Climate Statistics 101: see the Slide Show AOC Tried, and Failed, to Censor

Climate Statistics 101: See the Slide Show AOC Tried, and Failed, to Censor

This is the slide show and 20-minute talk that Representatives Alexandria Ocasio-Cortez and Chellie Pingree tried to censor at the LibertyCon 2020 conference in Washington, D.C. After Dr. Rossiter gave a climate talk at LibertyCon 2019, they wrote to sponsors of the event, such as Google and Facebook, and asked them not to fund any event with an appearance by “climate deniers” from the CO2 Coalition. See http://co2coalition.org/2019/01/30/representatives-ocasio-cortez-and-pingree-and-climate-change-debate/

LibertyCon indeed lost some sponsorship, but because of its commitment to the free exchange of ideas still invited Dr. Rossiter back to speak in 2020. This is the talk he had prepared, before the coronavirus crisis forced the cancellation of the conference.

As background to this topic, we suggest that you watch the CO2 Coalition’s “CO2-Minute” video, “Carbon Dioxide: Part of a Greener Future,” at https://co2coalition.org/studies-resources/video-and-media/.

Now, on to the talk! (You can also download and distribute the slides themselves in a PowerPoint file at: http://co2coalition.org/wp-content/uploads/2020/06/LibertyCon-Rossiter-Presentation-final_6-16-20.pptx)

Slide 1

I’m Caleb Rossiter, executive director of the CO2 Coalition of climate scientists, and a former statistics professor. Welcome to Climate Statistics 101, which shows how to test hypotheses about the impact of emissions of greenhouse gases like CO2.

Statistics uses logic and probability to test for causation, for whether one thing affects another. We take nothing on faith, everything on proof. Only in the law school do they teach ad homimen arguments – attacking or praising the messengers. Scholars just analyze their message.

Slide 2 – Normal Curve

This is life! It’s called the Normal Distribution or Bell Curve. It shows how far away from the average most physical and statistical things are. Things like people’s heights or the number of hurricanes in a decade.

We use the Normal Distribution to test the null hypothesis, the claim that there is no “statistically significant” difference between the average and what we actually observe. Most of the time, 68 percent of the time, observations are close to the average, within one standard deviation – the average distance of the data from the average itself. As you move farther from the average, you get less of whatever it is you are counting. There are a lot more six-foot guys than seven-foot guys.

Slide 2A

This formula, derived from our mathematics and amazingly confirmed in nature, determines the height of the Normal curve at every point. It tells us just how often what we observe will be, simply by chance, a certain number of standard deviations away from the average.

Slide 2B

This “Z-table” tells you exactly, to the third decimal place, how likely it is that an observation happened by chance. When we run an experiment, we only reject the null hypothesis, and say there is a statistically significant correlation, if the outcome would happened anyway one time out of 20, or five percent of the time. That makes us 95 percent sure that the two variables are correlated, or move together.

Slide 2C

Now, correlation is not necessarily causation. Life is not bivariate – based on just the two things you are measuring. Unless you can randomly assign subjects, life is multivariate, with other variables causing changes too. This is the most common error in public policy. In Latin it’s called post hoc ergo propter hoc: this thing happened after that thing, so it was caused by it. Here’s an example.

Slide 3 – Scouting and Delinquency

Does being a Boy Scout keep you out of trouble? Quick, hold a press conference: only nine percent of scouts are delinquents, versus 15 percent of non-scouts.

Slide 3A

The probability of getting such a big difference from the null expectation of no difference at 12 percent is …

Slide 3B

… less than one percent. You can see that in the column labeled “Probability?” 0.009 is less than 0.01, which is one percent. Scouting works!

Slide 4 – Subgroups

Now let’s control for another important variable in life, a family’s income level. But this slide shows that there is no difference in the low-income families in delinquency rates for Scouts and non-Scouts; both are at 20 percent.

Slide 4A

And in middle-income families, again no difference, at 12 percent. I guess all the difference in delinquency must come from the high-income families.

Slide 4B

Huh? No difference here either, with both groups at four percent delinquency.

Slide 4C

So, oops, the correlation disappears when controlling for income, which unlike scouting, is truly correlated with arrests.

Slide 5 – Trends in Crime

Another way that correlation is confused with causation is in trend lines. You see the drop in violent crime in a city. The mayor, of course, calls a press conference to take credit.

Slide 5A

Everybody has their own explanation.

Slide 5B

But they agree something caused the drop to happen.

Slide 5C

A rising trend line seems to say so.

Slide 5D

But a flat trend line …

Slide 5E

… or a falling line would seem to indicate that it’s all just random fluctuations. Cancel the press conference.

But the point here is that NONE of these things we are seeing with our eyes have ANY proof in them. A simple bivariate chart is inherently misleading. All these graphs are actually worse than useless, because they trick people into thinking they aren’t!

Slide 6 – Sea Levels

Let’s apply what we’ve learned to “climate change.” Take sea-level. There weren’t enough emissions for CO2 to be a factor until 1950, so we compare the rate of sea-level rise before and after 1950, and see if it has increased.

But the United Nations International Panel on Climate Change agrees that the difference in the two slopes isn’t statistically significant. No “climate change.” [See Testimony of Caleb S. Rossiter, Ph.D. before the Subcommittee on the Environment of the House Committee on Oversight and Reform]

Slide 7 – Sea Level by Presidents

Here’s a fun way to look at the same sort of data: sea-level has actually been rising since the 1800’s after the Little Ice Age ended, at the same rate for all sorts of presidents and levels of emissions!

Slide 8 – Hurricanes

How about hurricanes? If you just look at the trend from 1970 to 2010, you’d see a rise. But from 1940 to 2010, you’d see a drop. And from 1850 to 2010, you’d see no trend at all. It’s the same for floods, wildfires, and droughts: no long-term, statistically significant increases from the CO2 effect. “Detection and Attribution” studies claim to detect a rise in some extreme weather variable like hurricanes and then they attribute that rise to increased temperature from human activity. These studies lie in the realm of politics, not science, because there’s no way to tell if the increase in temperature was natural or based on industrial emissions.

Slide 9 – Global Mean Surface Temperature

And speaking of temperature, here is an iconic but misleading UN IPCC graph. It shows the average change in temperature at ground stations, along with uncertainty and a long-term trend line, in blue.

There’s a half degree rise from 1910 to 1940, a flat period until 1980, and then another half degree rise to 2010. With CO2 levels barely rising until 1950, and then zooming up since then, that’s a lot of variation that’s not explained by CO2 emissions. Chaos, natural fluctuation, and unknown or hard to quantify cycles are all part of this picture.

What’s so misleading? First, it’s hard to estimate a global or even local average temperature in tenths of a degree. You can see that by the uncertainty, which itself is a guess. Ground temperature stations are problematic, not just in 1880 but today. Second, the data from decades ago are constantly being adjusted with new rules to show more rise.

Slide 9A

We really should be looking at global surface temperature today, let alone in 1900, on a scale of degrees rather than tenths, like here. These are the exact same data. Hard to see a trend at all.

Slide 9B

Satellite and balloon readings of the troposphere are much more credible than the surface data, but they have only been gathered since 1980, so we can’t use them for longer trends.

Slide 10 – Warm days in Ohio

Here’s a typical temperature trick, courtesy of Tony Heller. The number of days per year over 90 degrees in this town has been decreasing from 1890 to 2017. Tony shows us how to reverse that.

Slide 10A

He moves the start until you can declare an increase! At 1955 you get the “climate change” graph you need. This sort of misleading shopping for a start date is often done in UN and U.S. climate reports. [See U.S. Government Climate Science vs. U. S. Government Climate Crisis]

Slide 11 – CO2 and Temperature

Here’s a famous UN slide of carbon dioxide and Antarctic temperature from ice cores.

Slide 11A

Well, the slide became infamous when Albert Gore Jr. gave us “correlation means causation”at its worst. Vice President Gore says CO2 drives temperature, but it’s mostly the other way around: lengthy cycles in earth’s orbit change the Sun’s impact and drive temperature, which drives CO2. Gore hops on a riser to convince us that as CO2 keeps going up from industrial emissions, it will drag temperature along.

Slide 11B

That prediction is already false, since temperature has barely budged on this scale.

Slide 12 – A Thousand Years of Temperature

Now, is it hotter now than any time in the past 1,000 years? It’s a silly question, because of minimal coverage for old data. But even if the answer were to be yes, it wouldn’t prove anything about what caused the recent rise. We have a lot of the “hottest years on record” recently only because the record is just one hundred years old and we happen to be in a period of slight natural warming. That started in about 1800, well before the CO2 era. Of course, during warming more recent years tend to be hotter than earlier years!

This graph appeared in the first UN report on climate change, in 1990. It represents a reconstruction by climate historians from diaries and proxies of what was roughly happening to the globe. It goes up about a degree in a Medieval Warm Period and down about a degree in the Little Ice Age, and then back up again as that ended, all from natural causes. Many in the public claimed that the image was embarrassing to the “climate change” narrative.

Slide 12A

Rather than try to educate the public about why this bivariate graph has no bearing whatsoever on what has caused our recent multivariate warming, the climate change establishment decided that the graph had to go, and in 2001’s UN report, it did.

Slide 12B

In place of Medieval warming we got a “hockey stick” with the blade rising only in the industrial era. Temperature, it seems, was naturally constant until bad fossil fuels came along.

Now, for all the creative math used in creating this chart from a few tree rings, it’s no better or more certain than the previous, hand-drawn one.

Slide 12C

And I’ll show you why.

Slide 12D

A key UN proxy set estimated by a researcher named Briffa shows that in recent years, temperatures calculated from tree rings go down, while we know temperature was rising. Rather than rethink using tree rings at all, the UN crowd – as revealed by their own emails in the Climategate scandal – just threw out the recent data to “hide the decline.”

We have a saying about complex calculations that rely on uncertain data: Garbage in, garbage out.

Slide 13 – Climate Models

Speaking of problems with data and calculations, let’s end with the mathematical computer models that drive the debate about dangerous warming. Global Climate Models are based on the General Circulation Models that predict local weather conditions on your TV every night. Weather models start with excellent local data on conditions and look at probabilities based on previous, actual weather in such conditions. For a few days then they can make an educated local guess.

The global models, though, use average data for very large blocks of air, land, and sea, then add in carbon dioxide emissions and run for decades into the future, where there can be no comparison to actual conditions. The models also use thousands of estimates, called parameters, to represent with just one number the effect of many complex and chaotic physical processes, like the Hadley cells, wind systems that move heat from the lower latitudes to the poles.

Legendary physicist Freeman Dyson dismissed such parameters as “fudge factors.” They’re all just guesses, like the Medieval warming graph, which are twiddled and tweaked until the temperature output in past decades is “tuned” to match the surface temperature record then.

Now, of course, the surface record is going to be wrong at the start – it’s a rough estimate itself – and the parameters are also going to be wrong as well– they’re guesses. What a mess! And then the true parameters will change both cyclically and chaotically as the model is run into the future, but the modeled parameters will not. Yikes!

The crucial output of these models is an estimate of “climate sensitivity” – the degrees of warming we’ll get from a doubling of CO2 – but that is completely determined by the modeler’s choice of the input parameter for how powerful CO2 molecules are at warming! Crazy … but true. Like the hockey stick, this turns out to be a waste of time. Again, how do we know?

Well, first we can test these models against their own projections, and these consistently run about three times too hot over the past 30 years. So the modelers constantly have to “retune” the parameters and project the future all over again.

But this graph here can’t be tuned away, because it tests even the up-to-date surface models against their projections in the troposphere, up to about 40,000 feet, where they can be checked by the far better satellite and balloon readings. Take a look: the models’ projections for the troposphere, the thick red line, also run three times too hot compared to actual temperatures, the line purple line, right now, without even waiting for the future.

Slide 14 – The Elephant Paper

You can understand models’ weaknesses from a cautionary tale about an elephant. John von Neumann was a legendary mathematician and atomic bomb maker who tried to build a climate model after World War II. He wanted to use it as a weapon, to create a drought in the Soviet Union.

When von Neumann gave up, he laughed that: “With four parameters I can fit an elephant, and with five I can make him wiggle his trunk.” Recently, though, three mathematicians wrote this paper showing how it could be done with functions of complex numbers. See https://publications.mpi-cbg.de/Mayer_2010_4314.pdf

Slide 15 – The Elephant

The first four functions draw the elephant on the left. Then, in the graph on the right, a fifth parameter is added and adjusted, giving us some different placements of the trunk…it wiggles, as required! The point here is that mathematical climate models are controlled by their thousands of convenient choices of parameters, and you can make those parameters do anything you want. And because models can’t be tested statistically, all we are left with again is art, not science.

Slide 16 – Questions

Well, this is Professor Caleb Rossiter, and I hope you have thought a lot and learned at least a little with this lecture on Climate Statistics 101. Please feel free to contact me with any questions or comments, at info@co2coalition.org.

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70 thoughts on “Climate Statistics 101: see the Slide Show AOC Tried, and Failed, to Censor

  1. Sea level has been rising not just since the end of the LIA, c. AD 1850, but since its Maunder Minimum depths, c. 1690.

      • I’d say it’s far too difficult for AOC to understand, which is why she wants it censored – then it won’t exist, you know, like the Laws of Thermodynamics, waaaaah.

        Why do I find myself making a sapphire and tonic?

        Loydo, with her pearls of wisdom, could help us in this discussion I’m sure.

        Three, two, one ……..

      • Censored? No! He should be censured! How dare he!! That’s not the script 97% of us colluded, sorry, concensused on. How dare he go rogue and think for himself!

    • Leftists are no stupider than conservatives. There are two quotes that sum it up better:

      It is difficult to get a man to understand something, when his salary depends upon his not understanding it! Upton Sinclair

      and Chesterton was talking about Marxian Socialists when he wrote,

      If you argue with a madman, it is extremely probable that you will get the worst of it; for in many ways his mind moves all the quicker for not being delayed by the things that go with good judgment. He is not hampered by a sense of humour or by charity, or by the dumb certainties of experience. He is the more logical for losing certain sane affections. Indeed, the common phrase for insanity is in this respect a misleading one. The madman is not the man who has lost his reason. The madman is the man who has lost everything except his reason. link

      AOC can’t understand climate science because that would be a disaster for her.

      • Wrong Commie

        As a libertarian since 1973, I belive I can judge leftists and conservatives without bias.

        There is no doubt in my mind that conservatives are brighter than leftists for the simple reason they debate their beliefs, while leftists just assert their beliefs.

        The one exception for conservatives is religion (I’ve been an atheist for almost 60 years).

        Debating a leftist on climate change is like debating a conservative about his or her religion.

        You hear science-free beliefs and predictions based on faith, not evidence.

        At least most of the 10 commandments make sense for civilization — the communist manifesto makes no sense at all

        • “Debating a leftist on climate change is like debating a conservative about his or her religion”.

          I think that means everyone is conservative when it comes to THEIR religion. Neither of you are wrong.

          • I have to add a Terry Pratchett quote: “All religions are right, for some definition of ‘right'”. Or Kipling “There are nine and fifty ways of constructing tribal lays – and every single one of them is RIGHT.”

        • “Debating a leftist on climate change is like debating a conservative about his or her religion.”

          That needs to be printed and sold on T shirts!

        • Their’s nothing wrong with taking religion on faith. So long as you know that is what you are doing.
          Science should never be taken on faith.

      • “Leftists are no stupider than conservatives.”

        Leftists are more prone to delusions, which does make them stupider than conservatives.

  2. Thanks fir that. However I have some comments.

    First the bell curve. Observables are mostly not following the bell curve. Log-normal is quite obiqitous and the number of hurricanes is more likely to follow a Poisson distribution.

    Secondly, there is a difference between the question: what fraction of observations do we expect (a priory) to deviate more than X from the average (the ‘frequentist’ approach) and the better question: given the observed deviation X what is the (a posteriory) likelyhood that the observation is drawn from the assumed distribution (the Bayesian approach). The literature is full of results that were a priory less than 5% probable and therefore deemed ‘significant’, which nevertheless turned out mostly spurious (implying that the a posteriory likelihood was much larger than the 5%). The 5% limit often used is in most case much to optimistic; 1% or even less may be more realistic.

    • EJ Zuiderwijk –

      I agree with you about the bell curve (normal or Gaussian distribution). Professor Roisster’s statement:

      “This is life! It’s called the Normal Distribution or Bell Curve. It shows how far away from the average most physical and statistical things are.”

      was what I was taught in school. However, when I began doing air pollution studies in the real world, I found that most observable physical things are NOT a normal distribution. Lots of mistaken conclusions have resulted from assuming the sample was from a normally distributed population, when in fact the population was not at all normally distributed.

      • If you don’t assume a Gaussian distribution then the use of a global average temperature blows up.

        In fact, what the climate alarmists use isn’t really an average at all. The baseline input is a “mid-range”, i..e the high reading minus the low reading divided by 2 at the measurement station. That isn’t an average at all. You don’t even know if the high and low readings are actually the maximum and minimum readings if the readings are taken at fixed points in time.

        With a skewed probability curve the median is quite likely to not equal either the middle value or the most likely value.

          • Steven,

            What don’t we understand? That minimum temps going up can raise the “average” temp just as easily as maximum temps going up? It’s sixth grade math!

            Yet all we hear from the climate alarmists is that the Earth is going to turn into a fireball because average temperatures are going up! A sixth grader would get an F on such a statement!

        • “If you don’t assume a Gaussian distribution then the use of a global average temperature blows up.”
          Calculating a global average temperature has nothing to do with Gaussian distribution.

          • ‘Calculating a global average temperature has nothing to do with Gaussian distribution.’
            Which is rather T G’s point.

          • Calculating a global average temperature back to the 1850’s, even though Phil Jones said the data from the Southern Hemisphere was mostly made up until the 1950’s, has nothing to do with any kind of distribution.

          • Nick,

            If you are calculating an AVERAGE temperature as a useful value then you are assuming a Gaussian distribution by definition.

            If you have a skewed distribution then the mean (i.e. average), the mid-range, and the most likely value are probably all different. Only very unique skewed probability curves have them come out the same.

            Why do you always think you can fool people on here? You *always* fail.

          • Steven, can you be kind enough, with your magnificence to make us, all dumbs, understand?

          • Nick. I was wrong he never mentions the 1950’s.

            “The issue Ray alludes to is that in addition to the issue
            of many more drifters providing measurements over the last
            5-10 years, the measurements are coming in from places where
            we didn’t have much ship data in the past. For much of the SH
            between 40 and 60S the normals are mostly made up as there is
            very little ship data there.”

            So tell me how they got the data accurate to .01C back to 1850?

          • “Steven, can you be kind enough, with your magnificence to make us, all dumbs, understand?”

            read the papers.
            download the code.

            I ain’t no liberal, personal tutoring is not free.

          • Nick, if you don’t assume a Gaussian distribution for all individual station data then tell us how folks propagate different variances from each station throughout the calculations of GAT.

            It’s been my experience when asking or looking at “papers”, this issue is absolutely and totally ignored. It certainly is never quoted when trend lines are graphed.

          • Moshpit says:
            download the code.

            Right! Paraphrasing Biden, we can all be coders now……

          • Jim
            “Nick, if you don’t assume a Gaussian distribution for all individual station data then tell us how folks propagate different variances from each station throughout the calculations of GAT.”
            You don’t have to propagate variances, or any other parameter of statistical distribution (except maybe mean) in averaging. The crude approach is to just sum the readings for a month and divide by the number. The proper approach is to weight the average by the area that each reading is supposed to represent. That doesn’t bring in any notion of statistical distribution, Gaussian or otherwise.

          • Nick,

            “You don’t have to propagate variances, or any other parameter of statistical distribution (except maybe mean) in averaging. The crude approach is to just sum the readings for a month and divide by the number. The proper approach is to weight the average by the area that each reading is supposed to represent. That doesn’t bring in any notion of statistical distribution, Gaussian or otherwise.”

            OMG! When you sum the readings and divide by the number of readings you are calculating a mean. That mean is only meaningful if you have a Gaussian distribution! If you have a skewed distribution then the mean, the mid-value, and the median are all different and the mean is probably the least useful.

            When you then try to determine an average of two different stations, perhaps thousands of miles apart, then you HAVE to take the variances of the readings at each station into account. Averaging the November monthly average temperature in Nome, AK with the November monthly average temperature in Galvaston, TX means exactly what? The temperature variation in Alaska during November is 5-8degF. In TX it is about 11-12degF. A LARGE difference.

            You would have us believe that averaging the average of two different locations will tell us something about a “global” climate! When it is obvious that it is meaningless.

            In a skewed distribution the median is a far better value to use. In a distribution with over-weighted or under-weighted data values or with a significant number of outliers the mean can be useless in actually representing the distribution, again the median or mid-range values are much better.

            The average global temperature is basically useless. It tells you absolutely NOTHING about the temperature profile of the globe let alone regional or local areas. And it is the temperature profile that determines actual climate, not some kind of an “average” that is meaningless. It’s why I remain an advocate of using heating and cooling degree-days (an integral of the temperature profile above or below a cutoff temperature.

  3. Much too complicated for bumper stickers and protest sign verbiage….and UN agencies on a mission and agenda media and politicos that came after Gore.

  4. The official global temperature chart is a fraud. That needs to be emphasized. You wouldn’t know that is the case reading this presentation.

    I would suggest comparing unmodified, regional Tmax charts, which show it was just as warm in the Early Twentieth Century as it is today, and therefore CO2 is not a significant factor in the Earth’s climate, to the fraudulent global temperature chart which has erased the Early Twentieth Century warming from the official record, in order to make it appear that temperatures are getting hotter and hotter, decade after decade and we are now at the hottest temperatues in human history. Nothing could be further from the truth, but the students won’t know that from this presentation.

    Ask your students why one set of charts shows one temperature profile, while the other shows a completely different profile. One of those profiles is wrong. Your students would benefit from knowing which one.

    And then the realization will sink into the heads of your students that since this whole Human-caused Climate Change crisis is based on a fraudulent Hockey Stick chart, the crisis is also a fraud.

    You didn’t point out the fraud. You left the students in the dark.

    Tmax charts from all over the world show it was just as warm in the recent past as it is today, which means CO2 is not a significant factor in the Earth’s climate.

    The bogus, bastardized global surface temperature Hockey Stick charts say we are living in the hottest times in human history. The Tmax charts show this is not true.

    The modern-era Hockey Stick chart is a Big Lie created to foist the Human-caused Climate Change hoax on the world. The Big Lie is easy to see: All you have to do is compare a few charts. It should be obvious they don’t look alike. One set of charts, the Tmax charts, are derived from actual observations, the other, the fraudulent Hockey Stick chart, is computer generated

    Teach the Children Well. Show them the truth, and show them the lies.

    • When you take an average you LOSE DATA. You can no longer determine the actual temperature profile and it is the profile that actually determines the climate, not the average temperature. In fact the term “average global temperature” is misleading because it is actually based on the “mid-range” value, not the average. (i.e. high value minus the low value divided by 2, This is *not* an average, it is a mid-range value) In many cases, especially for much of the historical data, we don’t even know if the high and low values are actually the maximum and minimum temperatures since much of the historical record is based on taking the readings at specific times. It is highly likely that the those times don’t occur at the maximum and minimum temperatures.

      • “… we don’t even know if the high and low values are actually the maximum and minimum temperatures since much of the historical record is based on taking the readings at specific times …”.
        Six’s max/min thermometer dates back to 1780 and most if not all meteorological thermometers would have been that type, presumably read at a particular time every 24 hours.
        How accurately or dutifully is anyone’s guess but they are the only direct temperature records there are which raises the matter of the myriad adjustments made long after the fact, particularly the NCDC and GISS records:
        “… it should however be noted, that a temperature record which keeps on changing the past hardly can qualify as being correct …” (Ole Humlum).

      • Is it of any importance for how long you have been at a certain value, or temperature in this case?

      • “it is the profile that actually determines the climate, not the average temperature”

        Yes, and the actual temperature profile, the one you get from unmodified (actual temperature readings) regional Tmax charts from around the world show that it is no warmer today than it was in the Early Twentieth Century. Pop! goes the Human-caused climate change narrative!

        If all regional Tmax charts show essentially the same temperature profile, then they represent the global climate.

        The real global climate warms for a few decades and then cools for a few decades and then warms again. Up a little, down a little, up a little, down a little.

        The bogus, bastardized Modern-era Hockey Stick chart shows the climate getting hotter and hotter and hotter for decade after decade and claims the Earth is now the warmest in human history. But the facts (regional Tmax charts) show this is not reality.

        We have two global temperature profiles. One derived from the Tmax temperature readings and one that is generated in a computer, subject to all sorts of manipulations.

        One of these two temperature profiles does not represent reality. One has to conclude that the Modern-era Hockey Stick chart does not represent reality because it does not agree wiith actual temperature readings from the past.

        Climate scientists should start using actual temperature readings if they want to know the truth about the Earth’s climate and CO2.

        Here’s an example of the difference in temperature profiles for a chart that uses actual temperature readings (Hansen 1999) and the bogus Modern-era “hotter and hotter” Hockey Stick chart.

        Notice how the US chart warmed from the 1910’s to the 1930’s, and then cooled from the 1940’s to the 1970’s (when climate scientists were worrying about Global Cooling) and then warmed again from the 1980’s to the present day. Hansen said 1934 was the hottest year in the recent past, being 0.5C warmer than 1998, which makes it 04C warmer than 2016. So the U.S. has actually been in a temperature downtrend since the 1930’s. Alarmists never mention this. How can CO2 be heating up the whole Earth if the US is in a temperature downtrend? Answer: the whole Earth is *not* heating up, it is doing just what the US is doing. Going by the regional actual temperature readings, that is.

        The bogus Hockey Stick chart, otoh, “disappears” the warming that took place in the 1930’s and it also downplays the cooling from 1940 to 1980. This was done to make it appear that temperatures are getting hotter and hotter and are now are the hottest in human history, and this was done to promote the Human-caused climate change hoax. Actual temperature readings put the lie to this narrative.

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

    • Tom, I’ve been around this — living in Boulder, CO, and online at sites like WUWT, climate audit and Pielke, Jr — for over 18 years. But I cannot recall the early 20th century Tmax charts.

      Will somebody please post a link!

  5. As a retired professor of computer science who has taught the art of computer modeling I am pleased to find a fellow traveler.

  6. Tom Abbott: I am happy to see corroboration of some of my recent work on diurnal temperature variations on how climate info is mis-reported. Alberta commissioned the Alberta Climate Future study. The authors, Hayhoe and Stoner, used only temperature data from 1950 to about 2015. They base all their forecasts on that. Working with Friends of Science Society, we tested Tmax and Tmin temperatures over the full historical record. In the three cases where data went back to 1884, the Tmax trend is downwards. The 1920 to 1940 period was hotter than today. That is opposite to the more recent trend and inconsistent with the long term trend. Warming is only happening in Alberta at night and in winter, increasing the average T. But summer days are not getting hotter as shown by the Tmax trends.

    • ” In the three cases where data went back to 1884, the Tmax trend is downwards.”

      That’s what the US shows, too. Tmax charts put the lie to Human-caused climate change/global warming. That’s why you never hear alarmist mention Tmax charts They want to pretend they don’t exist. You can understand why: It blows up their scam..

  7. About that 1950s decrease in frequency of 90-plus temperatures at Lancaster OH: The current location of the official thermometer is an airport that opened in 1959. Before that airport existed, the temperature for Lancaster OH was measured someplace different, probably in the city itself.

    • Yet another demonstration of why climate alarmism is a scam.The scientific debate is virtually won yet”we”
      are losing the political debate.What if Trump is not re-elected? Why is this happening?The answer of course is because a small group of determined “scientists” aided and abetted by a group of unscrupulous politicians determined an agenda.The media were cunningly brought on board and the message sold to a mainly non – scientific public and more opportunistic politicians.They corrupted peer review and,frankly,sold their ” message” brilliantly!Their message ,AGW,has been debunked endlessly on the net,yet politicians in the West (with the notable exception of Donald Trump) bow down to “Climate Change” and “Green Eco Warriors”.-and Greta!! Why?Mainly because of the media.Bad news sells!The “message” gets a life of its own thanks to ” Climate journalists” and of course television.Politicians take more heed of them than the net and accurate science promoted on it.Then they get frightened by ” public opinion ” and waste trillions trying to counter climate change.
      I believe ” they” have made a great mistake however.The ” message” has seriously frightened – and emotionally disturbed- many children.And with lies.This,therefore,is child abuse.If lies are proven to have caused child abuse then a bright light will shine on the whole scam.For example in the so-called BBC documentary “Climate Change -The Facts” Michael Mann said “We see the effects of climate change and it’s happening faster than we thought.” This is not an inaccuracy. It is a lie.Another scene depicted a car with two terrified occupants driving through a forest fire.The clear message being that global warming causes forest fires.I’m sure that others more qualified than me can point out other lies.Not inaccuracies or exaggerations but lies.What if an organization such as the BBC was sued by,say,a parent of a child who suffered psychological damage due to lies about climate change?(The BBC is of course just about the worst promoter of climate alarmism in the world).The bright light could then shine and -to coin a phrase – the can of worms would be opened.The light could shine very far down – maybe even as far as a proper investigation into the ” climategate” emails.I also believe that many scientists who fear to speak out ,even within the IPCC ,might gain courage.
      Yes,maybe I’m flying a kite too high.(ok 3rd cliche!) but Mann and co dont hesitate to use litigation and all sorts of dirty tricks – because they are politically savvy as well as totally unscrupulous.The good guys need to be at least politically savvy.
      A survey of children affected by climate change emotionally would be useful.
      A billionaire to fund a lawsuit also!
      Whatever,the LIES must somehow get to the main media – difficult I know because they spread a lot of them!

    • If the official station was actually inside the city limits prior to it being moved to the airport, then it was in violation of pretty much every NOAA standard.

      • This was common practice before airports became more common. Philadelphia is another example, having had its official thermometer at one downtown or semi-downtown place or another until it was moved to the then-new Philadelphia International Airport in 1948. For that matter, NOAA was created in 1970 and its predecessor absorbed in 1965 what was then called the Weather Bureau.

    • That’s twice now (that I know of) that a Democrat has openly pressured sponsors to stop dealing with “denialists,” the other being from the Chair of the House Select Committee on the Climate Crisis, again to the CEO of Google:

      https://tinyurl.com/y76po47n

  8. Okay, this is the first technical article on wuwt that I really do understand, because I have taken statistics, and know how to test a null hypothesis. I think, though, that if I hadn’t taken stats in a classroom, I’d be bewildered by most of the article.

    Of course, climate alarmists take advantage of the general innumeracy of most citizens and so you get otherwise intelligent people who believe that a season of wildfires in Australia proves the proposition that burning fossil fuels is making the Earth uninhabitable for humans.

    I concede that I may not understand the Math here, so feel free to clue me in if I’m wrong, but how is a two degree Celsius rise in average temperature a catastrophe? All that happens in a two degree rise is that the plot of daily average temperatures over a year shifts two degrees to the right. I live in Edmonton, Canada and, if the average annual temperature here were to increase by two degrees, it would just mean that the hottest day of the year would be 32 degrees C and the coldest would be -30. So big deal. We have cities two hundred miles to the south where that is the temperature range now, and the inhabitants of them are not thought to be living in climate hell. So what’s the big deal?

    As I type this, it is 36 degrees C. in Phoenix, Arizona. The population of Phoenix is 1.66 million. If heat is so bad, why don’t they move to Edmonton, where the temperature right now is 17?

    • It is mainly propaganda. The use of anomalies (long explanation) allow graphs where the whole y axis goes from 0 to 2 as an example. Looks like a tremendous change. Whereas a change from 70 to 72 looks like a pretty small change.

      Here in Kansas 2 deg would make us look like Oklahoma. We would be thankful!

    • There are numerous errors being propagated throughout the determination of a Global Average Temperature. The people doing it a numbers people, not measurements people.

      Significant digits are totally ignored.

      The Central Limit Theory is used to justify increasing the precision of measurements.

      Uncertainty associated with measurements is never defined or propagated.

      Populations of temp data (stations) are combined without recalculating a new variance. Anomalies are used to get them around this by reducing the variance.

      If you want to question any of this ask how variances, uncertainty, and precision are propagated.

      Lots of other issues, some of which have been mentioned elsewhere in this thread.

  9. “Only in the law school do they teach ad hominen arguments – attacking or praising the messengers. Scholars just analyze their message.”

    No – that’s the case for 90% of academia – as pointed out by Prof. Snow in his two cultures lecture.

    The 90% of academia that is the liberal arts and humanities have no grasp of physics, statistics and even basic math.

    There can be no proof that Rembrandt was a better artist than Picasso.
    Heidegger was a more accomplished philosopher that Hobbes.
    Hemmingway was a better writer than Dickens
    Ginsberg is a better Supreme Court Justice than Alito etc. etc.

    Such academics are fed a constant diet of opinions rather than facts.

    No wonder they are confused and worse wantonly ignorant when indulging in epistemic trespass into the arena of facts rather than opinions.

    The consensus is simply a consensus of ignorance !

  10. Were we really trying to understand climate, we’d be watching the chaotic distribution of enthalpy. At the sites of interest, it’d be interesting to watch the behavior for strange attractors. Putting other variables, such as dust, ice crystals in the stratosphere, etc. into the mix could lead to a much better understanding of our world. I know that until recently I was unaware that the American South got hit with dust from the Sahara during hurricane season.

  11. Hello, Pat. Don’t you worry: If I’m making too much sense today, this will in no way balance out the many days when I made too little. Or no sense at all, for that matter.

    But it does seem that the two degrees about pre-industrial levels idea is purely arbitrary. A lot of numbers you read in advocacy positions are similar, in that they seem to have been chosen, not on the basis of any real analysis, but because they are numbers that can be made plausible to the widest group of people.

    We used to have something called the Obesity Epidemic. (We don’t have it any more because now we have a COVID-19 epidemic, which really does kill people.) A number you’d read I the papers was that 60 percent of Canadians were overweight or obese. I know what a fat person looks like, and I’d go to a shopping mall on a Sunday, where I could see hundreds of randomly selected people walk by, and It just wasn’t true that 60 percent of them were fat. More like 25 percent. But the 60 percent figure just took hold, and most people believe it to be true. The 60 percent figure was just a number that diet and fitness enthusiasts found they could get most people to believe.

    97 percent of scientists agree that anthropogenic climate change is a serious problem. Well, that 97 is just a number they can get a lot people to accept on its face.

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