From Dr. Roy Spencer’s Weather Blog
2023 Was the Warmest Year In the 45-Year Satellite Record
The Version 6 global average lower tropospheric temperature (LT) anomaly for December, 2023 was +0.83 deg. C departure from the 1991-2020 mean, down from the November, 2023 anomaly of +0.91 deg. C.
The 2023 annual average global LT anomaly was +0.51 deg. C above the 1991-2020 mean, easily making 2023 the warmest of the 45-year satellite record. The next-warmest year was +0.39 deg. C in 2016. The following plot shows all 45 years ranked from the warmest to coolest.
The linear warming trend since January, 1979 still stands at +0.14 C/decade (+0.12 C/decade over the global-averaged oceans, and +0.19 C/decade over global-averaged land).
It might be partly coincidence, but the +0.51 deg. C number for 2023 from satellites is the same as the surface air temperature estimate from the NOAA/NCEP/NCAR Climate Data Assimilation System (CDAS). Note that the CDAS estimate is only partly based upon actual surface air temperature observations… it represents a physically consistent model-based estimate using a wide variety of data sources (surface observations, commercial aircraft, weather balloons, satellites, etc.). [UPDATE: it appears the CDAS anomalies are not relative to the 1991-2020 base period… I recomputed them, and the CDAS anomaly appears to be +0.45 deg. C, not +0.51 deg. C]:
Various regional LT departures from the 30-year (1991-2020) average for the last 24 months are:
| YEAR | MO | GLOBE | NHEM. | SHEM. | TROPIC | USA48 | ARCTIC | AUST |
| 2022 | Jan | +0.03 | +0.07 | +0.00 | -0.23 | -0.12 | +0.68 | +0.10 |
| 2022 | Feb | +0.00 | +0.02 | -0.01 | -0.24 | -0.04 | -0.30 | -0.49 |
| 2022 | Mar | +0.16 | +0.28 | +0.03 | -0.07 | +0.23 | +0.74 | +0.03 |
| 2022 | Apr | +0.27 | +0.35 | +0.18 | -0.04 | -0.25 | +0.45 | +0.61 |
| 2022 | May | +0.18 | +0.25 | +0.10 | +0.02 | +0.60 | +0.23 | +0.20 |
| 2022 | Jun | +0.07 | +0.08 | +0.05 | -0.36 | +0.47 | +0.33 | +0.11 |
| 2022 | Jul | +0.36 | +0.37 | +0.35 | +0.13 | +0.85 | +0.56 | +0.65 |
| 2022 | Aug | +0.28 | +0.32 | +0.25 | -0.03 | +0.60 | +0.51 | +0.00 |
| 2022 | Sep | +0.25 | +0.43 | +0.06 | +0.03 | +0.88 | +0.69 | -0.28 |
| 2022 | Oct | +0.32 | +0.44 | +0.21 | +0.05 | +0.17 | +0.94 | +0.05 |
| 2022 | Nov | +0.17 | +0.21 | +0.13 | -0.16 | -0.50 | +0.52 | -0.56 |
| 2022 | Dec | +0.05 | +0.13 | -0.02 | -0.34 | -0.20 | +0.80 | -0.38 |
| 2023 | Jan | -0.04 | +0.05 | -0.13 | -0.38 | +0.12 | -0.12 | -0.50 |
| 2023 | Feb | +0.09 | +0.17 | +0.00 | -0.10 | +0.68 | -0.24 | -0.11 |
| 2023 | Mar | +0.20 | +0.24 | +0.17 | -0.13 | -1.43 | +0.17 | +0.40 |
| 2023 | Apr | +0.18 | +0.11 | +0.26 | -0.03 | -0.37 | +0.53 | +0.21 |
| 2023 | May | +0.37 | +0.30 | +0.44 | +0.40 | +0.57 | +0.66 | -0.09 |
| 2023 | June | +0.38 | +0.47 | +0.29 | +0.55 | -0.35 | +0.45 | +0.07 |
| 2023 | July | +0.64 | +0.73 | +0.56 | +0.88 | +0.53 | +0.91 | +1.44 |
| 2023 | Aug | +0.70 | +0.88 | +0.51 | +0.86 | +0.94 | +1.54 | +1.25 |
| 2023 | Sep | +0.90 | +0.94 | +0.86 | +0.93 | +0.40 | +1.13 | +1.17 |
| 2023 | Oct | +0.93 | +1.02 | +0.83 | +1.00 | +0.99 | +0.92 | +0.63 |
| 2023 | Nov | +0.91 | +1.01 | +0.82 | +1.03 | +0.65 | +1.16 | +0.42 |
| 2023 | Dec | +0.83 | +0.93 | +0.73 | +1.08 | +1.26 | +0.26 | +0.85 |
The full UAH Global Temperature Report, along with the LT global gridpoint anomaly image for December, 2023, and a more detailed analysis by John Christy, should be available within the next several days here.
The monthly anomalies for various regions for the four deep layers we monitor from satellites will be available in the next several days:
Lower Troposphere:
http://vortex.nsstc.uah.edu/data/msu/v6.0/tlt/uahncdc_lt_6.0.txt
Mid-Troposphere:
http://vortex.nsstc.uah.edu/data/msu/v6.0/tmt/uahncdc_mt_6.0.txt
Tropopause:
http://vortex.nsstc.uah.edu/data/msu/v6.0/ttp/uahncdc_tp_6.0.txt
Lower Stratosphere:
http://vortex.nsstc.uah.edu/data/msu/v6.0/tls/uahncdc_ls_6.0.txt
I have shown two examples that demonstrate that certain theses have been accepted as certain in climate science, while observations contradict them. First, the ozone hole varies with the strength of solar flares and ozone production in the upper stratosphere and with the strength of the polar vortex.
Secondly, one can see a significant difference in sea surface temperature in the two hemispheres at middle and high latitudes, which can be linked to the Earth’s position relative to the sun and the amount of solar radiation available. Thus, it is impossible to speak of “global” warming.
Is the “consensus” in science scientific? Does it mean “I know I know nothing”?
The big problem is the extension of winter conditions in the northern hemisphere until April, when the Earth begins to move away from the Sun in orbit from January. Warm oceans in the northern hemisphere will produce large amounts of snow in the first part of winter, and low temperatures will continue until April.
By the 10th of January, the polar vortex in the tropopause will split into two centers consistent with the geomagnetic field. A harsh winter will befall North America and Europe.
This winter, sea ice in the Baltic Sea may be at a record level.
While the world between the poles appears to be getting warmer , the ice extents at both poles is creeping towards the median . Are the equator to pole currents reducing ?
I recall Dr. Lindzen saying that major climate change involves a decrease in contrasting temperature between the poles and the equator. The last glacial maximum was a 60C difference; now it is 40C. Lindzen suggests that the tropics are leading modern warming. If you follow Ron Clutz of Science Matters, he displays that with the data he uses. However, Dr Lindzen also cautioned, similar to what I, the Gormans, and karlomonte have been saying about temperature anomalies: the variance within a single anomaly is huge. As such, it is an unfit statistical methodology that is in dire need of correction.
I’ll repeat: Medical science is moving away from the use of the SEM as a measure of uncertainty in study results. Too many lawsuits over treatments justified by using large samples to lower the SEM have happened.
Climate science hasn’t had their significant emotional event yet that will wake them up to the misuse of the SEM. It *will* come. Sooner or later it always happens.
Do you accept Dr. Lindzen’s work? I ask because he uses adjusted data.
I accept his position on variance, because it’s correct.
Is that a yes? If so is it a yes because he uses adjusted data? Or is it a yes despite him using adjusted data?
What does adjusted data have to do with variance? And what do you mean when you say “do you accept his work?”
My question wasn’t focused on variance per se. It was just a general question of whether you accept Dr. Lindzen’s work. And by work I mean the research he has presented to the scientific community. I ask because you may not have been aware that he uses adjusted data and with that knowledge you may no longer accept his work and/or positions assuming you even accepted it to begin with.
Your question was a diversion away from the issue being discussed. Which you have *still* failed to address.
Why is medicine moving away from the SEM as the metric for measurement uncertainty while comate science is not?
I have to explore the other areas of Dr. Lindzen’s research.
I have yet*
BTW his position on variance has an implied assumption that he is okay with averaging since even the spot temperature measurements he speaks of in the video are actually averages themselves.
Nobody has a problem with averaging if the correct assumptions are used. The first thing statisticians must accept when dealing with physical measurements is how they are made. You never mention that a collection of temperature measurements, such as a month, is a random variable. That random variable has a mean and a variance that depends on the shape of the distribution.
You have in the past declared that the functional relationship to used to determine a single measurement is the average of, let’s say, a month.
That means the ~30 days of data are combined to into ONE measurement. You cannot claim that there is cancelation of any uncertainty since you do not have multiple measurements under repeatable conditions.
As I mentioned to Bellman, search the GUM for the word “dispersion”. See if anywhere in the document dispersion is used in the definition of “standard uncertainty of the mean”. The only place you find it is in relation to the “experimental standard deviation” and not the experimental standard deviation of the mean. Section E.5 is informative.
Ultimately, it means dividing by the √n is not the appropriate way to find the dispersion of values attributed to the measurand without making numerous assumptions.
No, he expressed, in the video, that relying on a single number to represent the global temperature isn’t a good idea. He also said an overall increase of 1C or any similar value at one station means it’s almost as likely to have cooled, because the spread of distribution is so large.
“ because the spread of distribution is so large.”
They simply can’t accept this. It invalidates their religious belief that if they can just calculate the average more and more precisely that they can then ignore the variance (and associated uncertainty) of the data.
The concepts of “precision” and “accuracy” are forever equal in their minds, there is no difference. Calculate the average more precisely and it’s more accurate. Uncertainty goes away and it can be ignored.
And they do is smash the red button when someone points that out to them ;-D.
As I’ve said numerous times I’ve never downvoted anyone.
It’s case of do as I say and not as I do. He relies on a single number to represent the global temperature in his own research and then tells other people not to do it.
That would only be true if the uncertainty was very large. For example, even if the standard uncertainty was u = 2 C there only be a 16% chance that it cooled.
“That would only be true if the uncertainty was very large. For example, even if the standard uncertainty was u = 2 C there only be a 16% chance that it cooled.”
As usual you assume a Gaussian distribution of “error”. But you never justify this assumption for temperature measurements. The uncertainty profile can be asymmetric for *any* measuring device. You need to justify why you never account for this.
It doesn’t really matter if he uses adjusted data or not. He still starts from a statistical point that is unjustifiable. (Tmax + Tmin)/2 is *ONLY* a good measure if you have a Gaussian distribution. Daily temperature is *NOT* a Gaussian distribution.
When you include the fact that *NO* weighting is done on a station-by-station basis for differing variances in their temperature profiles then be it raw data or adjusted data it is garbage. Garbage in ==> garbage out. It’s just that simple.
And then comes that fact that he, and the rest of climate science, assumes that all measurement error is random, Gaussian, and cancels so that the stated values are all 100% accurate. Again, GARBAGE IN ==> GARBAGE OUT!
And the use of anomalies fixes NONE of this. If the components of the anomalies are GARBAGE then the anomalies are GARBAGE as well. No amount of “averaging” can fix the problems.
I’ve been following the conversation on averages and why they are supposedly meaningless. I explored some weather data myself, and I find that while they, of course, don’t capture absolutely all variables, they are representative of large regions. Here’s data from three different stations in Northeast Maine on the average February temperature for two decades. I’ve lived here since 1974. Please explain why my thinking here would be erroneous.
No hockey stick.
Nice. Keep looking. As more and more graphs of these stations are shown, people should wake up and ask “WHAT?”
The three graphs are very similar. The variations (i.e. anomalies) in each are not.
Look at 2006 – 2007(?). The anomaly for the purple line is 9 and for the blue line is 5.5.
Yet climate science would have you believe that anomalies fix all problems with differences in micro-climate and natural variation. I haven’t taken time to calculate the variance for each set of data but I’ll bet they aren’t the same.
How do you justify adding either absolute values or anomalies from distributions with different variances without addressing the differing variances? Answer: Climate science doesn’t justify anything, they just ignore variance. Just like they ignore measurement uncertainty. They just make it all disappear by making magic motions with their hands!
I interpret his graph as indicating that thermometers register higher or lower temperatures when influenced by a pressure system entering his area, resulting in a corresponding anomaly shift. However, this isn’t the primary concern. Upon investigating the USCRN data in his graph, specifically focusing on one month, I examined the distribution of maximums and minimums, with the average (depicted by the green line) at the midpoint. Distinct temperature profiles emerge in the afternoon and morning, showcasing the effects of averaging them. The data points on his graph also exhibit variance, which means that changes in conditions during measurements could definitely yield a different trend. Given climate science’s tendency to derive trends from small increases, that is very important. That graph does not prove utility; the process involves averaging maximum and minimum values, resulting in several different days yielding the same average. Nothing surprising there. So what does that tell you about the weather conditions on those days? Absolutely nothing.
“The data points on his graph also exhibit variance, which means that changes in conditions during measurements could definitely yield a different trend.”
Not just variance but *different* variances for maximum and minimum.
“That graph does not prove utility; the process involves averaging maximum and minimum values, resulting in several different days yielding the same average.”
That’s because you are finding a median value, not an average value. It’s why San Diego, CA and Ramona, CA can have the same median value of temp with vastly different minimum and maximum temps.
I’m afraid I don’t understand. If the goal is to determine whether an area is experiencing warming or cooling, the anomalies appear to offer a solid foundation for comprehending that, especially considering their similarity, as you mentioned.
It’s a matter of uncertainty. If the anomaly is smaller than the uncertainty of the measurements then how do you know the anomaly’s sign let alone its value?
Measurement 1 = x_1 +/- u_1
Measurement 2 = x_2 +/- u_2
Anomaly equals M2 – M1
That becomes x_2 – x_1 +/- (u_1 + u_2)
If (u_1 + u_2) > x_2 – x_1 then how do you actually know what x2 – x1 is?
Let M1 = 20C +/- 0.5C
let M2 = 21C +/- 0.5C
M2 – M1 = 1C
The uncertainty becomes +/- 1C
So you have the anomaly of 1C +/- 1C
Is the anomaly 0C or 2C? You don’t know. It’s all part of the GREAT UNKNOWN.
When you consider that the measurement uncertainty of most field temperature measuring devices are 0.2C or greater the overall uncertainty of combining them will be 0.2C or greater. How do you identify anomalies out to the hundredths digit when your uncertainty is at lest in the tenths digit and is probably much, much higher.
The climate science crowd will tell you that more samples allow them to more precisely calculate the mean. They leave out the fact that more and more samples make the uncertainty of that precisely calculated mean more and more uncertain!
They start off confusing precision with accuracy. Then they will say that all measurement uncertainty is random, Gaussian, and cancels and thus the mean is quite accurate. But they NEVER justify that assumption that all measurement uncertainty is random, Gaussian, and cancels. In fact, the calibration drift in similar measuring devices, e.g. a liquid-in-glass thermometer, is typically in the same direction. Thus the combined random and systematic uncertainty tends to add and not cancel.
But we are supposed to just take the assumption that all measurement uncertainty cancels on faith.
My religious faith in climate science dogma was lost long ago. Call me a heretic I guess.
Don’t forget their handwaving about how with “enough” measurement stations, systematic error magically transforms into random error which they can ignore.
BTW, why do *YOU* think medical science is moving away from using the SEM as a metric for measurement uncertainty? You failed to address the actual point of my post. Why is that? Nothing to say? Your question is just a diversion, it’s a Red Herring argumentative fallacy.
” medical science is moving away from using the SEM as a metric for measurement uncertainty?”
Is this true? Can you cite sources to back this up? I aks because when I searched my first return was this.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10336232/
It’s pretty much all about reliability and measurement error.
I’m hoping you don’t deflect here as you did earlier with Mr. Stokes’s reasonable request.
You’ve been given the references before. Why do CAGW supporters never seem to remember the evidence they’ve been given?
go here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6999182/
Written in 2020 there is not a single mention of using the standard deviation of the sample means as the measurement uncertainty.
It even repeats the GUM: “Uncertainty is defined as a “non-negative parameter characterizing the dispersion of the quantity values being attributed to a measurand based on the information used” “
The standard deviation of the sample means (typically mischaracterized as standard error of the mean by statisticians) does not define the dispersion of the quantity values attributable to the measurand. It describes the dispersion of the sample means, not the dispersion of the population, i.e. the SD.
Documents on this are all over the internet. If you can’t find them then you aren’t looking. try google terms “medicine measurement uncertainty standard error”
“Written in 2020 there is not a single mention of using the standard deviation of the sample means as the measurement uncertainty.”
Because it’s not talking about the uncertainty of the mean – it’s all about measurement uncertainty of individual measurements.
No one cares about the standard deviation of the sample means when it comes to temperature. It is the accuracy of the temperature measurements that is, or should be, the most important factor to evaluate.
Instead, climate science is like you, the most important factor for temperature is how precisely you can calculate the average value with absolutely NO CONCERN FOR HOW ACCURATE THAT AVERAGE IS.
“You’ve been given the references before.:
What you mean “you” kemosabie? Not me, “you”, that’s for sure. If you gave them, to any “you” then you should have them. I bookmark what I “give”.
W.r.t. my link, I did more than what is required in superterranea and tried to chase down your claim. It’s what I found. And FYI, “Documents on this are all over the internet” don’t get it. Nor does imagineering search terms that you haven’t even used yourself. Earth to Tim, this is your claim.
AGAIN, per Chris Hitchens:
“That which can be asserted without evidence, can be dismissed without evidence.”
From your reference.
That is, σₜₒₜₐₗ = √(σ₁² + σ₂² + … + σₙ²)
Here are two references more directly applicable.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2959222/#
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3387884/
SSW is already in the middle stratosphere and will soon reach the lower stratosphere. Europe be ready for frost.
In the past few days, frost locally in Sweden reached nearly -44 deg C., but as it turned out, this was not the apogee of the cooling wave. The highest frost of -44.3 deg. C. the previous night was recorded in Enontekio, Finland. This is the highest frost in Scandinavia in at least several decades. Previously, powerful snowstorms occurred especially in Norway and Sweden. An example is the area of the town of Grimstad, where 70-100 cm of snow fell. There is much more in the snowdrifts. Skis have become the main means of transportation. Another example is the snowdrifts that have formed on the E22 between Kristianstad and Horby in Skåne in southern Sweden. Many drivers and their families were stuck in their cars overnight until help arrived.
Are these places north of the Arctic Circle?
https://www.ventusky.com/?p=62.0;22.3;3&l=temperature-5cm&t=20240104/0600
Looks to be just south of the Circle, but well inland from the oceans.