Michael Jonas
I have been working for quite a while on cloud data, and have now had a paper published on the behaviour of clouds, which challenges the way clouds are interpreted by the IPCC and in the climate models. The IPCC indicate that clouds (1) provide a positive feedback to (CO2-driven) climate change, and (2) have reacted to aerosols with an increasing cooling effect. The IPCC logic is essentially that man-made CO2 helped by #1 has been so powerful that it has more than overcome #2. My paper argues that clouds behaved largely independently of CO2 and had as much of a warming effect as CO2, maybe much more. As far as I can tell from searching the scientific literature, this analysis has not been presented before.
Abstract:
The patterns of behaviour of clouds, both for cloud area and cloud optical thickness, are studied over the period of available data, 1983 to 2017. There was a decrease in cloud cover over the study period, while global surface temperatures increased. The patterns of clouds and temperature indicate that the cloud cover decrease could not have been caused by the increased surface temperature. The clear implication is that the decrease in global cloud area must have been caused by some other unspecified factor, and was not caused directly or indirectly by CO2. Evaluation of the changes in clouds and CO2 over the study period indicate that this unspecified factor had as much positive impact as the increase in CO2, with respect to the amount of radiation reaching the surface (radiative forcing), and possibly a much larger positive impact. The climate models, which have zero or negative cloud impact on radiative forcing independently from CO2, need to take this into account in order to avoid over-estimating the influence of CO2.
The paper covers a fair amount of ground, and is open source so that everyone can read it, here. ‘Full text article in PDF‘ opens the full paper.
First, I should point out that whereas scientific papers tend to go into ever more intricate detail, this study stays at a much higher level, dealing only with global numbers or with global numbers split by sea and land. In other words, I am looking at the ‘forest’, while many scientific papers tend to look only at the ‘trees’. That’s particularly true of the climate models, which try to build a picture of the ‘forest’ from all the individual ‘trees’ – a futile approach in my view, since no ‘tree’ can be predicted more than a few days into the future.
A brief summary of the paper follows (NB. This is only a summary, if you want to disagree with it then please do so by disagreeing with the paper not with the summary):-
Available cloud data runs from mid 1983 to mid 2017 (34 years), so that is the paper’s study period. Global cloud cover went down over this period, while global surface temperature went up [Figure 1]. But if you look at cloud and temperature patterns over shorter time scales (a few months), warmer temperatures cause more cloud, not less [Figures 2, 3, 4]. The reducing cloud cover could therefore not have been caused by increasing temperature. The cloud behaviour must therefore have been independent of the temperature, ie, independent of man-made CO2.
The paper goes on to evaluate this independent cloud behaviour [Figures 5, 6]. Instrumental in this, is that cloud area decreased, while cloud optical thickness increased. But cloud area decreased very similarly over sea and land, while cloud thickness increased much more over sea than over land [Figures 7, 8]. The most likely explanation is that the increase in thickness over the sea was not caused directly by the unspecified factor mentioned in the abstract, but was a reaction to higher temperatures.
The conclusion is that the independent warming effect of clouds was comparable to the effect of man-made CO2, and possibly much larger. The IPCC and the models contain no provision for this [Figure 9]. If the models do take this into account, then it can help them to avoid running too ‘hot’.
Not stated explicitly in the paper (maybe it should have been), although it is implied, is that if the models do take this cloud behaviour into account, then the amount of warming from man-made CO2 necessarily becomes a lot smaller when the models are tuned against observed temperature. Hence the future effect of man-made CO2 becomes a lot smaller.
Also omitted from the paper is any attempt to identify the unspecified factor causing cloud cover to decrease. The reason is simply that I hadn’t studied it. I could possibly have mentioned things like cosmic rays or solar UV (ultra-violet) as possible causes, linking them to the IPCC reports which mention them, but I chose not to. It would only have been pure speculation.
The paper does state explicitly that the models’ projections are already known to be highly unreliable (citing the reasons given in the IPCC reports), so there is no excuse on that score for not adding in a provision for independent cloud behaviour.
Those who like to see error-bars on all numbers will be disappointed. The reason is that the calculations are all very approximate anyway, so error-bars would be meaningless. The final conclusions, “similar impact” and “possibly a much larger impact” are not numbers anyway.
Note: I have written before on WUWT about cloud feedback:
That study was restricted to ocean areas only. This paper starts with global data in order to make it more relevant for comparison with the IPCC re radiative forcing.
There are at least a dozen other papers quantifying the global reduction in clouds. This one was close to the average increase in SW energy reaching the surface the papers found.
“Late Twentieth-Century Warming and Variations in Cloud Cover”
John McLean
https://file.scirp.org/Html/22-4700327_50837.htm
“The reduction in total cloud cover is significant in the context of the energy budget described by Trenberth et al. [34] , which indicates that cloud reflect 23% of the 341 Wm−2 (i.e. 79 Wm−2) of incoming solar radiation. The reduction in total cloud cover of 6.8% means that 5.4 Wm−2 (6.8% of 79) is no longer being reflected but acts instead as an extra forcing into the atmosphere”
“According to the energy balance described by Trenberth et al. (2009) [34] , the reduction in total cloud cover accounts for the increase in temperature since 1987, leaving little, if any, of the temperature change to be attributed to other forcings.”
Thanks for posting that. I certainly had missed it, but it uses quite different methods to mine. It is quite a lot more complicated because it relates everything to global surface temperature and has to do things like removing the effects of ENSO and volcanoes. Their ‘6.8%’ cloud reduction figure is certainly compatible with mine, though for a different period. Where they relate the 6.8% decrease in cloud cover to 5.4 Wm−2 of forcing, they are looking only at reflection of sunlight. They do refer to another study with a much lower figure. My numbers are very much lower too, because I relate the cloud decrease to the net effect of clouds, not just to the effect on incoming sunlight.
My analysis specifically relates to radiative forcing and not to surface temperature. It doesn’t need to do things like removing other factors, because it simply compares radiative forcing change from clouds and CO2 – with a fair bit of logic, and a careful comparison only of like with like. It also looks at things like cloud feedback, which I didn’t see in the John McLean paper.
Anyway, I am happy to add my contribution to the mix.
The most important finding is that the increase in solar radiation is greater than the human forcing. The IPCC position that most of modern warming is AGW has been falsified by observation.
Tricky critters, those clouds.
When they’re not warming us, they’re cooling us.
It’s a pity that man-made CO2 is not as versatile.
Or just maybe it is 🤔
My subjective assessment is that they warm the surface during the night and cool it during the day.
Actually, the Great Greening (photosynthesis) is an endothermic (cooling) process. This sequestration of sun’s warmth can be approximated by burning the
plant material and measuring the energy given off. The approximation is somewhat lower than the actual amount because of energy used in non carbon components of of the plants’ construction. Also, resulting shading of the ground, retention of soil moisture, contribution of aerosols….
Yes, it is quite versatile. Due to Kirchhoff’s Law we know gases like to absorb and emit radiation equally. This means that any layer of the atmosphere will equilibrate to a temperature based on its mass and the available energy.
If the layer gets warmer than the supply of energy it will emit more energy (cool) and if it gets cooler than the supply of energy it will absorb more energy (warm). CO2 is the primary gas doing this work at higher altitudes. In addition, due to being a well mixed gas, each layer contains the same proportional amount of the gas as it does other molecules.
Why doesn’t CO2 pool at the bottom of the atmosphere?
It does. It’s called gravity. It’s why the atmosphere gets “thinner” the higher you go.
That denser layer near the earth should also be absorbing a huge chunk of the “back radiation” emitted from the GHG’s higher in the atmosphere before it actually gets back to earth.
Somehow this never gets shown in radiation budgets, no gradient for GHG’s at all.
To paraphrase Shakespeare, the fault lies not in our clouds, but in our politicized science.
Something the sun is/is not doing?
We can quite clearly see from our very long historical records in the Kk and Europe that cloud cover has reduced over the last 100 years as industrial pollution has decreased. This is especially true over the last 50 years.
It depends on a variety of factors (wind direction etc) as to the difference that makes, but generally foggy or grey pollution clouds covering the sun makes it cooler than if the sun is shining strongly.
Many of the finest impressio9nist paintings of the 19th century were due to the distorting effect of pollution from industry and many artists flocked to cities such as London to paint the skies.
To Industrial pollution must be added the tens of millions of domestic fires that were kept burning, often using low grade smoky fuel. All these factors had a bearing on temperatures especially as many were by definition recorded in the hubs of science-smelly polluted cities.
tonyb
Cleaning up the environment caused the Climate Emergency.
Is there any century-plus record or estimate of global pollution levels? While industry has been growing throughout the world (and shifting from here to there…), energy use has become more efficient and cleaner – cleaner fuels and more efficient stoves and heaters in the domestic case. So I’m guessing that even though total combustion of fuels has increased as the world’s population has increased and become more advanced, the total level of soot, SO2 and other particulates that could reflect light out of the atmosphere before it hits the ground has decreased. That change also caused by human efforts to limit wildfires as well.
I not saying it ain’t so, but the population increase and consequent increase in coal burning in China nd India must have done a great deal to offset the clean up and reduction of industry in the West. Not to mention the reduction of forests in the Amazon and elsewhere. much of that done by burn off. We basically exported our polluting industrial base. Also, I would have thought that all that particulate would have provided an outstanding amount of nucleating material for condensation of water vapour, no?
“The patterns of clouds and temperature indicate that the cloud cover decrease could not have been caused by the increased surface temperature”
The argument of the paper rests on this proposition. So what is the basis for it? It is this:
The slope is positive. But no error bars are placed on the trend. The scatter is large, the trend is small. It is very likely that the positivity is not significant. That is, had the apparently random scatter worked out differently, the trend could be negative.
You just can’t base such an extensive claim on a regression with no significance test.
See figures 3 and 4 as well. The argument did not rest on figure 2 alone.
Those figures don’t even show the scatter, let alone any error ranges. Impossible to estimate significance.
There’s a known mechanism too, and see reference [3] which supports it.
So why don’t you apply the same statistical rigour to climate models, Nick?
Your final sentence is extraordinary given confidence intervals and significance levels for computer models of a non-deterministic system involving numerous variables, many of which are co-linear, i.e. climate models, will always be so poor that the results are no better than throwing a couple of dice.
It’s staggering how climate ‘science’ completely ignores such a fundamental point. I’ve raised it on numerous occasions with climate ‘scientists’ and the excuses used to avoid it are quite unbelievable.
Given you apparently know something about statistics, maybe you can address it?
Without invalidating his position? I doubt he can..
If the data had been different, the results would have been different. The “apparently random” part …
‘Apparent’ is a nasty word. It can mean anywhere between ‘obviously’ and ‘seems to be’.
Given the complexity of the processes involved, the scatter on the graph could be entirely deterministic and correlated with something like plankton. ie. The data is not obviously random.
At very least, we can say that the data does not support the IPCC’s case.
Tossing a coin is deterministic. The spin of a roulette wheel is deterministic. It’s just that you don’t know how they are determined. And so the outcome is uncertain, and you have to allow for the uncertainty.
“A statistical distribution in which the variates occur with probabilities asymptotically matching their “true” underlying statistical distribution is said to be random.” link
You are mixing up probability and uncertainty. There is no uncertainty in a fixed value, e.g. the face of a 6-sided die. There *is* a probability distribution for the fixed values which makes predicting with 100% accuracy the next value (that has no uncertainty).
Just answer my question please, Nick, which is above in case you’ve missed it.
As for the toss of a coin or spin of a roulette wheel, neither is remotely deterministic for the simple reason that you cannot predict what the outcome will be for either. Ever.
The idea that climate can be predicted to levels of accuracy anywhere close to those claimed by climate ‘scientists’ is absurd. In fact it’s just plain stupid. As I said above, you might as well throw a couple of dice. Or toss a coin. Or spin a roulette wheel.
“ had the apparently random scatter worked out differently,”
The scatter is not random. Mechanisms caused them to take the values they did, you just don’t know what it was.
(I’ve always wanted to nit-pick Nick!)
“you just don’t know what it was.”
and hence apparently random. The proposition that a fitted slope is uncertain, with the CI’s determined by a random model, is basic and universal, and much insisted on here.
The scatter is still NOT random. Nor is it “apparently” random! Things caused the data to take the values they did. This is separate from “basic and universal models” involving randomness which is not what you were talking about. You were talking about observations not models.
I’m merely calling out some lack of precision in your words. Its frustrating isn’t it!
I think it likely clouds are both a cooling and warming factor at different times and so it’s unsurprising that their effect is unclear when measured. I think this result gives as definitive an answer is as warranted by cloud behaviour.
At the very least it is evidence against a strongly positive correlation that is claimed by IPCC.
Great work and good timing. I can incorporate some of this into my video on clouds that I’m finishing up this week.
Looking forward to nr. 5 Jim Steele!
Here’s Steele once again believing that false conclusions based on faulty analysis constitutes “great work”.
Auto-correlation of the data was not considered in this study. The cloud cover data is highly auto-correlated on a short term basis.
Failing to consider auto-correlation is a classic time series analysis error that easily results in false conclusions. That is the case here. The supposed short term positive correlation between cloud cover and temperature is, in reality, just a short term positive correlation between cloud cover and itself.
If you’re going to ridicule the guy, you should at least give some evidence.
I really don’t ‘see’ any autocorrelation in the cloud cover data – but I’m certainly no expert. But that said, the cloud cover decreases then levels off – during The Pause- and then continues to drop after the El Nino of 2016 (iirc) and the temperatures have the opposite reaction.
I don’t see the cloud cover repeating past levels or patterns.
PCman,
The cloud cover time series displays short term auto-correlation. It is much more likely to keep moving in the same direction (increasing or decreasing) than to reverse direction. It keeps moving in the same direction for 3, 4, 5, 6 months in a row far more often than random chance would indicate.
Lots of natural phenomena display this kind of auto-correlation. One of the more well known is rainfall: if it has rained today, there is a larger than average probability that it will also rain tomorrow.
Bit it’s obviously inverse correlated with temperature over the whole time period, decades not months. Cloud cover stayed roughly constant during the whole Pause period when temps seemed to level off, and then continued to decline after, and what do you know, temperatures started increasing.
I’m not saying which is the egg or chicken, just that they are dance partners.
“But it’s obviously inverse correlated with temperature over the whole time period”
Yes, exactly! As temperature increases, cloud cover decreases.
Mike Jonas is trying to pretend in his paper that this relationship “doesn’t exist” in shorter term time intervals, and “therefore” concludes (incorrectly) that the already well accepted conclusion that temperature increase has been driving cloud cover decrease is “wrong”.
It is the analysis by Jonas that is wrong, in many ways. Failure to account for auto-correlation is just one of his errors.
And Jim Steele, as he so often does, just blindly accepted incorrect analysis and totally wrong conclusions as “good work”.
If you’ ve still got an eye on this thread, Jim, see my comment June 4, 2022 3:12 pm and the chart from MGC above it. It dovetails neatly with “Proposition (b)” in the paper. I might be able to do you a chart that shows it more clearly (ask here) but as I’m sure you would appreciate there is a lot of noise in the data. Clarity isn’t always easy.
Mike Jonas –
My chart clearly demonstrates that there is no reason at all to imagine that “Proposition b” from your paper is correct. Remember, the cloud cover data is 3 months delayed; thus the cloud cover minimum actually occurs two months after temperatures have peaked.
I’ve reproduced my original chart (see below) but with the cloud cover data shifted to its correct position in time relative to the temperature peak.
The evidence here provides no reason whatever … none … to imagine that temperature increase has “not” been causing cloud cover decrease. There is no valid reason to invoke any mysterious Proposition (b) “X” factor.
NASA agree!
https://notrickszone.com/2019/08/29/nasa-we-cant-model-clouds-so-climate-models-are-100-times-less-accurate-than-needed-for-projections/
“NASA has conceded that climate models lack the precision required to make climate projections due to the inability to accurately model clouds. Clouds have the capacity to dramatically influence climate changes in both radiative longwave (the “greenhouse effect”) and shortwave.”
Indeed, they go further and say modelling climate is a hopeless task!
https://principia-scientific.org/top-nasa-climate-modeler-admits-predictions-mathematically-impossible/
1. It is fundamentally mathematically impossible for climate models to predict climate.
2. Climate proxies are far too inaccurate, unreliable, and sparse to prove anything about past global climate, e.g. that it was colder.
3. Scientific consensus is not proof of global warming, just publication and funding bias.
Not to mention that in 2011, they still stated that the sun was the prime driver of climate.. but mention CO2….
https://web.archive.org/web/20100416015231/https://science.nasa.gov/earth-science/big-questions/what-are-the-primary-causes-of-the-earth-system-variability/
“What are the primary forcings of the Earth system?
The Sun is the primary forcing of Earth’s climate system. Sunlight warms our world. Sunlight drives atmospheric and oceanic circulation patterns. Sunlight powers the process of photosynthesis that plants need to grow. Sunlight causes convection which carries warmth and water vapor up into the sky where clouds form and bring rain. In short, the Sun drives almost every aspect of our world’s climate system and makes possible life as we know it.
Earth’s orbit around and orientation toward the Sun change over spans of many thousands of years. In turn, these changing “orbital mechanics” force climate to change because they change where and how much sunlight reaches Earth. (Please see for more details.) Thus, changing Earth’s exposure to sunlight forces climate to change. According to scientists’ models of Earth’s orbit and orientation toward the Sun indicate that our world should be just beginning to enter a new period of cooling — perhaps the next ice age.”
Is less clouds due to less muons from outer space, and or less cloud seeding molecules?
fewer.
Or, as Peter Cook would say, “or bofe…”
Earth entered the current phase of glaciation around 500 years ago. It is very early in the first 10,000 year stage of glacier formation around the North Atlantic.
After 10kyrs, there will be little change for another 13kyr then the glaciers will advance at a faster rate.
There is still no evidence of glaciers advancing but boreal winters are already receiving less sunlight and this will result in more precipitation arriving as snow and eventually lead to accumulation. For the generations to come, expect to see glaciers advancing within the current millennium.
Nice summary. Am I presumptuous to assume that Dr. Thresher is on his way to becoming homeless?
Almost certainly!
If it was local cloud cover, over say the US or western europe, one could attribute it to various ‘clean air’ laws, such as the Euro laws on exhaust emissions from cars.
But globally, that IS weird. What can cause a global cloud cover drop, low cosmic ray count? High solar wind?
What can cause a global cloud cover drop, low cosmic ray count? High solar wind?.
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Heat plus water gives clouds.
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If the world heats up more clouds should be formed.
All the usual caveats.
Current world temperature and pressure range, etc.
No extreme conditions like a boiling or freezing earth.
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Clouds do benefit from seeding.
“Condensation happens with the help of tiny particles floating around in the air, such as dust, salt crystals from sea spray, bacteria or even ash from volcanoes. Those particles provide surfaces on which water vapor can change into liquid droplets or ice crystals.”
Possibly from ionization effects as well [cosmic ray count].
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Roy Spencer attributes a lot of global warming to the effect of missing clouds and less albedo.
He also tries to measure cloud cover and says it is a very inexact science.
Not to mention “NASA has conceded that climate models lack the precision required to make climate projections due to the inability to accurately model clouds.”
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So.
A drop in global temperature leads to decreased cloud cover.
This leads to lower albedo and a higher global temperature.
It is complicated.
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Nick Stokes makes some good points.
V “The patterns of clouds and temperature indicate that the cloud cover decrease could not have been caused by the increased surface temperature”The argument of the paper rests on this proposition.No error bars are placed on the trend. The scatter is large, the trend is small. It is very likely that the positivity is not significant. That is, had the apparently random scatter worked out differently, the trend could be negative”.
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As the world has been warmer there should have been more not less clouds.
The description of more clouds might be erroneous and associated with the poor modelling that computes the amounts of cloud over this time.
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Of interest is the association of extra tropical clouds associated with the cooling La Nina pattern.
Counterintuitive.
No explanation.
It may not be global?
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What can cause a global cloud cover drop?
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Lower global temperatures.
Less solar output.
Distance from sun.
Sun traveling over more ocean when it moves over the southern Hemisphere?
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In other words the usual suspects.
“As the world has been warmer there should have been more not less clouds.”. You are making the same mistake as the IPCC, so at least you are in good company. The major point of the paper is that clouds go in the opposite direction to temperature, so TEMPERATURE CANNOT BE THE CAUSE. Once you see that, new possibilities open up. Possibilities that have not been allowed for by the IPCC and the modellers in any shape size or form.
More is going on than the IPCC admit.
“As the world has been warmer there should have been more not less clouds.”
Or
“As the world has been cloudier there should have been more not less warmth.” Using the argument of green house gases.
Of course, by day less cloud = warmer.
I spent an amusing hour comparing the mean temperature of deserts and wetter places at the same latitude. To my surprise, the mean temperatures were identical, suggesting that clouds overall do not impact average temperature, only the highs and lows – deserts are much hotter by day and colder by night…
Perhaps then, mean temperatures and/or average temperatures don’t really tell us much.
Mid-range temperatures, (Tmax+Tmin)/2, tell you almost nothing. The mid-range value can stay the same as long as Tmax+Tmin stays the same, e.g. Tmax goes up while Tmin goes down, Tmax goes down while Tmin goes up, or Tmax and Tmin stay the same. How do you determine what actually happened? It is imperative to know in order to make rational decisions. Anomalies don’t help since you still can’t tell what is going on because you don’t know the absolute values and you can’t discern what caused the anomalies to change.
For threshold or non-linear effects, the mean tends to obscure what is going on. The mean is most useful for stationary data, or variables that increase linearly.
Even if other countries might not have the same rigor of environmental laws, technology marches on. Old inefficient coal plants get replaced with more efficient ones because that’s all that’s available and uses less fuel per KWh. Home use too, why bother picking up smelly cow patties to burn in an open fire at home when one can buy their poor wife a nice clean stove that runs for a long time off a LPG tank. Also the world’s population keeps getting more urbanized so cow patties aren’t readily available. I haven’t travelled that much through the world but I have noticed that the higher level of tech still makes its way into everday life, not because it’s legislated but because it’s better.
do we have the last 5 years of data for cloud cover as well? seems like since 2000 the trend is actually flat
As far as I can tell, there still isn’t any cloud data after 2017/6 – eg. https://www.ncei.noaa.gov/data/international-satellite-cloud-climate-project-isccp-h-series-data/access/isccp/hxg/
There’s a correlation between the cosmic rays and ionizing particles impacting the atmosphere and cloud extension. It seems that these particles ionize the higher atmosphere and in those points water vapour begins to condensate and forms clouds. Cosmic rays are deviated by the sun magnetic field: when the sun magnetic field is strong, less cosmic rays reach the earth. So in periods when the sun is magnetically active (high number of sun spots), cloud covers is small. The 2014-2018 period has seen the biggest amount of sun spots, in Europe weather was dry and temperature has risen. 2021 has had almost no sunspots and in fact we have had a lot of snow and rain and temperature went massively down.
https://www.sciencedaily.com/releases/2017/12/171219091320.htm
And also this one:
https://www.nature.com/articles/s41467-017-02082-2
“Ions produced by cosmic rays have been thought to influence aerosols and clouds. In this study, the effect of ionization on the growth of aerosols into cloud condensation nuclei is investigated theoretically and experimentally. We show that the mass-flux of small ions can constitute an important addition to the growth caused by condensation of neutral molecules. Under atmospheric conditions the growth from ions can constitute several percent of the neutral growth. We performed experimental studies which quantify the effect of ions on the growth of aerosols between nucleation and sizes >20 nm and find good agreement with theory. Ion-induced condensation should be of importance not just in Earth’s present day atmosphere for the growth of aerosols into cloud condensation nuclei under pristine marine conditions, but also under elevated atmospheric ionization caused by increased supernova activity.“
All interesting, but someone has to work out what ACTUALLY caused the cloud change.
There has been some interesting speculation over the years concerning Gaia theory and sulfur compounds emitted from ocean organisms, which contribute to aerosol nucleation.
Pretty simple. Fewer clouds allow more warming visible radiation to reach the oceans. The oceans warm, degassing CO2 in accordance with Henry’s Law. Where is the problem? How does CO2 fit in that model? La Ninas reduce the clouds over the oceans. There is your climate model in a nutshell and it doesn’t include CO2 as a cause, CO2 is an effect of warming oceans.
Correct. CO2 lags temperature (in ice core data) by a quarter cycle at all time scales.
“Also omitted from the paper is any attempt to identify the unspecified factor causing cloud cover to decrease. “
Watch the videos by Dr. Jim Steele, La Nina causes the jet stream to change in a manner that reduces clouds. Reduce clouds over the oceans, more warming visible radiation reaches the oceans, the oceans warm. No need for CO2 in that model.
“La Nina causes the jet stream to change in a manner that reduces clouds.”
Both El Nino & La Nina impact the Jet Stream. Please show how the La Nina jet stream “reduces clouds”.
“Reduce clouds over the oceans, more warming visible radiation reaches the oceans, the oceans warm”
Except during La Nina where stronger trade winds cause greater upwelling & cool the surface which overwhelms the warming SW insulation. The Sun shines on the Pacific the same during El Nino & La Nina.
Clouds must be punished for disobeying the models. They should have their minions flog them.
The paper says
But conversely it is reasonable a rise in temperature of the oceans indicates less evaporation (causing cooling) and therefore less cloud cover.
Cause and effect with clouds is a prickly problem to say the least.
See reference [3] – it shows increasing evaporation. Yes, clouds are a prickly problem, but once everyone can drag themselves away from the IPCC’s notion that cloud behaviour is all in reaction to climate change then some of the prickles go away. A major point in the paper, which I think is pretty well supported by the evidence, is that clouds have their own independent influence on climate.Yes, they react to the climate too, but that’s not the full picture.
I would love to have been able to say in the paper what the clouds’ drivers were, but I couldn’t because I hadn’t studied it. Others here and elsewhere have plenty of good ideas, and hopefully proofs will be found. If anyone finds anything useful, hopefully it can get published here.
The question is why the clouds are not so bright and reflective. Today we see increasing persistent hazes of microdrops instead of fully formed cloud. A change roughly 4%.
For low cloud there must be a hygroscopic process to bind the microdrops.
Condensed microdrops alone are actually hydrophobic due to their electric charge. They will not form into larger reflective droplets. Persistent hazes absorbing incoming solar directly, instead of reflecting.
So, we must introduce a hygroscopic substance to help bind the microdrops into poofy reflective clouds.
In the low atmosphere there are two available sources: salts and hygroscopic microorganisms. Airborne microbes – bacteria and fungi mostly.
Louis Pasteur noticed these things. The hygroscopic substance helps to bind the microdrops. The atmosphere is full of this stuff, variable over time.
These are sometimes called precipitation nuclei, necessary to produce large hydrometeors from microdrops e.g. raindrops.
It is not unreasonable to suggest that largely eroding soils and creating monocultures of crops (instead of diverse ecosystems) has reduced the availability and quality of hygroscopic bacteria transpiring from the continents (along with water vapor). Increasing periods of mid-summer drought from the eroded soils reduces growing time and transpiration as the plants frequently go dormant during the “growing season”.
Picture substantial streams of biota emanating from the continents high into the sky to nucleate large drops all along the jets. Today it is mainly observed in tropics, for example over rainforest.
We see more mineral dusts which encourages haze in mid-latitudes, and fewer hygroscopic biota to encourage effective cloud and precipitation efficiency.
Something I have been bouncing around in my head for some time is whether or not the large anthropogenic production of antibiotics might be having an impact on the abundance of bacteria in the atmosphere.
If you mean biocides/herbicides, I think it could have a substantial impact. Land left fallow and drying in the sun has a similar effect as chemical additions.
Try David Siegel’s 40 minutes new Climate Science 101 v2.2: https://www.youtube.com/watch?v=76Ln3Ou6jnc
Also “How this tiny Fish is Cooling our Planet” : https://www.youtube.com/watch?v=I8KpuydjfJI
Those lantern fish collectively weight as much as 80 billion humans!
Nor it seems has the Australian weather received the IPCC memo. 🙂
I blame CO2.
Many have the idea that because clouds look white they are shielding the surface and so the sun’s rays are prevented from heating the surface. Actually for those versed in thermodynamics the formation of a cloud is a reversible process, that is, the energy balance at cloud level is unaffected by the presence or absence of the cloud. High energy photons from sunlight reflected upward are replaced by low energy infrared photons which you cannot see, radiated both upward and downward from the cloud. What we can see is the day/night (diurnal) temperature swings under cloudy skies vs those under clear. Cloudy skies reduce the diurnal temperature range. Because radiation goes as the fourth power of absolute temperature, the reduced peak temperature means lower overall radiation power, a warming effect. But clouds bring rain, and the water cycle definitely does cool things down.
A very important subject. Satellite measurements show the great impact of clouds on radiation. And then you have the change in relative humidity, and correlation with clouds.
From my discussion of it at Science of Doom. Much of the same radiation change has happened from 1983. From ca 1950 to 1983 there was more global dimming.
I would like to present climate feedbacks, illustrated by the change of the most important components for the last 20 years. Measured from satellites from 2000 to 2020.
From Loeb et al 2021: Trend in EEI During the CERES Period
https://ceres.larc.nasa.gov/documents/STM/2021 05/35_Loeb_contrib_science_presentation.pdf
For the radiation at the earth
s surface we have the following numbers (Wild et al. 2019):t make much difference, and Methane stands for 22,9 % of trace gas warming, we get:Solar radiation absorbed: 160 W/m2 with an increasing trend.
Longwave cooling from increased temperature: -56W/m2 with an increasing trend.
Evaporation, without presentation of trend: -82W/m2
Sensible heat, conduction/ convection from surface: -21W/m2
Earth Energy Imbalance measurements tell us that there is a warming of 0,51 W/m2/dec from change in these variables, SWsurf down, LWsurf up, Evaporation, Sensible heat. The components behind these changes are Temperature change, Albedo change, Cloud radiation change, Water vapor Change, and Trace gases change. These are also the feedback components of climate change.
Loeb et al, 20 years of energy imbalance from 2000 to 2020:
Temperature surface radiation, Net LW cooling: -0,51 W/m2/dec
Albedo reduction. SW solar warming: 0,19 W/m2/dec
Cloud LW cooling (less clouds) -0,23 W/m2/dec
Cloud SW decreased absorption 0.44 W/m2/dec
Water vapor LW warming 0,33 W/m2/dec
Water vapor SW warming and latent heat. 0,05 W/m2/dec
Trace gas, aerosole LW warming 0,237 W/m2/dec
Trace gas, aerosole SW warming 0,002 W/m2/dec
If we assume that most trace gases and aerosols don
CO2 LW warming 0,185 W/m2/dec
Methane LW warming 0,055 W/m2/dec
With a warming of 19 degC/decade since 2000, we get the folowing feedbacks:
Temperature feedback (radiation from warmer surface): -2,68 W/m2/degree
Albedo feedback (Less reflection from atmosphere and surf)1,00 W/m2/degree
Cloud LW feedback (Less backradiation from Thiner clouds)-1,21 W/m2/degree
Cloud SW feedback (Less solar absorption of clouds) 2,31 W/m2/degree
SW water vapor warming feedback/forcing 0,26 W/m2/degree
LW water vapor absorption feedback/forcing 1,74 W/m2/degree
SW trace gas and aerosol warming feedback/forcing 0.01 W/m2/degree
LW trace gas and aerosol absorption feedback/forcing 1,25 W/m2/degree
Methane part of trace gas LW «trapping» «forcing» 0,29 W/m2/degree
CO2 part of trace gas LW «trapping» «forcing» 0,97 W/m2/degree
Sum of all feedbacks and forcings 2,68 W/m2/degree
A very little part of forcings and feedbacks has a warming effect on the atmosphere and earth`s surface (about 2% of total heat uptake, so about 0,01 W/m2/dec). Nearly all the absorbed energy becomes reradiated. But they have some impotant work to do. They have effects on the lapse rate. And shortwave radiation is warming liquid water and ice in clouds, resulting in evaporation and melting, potential heat and cloud dissipation. This may be the greatest contribution to global brightening, and is not a linear function of trace gases. CO2 stands for less than 20% of all positive forcings/feedbacks. So CO2 make only up a minor direct contribution to global warming.
https://scienceofdoom.com/2017/12/24/clouds-and-water-vapor-part-eleven-ceppi-et-al-zelinka-et-al/
Jonas: ” and have now had a paper published”
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Only cost you $30: https://wjarr.com/content/article-processing-charges#Article%20Processing%20charges%20for%20Foreign%20Authors
I can’t afford the 9,500 Euros for open-access publication in Nature. Unlike many others, I have no funding.
So, nothing in the Earth’s climate does anything close to what these morons keep screeching it does. Got it.
“Climate science” got just about everything wrong with clouds. It is specifically obvious, when occasionally more accurate information pops up. Let me quote Gavin Schmidt et al 2010..
The LW CRE (cloud radiative effect) is usually named to be ~30W/m2. This figure is vital for the claim clouds were cooling Earth. Schmidt uses a much smaller number here. It is to be read as a percentage of the GHE of 155W/m2. So the CRElw would be 14.5% x 155 = 22.5W/m2.
However the paper rightfully distinguishes between “single factor removal” (net) and “single factor addition” (gross). The gross CRE is indeed about 2.5times larger (= 36.3 / 13.5), or about 56W/m2 in this case. This figure exceeds the albedo effect of clouds and all of a sudden it is not so easy to tell if clouds are cooling, or warming.
Now since Schmidt downplays the CRE overall, let me point out the CRE is likely rather 30W/m2 net, and 75W/m2 gross. It is huge, but 60% of it are overlapped with GHGs. “Climate science” argues that the overlapped component, where clouds AND GHGs both cause a GHE, was attributable to GHEs only. This is a) the theoretic foundation to the claim clouds were cooling and b) totally wrong. You just cannot make such an arbitrary choive.
A more realistic depiction of the GHE will looks like below (I should probably decrease the cloud share from 80 to 75W/m2).
The net effect of GHGs is indeed pretty small, only about 40W/m2. And the role of clouds is rather warming than cooling. Something that can easily be confirmed by simply analyzing weather records. Something I pointed out a while ago..
https://notrickszone.com/2020/09/11/austrian-analyst-things-with-greenhouse-effect-ghe-arent-adding-up-something-totally-wrong/
And it is a variable that the GCMs can’t handle directly because of their size, and have to be parameterized, based on assumptions of questionable veracity.
How does your schematic account for the 40% of solar radiation absorbed in the atmosphere?
Using a material energy budget of 240 W m-2, 96 W m-2 solar is absorbed in atmosphere (40%), 144 W m-2 absorbed at the surface.
This energy gain at the surface is transported to the atmosphere by convective transport 104 W m-2 of latent and sensible heat (turbulent flux).
LW radiative flux from surface to atmosphere is 40 W m-2, as you mentioned (greenhouse gas effect).
104 convective transport + 40 GHG + 96 solar = 240.
The only relevant radiation temperature is 255K or 240 W m-2. The boundary layer is dominated by turbulent flux.
Total budget = solar/4 * (1-albedo).
A fixed relationship is that albedo change is about 1/2 that of cloud cover. A cloud cover decrease from 68% to 64% (4%) corresponds to an albedo decrease of 2%.
Absorbed solar grows by a factor of 2%/4 = 0.5%.
288K * 1.005 = 289.44K. From cloud reduction alone, warming should be about 1.5K (minus ocean surface change ~ 0.3K), boundary layer 1.2K.
This is all conjecture of course, and comments welcome. My argument is that we must account for material energy budgets (turbulence/convection/mechanical efficiency) and solar absorbed in the boundary layer.
The LW factor relates to plugging up the IR window viewed from the surface. But it should really be integrated up through the atmosphere. My understanding is that clouds reflection vs plugging IR window has a ratio of about 50/30, with a net cooling about 20 W m-2. Importantly, converting precipitating cloud to ineffective humid hazes actually increases Solar absorbed in the atmosphere. This is not a LW effect, per se.
Well that is what the orthodoxy claims. As pointed out above however, the “logic” behind is illicit.
If I am understanding correctly you are observing a positive correlation between cloud and temperature in local profiles.
I suggest the latent heat flux is being delivered to these locations (clouds), to condense. The heat has originated from elsewhere, usually a sunny area with rampant evaporation. The latent heat transport is not a local phenomenon. Vertical radiative profiles do not abide.
Furthermore, dense cloud will reduce local evaporation, shifting local profiles to a moist sensible heat regime until rain loosens things up. The magnitude of vapor pressure deficits impacts temperature.
The GHE definitions will never make sense from radiative profiles. This I agree with. We are dealing with fluid dynamics and heat in a turbulent system.
Or the LW effect of clouds is simply larger than their albedo effect. There are data implying this.
We know clouds strongly reduce night time cooling, by up to 80 or 85% with overcast scenarios. That is two years of data, average cloudiness in oktas (0-8) and sample size.
If you look at the chart below, you will see how clouds seem to cause cooling in spring, and more warming in autumn. This should be due to surface and atmosphere lagging behind in temperature relative to insolation. Of course it is not just temperature, but emissions lagging behind. This suggests it is indeed radiative effects of clouds responsible for the warming associated with them.
The cloud droplets, like other solid or liquid surfaces, are radiating continuous IR spectra in all directions. The clouds being at some height, perhaps 2-10km, are net exporters of heat from the system. They are relatively free to radiate OLR as compared to energy closer to the surface. The clouds represent the transmittance of latent heat from somewhere on the planet.
I do not dispute that clouds are associated with warmer air masses from a local perspective. And I do not dispute that clouds create discontinuities in radiative profiles.
I think a lot of people are expecting local profiles to balance surface flux and OLR. But the clouds represent the transmittance of heat sourced from other geographies, so OLR often has no relation to its associated local surface flux (cell).
For the system, the variable of interest for radiative energy budget is both cloud cover and cloud height, I think. A reduction in system average cloud top altitude, keeping cloud cover constant, can conceivably warm the system by IR effects. I do not think this parameter is known very accurately.
Not at all, because it only portrays the LW part, the GHE so to say. If we include the albedo effect of the atmosphere (mainly clouds indeed), then we talk about the atmosphere effect, which however is a lot smaller than the GHE. As a whole, the atmosphere only adds about 8K to the surface temperature.
Thanks Schaffer. I’ll read into it. cheers.
I think by this you mean the LW radiative effects.
So then we must factor in the important part, the material system response to solar input, which generates a great deal of kinetic energy and internal heat + potential energy. 2-3x the LW forcing, by turbulent process.
This turbulent process is coupled to OLR, in large part by cloud. The net effect of the water cycle is deliver buckets of latent heat to height to be exported in cloud condensation. The relevant radiating surface for much of the atmosphere is the cloud top temperature.
At the surface, you observe an associated reduction of IR window under cloud up to 30 W m-2, but a large amount of latent heat is discharging from cloud top easily to OLR.
I have felt for a number of years that the behavior of glaciers, particularly in Glacier NP, is best explained by decreased cloudiness rather than increased air temperature.