CO2 is Innocent but Clouds are Guilty. New

Key to me is “…Why is albedo change important?  Because the IPCC theory of CO2 effect on GW assumes that the earth’s albedo has been constant (or not changed much) and CO2 (and other greenhouse gases) thru Radiative Forcing effect GW.  The resent satellite data says this is not true...”

CO2 is Innocent but Clouds are Guilty.  New Science has Created a “Black Swan Event”**

2022  November 23

Charles Rotter

By Charles Blaisdell PhD ChE  

**From web sources: “,… in 1697 the Dutch explorer Willem de Vlamingh discovered black swans in Australia, upending the belief” (that all swans were white) “and transforming how we understand the natural world.  …the phrase “black swan event” came to refer to an event that suddenly proves something that was previously thought to be impossible.”

(This paper is a continuation of my previous paper   (1) with new data that reaches the conclusion that “CO2 is innocent but Clouds are Guilty” )

Part I:  CO2 is Innocent but Clouds are Guilty.

     Our tax dollars have been at work with NASA for the last 20+ years putting satellites in orbit to detect and measure the “CO2 effect” on Global Warming, GW.  After 20 years, the CERES satellite (and others) has discovered that cloud reduction is the major effect on GW for those 20 years. Two papers published in 2021 reach this conclusion, Dübal and Vahrenholt,  (2) and. Loeb, Gregory et al  (3) 

These new papers do claim some sign of CO2 effect (and other greenhouse gases) on GW; but the papers show the dominate effect on GW for those 20 years was the cloud reduction effect (albedo reduction- warming).   This paper will show that the observed cloud reduction will account for all the GW in those 20 years and back to 1975, leaving no GW left over for the CO2 effect on GW. Cloud reduction is albedo reduction, (albedo: color of the earth, black, 0.0, is hot and white, 1.0, is cool).  Another recently published paper (2021) by Goode et al (4) measuring earth’s albedo from moon shine also reports the same reduction in albedo as the CERES data of both Dübal and Loeb:  one can only conclude that for 20 years of data the albedo change is real.   

Why is albedo change important?  Because the IPCC theory of CO2 effect on GW assumes that the earth’s albedo has been constant (or not changed much) and CO2 (and other greenhouse gases) thru Radiative Forcing effect GW.  The resent satellite data says this is not true.   Cloud cover changes are best documented at “Climate and Clouds”(5) with links to the data source at “Climate Explorer” (6).  “Climate and Clouds” conclude that cloud change only accounts for 25% of the GW.  This paper will show an improved analysis of “Climate and Clouds” data agrees with the CERES data of Dübal and Loeb that cloud reduction is accounting for most if not all of the warming over CERES’s 20 years.  Figures 1 and 2 show a graphic representation of what Dübal and Loeb observed in the CERES data and what was expected from IPCC Radiative Forcing, RF, theory.  The shape (slopes) of the observed and expected are entirely different but the increase in the missing energy (Earths Energy Imbalance, EEI) is the same.  The missing energy, EEI, is used to warm the earth though the energy balance equation:

Energy In = Energy out + Accumulation (EEI)    Eq 1.

If the accumulation (EEI) is positive the earth warms if negative the earth cools.

     Cloud reduction effects GW by reducing the amount of highly reflective clouds covering the earth and letting in more sun light to warm the earth, Cloud Reduction Global Warming, CRGW.

Is Cloud Cover Changing?

     Yes, Cloud cover changes with seasons, hemisphere, altitude, and over time. Figure 3 shows the satellite data for cloud cover for the whole earth vs time (about 36 years).  The sine-al nature of the graph is a seasonal variation shown in Figure 4.  Figure 5 shows the hemispherical differences in cloud cover.  The hemispherical and seasonal variation in cloud cover is related to the tilt of the axis (23.5’ north) of the rotating earth favoring the northern hemisphere with more sun light and the larger land mass of the northern hemisphere (Total land mass of the earth is 39% of that 68% is in the northern hemisphere and 32% in the southern hemisphere).  It will be later shown that, these variables change the relative humidity which are responsible for the sine-al nature of the cloud cover.

     For global warming the change in cloud cover over years is the variable of interest.  The whole earth’s cloud cover (least squares fit from “climate Explorer” data) vs time in Figure 5 show a 0.075 % cloud change/year.  Note the high degree of variability in Figure 5, some of this variability is theorized by Dübal and Vahrenholt,  (2) to be due to the AMO (Atlantic Multi-decadal Oscillation) in the northern hemisphere which is a natural oscillation in ocean temperature with a period of 60-80 year and an amplitude of +/-0.2’C.  (a period up swing of the AMO occurred in the 1985 to 2020 range and could be related to the peak in 1997 and flatting after 2000 in Figure 5).  There is also a periodic swing in ocean temperature in the Pacific, PDO (Pacific Decadal Oscillation) in the southern hemisphere (commonly known as El Nino) with a period similar to the AMO of 60-80 years and a smaller amplitude of about +/- 0.1’C. The amplitude of each of these oscillations is smaller than the overall change in temperature and are not increasing over time.  The periods of AMO and PDO seem to be opposite and may have some canceling effect on a global basis.  Further explanation of these oscillations are best left up to the experts, in this paper, they are just potential noise makers to the cloud reduction data and emphasize the importance of long term data (the 36 years of cloud data may not be enough).  The 36 year cloud cover decrease of 0.75% per decade will be used in calculations of cloud effected energy changes.

     One more variable that needs to be considered in temperature vs cloud cover:  Time delay, when clouds decrease part of the sun light fall on land and the rest on water.  Land gives its energy back to the atmosphere quickly (over days), over water the energy is stored for years.  Some have calculated up to 80 years for a step change in energy into the ocean to come the full equilibrium  (20) and (21).   This time delay is another reason to use long term slope date to analyze cloud change data.  Our current 36 years of cloud data is probably not enough to complete our understanding of cloud cover and GW. It should be noted that surface sea temperature, SST, follows air temperature closely, questioning the significance of the time delay.

Cloud Cover Change vs Temperature Change

     An empirical way to relating cloud cover to temperature is to divide the least squares fits of the temperature change by cloud reduction change over the 36 years of data.  Figure 6 shows both least squares fits with the result of the ratio being -0.27 ‘C/% cloud change.  “Climate and Clouds”(5)  scatter plot of monthly temperature and cloud cover of the same data showed a least squares fit of -0.066 ‘C/% cloud cover; further emphasizing the need to use long term data to better understand cloud and temperature relationships.    [“Climate4You” (5) web site is a product of ISCCP:  (“Since July 1983, ongoing variations in the global cloud cover have been monitored by The International Sattelite Cloud Climatology Project (ISCCP). This project was established as part of the World Climate Research Program (WCRP) to collect weather satellite radiance measurements …”.) ] The “Climate4You” ratio only accounts for 25% of the observed ( 0.4 ‘C) 20 years of CERES data.  Figure 6’s -0.27 ‘C/% cloud cover accounts for all of the observed temperature change.

     Although significant, this ratio of temperature and cloud cover change is not the best way to prove the significance of cloud cover change.  The CERES data is energy data, cloud cover change must be related to CERES energy observations. Table 1 converts the observed albedo from Dübal (2) to energy change (Short Wave, SW, in – SW out at Top Of the Atmosphere, TOA) and is shown in Figure 7.  Table 2 uses the cloud cover from “Climate Explorer” least squares fit in Figure 5 and the Dübal “cloudy area” and “clear sky” albedo data to calculate the energy to the earth, the results are shown in Figure 7.  The comparison of the two calculation is close enough to claim: the cloud cover change can account for all the temperate change and energy change observed in the 20 years of CERES data.

     (Note: in Table 2 Dübal observed a small (but significant) change in the “Clear sky” albedo (decreasing). The “clear sky” albedo is the ground (land + ocean) color of the earth.  Holding the cloud change constant shows this small albedo “clear sky” albedo change can account for 15% of the observed energy in the 20 years of CERES data.  Cloud change is the major effect on GW)

Why a 1975 Zero for the CERES data?

     Many researchers have noticed that the temperature vs time curve since 1880 is not linear, the data better fits an exponential or 2ed degree polynomial.   One can also use two linear equations to fit the data, as shown in Figure 8.  The intersection of the two lines is about 1975.  The lower line has a poor R^2 and accounts for about 25% of the temperature rise.  The second line has a much higher R^2 and account for about 75% of the rise.  We have a lot more data in the 1975 to 2020 range so we should have a better chance of explaining GW in that range.

     The extrapolated data and 20 years of CERES data in Figure 7 are overlayed on Figure 8 – a good fit.  Table 1 and 2 show that albedo change and cloud cover change from 2001 to 2020 and from 1975 to 2020 can account for all the temperature change in each period.   CRGW is a valid theory and should be considered by the IPCC.

How did this significant change in scientific understanding occur?

     The 2021 papers by Dübal, Loeb, and Goode (and some others) verifying a 20-year change in the earth’s albedo is like a scientific “Black Swan Event” **.  The earth’s albedo and cloud cover changing over time was totally unexpected (“all swans are white”).  Albedo change being caused by cloud cover reduction was also unexpected prior to 2021.  All previous methods of measuring albedo and cloud cover showed no change.  There were modelers like Walcek (7) who predicted that if cloud cover changed it could be as significant as the predicted greenhouse gas GW.  The effect of greenhouse gases could be measured in the lower atmosphere and was known to be saturated (all ready enough, more would not change GW).  The IPCC needed a theory that could account for the observed GW with constant albedo and cloud cover – That theory was Radiative Forcing, RF.  RF is a plausible theory but needed to be measured in the upper atmosphere.  NASA sent up satellites to measure the RF (along with many other thing).   NASA’s satellites changed the method of measurement and the accuracy and with 20 years of data could see the small differences in big numbers needed.  And here we are today trying to get the IPCC to look at the “Black Swan”.

     Models are also a contributing factor.  There are climate models that use scientific laws and math (like IPCC’s Global Circulation Models, GMC’s) to calculate GW and like the simple models in Tables 1 and 2.  Other models use statistical multi variable analysis, SMVA, to predict GW.  The use of SMVAs can lead to some inaccurate conclusions.  Good multi variable analysis design an experimental grid to avoid confounded variables, it is difficult to do this with natural data.  In the case of GW, Cloud cover, relative humidity, albedo, specific humidity, CO2, and other GHGs are all confounded with the earth’s temperature change.  Variables with high accuracy in measurement and definite trends, like CO2, will dominate in SMVAs, even if they have nothing to do with GW.  Variables with poor measurement but good trends (but are the real effect on GW), like cloud cover, will show significance in SMVA’s but not eliminate variables like CO2.  Results from a SMVA are not a proof.  The IPCC’s SMVA model has a “dog’s breakfast” of variables in its AR6 model of GW, in AR6 cloud cover is not listed, but cloud density is, as a global cooling variable.   In all fairness, AR6 was issued in 2021 the same time at the Dübal and Loeb papers  – they may be looking at them now.

What is causing the reduction in Cloud Cover?

     Cloud cover is part of the earth’s water cycle:  the sun’s energy evaporates water, the water vapor makes clouds, and clouds make rain.  We are looking for a disturbance in this natural cycle

The water cycle variables that are a signature of cloud cover change:

Long term Signature of Cloud Cover Reduction

1.     Temperature increasing (less cloud cover – more sun’s energy to the earth, see Figure 8)

2.     Specific Humidity increasing (a result of higher temperature and more evaporation the atmosphere can holding more water, see Figure 9)

3.     Rain fall increasing (more energy in evaporates more water, (if not used for specific humidity increase) the water got to come back down.  A statistical increase has been observed but very low R^2 – graph not shown)

4.     Relative humidity decreasing (main effect on less clouds which leads to the other atmospheric variables, see Figure 10 and Figure 12)

     This is a unique set of atmospheric variables only associated with cloud reduction.

Relative Humidity and Cloud Reduction

     Relative Humidity, RH, has for a long time been associated with clouds.  Figure 11 show a page from Walcek (7)  1995 report which show the decline in cloud cover vs RH observed by him and other researchers.  The trend is there but the noise level is high.  Satellites have improved the observation.   “Climate and Clouds”(5) shows that different types of clouds form at different levels and that their formation may be triggered by things other than RH.  Particulates (aerosols) and cosmic rays have been documented as sources of cloud formation.  Even at 100% RH air can become super saturated and not form clouds.  All the variables are probably responsible for the noise in Figure 11; but the general trend is RH.  Of the three categories of clouds mentioned in “Climate and Clouds”(5) The only one that showed a significant reduction over time was the “Low Level” clouds, cumulus clouds.  Cumulus clouds are about 28% of the total 63% cloud cover of the earth.  The other clouds only create noise in the total cloud cover data.  In “International Satellite Cloud Climatology Project” (8) Cumulus clouds were the only cloud types of nine types of clouds that showed reduction over time, see Figure 13.   

     Cumulus clouds are the ones most affected by changes in RH from the earth surface in that they are the ones in contact with low RH air first.   The data in Figures 4, 5, and 6 contain all cloud types, but the yearly oscillations are related to similar changes in “low level” clouds and RH with time.  These oscillations can be used to make a plot of RH vs cloud cover for all the monthly data in Figure 3 to produce the scatter plot in Figure 14.  The data points used in the model in Table 2 are in red.  Note that these points are within the range of the natural variation of the data.

     The data in Figure 14 can be broken down into more detail to show the difference in monthly profiles between Northern Hemisphere (NH), Sothern Hemisphere and Time shift, see Figure 15.  Note the expected difference in shape of the NH and SH plots, in some months they cancel each other and in other complement each other giving the overall results in Figure 4.  In Figure 15 the cloud change in the Southern Hemisphere is greater than in the NH and the Sothern Hemisphere somewhat dominates the overall cloud change.  All the plots shift with time as the relativity humidity decreases.

The Missing Energy in the Earth’s EEI, Eq 1

     The missing energy in Figure 1  can go to the following paces  see Table 7 for details:

·      Warm the dry air in the atmosphere.  (Small but significant)

·      increase the moisture in the atmosphere and is the major use of EEI energy (specific humidity, Figure 9 and Table 7)

·      increase precipitation (small)

·      warm the land (small)

·      warm the oceans (small, with a time delay)

The bulk of the energy goes into water increase in the atmosphere.

    The Dübal and Loeb data can be used to estimate a degrees Celsius / W/m^2 energy change from short wave energy change of 0.3 ‘C per W/m^2.

Conclusion So Far

     There is no doubt that albedo of the earth has changed over the last 20 years (and longer) and that this albedo change is due to cloud cover reduction (and a little “clear sky” albedo change).  The cloud cover reduction is related to relative humidity reduction.  Relative humidity reduction has been going since 1948 (possible longer).  The cloud reduction data (starting in 1984) has been extrapolated back to 1975.  Cloud reduction has been around for a while.  CO2 is innocent but cloud cover reduction is guilty.  Leaving the question:

Part II.  Cloud reduction effects GW but ‘Man” is still Guilty.

What is affecting the Relative Humidity reduction?

 The observation of relative humidity decreasing (see Figure 10 and 12) has long puzzled climate scientist.  Most climate models show specific humidity increasing (which it does) and relative humidity staying the same.  Papers by J. Taylor (9) and K.  Willett (10)  both express that increasing SH and decreasing RH is inconsistent with CO2 (and other GHGs) effect on GW with no explanation as to why.  This paper gives an explanation.

     The theory Cloud Reduction Global Warming, CRGW, has been proposed (1): “Man’s changes to land use effects the production of low relative humidity, RH, hot air rising to where clouds could be prevented (or destroyed) thus reducing the albedo of the earth”.  This reduction in RH is triggered by a localized reduction (not an increase) in Specific Humidity, SH.  This reduction in SH is occurring only on land and is over whelmed by the increase in SH from evaporation (from oceans) due to the lower Cloud Cover, CC.  The relationship between SH, RH, and CC has a very large natural amplification factor.

  The key to CRGW is water evaporation, transpiration, or run off on land.  When water (rain or snow) falls on the land it can soak into the ground or run off.  On land when ground water is not available the relative humidity drops.  In any man-made structure that covers the virgin land prevents water from soaking in and increases the Run Off, RO.  When water is not available for Evaporation or Transpiration, ET, the relative humidity drops.  (ET is sometimes called Evapotranspiration.)  Some man-made effects (anthropological global warming, AGW) sources of relative humidity reduction are:

·      Cities

·      Any man-made structure that covers the natural ground

·      Forest to farm land or pasture land

·      Pumping water from aquifers

·      Forest fire land change.

·      Flood water prevention like dams and levees.

·      I am sure there are others

     Figure 13 shows a very good depiction of the water cycle on earth.  Of interest to the CRGW theory is the land part showing rain fall, evaporation, transpiration, and run off, RO.  Note that the rain fall is the sum of the evaporation, transpiration, and run off.  The evaporation is from water that has soaked in to the ground.  Transpiration is water that evaporates through any kind of vegetation, trees have the highest.  In the land water balance if any one of these changes it effects the others.   An example: if the virgin land is cover with asphalt or concrete that prevents water from soaking in to the ground where vegetation can evaporate the water then the water will run off and the ET will decrease.  Another example: If a forest is replaced with farm land or pasture the forest’s ground cover no longer holds water.  The crops that replaced the forest are only growing part of the year and do not have the leaf area as the tree’s many leaves and deep roots, all this decrease the ET.  A decreasing ET increases the Run Off, RO.  This change in ET sets in motion a series of events on land where ET has been restricted:

1.     RO increase making ET decrease, this lowers the local specific humidity, SH (SH % change is another measure of ET % change).  The land-based change in RH vs SH is shown in Figure 17, showing a 21:1 ratio.  Table 6 adjusts this relationship for the whole earth, down to 6.2 : 1.

2.     As the ET decrease, this creates low relativity humidity, RH, air with SH change equal to the change in -RO(%) and +ET(%).  In this step the RH and SH both decease.  Note at this point the local SH decrease is opposite the observed global increase.

3.     As the low humidity air rises (to where clouds form) the relative humidity, RH, drops even further. Another amplification occurs, 4.58 : 1, Figure 10 slope ratios, SH 850mb/SH 1000 mb..

4.     The low RH air spreads around the world (mainly off shore) and reduces cloud cover, CC,  this process has the smallest of amplifications, 1.2 : 1, In Table 3 dif. CC/dif. RH 850mb.

5.     Less CC lets more sun’s radiation in.  Table 6 shows the product of all these amplifications to be  34:1 change in CC per change in SH through this series steps of RH changes.

6.     More radiation warms all earth’s surfaces.  On land more radiation makes the relative humidity even lower.

7.     On the oceans the radiation increase warms the water and evaporates more water increasing the global specific humidity, SH.  This increase is greater than the local land decrease in SH resulting in the observed increase in SH.

8.     The result is the rising SH and dropping RH.  The localized short term ET effects are not seen on a yearly basis.  (Figure 1 in (1) shows city examples)

     This list of events is better seen by Figure 18.  Figure 18 is a blowup of a small part of a Psychrometric Chart, PC, that best describes the earth’s atmosphere.  Table 3 is a list of all the least squares fit data (from Figures in this paper) that are used to show the CRGW theory is valid.  Table 4 puts this data in to a “Free on Line PC “, (11) to test the fit of actual 1975 and 2020 data to calculated data from (11).  The low difference in Table 4 shows a good fit.  The “Free on-Line Psychrometric Chart” is a good calculator for atmospheric changes.

Estimating ET changes

     ET is somewhat like cloud cover; It varies a lot from season to season, hemisphere to hemisphere, and with land mass.   What we are looking for is small changes over time (smaller than the cloud changes – remember the amplification factor).      

     The easiest to explain change is ET is Cities or better known as Urban Heat Islands, UHI’s,   UHI’s got their reputation as heat island due the higher temperature from lower albedo and lower water, SH.  The effect on RH was not appreciated until this paper and the previous paper.  UHI’s temperature, SH, and RH behavior is predicted by a PC in (1).  The UHI’s change in ET is related to run off, RO, increasing, (if precipitation cannot soak into the ground, it runs off and is not available for ET).  The earths land surface is covered by 3% urban development, and about half of the population lives there.  The structure that the other half lives in also covers the earth with roof tops and drive ways that do not allow water to soak in and also increasing the RO.  That gives 6% of the earths land mass having an effect on the RO and ET.   The amount of RO change for UHI’s is hard to find data on but a lab experiment by U of Colorado College of Eng. (16) shows a 20%-30% increase in RO.  Table 5 uses 6% of land coverage and 25% change in RO.

     RO changes from land use changes are also hard to find.  One of the best reports on land change from satellite data is by Winkler K. et al (16)  with data claiming 32% of the land has been changed by man.  Most changes were virgin land to crop or pasture, some was reclaimed pasture back to forest.  Run Off in the Mississippi river basin computer simulations is documented by Tracy E. et al (18) showing a range of RO from +45% to -25% depending on what was being converted to what.     McMenemie C. (19)   paper singles out damming rivers and putting in levies to prevent flooding to have a significant effect on ET. 

     Depleting aquifers effects the water table to lower and reduce the water available for ET thus making more low RH air.  Most ground water from aquifers is recycled back as recharge yet the earths aquifers are decreasing.  According to the web “Typically, 10 to 20 percent of the precipitation that falls to the Earth enters water-bearing strata, which are known as aquifers.”  The 10 to 20 percent may not be shown accurately in Figure 16 (it is possible it is part of the RO).  No data could be found on the total earth effect of lower ground water on ET or RH – it should not be insignificant.  

     The study of ET is a relatively new monitoring field.  Most paper on the subject at less than 10 years of data and the emphasis of the studies are usually water management or carbon sequestration very little atmospheric specific humidity  or relative humidity data is reported.  Some satellite data is just started (< 5 yrs).  The results of current papers are not consistent.  Some examples: BaolinXue et al (23)   shows no change in 20 years from rural land-based stations in the FLUXNET dataset (as expected on non-land change areas and not urban data).  Samuel Zipper et al  (24) have a short (4 year) but very good study of Madison Wi USA UHI showing a 5%/year drop in ET in a 20 km^2 radius of Madison central (not a statistically significant time).  Qingzhou Zheng et al (25) study of a whole water shed (110 km^2) in China that included forest, crop-land, baron land, rivers, wetlands, and cities showed a 7% reduction in ET in 13 years, with the main factor being urban expansion.

     This paper will use estimates of land change RO that is in the range of the publish data as shown in Table 5, 30% land coverage and 10% RO change.  The man-made structure RO (% of global) added to the RO (% of global) totals 1.3% (% of global) (or -ET% change).   It is not the intension of this paper to be an expert on ET change only to show this change can account for all the GW at  1.3% (% of global) from 1975 to 2020.

Explanation of Figure 18 model of the Path taken by the 1975 to 2020 climate change.

      Figure 18 is a very blown-up Psychrometric Chart showing the chain of effects (the path) to the final 1975 to 2020 observed climate change.  The parallel energy (all atmospheric changes occur at constant energy or a shift in energy) lines are established from the observed 1975 and 2020 data of 33.54 kJ/kg(da) for 1975 and 35.72 kJ/kg(da) 2020.  The starting point is the 1975 SH at 7.7 g/kg(da) on the 33.54 kj/kg(da) energy line.  The ET change of -1.3% (above) is about -0.1 SH change shown at 7.6 g/kg(da) in the Figure 18 model.   The 34:1 natural amplification of this SH change (through RH changes) results in the CC reduction (see Table 6) and the energy shift to the 2020 parallel energy line of 35.72 kJ/kg(da).  The hot low RH air evaporates water (increase SH), cools the air and increases the RH to the 2020 end point.  This technique of tracing an energy, temp, SH, and RH path is standard in engineering heating cooling design.

Conclusions on the Effects of Relative Humidity Reduction Over Time.

     The high sensitivity of SH to RH to CC is a natural phenomenon.  The CC variation in Figure 3 is natural (effected by tilt of the earth and larger land mass in the NH) tracked by RH and SH.   These natural CC variations are greater than the CC reduction observed.   The natural laws used in the Psychrometric Chart show the high sensitivity of RH to SH.  Using an estimate of RO in the range of publish data shows a good fit to observed data.  This explains the rising SH and decreasing RH.

     The modelers of the 1990’s where on the right track – if clouds change the results would be as strong as the that expected from CO2.  The IPCC should evaluate CRGW theory. 

Figures and Tables

Figure 1,  Graph of what Dübal and Loeb both observed (all energy is TOA).

Figure 2,  Graph of what was expected in the 20 years of CERES data based on IPCC Radiative Forcing theory of greenhouse gases.

Figure 3, Cloud Cover over 45 years from “Climate and Clouds”(5) shows the seasonal and time reduction in global cloud cover.

Figure 4, Breaking down Figure 3 data by average/month shows a low in cloud cover in the summer month (NH) and a high in the winter months (NH) due to larger land mass and axis tilt in NH vs SH.  Later Figures shows the hemispherical contribution to this Figure.   Relative Humidity from “NOAA Physical Science Laboratory” was added to show the good fit.

Figure 5, From “Climate Explorer” (6).  Showing the difference in hemispherical cloud cover due to northern hemisphere getting more sun than the southern hemisphere.  All data is 3 year smoothed.

Figure 6, Combine satellite data for cloud cover and temperature on graph to get a -0.27 ‘C/% cloud cover ratio.  All data is 3 year smoothed.

Figure 7,  Observed from Albedo change and Calculated from Cloud Cover Change in the 20 years of CERES data.  A very good match.

Table 1, Albedo Change Model from Dubal (2) data.  Extrapolation to 1975.

Table 2, Calculated energy change using Cloud change data from “Climate Explorer” and Dübal data for “cloudy area” albedo and “clear sky” albedo.

Figure 8, Dividing Temperature vs Time into two parts and overlaying SW energy change from albedo and clouds.  Good fit to 1975 to 2020 data. All data is 3 year smoothed.

Figure 8a,  1975 to 2020 part of Figure 8 with actual data, least squares fit used for calculation.  All data is 3 year smoothed

Figure 9, Specific Humidity vs time, note the break point at 1975.  All data is 3 year smoothed

Figure 10,  Relative Humidity vs year for ground level and cloud level.  Cloud level RH much more sensitive than ground level RH.  Cloud level RH will be used.

Figure 11 Copy of page from Walcek (7) showing the 1995 correlation of clouds and RH.

Figure 12,  Relative Humidity at 850 mb (cloud level) vs time.  Note difference between NH and SH.  Good correlation, RH has been changing for a long time.

Figure 13,  “International Satellite Cloud Climatology Project” (8) of just Cumulus cloud cover over 27 years.  Cumulus clouds were the only ones changing of 9 cloud types studied.

Figure 14,  Scatter plot of all the monthly data in Figure 3 and 12 to obtain a correlation between RH and Cloud Cover.  Red dots are data used in the Model in Table 2.

Figure 15,  Monthly plot of cloud cover in both northern hemisphere and southern hemisphere.

Figure 16.  A good diagram of the water cycle on earth from Trenberth et al (13)

Figure 17.  Relative Humidity vs Specific Humidity the average slopes, 21, will be used in Table 6 to show the natural change in RH/SH.

Figure 18,  Path of energy and Specific Humidity, SH, change that accounts for the observed 1975 to 2020 change.


1.     “Where have all the Clouds gone and why care? “ web link:  Where have all the Clouds gone and why care? – Watts Up With That?  

2.     “Radiative Energy Flux Variation from 2001–2020” by Hans-Rolf Dübal and Fritz Vahrenholt  web link:  Atmosphere | Free Full-Text | Radiative Energy Flux Variation from 2001–2020 | HTML (

3.     “Satellite and Ocean Data Reveal Marked Increase in Earth’s Heating Rate” by 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

4.     “Earth’s Albedo 1998–2017 as Measured From Earthshine”  by P. R. Goode,E. Pallé,A. Shoumko,S. Shoumko,P. Montañes-Rodriguez,S. E. Koonin  First published: 29 August 2021  web link:  Earth’s Albedo 1998–2017 as Measured From Earthshine – Goode – 2021 – Geophysical Research Letters – Wiley Online Library

5.     “Climate and clouds” by web site  link    climate4you ClimateAndClouds

6.     Climate Explorer web site  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.

7.     “Clouds and relative humidity in climate models; or what really regulates cloud cover?”  by Walcek, C. web link Clouds and relative humidity in climate models; or what really regulates cloud cover? (Technical Report) | OSTI.GOV

8.     “International Satellite Cloud Climatology Project” Web page:  ISCCP: Climate Analysis – Part 7 (

9.     “Declining Humidity Is Defying Global Warming Models”  by James Taylor  web link  Declining Humidity Is Defying Global Warming Models (

10.“Investigating climate change’s ‘humidity paradox’”  by Dr Kate Willett  web link How is climate change affecting global humidity levels? | World Economic Forum (

11.“Free Online Interactive Psychrometric Chart”  by  Free Online Interactive Psychrometric Chart web link:  Free Online Interactive Psychrometric Chart (

12.“NASA Physical Sciences Laboratory” web site:  Monthly Mean Timeseries: NOAA Physical Sciences Laboratory

13.”Atmospheric Moisture Transports from Ocean to Land and Global Energy Flows in Reanalyses” by Kevin E. Trenberth1, John T. Fasullo1, and Jessica Mackaro1  web link:  Atmospheric Moisture Transports from Ocean to Land and Global Energy Flows in Reanalyses in: Journal of Climate Volume 24 Issue 18 (2011) (

14.“Met Office Climate Dashboard”  web link  Humidity | Climate Dashboard (

15.“Vital Signs”  Web link  Global Temperature | Vital Signs – Climate Change: Vital Signs of the Planet (

16.“Natural and Urban “Stormwater” Water Cycle Models” by U of Colorado college of Engineering web site  Natural and Urban “Stormwater” Water Cycle Models – Activity – TeachEngineering

17.“Global land use changes are four times greater than previously estimated” by  Karina Winkler, Richard Fuchs, Mark Rounsevell & Martin Herold  web site:  Global land use changes are four times greater than previously estimated | Nature Communications

18.“Effects of Land Cover Change on the Energy and Water Balance of the Mississippi River Basin”  by Tracy E. Twine1, Christopher J. Kucharik2, and Jonathan A. Foley3 web link Effects of Land Cover Change on the Energy and Water Balance of the Mississippi River Basin in: Journal of Hydrometeorology Volume 5 Issue 4 (2004) (

19.“Reasons for Increase in Global Mean Temperature and Climate Change”  By Conor McMenemie web site:  Reasons for Increase in Global Mean Temperature and Climate Change (

20.“How to Heat a Planet? Impact of Anthropogenic Landscapes on Earth’s Albedo and Temperature Mark Healey Lindfield”,  Web file:

21.“Analogy 04 Ocean Time Lag”  by Skeptical Science  web link  SkS Analogy 4 – Ocean Time Lag (

22.“Effect of groundwater pumping on the health of arid vegetative ecosystems” by Victor M. Ponce web link effect_of_groundwater_pumping.pdf (

23.Global evapotranspiration hiatus explained by vegetation structural and physiological controls by BaolinXue et al  web link Global evapotranspiration hiatus explained by vegetation structural and physiological controls – ScienceDirect

24.Urban heat island-induced increases in evapotranspirative demand by Samuel Zipper et al web link  (PDF) Urban heat island-induced increases in evapotranspirative demand (

25.Effects of Urbanization on Watershed Evapotranspiration and Its Components in  Southern China  by Qingzhou Zheng web link Microsoft Word – water-713292.docx (

Willie Soon speaks at the University of Chicago

The article has links to both his presentation and to the slides. It has hard to see the slides in the presentation.

By Andy May

Dr. Willie Soon gave a great presentation at the Federalist Society Chapter at the University of Chicago Law School on November 18, 2022. The title of his talk is:

“The Corruption of Environmental Rulemakings at the US EPA: Climate Change, Mercury Emissions, and Air Quality”Willie Soon, 2022

Dr. Soon’s slide deck is excellent reading and he has kindly sent it to me, you can download it here. If you prefer to watch his presentation, you can do so on YouTube here. Soon’s presentation starts about 22:46 minutes into the video.

Soon’s key points:

  • Given the daily, seasonal, and annual range of temperatures around the Earth, the warming of the past 125 years is trivial.
  • Except for ENSO variations, the global average surface temperature has hardly changed in over 20 years.
  • Willie humorously dismantles the article on him in Wikipedia and Gavin Schmidt’s criticisms, these slides are worth the download!
  • Willie plugs the article he wrote with 23 co-authors entitled: “How much has the Sun influenced Northern Hemisphere Temperature trends? An ongoing debate.” Seriously, this is probably the best climate change article written in the last thirty years in my humble opinion, I refer to it all the time. The bibliography alone is worth it. If you never read another climate article in your life, you should read this one. Download it here.
  • He destroys the Mercury pollution nonsense that is permeating the media. Possible spoiler, don’t drink Coca Cola!
  • Is it air pollution or weather?

Finally, President Dwight Eisenhower’s warning about “public policy [becoming] the captive of a scientific-technological elite” was correct:

“It is time to face a hard truth: the seventy-year experiment to federalize the sciences has been a failure. The task now is to prevent the Big Science cartel from further dehumanizing society and delegitimizing science. There is a second hard truth: the necessary reforms will not come from within. Rather, it will be the people and their representatives that will have to impose them. To restore science to its rightful and valuable place, break up the Big Science cartel.”(J. Scott Turner, Professor of Biology (emeritus), SUNY College of Environmental Science and Forestry, December 10, 2021)

I wish I had said that.

A Technical Study of Relationships in Solar Flux, Water and other Gasses in the upper Atmosphere, Using the October, 2022 NASA & NOAA Data

From the attached report on climate change for October 2022 Data we have the two charts showing how much the global temperature has actually gone up since we started to measure CO2 in the atmosphere in 1958? To show this graphically Chart 8a was constructed by plotting CO2 as a percent increase from when it was first measured in 1958, the Black plot, the scale is on the left and it shows CO2 going up by about 32.4% from 1958 to October of 2022. That is a very large change as anyone would have to agree.  Now how about temperature, well when we look at the percentage change in temperature also from 1958, using Kelvin (which does measure the change in heat), we find that the changes in global temperature (heat) is almost un-measurable at less than .4%.

As you see the increase in energy, heat, is not visually observably in this chart hence the need for another Chart 8 to show the minuscule increase in thermal energy shown by NASA in relationship to the change in CO2 Shown in the next Chart using a different scale.

This is Chart 8 which is the same as Chart 8a except for the scales. The scale on the right side had to be expanded 10 times (the range is 50 % on the left and 5% on the right) to be able to see the plot in the same chart in any detail. The red plot, starting in 1958, shows that the thermal energy in the earth’s atmosphere increased by .40%; while CO2 has increased by 32.4% which is 80 times that of the increase in temperature. So is there really a meaningful link between them that would give as a major problem?

Based to these trends, determined by excel not me, in 2028 CO2 will be 428 ppm and temperatures will be a bit over 15.0o Celsius and in 2038 CO2 will be 458 ppm and temperatures will be 15.6O Celsius.

The NOAA and NASA numbers tell us the True story of the

Changes in the planets Atmosphere

The full 40 page report explains how these charts were developed .

The Dirty Secrets inside the Black Box Climate Models

By Greg Chapman
“The world has less than a decade to change course to avoid irreversible ecological catastrophe, the UN warned today.” The Guardian Nov 28 2007
“It’s tough to make predictions, especially about the future.” Yogi Berra
Global extinction due to global warming has been predicted more times than climate activist, Leo DiCaprio, has traveled by private jet.  But where do these predictions come from? If you thought it was just calculated from the simple, well known relationship between CO2 and solar energy spectrum absorption, you would only expect to see about 0.5o C increase from pre-industrial temperatures as a result of CO2 doubling, due to the logarithmic nature of the relationship.
Figure 1: Incremental warming effect of CO2 alone [1]
The runaway 3-6o C and higher temperature increase model predictions depend on coupled feedbacks from many other factors, including water vapour (the most important greenhouse gas), albedo (the proportion of energy reflected from the surface – e.g. more/less ice or clouds, more/less reflection) and aerosols, just to mention a few, which theoretically may amplify the small incremental CO2 heating effect. Because of the complexity of these interrelationships, the only way to make predictions is with climate models because they can’t be directly calculated.
The purpose of this article is to explain to the non-expert, how climate models work, rather than a focus on the issues underlying the actual climate science, since the models are the primary ‘evidence’ used by those claiming a climate crisis. The first problem, of course, is no model forecast is evidence of anything. It’s just a forecast, so it’s important to understand how the forecasts are made, the assumptions behind them and their reliability.
How do Climate Models Work?
In order to represent the earth in a computer model, a grid of cells is constructed from the bottom of the ocean to the top of the atmosphere. Within each cell, the component properties, such as temperature, pressure, solids, liquids and vapour, are uniform.
The size of the cells varies between models and within models. Ideally, they should be as small as possible as properties vary continuously in the real world, but the resolution is constrained by computing power. Typically, the cell area is around 100×100 km2 even though there is considerable atmospheric variation over such distances, requiring each of the physical properties within the cell to be averaged to a single value. This introduces an unavoidable error into the models even before they start to run.
The number of cells in a model varies, but the typical order of magnitude is around 2 million.
Figure 2: Typical grid used in climate models [2]

Once the grid has been constructed, the component properties of each these cells must be determined. There aren’t, of course, 2 million data stations in the atmosphere and ocean. The current number of data points is around 10,000 (ground weather stations, balloons and ocean buoys), plus we have satellite data since 1978, but historically the coverage is poor. As a result, when initialising a climate model starting 150 years ago, there is almost no data available for most of the land surface, poles and oceans, and nothing above the surface or in the ocean depths. This should be understood to be a major concern.
Figure 3: Global weather stations circa 1885 [3]

Once initialised, the model goes through a series of timesteps. At each step, for each cell, the properties of the adjacent cells are compared. If one such cell is at a higher pressure, fluid will flow from that cell to the next. If it is at higher temperature, it warms the next cell (whilst cooling itself). This might cause ice to melt or water to evaporate, but evaporation has a cooling effect. If polar ice melts, there is less energy reflected that causes further heating. Aerosols in the cell can result in heating or cooling and an increase or decrease in precipitation, depending on the type.
Increased precipitation can increase plant growth as does increased CO2. This will change the albedo of the surface as well as the humidity. Higher temperatures cause greater evaporation from oceans which cools the oceans and increases cloud cover. Climate models can’t model clouds due to the low resolution of the grid, and whether clouds increase surface temperature or reduce it, depends on the type of cloud.
It’s complicated! Of course, this all happens in 3 dimensions and to every cell resulting in considerable feedback to be calculated at each timestep.
The timesteps can be as short as half an hour. Remember, the terminator, the point at which day turns into night, travels across the earth’s surface at about 1700 km/hr at the equator, so even half hourly timesteps introduce further error into the calculation, but again, computing power is a constraint.
While the changes in temperatures and pressures between cells are calculated according to the laws of thermodynamics and fluid mechanics, many other changes aren’t calculated. They rely on parameterisation. For example, the albedo forcing varies from icecaps to Amazon jungle to Sahara desert to oceans to cloud cover and all the reflectivity types in between. These properties are just assigned and their impacts on other properties are determined from lookup tables, not calculated. Parameterisation is also used for cloud and aerosol impacts on temperature and precipitation. Any important factor that occurs on a subgrid scale, such as storms and ocean eddy currents must also be parameterised with an averaged impact used for the whole grid cell. Whilst the effects of these factors are based on observations, the parameterisation is far more a qualitative rather than a quantitative process, and often described by modelers themselves as an art, that introduces further error. Direct measurement of these effects and how they are coupled to other factors is extremely difficult and poorly understood.
Within the atmosphere in particular, there can be sharp boundary layers that cause the models to crash. These sharp variations have to be smoothed.
Energy transfers between atmosphere and ocean are also problematic. The most energetic heat transfers occur at subgrid scales that must be averaged over much larger areas.
Cloud formation depends on processes at the millimeter level and are just impossible to model. Clouds can both warm as well as cool. Any warming increases evaporation (that cools the surface) resulting in an increase in cloud particulates. Aerosols also affect cloud formation at a micro level.  All these effects must be averaged in the models.
When the grid approximations are combined with every timestep, further errors are introduced and with half hour timesteps over 150 years, that’s over 2.6 million timesteps! Unfortunately, these errors aren’t self-correcting. Instead this numerical dispersion accumulates over the model run, but there is a technique that climate modelers use to overcome this, which I describe shortly.
Figure 4: How grid cells interact with adjacent cells [4]

Model Initialisation
After the construction of any type of computer model, there is an initalisation process whereby the model is checked to see whether the starting values in each of the cells are physically consistent with one another. For example, if you are modelling a bridge to see whether the design will withstand high winds and earthquakes, you make sure that before you impose any external forces onto the model structure other than gravity, that it meets all the expected stresses and strains of a static structure. Afterall, if the initial conditions of your model are incorrect, how can you rely on it to predict what will happen when external forces are imposed in the model?
Fortunately, for most computer models, the properties of the components are quite well known and the initial condition is static, the only external force being gravity. If your bridge doesn’t stay up on initialisation, there is something seriously wrong with either your model or design!
With climate models, we have two problems with initialisation. Firstly, as previously mentioned, we have very little data for time zero, whenever we chose that to be. Secondly, at time zero, the model is not in a static steady state as is the case for pretty much every other computer model that has been developed. At time zero, there could be a blizzard in Siberia, a typhoon in Japan, monsoons in Mumbai and a heatwave in southern Australia, not to mention the odd volcanic explosion, which could all be gone in a day or so.
There is never a steady state point in time for the climate, so it’s impossible to validate climate models on initialisation.
The best climate modelers can hope for is that their bright shiny new model doesn’t crash in the first few timesteps.
The climate system is chaotic which essentially means any model will be a poor predictor of the future – you can’t even make a model of a lottery ball machine (which is a comparatively a much simpler and smaller interacting system) and use it to predict the outcome of the next draw.
So, if climate models are populated with little more than educated guesses instead of actual observational data at time zero, and errors accumulate with every timestep, how do climate modelers address this problem?
History matching
If the system that’s being computer modelled has been in operation for some time, you can use that data to tune the model and then start the forecast before that period finishes to see how well it matches before making predictions. Unlike other computer modelers, climate modelers call this ‘hindcasting’ because it doesn’t sound like they are manipulating the model parameters to fit the data.
The theory is, that even though climate model construction has many flaws, such as large grid sizes, patchy data of dubious quality in the early years, and poorly understood physical phenomena driving the climate that has been parameterised, that you can tune the model during hindcasting within parameter uncertainties to overcome all these deficiencies.
While it’s true that you can tune the model to get a reasonable match with at least some components of history, the match isn’t unique.
When computer models were first being used last century, the famous mathematician, John Von Neumann, said:
“with four parameters I can fit an elephant, with five I can make him wiggle his trunk”
In climate models there are hundreds of parameters that can be tuned to match history. What this means is there is an almost infinite number of ways to achieve a match. Yes, many of these are non-physical and are discarded, but there is no unique solution as the uncertainty on many of the parameters is large and as long as you tune within the uncertainty limits, innumerable matches can still be found.
An additional flaw in the history matching process is the length of some of the natural cycles. For example, ocean circulation takes place over hundreds of years, and we don’t even have 100 years of data with which to match it.
In addition, it’s difficult to history match to all climate variables. While global average surface temperature is the primary objective of the history matching process, other data, such a tropospheric temperatures, regional temperatures and precipitation, diurnal minimums and maximums are poorly matched.
Even so, can the history matching of the primary variable, average global surface temperature, constrain the accumulating errors that inevitably occur with each model timestep?
Consider a shotgun. When the trigger is pulled, the pellets from the cartridge travel down the barrel, but there is also lateral movement of the pellets. The purpose of the shotgun barrel is to dampen the lateral movements and to narrow the spread when the pellets leave the barrel. It’s well known that shotguns have limited accuracy over long distances and there will be a shot pattern that grows with distance.  The history match period for a climate model is like the barrel of the shotgun. So what happens when the model moves from matching to forecasting mode?
Figure 5: IPCC models in forecast mode for the Mid-Troposphere vs Balloon and Satellite observations [5]
Like the shotgun pellets leaving the barrel, numerical dispersion takes over in the forecasting phase. Each of the 73 models in Figure 5 has been history matched, but outside the constraints of the matching period, they quickly diverge.
Now at most only one of these models can be correct, but more likely, none of them are. If this was a real scientific process, the hottest two thirds of the models would be rejected by the International Panel for Climate Change (IPCC), and further study focused on the models closest to the observations. But they don’t do that for a number of reasons.
Firstly, if they reject most of the models, there would be outrage amongst the climate scientist community, especially from the rejected teams due to their subsequent loss of funding. More importantly, the so called 97% consensus would instantly evaporate.
Secondly, once the hottest models were rejected, the forecast for 2100 would be about 1.5o C increase (due predominately to natural warming) and there would be no panic, and the gravy train would end.
So how should the IPPC reconcile this wide range of forecasts?
Imagine you wanted to know the value of bitcoin 10 years from now so you can make an investment decision today. You could consult an economist, but we all know how useless their predictions are. So instead, you consult an astrologer, but you worry whether you should bet all your money on a single prediction. Just to be safe, you consult 100 astrologers, but they give you a very wide range of predictions. Well, what should you do now? You could do what the IPCC does, and just average all the predictions.
You can’t improve the accuracy of garbage by averaging it.
An Alternative Approach
Climate modelers claim that a history match isn’t possible without including CO2 forcing. This is may be true using the approach described here with its many approximations, and only tuning the model to a single benchmark (surface temperature) and ignoring deviations from others (such as tropospheric temperature), but analytic (as opposed to numeric) models have achieved matches without CO2 forcing. These are models, based purely on historic climate cycles that identify the harmonics using a mathematical technique of signal analysis, which deconstructs long and short term natural cycles of different periods and amplitudes without considering changes in CO2 concentration.
In Figure 6, a comparison is made between the IPCC predictions and a prediction from just one analytic harmonic model that doesn’t depend on CO2 warming. A match to history can be achieved through harmonic analysis and provides a much more conservative prediction that correctly forecasts the current pause in temperature increase, unlike the IPCC models. The purpose of this example isn’t to claim that this model is more accurate, it’s just another model, but to dispel the myth that there is no way history can be explained without anthropogenic CO2 forcing and to show that it’s possible to explain the changes in temperature with natural variation as the predominant driver.
Figure 6: Comparison of the IPCC model predictions with those from a harmonic analytical model [6]

In summary:
Climate models can’t be validated on initiatialisation due to lack of data and a chaotic initial state.
Model resolutions are too low to represent many climate factors.
Many of the forcing factors are parameterised as they can’t be calculated by the models.
Uncertainties in the parameterisation process mean that there is no unique solution to the history matching.
Numerical dispersion beyond the history matching phase results in a large divergence in the models.
The IPCC refuses to discard models that don’t match the observed data in the prediction phase – which is almost all of them.
The question now is, do you have the confidence to invest trillions of dollars and reduce standards of living for billions of people, to stop climate model predicted global warming or should we just adapt to the natural changes as we always have?
Greg Chapman  is a former (non-climate) computer modeler.
Whilst climate models are tuned to surface temperatures, they predict a tropospheric hotspot that doesn’t exist. This on its own should invalidate the models.

Maricopa County Arizona Has Election Vote Counting and Tabulation Issues Again

Posted originally on the conservative tree house on November 8, 2022 | Sundance

The counties with ballot counting issues remain consistent over years until someone steps in and fixes the root cause of the problem, democrat election officials.  Nothing destroys election integrity faster than county election problems that repeat in the exact same precincts year after year.

Unfortunately, Maricopa County, Arizona, is one of those regional areas with major election integrity problems each voting cycle, this midterm 2022 election is no different.

According to multiple reports Maricopa County ballot tabulation machines are not working again.  Approximately 20% of the ballot tabulation machines in Maricopa County are not working which is causing delays, frustration and voter concern over the integrity of the election.  Voters have been told to leave their ballots in a box for tabulation later at a central location.  Many voters are not willing to ‘trust’ the process.

ARIZONA – Vote-counting machines weren’t working in about 20% of polling sites in Maricopa County, Arizona, as Election Day voting in the midterms began, county officials said.

The Maricopa County Recorder’s Officer said technicians were called to fix the tabulator machines that weren’t working, Fox10’s TV station in Phoenix reported. It’s not clear how many of the machines were malfunctioning in the state’s most populous county.

“About 20% of the locations out there where there’s an issue with the tabulator … they try and run (completed ballots) through the tabulator, and they’re not going through,” Maricopa County Board of Supervisors chairman Bill Gates said in a video posted on Facebook. Long lines of voters were appearing throughout the county as officials tried to reassure people that all votes would be counted. (read more)