Dr. Frank Stefani and colleagues from Helmholtz-Zentrum Dresden – Rossendorf and the Institute for Numerical Modelling, University of Latvia, have proposed a new physically consistent model of solar variability. It proposes that the known solar cycles, from the eleven-year Schwabe cycle to the 193-year De Vries cycle are related to planetary orbits and the 19.86-year solar oscillation around the solar system barycenter.
The paper, “Rieger, Schwabe, Suess-de Vries: The Sunny Beats of Resonance,” explains the details of the concept. The press release, which is easier to read, explains the implications.
Astronomers and physicists have long been writing about the possible solar tidal effects of the planets, see Scafetta and Bianchini’s review paper here. The planetary orbits must affect the solar plasma in some fashion and the orbital patterns do correlate to proposed solar and climate cycles. While the statistical correlation was good, the underlying physics of why it all worked remained elusive.
Stefani and his colleagues have created a physically consistent model of the 193-year De Vries solar and climate cycle, the longest solar cycle physically modeled to date. There are longer solar cycles, such as the famous ~2450-year Bray cycle and the ~1,000-year Eddy Cycle, that have been observed but not modeled as precisely yet. This is an important start.
NASA claimed that “Earth is warming at an unprecedented rate” and “human activity is the principal cause.” Others proposed spending trillions of dollars to control the climate. But are we humans responsible for climate change? And what can we do about it?
“The climate of planet Earth has never stopped changing since the Earth’s genesis, sometimes relatively rapidly, sometimes very slowly, but always surely,” says Patrick Moore in Fake Invisible Catastrophes and Threats of Doom. “Hoping for a ‘perfect stable climate’ is as futile as hoping the weather will be the same and pleasant, every day of the year, forever.”
In other words, climate change is normal and natural, and you can forget about controlling it.
For instance, a major influence of weather and climate are solar cycles driven by the Sun’s magnetic field over periods of eight to 14 years. They release varying amounts of energy and produce dark sunspots on the Sun’s surface. The effects of solar cycles on Earth vary, with some regions warming more than 1°C and others cooling.
Climatic changes occur as a result of variations in the interaction of solar energy with Earth’s ozone layer, which influences ozone levels and stratospheric temperatures. These, in turn, affect the speed of west-to-east wind flows and the stability of the polar vortex. Whether the polar vortex remains stable and close to the Arctic or dips southward determines whether winters in the mid-latitudes of the Northern Hemisphere are severe or mild.
In addition to solar cycles, there are three Milankovitch cycles that range in length from 26,000 to 100,000 years. They include the eccentricity, or shape, of Earth’s elliptical orbit around the Sun. Small fluctuations in the orbit’s shape influence the length of seasons. For example, when the orbit is more like an oval than a circle, Northern Hemisphere summers are longer than winters and springs are longer than autumns.
The Milankovitch cycles also involve obliquity, or the angle that Earth’s axis is tilted. The tilt is why there are seasons, and the greater the Earth’s tilt, the more extreme the seasons. Larger tilt angles can cause the melting and retreat of glaciers and ice sheets, as each hemisphere receives more solar radiation during summer and less during winter.
Finally, the rotating Earth, like a toy top, wobbles slightly on its axis. Known as precession, this third Milankovitch cycle causes seasonal contrasts to be more extreme in one hemisphere and less extreme in the other.
Moving from outer space to Earth, ocean and wind currents also affect the climate.
For instance, during normal conditions in the Pacific Ocean, trade winds blow from east to west along the Equator, pushing warm surface waters from South America towards Asia. During El Niño, the trade winds weaken and the warm water reverses direction, moving eastward to the American West Coast. Other times, during La Niña, the trade winds become stronger than usual, and more warm water is blown towards Asia. In the United States and Canada, these phenomena cause some regions to become warmer, colder, wetter, or drier than usual.
In addition to El Niño and La Niña, there is also the North Atlantic Oscillation, which is driven by low air pressure in the North Atlantic Ocean, near Greenland and Iceland (known as the sub-polar low or Icelandic low), and high air pressure in the central North Atlantic Ocean (known as the subtropical high or Azores High). The relative strength of these regions of low and high atmospheric pressures affects the climate in the Eastern United States and Canada and in Europe, affecting both temperatures and precipitation.
Similarly, Hadley cells are the reason Earth has equatorial rainforests that are bounded by deserts to the north and south. Because the Sun warms Earth the most at the Equator, air on either side of the Equator is cooler and denser. As a result, cool air blows towards the Equator as the warm, less dense equatorial air rises and cools, releasing moisture as rain and creating lush vegetation. The rising, drier air reaches the stratosphere blowing north and south to settle in regions made arid by lack of atmospheric moisture.
These and other phenomena influencing our climate are well beyond the control of humans.
This commentary was first published at Real Clear Markets on March 30, 2024.
CO2 Coalition Research and Science Associate Frits Byron Soepyan has a Ph.D. in chemical engineering from The University of Tulsa and has worked as a process systems engineer and a researcher in energy-related projects.
The Sun is approaching its solar maximum, the apex of its 11-year solar cycle. As it approaches that, as we’ve already seen over the last couple of years, we can expect more and greater activity from our solar system’s center. But that’s the thing, this solar-cycle might be far more intense than anyone expected.
The Sun as imaged on 29 November by NASA’s Solar Dynamics Observatory in 171 angstroms. (NASA SDO)
A new study has found that the impending peak in the Sun’s activity cycle will likely arrive significantly sooner than previously predicted.
According to an analysis by astrophysicists Priyansh Jaswal, Chitradeep Saha, and Dibyendu Nandy at the Center of Excellence in Space Sciences India, solar maximum is likely to hit in January 2024.
This is much, much sooner than the initial official prediction, which found solar maximum would take place in July 2025.
The finding suggests that there may be better ways of predicting the Sun’s behavior than the methods used for the official predictions for the current solar cycle, solar cycle 25.
Solar cycles are somewhat mysterious – it’s not entirely clear what drives them – but they are very normal.
Simply put, every 11 years or so, the Sun’s magnetic field reverses polarity. This is accompanied by a rise and fall of solar activity – sunspots, solar flares, and coronal mass ejections.
The Sun as seen by the Solar Dynamics Observatory on November 29. That’s a lot of eruptions, hey? (NASA SDO)
The point at which the poles switch places is known as solar maximum, characterized by a peak in activity. Solar minimum follows several years later; the Sun deescalates its activity before ramping up to the next maximum.
We track and predict when this will occur based on the number of sunspots that speckle the face of the Sun. However, this method has never been an exact science; we know roughly when solar maximum will occur, but official predictions are more of an estimate than a precision pinpoint.
The last solar minimum, marking the end of solar cycle 24, took place in 2019. Solar cycle 24 was relatively quiet, as solar cycles go; the NOAA prediction was that solar cycle 25 would follow suit, with subdued activity, and a peak in July 2025.
The Sun on November 29 in 211, 193, and 171 angstrom wavelengths. (NASA SDO)
Some scientists called it. Robert Leamon of NASA and Scott McIntosh of the US National Centre for Atmospheric Research predicted that solar maximum would be stronger than others believed, and take place in mid to late 2024. Last month, the NOAA revised its prediction, announcing that the maximum is now expected between January and October of 2024.
We’ve been getting by pretty well so far, but solar activity is tied to space weather – eruptions from the Sun can have an impact on Earth, and while our recourse is limited, there might still be ways to try and protect ourselves. So more accurate predictions of the solar cycles would be a nice thing to have.
Leamon and McIntosh based their analysis of the Sun on its internal magnetic activity, tracing larger patterns of behavior back over a long period of time. Jaswal and his colleagues have done something similar, studying decades-old data and linking magnetic activity to something known as the Waldmeier effect.
Sunspots freckling the face of the Sun on 29 November. (NASA SDO)
Formulated in 1935, the Waldmeier effect relates to sunspots and the length of the solar cycle. Basically, the faster sunspot activity ramps up, the faster solar maximum arrives. So, the stronger the cycle, the shorter it is.
Jaswal and his team looked at the rate at which the Sun’s poles weaken, studying data that dates back to 1976. They found that the rate of decay of the solar dipole correlates very neatly with the Waldmeier effect.
This means that they could use the Waldmeier effect to predict when the polar magnetic fields would weaken to zero and switch places – solar maximum.
Their findings predicted a timeframe around January 2024. There’s still some uncertainty and wiggle room, but if solar maximum takes place at this time, we’ll know that they’re onto something.
In all, however, the evidence so far suggests that we need to be looking more closely at the Sun’s magnetic activity, rather than just what’s happening on the surface.
In my previous post on multiple regression of known solar cycles versus HadCRUT5, I simply threw the solar cycles, ENSO, and sunspots into the regression blender and compared the result to various models that included CO2. Before reading this post, it is a good idea to read the previous one, since much of this post relies on the information in it. It was a very simple statistical analysis designed to show that the IPCC conclusion that rising CO2 and other greenhouse gases are “responsible” for “1.1°C of warming since 1850-1900” is probably erroneous. The difference between the HadCRUT5 1850-1900 average and the 2018-2023 (through all of 2022) is 1.18°C, so they are saying that essentially all the warming since the 19th century is due to humans. The analyses described in this post show they cannot be certain of their conclusion because they have ignored persuasive evidence that changes in the Sun caused at least some of the warming.
We have shown that various statistical combinations of known solar cycles correlated with HadCRUT5 as well as, or sometimes better than, changes in CO2 concentration. The way that the Sun might affect our climate is unknown. The IPCC only considers the direct effect of changing total solar irradiance (or TSI) directly on the Earth, as if the Sun were an incandescent light bulb over a piece of paper, but this cannot be correct. The climate effect of solar changes during a single 11-year solar cycle[1] is nearly an order of magnitude larger than the change in solar radiation can account for.
Recently great strides have been made in modeling and understanding the solar dynamo. However, modeling many important elements of the generation of solar cycles remains beyond our grasp. We only know their effect on Earth’s climate is much larger than the change in power received from the Sun during the cycle. We can examine the correlation of known (but poorly understood) solar cycles and climate change, but we cannot explain the mechanisms involved.
How additional CO2 can warm Earth’s surface is understood, but the climate sensitivity[2] to CO2 is not known. Recent published estimates of the sensitivity, range from near zero to over 5°C/2xCO2 (2xCO2 means doubling of the CO2 concentration). The IPCC claims that human generated CO2 and other human activities have caused all (or essentially all) recent warming. This is speculation. We do not know how much changing CO2 can affect climate, and we can’t explain the large observed effects due to solar changes,[3] so how can we know all the observed warming is due to CO2 and human activities? The advantage of the CO2 hypothesis is that the mechanism is known, but since the magnitude of the effect cannot be calculated accurately, quantitatively it is just as unknown as the solar effect, which the IPCC is clearly underestimating.[4]
In this post we will take a closer look at the correlation between solar activity and HadCRUT5, and address some of the many comments to my previous post. First overfitting.
Overfitting
Solar cycles are not understood but can be observed in cosmogenic isotope studies that have been used to document the very long Hallstatt (or Bray 2400-year, ±200 years) and Eddy (1000-year ±30 years) cycles. These two long cycles correlate with the most significant climate events in history, the Bray Cycle correlates with the Greek Dark Age (~ 1200 to 800BC) and the early part of the Little Ice Age (~ 1300 to 1600, we target 1470 as the Hallstatt low). The Eddy Cycle correlates with the Medieval Warm Period (~ 950 to 1250), the latter part of the Little Ice Age (~1500 to 1816, we target 1680 for the Eddy low), and the Modern Warm Period (~1940 to ~2005).[5]
The shorter cycles are not as climatically significant but noticeable. Both the “Pause” in warming and the cool period around 1910 correlate well with the Feynman Cycle, and the cooler period from 1945 to 1976 in the early part of the Modern Solar Maximum correlates with the Pentadecadal cycle. All these cycles are plotted for the instrumental period in Figure 1 along with HadCRUT5.
Figure 1. The known solar cycles plotted for instrumental era along with the HadCRUT5 global surface temperature record.
As some pointed out in comments on my last post, with this many cycles, multiple regression will always find a reasonable fit to almost anything trending upward. Further all the time series, including HadCRUT5, are strongly autocorrelated. The cycles are anchored to the solar lows or highs as specified in Ilya Usoskin’s 2016 and 2017 papers[6] or Joan Feynman’s 2014 paper.[7] The 22.1-year Hale Cycle is anchored to early 2020 during the solar cycle 24 minimum. It has been proposed that the de Vries Cycle is a beat period between the Hale Cycle and the 19.86-year orbit of the Sun around the solar system barycenter,[8] this configuration is consistent with this hypothesis.
As can be seen in figure 2, this regression relies mostly on the quasi-linear Hallstatt and Eddy Cycles. Frank Stefani does not like this idea and believes that only the better documented Feynman and de Vries Cycles and Log(CO2) are needed to model the period 1850 to the present. This is possible, Log(CO2) is also a quasi-linear series and is similar to the Eddy and Hallstatt series (see the first post), so all three can substitute for one another, this is an argument that will not be settled by observations soon.
Because the solar dynamo is not fully understood,[9] we have no choice but to choose the best regression of these cycles on HadCRUT5 as our solar model. I understand that regressions are possible with other configurations of the cycles, but we have a solid foundation for this configuration. The regression is shown in figure 2.
Figure 2. A multiple regression model of HadCRUT5 using only the well-known solar cycles. The coefficients (weights) for each of the cycles are listed and the regression statistics are given in the boxes. The decrease in global temperatures from 1944 to 1976 is not modeled very well, otherwise the model does a good job.
Because the input cycles and HadCRUT5 are autocorrelated the regression statistics (especially R2) shown are inflated to reality. Experimentation shows that most cycle configurations would result in R2 values above 0.8, although some were far lower than this. This R2 value of 0.83 is not great, but it is the best that can be obtained with these cycles, which is what we were after.
In this way, we created a single solar cycle predictor variable. The underlying reason for the cycles is very poorly understood. This is a statistical exercise, and it is the best match of these predictors to HadCRUT5, but that is all we can say.
Next we add other variables that proved significant in our residual and partial regression study. They are the Nino 3.4 index and the logarithm to the base 2 of CO2 or “Log(CO2)” time series. Oddly, adding the Nino 3.4 series, at least statistically, caused the sunspot series to become an insignificant (about 1%) addition to the regression. As a result, the sunspot series did not make the cut to be added to the regression, and the Nino 3.4 series was always significant at over 10%. This might be explained by the observed effect of the solar cycle on upper ocean temperatures described by Warren White and his colleagues at Scripps.[10] Figure 3 shows the regression with Nino 3.4 added.
Figure 3. Adding ENSO (Nino 3.4) to the composite solar function. Inputs are normalized to make the coefficients comparable. The cooling period during the early 1960s is still not modeled very well.
Adding Nino 3.4 to the composite solar series increases the R2 to 0.85, but the coefficients suggest that the addition of Nino 3.4 is significant, but small, at 15%. Nino 3.4 is about a 15% addition with or without sunspots. The normalized coefficients tell us that, statistically, 85% of the regression is from the combined solar series and 15% is from Nino 3.4.
The input series in these plots (figures 3, 4, and 5) are all normalized[11] so that the coefficients are comparable and can be used to compare the relative impact of the input series on the model. Figure 4 shows the result when Log(CO2) is added.
Figure 4. The regression when Log (CO2) is added. Inputs are normalized to make the coefficients comparable.
Figure 4 tells us that adding Log(CO2) does not change the R2 significantly, and it barely changes the regression model. The coefficients tell us that, statistically, the combined solar series added 79% to the model, ENSO is unchanged with a 15% addition, and Log(CO2) contributed only 6%. Finally, figure 5 shows the model created from just Log (CO2) and the combined solar series.
Figure 5. The combined solar series and the Log(CO2) series.
In figure 5, the R2 has dropped to 0.83, the solar time series supplies 87% of the result, and Log(CO2) only supplies 13%. Figure 6 compares the regression using the combined solar and Nino 3.4 to a regression using combined solar, Nino 3.4, and Log(CO2). As you can see, they are not exactly the same, but nearly so.
Figure 6. The models with the combined solar curve, ENSO, and CO2 compared to Solar and ENSO only. Although they appear to be exact overlays, they are slightly different. The suggestion is that CO2 did not add to the regression.
Figure 7 adds the combined solar plus Log(CO2) series to the plot. It now becomes apparent that once the solar cycles are combined into one predictor, it and ENSO produce the best regression model to predict HadCRUT5. How the solar cycles were created in the solar dynamo is unknown, but if our combined solar cycle series is correct, the major solar cycles are the dominant force behind recent warming.
Figure 7. Comparing all the models, solar plus ENSO plus CO2, solar plus ENSO, and solar plus CO2.
This analysis is not evidence that solar variability is the dominant cause of recent climate change. It merely shows that a statistically significant model of HadCRUT5 global average temperature series can be created from a combination of well-known and well-documented solar cycles. The physical reason for these observed solar cycles is unknown, although there are many plausible hypotheses that might explain them.[12]
All the current possible mechanisms show the Sun acting as an AC field generator with a period of about 22 years. The longer modulations are poorly understood. Observations and proxies show that the Sun varies over both short and long periods, which causes solar output to change, and results in climate changes on Earth. What is the driving force for the solar changes? They appear to depend on the complex fluid motions in the Sun’s interior which, in turn, might be influenced by the varying gravitational action of the orbiting planets, but all this is unclear.[13] The model we describe ignores all this complexity and only deals with the observed cycles. We created a very simple statistical model, but more elaborate and creative multiple regression solar models have been published recently, a quick summary of some of them follows.
Stefani, 2021
Frank Stefani uses double regression to model global sea surface temperatures (HadSST.4) with the aa index[14] of solar variability and Log(CO2). The aa index is a robust proxy of solar output and correlates well with the sunspot number (see here for more information). Stefani does a much more extensive check of regression parameters than we do here. He also uses his model to predict temperature into the next century. His predictions show a reduced warming rate over the coming century. He uses his model to compute a climate sensitivity of 0.6 to 1.6°C/2xCO2, much lower than reported in the IPCC’s latest report (AR6[15]). However, Stefani’s values are in line with other observation-based estimates of climate sensitivity.[16] (link)
Scafetta, 2023
Scafetta constructs multiple regression models that include solar forcing, volcanic eruption effects, and Log(CO2). He emulates the IPCC’s model results using their assumptions, although he computes a smaller climate sensitivity of 1.4 to 2.8°C/2xCO2. Using more realistic assumptions, the climate sensitivity is reduced to 0.9 to 1.8°C/2xCO2, consistent with Stefani’s estimate above. Scafetta regressed on HadSST4, HadCRUT4, and HadSST3 as well as HadCRUT5, all producing similar climate sensitivities. His model accounts for a delayed response due to ocean buffering of absorbed solar radiation. To account for the possibility of urban bias, some of Scafetta’s regression studies were done only on sea surface temperature datasets. His study shows that only 20% of the solar influence on global temperatures is due to increased radiation. Other factors such as modulation of cosmic rays, solar driven atmospheric/oceanic circulation changes, or other processes are probably more important. These latter processes, and other solar driven amplifiers, are not programmed into the IPCC climate models, which is possibly why they underestimate the climatic impact of the Sun. (link)
Soon, et al, 2023
Soon et al. did a regression study of solar, volcanic, and human forcings on two Northern Hemisphere datasets, one with rural temperatures and one with a blend of rural and urban datasets.[17] This paper is an extension of Soon and colleague’s earlier solar/CO2 regression study.[18] They used two solar forcing datasets, the TSI[19] dataset recommended by the IPCC, and another that was ignored in AR6.[20] They found that the choice of temperature and solar forcing datasets made a large difference in the study outcome. The temperature and TSI datasets are all possible, none are established as better or worse than the other, yet how much warming is attributable to human activities or nature depends on the datasets used. This casts doubt on the IPCC conclusion that humans have caused all, or nearly all, recent warming. (link)
It is important to realize that nearly everyone recognizes that urban areas are warmer than the surrounding countryside and urban areas have been growing rapidly globally over the past century, surrounding previously rural weather stations. This casts doubt on warming trends generated with combined rural/urban datasets. Further, there is no definitive record of solar radiation output (TSI), there are both low and high trend TSI datasets and no way to tell which is correct since proper records are too short and inaccurate. Thus, a proper study would use both, as Soon et al. do. Soon et al. found that 85% of the 1850-2018 warming, using their “rural-only” dataset could be explained by solar and volcanic forcing.
Stefani et al. 2023
Regression isn’t used in this paper, but it is of interest here because the authors connect the solar (Schwabe) and Hale Cycles to the de Vries (or Suess) Cycle via a 193-year beat period[21] between the 22.14-year Hale Solar Cycle and the 19.86-year orbit of the Sun around the solar system barycenter.[22] They note (as have many others) that the de Vries Cycle is probably responsible for the ~190-210-year spacing of Solar Grand Minima during Hallstatt-Bray Cycle lows. The most recent example being the Wolf-Spörer-Maunder minima between about 1300 and 1715, with the related Bray low at about 1500 (these are very similar to the values used in my model above). They also note that in some fashion, the de Vries and Bray-Hallstatt Cycles are related, or at least the de Vries Cycle appears to be modulated by the Hallstatt-Bray Cycle. (link)
Conclusions
These various multiple regression studies don’t prove anything, they aren’t even proper evidence of anything. But they do show that the IPCC assumption that the Sun had no effect on observed warming since 1750 is questionable. It also shows that their chosen TSI dataset and their assumption that the only impact of a changing Sun is the amount of radiation Earth receives is questionable. Both White and Haigh have established that amplifiers exist in Earth’s climate system that increase the impact of solar changes by a factor of four,[23] perhaps by a factor of ten,[24] yet this is ignored by the IPCC. The IPCC needs to go back to school and redo AR6 including all the research they ignored the first time.
I acknowledge the generous help from Dr. Frank Stefani and Dr. Willie Soon, but any errors in the post are mine alone.
Various writers refer to equilibrium climate sensitivity (ECS), the transient climate response (TCR), effective climate sensitivity (ECS). There are a bewildering number of ways to measure the effect of CO2 on climate, see here and here for a discussion. To avoid this confusion, we will only refer to “climate sensitivity” in this post. ↑
They are normalized by subtracting their respective means and dividing by their standard deviation. The model is not affected, but the coefficients become comparable when this is done. ↑
(Soon, Connolly, & Connolly, 2015), see also the summary here. ↑
TSI is total solar irradiance. The IPCC assumes that the increase or decrease in solar output is the only warming or cooling effect the Sun has on Earth’s climate. This is hotly debated, as there are recognized amplifiers in the climate system (Haigh, 2011). ↑
When two waves with dissimilar frequency interact, they cause an alternating constructive and destructive interference that is called “beating.” More here. ↑
The solar system barycenter is the center of mass of the solar system, which moves with the planets. The Sun moves about this barycenter in a complex orbit. More here. ↑
Global warming, climate change, all these things are just a dream come true for politicians. I deal with evidence and not with frightening computer models because the seeker after truth does not put his faith in any consensus. The road to the truth is long and hard, but this is the road we must follow. People who describe the unprecedented comfort and ease of modern life as a climate disaster, in my opinion have no idea what a real problem is.
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