February Fantasy Redux

Guest Post by Willis Eschenbach

This is an extension of my previous post entitled “February Fantasy Versus Reality“. Please read that to get the basic ideas. To recap, a study in Science magazine said

Despite the rapid warming that is the cardinal signature of global climate change, especially in the Arctic, where temperatures are rising much more than elsewhere in the world, the United States and other regions of the Northern Hemisphere have experienced a conspicuous and increasingly frequent number of episodes of extremely cold winter weather over the past four decades.

The Arctic is warming at a rate twice the global average and severe winter weather is reported to be increasing across many heavily populated mid-latitude regions, but there is no agreement on whether a physical link exists between the two phenomena.”

To test this claim of increasing “severe winter weather”, in my previous post I looked at the average February temperature of the continental US to see if it was cooling. It hasn’t been cooling.

However, a couple of commenters correctly pointed out that the issue discussed in the study was not average temperature. Instead, the authors were talking about “episodes of extremely cold winter weather” such as those Texas experienced in February of 2011 and 2021.

Looking for a more accurate measure of extremely cold winter weather, I got the daily temperature data for the Southern Great Plains from NOAA. Here’s a map of the area in question.

Figure 1. Map of the National Climate Assessment regions.

Then I calculated the standard deviation (a measure of how widely spread out the temperatures are) of the February temperatures. I reasoned that if there were short sharp cold spells, the standard deviation would be larger.

Figure 2. Standard deviations of February minimum daily temperature for the Southern Great Plains. Cold spells are indicated by an increase in the standard deviation.

In Figure 2, we can clearly see the Texas cold spells of 2011 and 2021. But is there a “conspicuous and increasingly frequent number of episodes of extremely cold winter weather over the past four decades”?

Well … in a word, no. Figure 2 shows there was a serious cold spell in 1951. And the Texas State Climatologist agrees, saying:

Jan.–Feb. 1951: Freeze. On Jan. 31.–Feb. 3 and again on Feb. 13–17, cold waves swept over the entire state, bringing snow and sleet. Heavy damage was done in the Lower Rio Grande Valley to truck and citrus crops, notably in the earlier of these northers. During the norther of Jan. 31–Feb. 3, the temperature went to –19°F in Dalhart.

However, during the thirty years after 1951, there was little in the way of “episodes of extremely cold winter weather” until the decade and a half from 1981 to 1996. During that time there were a number of cold episodes, although not as intense as in February 1951. In the coldest of these, in February of 1985, San Antonio got a rare snowfall, and they saw the coldest day ever recorded in Midland, Texas.

However, in the quarter century since 1996, there have only been the two extremely cold spells mentioned above, in 2011 and 2021.

If we divide the 72 years of the record into three 24-year periods, we have only one “episode of extremely cold winter weather” in the first period; six somewhat warmer episodes in the second period; and only two episodes in the most recent 24 years.

So no, in the Southern Great Plains, there is not a “conspicuous and increasingly frequent number of episodes of extremely cold winter weather. Nor is “severe winter weather … increasing” as they claimed. Neither of those statements is true.

Then I thought, “Well, maybe I’m looking too far south. Maybe the claimed effect is visible in the Northern Great Plains”. So it was back to the drawing board, and here’s what I found.

Figure 3. Standard deviations of February minimum daily temperature for the Northern Great Plains. Cold spells are indicated by an increase in the standard deviation

Although there is greater variation in the February minimum temperatures in the Northern Great Plains NCA region, the same situation prevails as in the Southern Great Plains—one February “episode of extremely cold winter weather” in the first 24 years, a half-dozen or so in the middle 24 years, and the two cold Februarys in 2011 and 2021 in the final 24 years. And there is no trend in the data.

Another beautiful theory runs hard aground on a reef of ugly facts.

Best to all,


Data Access—I’ve put the 72 years (1951-2022) of daily Southern Great Plains temperatures, both maximums and minimums, in my Dropbox for download. It’s a fairly small file entitled Great Plains South nClimDiv.csv, 588 KB, in CSV format so it can be opened in Excel or other programs.

My Usual—I can defend my own words. I choose them very carefully. I cannot defend your (mis)understanding of my words. So please, when you comment, quote the exact words you are discussing.

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Tom Halla
February 1, 2023 2:11 pm

How to report a null result. The weather records do go back farther than 1950, but are presumably not in searchable format. Besides, the claim was for a more recent change in severe weather.

Reply to  Willis Eschenbach
February 1, 2023 10:09 pm

The 1975 to 2015 warming that much more affected the Arctic than the tropics, reduced the temperature differential between the Arctic and the tropics in the Northern Hemisphere. The result SHOULD be better weather in the 1975 to 2015 period in the Northern Hemisphere, based on meteorology 101.

Reply to  Tom Halla
February 2, 2023 4:24 am


Yes, and as expected, cold extremes are decreasing in intensity, duration, frequency, and area.

February 1, 2023 2:16 pm

Data is to climate alarmism as Afghanistan is to empires. The rocks upon which so many are shattered.

Reply to  Giving_Cat
February 1, 2023 2:27 pm

Except Afghanistan is poor and these publishing empires of agenda science get monetary rewards from these publications via tenure and promotion and travel.

Reply to  Giving_Cat
February 1, 2023 10:17 pm

Of course there are no data for the futre climate, just predictions that are consistently wrong.

Unfortunately, the data for the past temperatures is controlled by the same people who predict much faster global warming in the future.

And their paychecks depend on such predictions.

That’s why they revise historical temperature data from raw numbers, to infilled numbers, to adjusted numbers, to homogenized numbers to pasteurized numbers, to readjusted numbers, to re-re-adjusted numbers

Here are some examples with GIF moving charts from my blog:

:NOAA US average temperature from 1920 to 2020, Raw Data vs. Adjusted Data presented to the public (science fraud) (honestclimatescience.blogspot.com)

Pre-1980 global average temperature “revisions” from 2000 to 2017 (science fraud) (honestclimatescience.blogspot.com)

Watch US climate history get changed to better support the CO2 is evil narrative (honestclimatescience.blogspot.com)


Last edited 1 month ago by Richard Greene
Norman Page
February 1, 2023 2:29 pm

Earth shows net cooling for 19 years and The Rules of the Lebensraum game.

1.A battle for Lebensraum, i.e. land, energy and food resources, broke out when Russia invaded Crimea.An associated covid pandemic, and global poverty and income disparity increases now threaten the UN’s Sustainable Development Goals. During the last major influenza epidemic in 1919 world population was 1.9 billion. It is now 7.8 billion+/ – an approximate four fold increase.

The IPCC and UNFCCC post modern science establishment’s “consensus” is that a modelled future increase in CO2 levels is the main threat to human civilization. This is an egregious error of scientific judgement.  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. Earth has now entered a general cooling trend which will last for the next 700+/- years.
Because of the areal distribution and variability in the energy density of energy resources and the varying per capita use of energy in different countries, international power relationships have been transformed. The global free trade system and global supply chains have been disrupted.

Additionally, the worlds richest and most easily accessible key mineral deposits were mined first and the lower quality resources which remain in the 21st century are distributed without regard to national boundaries and demand. As population grows,inflation inevitably skyrockets. War between states and violent conflicts between tribes and religious groups within states are multiplying.

2 The Millennial Temperature Cycle Peak.
Latest Data (1) https://www.nsstc.uah.edu/data/msu/v6.0/tlt/uahncdc_lt_6.0.txt
Global   Temp Data 2003/12 Anomaly +0.26 : 2023/01 Anomaly -0.04 Net cooling for 19 years
NH     Temp Data 2004/01 Anomaly +0.37 :  2023/01 Anomaly +0.05 Net cooling for 19 years
SH      Temp Data 2003/11 Anomaly +0.21:  2023/01 Anomaly  -0.14 Net cooling for 19 years  
Tropics  Temp Data 2004/01 Anomaly +0.22 : 2023/01 Anomaly  – 0.38 Net cooling for 19 years.
USA 48  Temp Data 2004/03 Anomaly +1.32 : 2023/01 Anomaly  + 0.12 Net cooling for 19 years.
Arctic    Temp Data 2003/10 Anomaly +0.93 :  2023/01 Anomaly  – 0.72 Net cooling for 19 years
Australia  Temp Data 2004/02 Anomaly +0.80 : 2023/01 Anomaly  – 0.50 Net cooling for 19 years 
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 +/-. (2)
 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)
Eschenbach 2010 (3) introduced “The Thunderstorm Thermostat Hypothesis – how Clouds and Thunderstorms Control the Earth’s Temperature”. 
Eschenbach 2020(4) in https://whatsupwiththat.com/2020/01/07/drying-the-sky  uses empirical data from the inter- tropical buoy system to provide a description of this system of self-organized criticality. Energy flow from the sun into and then out of the ocean- water interface in the Intertropical Convergence Zone  results in a convective water vapor buoyancy effect and a large increase in OLR This begins when ocean temperatures surpass the locally critical sea surface temperature to produce Rayleigh – Bernard convective heat transfer.

 Short term deviations from the solar activity and temperature cycles are driven by ENSO events and volcanic activity.comment image

Fig 1 Correlation of the last 5 Oulu neutron cycles and trends with the Hadsst3 temperature     trends and the 300 mb Specific Humidity. ( 5,6 )     

The Oulu Cosmic Ray count in Fig.1C 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 1 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. 

Reply to  Norman Page
February 1, 2023 3:23 pm

Soooo many assumptions must be accepted to allow for the conclusion. ie:
Earth has now entered a general cooling trend which will last for the next 700+/- years.”

Norman Page
Reply to  KevinM
February 1, 2023 9:02 pm
Reply to  KevinM
February 1, 2023 10:20 pm

No one has ever made accurate long term climate predictions.
Mr. Page is very unlikely to be the first.

Rud Istvan
February 1, 2023 2:42 pm

The abstract to the Science paper on which the Forbes article was based said they had used ‘observational analysis and modeling’ to reach their colder February results. Modeling, maybe. Observational analysis, nope. WE just provided that part.

Such study misrepresentation comprises academic misconduct. There are many other ‘climate science’ misconduct examples, including in Science. Marcott’s 2013 Science hockey stick paper is exhibit 1. I alerted then Science senior editor Marsha McNutt to it by providing written proof using Marcott’s own stuff. There was even a clear smoking gun. Her assistant acknowledged receipt, but no followup and no retraction. Details were then published as essay ‘A high stick foul’ in ebook Blowing Smoke.

Based on his famous (but dishonest) Science paper, Marcott got a tenure track assistant professorship at U Wisconsin Madison main campus.

Reply to  Rud Istvan
February 1, 2023 3:02 pm

Sicko Award to UWM

Rud Istvan
Reply to  ResourceGuy
February 1, 2023 3:29 pm

I don’t blame UWM. They thought they were getting a rising young academic star, famously published in Science. I don’t blame Science; peer review doesn’t screen for deliberate academic misconduct. I blame McNutt. She did nothing AFTER she was provided (receipt acknowledged) incontrovertible written proof from Marcott himself of academic misconduct (thesis fig4.3C versus Science fig1g is a smoking gun). She knew, yet did nothing.

Nick Stokes
Reply to  Rud Istvan
February 1, 2023 4:23 pm

“Such study misrepresentation comprises academic misconduct.”

No, you have misrepresented their paper. They said in that abstract:

” We use observational analysis to show that a lesser-known stratospheric polar vortex (SPV) disruption that involves wave reflection and stretching of the SPV is linked with extreme cold across parts of Asia and North America, including the recent February 2021 Texas cold wave, and has been increasing over the satellite era.”

They did show that SPV disruption has been increasing, and they did show that it is linked to extreme cold events. They didn’t claim to have observed colder Februaries; in fact February only came up because they mentioned the 2021 Texas event.

Nick Stokes
Reply to  Willis Eschenbach
February 1, 2023 9:01 pm

“No, Nick, they did not “show that SPV disruption has been increasing”. Their simplified MODEL, complete with a Tinkertoy imaginary “slab” ocean, fed with the output of another MODEL, did not “show” but merely claimed, that SPV disruption has been increasing.”

It is not the outcome of a model. They observed pressures at various levels, identified the patterns associated with the stratospheric polar vortex (SPV), and counted the days on which P4, the stretching pattern appeared.

This is laid out in a tableau in Fig 1 below. The top row are the stratospheric pressures P100, then the plot of frequencies, then pressures at P500 and sea level, and then surface temperatures. The evidence for increase of disruption is the P4 plot with the trend in red.

comment image

Nick Stokes
Reply to  Willis Eschenbach
February 1, 2023 9:12 pm

“And they did claim an increase in “extreme cold events” like the Texas February cold spell … but there was no such increase in the Texas area that they identified as being affected.”

No, they didn’t. They claimed that SPV events had been increasing, and gave the daily counts. And they demonstrated in some detail that the Feb 2021 event was associated with SPV stretching. They did that by analysing that winter, not by claiming a frequency increase.

comment image

Nick Stokes
Reply to  Willis Eschenbach
February 2, 2023 12:01 am

w: “First, those are not observations. They didn’t observe pressures at various levels. It is the output of a reanalysis model”
Well, you can call any calculation a model. But reanalysis in no way corresponds to your description 
“Their simplified MODEL, complete with a Tinkertoy imaginary “slab” ocean, fed with the output of another MODEL, did not “show” but merely claimed, that SPV disruption has been increasing”

No slab, and it isn’t fed but output of another model. Instead, as in your quote above, it is fed by observations:

“More recent updates to the model are presented in Molod et al. (2011). The GEOS-5 system actively assimilates roughly 2 × 10⁶ observations for each analysis, including about 7.5 × 10⁵ AIRS radiance data”

It is a way of transferring the observations onto a grid. Pressure observations mainly come from radiosonde.

w: “Second, their further results are the result of feeding the output of the model”

The “further results” are an analysis of the causal mechanism relating SPV and cold events. 

w: “Yes, they DID claim an increase in extreme cold events. From the paper:…”

Your quote is not from the paper or its authors. It is from commentary from a Science staffer, H Jesse Smith.

Dave Yaussy
Reply to  Nick Stokes
February 2, 2023 6:00 am

As someone who enjoys having you here on the site to offer a counterpoint, Nick, I think you should quit digging the hole you’re in. It’s pretty clear what they were claiming, and Willis has shown they were wrong.

Nick Stokes
Reply to  Dave Yaussy
February 2, 2023 11:59 am

It’s pretty clear what they were claiming”
How is it clear if no-one will actually quote what the paper said?
All Willis seems to have said is that they mentioned February 19 times.

Larry Kummer, Editor
Reply to  Nick Stokes
February 2, 2023 6:34 am

Your quote is not from the paper or its authors. It is from commentary from a Science staffer, H Jesse Smith.”

This is from the summary at the top of the Science page, and that sentence is the widely reported summary of the paper.

Nick appears to believe that this is an error. If so, the authors will contact Science and demand a correction.

Nick is almost certainly wrong, as usual – and there will be no correction.

Nick Stokes
Reply to  Larry Kummer, Editor
February 2, 2023 12:02 pm

Nick appears to believe that this is an error.”
No, I don’t believe it is an error. It is something said by HJS, may well be true. But the paper did not say it.

Nick Stokes
Reply to  Willis Eschenbach
February 2, 2023 12:53 pm

The basic problem here is that you are not following your own excellent advice:
“quote the exact words you are discussing.”

If you would quote upfront what the paper actually says that you disagree with, and then produce your refutation, then we could see whether the two things match. Instead you have produced an analysis of statistics of cold in the Southern Great Plains which really doesn’t relate to whatever the paper was saying about the SPV pattern and its occurrence.

Reply to  Willis Eschenbach
February 1, 2023 10:27 pm

Willie E. gets a 10-yard penalty for getting Nick the Stroker all wound up…. This is equivalent to walking past the monkey cage at a zoo and banging your steel drinking cup on the bars of their cage. Moderator Bait. Wake up Charles!

Last edited 1 month ago by Richard Greene
Reply to  Willis Eschenbach
February 2, 2023 12:22 am

“A man hears what he wants to hear.
And disregards the rest.”
Paul Simon, from his excellent song, “The Boxer”, one of the best songs in my collection of about 25,000 songs, although the ending stretches out too long:

Simon & Garfunkel – The Boxer (2021 Remaster) – YouTube

February 1, 2023 3:19 pm

conspicuous and increasingly … over the past four decades.

Do the authors believe Carl Sagan’s millions of years or don’t they? Every climate article ever written should have some short disclaimer about how relatively short a time how relatively small a community has been studying how chaotic a phenomenon.

Nick Stokes
February 1, 2023 4:12 pm

” And there is no trend in the data.
Another beautiful theory runs hard aground on a reef of ugly facts.”

This discussion seems to have gone off the rails. The original paper is here. It doesn’t make any special claims about February, anywhere. The Forbes mention of that seems to be based on a single reference to the Texas freeze of 2021. In fact the paper doesn’t have much data on February at all. Fig 1 and 2 have Oct-Dec patterns. Fig 3 caption starts
“Lower-stratospheric and tropospheric trends in late fall and early winter project onto SPV stretching precursors.”. For Fig 3 and 4 the data is for October snow cover and Oct-Dec sea ice.

Reply to  Willis Eschenbach
February 2, 2023 12:40 am

Its easy to check, so I did. Downloaded the paper from Nick’s link, ran the search tool in the PDF reader, and it does indeed show 19 occurrences.

Glanced through to see what they are about. Mostly they are about a range of observations for a period of some months including February, but there are quite a few with specific observations on the particular month of February.

I don’t know if this counts as ‘special claims about February’. But what’s clear is that whether or not there are remarks which count as ‘special claims’ is immaterial.

Willis’ point is not that the paper makes claims about February that can be called ‘special’. Its that February is a significant data point in the paper which is cited and discussed and that an examination of past February temperatures are therefore relevant to the paper’s argument.

He is right. No question about it. Anyone can verify this in five minutes by doing what I just did. I don’t understand why anyone would think they could dispute this.

Nick Stokes
Reply to  michel
February 2, 2023 2:01 am

there are quite a few with specific observations on the particular month of February”
In fact, those are when they analyse the specific event of Feb 2021. That accounts for 7 mentions. The rest is mostly mentioning a range like Oct-Feb. But the key thing is that they offer no specific February data, as I said. Only October snow or Oct-Dec Kara sea ice. Yet Willis concludes:

Although there is greater variation in the February minimum temperatures in the Northern Great Plains NCA region, the same situation prevails as in the Southern Great Plains—one February “episode of extremely cold winter weather” in the first 24 years, a half-dozen or so in the middle 24 years, and the two cold Februarys in 2011 and 2021 in the final 24 years. And there is no trend in the data.
Another beautiful theory runs hard aground on a reef of ugly facts.”

All the posts in the series have been about supposed cooling in February. There is no basis for that exclusive focus in the paper.

Reply to  Nick Stokes
February 2, 2023 1:25 pm

Do you think that extreme cold winter episodes have become more common in Texas in recent years?

If so, do you think this is due to global warming, and if so, why?

February 1, 2023 4:32 pm

Why is it that when the data is so clear once exemplary publications refuse to look at the data? Could it be that there are none so blind as they that don’t want to see? And if so, why don’t they want to see?

February 1, 2023 4:48 pm
  • I’ve put the 72 years (1951-2022) of daily Southern Great Plains temperatures, both maximums and minimums, in my Dropbox for download.” I have downloaded this data but its not weather station-level data but single values for each day so what are these values; means across stations? Like most regional, long-term weather station data sets the raw data is most likely a highly unbalanced spatio-temporal dataset. Simple averages across stations for a given day are not a reliable measure i.e. not an unbiased sample estimator of the regional population mean of the response variable(s), in this case minimums and maximums, due to non-random within-station missing values and non-random drop-in and drop-out of the station-level data series. Also those means will have a spatial as well as temporal variation, so have your standard deviations only considered the latter? see my paper (DOI: 10.9734/ARRB/2021/v36i1230460) for one way of dealing with such highly unbalanced spatio-temporal data using spatially-gridded means.
Joel O’Bryan
February 1, 2023 4:59 pm

Sadly, Science Magazine and its parent organization, AAAS, is just empty shell of its former self in terms of ethical behavior, adherence to the principles of science when it comes to any controversial topics like climate or racial diversity in the STEM fields. They have become, similar tyo what Cliff Mass noted about UW academia on his blog post, “censorship in the furtherance of free speech” Orwellian fascists. On any of these topics they push a desired narrative without regard to facts or data.

February 1, 2023 8:31 pm

In a releted vein, here is some UAH monthly data updated today to show a non-warming trend over Australia for the p[ast 10 years and 9 months.
Maybe, some day Aussie school children will graduate without ever having felt global warming at home. (Not that I say this other than to mock snow forecasts by former experts).
Geoff S

February 1, 2023 10:04 pm

This is the first article I read Thursday, and the first one on my list of what will grow to 12 to 24 recommended climate science and energy articles today: Honest Climate Science and Energy

As usual for Willie E., it is well written with easy to read charts.

But it seems to me that the February has increasingly frequent number of episodes of extremely cold winter weather over the past four decades is one of the weakest Climate Howler claims in the past year. Everyone living in northern states knows the winters are warmer than in the 1970s with fewer periods of extremely cold weather. That’s why we love global warming here in Michigan.

Willie E. debunked that bogus data mining in his first article on the subject, and then slam dunked the claim with this article. Two great articles.

Willie E. is one of our best weapons in the battel to refute Climate Howlers. He’s a good scientist and good writer too — two qualities that are rare in one person.

I’m confident William Happer could write a good article refuting claims made by Al “the climate blimp” Gore. But would that be a valuable use of his time?

Willie E. is free to write on whatever climate science subject interests him.

I can also hope his valuable time will be focused on the best Climate Howler propaganda. This February claim was inaccurate data mining — pretty bad propaganda even compared with the usual Climate Howler BS. An easy target.

I would just ask Willie E. politely to think about refuting bigger targets than this one. I only hope for one of our best scientists shooting at bigger targets than this one. … And this post is intended as a complement, and a suggestion, not an insult.

Last edited 1 month ago by Richard Greene
February 1, 2023 10:43 pm

This is statistical data. The alarmists don’t respond to statistics. It actually angers them. Remember AOC’s pronouncement that we make a mistake when we pay more attention to facts than the “truth” (paraphrase, with her point).

Not saying facts/statistics are to be ignored. But somehow we need to respond to her “truth” while using statistics rather than using statistics to refute her “truth”.

It’s like a recent argument I had with a relative about having solar panels installed on his roof.

The US government provided cost-benefit analysis of doing so with the slope of his roof. I noticed that, at Montana’s latitude, what counted was not the slope of his roof but the most efficient angle, which was 30* for a SW facing roof – because that was the angle that shed snow the best. Plus the cost didn’t include installation. Also asked how often he had to wash the panels because of dust grime, reducing their effectiveness. (I saw in the UAE that dust covered the solar panels there. Really dumb idea in places withoutwater or cleaners.)

The US government assessment was that he’d get payout in about 10 years, maybe 8. But factoring in the needed angle and dust problem, maybe 12 was payout. Lifespan was claimed to be 25 years but maybe 17 was more likely these days. And then the cost of removal and management if the toxic debris should be added into the cost.

They also claimed it would add 7% to his home value. No, it wouldn’t. Like repainting you’ll house and having bread baking in your oven during views doesn’t. Just speeds up the sale.

My point was not to not install solar panels, but to look clearly at what was the living “truth” (you don’t save money or reduce CO2 much) , not the sociopolitical AOC “truth” (the planet is under an existential threat for fosdil fuel use).

The saving money but was a persuasion technique called “thinking beyond the sale”. You forget to analyzing what you are doing today because you are focused on a sparkly possibility of tomorrow, including what your neighbors and friends might think of you.

So, how do we counter the “misinformation” of extreme cold weather events? I don’t know exactly at this moment. But stats are only the backup to persuasion efforts, not the cause.

Perhaps the focus could be: 2023! Back to the grand old ’80s! What a great climate we have! And then the stats.

Focus on the positive conclusion rather than stamping on the dumb claim with statistics.

February 1, 2023 11:32 pm

Perhaps you are using the “wrong” baseline Willis. Instead of using the data best fit trend line, if you used the modelled regional warming trend line, then you would see an outbreak of conspicuous cold episodes on the right of the graph /sarc.

John Hultquist
February 2, 2023 9:31 am

Great photo at the top. Chicago, Feb 2011 Groundhog Day 🤣🧑‍🎄
I tend to stay home on days like that.
(… and watch the flames in the wood stove.)

February 2, 2023 9:39 am

 I reasoned that if there were short sharp cold spells, the standard deviation would be larger.

nope bad reasoning. people make the same mistake looking for hotspells.

and heat waves.

the standard deviation will capture frequent rapid deviations from the mean. it wont capture
more severe winters. it wasnt designed to.

but you know when you have a hammer every problem looks like a screw

February 2, 2023 12:40 pm

I live in SE PA and work on a large virtual team. Most on the team were very alarmed by the warm winter so far in the mid-Atlantic region. They were surprised when I explained the typical La Nina weather pattern for the mid-Atlantic.. It explains the warm pattern to a T. To my surprise, no one challenged me with global warming and climate change mantras. Many were 30-50 years old with very poor weather memory and pointed to 1993 ( the perfect storm) and 1996 ( 33 inches in Philly, Post Minimum El Nino), and 2010 (Philly breaks annual snowfall record, Post Minimum El Nino) as normative for the typical PA winter They completely forgot the warm winters of 1989, 1997, 1999 and 2020. The coldest winter was 2013 with 11, 2 inch clippers, no one remembered that winter where it was sub-zero for 10 days with snow cover 1/16-4/5/2013. 2013 was the last of Bastardi’s post minimum, ENSO neutral predicted cold winters.

Last edited 1 month ago by JC
Reply to  JC
February 2, 2023 12:53 pm

I am glad we have excellent statisticians contributing to WUWT… its a science blog as it should be. My point is with my anecdote is that the media shapes people’s reality and memory of their experience. When people gettogether they just echo what they have be taught to believe and remember. This is the reason WUWT must remain a science blog not an extension of the world of political twits. People can form their own understand with good info……. this was my experience 15 years ago with WUWT. If it becomes too shrill people won’t trust the info. Most people are in the middle and they are tired of the crazy. Good info is good info. Good science can be trusted.

Reply to  JC
February 2, 2023 6:40 pm

I am glad we have excellent statisticians contributing to WUWT” I hope you do not mean Willis and that WUWT promulgator of amateur, home-grown, non-peer reviewed statistical theory (maths free of course) “Professor” Kip! Neither have any formal qualifications in statistical methods. Being able to calculate means and standard deviations and do some basic graphics in Excel does not a professional statistician make! I havent received a response on some good-faith questions about the analysis Willis posted for this WUWT post in my comment (February 1, 2023 4:48 pm). Like what happened in his analysis at the spatial level and so how did he calculate standard deviations when the raw data consists of measurements across weather stations as well as Julian days? How did he account for non-random missing values, and non-random station drop-in and drop-out? No details; no pre-print paper with a methods section, no access to computer code used for the data processing/analyses in say Supplementary Materials (in this case Excel functions – no serious professional statistician relies only on Excel for their analyses) to verify his methods, and no metadata to give detailed descriptions of the variables in the Excel datafile he provided via DropBox! WUWT posts like this have to be viewed as unverified analyses and that should limit their credibility until that verification can be carried out which requires all of the above i.e. write a legitimate scientific manuscript even if it only gets to preprint status on ResearchGate and then summarise it on WUWT. Then we can do a proper Open Source review in Comments. Over to you…Willis and co.

Reply to  steve_showmethedata
February 2, 2023 7:38 pm

Of course ignoring good-faith and technically proficient critiques is something authors can get away with on sites like WUWT when, despite its many failings, at least peer review requires these critiques to be seriously addressed and the technical merit of both the critique and the response by the author is also up for scrutiny as they should be – so power to the Open Source journals that also publish the review process.

Reply to  steve_showmethedata
February 2, 2023 8:53 pm

That should read “Open Access” not “open Source” journals.

Reply to  Willis Eschenbach
February 2, 2023 9:50 pm

The part of my post about you not having formal qualifications in statistical methods was a response to JC’s post (re:” I am glad we have excellent statisticians contributing to WUWT“) and not your work shown in the post. I did not let myself degenerate into personal attacks as you have done but simply reported the fact that you now confirm that you have no formal qualifications in statistical methods. So, no, both my posts directed at your article judged your article on its lack of detail on the methods you used contrary to your derogatory comments (“Like any true scientist, and unlike you, they judged me on my work and not my “qualifications”). You posted a link to a dataset with no accompanying metadata which is your responsibility to provide if you want to claim scientific credibility otherwise post links to the original source data (with metadata) and in your missing methods section describe how you processed this data further, if you did, for your analyses as required in peer-review journals (including any Supplementary Material).
“Next, I didn’t answer because I calculated standard deviations in the normal way. I took all the February days for a given year and took their standard deviation.” If your source data only has regional daily means (i.e. means across stations) then you should dig deeper since those means are not fit for purpose (to give valid unbiased means and standard deviations) if they involve, as is certainly the case, non-random within-station missing values and non-random drop-in and drop-out stations. This point you failed to acknowledge and it is important since different sets of stations contributing to different time points can inflate the variance and thus your sample standard deviation as an artefact of the sampling imbalance (technically, station random effects do not cancel out in unbalanced datasets like they do in balanced spatio-temporal datasets so marginally they contribute to the temporal variances and that process needs to be modelled).
Not sure what anyone would do differently”. Exactly, which shows your lack of knowledge and understanding of modern statistical methods for unbalanced spatio-temporal datasets. I gave you a link to a statistical methods paper of mine dealing with data similar in nature i.e. unbalanced spatio-temporal dataset, which has now some 280 downloads and over 750 views. Bottom line is unless you use methods such as that that take into account the lack of balance in the spatio-temporal dataset there is no way of knowing if your overly-simplistic analysis method has delivered misleading results.
I did not do any analyses myself because I am taking the part of an expert reviewer (expert in statistical methods) as I have done many many times as a referee for scientific journals over my career.
I do stand corrected that you do not restrict yourself to Excel, I was misinformed on that, and I humbly withdraw that comment. I have used FORTRAN, Apl, GLIM, GenStat, S-plus, and R (all my recent work is in R) as my programming languages over the last 46 years of my career as a professional statistician.

Reply to  Willis Eschenbach
February 2, 2023 10:34 pm

The data analysis you did is only one aspect. You failed to mathematically demonstrate that annual sample standard deviations in minimum temperature (means across stations) and the long-term trend fitted to them was a valid method for drawing the inference you aimed to draw about ‘increasing “severe winter weather”’ (i.e. increase in frequency of extreme cold weather events). If you failed to provide that mathematically explicit demonstration I would reject your paper until you did as an expert reviewer!

Reply to  steve_showmethedata
February 3, 2023 12:35 am

Here is an example of how you explicitly link the inference of interest to a specific parameter in a deterministic plus stochastic mathematical model of the data generation process (remembering that “all models are wrong, but some models are useful”) . Determine the null distribution of the parameter of interest, estimate, test, provide confidence intervals, estimate the power to detect the alternative hypothesis given a range of nominal parameter values. This all needs to be mathematically explicit (see Appendix of this paper of mine DOI: 10.9734/CJAST/2022/v41i333946).

Reply to  steve_showmethedata
February 3, 2023 1:10 am

I have published in mainstream statistical journals (see below) but these two papers didnt fit the political correct narrative so I had to go to ARRB and CJAST Open Access journals

DOI: 10.1007/s10651-014-0306-3
DOI: 10.1198/108571102302
DOI: 10.1023/A:1009662915320
DOI: 10.2307/2533104

Reply to  steve_showmethedata
February 3, 2023 4:00 am

OK I will walk back the comments related to my statement “if they involve, as is certainly the case, non-random within-station missing values and non-random drop-in and drop-out stations.” NOAA has used other valid methods to deal with this using the raw station-level unbalanced data. The above comment about mathematically explicit statistical inference still applies though. Even though I didnt do my research on the data well enough for this type of Blogging (I should stick to publishing papers in the scientific literature, and that’s now locked in), at least I didnt resort to inane personal insults.

Last edited 1 month ago by steve_showmethedata
Reply to  Willis Eschenbach
February 3, 2023 3:57 pm

Willis, you are even mis-representing my above mea culpa – you say that I “accuse NOAA of using improper methods in aggregating the nClimDiv data” whereas what I posted was “NOAA has used other valid methods to deal with this using the raw station-level unbalanced data”. My post is not very long so you couldnt have missed that so how do you construe that statement of mine as accusing NOAA of using improper methods?? Talk about ungracious.

You say “How do I know it’s valid?
Because it correctly and very visibly identifies the Texas cold snaps of 1951, 1981, 1985, 1989, 1996, 2011, and 2021.”

What is your mathematical definition of a “cold snap” (=extreme cold weather event, I assume)?

If you had any training (self or institutional) in statistical inference you would know that one cannot validate or justify a general method of statistical inference (i.e. standard deviations can be used to infer the frequency of “cold snaps”) using post-hoc comparisons with one or more realisations (i.e. your data) of a sampling process. You need to describe the inferential theory in mathematical terms employing a mathematical description of the sampling process, the statistical model of the process involving parameters of interest which are used to carry out the required probabilistic inferences. Its not called mathematical statistics without reason, and that was my major in my batchelor of science at the University of Sydney (1976) and my Masters and PhD were replete with mathematical statistics theories some of which were novel. Try publishing your method in a statistics or climate science journal and “it’s valid ; Because it correctly and very visibly identifies the Texas cold snaps of 1951, 1981, 1985, 1989, 1996, 2011, and 2021″ will not cut it. Its telling that you did not have a reference in the peer-review literature for your method, preferably in a statistics journal, which if you had I am sure you would have quoted since you like to promote your ability to re-discover statistical theories – so no published method papers in statistics on your CV then because you were always beaten to it??

Last edited 1 month ago by steve_showmethedata
Reply to  steve_showmethedata
February 3, 2023 4:45 pm

BTW the NOAA approach is to balance the unbalanced spatio-temporal data using gridding and methods of interpolation (thin-plate regression splines) to be able to provide data products that can be validly used without needing to directly model the unbalanced raw dataset using more complex models such is linear mixed models which is the approach I have used and is common in science since the researcher is dealing directly with the raw base dataset.

Reliance on single-day values and individual points is discouraged due to the significant uncertainty that is inherent in such a product, as a result of the spatial distribution of the underlying observations, differences in observation time between neighboring stations, and interpolation errors. Spatial and temporal averaging tends to reduce the effect of these uncertainties, and time series of such aggregated values can be shown to be suitable for climatological applications.” 

Reply to  Willis Eschenbach
February 4, 2023 4:21 am

Willis, I did ask for more detail “I have downloaded this data but its not weather station-level data but single values for each day so what are these values; means across stations?” and I hedged by saying “the raw data is most likely a highly unbalanced spatio-temporal dataset”. I was not fully informed until I checked out the source data at NOAA which showed me that they had “balanced” the dataset. It was my mistake to make the wrong assumption about the source data but it was partly because you did not have a methods section describing your source data in any detail. So at no time did I say in effect “NOAA got it wrong”.

You say “Oh, wait, I forgot. You don’t provide anything. For example, you don’t provide a description of exactly what is wrong with my method.” Your method is so poorly defined its hard to be specific except to point out what it lacks and its your proposed statistical method of inferring trends in the frequency of extreme weather events (e.g.”cold snaps”) not mine. So what’s lacking? 1. A rigorous mathematical definition of what defines a “cold snap” which you have not given. Minimum temperatures are a continuous variable, so is a “cold snap” a set number of consecutive days below a set minimum temperature? Note that involves two arbitrary values. How many of such “cold snaps” can potentially occur in the one month of February? You are somehow discretizing a continuous variable to define a type of discrete event.

The currency of statistical methods is providing the maths to show how your proposed statistic, the standard deviation, quantifies the expected frequency of extreme events (e.g.”cold snaps”). So what’s lacking? 2. The definition of an estimator, as a function of the above statistic, of the expected frequency of the extreme event for the month of February and how the sample estimates as realisations of this estimator trend across years. Note its not the trend in standard deviations that is important because that’s not the inference you are interested in, its the trend in the above estimator of the expected number of extreme events (i.e. cold snaps) in this particular month. If you can give such an estimator is it unbiased, consistent and minimum variance compared to alternative estimators?

So the lack of 1. and 2. above are what’s wrong with your method.

Your method involves a vaguely defined “cold snap”, that only you know how you define, some standard deviations that look like they are telling us something useful but we are not sure exactly what that “something” is. So many theoretical and practical questions to answer. Remember verified statistical methods used in practice have background mathematical theory behind them involving many important issues that professional statisticians consider, too numerous to consider here. Maybe you can collaborate with a professional statistician to address these shortcomings in what you have presented due to it having no theoretical underpinnings.

Reply to  Willis Eschenbach
February 4, 2023 4:46 am

Addendum: since I can also think practically, how does your method discriminate between Februaries containing unusually warm “spells” to those with cold “snaps” since hypothetically both could equally inflate the standard deviation through your unspecified statistical mechanism?

Reply to  JC
February 2, 2023 7:09 pm

Apparently, they also forgot how cold December was.

Short weather memory, decidedly.

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