for the uncertainty term. I’m sure I’ve had to explain this many times before.
“Since there is no complicated function when averaging temperatures all values have equal weight and the partial differential is “1” for each term.”
Wrong. See above. Really, why do so many here have such a hard time understanding how partial differentials work.
(x1 + x2 +… + x12) / 12 = (x1 / 12 + x2 / 12 + … + x12 / 12.
For all i, the partial differential is
∂f/∂xi = 1/12
I’d point you to a partial differential calculator, but last time I did that I was accused of using an argument from authority.
“Now why do you think additional scaling is necessary when summing variances of temperatures?”
Because if you don’t scale the monthly values by dividing by 12, you have a sum not an average.
“This is important and you need to explain better why it is necessary to further adjust by showing a reference that shows why adding variances requires more scaling than what GUM EQ 10 does.”
It isn’t. I’m applying exactly the weighting the equation requires, and you get the same result if you use the rules for adding random variables, or use the standard propagation of error rules which are derived from equation 10.
If you don’t agree with this why did you think it was correct to divide the standard deviation of the daily values by root 22?
“It might help for you to do a dimensional analysis and see what dividing by months^2 does to the values.”
Do you ask yourself the same when you divided the sd of the monthly values by root 12, or the sd of the daily values by root 22?
You can look at each monthly value as being a temperature times a time component with dimensions say KM, where m is the number of months (1). Then divide by 12 months to get an average with dimensions KM / M, or K.
When you calculate the variance you add the squares of the monthly values, so dimension K^2*M^2, and are dividing by M^2, so again this reduces to K^2. Then take the square root and your standard deviation is in K.
Or you could just average the temperatures in which case 12^2 has no dimension.
Why do you spend so much time trying to convince folks that you divide by 12^2 before you even find the combined uncertainty? You don’t do that.
You add the variances together, then you divide by twelve if you want an average uncertainty spread across each month.
Look at the Eq 10 that you referenced in the GUM. Do you see any division by the squared number of items involved?
You have not shown ONE REFERENCE that what you are doing is proper.
As I tried to show you, if you add the means of a random variable to get a total, then you also add the variances to get a total. If you want to divide the means by a number to get an average, then you also divide the total variance by that number to find and average variance.
“You add the variances together, then you divide by twelve if you want an average uncertainty spread across each month.”
You don;t want the average uncertainty (ask Tim), you want the uncertainty of the average.
“Look at the Eq 10 that you referenced in the GUM. Do you see any division by the squared number of items involved?”
Yes. In the partial differentials. For an average of 12 values each differential is (1/12).
“You have not shown ONE REFERENCE that what you are doing is proper.”
We keep going over this and you never learn. Take this site
https://apcentral.collegeboard.org/courses/ap-statistics/classroom-resources/why-variances-add-and-why-it-matters
ROFL!!! I have read this page multiple times. I even have it saved in my Microsoft OneNote program.
I have attached the whole segment of what you are talking about. You are cherry-picking again.
First, this is dealing with a “normal model”. It is also dealing with sampling where you sample a population multiple times. Do you understand what that means in terms of the CLT?
It means it is using the Weak Law of Large Numbers. What does that mean? It means you have multiple samples of a population, and each sample has the same mean “μ” and the same variance σ^2.
Why do you think the “nσ^2” factors out? All the variances are equal!
Look at the very last entry in the proof.
It is SD(xbar) = σ/ √N
SD(xbar) is the SEM or didn’t you know that? I have shown it multiple times.
Do you see the term random variable in this section at all? There is a difference between random variables and samples. Why do you think the section on random variables being added or subtracted has a different proof than the section on a sample means Standard Deviation?
In case you didn’t notice, the very first statement of each proof begins with a totally different assumption. There is a reason for that. As you were cherry-picking you must have failed to read the proofs and only looked at the last lines.
Another dead giveaway. The section on adding independent random variables is the use of the P -> probability function whereas the CLT section does not.
Quit cherry-picking in the hopes you’ll find the nugget that proves you correct.
I forgot to add the image.
There’s a surprise. Jim complains I never give him a reference. I give him a reference (one I know he’s quoted himself), and it’s the usual set of special pleading. “You don’t understand it. The assumptions are different. You’re just cherry-picking.”
Anything but admit the fairly obvious result that when scaling any random variable the variance must be scaled by the square of the scaling factor. It’s obvious from understanding what a variance is. It’s the average of the squares of the errors. Scale the random the variable by b and you scale all the errors by b.
Lets look as some of the objections:
No it isn’t. The part he quotes plainly says that the result is independent of the distribution you are sampling from. The reference to the normal model is about what the CLT says about the distribution of the mean. It approaches normal so you can model the distribution as normal.
Nope, other way round. The law of large numbers follows from this. The larger the sample size the tighter the less the variance.
No. It does not mean that at all. This is the usual misunderstanding of the CLT. It says that if you have one sample, you can understand that it’s an instance of a random variable that will have the known distribution. There is no need to actually have multiple samples, that’s just something you can do to try to understand what the CLT means.
Which is getting even more confused. If you take multiple samples they will all have different μ and σ^2. I suspect this confusion hints at a deeper misunderstanding.
Yes that’s the assumption in the calculation of SEM. Each element is a random variable taken from the same distribution. But this only matters for the later parts of the proof where you use it to simplify the result down to σ / √N. The part I’m talking about are the first few lines, where it shows that you have to divide the sum of the variances by N^2. That has nothing to do with them all being the same variance.
Why do you always have to say things like that? Of course I know it – I’ve been trying to explain it to you for years.
No there isn’t. The calculation of the SEM is based on treating is element in a sample as a random variable. It says it in the last line you quote “The mean is basically the sum of n independent random variables”.
Because they are proving different things. The whole introduction is saying they can derive the CLT from the rules for how random variables are added.
Your last sentence says it all!
THEY ARE PROVING DIFFERENT THINGS!
Yet you are wanting to use the CLT to justify the division by “n” when adding random variables?
THEY ARE TWO DIFFERENT THINGS DUDE.
You do realize how obvious it is that the more you write in all caps the more obvious it is you don’t understand what you are talking about?
Two separate proofs proving different things. I don’t see why you have a problem with that.
Theorem 1 – adding or subtracting two random variables results in the variances adding.
theorem 2 – taking an average of a random sample results in a standard deviation equal to σ / √N.
Why do you expect them to have the same proof?
The point is you can use 1 to derive 2, and in so doing you have to use the result, not stated in the article, that if you divide a random variable by N, you divide it’s variance by N^2.