From Watts Up With That?
By Charles Blaisdell PhD ChE
- Mount Pinatubo ash in the atmosphere and Amazonia deforestation may be seen in the cloud data.
- A correlation of measured “Temperature – Dew point Temperature”, T-Td, to Cloud Cover was found.
- The Temperature – Dew point Temperature variable suggests Cloud Reduction has been going on before 1975.
- 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:
- Clear Sky albedo change, Acs (could be related to land albedo changes)
- Cloudy Areas albedo change, Aca (could be cloud reflectivity, or cloud thinness, or cloud temperature as Dübal points out, or all)
- 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.
- Where have all the Clouds gone and why care? – Watts Up With That?
- CO2 is Innocent but Clouds are Guilty. New Science has Created a “Black Swan Event”** – Watts Up With That?
- Monthly Mean Timeseries: NOAA Physical Sciences Laboratory
- Humidity | Climate Dashboard (metoffice.cloud)
- Free Online Interactive Psychrometric Chart (flycarpet.net)
- Mount Pinatubo: Eruption and Climate Change – Philippines Tour Guide (phtourguide.com)
- Downwind footprint of an urban heat island on air and lake temperatures | npj Climate and Atmospheric Science (nature.com)
- Hans-Rolf Dübal and Fritz Vahrenholt web link: Atmosphere | Free Full-Text | Radiative Energy Flux Variation from 2001–2020 | HTML (mdpi.com)
- 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
- “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
- Climate Explorer: Select a monthly field (knmi.nl) 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.
- Selective logging leads to clear-cutting in Amazon (scidev.net)