More on Cloud Reduction.  CO2 is innocent but Clouds are guilty.

By Charles Blaisdell PhD ChE


This is a continuation of previous papers (1) and (2) on Cloud Reduction.  Further analysis of cloud data has revealed four new observations:

  1.  Mount Pinatubo ash in the atmosphere and Amazonia deforestation may be seen in the cloud data.
  2. A correlation of measured “Temperature – Dew point Temperature”, T-Td, to Cloud Cover was found.
  3. The Temperature – Dew point Temperature variable suggests Cloud Reduction has been going on before 1975.
  4. A simple model shows that Clouds either by reduced Cloud Fraction, decreased Cloud Albedo (lower reflectivity), or both can account for most of the observed Radiation and the associated Global Warming, GW.

CO2 is innocent but Clouds are guilty.


Climate change leaves a multi variable data finger print in the Atmosphere that is useful in drawing conclusions and testing theories.  The first of these finger prints is shown in Figure 1 where Cloud Cover, Temperature, Specific Humidity, and Relative Humidity (ground and 850mb) are shown on the same time scale.  None of Figure 1 graphs is a flat line any theory on GW should account for all these observations.  Figure 1 is NOAA data from “NOAA Physical Science Laboratory”, (3) average Northern and Southern Hemisphere.  In Figure 1 note that relative humidity at 1000mb is much less sensitive than the relative humidity at 850mb(where cumulus cloud are).  Cloud Data is from Climate Explorer, (11)

 Another data finger-print data set is shown in Figure 2 from “Met Office Climate Dashboard” (“HadISDH” data), (4)  (station and buoy data).  Note that the Met Data has a much better relative humidity correlation.  The relative humidity is significant variable in the Dew Point temperature calculation, Figure 2 (e).

Methods and data Calculations

The raw data from Figure 1 and 2 graph sets was tabulated in excel.  The actual (vs published temperature anomalies) temperature needed for the Dew Point temperature was obtained by adding 13.7 to the temperature anomaly data.  The Dew Point was obtained from “Online Psychrometric Chart”, (5)  The HadISDH data for Relative Humidity (1000mb) was used for the T-Td calculation.

Pinatubo and Amazonia

            The cloud data from Climate Explorer, (11) can be displayed by Northern and Southern Hemisphere, see Figure 3.  Mount Pinatubo erupted in 1991, Pinatubo’s ash remained circulating in the atmosphere for 3 years, (6) In Figure 3 the Pinatubo ash perturbation is seen in the Northern Hemisphere but not in the Southern Hemisphere graphs, as expected (Mount Pinatubo is in the Northern Hemisphere).  The Figure 1 finger print graphs all show some sign of a perturbation in that time period.  The ash could have made cloud formation a higher probability.  The Ash could also have been viewed by the satellite as clouds. (Or both.)  Since the Figure 2 graphs all show some expected response to more clouds it suggests the ash helped make more clouds. (Expected response to less clouds are: increased temperature, increased specific humidity, decreased relative humidity, and increased T-Td.   All reversed for cloud increase)

Logging in the Amazon rain forest has been going on since before 1970.  From 1977 to about 1998 most of the logging was a sustainable selective type of logging that left some forest canopy and allowed the forest to regrow and did not upset the natural water cycle.  Started in about 1998-1999 to 2004 a clear-cutting practice was started, (12). Clear-cutting destroyed any remaining forest canopy and drastically altered the water cycle.  Crop and pastureland replaced the rain forest after 2004.  Current farm and pasture land made from that deforestation is about 70,000km^2  (the size of West Virginia) but only 0.05% of the earths land mass.  This clear-cutting may have been captured in the Southern Hemisphere graph in Figure 4.  Does a plume of warm low humidity air rise out of this area and have a greater effect in the upper atmosphere (where the clouds are) than the area it came from?   We can see plumes from forest fires and cooling towers rising to cloud level then making a downwind plume much bigger than the area they came from.  We cannot see warm lower humidity air plumes. All the other graphs in Figure 2 move in the expected direction (less clouds) with the 1999 to 2004 Amazon event.  Figure 2 (d) the T-Td graph seems to capture the Amazon event.

Models of Urban Heat Islands, HI’s, warm dry air plumes suggest plumes 2-4 times larger than the area they came from (7)

Dübal (8) suggest the 1998-2004 inflection in the Cloud Fraction data is related to changes in the “Atlantic Multi-decadal Oscillation”, AMO.  Inspection of the AMO cycles in the 1998-2004 period reveals little activity; but the “Pacific Decadal Oscillation”, PDO does have activity in that time frame.

Loeb (9) shows a red spot (his Figure 3(e)) in humidity change pictures of South America where the Amazon clear-cuts would be, indicating a big humidity change occurred at that location.

T-Td an indicator of cloud cover

The variable (Temp -Temp(dew)) is a common-sense variable representing how close the temperature is to the saturation point; which, should be related to the cloud point. (T-Td is nothing new, a T-Td correlation is used by pilots to get an idea of probable cloud ceiling, (10) )  The cloud point is not an exact variable, aerosols, particulates, and cosmic rays can cause the cloud point to occur sooner while in a lack of these items the atmosphere can become supersaturated and delay cloud formation.  The variable Temp-Temp(dew) should be thought of as a probability function.  As the T-Td decrease the probability of cloud formation increases.

Figure 4 is graphical representation of the sensitivity of T-Td to atmosphere variables.  Note that the probability of cloud formation decreases with temperature and increasing relative humidity.  Also note the same probability of cloud formation can occur at equal T-Td’s combination of lower temperature – low relative humidity and high temperature – high relative humidity.

The Cloud data in Climate Explorer, (11), has an oscillation with the seasons.  The oscillations are opposite each other in the two hemispheres; therefore, looking at each hemisphere separately reveals a good correlation of T-Td to cloud Cover over short time periods, see Figure 5.   Figure 5 groups the monthly data in years 1983-6 and years 2016-8 to show the T-Td vs Cloud Fraction line in the Northern Hemisphere is shifting with time.  Figure 6 uses the average yearly data to show the T-Td correlation for the 36 years of cloud data.  This correlation can be used in Climate Change models to test theories on how local changes in water balances can change global water balance.

The T-Td variable is used in Figure 7 to show Cloud Cover has been changing before 1975.

In the use of the T-Td variable it is noted that T-Td is confounded with Temperature and Relative Humidity and only slightly improves Cloud Cover correlations.  The higher sensitivity to relative humidity at 850mb (where the changing clouds are) suggest a T-Td at 850mb may be a better indicator of Cloud Cover.

Relating Change in Cloud Cover to the Earth’s Short Wave Radiation.

            The following relies on the data and correlations published by Hans-Rolf Dübal and Fritz Vahrenholt (8).   Norman G. Loeb,Gregory C. Johnson,Tyler J. Thorsen,John M. Lyman,Fred G. Rose,Seiji Kato (9) data could just as easily been used.   Table 1 shows the data used for the following calculations.  In the Hans-Rolf Dübal and Fritz Vahrenholt (8) paper the Cloudy Areas Radiation (CAR) is a calculated number from Total Radiation to the Earth (TR), Clear Sky Radiation (CSR), and the Cloud Fraction(Cover) (CC) :

 (Eq 1) CAR = ( TR – (1-CC) * CSR ) / CC 

The cloud fraction(cover) used by Dübal and the cloud fraction from Climate Explorer, (11), is show in Table 1.   The Dübal cloud fraction change is -0.1%/decade for Years 2001 to 2020 and the Climate Explorer cloud fraction change is -0.75%/decade for 1982 to 2018 both are statistically correct for their data source.   Figure 1(a) has a very low slope from 2001 to 2020.  Both will be analyzed.

            This analysis will only deal with the Short Wave Radiation, SWR, in and out.  Table 2 shows the total earth albedo calculation of the CERES data and extrapolation to 1975.  The observed temperature anomalies from NASA data remain proportional to the albedo change at 0.27’C/Wm^2, in Table 1.  (The 0.27’C/Wm^2 assumption is used assuming LW out and the calculated EEI are proportional to the SW in. This assumption is only valid if CO2 is not affecting Long Wave out radiation (CO2 is innocent).  Figure 8 shows they are not exactly proportional but are (with in statistical error) statistically proportional.

In calculating the Cloud Fraction (Cover) contribution to albedo change the nature of the collected data gives three possible sources of radiation change:

  1.  Clear Sky albedo change, Acs  (could be related to land albedo changes)
  2. Cloudy Areas albedo change, Aca (could be cloud reflectivity, or cloud thinness, or cloud temperature as Dübal points out, or all)
  3. Cloud Fraction (Cover) change, CC (clear sky vs cloudy areas)

The Earth’s Short Wave Radiation, SWR, balance can be calculated two way:

(Eq 2) SWR(earth) = SWR(sun) * (1- Ae)                 

( Eq 3) SWR(earth) = SWR(sun) *((1-Aca) * CC + (1-1Acs) * (1- CC))


SWR(sun) = Short Wave Radiation flux from the Sun from Dübal data.

Ae = total Albedo of the earth from Dübal data

Aca = Albedo of cloudy areas calculated from Dübal  or Climate Explorer data

Acs = Albedo of clear sky area from Dübal data

CC = Cloud fraction (cover) from Dübal or Climate Explorer data

Table 3 Model calculates the Radiation totals from Climate Explorer data with a 0.75%/decade change in Cloud Cover.  Clear Sky albedo and Cloudy area albedo are calculated from Dübal data (note Cloudy area albedo mathematically becomes constant because Cloud Cover change is high enough to account for all the radiation change).  Table 4 Model calculates radiation from a much lower Cloud cover change (Dübal’s) which mathematically causes the Cloudy area albedo to change more.  In either table the total SW radiation to the earth is about the same.  The pie charts below the Tables shows the big shift in where the radiation change is coming from; Change in Cloud Fraction or Change in Cloud albedo or both.  In either case clouds have caused a change in the earth’s albedo that is not considered by the IPCC.

            The LW radiation from cloudy areas can also be calculated from Equation 3 and has the same change in slope as seen with the SW radiation.  In fact, some LW radiations in cloudy areas reverse slope when using Climate Explorer vs Dubal cloud cover data, not shown.

Extrapolation of Dübal data shows the uncertainty in the CERES data.

            Figure 8 show the Dübal data (least squares fit) extrapolated to 1975.  Note the Short Wave to the Earth’s surface line crosses the LW radiation out line.  This should not be the case.  The LW radiation out should always be less than the SW radiation to the earth.  As Dübal noted this could be the result of subtracting large members.  But, Figures like 8 add to the uncertainty of using the data.


The variable T-Td is a useful tool in predicting cloud fraction.  (not perfect but useful)

The Climate Explorer, (11) data in Figure 3 may have captured the Mount Pinatubo ash in the atmosphere and the clear-cut deforestation in the Amazon.  If the clear-cut observation is true (related to the 1998-2004 reduction in cloud cover), it supports the theory proposed in (2) that: if a localized change in the evapotranspiration, ET is big enough (including it’s plume) the low relative humidity and warmer temperatures from that location (and ones like it) could mix in the atmosphere where clouds form and cause Cloud Reduction Global Warming, CRGW.

Simple models using cloud reduction and calculated cloud albedo data showed that either or both could be related to the Earth’s albedo change observed by CERES data.   If low relative humidity – warm air plums from areas like the Amazon clear-cuts raise to cloud altitude in the atmosphere it could cause either cloud reduction or thinner less reflective clouds (lower albedo) or both at different conditions.  If this is true, cloud reduction and lower cloud albedo should be thought of as one variable.

Statistical uncertainty in the CERES and Cloud data seem to retard acceptance of alternative GW theories.

The IPCC should try this type of model in one of their Global Circulation Models, GCM’s.

CO2 is innocent but Clouds are guilty.


Table 1.  Basic Data used for this paper.  All cloudy area data is calculated from Clear Sky, All Sky, and Cloud Cover (Fraction) data.

Table 2.  Using equation 2 to calculate the SW radiation to the earth with Dübal total earth albedo.

Table 3.  Calculating the SW in distribution of radiation using Climate Explorer data. Of -.75%/decade.  Note this amount of Cloud Change is enough to make the Cloud albedo constant.

Table 4.  SW in radiation distribution using Dubal cloud fraction.  Note this -1%/decade cloud fraction change causes the cloud albedo to change over time.

Pie chart for Table 3

                                  Pie chart for Table 4


Figure 1.   Atmospheric Finger Print of Cloud data from Climate Explorer and atmospheric data from NOAA.  Yellow area is years Mount Pinatubo ash was in the atmosphere and green area is years of clear-cut logging in Amazonia.

Figure 2.  Atmospheric Finger Print of Cloud data from Climate Explorer and atmospheric data from HadISDH.  Yellow area is years Mount Pinatubo ash was in the atmosphere and green area is years of clear-cut logging in Amazonia.

Figure 3.  Cloud Fraction (Cover) for Northern and Southern Hemispheres from Climate Explorer.  Note a perturbation in the Northern Hemisphere that may be related to the Mount Pinatubo ash circulating in the atmosphere but not in the Southern Hemisphere, as expected.   A strong perturbation in the Southern Hemisphere may be related to a switch to clear-cut logging in the Amazon rain forest and is not so strong in the Northern Hemisphere, as expected.

Figure 4.  Temperature vs Specific Humidity with constant Relative Humidity line (a psychrometric chart) showing the Temperature – Temperature(dew), T-Td.

Figure 5.  T-Td vs Cloud Fraction for the beginning and the end of the Cloud data. The normal seasonal variation of Cloud Fraction is shifting with time.

Figure 6.  T-Td vs Cloud Fraction.  Useful correlation in Models (wish it was better R^2)

Figure 7.  T-Td vs time from HadISDH data.  Cloud change has been going before the 1970’s

Figure 8.  Dübal CERES data extrapolated to 1975.  The three least squares fit of the data do not come together exactly in 1975 but they are statistically close.  The LW radiation should always be less than the SW radiation.


  1. Where have all the Clouds gone and why care? – Watts Up With That?  
  2. CO2 is Innocent but Clouds are Guilty.  New Science has Created a “Black Swan Event”** – Watts Up With That?
  3. Monthly Mean Timeseries: NOAA Physical Sciences Laboratory
  4. Humidity | Climate Dashboard (
  5. Free Online Interactive Psychrometric Chart (
  6. Mount Pinatubo: Eruption and Climate Change – Philippines Tour Guide (
  7. Downwind footprint of an urban heat island on air and lake temperatures | npj Climate and Atmospheric Science (
  8. Hans-Rolf Dübal and Fritz Vahrenholt  web link:  Atmosphere | Free Full-Text | Radiative Energy Flux Variation from 2001–2020 | HTML (
  9. Norman G. Loeb,Gregory C. Johnson,Tyler J. Thorsen,John M. Lyman,Fred G. Rose,Seiji Kato  web link  Satellite and Ocean Data Reveal Marked Increase in Earth’s Heating Rate – Loeb – 2021 – Geophysical Research Letters – Wiley Online Library
  10. “Relative Humidity and Dew Point as a Function of Altitude — A Way to Estimate Cloud Ceilings”  by David Burch Navigation Blog  web link: David Burch Navigation Blog: Relative Humidity and Dew Point as a Function of Altitude — A Way to Estimate Cloud Ceilings
  11. Climate Explorer: Select a monthly field (  go to “Cloud Cover”  click “EUMETSAT CM-SAF 0.25° cloud fraction”  click “select field” at top of page on next page enter latitude (-90 to 90) and longitude (-180 to 180) for whole earth.
  12. Selective logging leads to clear-cutting in Amazon (
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Stephen Wilde
April 13, 2023 11:50 pm

Wavier, more meridional jet stream tracks result in more clouds due to longer lines of air mass mixing.
Solar variations appear to affect jet stream waviness/ meridionality.
Cloudiness affects global temperature by regulating the proportion of incoming solar energy that is able to enter the oceans.
Such effects are so much larger than those from changes due to variations in CO2 that we would never be able to discern the latter from natural variability.
It is looking likely that large short term variations in atmospheric CO2 are not adequately captured within the ice core record which is our current primary source for past changes in atmospheric CO2.
It is likely that such short term CO2 changes are the consequence of changes in solar input to ocean surfaces beneath the large sub tropical high pressure cells which are largely cloud free.
We see plumes of CO2 downwind of sun warmed ocean surfaces but not downwind of human population centres..

Richard M
Reply to  Stephen Wilde
April 14, 2023 5:15 am

Changes in the main ocean cycles (AMO/PDO) will lead to changes in the jet stream as well. In fact, it may very well be the warming/cooling effects we see are really due to cloud changes driven by this jet stream movement.

Torbjörn Pettersson
April 13, 2023 11:54 pm

The problem with CERES (and all calculation of the Eart’s Energy Imbalance) is that they don’t calculate all of the incoming solar radiation.
They use the value for TSI, which would be correct for a planet without atmosphere, but the atmopshere is acting like a lens, bending radiation towards the center of the Earth and intensifying the radiation/m2.
The missing radiation is somewhere between 4-5Wm-2 at TOA, and should be added to TSI

Reply to  Torbjörn Pettersson
April 14, 2023 6:07 am

That’s only one of the problems! Another one is that they don’t specify what temperature they are using to calculate this radiant power (i.e. the temperature of the sensor on the CERES satellite). Power developed depends on two temperatures, one (“hot”) being the sun in this case. But the “cold” sensor temperature on the satellite is unlikely to be the same as the temperature of any part of Earth’s surface or atmosphere, so the amount of radiant power detected by the satellite will be different than the amount developed on any part of Earth. No one wants to mention this, though…

Clyde Spencer
Reply to  Torbjörn Pettersson
April 14, 2023 9:18 am

And the total reflectivity is underestimated because the specular reflection is ignored, relying on just diffuse reflection and, for oceans, a combination of diffuse reflection from suspended particles and low-angle of incidence specular reflection.

Reply to  Torbjörn Pettersson
April 15, 2023 7:27 pm

And high energy UV photons carrying substantial portions of TSI energy are absorbed when encountering O₂ manufacturing O₃, ozone.

Javier Vinós
April 14, 2023 12:12 am

The changes in cloud cover observed in figure 3 from 1997 to 2005 are just part of the changes observed throughout the climate system, and the result of a climate shift similar but opposite to the one that took place starting in 1976. This is explained in my book and in this article. Clouds are not the cause but one of the many climate features affected. The result of this shift was the pause from 1998. It seems counterintuitive that a cloud reduction would stop global warming because it is. The cloud change did not result in the Pause. It is just part of the increased poleward transport of heat and moisture that resulted in fewer clouds in mid-latitudes, more heat and moisture reaching the Arctic, less warming in mid-latitudes, and more warming in the Arctic.

Richard M
Reply to  Javier Vinós
April 14, 2023 5:03 am

The +AMO switch occurred around 1996 and is the likely driver of the shift you mentioned. It doesn’t happen all at once. It started the Arctic ice melting which is why the effect is slow. The ice reached a new equilibrium in 2006.

The 1976 climate shift was tied to the +PDO phase change. Not sure these are opposites, just different factors involved. The PDO changed back in 2006 which could be another factor in the end of the Arctic melt, however it was only for 8 years and switched back positive again in 2014 which is the same time the clouds showed another thinning.

One final factor that could be affecting clouds is human ocean pollution. Micro-plastic stays near the surface which could be reducing evaporation. This could lead to a decrease in clouds.

Reply to  Javier Vinós
April 14, 2023 5:26 am

Yes, there has been more arctic cloud cover. It pretty much entirely compensates for sea ice declines re albedo.

Reply to  Javier Vinós
April 17, 2023 2:34 pm

The CRGW theory in this paper is smaller than the changes in climate discussed in your paper. CRGW theory has since about 1975 gotten big enough to be seen and may grow to the scale of events in your paper. Prior to 1975 the population of the Earth was not large enough to make significant changes to the Earths ET.

Chris Hanley
April 14, 2023 12:28 am

CO2 is innocent but Clouds are guilty

Guilty of what?
Maybe the author is being facetious; if the answer is the global warming during the past forty years why the pejorative, warmer or cooler are objectively neutral terms.
I’m not equipped to analyze the study in detail, obvious questions:
Do clouds control temperature or does temperature control clouds, or both?
Does the analysis eliminate all the other possible climate factors including CO2?

Reply to  Chris Hanley
April 17, 2023 2:21 pm

Clouds are the Earth’s thermostat. Less (or thinner) clouds the Earth gets cooler and vis a visa. Prior to satellites the assumption was that cloud cover was constant over the years. We now know that is not true. The question now is what causes cloud cover to change? This paper says CRGW. CO2 may be eliminated in time as the uncertainty in the cloud data improves. At this point in time, it could be both.

Peta of Newark
April 14, 2023 1:00 am

Assertion: CERES cannot measure diddly squat.
The data, measurements and recordings that come out of it what its ‘pilots’ think should come out of it.

No matter, Feare Ye Notte, Brave Enquiring Mind, guidance and help is at hand – see it in a mirror.

  • Get yourself any audio amplifier, remove all its inputs, switch it on and turn up the volume. You will hear ‘noise’
  • Take any radio receiver, detune it from any station, remove its aerial and or put it in a metal box and turn up its volume. You will hear more ‘noise’
  • Take any TV set, do similar and see what you see on the screen. You will see ‘noise’
  • There are devices out there you might play with and that work at much
    higher frequencies and they will ‘see’ the exact same: more ‘noise’

What you will be seeing and hearing is near-perfect White Thermal Noise..
It is the sight and sound of Planet Earth obeying the 2nd Law as it down-shifts incoming short wavelength solar energy to progressively longer and longer wavelengths via literally countless absorptions and re-emissions.
It answers the question asked by Sadi Carnot, when he wondered: “Where does the energy go?”
It is why no heat engine that can ever be devised in this universe will never have 100% efficiency.
(In direct contravention of the GHGE – unquestioning and blindly obedient slave as it is the The 1st Law)

The problem for CERES, its operators and acolytes is that it is an ‘electronic device’ – exactly like your audio amp, radio and your TV
i.e. It will be generating its own internal noise but the very signal its looking to measure, is that Perfect White Thermal Noise created by Planet Earth

Question: How do NASA engineers and scientists know which is which?
Answer: They don’t know and they can never know

So what do they do ‘keep face’: They make it up. They lie.
They appeal to their own authority ##

i.e. They tell the world what they think the world wants to hear and say to the world what their paymasters tell them to say

## Just as Ancel Keys did and look at the damage he did.
i.e. 88% of US population has, at least one, chronic metabolic disorder which is costing them $20,000pa just to manage the symptoms. With no cure (seemingly) available

Meanwhile, back on topic:
What a lovely article describing soil erosion.
i.e. There are less clouds, because there is less water in the landscape and there is less water because there is less Soil Organic Material retaining moisture (in the landscape)
We know where the SOM went, into the sky.
We know where the water went = sea level rise

And as all Soil Organic Material comprises bacteria, the essay above throws light upon the ongoing mass extinction
i.e The extinction of the bacteria

They are = Life on Earth, we are the fleas on their back
when they go, we go.

Reply to  Peta of Newark
April 14, 2023 5:26 am

SOM including Bacterial & Fungal Communities.

Increasingly exposed soils to the life killing UV rays + biocides (glyphosate) with direct consequences to microbial & fungal biomass.

Consequences: reduced soil sponginess; increased bulk density; reduced evaporative fraction; increasing runoff or P-E; reduced airborne hygroscopic bacteria (a la: Louis Pasteur); reduced cloud condensation nuclei; increased condensation altitude; reduced cloud condensation temperature; increasing blocking high pressure; reduced continental inflows; disrupted trade-winds; disrupted (“natural”) oceanic oscillation e.g. ENSO; increasing oceanic Shortwave Absorption.

April 14, 2023 3:28 am

Thank you for this presentation. This is the most important that we can know about climate change.

It is too easy to say that CO2 and other climate gases are without any effect. I think CERES data show that climate gases may be “responsible” to one third of the warming.

As Martin Wild has shown. There is a remarkable global brightening since about 1983, which was following the global dimming since about 1950.

I have been discussing this at Science Of Doom.

“I would like to present climate feedbacks, illustrated by the change of the most important components for the last 20 years. Measured from stellites from 2000 to 2020.
From Loeb et al 2021: Trend in EEI During the CERES Period 05/35_Loeb_contrib_science_presentation.pdf
For the radiation at the earths surface we have the following numbers (Wild et al. 2019):
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
t make much difference, and Methane stands for 22,9 % of trace gas warming, we get:
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 following 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 thinner 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 2,68 W/m2/degree
A very little part of 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.”

“What do we know of the mechanisms behind cloud formation and disappearance?
First of all it is about relative humidity and temperature. From a post at WUWT by Charles Blaisdell, where-have-all-the-clouds-gone-and-why-care: «The basics of cloud formation and disappearance is temperature and relative humidity, RH. Clouds form with combinations of lowering temperature and higher RH approaching the dew point; and disappear with combinations of higher temperatures and lower RH moving away from the dew point. Cold air meeting warm humid air is the most common way clouds are formed.» And. «Global maps of study variables in Loeb et al show that the changes in heat flux (W/m^2) are not evenly distributed for all variables. Cloud cover and humidity stand out as localized changes over the 20 years of study. The cloud cover change in heat flux is most noted downwind of UHI areas and the humidity increase in heat flux is located in the converted Amazonia crop land. One other noted area of cloud change is the dark change in the Pacific Ocean which is the known Pacific Decadal Oscillation (PDO) temperature oscillation.»
A guest post at Carbon Brief by Kate Millet take up the same subject.
«According to the Clausius-Clapeyron equation, the air can generally hold around 7% more moisture for every 1C of temperature rise. Therefore, for relative humidity to stay the same under 1C of warming, the moisture content in the air also needs to increase by 7%. 
In theory, if there are no limiting factors, then this is the rate of increase we would expect to see. However, the real world does have limiting factors – and so relative humidity is decreasing.
The Earth’s land surface has been warming faster than the oceans over the past few decades. But, while the oceans contain an inexhaustible supply of water to be evaporated, the same is not the case for land.
In fact, we know that most of the water vapour over land actually originates from evaporation over oceans. This moist air is moved around the globe thanks to the atmospheric circulation and some then flows over land. 
The slower warming of the oceans means that there has not been enough moisture evaporated into – and then held in – the air above the oceans to keep pace with the rising temperatures over land. This means that the air is not as saturated as it was and – as the chart below shows – relative humidity has decreased.
Focusing on the world’s oceans, observations indicate that – as expected – specific humidity has increased in the air over oceans. This has been shown in a new global dataset that my colleagues and I have recently published in the journal Earth System Science Data.
Interestingly, this new dataset shows that relative humidity has actually decreased over many regions of the oceans. This is enough to make the global ocean average relative humidity decrease. 
This decrease is difficult to explain given our current physical understanding of humidity and evaporation. For example, the expectation from climate models is that ocean relative humidity should remain fairly constant or increase slightly.»
So a conclusion can be that global brightening and global dimming is difficult to explain. Especially what looks like natural variations in cloud cover can be difficult to understand.”

Last edited 1 month ago by nobodysknowledge
Torbjörn Pettersson
Reply to  nobodysknowledge
April 14, 2023 4:47 am

As I wrote earlier, CERES doesn’t include all solar radiation, then every calculation will be wrong, Loeb and Kato will have to correct their model

Reply to  nobodysknowledge
April 15, 2023 8:18 pm

In fact, we know that most of the water vapour over land actually originates from evaporation over oceans.”

Maybe for a few locations, not for most continents, including Africa, North America, South America, Australia and Antarctica.
Mexico, California, Oregon, Washington are all arid areas.

The Atacama desert is adjacent to the South Pacific. It is considered the driest location on Earth.

The plain facts of the matter is that air does not efficiently heat up water enough for H₂O to phase transition to vapor.
The same goes for most light wavelengths.

Cold oceanic waters are terrible at increasing atmospheric water vapor.

Joe Bastardi postulates that El Niño is a water vapor pump and that is the source for most of the water vapor increases in the Arctic.

April 14, 2023 4:12 am

The models also neglect the speeding up of the hydrological cycle. The energy absorbed by additional water vapor is likely trivial compared to the movement of latent heat. If the water spends less time in the air, it also absorbs less heat.

Richard M
Reply to  aaron
April 14, 2023 5:12 am

When that hydrological cycle is sped up water vapor is driven higher into the atmosphere where more water vapor is condensed. This reduces the high altitude absorption of energy (greenhouse effect). This is what leads to less heat.

April 14, 2023 5:28 am

Mount Pinatubo also increased cloud cover by increasing phytoplankton populations. Phytoplankton create cloud condensation nuclei and change ocean surface albedo, increasing evaporation – their effect is pretty similar to trees.

Their populations are constrained by lack of minerals, especially iron. Volcano ash provides iron, spurs their growth.

This growth is sometimes masked in satellite ocean primary productivity data because they produce clouds that the satellites can’t see through.

Reply to  vboring
April 14, 2023 8:26 am

Enhanced plant growth also likely lead to increased water capture on land.

April 14, 2023 5:30 am

There has been an increase in cloud cover in the arctic where sea ice has declined.

Reply to  aaron
April 14, 2023 5:40 am

Recent paper increased uncertainty.

E. Schaffer
April 14, 2023 7:33 am

Clouds not only affect SW, but also LW radiation and contribute to the GHE. As I have pointed out, their LW effect exceeds their SW effect, so that they are actually warming. Anyway, both sides will have to be considered.

The article claims..

(241.667-238.696) * 0.27 = 0.8K

in SW forcing. The obvious question is, where is the LW forcing?

Reply to  E. Schaffer
April 14, 2023 7:41 am


Reply to  E. Schaffer
April 15, 2023 4:51 am

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

Reply to  E. Schaffer
April 17, 2023 12:44 pm

The 0.27’C/W/m^2 factor assumes no or very little CO2 forcing. With that assumption LW out is proportional to SW in so the SW in factor is all that’s needed to calculate temperature. If CO2 forcing (or any other forcing) is significant another factor is needed and as you pointed out the LW out would play a role in the factor.

April 14, 2023 7:34 am

At least with CO2 we were near a saturation point. With this analysis if I see it correctly heating results in less cloud cover which produces more heating and less cloud cover …….

Reply to  DWM
April 17, 2023 12:32 pm

Almost right. The reduction in cloud cover (and heating) stops when local ET decrease stops. Likewise, if local ET increases the temperature will decrease. Cloud cover is the Earth’s thermostat.

Clyde Spencer
April 14, 2023 8:56 am

None of Figure 1 graphs is a flat line any theory on GW should account for all these observations.

What is this sentence trying to say?

Neo Conscious
April 15, 2023 6:49 am

My layman takeaway is that clouds impact climate to a significantly greater degree than CO2. Factors that increase cloud formation (dust, smoke, and solar charged particles) lead to increased reflection of solar radiation and decreased solar heating of the earth’s surface. Factors that decrease cloud formation such as decreased evaporation from deforestation lead to increased solar heating.

Deforestation resulting from human clearing of forests for grazing and agriculture has been ongoing since the discovery of fire usage several hundred thousand years ago, and that is likely what led to the end of the Last Glacial Period.

However, increased carbon sequestration into our ecosystem from fossil fuels is leading to increased plant growth which will impact cloud formation. More plants will lead to increased evapotranspiration and cloud cover. Additionally, the increased accumulation rate of dead plant material is leading to increased smoke from wildfires that will also increase clouds, and thus eventually lead to global cooling.

Josh Scandlen
April 20, 2023 3:39 pm

Of course cloud cover has something to do with global warming. That’s why they’re doing chemtrails, ahem, Geo-seeding.

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