“Personally, when I take mushrooms, the last thing I want to do is think about climate change. But that’s apparently what I should be doing, according to … Psychedelics for Climate Action.” (Emily Atkin, Heated)
At the anti-fossil-fuel Substack Heated, Emily Atkin outdid herself. “I fell down the rabbit hole of Psychedelics for Climate Action,” she confessed. “Then I came back to reality.” She continued:
Personally, when I take mushrooms, the last thing I want to do is think about climate change.
But that’s apparently what I should be doing, according to a new advocacy group. Psychedelics for Climate Action, or PSYCA, argues that the use of mind-altering substances and Indigenous plant medicines—like ayahuasca, psilocybin, ibogaine, ketamine, and LSD—can inspire people to help solve the climate crisis.
PSYCA held its official launch at the Psychedelic Assembly in New York City last month. I decided to attend, because frankly, I’ve been pretty bummed out about the state of the world lately. I figured at the very least, going to this would be fun.
It was—but it was also genuinely thought-provoking. So today, I’m going to tell you how I fell down the rabbit hole of tripping balls for the planet—and how I ultimately came back to reality.
Crazy is as Crazy does…. Defacing Stonehenge. Throwing paint on museum masterpieces. Disrupting sporting events. Disrupting traffic.
Emily Atkin faces a bleak outlook if she is at war against dense, reliable, affordable, consumer-driven, taxpayer-neutral, infrastructure-light, global-greening energies. And she is tripping to believe that industrial wind power, solar arrays, and battery packs are good for the environment.
It is past time for her to study the issues with an open mind toward human betterment. And then enter back to reality. And then fight back against the Climate Industrial Complex and the elitism behind it with her being a case study of reason over emotion.
Recent extreme weather events, such as Hurricane Beryl that left more than two million Texas households and business without electricity for days and then went on to flood parts of the Midwest and New England, has drawn renewed attention to the vulnerabilities of America’s power grids. As climate models predict even more severe weather in the future, utilities across the nation are taking measures to “harden the grid” such as replacing wooden poles with steel or concrete, putting more transmission and distribution lines underground, and installing transformer circuit breakers that can work underwater.
But the recent focus on improving grid resiliency may be masking a more serious energy issue—namely, a projected deficit in baseload power generation relative to the expected growth in electricity demand over the next several decades. The U.S. Energy Information Administration sees electricity consumption reaching record highs in 2024 and 2025 and is also projecting that demand will jump from its current level of about 4,100 terawatt hours today to more than 5,200 terawatt hours by 2050, a 27% increase.
This rapid growth in demand will be driven by a number of factors. First is the expansion of power-hungry server farms that are expected to consume more than one-third of new capacity in the years ahead as the incorporation of artificial intelligence requires even more power than traditional data centers. Hydrogen production, which uses huge amounts of electricity, is another factor pushing up power demand as is the overall electrification of the U.S. economy.
The huge growth of investment in renewables in recent years, especially wind and solar, has created the perception that plenty of generation will be available to meet future demand. Over the past decade, installed wind generation capacity has jumped from 60 gigawatts to more than 150 gigawatts while solar farms now produce 150,000 gigawatt hours of electricity per year compared with a mere 10,000 ten years ago. But data centers, crypto-miners, and other critical information technology infrastructure need reliable, 24/7 power sources. They can’t rely solely on renewables because of their intermittency. For example, in 2023 wind generation in the U.S. actually declined by 9 terawatt hours despite the addition of hundreds of wind turbines to the grid.
Meanwhile, investment in baseload natural gas, nuclear, and coal plants has languished. According to the Energy Information Administration, additions to natural gas capacity are at a 25-year low of 2.5 gigawatts this year, or just 4 percent of total planned capacity. This is down from 21% of planned capacity as recently as 2020. At the same time, according to the North American Electric Reliability Corporation, the power grid has lost more than 50 gigawatts of natural gas, coal, nuclear, and hydro power over the past decade while more than 1,000 coal plants have been shuttered. In response, a growing coalition of industry leaders, regulators and independent experts is warning that with power demand booming while coal and nuclear plants are going offline, the nation’s grids are more susceptible to electricity shortages than at any time in the past 50 years.
Making matters worse, new “carbon capture” rules from the Environmental Protection Agency (EPA) may not only retard new investment in natural gas plants but may actually hasten both gas and coal-fired power plant closures. Under the rules, new gas plants that will operate after 2039 must install carbon capture and storage (CCS) technologies, run them at 90% efficiency, and store the captured CO2 underground. The EPA’s rules also apply to existing coal plants which are required to reduce their carbon emissions by 90% by 2032 if they plan to operate past 2039. Not only will complying with these rules prove expensive, the new CCS standards may not even be technically achievable.
Reducing or eliminating policies that cause market distortions, such as the huge subsidies for wind, solar and batteries, would be one option for sustaining baseload power generation. But these incentives remain popular and were actually extended by the 2022 Inflation Reduction Act. Instead, the next administration in Washington should move to rescind the new EPA CCS rules in order to maintain a level playing field for all forms of electricity generation and to encourage more investment in baseload power plants.
In reality, baseload, dispatchable power—namely coal, nuclear and large gas-fired plants—will remain the backbone of America’s energy supply for the foreseeable future. Despite the continued expansion of renewables, there is no other way to maintain the reliability of the power grid while meeting the growing electricity needs of American industry.
Bernard L. Weinstein is retired associate director of the Maguire Energy Institute at Southern Methodist University, a professor emeritus of applied economics at the University of North Texas, and a fellow of Goodenough College, London.
This article was originally published by RealClearEnergy and made available via RealClearWire.
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.
Figure 1. CC vs time, data from Climate Explorer (5).
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).
Figure 2 VPD and T-Td vs time.Figure 3 CC vs timeData for both graphs from Climate Explorer data (5)
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?
Figure 4. VPD Land and Marine vs time from Meto Office Dashboard (10).
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).
Figure 6 Monthly VPD vs CC for year ranges 1982-6 and 2015-8, note shift in pattern
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 7. 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 (albedo) than the clear sky, see Figure 9. 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 (Figure 8) 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 10.
Figure 7 Earth’s Albedo vs time from Dubal Figure 8 Cloud cover, VPD, and Dubal CCFigure 9. Albedo of clear and cloudy areasFigure 10. VPD vs Albedo for Dubal’s 19 yearsClimate changes is more complicated than just the albedo. The long wave radiation, LW, out is part of the overall equation: Climate Change = SW out – LW out = Earth’s Energy (Radiation) Imbalance, EEI.
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.
Climate Explorer web siteClimate 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.
Iowa Climate Science Education 