VPD, Vapor Pressure Deficit a Correlation to Global Cloud Fraction? – Watts Up With That?

By Charles Blaisdell, PhD ChE                                                            

Abstract

The term “Vapor Pressure Deficit”, VPD is not a new term it has been used in agricultural management for many years with correlations to plant growth and CO2 absorption.  VPD is the difference between the atmospheric saturated water partial pressure, Psw, and the actual water vapor pressure, Pw.  It is common knowledge that as VPD approaches zero at any temperature that clouds are likely to form.  This paper will explore the relationship between global VPD and global Cloud Fraction (Cover), CC.

In a previous paper, Blaisdell (2023) (4), “Temperature – Dew Point Temperature”, T-Td, was explored as a global correlation to CC.  This paper will show that VPD is a better correlation than T-Td.  From 1975 to 2022, VPD tracks the increase in climate change.  It is common sense that if cloud cover decreases that the earth temperature will increase (assuming all other variables remain constant).  VPD correlation to CC is not a perfect correlation but is a hint it may be on the right track of exploring VPD’s role in climate change.

VPD may correlate to cloud cover reflectivity (albedo) in the Dubal et al (2022) CERS data analysis.  Dubal et al (2022) (6) showed that the reflectivity of cloudy areas where 2x the reflectivity (short wave radiation out) of clear sky areas, resulting in albedo reduction of the earths cloudy areas  being 85% of the total earth’s albedo for the 19 years of data.

A correlation of VPD to cloud cover is a key variable in the “Cloud Reduction Global Warming”, CRGW, theory presented in Blaisdell (2023) (4), CRGW theory starts with localized land-based reduction in Evapotranspiration, ET, (reduction in Specific Humidity, SH).  A land-based reduction in SH is mathematically related (through Clause- Chaperon equations) to a VPD increase.  The VPD increase is correlated to Cloud Cover decrease: The subject of this paper.  The cloud cover decrease lets in more sun which increases temperature and evaporates more water, global SH increases.    The result of this natural process can be seen in the current atmospheric “fingerprint” over time:  Increasing temperature, increasing specific humidity, decreasing relative humidity, decreasing cloud fraction, and increasing VPD.  Initial results indicate cloud reduction could account for a significant part of the current global warming.

CO2 is innocent but clouds are guilty.

Introduction

Scientist have long known that cloud cover, CC, (fraction) of the earth is a key part of seasonal and yearly climate change (11).  The pursuit of a cloud model has been going on for years.  The earliest models had poor correlation of CC to relative Humidity (11).   NASA is working on a computer model, CHIMP6, to predict cloud cover with some success (9).  Current International Pannel on Climate Change, IPCC, models assume cloud cover (fraction) is yearly constant (no data to say otherwise per IPCC).  NASA satellite data reported in “Climate and Clouds” (12) suggest cloud cover may have decreased since 1982.  There is currently no agreement on how much CC has changed or if CC has changed, therefore IPCC climate change models contain no CC change.  There is agreement in the scientific community that if CC has changed it should be included in any climate model (11).  “Climate and Clouds” (12) (also in (5)) CC data is all this paper must go on.  The “Climate and Clouds” (12) data shows that CC can range from 57% to 68 % depending on the hemisphere.  The global seasonal range is 59.6% to 65% (range = 5.4%).  Modeling (4) calculates a -3.4% change in the average CC could make a +0.85 ⁰C change in global climate.  The monthly variability makes small annual averages difficult to see, averages over several years are needed to see any change.

The repetitive seasonal variation of cloud cover (fraction), CC, is show in Figure 1.

The Southern Hemisphere seams to rule the global cloud fraction, CC, (assumed, due to the Earth’s tilt (less sun, cooler) and less land surface (more ocean for water evaporation along with hemispherical interactions).   In search of atmospheric variables that correlated to CC the previous paper, (4), presented the relationship between “Temperature – dew point Temperature”, T-Td, and CC to use in the CRGW model for climate change.  This paper will introduce the “Vapor Pressure Deficit”, VPD, correlation to CC.  VPD is defined as the saturated vapor pressure of water, Pws, – vapor pressure of water, Pw).

Pws = 6.116441*10^(T*7.591386/(240.7263+T))     Eq 1
(Note: the above is not an Arrhenius equation but give similar results.)
Pw = SH*1000/(621.9907+SH)                                 Eq 2
VPD = Pws – Pw                                                       Eq 3
Since:  RH = Pw/Pws                                                Eq 4
VPD = Pws * (1-RH)                                                  Eq 5                               

Where:

Pws = the saturated water pressure in hPa.
Pw = the actual water pressure in hPa
T = temperature in ‘C
SH = specific humidity in g/kg(da)
RH = relative humidity, %
Eq 1 and 2 are from from Vaisala Oyj (2013) (14):         

VPD is used in ET papers on agricultural water management and plant growth models.  An excellent summary of VPD in agriculture can be found in Novick et al (2024) (13).  In this paper we will look at VPD as a deficit that retards cloud formation.  VPD and T-Td both increases over time, Figure 2.  Cloud Fraction decrease over time, Figure 3.  VPD and T-Td both use the same input of Temperature and Specific Humidity, SH, in different equations. 

The result is VPD is more sensitivity to Temperature and SH.   Cloud Fraction, CC, vs time has a lower R^2 than VPD because CC is binary, only covers clouds or no clouds.  The “cloudy areas” could have variability in radiation reflectivity that is not included in the CC number that affect VPD (such as lower amount to “partly cloudiness” or cloud density).  Using Dubal (2022) (6) data, Blaisdell (2023) (3) showed the cloud reflectivity variability in the years 2000 to 2019, decreased while the CC was relatively constant (to be discussed later). 

Land vs Marine VPD

Figure 2’s VPD data can be divided into Land and Marine (Meto Office Dashboard (10)), showing the land VPD vs time has a significant increasing slope (cloud reduction).  The marine slope is slightly increasing (low R^2), See Figure 4.  The differences in slopes suggest the source of the global VPD increase is from the land – consistent with CRGW theory.  Mero Office (10) commentary suggest CO2 is the reason for the slope difference?

VPD vs CC

Correlating VPD or T-Td to CC is shown in Figure 5.   VPD, (Pws-Pw), is a better correlation to cloud cover.  Note the Mt Pinatubo years 1992 to 1998 were removed (Mt Pinatubo ash increased cloud cover in those years).

Figure 5  VPD and T-Td vs CC  basic data from Climate Explorer (10)

Interesting Side Bar

Figure 2 uses yearly data because monthly VPD data contains a repetitive strange variability.   VPD plotted monthly in Figure 6 showing a strong monthly hysteresis to VPD.   (T-Td) has the same hysteresis pattern but less pronounced, not shown). 

This author is not a climatologist thus can only guess that the strange pattern in Figure 6 is due to hemispherical interactions and/or the wetter climate ET going from spring to summer vs dryer ET fall to winter.  For global climate change, this strange pattern is not of interest but the shifting with time is.   Figure 2 plots this shift with time.

VPD and the Dubal et al (2024) CERES data (or Loeb et al (2021)

The Dubal et al (2024) (6) and Loeb et al (2021) (7) 19-year study of CERES data are currently the most accurate measure of the earth’s albedo, decreasing from 0.293 to 0.288 over the 19 years, see Figure .  The global temperature increased 0.36’C over the 19 years.  The cloud cover from Climate Explorer for those years was essentially flat, see Figure xx8, suggesting that cloud cover was not a factor in climate change for those years.  Dubal et al (2022) separated the reflectivity (SW radiation out) in to “clear sky”  and “cloudy areas” to show that the cloudy areas was 2x more reflective than the clear sky, see Figure .  Considering the cloud cover (67% in Dubal) of the earth the cloudy areas account for 85% to the total earth’s albedo change for those 19 years.  The earth’s VPD was increasing for those years suggesting that clouds were thinning or there were more partly cloudy skies in the cloud cover number.  VPD vs earth’s albedo is not that good (not enough years) but in the right direction, see Figure

Cloud cover can affect the LW out by reflecting (down) (or absorbing) LW back to the earth: less clouds = more LW out = cooler.  This cloud cover affect is overwhelmed by the main effect of reduced clouds:  less cloud cover = increased SW in (to surface) = increased LW out = increased EEI = warmer (see Blaisdell (2023) (2) figure 1 and 2).  VPD is only correlated with the SW reflectivity, albedo, Figure 10.

Discussion

Why study a variable the predicts cloud cover?  NASA’s data on cloud cover only goes back to 1983 and is very noisy.  CERES data is only 19 years. When did cloud cover start to change?  VPD may give some clues.  Specific Humidity data for the VPD calculation only goes back to 1948 and is increasing to 2022 (not shown) suggesting that cloud cover has been decreasing since 1948.

 VPD is an improvement over T-Td vs cloud cover, but not without a high degree of variability.  Correlations with Dubal data showed that cloud cover number is not always correlated to VPD or earth’s albedo.   Suggesting that VPD at times is increasing while CC is not decreasing, but the reflectivity of the cloud cover is decreasing, possibly explaining the high variability of CC vs time.   VPD vs earth’s albedo would be a better metric if more than 19 years was available.

How much has cloud cover or thinning reduced (VPD suggest about 3% reduction in CC since 1975) and what causes global VPD to change?  The CRGW theory gives one possibility:  Land changes that reduce the water vapor into the atmosphere.  It has been almost 5 years since the last NASA report on clouds – time for another.  The IPCC is theorizing CO2 and other greenhouse gases.

CO2 is innocent but clouds are guilty.

Bibliography

1. “Where have all the Clouds gone and why care? “  by Charles Blaisdell (2022) web link:  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”**”  by Charles Blaisdell (2022) web link CO2 is Innocent but Clouds are Guilty.  New Science has Created a “Black Swan Event”** – Watts Up With That?
3. “More on Cloud Reduction.  CO2 is innocent but Clouds are guilty” by  Charles Blaisdell web link    More on Cloud Reduction.  CO2 is innocent but Clouds are guilty (2023). – Watts Up With That?
4. “An Unexplored Source of Climate Change: Land Evapotranspiration Changes Over Time.” (2023)  By Charles Blaisdell web link  An Unexplored Source of Climate Change: Land Evapotranspiration Changes Over Time. – Watts Up With That?
5. Climate Explorer web site  Climate Explorer: Select a monthly field (knmi.nl)  go to “Cloud Cover” or any other data set, for CC  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. Raw data link is above the graph.
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10. Met Office Climate Dashboard  web Link Humidity | Climate Dashboard (metoffice.cloud)
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14. “HUMIDITY CONVERSION FORMULAS” by Vaisala Oyj (2013) web link  Humidity_Conversion_Formulas_B210973EN-F (hatchability.com)