One of the most frightening aspects of global warming, aka “climate change” is the graphs produced from temperature data for public consumption and trumpeted by an unquestioning and compliant media. When it comes to measuring climate, in order to actually see any temperature differences over the last century, they must be highly magnified using the temperature anomaly method.
The most often cited global temperature anomaly graph is from the NASA Goddard Institute of Space Studies (GISS), showing yearly average temperatures since 1880, as seen in Figure 1 below.
To the untrained and uninitiated (i.e. the general public) it looks like Earth’s temperature is on a trajectory for a hot and terrible future.
Sometimes, media outlets such as the daily-doom newspaper known as The Guardian, will take that data and make their own graphs, making them look even steeper and scarier, such their highly statistically amplified graph from their 2019 article as seen in Figure 2.
Written by the ever-alarmed and always unreliable Damian Carrington, it is no wonder some children think they have no future due to “climate change”.
But in the real-world, people don’t experience climate as yearly or monthly temperature anomalies, they experience weather on a day to day basis, where one day may be abnormally warm, and another might be abnormally cold. Sometimes new records are set on such days. This is normal, but such records are often portrayed by the media as being evidence of “climate change” when if fact it is nothing more than natural variations of Earth’s atmosphere and weather systems. In fact, is doubtful humans would even notice the mild warming we’ve had in the last century at all, given that the human body often can’t tell the difference between 57°F and 58°F in any given moment, much less over a long term.
Essentially, what we know as climate change is nothing more than a man-made statistical construct. You can’t go outside and hold an instrument in the air and say “I’m measuring the climate.” Climate is always about averages of temperature over time. It’s a spreadsheet of data where daily high and low temperatures are turned into monthly averages, and monthly averages are turned into yearly averages, and yearly averages are turned into graphs spanning a century.
But, such graphs used in press releases to the media and broadcast to the public don’t really tell the story of the data honestly. They omit a huge amount of background information, such as the fact that in the last 40 years, we’ve had a series of El Niño weather events that have warmed the Earth; for example, 1983, 1998 and in 2016. The two biggest El Niño events are shown coinciding with temperature increases in Figure 3.
These graphs also don’t tell you the fact that much of the global surface temperature measurements are highly polluted with Urban Heat Island (UHI) and local heat-sink related siting effects that bias temperatures upward, such as the wholesale corruption of climate monitoring stations I documented in 2022, where 96% of the stations surveyed don’t even meet published standards for accurate climate observations. In essence – garbage in, garbage out.
But, all that aside, the main issue is how the data is portrayed in the media, such as The Guardian example shown in Figure 2.
To that end, I have prepared a new regular feature on WUWT, that will be on the right sidebar, combined with the long-running monthly temperature graphs from the state of the art (not polluted or corrupted) NOAA operated U. S. Climate Reference Network and the University of Alabama Huntsville (UAH) satellite derived temperature global record.
I’m utilizing the NASA Goddard Institute of Space Studies GISTEMP global dataset. The difference is simply this – I show both the absolute (measured) and the anomaly (statistically magnified) versions of the global temperature. This is accomplished by doing the reverse procedure as outlined in UCAR’s How to Measure Global Average Temperature in Five Easy Steps.
In this calculation, the “normal” temperature of the Earth is assumed to be 57.2°F. and that is simply added to the anomaly temperature reported by NASA GISS to obtain the absolute temperature. The basis of this number comes from NASA GISS itself, from their FAQ page as seen in August 2016 as captured by the Wayback Machine.
Of course GISS removed it from that page as seen today, because they don’t want people doing exactly what I’m doing now – providing the absolute temperature data, in a non-scary graphical presentation, done in the scale of how humans experience Earth’s temperature where they live. For that I’ve chosen a temperature range of -20°F to +120°F, which is representative of winter low temperature near the Arctic Circle and high summer temperature in many populated deserts, such as in the Middle East.
Can you tell which graph visually represents a “climate crisis” and which one doesn’t?
Feel free to check my work – the Excel spreadsheet and the calculations are here:
To create the graphs above in Figures 5 and 6, I used the data from the Excel Sheet imported into the graphing program DPlot.
Note: some typos in this article were fixed and some clarifications added within about 30 minutes of publication. -Anthony
Although global warming by about 2 degrees F is not much for how people feel the temperature, there is the matter of the Arctic having warmed a lot more than the world as a whole because of a known regional positive feedback mechanism. This positive feedback mechanism also exists substantially in the peninsula and near-peninsula region of the West subset of Antactica. The main actual concern is ice sheet melting causing problematic sea level rise. Reconstructions of the previous interglacial period (with the Milankovich cycles favoring an earlier-starting longer-duration for the previous interglacial than for the current one) indicate that global temperature spending much time more than 2 degrees C above the 1850-1900 average is likely to cause a problematic sea level rise, and 3 degrees C above 1850-1900 average melted much maybe a majotity of Greenland’s ice sheet within a few centuries and caused sea level rise of multiple meters.
“The main actual concern is ice sheet melting causing problematic sea level rise”
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Greenland and Antarctica are below freezing nearly everywhere nearly all of the time. The tiny melting that occurs for a few weeks during very the short summer represents a tiny percentage of the ice loss due to the calving of icebergs year round. The ice mass balance is a function of snow that fell years or millennia ago and icebergs calving into the sea. Greenland and Antarctica might be losing ice, but temperature has precious little to do with it.
Sir,
You are quite wrong about the cause of Antarctic peninsula warming compared to East Antarctica. the western part of that continent is subject to plate tectonic action with the Pacific/Southern Ocean plate sliding under the Antarctic plate causing strong thermal underplating and many volcanos that are warming the region.
So, we can expect some gradual loss of glaciers and ice shelves, but nothing sudden or catastrophic, as the continent as a whole is slowing cooling towards the next glaciation within 1500-3500 years.
“So, we can expect some gradual loss of glaciers and ice shelves, but nothing sudden or catastrophic”
Does not follow – depending on how one defines gradual, sudden or catastrophic.
A lot of gradual change can make a sudden change inevitable.
“problematic sea level rise”
Who decided what problematic means?
My only question is: shouldn’t the temperature scale be Kelvin? To my knowledge all thermodynamic analysis must be done in Kelvin.
The difference is that the 2 degree difference(1880-2020) in Celsius is 3.5% but in Kelvin it is 0.7%.
Perspective is everything.
Applying that method to human body temperature, a 2 degree difference from normal in Celcius is 5.1% but in Kelvin it is only 0.6%. Perspective may be everything, but if you were 2 degrees (K or C) off normal body temperature you’d still be very ill.
“A mild fever (up to 39°C) can actually help the immune system to get rid of an infection.” https://www.betterhealth.vic.gov.au/health/conditionsandtreatments/fever
The planet does not have a fever, Greta.
>> Can you tell which graph visually represents a “climate crisis” and which one doesn’t?
Both seem to show a warming signal of about 2F in the last 40 years, to the relevant information is the same, isnt it? (one seems trying to hide it a little, so it is deceptive)
Are you trying to imply that there is no warming or 0F is a relevant reference point?
I am sorry to say so, but this is one of the weakest posts, I have ever seen you writing!
You could try that trick from Monkton and his flawed feedback math and try to show all this all relative to absolute zero, so the increase looks visually even smaller .. it is still there and still about 2F over 40 yeras..
But maybe I misunderstand something?
Well, I know everybody loves a critic, but if that warming would be real and mostly anthropogenic, you color it alarming…
morfu ==> No one, particularly not I or Mr. Watts, denies that it has warmed some since the end of the Little Ice Age, or that it has seemingly warmed more at the northern pole.
In my case, I am grateful that it has warmed as it has improved the lot of mankind and a vast majority of all living things. Why anyone would considered it “alarming” is a mystery.
It was alarming when temperatures in Europe and North America dropped into the Little Ice Age. The first European colonizers to what we call New England today, had a very rough time of it due to the severity of the winters.
The 2°F that you seems to impress you would not even begin to concern an OSHA inspector responding to complaints of too warm/too cold at the office. OSHA’s comfort range is 9°F wide.
At what point would an increase or decrease in the global average temperature concern an OSHA inspector? Why would an OSHA inspector care about the global average temperature anyway? Would it even be appropriate for an OSHA inspector to work with the global average temperature or any average temperature for that matter since one cannot average temperature?
Why would the OSHA inspector be worried about the global average temperature? The inspector is worried about the temperature variation at a specific location! It is the RANGE of temperatures that the OSHA inspector is concerned with. That same RANGE applies to the GAT as well. Why is that so hard to understand?
bd ==> I suggest reading that essay — it is quite good.
I did read the essay.
bdgwx ==> well then you should know that you questions are misplaced.
Why? Because averaging intensive properties is something only you and a few select individuals are allowed to do but no one else?
Stop trolling. Hoisting those who use temperature on their own petards is actually fun – whether you like it or not.
Is 100F the same in Las Vegas and Miami? If not, why do climate scientists average them together?
“If not, why do climate scientists average them together?”
They don’t. They average anomalies.
Averaging anomalies fixes nothing. The anomalies carry the very same measurement uncertainties of the components used to calculate the anomaly. The uncertainty of the baseline average and the uncertainty of the current temp ADDS, actually making the anomaly MORE uncertain than either of the components alone.
Just as the anomaly components hide the variance of the actual measurement data the anomalies do the same thing. Climate is made up of the entire temperature profile, not just the median temps. When you lose the variance you lose the temperature profile. Anomalies don’t fix this.
In fact, the term “average” is a mischaracterization of the combination of the max and min temperatures used for daily calculations. They are MEDIAN temps. Because the daytime temps and nighttime temps come from different distributions (sinusoid vs exponential) combining them results in a skewed distribution where the average and the median are *not* the same.
The Global Average Temp is *NOT* an average. It should be called the Global MEDIAN Temp!
bdgwx ==> Can you PLEASE try to be a responsible, contributing commenter? You “say” you have read my essay on this point, but demonstrate over and over and over that you did not understand it.
Have a sixth-grader read it to you and explain it as he goes along.
You said…
and
and
and finally and most succinctly
…which I understand as one cannot average temperature…no buts.
Pretend I’m a sixth grader. Use as many ad-hominem attacks as you want if it helps you articulate better. So if you didn’t mean that an average temperature is nonsensical, meaningless, and cannot be done no buts then what did you mean and why do you and Anthony Watts get a free pass to post average temperatures?
No one is getting a free pass. They are merely using what is presented to them – and it doesn’t matter if it has any physical meaning or not.
Temperature is not a valid measure of enthalpy. Enthalpy depends on several other variables as well. It truly is that simple. 100F in Las Vegas is *not* the same as 100F in Miami. Yet in climate science they are both included in the same data set used to calculate GAT.
If you can’t understand this simple fact after reading KH’s posts then you are just being willfully ignorant or are trying to defend something that you know is not fit for purpose
bdgwx ==> You want a philosophy of public science communication lecture now? I haven’t the time — but I’ll give the the nut of it: Certain groups worldwide are using the metric GAST (whether I agree that it is in any way valid or not, they use it and the public believes it is something important) not only to scare the pants off people (and most insidiously, children) and to try to change the world’s standard of living for the worse by reducing and banning the use of fossil fuels. They do that by exaggerating the magnitude and importance of the metric as they produce it and present it.
On the other side of the issue, Anthony and I and many others, try to combat that evil by reassuring the general public with a more honest view of the topic.
Then perhaps you should make that your schtick instead because from my vantage point saying one cannot average temperature no buts and then not calling out Anthony Watts is turning a blind eye at the least. My hunch, however, is that you aren’t as convicted about averaging temperatures as you let on otherwise you’d have to chastise the use of all of temperature observations including Tmax and Tmin since they are themselves averages.
You’ve never really bothered to read what Kip has posted in the past even though you say you have.
AW is using what the climate science cadre puts out and has shown how they distort the context of it. He is hoisting them on their own petard. Do you even know what that means?
For some reason you simply can’t get it through your head that he is playing on *their* field, just as Kip is. That is not the same thing as accepting their “field” as correct. You can play soccer on an American football field but that doesn’t mean you accept the size of the American football field as correct for soccer. Typical soccer fields are about 9000 yd^2 while the football field is about 6400 yd^2.
You are revealing your bias towards being a mathematician and not a physically trained engineer or scientist.
The biggest reason for not averaging is you are not measuring the same thing but also using different devices subject to different calibrations and microclimates. In other words, you have not determined that the enthalpy at each station is the same so that temperature could be an adequate proxy for both locations.
Stop trying to prove that math tricks can somehow create better resolution of measurements out of thin air, and also reduce uncertainty far below what each measurement is subject to. Those are reasons why there is such concern with the replication crisis in all science fields.
Use the method that NIST has explained in TN 1900. If you do that separately one Tmax and Tmin at individual stations, you may reach a better appreciation about what GAT truly means.
Bravo! You hit the nail right smack dab on the head!
Excellent response!
Einstein said it best when he exclaimed that it only took one person to show him wrong. As more and more localized data is examined, it becomes clear that CO2 is simply not the driver of the globe getting warmer. Trying to eliminate it is not going to accomplish anything.
If bdgwx and bellman and others want to really prove their point, they need to find large areas of very high warming that offset those locations with no warming or even cooling.
It would be advantageous to all to read this study carefully.
https://www.nature.com/articles/s41598-018-25212-2
It very well demonstrates that last frost and first frost dates are changing due to Tmin. It also confirms that this is a good thing for crop production in the U.S. If the temperature increase was harming food production it should have shown up in this study – it did not.
I really do not see you point in regards to this debatte!
How would you answer AW´s question then:
>> >> Can you tell which graph visually represents a “climate crisis” and which one doesn’t?
>> The 2°F that you seems to impress you
Please stay away from personal comments in any from, that is just unprofeesional.
In this case it is not the question how I might react to it, but that is the relevant information shown in the graphs, is it not?
morfu ==> Sorry, sir or mam’, these comments are conversations, and exchanges of ideas and opinions. It is wholely appropriate to ask another commenter his or her opinion on the topic of conversation.
The question, rather bluntly, IS about how people (even you) react to it. That’s the whole point about the scaling of the data — all are using the same data only different scales for presentation. Read both Huff (free copy linked by me somewhere in here, use your browsers page search for it) and Mosher’s beloved Cleveland which quotes Huff).
You are not only not required to answer, you are not required to comment at all.
Well, okay then, I am not impressed with that 2F, as any dataset using the MSU 14 channel is erroneous and for example J. Vinôs offers a recent good explanation for some of this warming highlighting the role of the sun.
>> The question, rather bluntly, IS about how people (even you) react to it.
Asked and answered:
“””Both seem to show a warming signal of about 2F in the last 40 years, to the relevant information is the same, isnt it? (one seems trying to hide it a little, so it is deceptive)”””
Aww, maybe it´s your turn to answer questions . .
If you agree that data might show some warming, why trying to hide it?
The anomaly trend seems a good way to show how mach warming there is.
Feelings about alarmism seem unscientific- on either side of the spectrum. Just the facts, please.. what could that warming imply?
Just greening of this planet or a methane flood?
The most often cited global temperature anomaly graph is from the NASA Goddard Institute of Space Studies (GISS), showing yearly average temperatures since1880, as seen in Figure 1 below.
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It needs to be pointed out that the change since 1880 is about ONE degree which at any time of day or year won’t be noticed by anyone. It should then follow that the proper response to that should be: “So What?”
Readers ==> Global temperature anomaly graph is from the NASA Goddard Institute of Space Studies (GISS)
Figure 1 shows almost exactly 1°C of warming since 1880. Apparently it matters which GISTEMP chart you choose to look at.
I believe there is still a great deal of perception deliberately added.The idea of a “normal” amount of variation is key to understanding whether something is unusual. So filtering to an annual figure helps people see only the low frequency trend instead of the huge variation we see month by month, week by week, day by day or even minute by minute (cue The Doobies)
So compare the unfiltered plot on the left below with Figure 1 or 3 above. We see that the variation within a year is a significant component.
On top of that, by drawing a line between data points we are implying that the values between points should be some form of interpolation where we actually have no evidence that this is the case. The more correct plot is shown on the right hand side below providing no prejudice as to what lies between. I have never seen a sub-monthly sampled global dataset, but I would be hugely surprised if there isn’t far higher frequency content to the signal than we typically see monthly. Eyeballing my graph, the annual variability is about half a degree in a 150 year 1.5 degree change. If we were to manage to get a daily global record, I suspect (just a guess really) that the width of the annual variation would increase to maybe 1 degree, and swamp the trend significantly
Unfortunately, children (and adults) in the UK just don’t know what Fahrenheit is anymore!
Both charts show the same amount of warming. One just makes it easier to see than the other. Why would you want to produce a chart that makes the change you’re measuring more difficult to see? You’d only do something like that if you were trying to hide or minimise the extent of the change… oh.
Adding a least squares or some such line to the CRN graph would be helpful.
The NASA GISTEMP graph is a fraud. It really is that simple. It is impossible to create the shown graph from actual measured data.
“It is impossible”
Huh? They did it. I’m not saying its true, I’m saying it exists.
Of course it exists. But it’s not from measurements. 1921 was a hot year around the world. Anyone telling you that the 70s were warmer than 1921 is just wrong.
Nelson said: “1921 was a hot year around the world.”
Can you post a global average temperature dataset showing that.?
JRA-55 posits a global warming of 1C since late February 2023.
20% of an ice age as Schmidt and Dessler like to say.
It really is all a matter of perspective, innit?
1C over 1 month. 1C over 1 year. 1C over one century. 1C over one millennium.
Our sense of scale most acutely tied to our expected lifespan, i think.
Everything will either to heck or must be resolved before we expect to die.
For we want to bear witness. So we can say, “see”, I told you so.
Climate science packed full of braggarts and egoists.
The time scale, y-axis visualization, and the associated perspective [noise or not noise?] selection process objectively arbitrary in reality.
I comes down to feelings, after all.
The funny thing is 1°C means end of April typical temperatures (15-17C°) for northern hemisphere. But a UK has a weather warning for snow and ice for Tuesday. Not normal for middle of March. This is why people question the 1°C claim.
Such secular changes and the popular belief of permanent progressive climate change over generations has been discussed for centuries. Here from 1908. https://wellcomecollection.org/works/jzddphyp/items?canvas=1
JCM ==> Terrific link! Thank you!
“To the untrained and uninitiated (i.e. the general public) it looks like Earth’s temperature is on a trajectory for a hot and terrible future.”
But…but…but… Al Gore said we’re already there when he said “the oceans are boiling”. /sarc
Essentially, what we know as climate change is nothing more than a man-made statistical construct. You can’t go outside and hold an instrument in the air and say “I’m measuring the climate.” Climate is always about averages of temperature over time. It’s a spreadsheet of data where daily high and low temperatures are turned into monthly averages, and monthly averages are turned into yearly averages, and yearly averages are turned into graphs spanning a century.
you cant go outside and measure inflation its nothing more than a man made construct
this cant go outside and measure GDP, you cant measure the curvature of the earth.
climate is not a spreadsheetof data. this tells me the writer cant program like most amateurs they download data into exell
they probablly think averaging is adding up numbers and dividing by n.
“Climate is always about averages of temperature over time. It’s a spreadsheet of data where daily high and low temperatures are turned into monthly averages, and monthly averages are turned into yearly averages, and yearly averages are turned into graphs spanning a century.”
Climate isn’t even really about averages. Two different locations with different climates can have the same average daily temperature, average monthly temperature, and average yearly temperature. Climate is based on the whole temperature profile at a location including the variance of daily, monthly, and yearly temperatures. You lose all track of the variance of the data set when you use an average. You have no idea of what the actual climate is from an average value.
In fact, these aren’t even averages, they are *median* values. (Tmax + Tmin)/2 is *not* an average, it is a median. Daytime temps and nighttime temps have different data distributions (sinusoid vs exponential decay). Combining the two almost always will generate a skewed distribution where the mean (average) and median are different. When you combine daily median values to get a monthly value what are you actually getting? If the daily median temperatures represent a skewed distribution (think September where temps early in the month can be vastly different than temps later in the month) then the monthly value is also a median and not an average.
You are so very full of crap. You are an academic mathematician with no appreciation of physical measurements. I know you are aware that EVERY MEAN also has a VARIANCE associated with it. Yet you never once mention what that variance is nor what is size actually tells you. Every spreadsheet like excel, libre office, or wps has a variance calculator built in. Most packages like matlab or ‘r’ have a similar function. Why is it so hard to use and inform the public about the variance?
Every time means are added or subtracted directly, their variances add directly. You know that I’m sure. Tell us what values you obtain from this anomaly calculation.
I have attached an image of one month, January, for one station for separate Tmax and Tmin temperatures. The variances were calculated exactly how the NIST TN 1900 Example 2 calculated the 95% confidence interval of the uncertainty in the mean. I include text from the Technical Note here.
The labels of RSS are the Root Sum Square addition of the variances for the monthly average minus the baseline average calculated by using all of the years available. This does provide a smaller value for the combined variance than simple addition would show.
You should be aware that anomalies that are inside these uncertainty values are truthfully unknown. There can be no assumptions that values are within the uncertainty interval are the true value. I should also point out that the uncertainty intervals do not vary much at all over all the years. This should indicate that variances are fairly consistent over time.
I understand that there are a number of missing years, however, the stability of the values both before and after the missing interval does make a statement of what has occurred over the entire period.
“Why is it so hard to use and inform the public about the variance?”
Try it as s Six-Sigma instructor teaching business majors.
When I went through Deming training from Western Electric it was hard for folks to understand the concept of zero defects. Most could understand do it right the first time, at least partially. There were always some who continued to think that rework wasn’t a problem.
Graphs spanning a century would be scarier for an Earth younger than 10,000 years.
It’s pitiful that climate scientists get away with methods that would be condemned in other fields. This article describes an especially outrageous example.
A graph of percent change over time is often used in fields other than climate science. It shows changes in context, such as the total percent change since a start date. Climate scientists well understand this, but don’t want you to know this info.
See this for the US public debt. The great FRED website gives several ways to do this.
https://fred.stlouisfed.org/series/FYGFDPUN#
Ah, but the FRED graph measures public debt in dollars relative to 1982. More meaningful would be as percent of GDP, as bad as that metric is.
You are, of course, correct. Comparisons with other relevant metrics provide additional information. That why sophisticated financial and economic numbers are often expressed as ratios. The presentation of data is an endless ladder.
But this post discusses the essential first step: understanding the data being discussed. Without that, more complex measures are as likely to confuse as to help.
More meaningful is interest on the debt as a percentage of GDP, which fell from about 5% of GDP in the 1985 to 1990 period to about 3%of GDP last year.
Interest on the debt will be paid.
The actual debt will never be reduced.
Richard,
‘Yes, that’s the kind of analysis that financial ratios make possible. Unlike the ones usually given to the public by climate scientists – or the deliberately misleading ones given here by Bdgwx and Mosher.
As for your bold confident prediction, that’s too off-topic to discuss here. But – we can only guess about such long-term economic outcomes. The success rate for them is …discouraging.
Here is the same data using the same relative scaling that Anthony Watts chose. See, nothing to worry about.
Bdgwx gives an excellent example of what the great Edward Tufte (author of “The Visual Display of Quantitative Information”) calls ‘ chart junk.’ Its worse than aggressive ignorance, it is designed to hide context.
Numbers only convey meaning with a relevant context. Simple displays of the numbers, as Watts does – in this case, magnitude, are a useful first step.
Bdgwx uses a scale to hide the relevant information. He is working to keep people ignorant, a favorite tactic of climate activists.
The chart uses the tactic advocated by Anthony Watts. I’m glad you agree that it is designed to hide context. Can you help me, Bellman, Nick, TheFinalNail, etc. convince AW of that?
I suspect WUWT is making a rod for its own back with this nonsense. Let them carry on.
You are just trolling, deliberating misinterpreting what Watts said. Such comments deserve only contempt.
So Anthony Watts deliberately distorts a graph by unreasonably expanding the y-axis to hide the variation and I’m the troll here?
Now increase the y-axis by 100%, way above the level required by the data it represents, then stretch the years back to 1880. All useful information is thus obscured. That’s exactly what this silly chart trick does with the GISS global temperature data. A child could see through it.
Even chart junk like given by Bdgwx provides insight. From a sufficiently large perspective, everything we’re concerned with is meaningless. Perhaps that’s helpful to a philosopher or theologian.
For example, see the famous scientist James Lovelock discussing end-of-the-world scenarios with Charles Sheffield. From the essay “Unclear Winter”.
”A detached attitude to such calamities is hard to achieve, but possible. I was driving with James Lovelock, originator of the “Gaia” concept down to the Museum of Natural History in Washington. We were discussing all-out nuclear war. Locklock surprised me by remarking that it would have very little effect. I said ‘It could kill off every human.’ He replied, ‘Yes it might do that; but I was thinking of the effects on the general biosphere.’”
If we’re discussing scientific data on a supposedly scientific site, then maybe charts useful to scientists, rather than philosophers or theologians, should be preferred.
Yes, I agree. But Lovelock’s insight was too fun not to post!
TheFinalNail – How many years of data would be required to link CO2 concentration to global temperature if both data sets were nearly perfectly accurate?
How many years? Infinite. Temperature as examined is a time series, not a functional relationship that takes CO2 as an input and spits out a temperature.
Here are some GUM specifications for measurands.
The last two are probably the most important. Especially, “obtained by measurement”. A global temperature is not measured, nor is it determined by a functional relationship involving discreet measurements.
A global temperature is a statistical calculation to determine a mean of a distribution of samples of different things. That mean is NOT a measurement, regardless of the folks here who want to define an average as a functional relationship describing “attribute of a phenomenon, body or substance”.
The only time a “mean” of different things is used in measurement is to determine an experimental value of measurement and its uncertainty when multiple trials are required. However, this is only done when repeated measurements of similar things are taken. Examples would be duplicated and repeated chemical reactions, the repeated measurements of length of a spring stretch when a given weight is attached, or timings of the same object falling the same distance. NIST TN 1900 Example 2 is a good example of finding the variance associated based on a collection of data. It should be used to find the variance associated with each average used in calculating the mean of the GAT.
It should be obvious that a mean of various temperatures is not repeated trials of duplicated experiments. Worse, it is an average of an average of an average. It is pure statistics, used only to derive a metric.
“Temperature as examined is a time series, not a functional relationship that takes CO2 as an input and spits out a temperature.”
Happy to help.
So you believe CO2 and temperature has a causal relationship?
You need a CAUSAL relationship to define a functional relationship. Correlation is not causation and therefore doesn’t define a functional relationship.
go here for an example: https://web.stanford.edu/class/hrp259/2007/regression/storke.pdf
You also have a conundrum to address if you believe there is a causal relationship – does CO2 drive temp or does Temp drive CO2?
“So you believe CO2 and temperature has a causal relationship?”
I think it’s probable that CO2 causes changes in temperature, but that isn’t something proven by that graph. It just demonstrates that you don’t have to just “examine” temperature as a time series and ignore CO2. There’s clearly a correlation between CO2 and temperature, but correlation does not prove causation.
“You need a CAUSAL relationship to define a functional relationship.”
Why do you think there has to be a “functional relationship” between CO2 and temperature? Is there a functional relationship between sunshine and temperature? Does that mean there’s no causal relationship?
Nobody apart from you two thinks that CO2 is the only thing that has an effect on temperature, that doesn’t mean that it doesn’t have an effect.
“Correlation is not causation and therefore doesn’t define a functional relationship.”
Completely backwards. “correlation does not imply causation” is not the same as saying “causation does not imply correlation.” Any causal relation will have correlation. Showing a correlation may not prove causation, but it certainly doesn’t disprove it.
“You also have a conundrum to address if you believe there is a causal relationship – does CO2 drive temp or does Temp drive CO2?”
Not really a conundrum. The answer is that both are true, but at present much more the former than the later. I’ve explained why many times, and have no intention of going through it all again at this point in the discussion. But for one thing, there is no need for a temperature based explanation of the current rise in CO2. Human activity is more than sufficient to explain all the modern rise in CO2.
“I think it’s probable that CO2 causes changes in temperature, but that isn’t something proven by that graph.”
If it doesn’t define causality then of what use is it?
“Why do you think there has to be a “functional relationship” between CO2 and temperature? “
If there isn’t a functional relationship they of what use is it? You didn’t even bother to look at the stork article, did you? It’s where the growth in stork population correlated with births. It’s a meaningless correlation!
“Is there a functional relationship between sunshine and temperature?”
YES!
You obviously don’t even understand what the terms “causal” and “functional relationship” mean. You lack of knowledge of the real world is showing again.
“Nobody apart from you two thinks that CO2 is the only thing that has an effect on temperature, that doesn’t mean that it doesn’t have an effect”
Then why aren’t we spending trillions of dollars on those other things? Methane is over 10% of emitted GHG’s. Why aren’t we spending trillions to get rid of natural gas *everything*?
tg: “Correlation is not causation and therefore doesn’t define a functional relationship.”
bellman: “Completely backwards.”
wikipedia: The phrase “correlation does not imply causation” refers to the inability to legitimately deduce a cause-and-effect relationship between two events or variables solely on the basis of an observed association or correlation between them.[1][2] The idea that “correlation implies causation” is an example of a questionable-cause logical fallacy, in which two events occurring together are taken to have established a cause-and-effect relationship. This fallacy is also known by the Latin phrase cum hoc ergo propter hoc (‘with this, therefore because of this’). This differs from the fallacy known as post hoc ergo propter hoc (“after this, therefore because of this”), in which an event following another is seen as a necessary consequence of the former event, and from conflation, the errant merging of two events, ideas, databases, etc., into one.
You’re back on the bottle again aren’t you?
“Showing a correlation may not prove causation, but it certainly doesn’t disprove it.”
To prove causation you need to show the functional relationship! So what is the functional relationship?
“The answer is that both are true”
Jeesshhh! Nice waffle!
“Human activity is more than sufficient to explain all the modern rise in CO2.”
Red herring again! The issue isn’t whether CO2 emissions are going up. The issue is what it is causing!
“You obviously don’t even understand what the terms “causal” and “functional relationship” mean.”
Then you are going to have to supply your definition of a functional relationship, because it doesn’t agree with any I know.
I assume you mean a deterministic relationship as opposed to a statistical one.
https://online.stat.psu.edu/stat415/book/export/html/875
“A relation is simply a set of ordered pairs. Not every relation is a functional relationship.”
“A function exists when each x-value (input, independent variable) is paired with exactly one y-value (output, dependent variable). This pairing is also referred to as a functional relationship.”
A statistical relationship is *not* a functional relationship because you can have multiple y-values for a single x-value.
I’m not surprised this definition doesn’t agree with any you know. That’s just an indication of how much you know and not an indication that the term “functional relationship” has multiple meanings.
“I’m not surprised this definition doesn’t agree with any you know.”
No, that’s exactly the definition I use. I don’t see why you think the relationship between annual temperature and CO2 levels could be functional. That does not mean that CO2 isn’t a cause of the temperature rise.
You claimed the relationship between the sun and temperature was functional. This is obviously just as false as the idea that there is a functional relationship between CO2 and temperature.
From the GUM.
“””””B.2.3
true value (of a quantity)
value consistent with the definition of a given particular quantity “””””
An average of temperatures does not have a value consistent with a given particular quantity. Why? Because the average is a statistical descriptor of a distribution and not a definition of determining a measurement of a measurand.
“””””B.2.5
measurement
set of operations having the object of determining a value of a quantity “””””
A “mean” is a statistical descriptor of a distribution. It is not the measurement of a physical quantity.
The only time a mean is used in measurement is when there are repeated measurements of the SAME measurand and the errors are random.
Those are two DIFFERENT things entirely.
And yet you have no problem regarding taking 22 maximum temperatures on 22 different days and regarding them as measuring the same thing under repeatable conditions. That is if you still accept TN1900.
In that example the measurand is the average maximum daily temperature for that month, and each measurement. You seem to accept that measuring different temperatures on different days is still measuring the same thing (the thing being the daily average), yet won’t accept measuring different temperatures at different locations may be measurments of the same thing (the thing being the global average).
And none of this has anything to do with the comment you are replying to, which was about the definition of a functional relationship.
“And yet you have no problem regarding taking 22 maximum temperatures on 22 different days and regarding them as measuring the same thing under repeatable conditions. That is if you still accept TN1900.”
This has been explained to you at least three times in the past two months. And you just can’t seem to understand.
These assumptions should be familiar to you, they are part of your statistical bible from the way you apply them.
With these assumptions you can treat the measurements as measurements of the same thing using the same device and the variation in the stated values become the measure of uncertainty.
The exact same thing you always do.
Who says no one has a problem with this? I do. I’m sure Jim does. We’ve both used the method in TN1900 as an attempt to hoist the climate alarmists on their own petard. It’s their playing field and reality beats them everytime.
“yet won’t accept measuring different temperatures at different locations may be measurments of the same thing (the thing being the global average).”
Are are you a “bubble boy” by any chance? In what reality is measuring different temperatures at different locations measurements of the same thing? Even in TN1900 all the measurements are from the SAME location using the SAME device!
Have you actually bothered to read TN1900? It’s honestly not obvious that you have.
“measurments of the same thing (the thing being the global average).””
Where do you go to measure the global average temperature? Your backyard maybe?
“Who says no one has a problem with this? I do. I’m sure Jim does. We’ve both used the method in TN1900 as an attempt to hoist the climate alarmists on their own petard. It’s their playing field and reality beats them everytime.”
How pathetic. So you don’t actually agree with the method in TN1900. But you keep going on about it if you can make some political capital from them.
Yet whilst you want me to hoist myself with my petard, every time I’ve pointed out why I think the example doesn’t mean what you seem to think, or point out why you are using it wrong, you say I must be wrong because the example says so. I think if anyone’s getting hoisted it isn’t me. Put your own petard down and run away.
“””””How pathetic. So you don’t actually agree with the method in TN1900. But you keep going on about it if you can make some political capital from them.”””””
Don’t mistake not agreeing with TN1900, as a problem. It is a much better way to determine variance in an extremely small sample of one piece of the current temperature data. Look at the attached image. Do you think a one hundredths or one thousandths anomaly is statistically significant with an expanded uncertainty of this amount? You can’t know what a true value is inside the uncertainty interval.
The real issue is that with ASOS stations and 5 minute data there is no reason climate science shouldn’t move to an integral of the temperature distribution throughout the entire 24 hour day rather than relying on a “traditional” method using extremely small samples. HVAC and others are currently doing this for heating/cooling degree days. They have given up on the old method and moved to a better method.
You forgot the image.
”
How pathetic. So you don’t actually agree with the method in TN1900. But you keep going on about it if you can make some political capital from them.”
In other words the truth hurts you and you just pretend it doesn’t.
“you say I must be wrong
Because you *are*! You can’t seem to get anything about reality correct.
“I don’t see why you think the relationship between annual temperature and CO2 levels could be functional. That does not mean that CO2 isn’t a cause of the temperature rise.”
A functional relationship defines a causal relationship. If there is no causal relationship between CO2 and temperature then CO2, by definition, cannot affect temperature.
“You claimed the relationship between the sun and temperature was functional. This is obviously just as false as the idea that there is a functional relationship between CO2 and temperature.”
You still don’t understand physical science at all, do you?
I gave you the relationship of the sun and temperature a long time ago. You didn’t understand it, refused to even try to understand it, and just blew it off.
The relationship between the sun and the earth is a sinusoid. The position of the sun with respect to the earth varies by location, i.e. latitude. Therefore the sun creates a sinusoidal effect on the temperature at any location on earth. It’s why the daytime temperature profile is at least an approximate sinusoid. THAT IS A FUNCTIONAL RELATIONSHIP. I gave the equations to use and at least two web sites that explains the geometry of the sun’s movement.
The full functional relationship for temperature has many components including, wind, rain, clouds, elevation, geography, terrain, humidity, pressure, AND THE SUN. Since the main driver is the sun (there is no other heat source of any consequence) the daytime temperature is a sine wave (caused by the sun) modulated by the other factors. All of those factors can be measured at any point in time and the functional relationship determined. Clouds, for instance, cause a change in albedo. That can be measured and included in the functional relationship. Rain causes effects associated with latent heat. That can be measured and included in the functional relationship. Same for pressure (PV = nRT). I’ve seen estimates of the effects of all of them – except CO2.
You are stuck in a “statistics” box and its obvious that you’ll never get out of it.
“If there is no causal relationship between CO2 and temperature then CO2, by definition, cannot affect temperature. ”
and if there is it can.
I think this whole rabbit whole is still to do with what we think any of these words mean. To me a causal relationship between X and Y is one where to some extent X affects Y. It does not mean that X is the only factor in Y. If X were the only factor then you would have a functional relationship as well as a causal one, but the lack of a functional relationship does not mean no causal relationship exists.
You seem to be implying the opposite, that only functional relationships can be causal.
“I gave you the relationship of the sun and temperature a long time ago.”
You gave me a vague badly worded description, which made no sense to me. I tried to figure out what you were getting at, but as usual you just insulted me and refused to consider the possibility that you needed a better model or explanation.
“Therefore the sun creates a sinusoidal effect on the temperature at any location on earth.”
Yes, and that’s a causal relationship. Amount of sun affects temperature. But it isn’t a functional relationship. You cannot take the height of the sun and the latitude and get predict the exact temperature. A functional relationship between sun, latitude and temperature would require that given the same values you would always get the same temperature.
“THAT IS A FUNCTIONAL RELATIONSHIP.”
And writing in all caps doesn’t make it true.
“The full functional relationship for temperature has many components including, wind, rain, clouds, elevation, geography, terrain, humidity, pressure, AND THE SUN.”
Thus proving there is not a functional relationship between sun and temperatures.
This is no different to that and the relationship between CO2 and temperature. An annual temperature will be affected by many factors including ENSO, Volcanic activity, clouds, ocean circulations, solar activity, AND THE CO2.
“To me a causal relationship between X and Y is one where to some extent X affects Y.”
Not just to some extent, the change in x DETERMINES the change in y.
“ It does not mean that X is the only factor in Y. “
You are dissembling because you know you are wrong. Again, V = πR^2H does not have a single factor. But it *IS* a functional relationship, not a statistical statistical association.
Again:
A statistical association, i.e. a correlation, is *NOT* a functional relationship.
“You seem to be implying the opposite, that only functional relationships can be causal.”
Only functional relationships can be causal. If you can have multiple y values for the same x you don’t have a functional relationship and you do not know the causal connection between y and x.
“You gave me a vague badly worded description, which made no sense to me.”
Nothing in the physical sciences *ever* makes any sense to you!
“Yes, and that’s a causal relationship. Amount of sun affects temperature. But it isn’t a functional relationship. You cannot take the height of the sun and the latitude and get predict the exact temperature. “
You just said the climate models can’t work. Is that *really* what you meant to say?
The path of the sun and the latitude *is* part of the functional relationship. Don’t you remember that I gave you this definition:
T = sin(sun, latitude, humidity, pressure, elevation, terrain, geography, wind, etc)
That defines a functional relationship.
From the GUM
————————————————————
5.2 Many measurements are modelled by a real functional relationship f between N real valued input quantities X1, . . . , XN and a single real-valued output quantity (or measurand) Y in the form Y = f (X1, . . . , XN ).
(1)This simple form is called a real explicit univariate measurement model; real since all quantities involved take real (rather than complex) values, explicit because a value for Y can be computed directly given values of X1, . . . , XN , and univariate since Y is a single, scalar quantity.
What I have given you is no different. I can define X1 as the sun’s path, X2 and the latitude, X3 as the humidity, X4 as ……
————————————————(bolding mine, tpg)
There is no “statistical association” in this definition.
“Thus proving there is not a functional relationship between sun and temperatures.”
You *STILL* don’t get it. You never will. You are stuck in that statistics box of yours and you refuse to join the rest of us in the real world.
“An annual temperature will be affected by many factors including ENSO, Volcanic activity, clouds, ocean circulations, solar activity, AND THE CO2.”
But *NOT* by your simple statistical relationship of T = .01 * CO2 or whatever it was you came up with! That is *NOT* a functional relationship, it is a curve fitting equation relating two statistical descriptors.
“Again, V = πR^2H does not have a single factor. But it *IS* a functional relationship, not a statistical statistical association.”
Correct. The relationship between the pair of (R, H) is functional to V. But that does not mean that H has a functional relationship with V. The same H can give different Vs.
“Only functional relationships can be causal.”
And that’s where I disagree, though it might depend on exactly how causal relationship is defined. Would you say there is a causal relationship between H and V?
“You just said the climate models can’t work. Is that *really* what you meant to say?”
No model is perfect. Not even yours. No model can tell you exactly what the temperature will be at a specific time of day at a specific place, or even what the global average annual temperature will be. There are just too many factors and weather is too chaotic. That does not mean that they can’t be useful.
“The path of the sun and the latitude *is* part of the functional relationship.”
“part of”. That’s my point.
“T = sin(sun, latitude, humidity, pressure, elevation, terrain, geography, wind, etc)”
Then write it out and we can test it. Though as it stands you are saying all temperatures will be between -1 and +1, which seems unlikely.
Really, it would be much easier if you stopped obsessing over sin and just wrote
T = f(sun, latitude, humidity, pressure, elevation, terrain, geography, wind, etc)
“There is no “statistical association” in this definition.”
Indeed not. It’s describing a functional relationship. I’m not sure what relevance you think this has to your claims. If you want to define temperature by a functional relationship of many many measurements, you could (if you actually knew what the function was). But why would you when you could just take the temperature. All you are trying to do is derive a temperature from a multitude of different measurements which will leave you with a very uncertain temperature measurement.
“You *STILL* don’t get it. You never will. You are stuck in that statistics box of yours and you refuse to join the rest of us in the real world. ”
I look forward to reading your real world paper explaining how you can predict temperature knowing only the position of the sun, the latitude, the humidity,m the pressure, the elevation,t he terrain, the geography and the wind, etc. Until then I’ll take your claims of living in the real world with a grain of salt.
“But *NOT* by your simple statistical relationship of T = .01 * CO2 or whatever it was you came up with!”
I never claimed it was an accurate model, just that it shows there is a correlation between CO2 and temperature, and it’s a better correlation than time and temperature. That was the point if I remember all that time ago. Jim complaining that we were only looking at the relationship between time and temperature and not between CO2 and temperature.
“””””The relationship between the pair of (R, H) is functional to V. But that does not mean that H has a functional relationship with V. The same H can give different Vs.”””””
You have no idea about functional relationships do you?
I suppose the Ideal Gas Law is not a functional relationship either. You mentioned deterministic and that is closer than you have ever been. Take the IGL, and put all the variables on one side and see if you have a constant.
(PV) / nT = R (R is the gas constant)
V / R^2H = π (π is a constant)
Let’s see about yours.
A = 0.01 * CO2 – 3.2 A / (0.01 • CO2) = -3.2
This can’t work because the effect of CO2 is logarithmic not linear.
Let’s try => A = log2 CO2. => A / log2 CO2 = 1
When A = 0.1 what does log2 CO2 have to be? -> 1.075
When A = 0.5 log2 CO2 -> 1.416
When A = 1.0. log2 CO2 -> 2.0
Your log formula doesn’t work as a functional relationship does it?
You are curve fitting again to show a correlation. It is obvious you took the log2 CO2 for the x-axis, then artificially put the values you wanted on the y-axis. What you did was convert x-axis values from concentration to log2 CO2.
Naughty, naughty.
That’s it. That’s what’s meant by a functional relationship. If you mean something other than that you need to explain what you mean by it.
“(PV) / nT = R (R is the gas constant)
V / R^2H = π (π is a constant)”
Now you have two constant functions. That is a functional relationship, trivially so.
“A = 0.01 * CO2 – 3.2 A / (0.01 • CO2) = -3.2”
Still no idea what the point of all this is.
“This can’t work because the effect of CO2 is logarithmic not linear.”
It doesn’t matter if “it works”. The point was it’s an example of a functional relationship. I never claimed it would work for all values, I’m just showing there is a correlations. As I keep pointing out you can do the same with log2(CO2). I’ve done it and given the equations. Again I don’t claim this is the one and only correct equation, just that there is a correlation.
“Let’s try => A = log2 CO2. => A / log2 CO2 = 1”
Not my equation.
“When A = 0.1 what does log2 CO2 have to be? -> 1.075”
I’ve already given you the linear regression I got for log2 CO2 compared with NOAA temperatures.
Estimate Std. Error t value Pr(>|t|)
(Intercept) -19.60612 0.53171 -36.87 <2e-16 ***
log2(CO2) 2.35907 0.06369 37.04 <2e-16 ***
Anomaly = 2.4 * log2(CO2) – 19.6
When A = 0.1 what does log2 CO2 have to be? -> 8.2
When A = 0.5 log2 CO2 -> 8.4
When A = 1.0. log2 CO2 -> 8.6
“Your log formula doesn’t work as a functional relationship does it?”
Of course it does. Plug in any value for CO2. How many different results will you get? Unless the answer isn’t 1, you have a functional relationship.
“You are curve fitting again to show a correlation.”
Yes, that’s exactly what a linear regression does. And in this case it shows a significant correlation.
“It is obvious you took the log2 CO2 for the x-axis, then artificially put the values you wanted on the y-axis.”
An absolute lie. I literally just put the existing data in to the R function and showed the result.
“What you did was convert x-axis values from concentration to log2 CO2.”
That’s what you do if you want a logarithmic linear regression. Your attacks are becoming more deranged.
If you think I’ve cheated in some way produce your own results using your own data.
“””””Now you have two constant functions. That is a functional relationship, trivially so.”””””
Trivially? Trivially? Holy crap dude! Google all you want, but every, and I do mean every, function describing a physical relationship of measurements ends up equaling a constant if all variables are put on the left side of the function.
e/m = c^2
PV = k (with constant mass and temperature)
Eλ = hc
V / (l•w•h) = 1
You show one that does not.
Calm down. All I said was that a constant function is trivially a functional relationship.
Perhaps if you explained what you were trying to do I wouldn’t need to point things out. All you are saying is you can take a functional relationship, divide through by the dependent variable and get a new functional relationship which is just relating to a constant. Maybe you have a good reason to do that, but at present all I see is someone trying to confuse the point.
“Correct. The relationship between the pair of (R, H) is functional to V. But that does not mean that H has a functional relationship with V. The same H can give different Vs.”
I see that Jim answered you before I got around to it!
H = V/(πR^2). You see, a functional relationship defines the relationship for *all* factors, not just some of them. The same H doesn’t give different V’s. To get different V’s with a constant H implies that either π or R^2 has to change.
Did you take high school algebra by any chance?
“And that’s where I disagree, though it might depend on exactly how causal relationship is defined. Would you say there is a causal relationship between H and V?”
Of course there is a causal relationship between H and V! As H goes up, V goes up by a fixed relationship. Weld two 50 gal barrels together end to end and the volume goes up by H, the height. You can’t get more causal than that!
“No model can tell you exactly what the temperature will be at a specific time of day at a specific place, or even what the global average annual temperature will be. ” (bolding mne, tpg)
If they can’t tell you what the GAT will be then of what use are the models? The issue is that they can’t even get *close* to being right!
“Indeed not. It’s describing a functional relationship. I’m not sure what relevance you think this has to your claims.”
The issue is *YOUR* claim that a functional relationship can only have one factor. *YOUR* claim that if the sun, by itself, can’t predict the temperature then it can’t be a functional relationship.
“But why would you when you could just take the temperature.”
Your lack of physical science training is showing again! Heat is not temperature, enthalpy is. Enthalpy depends several factors OTHER than temperature. Enthalpy can go up while temperature stays the same, all that is required is that the humidity goes up. It’s why the temperature in Las Vegas and Miami can be the same but the heat content of the air can be so different, “dry heat” vs “wet heat”.
Temperature is just a piss-poor proxy for heat because its only one factor in a functional relationship.
“All you are trying to do is derive a temperature from a multitude of different measurements which will leave you with a very uncertain temperature measurement.”
No kidding, Sherlock! What do you think we’ve all been trying to teach you over the past two years! Is it finally starting to sink in a little bit?
“That’s my point.”
No, that was *NOT* your point.
bellman: “Amount of sun affects temperature. But it isn’t a functional relationship. “
“Then write it out and we can test it. Though as it stands you are saying all temperatures will be between -1 and +1, which seems unlikely.”
Oh, jeeesh! Now you are nitpicking because I didn’t include the weighting values for each factor? That’s just one more argumentative fallacy don’t you know!
You can’t even remember that the original equation I gave you was T = Tmax * sin(t). That does *NOT* give a value between -1 and +1.
“T = f(sun, latitude, humidity, pressure, elevation, terrain, geography, wind, etc)”
Nitpicking again. At least you are finally figuring out what a functional relationship is! Pardon me for leaving off the “f”.
“I look forward to reading your real world paper explaining how you can predict temperature knowing only the position of the sun, the latitude, the humidity,m the pressure, the elevation,t he terrain, the geography and the wind, etc. Until then I’ll take your claims of living in the real world with a grain of salt.”
Unfreakingbelievable. Those are *NOT* the only factors. You can’t even get this one right. Those are EXAMPLE factors. You left out clouds and humidity for one thing.
You are whining now that you’ve been shown to be wrong. Where do I send the tiny violin and crying towels?
“I never claimed it was an accurate model, just that it shows there is a correlation between CO2 and temperature, and it’s a better correlation than time and temperature.”
It’s a statistical correlation curve matching equation, it’s *NOT* a functional relationship. You *still* don’t get it. I can write a curve matching equation to related the growth in the stork population in Denmark to the number of baby births in Denmark – DOES THAT DEFINE A FUNCTIONAL RELATIONSHIP BETWEEN THEM?
You are *still* stuck in your statistical box.
“You see, a functional relationship defines the relationship for *all* factors, not just some of them.”
Yes. That’s the point. If you have multiple input values in a function, then you don’t have a function on just one.
“The same H doesn’t give different V’s.”
It does. That’s why you need R. A cylinder H = 5, could give completely different values for V, say if R = 1 compared to R = 2.
“To get different V’s with a constant H implies that either π or R^2 has to change.”
Yes, as I’m saying R can change.
“Did you take high school algebra by any chance?”
No. And if you’re an example of that system then I’m pretty glad I didn’t.
“The issue is *YOUR* claim that a functional relationship can only have one factor.”
I hope I’ve never claimed that. What I said is the opposite, that in the equation for the volume of a cylinder the function has two factors, H and R, and together they provide a functional relationship with V.
“*YOUR* claim that if the sun, by itself, can’t predict the temperature then it can’t be a functional relationship.”
Yes that’s my “claim” and it’s patently obvious if you would only take the time to read the definition of a functional relationship. How can there be a functional relationship between the sun’s position and temperature if the same sun in the same position can give you different temperatures?
“You can’t even remember that the original equation I gave you was T = Tmax * sin(t). That does *NOT* give a value between -1 and +1.”
Indeed not. It gives you value between Tmax and -Tmax.
You can call it nit picking if you want. But you keep throwing out these nonsensical sin functions which never seem to come close to describing an actual temperature, then complain if I didn’t turn it in to something more meaningful for you.
“Pardon me for leaving off the “f”.”
It isn’t a question of what you call the function, it was the fact that you felt just putting a load of variables into a sin function was in someway meaningful. I was trying to help you. If you don’t start of by assuming what the function is it means you can just say there is some function, you just don’t know what it is yet.
“Unfreakingbelievable. Those are *NOT* the only factors. You can’t even get this one right. Those are EXAMPLE factors. You left out clouds and humidity for one thing. ”
I take it you didn’t notice the “etc”? (Understandable given the number of typos. )
“If it doesn’t define causality then of what use is it?”
I said the graph doesn’t “prove” causality. Not “define”.
But I keep forgetting that you live in a world of absolutes. If something isn’t 100% sure then it has absolutely no use.
“It’s where the growth in stork population correlated with births. It’s a meaningless correlation!”
And you again fail to understand my point. Saying “correlation does not imply causation” is not the same saying “correlation implies no causation”. In other words, some correlations can be spurious, but that does not mean all correlations are spurious.
““Is there a functional relationship between sunshine and temperature?”
YES!”
Then show me your graph showing an exact functional relationship between the amount of sunshine in a day and the maximum temperature.
“Then why aren’t we spending trillions of dollars on those other things? ”
Because “other things” are not in our control and generally have less or no long term impact. As I keep saying ENSO is probably the biggest other thing regarding year to year variability, but how could you stop it, and it’s effect is still trivial compared to the CO2 caused warming.
“Methane is over 10% of emitted GHG’s. Why aren’t we spending trillions to get rid of natural gas *everything*?”
I believe people are looking at ways of reducing methane emissions. But as I understand it methane is still less of a problem than CO2, because it doesn’t last in the atmosphere in the same way as CO2 does.
“wikipedia”
As so often you quote a passage that entirely agrees with what I’m trying to say.
“To prove causation you need to show the functional relationship!”
You do not. I’m really puzzled as to why you keep saying this. I assumed it was because you didn’t know what a functional relationship was, but you’ve now demonstrated you do. So why do you think you need to show a functional relationship to show causation?
“Red herring again! The issue isn’t whether CO2 emissions are going up. The issue is what it is causing!”
This in response to me saying that “Human activity is more than sufficient to explain all the modern rise in CO2.” In case you failed to get my point, I’m saying that in my opinion rising CO2 is almost entirely caused by human emissions. I point out the fact that human emissions are more than sufficient to cause the observed rise, as evidenced for that proposition.
“I said the graph doesn’t “prove” causality. Not “define”.”
Once again, answer the question – what use is it then?
“But I keep forgetting that you live in a world of absolutes. If something isn’t 100% sure then it has absolutely no use.”
No, I live in the real world, not your statistical fantasy land. If I have 100 ‘6 boards and 100 8′ boards I know that in reality I have no 7′ boards. You, on the other hand, believe the 7′ board exists because it is the statistical average of the 6′ and 8’ boards.
“In other words, some correlations can be spurious, but that does not mean all correlations are spurious.”
But you can’t just ASSUME that correlation means causation. You must *PROVE* it. When and where has the physical functional relationship between CO2 and temperature been proven?
“Then show me your graph showing an exact functional relationship between the amount of sunshine in a day and the maximum temperature.”
I don’t need a graph. I can measure Tmax and from there I can figure out the daytime temperature profile pretty closely. Tx = Tmax * sin(Angle_sun). Tmax occurs when Angle_sun is π/2 + ⱷ where ⱷ is the thermal inertia of the atmosphere and the surface. I.e. Tmax doesn’t occur at π/2 but at some point in time later.
Tmax will be modulated by storm fronts (wind, pressure), by clouds (albedo, rain(latent heat)), elevation, humidity, etc. All of which are measurable and quantifiable – i.e. how you define a functional relationship.
What do you think the climate models try to do? Their problem is that they are trained using inaccurate temperature profiles and by effects that they haven’t yet figured out the functional relationship for – e.g. time varying albedo, time varying clouds, time varying pressure fronts, and on and on and on …. ad infinitum. So they try to parameterize all these, ignore the time varying of the effects, and ignore all the unknown unknowns. So they wind up basically with a y = mx + b linear projection of the future. If it is warming now it’s going to warm forever till the earth turns into a cinder! What do you think “CO2 trapping heat” means? Trapping more and more heat has only one final consequence.
“Once again, answer the question – what use is it then?”
How many more times. It shows there is a statistically significant correlation between CO2 and temperature. That means you have not falsified the hypothesis that CO2 will affect temperatures,, and under any reasonable interpretation have provided evidence that agrees with the hypothesis.
Only in your binary world is that of no use.
“No, I live in the real world, not your statistical fantasy land. If I have 100 ‘6 boards and 100 8′ boards I know that in reality I have no 7′ boards. You, on the other hand, believe the 7′ board exists because it is the statistical average of the 6′ and 8’ boards. ”
How many times are you going to repeat this meaningless nonsense. No. If you have no 7′ board you have no 7′ board. Saying the average length of all the boards is 7′ is not saying that all boards are 7′, or even that one board that is 7′ exists. It means, and I know this might be a little complicated for you to understand, but it means that the average length of a board is 7′.
And none of this has any relevance to my point, which was about your claim that if a correlation didn’t prove causation, then it was of no use.
“You must *PROVE* it.”
You can’t prove it. Science and statistics doesn’t work like that. You can show that something is consistent with a proposition, you can show that you haven;t falsified a proposition, and if you are daring you might even show that it increases the probability of it being true – but you can never prove it to be true.
“I don’t need a graph. I can measure Tmax and from there I can figure out the daytime temperature profile pretty closely. Tx = Tmax * sin(Angle_sun). Tmax occurs when Angle_sun is π/2 + ⱷ where ⱷ is the thermal inertia of the atmosphere and the surface. I.e. Tmax doesn’t occur at π/2 but at some point in time later.”
In other words, there isn’t a functional relationship between sun and temperature. The fact you need to include thermal inertia of the atmosphere and the surface, shows that.
Oh, there’s more.
“Tmax will be modulated by storm fronts (wind, pressure), by clouds (albedo, rain(latent heat)), elevation, humidity, etc. All of which are measurable and quantifiable – i.e. how you define a functional relationship.”
So defiantly not a functional relationship. The question was if there was a functional relationship between the sun and temperature, not between hundreds of other factors. Just as you want a functional relationship between CO2 and temperature, whilst ignoring all the other factors that determine temperature.
“How many more times. It shows there is a statistically significant correlation between CO2 and temperature.”
Correlation without causation is meaningless. If it was then the correlation between stork population growth and the number of babies being born would be meaningful.
“If you have no 7′ board you have no 7′ board.”
Then of what use is the average? In fact this is a perfect bi-modal distribution and the use of the average as a statistical descriptor is meaningless. Yes you can calculate one but it describes the population in no way, shape, or form.
“which was about your claim that if a correlation didn’t prove causation, then it was of no use.”
That is *NOT* what I said. Stop making things up. You didn’t even provide a quote of me saying that. I *said* that if there is no functional relationship then correlation is meaningless. Correlation between postal rates and the DJI has no functional relationship – it is a useless correlation. Correlation between growth in the stork population and the number of babies born has no functional relationship, it is a useless correlation. Without a functional relationship the correlation between temp and CO2 is meaningless – can *you* provide a functional relationship for the two?
“n other words, there isn’t a functional relationship between sun and temperature. The fact you need to include thermal inertia of the atmosphere and the surface, shows that.”
Your lack of training in the physical sciences is showing again. The volume of a barrel is π/R^2H. That is a functional relationship. Does the fact that H alone does not define volume make this NOT a functional relationship? Does the fact that you must also know the radius make the relationship not a functional one? sin(t) and sin(t+ⱷ) is no different. As we discussed before and you just absolutely refused to consider, you can even add latitude into the equation. Since the contribution from latitude is cos(l), i.e. when l = π/2 the cos(l) = 0, this would be at the poles. The trig identity of sin(t + π/2) = cos(t) all you have to do is include ‘l’ in the equation, Tx = Tmax * sin(ɑ + ⱷ + l). That *IS* a functional relationship. Functional relationships do *NOT* require that there be only one independent variable.
“So defiantly not a functional relationship.”
Go learn some physical science, get out of your statistics box. Functional relationships can have multiple independent variables. That may be an inconvenient truth for you to accept but it is the truth nonetheless!
“Then of what use is the average? ”
Don’t ask me – it’s youir average. Why did you want to know the average of all your boards if you didn’t think it would be useful?
Honestly. Jest because stupid people do stupid things with statistics, doesn’t mean that all statistics are stupid.
Usually if you are looking at averages it’s because you want to test a hypothesis such as are two samples likely to be from the same population or not.
An example which is not unlike your example – suppose you weren’t sure if a coin was fair. You toss the coin a number of times and look at the average number of heads (that is you count a head as 1 and a tail as 0). You expect the average of a fair coin to be 0.5. Say the average of my coin tosses was 0.7. That’s more than expected, and if this difference was significant I could say it’s unlikely this is a fair coin.
You however would argue that a coin toss of 0.5 doesn’t everything about that test is meaningless and the result tells you nothing.
“You however would argue that a coin toss of 0.5 doesn’t everything about that test is meaningless and the result tells you nothing.”
You are full of it. You just proved my point. The *MEDIAN* remains 0.5, (0+1)/2. The average changes so the median and the average are different in a skewed distribution AND the median (Tmax+Tmin)/2 doesn’t tell you anything about the distribution. You need the rest of the five number description. AND the average doesn’t describe the range and variance so it is not a full description either!
Why do you have such a hard time relating to reality?
You keep demonstrating that you don’t know what a median is. Look it up. If I toss the coin 100 times and get 48 heads and 52 tails, what is the median?
And if you keep saying that a mean is of no value if it doesn’t have a real world representative, e.g no 7′ boards in your example, why do you accept a media that also is not existing in the real world, such as the median of the coin toss? What is the median length of 100 6′ boards and 100 8′ boards?
You are confusing the mean with the median. You can’t even keep them straight for 24 hours.
If you are going to make an accusation like that, have the courage to show where I made such a mistake.
Using these petty insults rather than answering my questions suggests you really don’t know what a median is.
“And if you keep saying that a mean is of no value if it doesn’t have a real world representative, e.g no 7′ boards in your example, why do you accept a media that also is not existing in the real world, such as the median of the coin toss?”
Who says I don’t accept that a median is a median. The issue is whether it is meaningful in the real world or not.
YOU CAN’T KEEP THE ISSUES STRAIGHT FOR 24 YOURS, TROLL!
So to return to the point – do you think an average for a coin toss of 0.5, whether calculated as a mean or a median is meaningful, given that a toss of 0.5 doesn’t exist in the real world?
“You keep demonstrating that you don’t know what a median is.”
He does.
“So why do you think you need to show a functional relationship to show causation?”
How else do you show that growth in he stork population does *NOT* cause growth in the number of babies being born?
“””””The answer is that both are true, but at present much more the former than the later.”””””
This answer destroys the idea that there is a casual relationship described by a functional relationship.
A relationship can be direct, both up or down, or inverse, one goes up the other down.
To do what you propose requires a third variable with a much larger effect than CO2. Why aren’t we dealing with that?
You also need to explain what you mean by “functional relationship”. Again, nobody thinks there is a functional relationship between CO2 and temperature. Air temperature in any given year can be the result of many inputs, and will always have an effectively random component.
I’ve no idea why you think a cycle requires a larger third factor. It’s quite simple. The amount of CO2 in the atmosphere affects the global temperature. The global temperature affects the amount of CO2 in the atmosphere. No third variable required.
“Again, nobody thinks there is a functional relationship between CO2 and temperature. “
Really? Then the correlation is spurious and meaningless. Which invalidates the current political push to limit CO2 and makes the climate models meaningless.
“The amount of CO2 in the atmosphere affects the global temperature.”
You just said there is no functional relationship between CO2 and temperature. So which is it?
“Then the correlation is spurious and meaningless.”
Why? No correlation regarding climate in the real world will be “functional”. There will always be a multitude of other factors that determine the temperature on any day or year. That doesn’t mean all correlations are meaningless. I asked about the correlation between sun and temperature before. You ever need to demonstrate how that’s a functional relationship, or accept that the lack of such a relationship does not make the correlation meaningless.
“You just said there is no functional relationship between CO2 and temperature. So which is it?”
The difference between “affects” and is the sole cause of.
Just to make sure I wasn’t mixing up affects and effects, I checked the dictionary
https://www.merriam-webster.com/dictionary/affect
Here’s the first definition and examples:
to act on and cause a change in (someone or something)
Rainfall affects plant growth.areas to be affected by highway constructionThe protein plays a central role in metabolism … which in turn affects the rate of agingThe 1883 eruption of Krakatau in what is now Indonesia affected global sunsets for yearsBefore the 1980s it was not at all clear how nicotine affected the brain.
Do you think any of those examples are functional relationships?
“No correlation regarding climate in the real world will be “functional”.”
Malarky! I can write a functional relationship between the sun’s insolation at a point on the earth and its orbital relationship to that point on the earth.
“There will always be a multitude of other factors that determine the temperature on any day or year.”
That does *NOT* mean that you can’t write a functional relationship between CO2 and its contribution to the temperature. It may be part of a sum but it will still be a functional relationship.
“That doesn’t mean all correlations are meaningless. “
I didn’t say that. Stop putting words in my mouth. A correlation that *can* be described by a functional relationship does have meaning.
The growth in the number of storks and the number of babies being born is *NOT* a functional relationship but can have a correlation!
“I asked about the correlation between sun and temperature before. You ever need to demonstrate how that’s a functional relationship, or accept that the lack of such a relationship does not make the correlation meaningless.”
I gave you the start of the development of a functional relationship and you just blew it off because you couldn’t understand it. It was a sinusoidal function with a number of factors including elevation, humidity, geography, wind, terrain, latitude, longitude, and etc. You didn’t even have a clue as to how to calculate the path of the sun across the earth based on latitude! You *still* don’t. If its anything with the real, physical world it just seem to go right over your head!
“ust to make sure I wasn’t mixing up affects and effects, I checked the dictionary”
bellman: “Again, nobody thinks there is a functional relationship between CO2 and temperature. “
bellman: ““The amount of CO2 in the atmosphere affects the global temperature.””
I’m not even sure English is your first language.
effect – noun
affect – transitive verb : to produce an effect upon (someone or something):
If CO2 “affects” the temperature so as to cause an “effect” in the temperature then there is a functional relationship between the two.
What is it?
“Malarky! I can write a functional relationship between the sun’s insolation at a point on the earth and its orbital relationship to that point on the earth.”
Anybody can write a functional relationship. I’ve written a functional relationship for CO2 and temperature. 0.01 * CO2 – 3.2. I think it was. That’s a functional relationship.
“That does *NOT* mean that you can’t write a functional relationship between CO2 and its contribution to the temperature.”
But that wasn’t what you were asking about. You were talking about the relationship between CO2 and temperature, not it’s contribution to temperature.
“I didn’t say that. Stop putting words in my mouth. A correlation that *can* be described by a functional relationship does have meaning.”
All correlations can be described by a functional relationship. 0.01*CO2 – 3.2 is a functional relationship that describes the correlation between CO2 and temperature. It doesn’t mean you’ve proven the correlation is causative.
“If CO2 “affects” the temperature so as to cause an “effect” in the temperature then there is a functional relationship between the two.”
No there isn’t. I just don’t get your problem. You quote the correct definition of a functional relationship, yet seem incapable of understanding it. I’m suspect you don’t actually mean “functional relationship”, you probably mean “causal relationship”. But for some reason you’ve got “functional” stuck in your brain, and no amount of argument will dislodge it.
“Anybody can write a functional relationship. I’ve written a functional relationship for CO2 and temperature. 0.01 * CO2 – 3.2. I think it was. That’s a functional relationship.”
That’s not a functional relationship.
It is *NOT* a functional relationship.
On the other hand, the path of the sun at any specific point *is* physical science. I gave you two web sites that analyze that in detail. The fact that latitude is a sine wave function is also physical science. The two web sites discussed how the sun’s insolation at a point is based on latitude. The fact that thermal inertia exists is also a physical fact. Thermal inertia, or thermal mass or thermal admittance, is also a physical fact. Just as an object has inertia that is proportional to its mass, objects also have thermal inertia, sometimes described as the delay of the instant in which the temperature peaks on the inside, compared to when that peak was produced on the outside.
Get out of your statistics box. Data fitting to something that doesn’t exist isn’t physical science, it’s just mental masturbation.
I asked you if you understood what functional meant. You gave me the correct definition. But with every post you make it clear you don’t understand the definition.
It’s not that hard. A relationship is functional if any input will give you just one output. That is all. Nothing about whever you believe it or not, nothing about if there is a physical cause or if you like the output, just if the relation is a function .
The linear function is definitely a function. Any value of CO2 will give you one and only one temperature. That makes it a functional relationship. Your personal assumptions make no difference to that fact.
“But that wasn’t what you were asking about. You were talking about the relationship between CO2 and temperature, not it’s contribution to temperature.”
You have yet to define the relationship between CO2 and temperature. Curve fitting to something that doesn’t exist in reality is *NOT* defining a relationship. You keep assuming that correlation implies causation even though you deny it. It’s just like the fact that you always assume that all measurement error is random, Gaussian, and cancels. You deny that you do that as well. But in EVERYTHING you post you always circle back to such assumptions, unstated of course but that doesn’t mean you don’t assume them.
“All correlations can be described by a functional relationship. 0.01*CO2 – 3.2 is a functional relationship that describes the correlation between CO2 and temperature. It doesn’t mean you’ve proven the correlation is causative.”
That’s not a functional relationship. Stork population growth (SP) and baby births (BB) correlate. But BB ≠ k * SP! Not all correlations have a functional relationship. You don’t live in the real world at all. *YOU* must think that somehow baby births *are* determined by SP growth. Sorry to burst your bubble but they aren’t. Correlation is not causation. It is causation that is explained by a functional relationship.
Get out of your statistics box!
“But for some reason you’ve got “functional” stuck in your brain, and no amount of argument will dislodge it.”
Causation means you get one value of output for every value of input – a functional relationship. If the sun is directly overhead at noon (i.e. at π/2) then sin(π/2) = 1 and you get maximum insolation.
The GAT is a statistical descriptor and although no one in climate science will admit it, it carries a standard deviation with it which is associated with propagated systematic bias (as a minimum). Thus you can get more than one output value for one input value. I.e. a statistical association can give multiple output values for one input value. That is *NOT* a functional relationship.
That is why error bars *must* be included with any graph of GAT but they never are. If they were included it would be obvious that you are *NOT* talking about a functional relationship but a statistical one.
Get out of your statistics box and join the rest of us in the real world.
Then why, in some mappings of CO2 level from OCO-2 data, are the highest CO2 levels located where broad leaf vegetation is at its highest levels, for example the Amazon Rain Forest?
You might want to check your math here. Your x&y axis both have linear spacing so your line should be described by a linear equation.
Let’s see:
y-intercept =~ -0.3
Slope => (320, 0) & (400, 0.8)
slope = (0.8 – 0)/(400 – 320) = 0.01
a = mx + b
a = 0.01(320) – 0.3 = 2.9 ?????
a = 0.01(400) – 0.3 = 3.7 ?????
What equation did you use?
It’s not my maths, it’s just what R gave me. True, for simplicity I used a linear scale for CO2, as it doesn’t make much difference over this time scale.
Attached is the same using Log2 CO2.
Sorry, attached the wrong graph.
The equation for the linear scale is
-3.2 + 0.01 * CO2
Or more exactly
I think your problem is you think the intercept is -0.3, rather than 3.
Hence
0.01 * 320 – 3.2 ≈ 0.0
0.01 * 400 – 3.2 ≈ 0.8
For completeness, here’s the log equation
-3.2 + 0.01 * CO2″
You do realize that the equation you show here is of the slope intercept form, right? That is:
y = mx +b
Where m is the slope and b is the “y” intercept.
So your equation is y = 0.01x + (-3.2)
See that (-3.2), that is supposed to be the y intercept. Now, you can move the y-axis to where CO2 = 0, but if you do that with your x-axis spacing, the y-intercept is going to be far below -3.2.
You may not realize it, but you are simply curve fitting. You can draw no conclusions from that.
“but if you do that with your x-axis spacing, the y-intercept is going to be far below -3.2.”
No, it’s -3.2. See attached graph. Really I don’t know why you would think the maths is going to be wrong. It’s a simple linear fit generated by a decent statistical package. And yes, it’s curve fitting. I never said it was anything else. It shows the correlation between the two over the local domain.
If what you mean is that -3.2 is not going to be a realist value for 0 CO2, that’s obviously correct (assuming you accept the greenhouse effect is true.). If you want a slightly more realistic equation, see the one above using a logarithmic scale. That puts the y-intercept at -19.6°C. Of course, that isn’t going to be the actual temperature you would expect at 1 ppm. You can’t extrapolate too far outside the data range.
Here’s the graph with a linear regression on y ~ log2(x).
Here is the problem with your graph. See my image.
Look real closely at your temps. They show a flat period right at the end.
Also can you justify the large negative anomalies with low CO2 from the past. Maybe during the LIA. In other words you are using a small, small sample or temps to justify your graph. What is the range of temps experienced over the last 500,000 years at 300 ppm?
You can’t get away from the fact that rising CO2 doesn’t determine temp by itself.
You mean, there’s a problem with my graph which shows a good fit, because you can produce a graph with a much shorter period and no attempt to find the actual fit?
Here’s a quick graph showing the CO2 scaled to the best linear fit for UAH.
“They show a flat period right at the end.”
AS we keep establishing, no-one should expect a functional relationship. Individual years fluctuate about the predicted values. That’s what anyone who has the slightest understanding of how the planet works, would expect.
“Also can you justify the large negative anomalies with low CO2 from the past.”
CO2 has been around the 270 – 280 mark for the last 2000 years before the 20th century. Using the linear trend derived from the NOAA data, this would mean a predicted anomaly of about -0.4°C. Slightly lower than NOAA data for the end of the 19th century, but hardly a large negative anomaly. As I said, I wouldn’t try to extrapolate too far outside the range of the actual data.
“You can’t get away from the fact that rising CO2 doesn’t determine temp by itself.”
I don;t know how many times I have to explain this before it sinks in – I do not expect CO2 to be the only factor determining temperature.
since 2013 absolutes have been on the web
we did this precisely because skeptics asked for it
now they pretend it doesnt exist. well you cant deny it anymore
Observational data shows 2022 absolutes as end of each month values.
% Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2.5 2.2 1.7 2.4 5.9 6.3 6.6 6.3 4.4 4.0 2.9 2.68
NH 0.74 1.73 2.65 6.67 13.94 16.19 16.74 16.33 11.99 7.68 3.13 0.90
SH 4.29 2.68 0.67 -1.84 0.77 -3.94 -3.62 -3.79 -3.15 0.28 2.71 4.46
What in the world do those “estimated” numbers actually mean?
Especially when stated out to the hundredths digit! What measurement device was used to get that kind of resolution in temperatures?
My ruler is graduated in sixteenths of an inch. This allows me to write 11 1/16 inches as 11.0625 in. See? Easy.
You surely can. Now tell us what the measurement uncertainty, both random and systematic is with that single measurement!
Steven,
Watts’ observed how rising temperatures are reported to the public, not that “denying” that the data is available:
”But such graphs used in press releases to the media and broadcast to the public don’t really tell the story of the data honestly.”
If press releases started putting out these rectangle graphs, they would be laughed at. And for good reason. They convey no information. They all look the same.
From that can we assume that your main purpose is to frighten the public about what is happening? Is trying to reassure the public that the end is NOT near a worthwhile endeavor? Which purpose is the true one, and how do you tell the difference?
Mosher ==> From that we can estimate the 1951-1980 average of monthly averages to be 14.1.
Far below the global temperature for this planet to qualify as an “Earth-like planet” (according to NASA).
That means we need to warm up a bit more.
the new sidebar chart is a perfect example of statistical abuse and amatuer graphics
practice.
plot sunspots that way, plot the stockmarket, inflation, visits to WUWT.
BANK to 45 is a good rule
https://blogs.sas.com/content/iml/2016/01/20/banking-to-45-aspect-ratio-time-series.html
Essentially, what we know as climate change is nothing more than a man-made statistical construct. You can’t go outside and hold an instrument in the air and say “I’m measuring the climate.”
next youll tell us gender is a social construct.
what kind of liberal marxist claptrap is this? man made construct? all of physics
is man made constructs.
furthermore you can’t hold an instrument in the air and say im measuring the temperature
In physics, the temperature is the average kinetic energy of the moving particles in a substance
you are NOT measuring the average kinetic energy of the molecules.
temperature itself is a man made statistical construct.
didnt think about that did you.
molecules hit your thermometer over time it creates an average of the kinetic energy of
different molecules.
another hint. you dont measure the same molecules TWICE,
average kinetic energy = 3/2 kT, where k = 1.3807E-23 J/K and T is in Kelvin. A fixed relation.
How one feels about a climate change is not comparable to the fixed relations in the Kinetic theory.
It’s the feelings that we introduce about the magnitudes of climate variations in time and space which makes it a touchy feely “human” construct.
It’s really a matter for the philosophers to sort out.
For example, using a Koppen style classification, have climates changed? If an individual’s ancestors lived in a temperate continental type climate characterized oak savannah ecosystems, and this condition still exists today, has their climate changed?
My view is that only a philosopher can adequately provide this perspective.
Pure scientists are not well equipped in tools and training to answer this, as they tend to get caught up in technicalities. They might say a change in average (average) kinetic energy is a change to climate. But things are much more gray than that and necessarily introduces some ‘feelings’ about the subject.
The usda has not changed their hardiness zones since 2012 that I know of. The previous change before that was in the 1990’s. So apparently the climate in the US isn’t changing much!
I think you have been drinking cans of Colt 45 malt liquor, Mr. Masher
Heinekens out of a can taste different than Heinekens out of a bottle.
The can doesn’t have the “skunk” taste that the bottle often has, at least here in the US.
Mosh may prefer the bottle in what he takes in?
You are only making the argument about temperature data being unfit for purpose to be stronger. You should realize that calibrated thermometers do have set points that are very accurate by measuring well defined states of water. Does that make what temperatures are measured an accurate depiction of the total energy in a system accurate? Will a thermometer read latent heat which is a large part of the energy in the atmosphere? Do they take into account the lapse rate for calibration. Topeka is at 925 feet elevation above sea level. That is ~minus 5 F lower than sea level just due to the lapse rate. Are temps adjusted to sea level to insure a correct comparison? Are anomalies directly comparable?
This is great!
How many people and kids are scared because they’ve been given false impressions of the dangers of “GAGW” and/or “Climate Change”?
They’ve been given cellphone videos of fires and floods and storms and hurricanes presented as if no one have ever seen such things before.
Because we have color pictures and videos current events are worse than ever!
Putting things into a proper perspective is important.
It reminds of a couple of stories I saw a couple of decades ago. (I don’t remember the actual numbers.)
The story that wanted to vilify “greedy” insurance companies (plural. lots of insurance companies) stated that they made a bunch of billions in profits that year.
Another story presented their profits increasing from something like 2% to 3%.
Which story was out to leave what impression?
When reporting on Man’s CO2 from fossil fuels, what impression are those which report that gas in tons vs those who report it as % of the CO2 nature puts into the atmosphere? Or even as a % of the atmosphere itself?
PR reporting to leave the desired impression.
(Lots of comments already made. This will probably be lost.)
finally!
been pointing out for years the anomaly, while useful, really needs the absolute temperature given as a reference for context
it’s also important to keep in mind that, technically speaking, the climate could become quite horribly extreme without changing the average temperature at all if hot regions get hotter while cold regions get colder
and of course conversely it’s entirely possible for every region to moderate, improving conditions everywhere
so rather than focusing on average or regional temperatures, we should really develop a “livability” index with a scale reaching the size of (say) the average small town
because after all, if Earth was an unsettled exoplanet we’d just discovered, we would probably rate most local conditions as marginally habitable due to cold
I took the GISP2 ice core temperature data from here and plotted it using the same relative scaling as AW with the y-axis upper bound being 92σ above the max. The y-axis lower bound is quite a bit higher than the 115σ value below the min because -273 C is the lowest possible so I took the liberty of constraining it above what AW would have done. Anyway, notice how there is hardly any difference between the peak of the glaciation and the Holocene Climate Optimum. See, nothing to worry about.
At standard pressure it is not possible to have ICE above 1 C this is a really dumb graph. Graphing something that can’t exist is weird. In AW graph temperatures can exist between his min/max.
You even admit physical limits exist by stopping at -273 C saying it was “lowest possible”.
I’m glad we agree that it is a dumb way to make a graph. Now can you help me explain this to Anthony Watts?
bdgwx,
Good, now please add uncertainty bounds to your line and show us the result in a picture.
But first, you have shown temperatures estimated from (presumably) isotope ratios from selected parts of a core of ice drilled in an old, remote area. Can you admit that there is some uncertainty in assuming that the isotope ratio has remained related to temperature estimates in a linear, predictable way over many centuries. How do you quantify the uncertainty of that link, given that we know of no method to validate it. What statistics are applicable to “good faith” assumptions? Another example is statistically looking at sea level change, with the “good faith” assumption that the basin volume has not changed (the rock walls holding the oceans are free enough of large movement).
I think that it is scientifically invalid to fraudulent to quote uncertainties without adequate or even any mention of good faith assumptions. It follows that all such graphical representations that we are discussing here are meaningless without mention of uncertainty and its difficulties.
There is uncertainty in the temperature reconstructions just like there is uncertainty in the instrumental temperature record that Anthony Watts removed in the process of also expanding the y-axis bounds beyond a reasonable range. I’m glad you agree that this graph is meaningless. It means I’ve proved my point. Can you help me convince Anthony Watts of this?
As an economist, I like to think about “optimal” conditions. Can those who understand these measurements far better than me please answer a few questions: (1) what is the optimal temperature of the earth and how is that even determined? (2) why is everything measured against a “pre-industrial” temperature, but nobody seems to agree on what time period that should reflect, nor how accurate the measurement is? (3) Isn’t everything before 1850, or whatever year is the demarcation for when industrialization began “pre-industrial”? Shouldn’t that longer period be used? If not, why not? (4) why don’t we look at longer-term pre-industrial temperatures (say from zero CE to 1850?
Then there are a few economic questions: (5) if temperatures have increased by 1.5 degrees C since 1880 (or whatever year one wishes to use), then by how much would we be better off? How much higher would world GDP be if the average temperature was still at the pre-industrial value? How much higher would ag productivity be? How much less disease would there be? I could go on, given the various predictions of what climate change will cause.
(1) There isn’t a be-all-end-all temperature that is optimal for every situation.
(2) Not everything is. UAH, BEST, and most reanalysis datasets provide the absolute temperature.
(3) It depends. Some use 1750 as the demarcation.
(4) We do. There are many reconstructions of temperature going back millions of years.
(5) I don’t know.
Your #1 is pretty much an adequate response to any CAGW activist.
They say that the optimal temperature is that which the advanced economies have already adapted to with existing infrastructure and zoning.
A global average temperature change is not intrinsically bad. This is often misunderstood.
The claim is that populations have chosen to live (and we’ve built things) in places with the expectation of a certain kind of weather and seasons. So, changing weather and seasons will result in a non-optimal population distribution and build environment.
Thank you, Anthony, another small step toward damping the CAGW nonsense. However, “climate” is never about “average temperature”, neither daily nor annual, but depends on temperature variations, both daily and annual, to be useful, partial, descriptors, along with seasonal variations of precipitation, clouds, humidity, etc., of a location’s “climate”.
See quotes from my Blogpost update https://climatesense-norpag.blogspot.com/
” A Millennial Solar ” Activity” Peak in 1991 correlates with the Millennial Temperature Peak at 2003/4 with a 12/13 year delay because of the thermal inertia of the oceans. Since that turning point Earth has entered a general cooling trend which will last for the next 700+/- years.
The amount of CO2 in the atmosphere is .058% by weight. That is one 1,720th of the whole. It is inconceivable thermodynamically that such a tiny tail could wag so big a dog.There is no anthropogenic CO2 caused climate crisis………………………..
.Earth’s climate is the result of resonances and beats between the phases of natural cyclic processes of varying wavelengths and amplitudes. At all scales, including the scale of the solar planetary system, sub-sets of oscillating systems develop synchronous behaviors which then produce changing patterns of periodicities in time and space in the emergent temperature data. The periodicities pertinent to current estimates of future global temperature change fall into two main categories:
a) The orbital long wave Milankovitch eccentricity, obliquity and precession cycles. These control the glacial and interglacial periodicities and the amplitudes of the corresponding global temperature cycles.
b) Solar activity cycles with multi-millennial, millennial, centennial and decadal time scales.
The most prominent solar activity and temperature cycles are : Schwab-11+/-years ; Hale-22 +/-years ; 3 x the Jupiter/Saturn lap cycle 60 years +/- :; Gleissberg 88+/- ; de Vries – 210 years+/-; Millennial- 960-1020 +/-. (1)
Fig. 1 Greenland Ice core derived temperatures and CO2 from Humlum 2016 (2)
Fig.1 shows that Earth has passed the warm peak of the current Milankovitch interglacial and has been generally cooling for the last 3,300 years. The millennial cycle peaks are apparent at about 10,000, 9,000, 8,000, 7,000, 2,000, and 1,000 years before now.
Climate, and in particular precipitation, is dominated mainly by the Obliquity modulated by the Precession. J. H. C. Bosmans et al 2015 (3)”Obliquity forcing of low-latitude climate” shows that obliquity induced changes in the summer cross-equatorial insolation gradient explain obliquity signals in low latitude paleo climate records more usefully than the classical 65 degree north insolation curve alone. Yi Liu et al 2015 (4) in “Obliquity pacing of the western Pacific Intertropical Convergence Zone over the last 282,000 years ” ” … shows that the western Pacific ITCZ migration was influenced by combined precession and obliquity changes. The obliquity forcing could be primarily delivered by a cross-hemispherical thermal/pressure contrast, resulting from the asymmetric continental configuration between Asia and Australia in a coupled East Asian–Australian circulation system. “
The Oulu Galactic Ray Count is used in this paper as the “solar activity ” proxy which integrates changes in Solar Magnetic field strength, Total Solar Insolation , Extreme Ultra Violet radiation, Interplanetary Magnetic Field strength, Solar Wind density and velocity, Coronal Mass Ejections, proton events, ozone levels and the geomagnetic Bz sign. Changes in the GCR neutron count proxy source causes concomitant modulations in cloud cover and thus albedo. (Iris effect)…………………………………………………..
3 The Millennial Temperature Cycle Peak.
Short term deviations from the solar activity and temperature cycles are driven by ENSO events and volcanic activity.
Latest UAH Satellite Data (7)
Global Temp Data 2003/12 Anomaly +0.26 : 2023/02 Anomaly -0.04 Net cooling for 19 years
NH Temp Data 2004/01 Anomaly +0.37 : 2023/02 Anomaly +0.17 Net cooling for 19 years
SH Temp Data 2003/11 Anomaly +0.21: 2023/02 Anomaly 0.0 Net cooling for 19 years
TropicsTemp Data 2004/01 Anomaly +0.22 :2023/02 Anomaly – 0.11 Net cooling for 19 years.
USA 48 Temp Data 2004/03 Anomaly +1.322023/02 Anomaly + 0.68 Net cooling for19 years.
Arctic Temp Data 2003/10 Anomaly +0.93 : 2023/02 Anomaly – 0.24 Net cooling for 19 years
Australia Temp Data 2004/02 Anomaly +0.80 : 2023/02 Anomaly – 0.12 Net cooling for 19 years
Fig 2 Correlation of the last 5 Oulu neutron cycles and trends with the Hadsst3 temperature trends and the 300 mb Specific Humidity. ( 8,9 )
The Oulu Cosmic Ray count in Fig.2C shows the decrease in solar activity since the 1991/92 Millennial Solar Activity Turning Point and peak There is a significant secular drop to a lower solar activity base level post 2007+/- and a new solar activity minimum late in 2009. In Figure 2 short term temperature spikes are colored orange and are closely correlated to El Ninos. The hadsst3gl temperature anomaly at 2037 is forecast to be + 0.05.
https://blogger.googleusercontent.com/img/a/AVvXsEjEbcj5Rk2czupOsD4PnxjTI-dNoIAxcMG7yKIGiTboHkXgmlF-HR1m87NYfqMPtiJwwLrIvGpQBvedJLU9dgcqsm-EV63Xuz7VyuiLjy7aqL2p6NaMD9mt9TOO-iDEeT_GIcBDpyAFUkX5-gJwoywFuphiM6-20iV3lXEUvLpz1Ln0mdmiRqpfyAR_3w=w631-h410
Fig.3 Northern Hemisphere 2000 year temperature reconstruction and a Millennial Temperature Turning Point. (MTTP). (7)
Because of the data quality, record length and methods used, the NH Christiansen et al 2012 series was selected as the “type reconstruction” to represent the NH trends. The de Vries, Maunder, Sporer and Wolf minima are noted. Important volcanic cooling events are marked with a V. An MTTP occurs at about 990. The Millennial cycles are asymmetric with a 700+/- year down-leg and a 300 +/- year up-leg.
https://blogger.googleusercontent.com/img/a/AVvXsEi_Nb8FScfi4vU39WRnt85Ua6CSHHmuRkp9lCLYNQqXLsAyLu5iOnnIgr2Jj32od9RAOhpiG6MHcdUVBUb-3_ATOd7Wue73fogFWBHPBRMEu2gsmZ7wn5ZEuLlxxCCjK2T47UdKng6Wbt36Igdf659FOWPLsuAlR19WeFoGaXjbVZn6qctZd6RnLFj1gQ=w446-h516
Fig 4 The NRLTSI2 Solar Activity – CET Relationship 1600- Present (8,9,10)
In Fig.3 the Roth & Joos Cosmogenic Index (CI) is used as the emergent proxy for the solar activity driver of the resulting emergent global and NH temperature data.
The effect on observed emergent behaviors i.e. global temperature trends, of the combined effect of these solar and GCR drivers will vary non-linearly depending on the particular phases of the eccentricity, obliquity and precession orbital cycles at any particular time.
Figure 3 shows an increase in CI of about 2 W/m 2 from the Maunder minimum to the 1991 activity peak. This increase, together with the other solar “activity” variations modulate the earth’s temperature and albedo via the GR flux and varying cloud cover.
The emergent temperature time series trends of the combined orbital, solar and GCR drivers also reflect turning points, changes of state and important threshold effects created by the interactions of the underlying physical processes. These exogenous forcings are also simultaneously modulated by changes in the earth’s magnetic field and length of day.
The temperature increase since the 1680s is due to the up- leg in the natural solar ” activity” Millennial cycle as shown by Lean 2018 “Estimating Solar Irradiance Since 850 AD” (ibid) Figure 3 also shows the correlation between the CI driver and the Central England Seasonal Temperatures. (ibid). The 1650 – 1700 (Maunder), 1810 – 20 (de Vries/Dalton), and the 1890-1900 (Gleissberg) minima are obvious.
These temperature changes correlate very well with the changes in energy flow from the sun shown in Figure 1 C without any measurable effect of C02.There is no anthropgenic CO2 caused climate crisis …………………………………..
AleksanderZhitomirskiy 2022,(16) says:
“The molar heat capacities of the main greenhouse and non-greenhouse gases are of the same order of magnitude. Given the low concentration of greenhouse gases in the atmosphere, their contribution to temperature change is below the measurement error. It seems that the role of various gases in the absorption of heat by the atmosphere is determined not by the ability of the gas to absorb infrared radiation, but by its heat capacity and concentration. ”
Zaichun Zhul et al 2016 (17) in Greening of the Earth and its drivers report “a persistent and widespread increase of growing season integrated Leaf Area Index (greening) over 25% to 50% of the global vegetated area from 1982 – 2009. ………. C02 fertilization effects explain 70% of the observed greening trend.”
Policies which limit CO2 emissions or even worse sequester CO2 in quixotic CCS green-washing schemes would decrease agricultural food production and are antithetical to the goals of feeding the increasing population and bringing people out of poverty.
.See Figs 3 and 4 at https://climatesense-norpag.blogspot.com/
Norman Page said: “The amount of CO2 in the atmosphere is .058% by weight. That is one 1,720th of the whole. It is inconceivable thermodynamically that such a tiny tail could wag so big a dog.”
The amount of Fentanyl a dose of 5 mg in the human body is 0.0000083 % by weight. That is one 12,000,000th of the whole. It is inconceivable chemically that such a tiny tail could wag so big a dog.
You’re comparing apples to rocks.
CO2 is a natural and vital part of our atmosphere and one of the things that makes life on Earth possible.
Fentanyl is a drug that is not natural nor a vital part of the human body.
You’re comparing apples to rocks.
Gunga Din said: “CO2 is a natural and vital part of our atmosphere and one of the things that makes life on Earth possible.”
It is doubly amazing then that such a tiny tail could wag so big a dog. Think about it…one 1720th of the whole makes life on Earth possible!
“CO2 is a natural and vital part of our atmosphere and one of the things that makes life on Earth possible.”
CO2 levels have been higher and they’ve been lower. Life goes on.
Ma’ Nature, the original “recycler”.
(Are you saying she’s a dog?) 😎
Norman isn’t insinuating that CO2 levels have not changed. He is insinuating that it has no effect.
Maybe.
But what are you insinuating?
Man’s CO2 is “wagging the dog?” of “CAGW”/”Climate Change”?
Is CO2 even the “tail”?
You seem to think so.
I’m not the one insinuating. That was Norman. I’m just here to report that the insinuation that small things cannot have a noticeable effect is wrong.
So is comparing chemistry with physics.
There’s going to be a Moshism in here somewhere.
In this calculation, the “normal” temperature of the Earth is assumed to be 57.2°F. and that is simply added to the anomaly temperature reported by NASA GISS to obtain the absolute temperature.
no.
you did 2 things to hide the incline
gosh i get tired of explaining algebra to non english majors
so you simply added the wrong number, using annual absolutes as opposed to monthly absolutes. HINT all the underlying data is monthly.
Thanks for the new method of temperature record presentation. Common sense is always to be desired. Now could we have atmospheric CO2 levels presented as a percentage of atmosphere? That would be an eye-opening experience for many.
OK then smartass, YOU DO IT. I get tired of English majors trying to “correct” other peoples work, when they show none of their own.
The data I used is yearly, not monthly. If it WERE monthly data, then yes I’d use the MONTHLY value.
Also, I didn’t hide anything, I showed the scale of human temperature experience. Tough noogies if you don’t like it.
Somehow I missed the original comment. It really doesn’t matter what the absolute value is as long as it is close. Anomalies are calculated by subtracting a value, you can reverse that operation by adding.
All Mosher’s complaint does is confirm that one of the purposes of averaging anomalies is that it hides what is behind the number. It is why the variance of the distributions making up the anomalies is very important. Hint, when two random variables are subtracted, their variances add to determine the variance of the difference. You can’t just compute the variance of the anomalies that you end up with.
Only thing to add is the bandwidth of inaccuracy of the measurements.