I didn’t vet this before posting and have no idea as to its real strengths or weaknesses. Have at it.~ctm
J. KAUPPINEN AND P. MALMI
Abstract. In this paper we will prove that GCM-models used in IPCC report AR5 fail to calculate the influences of the low cloud cover changes on the global temperature. That is why those models give a very small natural temperature change leaving a very large change for the contribution of the green house gases in the observed temperature. This is the reason why IPCC has to use a very large sensitivity to compensate a too small natural component. Further they have to leave out the strong negative feedback due to the clouds in order to magnify the sensitivity. In addition, this paper proves that the changes in the low cloud cover fraction practically control the global temperature.
1. Introduction
The climate sensitivity has an extremely large uncertainty in the scientific literature. The smallest values estimated are very close to zero while the highest ones are even 9 degrees Celsius for a doubling of CO2. The majority of the papers are using theoretical general circulation models (GCM) for the estimation. These models give very big sensitivities with a very large uncertainty range. Typically sensitivity values are between 2–5 degrees. IPCC uses these papers to estimate the global temperature anomalies and the climate sensitivity. However, there are a lot of papers, where sensitivities lower than one degree are estimated without using GCM. The basic problem is still a missing experimental evidence of the climate sensitivity. One of the authors (JK) worked as an expert reviewer of IPCC AR5 report. One of his comments concerned the missing experimental evidence for the very large sensitivity presented in the report [1]. As a response to the comment IPCC claims that an observational evidence exists for example in Technical Summary of the report. In this paper we will study the case carefully.
2. Low cloud cover controls practically the global temperature
The basic task is to divide the observed global temperature anomaly into two parts: the natural component and the part due to the green house gases. In order to study the response we have to re-present Figure TS.12 from Technical Summary of IPCC AR5 report (1). This figure is Figure 1. Here we highlight the subfigure “Land and ocean surface” in Figure 1. Only the black curve is an observed temperature anomaly in that figure. The red and blue envelopes are computed using climate models. We do not consider computational results as experimental evidence. Especially the results obtained by climate models are questionable because the results are conflicting with each other.
In Figure 2 we see the observed global temperature anomaly (red) and global low cloud cover changes (blue). These experimental observations indicate that 1 % increase of the low cloud cover fraction decreases the temperature by 0.11°C. This number is in very good agreement with the theory given in the papers [3, 2, 4]. Using this result we are able to present the natural temperature anomaly by multiplying the changes of the low cloud cover by −0.11°C/%. This natural contribution (blue) is shown in Figure 3 superimposed on the observed temperature anomaly (red). As we can see there is no room for the contribution of greenhouse gases i.e. anthropogenic forcing within this experimental accuracy. Even though the monthly temperature anomaly is very noisy it is easy to notice a couple of decreasing periods in the increasing trend of the temperature. This behavior cannot be explained by the monotonically increasing concentration of CO2 and it seems to be far beyond the accuracy of the climate models.
![Figure 2. [2] Global temperature anomaly (red) and the global low cloud cover changes (blue) according to the observations. The anomalies are between summer 1983 and summer 2008. The time resolution of the data is one month, but the seasonal signal is removed. Zero corresponds about 15°C for the temperature and 26 % for the low cloud cover.](https://i0.wp.com/wattsupwiththat.com/wp-content/uploads/2019/07/Screenshot-2019-07-11-21.32.34.png?resize=634%2C488&quality=75&ssl=1 "Screenshot 2019-07-11 21.32.34")
Oh ! RT publised this paper….
https://www.rt.com/news/464051-finnish-study-no-evidence-warming/
Non-peer-reviewed manuscript falsely claims natural cloud changes can explain global warming
https://climatefeedback.org/claimreview/non-peer-reviewed-manuscript-falsely-claims-natural-cloud-changes-can-explain-global-warming/
The source of the LCC data can be found in this comments section.
The physics involved are valid, indeed uncontroversial.
Overcoming consensus is exactly how science is supposed to work. In 1543, the consensus was that the sun goes around the earth. In 1667, the consensus was that fossils grew in rocks. In 1755, the consensus was that the earth is young. In 1778, the consensus was that phlogiston controls combustion. In 1858, the consensus was that species are immutable. In 1861, the consensus was that miasmas and humors caused disease. In 1892, the consensus was that the earth was about 100 million years old. In the first half of the 20th century, the consensus was that continents don’t move. In 1998, the consensus was that the universe was expanding but not at an accelerating rate (the jury might still be out on this one).
Catastrophic Anthropogenic Climate Change Alarmism (CACCA) dogma overestimates the effect of radiative physics in the atmosphere and undervalues or ignores effects such as clouds, evaporative cooling, convection, oceanic circulations and oscillations. Consensus “climate science” is not real climatology but false assumption-based GIGO computer gaming.
“The source of the LCC data can be found in this comments section.”
People have tried to guess. Nobody actually knows.
Proper, actually reviewed science, which states its sources, says low cloud behaved quite differently.
“A test comparing the cloud record from ship reports to that from observations on islands in the central Pacific suggests that many of these long-term variations are spurious, particularly those that are coherent across many latitude zones. It is possible that a change over time in the fraction of nations contributing ship reports could be responsible for these spurious variations; however, an exact cause is yet to be identified.”
Yes the observations fell by two thirds from the mid 1980’s. (figure 1)
“Using adjusted data, anomalies of cloud amounts are shown to correlate with SST and LTS. A strong negative correlation is seen between low stratiform cloud cover and SST, while a positive correlation is seen between low stratiform cloud cover and lower-tropospheric stability in regions of persistent marine stratocumulus. This is an expected result, given previous work. Other factors such as sea level pressure and relative humidity also correlate with variations in low cloud cover. High clouds show a less substantial, but consistent, positive correlation with SST.
Given the subtle long-term variation in cloud cover shown on the global-scale, spurious variation makes finding trends on a large scale a perilous pursuit. Looking at smaller regions (adjusted for the long-term global variation), a possible increase in total cloud cover is observed in the central Pacific, while possible declines are seen in stratiform cloud cover in regions of persistent MSC. The decline in MSC is accompanied by an increase in SST between 1954 and 2008. Lower-tropospheric stability and sea level pressure show long-term increases, apparently inconsistent with the changes in MSC and SST. An evaluation of previous works lends more confidence toward the SST changes, though in future work aerosol and circulation changes should also be taken into consideration. This decline in MSC and the warming of the sea surface, taken together with the negative correlation between MSC and SST, suggests a positive feedback to warming in regions of persistent MSC. In the central Pacific, the change in the ENSO index is not substantial enough to account for the observed cloud changes.”
So apart from the central tropical Pacific, the general rule is that higher SST’s lead to reduced cloud cover. The final conclusion though lacks the understanding of how weaker indirect solar drives the warmer phases of ENSO and the AMO, such that the warm ocean phases ARE a negative feedback to weaker indirect solar.
https://journals.ametsoc.org/doi/full/10.1175/2011JCLI3972.1
My general rule is, anyone can make any point they wish via such convoluted logic, hand picked anecdotal reports, and using spotty information with no prior discussion of why anyone should take it as authoritative or even relevant.
““A test comparing the cloud record from ship reports to that from observations on islands in the central Pacific suggests that many of these long-term variations are spurious, particularly those that are coherent across many latitude zones.”
OMG!
This is science?
Besides for the inanity of it, the language is a mish-mash of uncertainties.
Suggests.
Particularly those.
Coherent across many.
Even latitude zones. What exactly is a latitude zone?
How many is “many”.
Think about this sentence:
“All paint requires two coats, particularly latex paint.”
Once the phrase particularly is used, what exactly is meant by the previous part of the sentence?
It renders it meaningless.
Such language is not indicative of science, it is the language of opinion.
Cart before the horse. Less cloud cover causes higher SST. Deuh. How does that work??
So the data gets adjusted. We know the adjustment are correct since they then provide “expected result”.
Hey , climatology 101. If it don’t fit: adjust until it does. Homogeneous data is beautiful.
No, John, the source of the LCC data is NOT found in the comments section. All we find there is speculation with no certainty.
As to the physics being uncontroversial, see my comment to Nick Stokes. Sometimes the sun rules the temperature, and sometimes the temperature rules the sun … hardly “uncontroversial physics”.
w.
Data have gotten altogether new meaning in climate science.
“Whatever happened to the Global Warming Hiatus ?”
http://clivebest.com/blog/?p=8934
I might add that in 1905, the consensus was that space and time are absolutes, while gravity acts instantaneously. Then it was discovered that spacetime is relative and that gravity operates at about the speed of light.
John Tillman
It is well established (consider Snell’s Law) that the speed of light is different for different materials. Does the speed of propagation of gravity vary with the substance it passes through, allowing for refraction?
Richard Betts, how do you explain this decline in cloud cover?
From the paper which Prof Richard Betts cites in his linked rebuttal:
So, if there is a trend in the data for which there is no known explanation, it must be erroneous and thus eliminated by suitable filtering. The fact that known reason for such a bias can be found, or that suggested logic for a bais is found to be unsubstantiated, does not matter.
If our current understanding of climate can not explain it , the most logical explanation is that the data must be wrong and requires “correcting”.
The degree of hubris and lack of scientific integrity in this field is astounding.
So if we can change the climate by varying the amount of CO2 we pump into the atmosphere, can we control the climate by varying the amount of H20 we pump into the atmosphere to create more or less clouds? No more droughts, no more floods, nirvana awaits. Climate emergency solved.
It’s remarkable how confused a grasp of the role of clouds is manifest throughout the discussion here, which seems more concerned with publishing process and statistical relationship than with physical comprehension.
The actual driver of the climate system in any energetic sense is insolation, which low clouds, in particular, strongly modulate at the surface. While sound science is often conducted without any code or data, there’s no climate to speak of without insolation. To be sure, the process of cloud formation is not a simple one, with various temperature effects upon evaporation and condensation, but there’s no denying what supplies the effective energy throughout the system. This includes the mechanical energy involved in creating spatial pressure differentials along with surface winds and currents. No matter what the LOCAL correlation between clouds and surface temperatures proves to be be, the GLOBAL physical significance remains indubitable.
“The basic task is to divide the observed global temperature anomaly into two parts: the natural component and the part due to the green house gases.”
Lol. More epicycles trying to explain the SIZE of the pink elephant
Well I agree with everyone who has said that this is an absolutely terrible science paper. Not only is the data not referenced, sourced or well explained, but the conceptual model they are using is appalling for what they are trying to do.
a) It is memoryless, which is silly given their attempt to draw inferences from frequency content
b) They use an estimate of CO2 forcing change (only) rather than total forcing for temperature prediction
c) They confuse TCR and ECS
d) The response parameters are separately estimated rather than simultaneously obtained by minimising a well-defined objective function
Despite the above problems, it would not surprise me if their conclusions might turn out to be of some interest value, but the paper needs a complete re-write to have any credibility at all.
Yet the warmist interpretation of the greenhouse gas effect is pseudoscience. Making concepts like TCR and ECS basically pseudoscience.
They should also ask a native English speaker to rewrite their awkward English.
At least the paper attempts to address the seminal issue of whether low cloud cover feedbacks are negative or positive. Of course the orthodox narrative is that CO2 driven warming of the sea surface temperatures drives a reduction in low cloud, acting as a positive feedback. Which is in full contradiction to how increased negative NAO/AO states drive a warm AMO phase and increased El Nino conditions during centennial solar minima. CO2 uptake in the North Atlantic is greatly reduced during the warm AMO phase, providing an additional negative feedback.
Here’s a 11-2018 Roy Spencer post on WUWT detailing the same observation
https://wattsupwiththat.com/2018/11/01/data-global-temperatures-rose-as-cloud-cover-fell-in-the-1980s-and-90s/
I further read there about the ozone and cloudines.
Isn’t ozone built by the UV radiation, changing strong with suns activity, see TCI variations ?
About a decade ago I debated a modeler from Boulder at a Congressional/ Legislature joint public workshop in our state. As far as I could determine from the people sending me to the meeting and what was said in opening remarks was the meetings intent was to explain to the public how serious CAGW was. The modeler was there as their scientific expert. The conversation got a bit heated when I asked about the role of water vapor, clouds, the atmospheric-ocean interface and how they were all addressed in the climate models. The modeler began to sputter obviously not wishing to answer my questions. Finally the chairman told the modeler to do so. The answer was a bit long but fundamentally the modeler said, (1) water vapor and therefore clouds were extremely complex, (2) the models did not address the ocean-atmosphere interface well at all contrary to what some might tell us. (3) it was his opinion that without a far better understanding of climate systems generally, especially the oceans and substantially more computing power the models being used and developed could only be considered advisor at best. As we should have all learned years ago from a Shoe cartoon, ‘models are a small imitation of the real world.”
Javier July 13, 2019 at 12:15 pm Edit
Calling my objections “silly” is scientifically meaningless, as you know.
I’m sorry, but the numbers make no sense. What month is “1955.8” referring to? I’ll assume it is month 8 (August) but that’s far from clear.
In any case, I tried your method. I quickly noted that about 6% of random results gotten by just shifting your selected time periods earlier or later gives a value LESS than your result.
Next, you have not included the Bonferroni correction. You’ve selected a total of about 9% of the data. If you were to change your arbitrary criteria, you’d find a different result. You need to include that fact in your analysis.
Conclusion? Sure, if you cherrypick some 9% of almost any dataset, you can get results that appear to be significant. However, once you include the Bonferroni correction for the fact that you are arbitrarily subsetting the data, they’re not significant in the slightest.
Regards,
w.
When I read that comment from Javier, I could not really pay much attention to anything else he had to say.
Just sayin’.
Disagreeing is fine.
When something is actually silly, we all know it.
We see enough silly crap to know it.
So making dismissive comments just renders everything efter an unthoughtful opinion.
IMO.
Then you don’t under stand what Javier was saying.
How’s this.
Over the history of the ENSO index the likelihood of an la niña event at the start of a solar cycle is to strong for it to be random chance. Therefore, given the variable length of solar cycles, the sun’s solar cycle must in some way be a driver for at least those la niña events.
Willis’s initial “silly” comment was that using averaged data to determine the start of a solar cycle is wrong and makes the analysis useless.
Later he said if it is done, it must be put through a correction algorithm.
I’m not enough of a statistician to agree with either Javier or Willis, but Willis is the one coming across as the one taking pot shots instead of making positive criticisms.
Obviously, there is a lot of water under the bridge of contention between the 2 of them.
I have no idea why anyone would apply a 12mo running average to SSN. I contacted the Belgian observatory about this a few years back. They agreed but carry on doing so because they always have done. They made minor concession in separating the “smoothed” data from the monthly means.
ONI is also a sloppy running mean ( 3mo ) these will actually reduce any real correlation which may be there.
Any kind of low-pass filtering reduces the degrees of freedom and affects what degree of correlation is “significant”. Also cherry picking only one part of the year requires correction: it’s a sample bias.
Selecting a part of the SSN record during which it correlates with SST also makes a bias. This may appear legit if one says “that is the length of the ONI record” but it still may produce a false result. When it is known that it decorrelates earlier in the record should be a red flag warning that there is something more to this and Javier’s result could be spurious.
I decided to ignore this obvious crock of sophistic malarkey when I took a carefully at the individual cells in figure one.
North America, and pretty close to everywhere else, showing a sharp warming trend extending right to 2010?
Nope.
Why pay any attention to anything that starts with BS?
I read the paper, it is pretty straight forward. The only part that really needs checking is the percent of cloud cover data. They use the PC surface temperature data so that is traceable. Cloud cover is referenced so it is check-able. It must exist since we have weather satellites which look at cloud cover and have for decades.
Those of you who have access to the cloud cover data can verify this paper in less than a day. Why not do it?
I looked around for some data on actual cloud cover as the model in this study assumes the cloud cover is decreasing to correspond with the increased temperatures in their Figure 2 which are most likely Giss though the source is not actually stated. NASA has cloud data from two satellites going back to 2000. If you look at Atmosphere Discipline Team Imager Products you find a graft that was produced in the changes section MODO8_M3 ocean only which I seem to be unable to copy to here. It shows no cloud change from 2000 to 2018. That presents a problem for figure 2 of this paper assuming the MODO8_M3 graft for cloud cover is correct. Perhaps one of these readers can visit NASA and re look at the cloud cover data.
NOAA STAR Microwave Sounding Calibration and Trends MSU/AMSU-A/SSU Global Mean Layer Temperature Anomaly Time Series shows the world cooling since 1978, about 3 K. If the authors of this paper have a verifiable source which is correct for the cloud cover their paper may be correct since it corresponds to the drop in the Star data K temperature. as shown in their figure 2. The Giss data could be simply be the problem.
https://climatefeedback.org/claimreview/non-peer-reviewed-manuscript-falsely-claims-natural-cloud-changes-can-explain-global-warming/ I sure hope you and all of your kind get dragged out into the streets and tarred and feathered by the same people you’ve been selling this garbage to for far too long now.
Critics never showed this paper to be fraud but they claim as much. Critics showed the paper uses cloud cover data which isn’t widely available. I’m sure the authors can provide the data is asked nicely. This paper is easy to replicate.
Meanwhile the so-called consensus greenhouse gas effect certainly is fraud:
1. never tested nor validated;
2. ignores real behaviour of CO2 absorbing & emitting infrared radiation
3. assumes impossible = violates 2nd Law of Thermodynamics
4. is unsupported by observation
Science Matters has a wider perspective on this paper: https://rclutz.wordpress.com/2019/07/12/more-2019-evidence-of-natures-sunscreen/
Dear scientists and engineers,
i am now confused, tell me please who is well oriented in all these papers models and peer-reviewed publications, if Michael Mann and his models, and his papers for IPCC, and all the other important papers, stand or these are going to be deconstructed as dangerous fuel for unholistic and irrational carbon dioxide politics – and so its uncertain how much carbon dioxide influenced or is going to influence future climate and global temperature…. and if the 97 per cent consensus still stands…. ? I.e. that destruction of environment and resulting climate change is not about carbon dioxide only, and that agriculture /livestock/, deforestation and methane gas plays perhaps possibly even more critical role, not just burning coal only.
Thanks for explanation on this problem with Michael Mann models and papers.
Let me tell what is certain.
Its the imperative to stop endlessly devastating nature and natural enviroment for us all on this planet Earth. Who cares about Amazon forest and reforestation/deforestation, carbon dioxide fanaticism without taking into account trees, forest and agriculture, is bad.
Kind regards,
Radek