The Quest for Climate’s “Golden Fleece” – Watts Up With That?

Arvid Pasto

August 2024

A major climate parameter called “climate sensitivity” has been the subject of scientific inquiry for literally hundreds of years. This parameter, basically, is the amount of global temperature increase caused by a doubling of atmospheric CO₂. If the parameter’s value is 2, then the world’s temperature will increase by 2°C when the CO₂ level in the atmosphere reaches twice what it is now. It is therefore, by implication, a constant number. Scientific estimates have ranged very widely, from much less than 1 to well over 6 (see later). Exact knowledge of its value is “priceless” to climate modelers and alarmists, much like the Golden Fleece was to its owner(s) in Greek mythology.

Inasmuch as global warming is considered to arise from the presence of greenhouse gases in our atmosphere, a great deal of attention has been paid to assessing the effects of these gases (principally CO₂, methane, and nitrous oxide, along with a myriad of gases typically occurring at very low concentrations).

Blaming global temperature change on any single factor, such as CO₂, is folly. Beer’s Law¹ describes the well-known phenomenon of “saturation”, wherein as CO₂ increases, atmospheric temperature will rapidly increase starting at a CO₂ level of 0, but as it increases, the temperature increase is NOT linear. Instead, it decreases, ultimately appearing to level off. (Figure 1) Thus, you can see that, using only one factor, climate sensitivity is NOT a constant. However, at high levels of CO₂, the climate sensitivity can be considered to be “nearly” constant. There is a perfectly good reason for that behavior, as can readily be shown¹. The major result of this expression is that, after some given amount of CO₂ has been added to the atmosphere, any additional amounts will cause smaller temperature increases. At some level, these increases will be insignificant.

Figure 1. The effect of CO₂ Concentration on Atmospheric Warming

(From page 8 of The Skeptics Handbook, Joanne Nova 2009)

The same non-linear relationship is true for any gaseous species in the atmosphere. And of course, our atmosphere is composed of anything but a single gas. Each will exhibit its own self-flattening concentration-temperature curve, depending on its properties.

Theoretically, knowing the atmosphere’s exact composition, and the requisite spectroscopic properties for each gas, it should be simple to calculate the resultant absorption curve. Earth’s global temperature is thought to be governed by a balance between the incoming solar radiation plus the earth’s own internally-generated heat, and the outgoing heat (radiation).

Gaseous molecules will absorb heat from both the incoming and outgoing radiation. The incoming heat is absorbed primarily via electronic transitions, while the outgoing heat is absorbed via vibrations and/or rotations of the molecules. These interactions can be easily measured via spectroscopy, with each species showing a distinct spectrum of absorption versus radiation wavelength (or frequency). See Figure 2.

Figure 2. Absorption of energy by several gases as a function of incident radiation wavelength, and their affect on incoming solar and outgoing earth radiation. (From a presentation by Dr. William Happer, of Princeton University, at Marshall University)

One can then calculate how much heat will be generated in the atmosphere via these processes, since the specific heat of each component is known. The calculation basically asks: how much heat is generated, and then how much temperature change does that heat cause.

Such calculations have been done for many years, with results such as shown below. These calculations allow for a more precise estimation of the “greenhouse gas effect” of a certain gas, since one can simply double the concentration of the species of interest in the calculation and see what happens. (Figure 3)

Figure 3. This figure shows that increasing the CO₂ level from 0 ppm (green line), to 400 ppm (around today’s value, black line) has a significant effect, raising earth’s temperature from what it would be with no CO₂ (ca. 16°F) to today’s level (ca. 60°F). You can plainly see that doubling the CO₂ to 800 has almost no effect (red line). [Ron Clutz.com 2021]

The two principal “evils” of climate change, CO₂ (carbon dioxide) and CH₄ (methane) are easily shown to be unlikely to cause any significant global warming, through calculations like those discussed above. Both are known to be continuously increasing (Figures 3 and 4), and have been painted as the major cause(s) of global warming for decades.

Figure 3. CO₂ concentration in earth’s atmosphere through 2023².

Obviously, then, something is missing from these global warming calculations. Many factors, other than atmospheric gaseous heating, can be easily inferred. These include assumptions about how much incoming solar radiation is reflected or absorbed by clouds or the oceans, how much incoming light is scattered by clouds or “dust” in the atmosphere, how much incident light is reflected by clouds, or ice and snow patches on the earth, and numerous others. The latest climate models have defined all of these terms in great detail (Figure 5).

Additional hard-to-account-for factors include extraneous heat sources (underwater volcanoes, underground coal- or- methane fires), soot deposited on glaciers (heat absorbing), the recent huge world-wide wildfires (which produce heat, soot, and CO₂), and others.

Finally, there are the so-called “feedbacks”: these include interactions between two gases such as CO₂ and water vapor, and others. These can be positive, wherein the presence of one gas, e.g.- CO₂, is thought to cause an increase in the presence of another, e.g.-water vapor, from enhanced evaporation from the oceans.  They can also be neutral or negative. (Figure 6) The heat inputs, called “forcing factors” are added to the “feedbacks” in climate models.

Figure 6. Climate feedback considered in global climate models. [From IPCC AR4 report.]

The water vapor feedback is especially troublesome: it is NOT known to be “positive”, much less as positive as climate scientists claim to know.

Because there are so many forcing factors, and so many complicated feedbacks, climate sensitivity is impossible to calculate a priori. Assumptions must always be made. Yet scientists have tried in vain for decades to do just that. (Figures 7, 8, 9)

Figure 7. Scientific estimates of climate sensitivity. In 2013, Nobel Prize-winning physicist Nur Shaviv said, about this graph, “More seriously, let me put this in perspective with the most boring graph I have ever plotted in my life. Below is the likely range of climate sensitivity as a function of time. As you can see, with the exception of AR4 with its slightly smaller range mentioned above, the likely range of climate sensitivity did not change since the Charney report in 1979. In other words, after perhaps billions of dollars invested in climate research over more than three decades, our ability to answer the most important question in climate has not improved a single bit!”

( https://www.climatedepot.com/2013/10/09/award-winning-israeli-astrophysicist-dr-nir-shaviv-the-ipcc-and-alike-are-captives-of-a-wrong-conception-the-ipcc-is-still-doing-its-best-to-avoid-the-evidence-that-the-sun-has-a-large-effec/ )

Figure 9. Historical estimates of climate sensitivity.

It turns out that climate sensitivity is actually a physically useless term, except to help show what influence certain forcing factors or feedbacks may have on global warming. Global atmospheric temperature can now be measured directly 24 hours per day via satellite and/or weather balloons, and ocean temperature via diving buoys, and “earth” temperature via thermometers located on every continent. (The latter two have their own problems, especially the surface measurements, but these will not be discussed here).

Global climate models currently utilized on supercomputers worldwide do not actually use “climate sensitivity” as an input, but their output can be used by generalists to suggest its value from the computer’s input and output.

Given the results of actual global temperature versus the results of computer climate models (black line on Figure 10), one can see that earth’s temperature is NOT increasing at anything like the rate that would be predicted by a “climate sensitivity” of over 2.

The global CO₂ level in 1976 was² 332 ppm, and the starting temperature change is taken as 0.00. In 2016, CO₂ was 404, and the rise in temperature was 0.3°. At an average rate of 2 ppm/year increase, it would take 166 years to double from 1976. {I use 2 ppm/yr, even though from 1976 to 2016 it was 1.8 ppm/yr, but it has been increasing to over 2 ppm/yr lately}. Now, 40 years represents 0.24 of that time difference (40/166). Thus the “climate sensitivity” starting in 1976 would be 0.3°C/0.24, or 1.25. If the climate sensitivity (C.S.) had been 2, the expected temperature rise would have been 0.48°, and if C.S. was 3, then the rise would have been 0.72°C, and etc. The average of the climate models shown on the graph gives between 4 to 5 for the climate sensitivity (and increasing).

Figure 10. Comparison of computer climate model forecasts and actually observed global temperature. [From Dr. John Christy, Univ. Alabama-Huntsville]

If the rate of rise rate in CO₂ dramatically increases over the next few decades, then the climate sensitivity will also increase, and so will the predicted global temperature. Actually, the CO₂ emission rate is expected to slow, which will extend the CO₂ doubling time, and decrease the climate sensitivity.

However, since actual temperature and CO₂ data result in a current climate sensitivity of ca. 1.25, there is plainly something wrong with the current “fad” for climate sensitivities of 3 or more.

AND…the real problem with the term “climate sensitivity” is that it implies that CO₂ is the driver of climate change, which it is not.

REFERENCES

1. M. N. Berberan-Santos, “Beer’s Law Revisited”, Jour. Chem. Ed. 67, Sept. 1990
2. https://www.statista.com/statistics/1091926/atmospheric-concentration-of-co2-historic/q