By P Gosselin on 30. October 2021
Satellite data raise doubts about man-made climate change
Climate models do not represent reality. They sre running too hot.
Climate modeling has been awarded the Nobel Prize. But two German researchers prove that observations are more important than calculations in climate research.
By Alex Reichmuth (Switzerland)
(Translated, edited by P. Gosselin)
There was great satisfaction among protagonists of man-made climate change when the Nobel Prize Committee awarded at least half of this year’s Nobel Prize in Physics to American Syukuro Manabe and German Klaus Hasselmann. The two researchers are pioneers of what is known as climate modeling – that is, the attempt to trace and predict climatic developments with the help of mathematical models.
The prize recognizes “that our knowledge of the climate rests on a solid foundation, based on rigorous analysis of observations,” praised Thors Hans Hansson of the Nobel Committee as he announced the winners. Of “balm for the beleaguered souls of climate researchers,” wrote the “Tages-Anzeiger.” It will now be “even more difficult to ignore and discredit climate research.” Climate models were based on “solid physics,” it said.
Climate models have failed
With so much applause, it was lost how big the scientific problems are that go hand in hand with climate modeling. This was evident again just recently with the models called CMIP6, which form the basis of the new Intergovernmental Panel on Climate Change report issued in early August. The CMIP6 models are not able to correctly reproduce the real temperature development of the past decades and simulate a warming that is much stronger than the real data show. Thus, there can be no confidence in these models to correctly predict future warming. The Intergovernmental Panel on Climate Change has nevertheless relied on them.
Nobel laureate Klaus Hasselmann
Critics of climate modeling include, in particular, Steven Koonin, a highly accomplished American physicist and climate scientist who once served U.S. President Barack Obama and recently published a “climate skeptic” book. He notes that climate models have failed time and again because they fail to prove human influence on global warming. Discrepancies among individual climate models showed “that the science is far from settled”.
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Almost all models are running too warm. Nobody knows why. Image: Legate’s presentation Heartland 14th Climate Conference Las Vegas 15 October 2021
Data from NASA’s Ceres project used
In general, real-world data repeatedly calls into question the results of climate models and thus the tone-setting climate science. This is also the case with a study by German researchers Fritz Vahrenholt and Hans-Rolf Dübal, which has just been published in the peer-reviewed scientific journal Atmosphere.
Vahrenholt and Dübal are originally chemists, but have worked extensively on climate science in recent decades. The study is based on data from NASA’s Clouds and the Earth’s Radiant Energy System (Ceres). Ceres has been using satellites to record the radiation that reaches, and is emitted by, the Earth since 1998. The project’s goals include a better understanding of the role of clouds and the Earth’s radiation balance with respect to global warming.
Cloud cover has decreased by two percent
And it is precisely these data from Ceres that throw a wrench into the thesis of man-made climate change. Vahrenholt and Dübal conclude that it is not man-made enhancement of the greenhouse effect that is the main cause of warming over the past 20 years, but a two percent decrease in cloud cover during that period. According to Vahrenholt and Dübal, the weaker cloud cover has resulted in more shortwave radiation from the sun reaching Earth. This increase in solar radiation has been a major driver of global warming.
NASA researchers led by Norman Loeb, as well as Finnish researcher Antero Ollila, have each pointed out in a study that shortwave solar radiation increased from 2005 to 2019 due to a decrease in low clouds. Dübal and Vahrenholt have now studied radiation fluxes for the entire period from 2001 to 2020 – both near the ground and at an altitude of 20 kilometers – and related them to changes in cloud cover.
Greenhouse effect had only a small impact
In fact, the satellite data from Ceres show that the shortwave radiation emitted into space by the clouds has decreased by about two percent in both the northern hemisphere (NH) and the southern hemisphere (SH). With solar radiation remaining nearly constant, this means that more shortwave radiation has reached the Earth’s surface and contributed to warming. At the same time, the fraction of longwave radiation that is reflected back to Earth from the atmosphere has only warmed the planet to a lesser extent. This radiation back to the earth is the greenhouse effect, which has been intensified by the emission of climate gases. According to Fritz Vahrenholt and Hans-Rolf Dübal, this enhanced greenhouse effect has even been largely compensated by the aforementioned decrease in cloud cover: The decrease in clouds has resulted in more longwave radiation reaching space from the earth.
IPCC relies on model calculations instead of real data
The study results of the two German researchers contradict the claims of the Intergovernmental Panel on Climate Change (IPCC), according to which the observed warming occurred solely because the proportion of long-wave radiation reflected back to Earth from the atmosphere increased (due to the stronger greenhouse effect). The IPCC attributes 100 percent of the warming to this enhanced greenhouse effect – but justifies it with model calculations rather than real data.
“The warming of the last 20 years has been caused more by changes in clouds than by the classical greenhouse effect,” say study authors Fritz Vahrenholt and Hans-Rolf Dübal
In their study, Vahrenholt and Dübal also looked into the background of the observed stronger heat absorption by the Earth. The corresponding explanations can quickly exceed the understanding of laymen: Based on observations of the so-called enthalpy of the climate system and oceanic heat uptake, it was shown that there have been two warming periods on Earth since 1850, each lasting 20 to 30 years. A third warming period began in 1990 and continues to this day.
The onset of each of these three warming episodes was accompanied by changes in the Atlantic Multidecadal Oscillation, a natural periodic ocean current in the Atlantic that significantly determines the climate…
End of the warming period could mean end of global warming
The third warming period coincides with the observed decrease in cloud cover. Whether this warming period, like its two predecessors, will end soon must be clarified by measurement data in the coming years. If the warming period ends soon, global warming should decrease and the announced “climate catastrophe” will largely fail to materialize.
To date, it is unclear what is causing the observed cloud thinning. According to the study authors, changes in ocean currents are cited in the literature as possible causes Study authors are cited in the literature as possible causes, but also a decrease in aerosols in the air and warming due to more CO₂ in the atmosphere. However, Vahrenholt and Dübal emphasize: “The warming of the last 20 years was caused more by changes in clouds than by the classical greenhouse effect.”
The Intergovernmental Panel on Climate Change is thus challenged to review its findings.
Hat-tip: EIKE
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Never fear “Karl et al.” will take care of it.
In taking a closer look at the graph in the article, I notice that the trends are not the only thing that varies from one model to another. Clearly there is absolutely zero correlation in the year to year projections between the models themselves and the models and measured changes. Why would I trust a model’s trend when all of them project a completely scattershot range of temperatures for any given year? Half the models project higher temps next year, and half project lower temps next year, with a total range of about 3 degrees F.
It would be like making a chart of all of the Beatles’ songs written out in musical notation and connecting the dots. Then claiming that any songs that start out low and end up high are correlated and obviously the same song. It’s getting that ridiculous.
Show me the weather! Climate models have demonstrated no skill to hindcast, let alone to forecast. Science is, with cause, a philsophy and practice in the near-domain, a limited frame of reference from the observer.
The mind-numbed prattling of climate alarmists never ceases to amuse me! They keep trying to claim the mantle of “the science” while wrapping themselves in bogus studies and models! Apparently you aren’t too bright so I’ll keep it simple for you!
CO2 follows temperature in the ice core data; you are putting the cart in front of the horse!
The Earth has been warming since coming out of the depths of the Little Ice Age, and CO2 levels will continue to rise as that heat penetrates the ocean deeps! Additional heating from changes in solar flux or GCR flux won’t be mitigated by cutting back the minuscule amount of CO2 that humans add to the mix!
The geologic record clearly shows that Earth has been COOLING for the last 50,000,000 years! During that period CO2 levels have fallen dramatically, driven by bio-mineralization and deposition!; but there is correlation shown! Often temps and CO2 are moving in opposition to one another!
The worst part of the Gretatard drivel is the claim that CO2 is a pollutant! Not only is it the basis of ALL life, it is still in seriously short supply despite the small recent increase! Trying to reduce CO2 in the atmosphere is akin to a patient suffering breathing difficulties having their oxygen levels reduced! That might actually be an explanation for the alarmist brain damage we see!
Whoops!
Paragraph three, penultimate sentence should say: NO correlation shown!
RADIATIVE FORCING BY CO2 OBSERVED AT TOP OF ATMOSPHERE FROM 2002-2019
https://arxiv.org/pdf/1911.10605.pdf
New confirmation that climate models overstate atmospheric warmingReposted from Dr. Judith Curry’s Climate Etc.
https://wattsupwiththat.com/2020/08/27/new-confirmation-that-climate-models-overstate-atmospheric-warming/
Study suggests no more CO2 warminghttps://wattsupwiththat.com/2020/10/26/study-suggests-no-more-co2-warming/
High end of climate sensitivity in new climate models seen as less plausiblehttps://wattsupwiththat.com/2021/03/03/high-end-of-climate-sensitivity-in-new-climate-models-seen-as-less-plausible/
Those last 2 links ran together:
https://wattsupwiththat.com/2020/10/26/study-suggests-no-more-co2-warming/
https://wattsupwiththat.com/2021/03/03/high-end-of-climate-sensitivity-in-new-climate-models-seen-as-less-plausible/
You say that “The IPCC attributes 100 percent of the warming to this enhanced greenhouse effect”. In fact, the AR6 attributes 200% of warming to humankind with aerosols reducing this to only 100%.
The so-called [catastrophic] anthropogenic anomaly in a climate stasis.
The simple scalar fails to model climate sensitivity in both hindcasts and forecasts. The Greenhouse effect proposes a net positive change from lower to higher energy states. The recorded evidence suggests that CO2 is a lagging indicator of terrestrial temperature changes and a peculiar lack of hot spots in the atmosphere. There is no significant change in catastrophic events. Temperature anomalies are within the margin of error. The world is greening with benefits for flora, fauna, and people. Any climate change is following a path notable for its constrained irregularity in time and space.
The IPCC trick is that global warming is defined as surface warming, making the satellite readings irrelevant. The highly adjusted surface statistics show a lot more warming, so the models look much better. Ask Google for the definition of “global warming”.
“The IPCC trick is that global warming is defined as surface warming, making the satellite readings irrelevant.”
That’s the same trick the alarmists on this website use.
When NASA first proposed using satellites to determine the temperatures, they said the satellites would be more accurate than previous ways of measuring the global temperature.
Since the UAH satellite shows cooler temperatures than the bastardized temperature record, the alarmists spend a lot of time trying to trash the UAH satellite results.
Not to mention that the UAH satellite readings put the lie to the NOAA/NASA meme of “hotter and hotter and hotter” that they have lied about since 1998, started a cooling trend.
They couldn’t make the claim of “Hottest Year Evah!” if they went by the UAH satellite readings.
Here’s the UAH satellite chart. From the year 2000 to 2015, NASA and NOAA proclaimed 10 of those years as being the “hottest year evah!” going by their bastardized temperature charts.
But as you can see, NASA and NOAA could not make such claims if they went by the UAH satellite record. So they ignore the UAH satellite record because it doesn’t tell the story they want told, the “hotter and hotter” story. See for yourself:
David Legates said he created his graphs using KNMI. The problem is that KNMI does not have all of the CMIP6 data uploaded yet and so is overweight on the outlier model CanESM5. That makes it difficult to fairly assess how well it performed.
Here is the CMIP5 RCP45 ensemble mean from KNMI. As can be seen it is indeed running hot. I don’t know if it is fair to call that a failure though. Afterall UAH differs from the composite of 7 datasets by 0.052 C/decade whereas CMIP5 differs by 0.045 C/decade. So if CMIP5 is a failure in predicting the temperature then perhaps UAH could be described as a failure at measuring it.
” The problem is that KNMI does not have all of the CMIP6 data uploaded yet”
No, the problem is that people like you actually think that this CMIP GARBAGE is worth more than wrapping up your fish in. That’s the ACTUAL problem..
KNMI does not have all of the data for CMIP6 yet so I can’t comment on it too much. But CMIP5 is all there so I can say that the RMSE of it relative to BEST is 0.15 C and 0.10 C for monthly and 13-month centered averages respectively. Its far from perfect, but I don’t know if it is fair to call it garbage.
”KNMI does not have all of the data for CMIP6”
Not data. Bullshit.
Mike
What about coming here with a proof for your ‘bullshit’ allegations concerning KNMI’s CMIP data?
Here is some proof. What percent of actual temperature does 0.05 consist of. How about 0.05 / 15 = 0.3 percent. Do you realize that models are dealing with how many angels can reside on the head of a pin. The uncertainty in measurements is 3 – 4 percent. The uncertainty is 10 times the calculated values –> bullshit.
JG said: “What percent of actual temperature does 0.05 consist of. How about 0.05 / 15 = 0.3 percent.”
Where are getting 0.05 and 15?
From you!!
And the general consensus that the global temperature is around 15C.
Why don’t you address the real issue about recognizing this small of a change when the uncertainty is ten to hundred times as great.
I thought so. Let’s check your math.
0.05 C/decade / 15 C = 0.003/decade = 9.5e-12 Hz.
I don’t know what 9.5e-12 Hz even means in this context, but I do know that it is not the same thing as 0.3 percent. They’re not even the same units.
Huh? (C/decade) * (1/C) = 1/decade
Where did the decade come from? How did you convert (1/decade) to Hz? 1/sec is not Hz. Cycles/sec is Hz. There are no cycles in a time interval by itself.
.05C / 15C = .0033 which 0.3%
Say the difference is 15C to 15.05C over a decade. The percentage change is (15.05 – 15)/15 = .05/15 = 0.3% QED
TG said: “Where did the decade come from?”
The units on that 0.05 figure is C/decade.
TG said: “How did you convert (1/decade) to Hz?”
1 decade-1 = 1/315567360 s-1.
1 s-1 = 1 Hz
TG said: “There are no cycles in a time interval by itself.”
I know. That’s my point. That’s what makes JG’s math wrong.
TG said: “.05C / 15C = .0033 which 0.3%”
Nope. Your .05C value should be 0.05 C/decade. Go up and look at my post. I clearly labeled the 0.052 and 0.045 trend figures as C/decade.
ΔC is ΔC. The time period doesn’t figure in the calculation.
Batting 400 is figured from pitches/pitches. Percent change is figured from C/C. If you want to figure percentage per decade then give the ΔC for the decade. If you want to figure the percentage per year then give ΔC for the year. If you want to figure percentage per day then give ΔC for the day.
Same thing you do for batting averages. I *still* don’t think you live in the real world!
Com on dude. You reveal your ignorance each time you post. (0.05 C / decade) / 15 C = 0.3 percent per decade. You. can’t even do dimensional analysis correctly. Heck make it annually –>. 0.3 percent / 10 years = 0.03 percent per year.
Do you really think anyone really believes you can determine temps to that precision? Especially when the instruments used to gather the data had a 3 – 4 percent uncertainty!
I’m still waiting for you to give us the statistical parameters associated with the Global Average Temperature! All averages of a distribution have a variance and standard deviation. WHAT ARE THEY.
I’m beginning to think all you mathematicians haven’t a clue how to characterize a distribution. You just average wily nily and expect everyone to bow down to your expertise. It don’t work that way in the real world. If your averages had to be legally accountable, you would need to explain your statistical parameters. Believe me, it is probably headed that way as economies spiral down and energy costs go berserk. Somebody will be held accountable, and you can bet it won’t be the politicians.
JG said: “Com on dude. You reveal your ignorance each time you post. (0.05 C / decade) / 15 C = 0.3 percent per decade. You. can’t even do dimensional analysis correctly. Heck make it annually –>. 0.3 percent / 10 years = 0.03 percent per year.”
You didn’t say percent per decade. You just said percent which is wrong. Nevermind that percent in the context temperatures is meaningless anyway. Let me illustrate.
15 C = 288.15 K
15 C = 59 F
15 C = 518.67 R
0.05 C/decade = 0.05 K/decade
0.05 C/decade = 0.09 F/decade
0.05 C/decade = 0.09 R/decade
0.05 C/decade / 15 C = 0.0033 decade-1 = 0.00033 years-1
0.05 K/decade / 288.15 K = 0.00017 decade-1 = 0.000017 years-1
0.09 F/decade / 59 F = 0.0015 decade-1 = 0.00015 years-1
0.09 R/decade / 518.67 R = 0.00017 decade-1 = 0.000017 years-1
So which is it? Is it 0.033 percent per year, 0.0017 percent per year, or 0.015 percent per year? Do you see the problem now with trying to use percent with temperatures especially for scales that are not anchored on absolute zero? Not that being anchored on absolute zero (like Kelvin) helps you here because it is still scaled by an arbitrary amount. Note how Kelvin and Rankine are both anchored at absolute zero, but are scaled by different amounts leading to a contradictory answer if trying to force percent in this context on unsuspecting readers. I was fooled when you first tried it as just percent and I’m not fooled now that you changed it to percent per year.
Do you know what the deflection means?
The issue is how the per decade amount compares to the average percent uncertainty in measurement.
So let’s use your figures.
0.0333 percent / year –> 3 – 4 % uncertainty
0.0017 percent / year –> 3 – 4 % uncertainty
0.015 percent / year –> 3 – 4 % uncertainty
0.0017 percent / year –> 3 – 4 % uncertainty
You claim that the precision of the measurements allow you to have very, very small relative uncertainty. Yet the devices used to make the temperature measurements have much, much larger relative percent uncertainties [(0.5 / 15) * 100 = 3.3%].
You need to explain how this can this be?
It kinda goes along with the question I’ll keep reminding you of. Averages have no meaning if there is no variance and standard deviations associated with the mean of a distribution.
What is the variance and standard deviation of the Global Average Temperature?
It’s still not percent/year. The units are just year-1.
If you are insistent on a percent then you need to find a denominator with units that match the numerator. Those units are C/decade.
You keep displaying your lack of physical phenomena! Why do you think you have no label in the numerator?
[(deltaC / Ctot)*100] / year = percent / year Why do you think you have no label in the numerator?
(delta $ / total $) = percent
(1 gram / 10 grams) * 100 = 10 percent
Stop deflecting and answer the question. How do you get that kind of precision when the uncertainty of each and every measurement has an uncertainty of 3 – 4 %? A simple “I don’t know would suffice.”
JG said: “Why do you think you have no label in the numerator?”
For the calculation you attempted there is a unit label in the numerator. It is C/decade. And the unit label for the denominator is C. The final unit label is thus decade-1. Percents have no units. That’s how I know your calculation does not produce a percent.
JG said: ” How do you get that kind of precision when the uncertainty of each and every measurement has an uncertainty of 3 – 4 %?”
First…I didn’t make a claim of uncertainty for the trends.
Second…if you’re getting 3-4% by doing 0.5 C / 15 C (which at least does produce a percent) then that isn’t event relevant to a trend which has units of C/decade.
Third…if you’re getting 3-4% by doing 0.5 C / 15 C (which at least does produce a percent) then I reject your 0.5 C figure because no one except Pat Frank thinks it is that high. And Pat Frank’s analysis 1) does not show how station uncertainty propagates through the grid mesh averaging and 2) is not consistent with the differences between global mean temperature measurements and 3) does not agree with the various uncertainty analysis accompanying many of these datasets and which no one has found an egregious error with.
If you are batting 400 what is the dimension for that figure?
ΔC/C is a percentage. It isn’t (ΔC/decade), it’s just ΔC.
TG said: “If you are batting 400 what is the dimension for that figure?”
None. That’s exactly my point.
TG said: “ΔC/C is a percentage. It isn’t (ΔC/decade), it’s just ΔC.”
First…I never said anything was ΔC/decade.
Second…0.05 C/decade is not the same thing as ΔC. It’s still just C/decade. And 0.05 C/decade / 15C = 0.003 decade-1. Which is still not a percent.
Then how did you come up with 1/decade as a dimension?
Dude! You *really* need to think about starting a memory enhancer.
“Second…0.05 C/decade is not the same thing as ΔC.
The percentage is figured as the change divided by the base. Of course the dimensions for both are “C”!
You say “0.3% IN A DECADE”!
Or “0.3% IN A YEAR”.
Is the dimension on batting average .4/year? What’s the dimension on lifetime batting average? .4/lifetime?
1st) Your label for the numerator is simply “C”. The label for the denominator is “decade”. If you divide the numerator by a number labeled “C” the answer will have no label and will be “# / decade”. Since # is a descriptor of the growth expressed as a decimal value you can multiply it by 100 to get percent.
This isn’t complicated math. Calculating percents is grade school math.
Here is the math including how to change a decade to an annual amount.
A percentage is a ratio or proportion and is signified by the symbol % since there is no measurement label.
If 0.5 degrees has any relation to a baseline, then it can be expressed as a ratio of that baseline value. Use 14.25 or 16.63 if you wish. The issue is that it is very small change in comparison to the baseline temperature. It is also much smaller than the uncertainty in measurement. If you should have learned anything lately, you should have recognized that uncertainty in original measurements DOES NOT disappear through any kind of mathematical calculations. You can’t average uncertainty away, it only grows.
“Afterall UAH differs from the composite of 7 datasets by 0.052 C/decade”
That much, huh? They differ by five one-hundredths of a degree.
That’s five one-hundredths of a degree C per decade.
Appropriate post for Halloween.
It’s appropriate any day of the year because C is the not the same thing as C/decade.
That’s even worse on a percent basis. 0.03 percent vs 3 – 4 percent on an annual basis! How frightening!
Averaging the outputs of these computer models is absurd.
Carry On Predicting.
“Cloud cover has decreased by two percent
And it is precisely these data from Ceres that throw a wrench into the thesis of man-made climate change. Vahrenholt and Dübal conclude that it is not man-made enhancement of the greenhouse effect that is the main cause of warming over the past 20 years, but a two percent decrease in cloud cover during that period. According to Vahrenholt and Dübal, the weaker cloud cover has resulted in more shortwave radiation from the sun reaching Earth. This increase in solar radiation has been a major driver of global warming.”
This would go along with Muller’s claim that a two percent increase in cloud cover would offset all human-caused CO2 global warming. So a two percent decrease in clouds accounting for the warming that CO2 is supposed to have caused, falls right in line.
From the article: “Based on observations of the so-called enthalpy of the climate system and oceanic heat uptake, it was shown that there have been two warming periods on Earth since 1850, each lasting 20 to 30 years. A third warming period began in 1990 and continues to this day.”
It would be helpful if these three warming periods had been named in the article.
Let me guess: The first warming period was from 1850 to the 1880’s, where a highpoint was reached. Then cooling occurred for a few decades to 1910, and the the second warming period began in the 1910’s and warmed to the 1930’s, where the temperatures reached about the same height as the temperatures in the 1880’s, and the third warming period began in the 1980’s, culminating in a highpoint in 1998 that was equal to the highpoints of the 1880’s, and the 1930’s, then it cooled for a few decades, and then warmed back up to the highpoint of 2016/2020, which still did not exceed the highpoints of the past before cooling began.
And now the temperatures are cooling 0.4C below all these highpoints.
From the article: “Based on observations of the so-called enthalpy of the climate system and oceanic heat uptake, it was shown that there have been two warming periods on Earth since 1850, each lasting 20 to 30 years. A third warming period began in 1990 and continues to this day.”
It would be helpful if these three warming periods had been named in the article.
Let me guess: The first warming period was from 1850 to the 1880’s, where a highpoint was reached. Then cooling occurred for a few decades to 1910, and the the second warming period began in the 1910’s and warmed to the 1930’s, where the temperatures reached about the same height as the temperatures in the 1880’s, and the third warming period began in the 1980’s, culminating in a highpoint in 1998 that was equal to the highpoints of the 1880’s, and the 1930’s, then it cooled for a few decades, and then warmed back up to the highpoint of 2016/2020, which still did not exceed the highpoints of the past before cooling began.
And now the temperatures are cooling 0.4C below all these highpoints.
https://tallbloke.wordpress.com/2012/02/13/doug-proctor-climate-change-is-caused-by-clouds-and-sunshine/
Yep. Even crude analyses showed the probable causative correlation of sunshine hours and maximum temperatures (in Central England) over decades (1930 – 2010).
Interesting. I do wonder when the AMO will reverse. Anyhow, until then, I’m sure the alarmists will merely claim that the reduction in cloud cover is somehow the result of human CO2.
The models run hot compared to the data. The data shows warming because cloud cover has decreased. Cloud cover has decreased because….?
JF
I won’t repeat my suggestion as to why, there’s dozens of my posts that suggest why here and elsewhere. I have a post at a blog called Independence Daily entitled First We Guess, then we do science (Feynman’s explanation of the scientific method) which suggests various mechanisms. I usually comment on Willis’s excellent posts here but he never listens.
I can even guess about Prof. Wigley’s blip
Too warm? Correction: Too hot.
if and when warming ends, here are your ready made excuses:
Its a temporary blip… it will all kick off again soon
The planet is still in crisis with floods, wild fires, pollution, sea level rise, loss of habitat…. stop burying your head in the sand!
Our trillions of dollars have done it !!!
This story should be pinned to the top of the page