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Climate Change, due to Solar Variability or Greenhouse Gases? Part B.

By Andy May

In a previous post, Part A here, we discussed the role of oceans, the Earth’s orbit, and human greenhouse gas emissions on climate change. In this post we discuss the impact of solar variability.

How does the Earth naturally respond to warmer temperatures?

Clearly the Earth has been warming for the past 150 years or so, the warming could be natural or human-caused or both. Will it continue warming until dangerous temperatures are reached? Newell and Dopplick (Newell and Dopplick 1979) and Sud, et al. (Sud, Walker and Lau 1999) have pointed out that evaporation from the oceans limits maximum average atmospheric temperatures to 303K (30°C or 86°F). Evaporation stores latent energy (heat) in water vapor that is released when the water vapor cools and precipitates as liquid water. The water vapor can be transported great distances, often in clouds, before the precipitation occurs and the heat is released. This process of evaporation, convection and precipitation is the primary mechanism for cooling the surface of the Earth according to (Pierrehumbert 2011), (Benestad 2016) and others. Thus, as long as the oceans exist, it is highly unlikely dangerous temperatures will be reached on Earth (Miskolczi 2014).

Christopher Scotese has compiled a detailed reconstruction of Earth surface temperatures for the past 550 million years (Scotese 2015). The Earth’s surface average temperature has been as warm as 28°C in the past and as cold as 12°C. The average surface temperature over the last 550 million years is around 20°C (68°F), some 5°C warmer than today. At no time in the Earth’s past do we see average temperatures that exceed 30°C, which agrees with the conclusions of Newell and Dopplick and Sud, et al.

Ferenc Miskolczi’s (Miskolczi 2010) analysis of weather balloon data suggests that specific humidity has decreased over the last 60 years which counteracts the effect of increasing CO2 and methane concentrations. Because water vapor is a much stronger greenhouse gas than CO2, a very small change in water vapor concentration can counteract a larger change in carbon dioxide concentration. While Miskolczi’s data was from NOAA, Rasmus Benestad used data from Europe that also suggests that atmospheric water content has dropped 0.018 kg/(m2 decade) over the last several decades (Benestad 2016). Vonder Haar, et al. have also reported that the water vapor concentration in the atmosphere has declined or stayed the same over the past 30-70 years (Vonder Harr, Bytheway and Forsythe 2012). This is at odds with Pierrehumbert’s assertion that if CO2 were to increase the surface temperature, water vapor would increase per the Clausius-Clapeyron relation. The Clausius-Clapeyron relation predicts that specific humidity (total precipitable water) will increase 6-7% per degree of warming and it is a solid relationship in the laboratory. We appear to be witnessing water vapor decreasing or staying the same as CO2 and temperatures increase. Perhaps because precipitation increases faster than evaporation? Or perhaps there are other contravening effects? While statistically significant proof does not exist, Miskolczi and Spencer (Spencer 2010) think the atmosphere compensates for higher temperatures by altering humidity and cloud cover. Lindzen, Chou, and Hou have suggested that cloud cover adjusts to cool the Earth when surface temperatures rise (Lindzen, Chou and Hou 2001).

The solar radiation variability hypothesis

This hypothesis suggests that the major influence on the Earth’s climate is the variation in solar radiation received by the Earth and the distribution of the radiation by latitude. The Earth’s orbit is elliptical, and the Earth is tilted relative to the orbital elliptical plane, these factors vary with time and all affect the amount of radiation the Earth receives from the Sun and where on the Earth it falls. Over the geological short term, meaning a few tens of thousands of years, the average yearly amount of solar radiation striking the Earth doesn’t change much. But, the amount striking a particular latitude does change significantly. Obliquity (the tilt of the Earth’s axis) changes the amount of thermal energy striking the tropics versus the amount striking the poles. This obliquity change is a cumulative and over time the difference between the polar insolation and the equatorial insolation can be very large. This changes the pole-to-equator temperature gradient, which affects climate.

Orbital precession (the wobble of the Earth’s axis) changes the amount of radiation striking the Northern Hemisphere in the summer versus the amount striking the Southern Hemisphere in the summer and winter. This is because one hemisphere faces the Sun at perihelion and the other faces the Sun at aphelion during their respective summers. This affects “seasonality” or the difference between summer and winter in each hemisphere. Unlike obliquity, the total amount received in each year at a given latitude does not change, but the timing throughout the year does change and the change is modulated by orbital eccentricity. These orbital effects can radically affect climate, even though the total amount of radiation reaching the Earth, on a yearly average, changes very little.

The Earth also reflects sunlight into space and the reflectivity or albedo of the Earth changes with time. Because of all these factors and because the Earth is nearly spherical, while ~1361 W/m2 of radiation reaches the average distance of the Earth from the Sun (one astronomical unit or 1AU) only a global average of ~340 W/ m2 (~25%) enters the Earth’s atmosphere (Kiehl and Trenberth 1997). While we will be discussing yearly averages of solar radiation reaching the Earth at its average distance from the Sun in this section, it is important to realize that the Earth’s orbit is elliptical and that the radiation reaching the Earth varies a lot during a year. The maximum radiation reaches the Earth in January currently and the minimum in July, the annual range is 88 W/m2. See Figure 6.

Figure 6. The average radiation reaching the Earth from the Sun, by month. Source (Soon, Connolly and Connolly 2015).


Besides the effect of the Earth’s orbit around the Sun, the Sun is a variable star. Solar output and the solar magnetic field vary with time. The solar wind and the solar magnetic field interfere with the Earth’s magnetic field and measures of the Earth’s magnetic field variations (like the “aa-index“) do correlate well with the Earth’s climate (Cliver, Boriakoff and Feynman 1998). Cliver, et al. showed a strong correlation between the minima of the geomagnetic “aa index” and the Earth’s average surface temperature. They also conclude that their data suggests that 50% or more of the global warming since the depths of the Little Ice Age in the 17th century might be due to solar forcing.

The aa-index is a measure of the disturbance level of the Earth’s magnetic field based on magnetometer measurements at two stations on opposite sides of the Earth. The two stations have always been in Australia and England. The aa-index is plotted in Figure 7 and it shows a steady increase from 1900 to 1997-2005. After 2005 it drops dramatically and still has not recovered by 2017. The aa-index data was downloaded from the UK Solar System Data Centre. The orange line is the HADCRUT4 temperature anomaly for the same period. The best correlation is between the minima of the aa-index and average temperature. The sudden drops in aa-index after 1997 and 2005 suggests that the solar component of climate change is decreasing (Cliver, Boriakoff and Feynman 1998).

The aa-index reflects the character of the solar wind and solar flares as observed near the Earth, it is a function of the solar wind speed, squared and the intensity and direction of the magnetic field. It measures the solar influence on the Earth’s magnetic field. It is interesting that the aa-index is very similar in 2009 to what was calculated for 1902, possible reasons for this are discussed in Feynman and Ruzmaikin in a Journal of Geophysical Research: Space Physics paper (Feynman and Ruzmaikin 2014).

Figure 7. Yearly mean aa-index versus HADCRUT global temperature anomalies. Sources: UK Solar System Data Centre, download here. HADCRUT4 data, download here.


Cliver, et al. state that it is well established that geomagnetic activity is driven by the solar wind. The aa-index is well correlated to sunspot peaks as shown in Figure 8 (Cliver, Boriakoff and Feynman 1998). The aa-index is a measure of the Earth’s magnetic field, but the correlation to sunspots suggests it is driven by solar activity and not an internal (to the Earth) effect. The aa-index has increased dramatically from 1900 until 1990, or perhaps 2005, and it may not be a coincidence that the first large decrease in 1997 was also the beginning of the pause in warming. There are problems with the aa-index and it should only be used qualitatively (Love 2011). However, although there probably is some long-term bias in the measurement, it is likely that the upward trend in the aa-index lows is real.

Figure 8. Plots of the aa-index (red) and annual sunspot number (yellow). Source NOAA here.


Besides the solar activity indicators discussed above there are several more, some of these are listed and plotted in Figure 9. The plot is from Joanna Haigh’s report on the “Solar influences on Climate” (Haigh 2011).

Figure 9. Some solar activity proxies. These are plotted so higher solar activity is up and lower activity is down. Source: (Haigh 2011)


Aurorae (the Northern Lights) are also a good qualitative indicator of solar activity. These lights are triggered when the Sun emits high energy, ionized particles that are carried to the Earth by the solar wind and interact with the Earth’s atmosphere. The resulting bright aurorae are associated with an electric current in the upper atmosphere that causes disturbances in the Earth’s magnetic field (Haigh 2011). The magnetic disturbances are also seen in the aa-index plotted in Figures 7 and 8.

The solar wind and the solar magnetic field help protect the Earth from cosmic rays. Cosmic rays produce neutrons when they strike the atmosphere and the neutron count, measured on the surface since 1950, is a function of the level of cosmic rays entering the atmosphere. A high level of cosmic rays suggests a weaker solar magnetic field, solar wind and lower solar activity. The neutron count rate in Figure 9 is inverted as a result.

The isotopes beryllium-10 (10Be) and carbon-14 (14C) are only created in nature by cosmic rays. Humans can create them with atomic bombs, so the records since World War II are tainted, but before 1945, these two isotopes are useful proxies for solar activity. Carbon-14 is recorded worldwide in tree rings. Beryllium-10 is recorded in ice cores through precipitation and has a short lifetime in the atmosphere which makes these records very useful. The scale in Figure 9 is inverted for convenience.

Another possibly useful solar proxy is the level of 10.7cm solar radio flux. It has been measured consistently in Canada since 1947. It is a useful solar activity proxy that can be measured under all weather conditions on the surface of the Earth. The 10.7 cm radio flux from the Sun is plotted with the SORCE satellite TSI in Figure 10.

Figure 10. The 10.7 solar radio flux and the SORCE satellite TSI from 1947 through 2017. Data sources: TSI here and 10.7 flux here.


While the TSI (total solar irradiance) and the 10.7 cm radio flux show the same cycle, the characteristics of the trough and peak appear different. This suggests they are seeing different aspects of the same thing. Generally, this is true of all the solar activity proxies. They see the same solar activity cycles, but they do not agree in detail. Figures 8, 9 and 10 all show this. None of the solar activity proxies are quantitative, including the satellite measurements and the sunspot counts, all are qualitative. The reason is simple, we cannot compute a well-defined measure of solar activity to compare them to. Each proxy roughly correlates to the others and to some aspect of Earth’s climate, but beyond these rough correlations, there is nothing quantitative.

Comparing Solar Activity Proxies

Galactic cosmic rays (GCR) anticorrelate with TSI, with a correlation coefficient of -0.68 (R2=0.46), significant at the 99.99% confidence level (Lockwood 2006). But, as you can see from the correlation coefficient it is not a perfect match, they are influenced by multiple different factors. The physics behind this connection as well as the other solar proxies discussed above is far from clear. But, a general qualitative connection between the aa-index (Figure 7), the sunspot count (Figures 8 and 18), and the other solar proxies is apparent. However, exactly how this works is unknown. It is assumed that this connection can be extended into the past by using 10Be and 14C, but since we have little understanding of these proxies, this is just an assumption (Friedhelm Steinhilber, et al. 2012).

The solar proxies can be used to estimate TSI (total solar irradiance). Then some researchers and the IPCC assume TSI is the only solar variable that influences our climate (IPCC 2013). This is a logical assumption since the total solar energy the Earth absorbs is obviously important to our climate, but it is an assumption. Other natural factors could also play a significant role in our climate, in particular there is considerable circumstantial evidence that variations in the solar magnetic field and variations in high frequency radiation, such as UV and EUV, play a role in climate and are only loosely related to variations in TSI (Lockwood 2006) (Floyd, Tobiska and Cebula 2002). The current emphasis on TSI, ignores evidence that the climate impact of variations in the shorter solar wavelengths, for example UV wavelengths, are different than the longer wavelengths. We have also seen that the solar output of longer wavelengths varies less than the output in the shorter and more energetic wavelengths (Woods and Rottman 2002).

Another key issue is the difficulty in measuring TSI accurately. Figure 11 shows the recent attempts to measure TSI by satellite.

Figure 11. Plots of the (a) original satellite measurements of TSI, corrected to 1AU, and (b) one of the spliced composites made from the raw data. Source (Haigh 2011).


The oscillations in TSI are the ~11-year “solar cycles.” The lows in the cycles are periods of low solar activity and the highs are periods of high activity. The various satellites all show this pattern, but the baseline TSI values are different for every satellite due to calibration and design problems. As (Shapiro, et al. 2011) have written:

“Each of [the satellites] suffered from degradation and individual systematic effects, which renders the direct comparison of the measurements impossible.”

Further, no satellite has functioned for two cycles, so there are no consistent measurements of the TSI baseline. The PMOD composite in the lower part of Figure 11 shows a declining baseline. Scafetta and Willson’s “ACRIM”composite shown in Figure 12 shows an increasing baseline TSI (Scafetta and Willson 2014).

Figure 12. Scafetta and Willson’s ACRIM composite TSI versus the PMOD TSI composite. The two composites show opposite underlying TSI trends. Source: (Scafetta and Willson 2014)


A debate has raged for many years about the TSI baseline, is it increasing, decreasing or flat? Bottom line is we don’t have a clue and will not until the satellite instrumentation becomes more accurate and stable. The new Total and Spectral Solar Irradiance Sensor (TSIS-1) instrument on the International Space Station became operational in March, 2018 and it is recoverable, so the calibration can be checked after it is used and that may help resolve this issue. But, currently we must accept that we do not know if the Sun is varying in a longer-term (longer than the basic ~11-year Schwabe Cycle) way or not. Since the raw data (corrected to 1 AU, the average distance between the Sun and the Earth) spans a total interval of 13 W/m2 and the baselines span 10 W/m2 we really don’t know how much solar radiation reaches the Earth with any degree of accuracy. As described above, 10 W/m2 reaching the top of the Earth’s atmosphere, translates to a global average of 2.5 W/m2 after considering the Earth is approximately a sphere. This error is larger than the IPCC estimate of radiative forcing due to CO2 of 1.68 W/m2 (IPCC 2013, 13). As discussed above, TSI, and the variation in TSI, is obviously important to our climate, but it has not been shown that it is the only solar variation that is important.

Figure 13 is a plot of the aa-index and the SORCE satellite TSI. It suggests the baseline should be increasing, at least through 1990 or so.

Figure 13. Plot of the aa-index and TSI. Data sources: TSI here, aa-index here.


The peaks in the number of sunspots appears to show an increase until 1960 or so and then decrease as shown in Figure 14. Thus, the sunspot record supports the PMOD composite trend and the aa-index supports the ACRIM composite trend. The sunspot number minima have no clear pattern, unlike the minima in the aa-index, this suggests there is little information in the sunspot minima.

Figure 14: A plot of the SILSO sunspot number versus SORCE TSI.


As Figure 10 shows, the 10.7 cm radio flux also supports the PMOD composite trend. The 10.7 cm radio flux (abbreviated F10.7) correlates well with sunspots if solar activity is high, the relationship breaks down when solar activity is low (Svalgaard and Hudson 2010). The F10.7 flux originates in the coronal plasma high in the solar atmosphere.

In Figure 15 we plot a TSI reconstruction by Greg Kopp (Kopp 2018), using a code and methodology written by Coddington (Coddington, et al. 2016) and another TSI reconstruction based on an interpretation by Egorova, et al. in press in Astronomy and Astrophysics in 2018 (Egorova, et al. in press) which is an update to (Shapiro, et al. 2011). Dr. Tatiana Egorova’s reconstruction specifically addresses some criticisms of the Shapiro, et al. reconstruction. These two reconstructions are representative of the extremes of TSI reconstructions produced from sunspot records. Figure 15 compares the two reconstructions to the HADCRUT4 global temperature anomaly and Figure 16 compares the reconstructions to the aa-index.


Figure 15. The Coddington/Kopp TSI reconstruction and the Egorova, et al. reconstruction compared to the HADCRUT4 global temperature anomaly. Coddington/Kopp data is here, the Egorova, et al. data is from Dr. Tatiana Egorova, Physikalisch-Meteorologisches Observatorium Davos World Radiation Center, and the HADCRUT4 data is from here.


TSI reconstructions like the Coddington/Kopp and Egorova, et al. reconstructions shown in Figures 15 and 16, that go back into the pre-satellite era, must rely on proxies of solar activity. We have already discussed the aa-index, sunspot numbers, the 10.7 cm radio flux, neutron counts, aurorae, and the cosmogenic isotopes 10Be and 14C. In addition, there are other measures, related to sunspots that are important. These include the solar equatorial rotation rate, the structure of the sunspots, the sunspot decay rate, and the number of sunspots without umbrae (Hoyt and Schatten 1993). Three important solar proxies allow solar activity to be modeled back to the mid-1700s, the length of the solar cycle, the decay rate of the solar cycle and the mean level of solar activity. It is important to recognize that these proxies are not perfect indicators of solar activity or TSI and that only 50% of the solar variance is modeled. Further, we must realize that although it is obvious that TSI influences our climate we don’t know how much of climate change is influenced by TSI and related factors.


Figure 16. The Coddington/Kopp TSI reconstruction and the Egorova, et al. (2018) reconstruction compared to the UK Solar System Data Centre aa-index.


In Figure 16 we plot the aa-index in gray, it compares much better with the Egorova, et al. TSI reconstruction than the Coddington/Kopp reconstruction. In Figure 15 we see that the HADCRUT4 global temperature anomaly also compares better with the Egorova, et al. reconstruction. These are only two of many TSI reconstructions that have been developed in recent years. But as Soon, Connolly, and Connolly noted in their Earth Science Reviews paper in 2015 (Soon, Connolly and Connolly 2015), all the various TSI reconstructions fall into two large groups, the high solar variability group, like Egorova, et al. and the low solar variability group like Coddington/Kopp. For more examples of reconstructions in each group and a discussion of them I refer you to their paper.

For now, it is important to realize that the particular TSI reconstruction used to compute human influence on climate change is very important. The Egorova, et al. TSI reconstruction can explain much of the warming of the last 150 years by itself, only requiring a small contribution from carbon dioxide emissions to balance the energy flow. However, if one chooses to use a low solar variability reconstruction, like Coddington/Kopp, then a larger contribution from carbon dioxide and other anthropogenic forces is required. As you might have guessed the IPCC AR5 computation of human influence uses a low solar variability reconstruction as illustrated in Figure 4 of Part A. There is little, or no, justification in the data for choosing one over the other. Critics of the Egorova, et al. reconstruction admit that it is within the variability seen in Sun-like stars and that we do not have enough data at this point to discount this TSI reconstruction (Judge, et al. 2012). Judge, et al. conclude, referring to the (Shapiro, et al. 2011) reconstruction, which is very similar to the (Egorova, et al. in press) reconstruction:

“… when looking for long-term (>22 year., say) variations among the stars, there is no substitute for a time series of the duration needed. Thus, to see the kinds of large secular changes reconstructed by [Shapiro, 2011] over some 30-50-year periods … actually occur in stars, one must observe the stars for 30-50 years. Unfortunately, we have data only for 15 years or so, and equally unfortunately, decadal time scales [also] correspond to the typical variations associated with stellar spot cycles (Baliunas et al. 1995). A reasonable interpretation of our result is that we cannot yet discount such large secular solar variations on the basis of a comparison with stars, using the existing photometric data. It appears necessary to continue to observe this stellar sample for a couple more decades …” (Judge, et al. 2012)

So, we really don’t know how much TSI varies over long periods of time and will not know until we gather more data. In the words of (Egorova, et al. in press):

“There is no consensus on the amplitude of historical solar forcing. The estimated magnitude of the total solar irradiance difference between the Maunder minimum and the present time ranges from 0.1 to 6 W/m2 making the simulation of the past and future climate uncertain.”

We also do not know enough about how TSI affects climate over other factors, nor do we know enough about how TSI varies as a function of sunspot records.

One might argue over which sunspot record is better, but that matters little, the big questions are what do sunspot records tell us about solar activity and how does climate vary with solar activity? Are small changes in TSI amplified in some way? Or are the changes larger than we have currently measured? We do not know. The main question is quantitative, how much does solar variation affect the climate versus other factors like human activities? In the words of Shapiro, et al.:

“During the past 10 000 years, the Sun has experienced the substantial variations in activity and there have been numerous attempts to reconstruct solar irradiance. While there is general agreement on how solar forcing varied during the last several hundred years – all reconstructions are proportional to the solar activity – there is scientific controversy on the magnitude of solar forcing.”

That really hits the nail on the head. Qualitatively, the indicators of solar activity follow the same pattern, the pattern of solar change is undisputed. The magnitude of the change and the magnitude of impact on Earth’s climate are unknown. Sunspot statistics help us show a pattern of solar activity, they do not help us determine the absolute magnitude of long term variations in solar activity. To get at that longer-term trend we need to make assumptions, if we make the assumptions Shapiro, et al. made we get a highly variable Sun, if we make the assumptions Coddington made, we get a quiet Sun. No one can know which is correct, our current TSI records are too imprecise and too short.

By comparing the aa-index with the sunspot record we can see a distinct solar minimum at 1902, followed by one that occurs between 1997 and 2006, with the lowest point (so far) at 2008-2009. Feynman and Ruzmaikin call this the Centennial Gleissberg Cycle (Feynman and Ruzmaikin 2014). Whether or not this is the cycle Gleissberg described is controversial, so we will simply call it the Centennial Cycle. While the name “Gleissberg” is used for a number of potential solar cycles, a centennial cycle of about 104-106 years is apparent in 10Be and 14C records, see Javier here, (McCracken, et al. 2013) and (Feynman and Ruzmaikin 2014). Figure 17 shows the past 150 years of sunspot and aa-index records and they seem to show a cycle of this length. Besides the cosmogenic isotope record there is other evidence for the Solar Centennial Cycle as discussed in Feynman and Ruzmaikin (2014).

Figure 17. The SILSO sunspot record and the UK Solar System Data Centre aa-index (see here) showing the ~1900 extended solar minimum and the ~2000 extended solar minimum.


The presence and persistence of the cycle or oscillation is debated, as it does not always have a strong signature. The Centennial Cycle is persistent but has reduced amplitude in periods without grand minima.

It is interesting that the sunspot peaks increase to about 1960 and the sunspot minima are always near zero and have no distinct trend. The aa-index provides more detail in the solar activity lows and the lows in the aa-index increase (except one) until 1990 and then fall dramatically until 2006-2009. The HADCRUT4 temperatures increase from 1960 to 1997 and, except for a large El Nino in 1998 and again in 2016 they are flat, to slightly increasing. Thus, the aa-index might be giving us more climate information than the sunspot record. I have not read an explanation for the anomaly in both records between 1960 and 1979, but this was a cold period in the Northern Hemisphere (see Figure 7).

It is clear that sunspot lows contain little information because the solar cycle did not stop, and the modulation potential did not fall to zero during the Maunder minimum (Shapiro, et al. 2011) when there were very few sunspots, see Figures 9 and 18. It appears that sunspots do not form below some lower limit of solar activity. They may not form at all below a local solar magnetic field strength of 1,500 Gauss (Penn and Livingston 2006) (Soon, Connolly and Connolly 2015). Figure 18 shows the group sunspot number in red during the Maunder Minimum. It also shows the solar magnetic field modulation parameter (ɸ) from 14C (yellow), 10Be (cyan), and a model of solar flux in green. The black line is the mean of a composite solar modulation parameter for each solar cycle. As you can see sunspots are essentially flat-lined from 1645 to 1700, but the modulation measures are active and greater than zero (Lockwood, Owens, et al. 2011). There is considerable solar variability during the Maunder Minimum.


Figure 18. The Maunder Minimum group sunspot number (red, RG), a composite solar modulation parameter (ɸS) in black, and the modulation parameter from 14C in yellow, 10Be in cyan, and a model of solar flux in green. The gray bars are even numbered Schwabe cycles that are numbered at the top of the figure. Source (Lockwood, Owens, et al. 2011).


Conclusions and Discussion

I’ve tried to provide a brief overview of the actual data used to support the poorly defined anthropogenic “greenhouse gas effect” hypothesis in part A and the natural climate change due to solar variability hypothesis in this post.

There has been a great deal of debate about natural (mostly solar) influence on our climate versus human influence. The key point of this article is that it is likely that both solar variability (including orbital effects) and humans affect the climate. Other major plants and animals, like trees and phytoplankton, have some influence as well. Depending upon the time frame being discussed it is also likely that ocean circulation affects the climate on scales of several years (ENSO) to over a thousand years (the thermohaline circulation, tidal effects, and ocean CO2 uptake). It is not clear which of these influences is the largest, we all have an opinion, but there is no quantitative data to support the idea that any of them are dominant.

The Sun is a variable star but measuring the amount of variation and its effect on climate eludes us, especially in the longer term. Multiple proxies confirm this, although some solar proxies appear to vary in phase with climate proxies, we don’t really know much more than that. These are all proxies and hypothetical mechanisms that provide cause-and-effect between the Sun’s variability and climate abound, but none are quantitative or have been proven by making predictions that are later verified. Since the modeled difference between “natural” climate change and observations of global mean temperature are used to compute human influence, the uncertainty in the magnitude of solar and other natural climate change casts considerable doubt on the claimed magnitude of human influence on the climate.

The long and short of it is, we can be reasonably certain that human greenhouse emissions affect climate, but we don’t know by how much. We can also be reasonably certain that solar variability affects climate, but we don’t know by how much. We know that most thermal energy is absorbed by the oceans and that it redistributes this energy around the Earth, but we don’t know how much or how fast. The debate over the cause of climate change is fierce for the simple reason that we don’t know very much about it.

There is a reasonable chance that we will enter a solar activity minimum comparable to the Maunder Minimum soon (Lockwood, Owens, et al. 2011), and if this occurs it will be an opportunity to evaluate the solar effect on our climate. Usoskin, et al. defined a grand solar minimum as a period when the smoothed sunspot number is less than 15 for two or more consecutive decades (Usoskin, Solanki and Kovaltsov 2007). Previous periods that contain clusters of grand solar minima are 8,300BP to 7,100BP (1,200 years), 6,300BP to 4,700BP (1,600 years), and 3,300BP to 2,200BP (1,100 years). The current period began about 1,040BP and could well go on for another 100 years or more. If the solar effect can be quantified, we will be able to estimate the total effect of the oceans, humans and other plants and animals. From this composite measure we may be able to eventually extract the human impact. But, for now, we simply do not know what it is.

As my friend Javier says, debate, arguments and disagreements fuel science. Let the debate continue but remember to disagree in an agreeable fashion. We will get to the truth quicker and healthier that way. If I disagree with you, it isn’t personal, I just disagree. Science is easier if you don’t “marry your opinions” and you carefully separate what you know from what you don’t know. Do some research, write a post explaining why you are correct, and we will respond. This is how science is supposed to work, unless you are Michael Mann or the city of San Francisco, then you sue anyone who disagrees with you.

The bibliography can be downloaded here.

The data used to make many of the figures in this post can be downloaded here.

Andy May has just published his first book: “Climate Catastrophe! Science or Science Fiction?” It is available from and

Javier provided many helpful comments on this post.

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