IPCC Politics and Solar Variability

By Andy May

This post is about an important new paper by Nicola Scafetta, Richard Willson, Jae Lee and Dong Wu (Scafetta, Willson and Lee, et al. 2019) on the ACRIM versus PMOD total solar irradiance (TSI) composite debate that has been raging for over 20 years. ACRIM stands for Active Cavity Radiometer Irradiance Monitor, these instruments recorded solar irradiance from space for many years. Richard Willson is the principle investigator in the laboratory that studied the results, Nicola Scafetta worked in the laboratory, until he accepted a position as a professor at the University of Naples Federico II.

The paper casts a spotlight on the political problems at the IPCC. In order to properly put the ACRIM vs PMOD debate into context and to show why this obscure and complicated scientific and engineering debate is important, we need to also discuss the messy politics within and between the IPCC and the UNFCCC.

The ACRIM composite TSI record shows an increase in solar activity from the 1980s until about the year 2000, when it flattens and then begins a decline. The ACRIM composite was introduced by Richard Willson in an article in Science in 1997 (Willson 1997). This composite was updated in 2014 by Scafetta and Willson (Scafetta and Willson 2014).

The next year, 1998, a rival composite was published by Claus Fröhlich and Judith Lean (Fröhlich and Lean 1998), it uses the same data but shows a declining solar irradiance trend from the 1986 to 1997. The late Claus Fröhlich worked for the Physikalisch-Meteorologisches Observatorium Davos and World Radiation Center, which is abbreviated “PMOD” and this is where the composite gets its name. The two composites are compared in Figure 1.

Figure 1. A comparison of the ACRIM and PMOD total solar irradiance (TSI) composites. The upper graph shows the ACRIM composite increasing from 1986 to 1997, contrast this with the slightly decreasing trend shown in the lower PMOD graph. Most of the difference between the two is how they handle the “ACRIM gap” from 1989.5 to 1991.8. Source: Modified from (Scafetta, Willson and Lee, et al. 2019), their figure 3.

There were three ACRIM satellites and their measurements are accurate and generally undisputed, except by the PMOD group. The dispute between PMOD and ACRIM revolves around how to handle the “ACRIM gap” from 1989.5 to 1991.8. This gap was created when the Challenger disaster delayed the launch of the ACRIM2 instrument. Another significant difference is the PMOD team chose to use the Virgo results in place of ACRIM3. To emphasize why this dispute matters, let us look at what the authors say. The first quote is from the abstract of Richard Willson’s first ACRIM composite paper in Science:

“The trend follows the increasing solar activity of recent decades and, if sustained, could raise global temperatures. Trends of total solar irradiance near this rate have been implicated as causal factors in climate change on century to millennial time scales.” (Willson 1997)

Judith Lean, who was the lead author in charge of the relevant section of the IPCC AR4 report (Chapter 2.7, p. 188, “Natural Forcings”) and a Senior Scientist for Sun-Earth System Research at the U.S. Naval Research laboratory, and the late Claus Fröhlich, wrote the following in the conclusion of their 1998 PMOD introductory paper (Fröhlich and Lean 1998):

“these results indicate that direct solar total irradiance forcing is unlikely to be the cause of global warming in the past decade, the acquisition of a much longer composite solar irradiance record is essential for reliably specifying the role of the Sun in global climate change. Detection of long-term solar irradiance trends and validation of historical irradiance reconstructions rely on the acquisition of a much longer irradiance time series than is presently available.” (Fröhlich and Lean 1998)

Judith Lean later told a NASA reporter, Rebecca Lindsey, one of the reasons she decided to help create an alternative TSI composite:

“The fact that some people could use [the ACRIM group’s] results as an excuse to do nothing about greenhouse gas emissions is one reason, we felt we needed to look at the data ourselves. Since so much is riding on whether current climate change is natural or human-driven, it’s important that people hear that many in the scientific community don’t believe there is any significant long-term increase in solar output during the last 20 years.” (Lindsey 2003)

It seems that Judith Lean had some political motivation to challenge the ACRIM composite. But there is more to the story and to tell it properly we need to review the IPCC climate change reports briefly. As we will see the only way the IPCC could compute how much of climate change is human-caused and how much is natural, is to model the natural component and subtract it from the observations to derive the human component. The natural component is very complex and works on multiple time frames, at its root it is driven by solar variability and ocean oscillations, these are poorly understood and the IPCC and CMIP models may not be accurately modeling it. The IPCC does a lot of research on many topics, but we will focus only on the most important, how much do humans influence climate change?

The first IPCC Report

The IPCC (Intergovernmental Panel on Climate Change) is an independent body founded under the auspices of the World Meteorological Organization and the United Nations Environment Programme. The IPCC states that its goal is:

“The [IPCC] is the international body for assessing the science related to climate change. The IPCC was set up in 1988 by the World Meteorological Organization (WMO) and United Nations Environment Programme (UNEP) to provide policymakers with regular assessments of the scientific basis of climate change, its impacts and future risks, and options for adaptation and mitigation.” (IPCC 2020)

The UNFCCC (United Nations Framework Convention on Climate Change), which is not directly connected to the IPCC, states that the mission of the IPCC is:

“The Intergovernmental Panel on Climate Change (IPCC) assesses the scientific, technical and socioeconomic information relevant for the understanding of the risk of human-induced climate change.” (UNFCCC 2020)

According to the IPCC they investigate the risks of climate change without any mention of the cause. According to the IPCC they advise on both mitigation (of fossil fuels presumably) and adaptation, such as sea walls, levees, air conditioning, heating etc. According to the UNFCCC they are to investigate human-induced climate change, these statements are different. In a similar fashion the two bodies define “climate change” differently. The politically oriented UNFCCC defines it as

“[A] change in climate which is attributed directly or indirectly to human activity.” (United Nations 1992)

This contrasts with the IPCC definition of climate change, which is less political and more scientific:

“A change in the state of the climate that … persists for an extended period, typically decades or longer. Climate change may be due to natural internal processes or external forcings, or to persistent anthropogenic changes in the composition of the atmosphere or in land use” (IPCC 2012)

We can easily see that the UNFCCC and the IPCC have a potential conflict. In fact, if the IPCC does not find that humans have a significant impact on climate, the UNFCCC has no reason to exist. In the first IPCC report, published in 1990 and usually called “FAR” for “first assessment report,” they were unsure whether global warming was human-caused or natural, their conclusion was:

“global-mean surface air temperature has increased by 0.3°C to 0.6°C over the last 100 years … The size of this warming is broadly consistent with predictions of climate models, but it is also of the same magnitude as natural climate variability. … The unequivocal detection of the enhanced greenhouse effect from observations is not likely for a decade or more.” (IPCC 1992, p. 6)

Given the wide range of opinions in the scientific community and the lack of any solid evidence of human influence on climate, this was a logical conclusion. But this statement caused political problems for the UNFCCC. Its entire reason for existence was human-caused climate change. If the IPCC could not determine climate change was human-caused, they were in trouble. Enormous pressure was put on the scientists working on subsequent reports to attribute climate change to human activities.

The political state-of-mind at the time can be seen with this quote from Senator Tim Wirth at the 1992 U.N. Earth Climate Summit in Rio de Janeiro:

“We have got to ride the global warming issue. Even if the theory of global warming is wrong, we will be doing the right thing in terms of economic policy and environmental policy.” (National Review Editors 2010)

The Second Report, SAR

All subsequent reports did attribute most climate change and global warming to humans. The second report (“SAR“) barely stepped over the threshold with the following conclusion:

“The balance of evidence suggests a discernible human influence on global climate.” (IPCC 1996, p. 4)

Ronan and Michael Connolly (Connolly 2019) explain that this statement was included in SAR because Benjamin Santer, the lead author of the SAR chapter on the attribution of climate change, presented some unpublished and non-peer-reviewed work he had done that he claimed identified a “fingerprint” of the human influence on global warming. His evidence consisted of measurements that showed lower atmosphere (tropospheric) warming and upper atmosphere (stratospheric) cooling from 1963-1988. This matched a prediction made by the climate models used for SAR. Supposedly, additional CO2 in the atmosphere would increase warming in the troposphere and increase cooling in the stratosphere. He did not connect these measurements to human emissions of CO2, or to CO2 at all, he simply said that they showed something like what the models predicted.

It was weak evidence, and it was evidence that had not been peer-reviewed or even submitted for publication, but it was accepted. Further, the paper admits that they did not quantify the relative magnitude of natural and human influences on climate. They had simply shown a statistically significant similarity between some observations and their model’s predictions.

The political pressure from the UNFCCC to blame humans was unrelenting and they had to do something. There were some persistent rumors that someone was secretly changing the text in SAR after the authors had approved the text and doing it in such a way that supported the conclusion above and removed dissenting statements. These allegations may or may not be true.

Unfortunately, when Santer’s paper (Santer, et al. 1996) was finally published it ran into a firestorm of criticism. In particular, Patrick Michaels and Paul Knappenberger (Michaels and Knappenberger 1996) pointed out that the tropospheric “hot spot” that comprised Santer et al.’s “fingerprint” of human influence disappeared if the 1963-1987 range was expanded to the full range of available data, 1958-1995. In other words, it appeared Santer, et al. had cherry-picked their “fingerprint.”

Besides cherry-picking a portion of the data, there were other problems with Santer et al.’s interpretation. The warming and cooling trends that they identified may have been natural, as explained by Dr. Gerd R. Weber. The beginning of Santer, et al.’s selected period was characterized by volcanism and the end of the period by strong El Ninos.

The Third Report, TAR

The IPCC had been embarrassed by the revelation that Santer et al. had fudged the data in SAR, but they still needed some way to blame humans for climate change. They found another study that seemed to make the case and highlighted it in the third report, called TAR (IPCC 2001). In 1998, Michael Mann, Raymond Bradley, and Malcolm Hughes published a Northern Hemisphere temperature reconstruction of the past 600 years (Mann, Bradley and Hughes 1998), based primarily on tree rings. This paper is often abbreviated as MBH98. This reconstruction appeared to show that the recent warming period was unusual, so it was easy to assume humans did it.

A simplified version of the MBH98 graph, often called the “Hockey Stick” because of its shape, extended to 1000 AD (Mann and Bradley 1999, MB99) was featured prominently on page 3 of the TAR Summary for Policymakers, it is reproduced here as Figure 2.

Figure 2. The infamous “Hockey Stick” from (Mann and Bradley 1999). It purports to show that the recent warming is unusual. Source (IPCC 2001, p. 3).

The reconstruction in Figure 2 generated a firestorm of criticism that made the Santer et al. debacle look like a campfire. But the graph was used to increase the certainty that human greenhouse emissions caused recent warming, the conclusion of TAR:

“In the light of new evidence and taking into account the remaining uncertainties, most of the observed warming over the last 50 years is likely to have been due to the increase in greenhouse gas concentrations.” (IPCC 2001, p. 699)

The criticisms of the MBH98, MB99 and TAR temperature reconstructions are too numerous to list here, but they were devastating. An entire 320-page book by Mark Steyn, A Disgrace to the Profession, was written to list them (Steyn 2015). The fraudulent Hockey Stick and Michael Mann were even memorialized in a song.

The Hockey Stick not only appeared as Figure 1 of TAR but was prominently displayed in Al Gore’s movie “An Inconvenient Truth.” Steyn’s book clearly shows that the graph and the movie have been thoroughly discredited by hundreds of scientists who attempted and failed to reproduce Michael Mann’s hockey stick. Further, MB99 attempts to overturn hundreds of papers that describe a world-wide Medieval Warm Period from around 900AD to 1300AD.

When Michael Mann’s hockey stick was chosen to be Figure 1 of the TAR summary for policy makers, Mann had just received his PhD. As many have noted, the ink was not yet dry on his diploma. Yet, in addition, he was made one of the lead authors of the very section of TAR that presented his hockey stick (see the TAR Chapter 2 author list on page 99 and figure 2.20 on page 134). As a result, it was up to him to validate his own work.

The MBH98 paper was deeply flawed. In 2003, Soon, et al., presented evidence that the Little Ice Age and Medieval Warm Period were global events (Soon, Idso, et al. 2003). This meant the flat hockey stick handle was incorrect.

Two years later, in 2005, it was thoroughly debunked by Steve McIntyre and Ross McKitrick (McIntyre and McKitrick 2005). They showed that using the statistical technique invented by Michael Mann even random number series (persistent trendless red noise) will generate a hockey stick. Basically, Mann had mined many series of numbers looking for hockey stick shapes and gave each series that had the shape he wanted a much higher weight, up to a weighting factor of 392! This was truly a case of selecting a desired conclusion and then molding the data to fit it. Prominent statisticians Peter Bloomfield, Edward Wegman and Professor David Hand said Michael Mann’s method of using principle components analysis was inappropriate and misleading and exaggerated the effect of recent global warming.

The Fourth Report, AR4

By the time the fourth report was written, the MBH98 hockey stick was thoroughly discredited and in light of this, the lead author of the relevant chapter (Chapter 6), Keith Briffa, admitted that the recent warming was not unusual. He wrote:

“Some of the studies conducted since the Third Assessment Report (TAR) indicate greater multi-centennial Northern Hemisphere temperature variability over the last 1 kyr than was shown in the TAR” (IPCC 2001, Ch. 6 p. 1)

It is a weak admission of failure, as we might expect, but he acknowledges that the hockey stick handle was too flat and that temperatures during the Medieval Warm Period might have been higher than today. This admission certainly took the wind out of the sails of TAR, so what can they do now? There seemed to be no data to support the idea that humans were causing global warming.

The IPCC decided to emphasize their climate models in AR4, rather than paleo-temperature reconstructions, atmospheric “fingerprints,” or any other observational data. Human causation was the goal, they needed to shape the evidence to support it. For twenty years they had looked for evidence that humans were the major cause of recent warming and failed to find any. But, they “discovered” that if their climate models were run without any human climate forcings the resulting computed global temperatures were flat. You can see this in Figure 3b.

Figure 3. The IPCC model calculation of human influence on climate change. There are two model averages shown in both graphs. The blue one is from AR4, CMIP3. The red from AR5, CMIP5. Both are compared to observations, in black. The upper graph shows models that include both human and natural climate forcings, the lower shows natural forcings only. Source: (IPCC 2013, Ch. 10, page 879).

Then they can rerun the model with human plus natural climate forcings (Figure 3a) and the model temperatures will rise. Voila! We have shown human-caused global warming and did not need a shred of observational data! With this proof they triumphantly write:

“Most of the observed increase in global average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations.” (IPCC 2007, p. 10)

We have shown the comparison between models and observations from AR5 in Figure 3 already. In Figure 4 we show the similar figure from AR4.

Figure 4. A comparison of a natural warming model (b) to human plus natural (a) and observations in black. Source: (IPCC 2007, p. 684).

In these climate simulations the only natural forcings, that make any difference, are volcanic eruptions. Solar variations and ocean oscillations are assumed to net to zero over the period studied. The volcanic eruptions are labeled in Figure 4. The lack of a robust model of natural climate change can be seen in the poor model-observation match from 1910 to 1944 in both Figures 3 and 4. Given the abundant literature supporting significant solar variability (Soon, Connolly and Connolly 2015) and natural ocean oscillations (Wyatt and Curry 2014) it is easy to doubt any calculation of human forcing made from the models shown in Figures 3 and 4. Thus, the conclusion given in AR4 and the similar conclusion reached with similar logic in AR5 are suspect.

The Fifth Report, AR5

AR5 is essentially a redo of AR4, they do the same thing, take the same approach. No new data supporting human involvement in climate change is presented, the same models are rerun with a few tweaks here and there and they reach essentially the same conclusion for the same reasons as in AR4:

“More than half of the observed increase in global mean surface temperature (GMST) from 1951 to 2010 is very likely due to the observed anthropogenic increase in greenhouse gas (GHG) concentrations.” (IPCC 2013, p. 869)

TSI and the IPCC

Soon, Connolly and Connolly (Soon, Connolly, & Connolly, 2015) identified several valid, peer-reviewed solar activity reconstructions that could explain a lot of the warming since 1951 and earlier. These reconstructions were ignored by the IPCC.

At the time AR4 was being written, the accepted solar activity (TSI or Total Solar Irradiance) composite from satellite solar radiation measurements was the ACRIM composite shown in Figure 1 (Willson 1997). It showed an increasing trend of solar activity from the 1980s to the 1990s. This supported the idea that at least some of the warming seen then was due to increasing solar activity. Scafetta and Willson in 2014 reported:

“Our analysis provides a first order validation of the ACRIM TSI composite approach and its 0.037 %/decade upward trend during solar cycles 21–22 [1986-1997]. The implications of increasing TSI during the global warming of the last two decades of the 20th century are that solar forcing of climate change may be a significantly larger factor than represented in the CMIP5 general circulation climate models.” (Scafetta and Willson 2014)

As we saw above, Judith Lean led the development of the rival PMOD TSI composite and admitted that part of the reason was political. Fröhlich and Lean conclude that TSI is unlikely to have caused any global warming, then say they do not have enough data to be sure. While the ACRIM composite was more accepted at the time, the PMOD composite also had a lot of support.

The PMOD and ACRIM composites are complex because the satellite measurements must be scaled properly so they fit together end-to-end. The process is discussed in some detail in Scafetta and Willson’s 2014 paper and in (Scafetta, Willson, Lee, & Wu, 2019). The process used by Fröhlich and Lean is different, they make changes to the raw data that are not supported by the satellite teams (Scafetta, Willson and Lee, et al. 2019). This is an important controversy and it directly affects the calculation of human influence on climate. Reconstructions of solar activity depend heavily upon proxies, how the proxies are converted to TSI in Watts per square meter (W/m2), depends heavily on the modern TSI composite used. It appears the IPCC and CMIP decision to ignore the ACRIM composite and the more active TSI reconstructions was a political decision. As we saw above, Judith Lean admitted as much to Rebecca Lindsey of NASA. The Hoyt and Schatten (Hoyt and Schatten 1993) “active” reconstruction, calibrated to ACRIM, is compared to the “quiet” reconstruction by (Wang, Lean and Sheeley 2005) and (Kopp and Lean 2011) in Figure 5. The latter, quiet reconstruction, is the one the IPCC encourages the climate modelers to use.

Figure 5. Two example TSI reconstructions extended to 1700AD using proxy data tied to satellite measurements. The green curve is from (Wang, Lean and Sheeley 2005), but rescaled to the TSI base value given in (Kopp and Lean 2011). The red curve is from (Scafetta and Willson, ACRIM total solar irradiance satellite composite validation versus TSI proxy models 2014, Their Figure 16). The green TSI curve is the curve the CMIP5 organizers strongly recommended that the climate modelers use for AR5 (Scafetta and Willson 2014). Notice how short the period of actual satellite measurements is relative to the reconstructions (blue line).

As explained by Ronan and Michael Connolly (Connolly 2019), of the five models that contributed to the “natural forcings only” AR4 dataset illustrated in Figure 4(b), four used low variability solar reconstructions recommended by Lean. The quieter or low variability reconstructions tend to rely heavily on sunspot numbers and other measurements that are representative of the active regions of the Sun for their TSI reconstructions (Soon, Connolly, & Connolly, 2015) and (Scafetta, Willson and Lee, et al. 2019). This creates problems since when there are no sunspots, their number (zero) implies no solar variation, yet in periods of no sunspots, other indications of solar activity show there is still solar variability, see Figures 6, 7, and 8. The more active reconstructions used proxies that are sensitive to the less active portions of the Sun and are less reliant on sunspot number (Scafetta, Willson and Lee, et al. 2019).

The next three figures show recent TSI measurements by the SORCE TSI instrument, which measured TSI continuously, with one notable gap in 2013, from 2003 until February of 2020. The first figure shows an overview of the data, the next shows periods of zero sunspots before cycle 24 and the last figure shows the recent period of zero sunspots. Notice that the TSI varies quite a lot even when there are no sunspots.

Figure 6. Overview of Solar Cycle 24. The gray line is TSI from SORCE and the blue line is the sunspot record from SILSO.

Figure 7. The solar minimum before Solar Cycle 24. Notice the activity in TSI when there are no sunspots.

Figure 8. The solar minimum at the end of Solar Cycle 24, notice the TSI activity when there are no sunspots.

By ignoring the more active TSI reconstructions, the IPCC and CMIP have not considered a major source of uncertainty. Both the ACRIM- and PMOD-based reconstructions should have been used, or the reason for rejecting the ACRIM composite altogether explained to everyone’s satisfaction.

The use of models to “show” that humans are causing climate change is perfect for the politically motivated IPCC and UNFCCC since you can get a model to do anything you want if you feed it the appropriate data and tweak the adjustable parameters properly. One of the key elements to adjust in the IPCC models was solar variability. If it is invariant, which is difficult for a variable star like the Sun, most of the warming can be attributed to humans.

ACRIM v. PMOD

Whether the ACRIM or the PMOD composite is used to calibrate the solar proxies makes a difference (Fröhlich and Lean 1998). It is not the sole reason for the difference between the two representative solar reconstructions shown in Figure 5, but it is a big part of it. Scafetta et al. (Scafetta, Willson and Lee, et al. 2019) take a look at the differences in the two composites and provide some evidence that the ACRIM composite is preferred.

The most significant difference between the two composites is the overall TSI trend from 1986 to 1997, these are the minima before and after Solar Cycle 22, see Figure 1. The reason that they are so different is that they handle the so-called “ACRIM gap” differently. The ACRIM gap, from mid-1989 to late 1991, had no functioning high-quality TSI-measuring satellite. Only the Nimbus7/ERB and the ERBS/ERBE satellites were functioning and they had opposite trends. The Nimbus7/ERB measurements trended up 0.26 W/m2 per year and the ERBS/ERB trends down 0.26 W/m2 per year (Scafetta, Willson and Lee, et al. 2019). This difference was enough that one of the satellites had to be wrong.

The PMOD group used solar proxies and a proxy model to attempt to show that the Nimbus7/ERB instrument had problems. During the gap, the PMOD group then significantly changed the TSI measurements of this instrument and changed the slope of the readings in the gap from positive to negative (Fröhlich and Lean 1998). Then they further modified measurements from the very accurate ACRIM1 and ACRIM2 instruments, claiming they had sensor problems. The PMOD modifications were made without consulting with the original satellite experiment science teams or examining the raw data. Their idea was that their solar proxy models were superior to the data and could be used to “fine-tune” the observations (Scafetta, Willson and Lee, et al. 2019) and (Fröhlich and Lean 1998). Regarding the “corrections” the PMOD team made to the Nimbus7/ERB satellite data, the leader of the Nimbus7 team, Douglas Hoyt, wrote:

“[The NASA Nimbus7/ERB team] concluded there was no internal evidence in the [Nimbus7/ERB] records to warrant the correction that [PMOD] was proposing. Since the result was a null one, no publication was thought necessary. Thus, Fröhlich’s PMOD TSI composite is not consistent with the internal data or physics of the [Nimbus7/ERB] cavity radiometer.” (Scafetta and Willson 2014, Appendix A)

In Lean, 1995:

“Deviations of the SMM and UARS data from the reconstructed irradiances in 1980 and 1992, respectively, may reflect instrumental effects in the ACRIM data, since space-based radiometers are most susceptible to sensitivity changes during their first year of operation.” (Lean, Beer and Bradley 1995)

Yes, Judith Lean is saying that her models “may reflect” that the instruments are wrong. Modifying the measurements to match an unvalidated model is not an accepted practice. Besides the original “corrections” to the satellite measurements made by Fröhlich and Lean, there are new “corrections” suggested by Fröhlich (Fröhlich 2003). Which set should we use? Scafetta, et al. comment on the “corrections:”

“a proxy model study that highlights a discrepancy between data and predictions can only suggest the need to investigate a specific case. However, the necessity of adjusting the data and how to do it must still be experimentally justified. By not doing so, the risk is to manipulate the experimental data to support a particular solar model or other bias.” (Scafetta, Willson and Lee, et al. 2019)

The ACRIM group and Douglas Hoyt believe that the upward trend in the Nimbus7/ERB data is more likely correct than the modeled downward trend created by the PMOD group. Further, the Nimbus7/ERB trend is supported by the more accurate ACRIM1 instrument. The downward trend of the ERBE instrument is in the opposite direction of the ACRIM trend and was caused by well-documented degradation of its sensors. The ACRIM team also investigated the PMOD “corrections” to the ACRIM1 and ACRIM2 data and found that they were not justified.

Conclusions

The IPCC appears to have hit a dead end. They have been unable to find any observational evidence that humans contribute to climate change, much less measure the human impact on climate. They are reduced to creating models of climate and measuring the difference between models that include human forcings and those that do not. This was the approach taken in both AR4 and AR5, the approach was similar and the results similar. AR5 was simply a redo of AR4, without any significant improvement or additional evidence.

It appears that a significant problem with the AR4 and AR5 results was they used low variability solar variability reconstructions and ignored the equally supported high variability reconstructions. This reduces the computed natural component of climate change and enlarges the computed human component. Part of the problem with the low variability reconstructions is they are “tuned” to the PMOD TSI composite, which is also based upon a proxy model. Thus, we have used a model to alter satellite measurements, then used the altered measurements to calibrate a proxy model. The proxy model is then projected back to 1700AD. Not very convincing.

What Scafetta, et al. did in their paper was reverse the PMOD process. They used the uncontroversial TSI observations before and after the ACRIM gap period to empirically adjust the low-frequency component of the TSI proxy models to fill in the gap. Their process explicitly allows for the models to be missing a slow varying component in the quiet sun regions, allowing variation from solar minimum to solar minimum. They tackled the ACRIM gap problem without using the Nimbus7/ERBS or ERBS/ERBE lower quality TSI records. They simply evaluated how the proxy models reconstruct the ACRIM gap. The proxy models underestimated the TSI increase to the solar cycle 22 peak and overestimated the decline. There were also problems properly reconstructing solar cycles 23 and 24.

Scafetta, et al. then adjusted the models to correct the mismatch (rather than changing the data!) and produced a TSI composite that agreed well with the ACRIM composite and another composite created by Thierry Dudok de Wit (Dudok de Wit, et al. 2017). Both the new composite and the Dudok de Wit composite show an increasing trend from 1986 to 1997, like the ACRIM composite and unlike PMOD.

The new composite shows an increase in TSI of 0.4 W/m2 from 1986 to 1996 and twice that much from 1980 to 2002. It decreases after 2002. This is like the ACRIM composite. The PMOD composite goes down from 1986 to 1996. PMOD appears to have been discredited by this paper, it will be interesting to follow the discussion over the next year or so.

The Bibliography for this post can be downloaded here.

This post was improved by many helpful suggestions from Dr. Willie Soon and Dr. Ronan Connolly.

112 thoughts on “IPCC Politics and Solar Variability

  1. ah ya scafetta again.
    McIntyre asked him for data. he refused.

    I asked his co author for data. He wanted to give it but scaffeta refused.

    • Steven, He has sent me some data and is still working on getting me some more. What data do you need?

      My understanding is he is working around the clock on a COVID-19 project he is needed on. He has very little free time right now. Hopefully that mess will be over with soon. First I’ve heard of him refusing anything, he’s usually very responsive. Do you and he have some history?

      • I made my request for code and data to his co author Loehle.
        That way I would know that it was in fact the code and data USED. because I know
        and trust Craig from our interactions on Climate audit
        I’m not interested in hearsay materials you might provide.

        The real source Andy. not third hand stuff. if you gave me stuff, I’d have to ask
        Is this the code and data that Craig and Scafetta actually used?

        primary sources mate.

        You should know this

        • Steven, wrong paper. This post is on Scafetta, Willson, Lee and Wu, 2019 and Scaffetta and Willson, 2014. Loehle was not involved in either that I know of. You are on your own.

    • Steven M, “The paper casts a spotlight on the political problems at the IPCC.” is the Andy May comment about the Scafetta et al paper, and Andy goes through a long list of what certainly appears to be scientific misconduct, wherein data is (manufactured? tortured? changed? cherry-picked?) selected to support a political agenda, and the money comment is Scafetta won’t give you data? What about all of the other stuff in the Andy May report, herein?

  2. https://leif.org/research/Calibration-TSI-Magnetic-Flux.pdf
    Solar surface magnetic field seems to be able to explain variations in Total Solar
    Irradiance on timescales from hours to decades. Using magnetograms from spacecraft
    (MDI and HMI) and ground-based observatories (MWO and WSO) I build a composite
    dataset of the Total Line-of-Sight Unsigned Magnetic Flux over the solar disk stretching
    back to 1976, validated by excellent correlations with the solar microwave flux (F10.7)
    and the Sunspot Group Number. Direct measurements of TSI by space borne sensors
    have been carried out since late 1978. The early instruments were plagued by scattered
    light entering the aperture, but this construction flaw can be corrected for. At the AGU
    2018 meeting, a new TSI composite has been proposed based on a novel mathematical
    method vetted by representatives from all current and most past TSI instruments.
    Although an ‘official’ release of the dataset has not been offered yet, a preliminary
    version is available. Anticipating that any last-minute changes might be minor, I compare
    this new version with the magnetic flux composite. It is clear that we have two TSI
    populations: values before 1993 that are seriously too low and values from 1993 onwards.
    I elect to normalize the magnetic flux (the driver of variations of TSI) to a New TSI using
    the regression equation for the recent population with the smallest uncertainty. With this
    normalization, there is now total agreement between the variation of the magnetic flux
    and of the New TSI as well as with the F10.7 and Group Number proxies. We now have
    two choices: (1) the Sun underwent a dramatic change in how its magnetic field drives
    variation of TSI or (2) the New Consensus TSI reconstruction does not work and the new
    dataset is premature and not useful neither for solar nor for climate research. Following
    David Hume, we should always believe whatever would be the lesser miracle, which in
    our case would be choice (2).
    See also slide 23 of
    https://leif.org/research/Interocular-Comparisons-of-GN-Reconstructions.pdf

    Bottom line: the ACRIM and PMOD and Consensus TSI are all wrong.

    • Leif, Thanks for the paper and ppt. Certainly a well grounded proxy model like yours needs to be taken seriously and it is sufficient to suggest that the satellite measurement composites may be wrong. Certainly they cannot all be right! But, a proxy model is still a proxy model and not a measurement. More data is needed. I’m comfortable that Scafetta, et al. have demonstrated that PMOD is incorrect and was constructed in an invalid way, but it is not proof that ACRIM or SORCE are correct, only proof that PMOD is not.

      • but it is not proof that ACRIM or SORCE are correct
        Well, they are also wrong. It is generally accepted that TSI should vary like the sun’s magnetic field, which IS measured and agrees with several proxies. The systematic errors in TSI are large and makes the ‘data’ suspect. The cry for more ‘data’ does not do anything for the historical record, so ‘more data’ does not fix the record. Proxies are measures of the EFFECT of TSI, which is actually what we want.

        • We’ll see, the measurements are getting better all the time. And without good measurements, or a complete understanding of the sun which isn’t likely anytime soon, the proxies are just proxies. Further, very tiny changes in the sun’s output can make a big difference, it’s hard to measure an atom with a ruler.

          • Further, very tiny changes in the sun’s output can make a big difference

            How so? Yes, I’ve long heard all the suggestions — changing UV, magnetic effects, ozone, cosmic rays & cloudiness, etc — but nothing convincing to me other than changes in TSI change temps proportionally. IOW, only slightly.

            IMHO, cycles such as the medieval warm period & little ice-age can be attributed to internal variations with the general longer period decline in the background from the Milankovitch cycles.

    • Leif,
      In the paper you cite in your link you mentioned the DuDok de Wit “consensus” TSI. I mention it in my post as well. It is “not for publication,” but I have plotted it and thought I would put the “not for publication” plot here in the comments, where presumably it is OK. The plot is of the unaltered original satellite data, the dataset contains data with one or the other of Frohlich’s “corrections,” but I didn’t use it. Notice the steep increase from 1986 to 1997, opposite of PMOD.
      Consensus TSI

      • As the direct measurements of the solar magnetic field does not show that the minimum in 1996 was any higher than the surrounding minima a TSI series that is higher in 1996 [as ACRIM] is simply wrong. Not difficult to understand.

      • My point is that ALL the reconstructions of TSI are wrong in various ways and that therefore it is premature [immature?] to discuss climate response in terms of one’s favorite choice of TSI. And that therefore there are no ‘important’ papers on this topic. Only papers that by proper [‘clever?] selection conforms to one’s bias.

        • Leif, We agree on that. We don’t know. But, any paper that eliminates one of the possibilities, in this case PMOD, is an important paper. I disagree with you on the 1996 minima. To me the evidence suggests that may have been close to a grand solar maximum. TSI may go down before that minima and after. I can’t prove it, but that is (at least to me) the most likely explanation of the data I’ve seen. You seem to think everything is flat, a possibility, I suppose, I can’t disprove that, but the Sun is a variable star and other variable stars don’t work that way. To me, you seem to be misled by the sunspot number=0 periods. That does not mean solar variability goes to zero.

          • Solar variability is not the only possible solar factor of significance. The level of solar activity may be a factor too.

            Leif (if I understand him correctly) argues that solar variability has been around zero for a few decades. The IPCC does too – “[The IPCC] are reduced to creating models of climate and measuring the difference between models that include human forcings and those that do not.” – but they can safely be ignored because their models assume zero solar variability in the first place. ie, they are using circular logic.

            There may or may not be solar variability, but over a period of a few decades the level of solar activity (not its variability) can matter. In other words, after a change in solar output Earth’s temperature can continue changing long after the sun’s output has stopped changing. A few decades is a pretty short period in this context.

            So I don’t think anything has been resolved yet. Except that the IPCC logic is simply wrong.

          • I disagree with you on the 1996 minima.
            You are not paying attention. There is general agreement that the solar magnetic field determines TSI on time scales longer than one day [Yeo et al. A&A 570, A85 (2014) DOI: 10.1051/0004-6361/201423628]. We have good and direct measurements of the magnetic field [that largely validate PMOD], so one cannot disagree on the minima [unless one ignores the direct evidence].

          • To fully appreciate the issue you should study carefully Kok Yeo’s thesis: https://leif.org/research/Dissertation_2014_Yeo__Kok_Leng1.pdf
            From the Summary:
            “Apart from the NRLSSI, the semi-empirical model SATIRE-S, recently updated by Yeo et al. (2014b), gives the only other daily reconstruction of the solar spectrum spanning the ultraviolet to the infrared from present-day models to extend multiple solar cycles, covering 1974 to 2013. Of the three divergent TSI composites, the model found the greatest success in replicating the solar cycle variation in the PMOD composite.”
            BTW, the Scafetta et al. paper you cite does not ‘prove’ or even make it plausible that PMOD is wrong.

          • Leif, the PMOD group modified the raw satellite data to match a proxy model like Satire-S, of course PMOD matches SATIRE-S, they forced the data to match the model! Scafetta uses SATIRE-S in his study and shows that it does not reproduce TSI accurately for solar cycles 22, 23, or 24 (see pages 15-17). This is pretty much what you are saying, but you believe SATIRE and say the measurements are wrong. Scafetta sees the same mismatch and says the model is wrong. I agree with Scafetta.

        • Leif, There is no argument about the mismatch between SATIRE-S and the observations, but which is wrong? From Scafetta, et al. 2019, page 13:

          Both SATIRE-S and NRLTSI2 model show a good linear behavior for the period 1996–2016 but for the period 1981–1996 a different scaling appeared together with some non-linearity. The most noticeable discrepancy in the TSI composites and the proxy models was the different scatter-plot slopes between the 1981–1996 and 1996–2016 periods. This slope divergence, or scaling error, was particularly evident for SATIRE-S. NRLTSI2 appeared to perform better when compared against the second TSI composite by Dudok de Wit et al. [28] using the PMOD modified TSI database. However, this good behavior was likely due to mutual-calibration issues. In fact, NRLTSI2 was carefully calibrated against the SORCE/TIM (2003–present) TSI record [15] and, simultaneously, the PMOD modifications of the original TSI database before 2000 used in the second TSI composite were partially justified by a TSI proxy model using the same constructors of NRLTSI2 [35,40]. Due to these scaling and non-linearity errors, these proxy models could not accurately represent the original TSI observations.

        • Consider this. If ~1996 was the peak or near the peak of a grand solar maximum, a real possiblity. Then a model based on SORCE would only see the downward trend and would not match the upward trend to 1996. Just speculation, but it may be what is happening, and it would explain the differences before and after 1996. That is the problem with proxy models, you make them on a postage stamp of data and then try and apply them to a football field.

          • It is not about the models that just do curve fitting to questionable data. There is no doubt that the solar cycle variation of TSI is caused by the variation of the magnetic field. One neat way to show that is to consider the rotational variation: as magnetic elements rotate into view, TSI changes in sync with that. Here one can directly measure the relationship between magnetism and TSI [no model needed]. So, by direct observation we establish how much magnetic fields cause variation of TSI [taking into account the different contributions from bright and dark magnetic features]. The magnetic field has been directly measured for many cycles and so TSI can be calculated from the measured field. Since the 1996 minimum did not have magnetic fields larger than the 1986 minimum, TSI would not be higher in 1996 than in 1986. Simple as that. One can then compare the so derived variation with the various TSI series and here ACRIM falls short while PMOD does not [or rather: falls less short], regardless of how PMOD is derived. It is clear that you did not [or at least have not] understood the process so clearly laid out in Yeo’s thesis. Perhaps you should look at it [if not done already]. Tom Woods explains here yet another technique that overcomes the long term calibration problems:
            https://link.springer.com/article/10.1007/s11207-018-1294-5

          • Leif, You have every right to challenge the measurements. But, models never supersede measurements. The PMOD composite is a model, based on a model, of course it matches your model! Total circular reasoning. We will never agree, but with more measurements, maybe we can find out who is correct on the 1996 minimum. I agree the magnetic field correlates with solar cycles, but this does not mean that the magnetic field controls TSI trends exclusively, that is an assumption on your part. We are talking about longer term trends anyway, not just solar cycles. Grand solar maxima and grand solar minima may have additional drivers that cannot be seen in your current proxy models, we don’t have good data over long enough periods.

          • I agree the magnetic field correlates with solar cycles, but this does not mean that the magnetic field controls TSI trends exclusively, that is an assumption on your part
            No, it is the result of the very careful work of Kok Yeo et al. [and others].
            It is actually quite simple: magnetic elements [outside of sunspots] are bright [i.e. hot and radiating more, because we can see deeper layers of the sun in them]. We measure the radiation from every one [of the millions of them] and add it all up. Sunspots are cooler and thus darker. We add up the radiation from them. The sum of the bright and the darker contributions is the variation of TSI. No assumptions involved. The thus derived contribution to TSI matches the spacecraft observations [on a day-by-day basis] with 96% accuracy.
            It is clear that you have not read Yeo’s thesis [or at least not understood anything].
            And BTW, there was not a Grand Solar Maximum peaking just in 1996:
            https://leif.org/research/Three-Centuries-of-Validated-Sunspot-Group-Numbers.pdf
            Don’t let wishful thinking lead you astray.

          • A lot of talk about TSI and Anthropogenic warming.
            The formula is TSI change (A) + Anthropogenic change (B) = total climate change (C).
            I am told that TSI variability can not account for climate change so it must be Humans that make up the difference.
            Not a word about Solar Energetic Particle Forcing Variability (non A, non B). I.E Solar Wind Variability. (Which is a function of coronal holes, not sun spots.) Solar variability if far more than just TSI.
            I stumbled across an article from NASA:
            https://www.jpl.nasa.gov/news/news.php?feature=7632
            Headline: “Data From NASA’s Cassini May Explain Saturn’s Atmospheric Mystery”
            “The upper layers in the atmospheres of gas giants – Saturn, Jupiter, Uranus and Neptune – are hot, just like Earth’s. But unlike Earth, the Sun is too far from these outer planets to account for the high temperatures. Their heat source has been one of the great mysteries of planetary science.
            New analysis of data from NASA’s Cassini spacecraft finds a viable explanation for what’s keeping the upper layers of Saturn, and possibly the other gas giants, so hot: auroras at the planet’s north and south poles. Electric currents, triggered by interactions between solar winds and charged particles from Saturn’s moons, spark the auroras and heat the upper atmosphere. (As with Earth’s northern lights, studying auroras tells scientists what’s going on in the planet’s atmosphere.)
            By building a complete picture of how heat circulates in the atmosphere, scientists are better able to understand how auroral electric currents heat the upper layers of Saturn’s atmosphere and drive winds. The global wind system can distribute this energy, which is initially deposited near the poles toward the equatorial regions, heating them to twice the temperatures expected from the Sun’s heating alone.”

            Wow, Twice the heating than from TSI!

            I ask myself, “Self, if the solar wind inductive heating can heat the atmosphere of Saturn Jupiter Uranus and Neptune, why is it not part of the Earth energy calculation?”

            “Self, is Solar Energetic Particle Variability lumped in with Human caused global warming (B), seeing as how it is not part of TSI (A)?”
            The answer apparently is yes. (If yes this is a huge and misleading error.) It is my understanding that Solar particle forcing is counted as Anthropogenic forcing by default. (If I am mistaken, please disabuse me.)
            The newest iteration of CMIP(6) is supposed to include particle forcing, finally.

            I long ago came to the harsh conclusion that the IPCC was a political tail wagging the climate dog for money (and political) treats. By the way, I studied Ecology back when the scare du jure was the pending ICE AGE. 1970’s. Boy was that a bust.

            “There are more things in heaven and earth, Horatio,
            Than are dreamt of in your philosophy.”
            – Hamlet (1.5.167-8), Hamlet to Horatio

            The climate is variable, naturally. There is a range of natural variability. We are still within that range. We are part of that complex variability, no more than a part.

          • Richard G., Thanks! Very helpful comment and a terrific link. Indeed, there is much we do not know Horatio.

          • Richard G

            Particle effects aren’t lumped in with AGW by default. In order for them to have a significant climate effect, they have to affect ocean temperature significantly.

            The evidence indicates TSI/insolation drives the ocean and whatever happens in the high atmosphere where solar particle joule heating occurs doesn’t breach the ocean surface.

          • I ask myself, “Self, if the solar wind inductive heating can heat the atmosphere of Saturn Jupiter Uranus and Neptune, why is it not part of the Earth energy calculation?”
            The solar wind and UV from the sun also heats the Earth’s upper atmosphere to more than 1000 degrees, but the air up there is so thin (million times less dense than at the surface) that the actual amount of energy is very, very small, and therefore, obviously, does not play a role in the energy budget.

          • Bob Weber says:
            The evidence indicates TSI/insolation drives the ocean and whatever happens in the high atmosphere where solar particle joule heating occurs doesn’t breach the ocean surface.

            Agree. The tail does not wag the dog. Earth’s thermosphere has negligible mass & can’t heat anything significantly.

          • Bob,
            Ah, but as Cassini demonstrated, inductive heating effects atmospheric circulation winds aloft. Energetic Electron Precipitation causes chemical changes in the constituent gasses. Dependent variables cascade through the system we call weather. Changes in cloud cover and precipitation distribution, albedo, density, top of atmosphere height, changes in effective atmospheric surface area. A lot going on before we reach the oceans.
            The formula should read TSI(A) + solar particle forcing(B) + AGW(C) = Climate change(D).
            We need to better understand these dependent variables. I think we are starting to do a better job.

          • Leif, I’ve taken a look at Yeo’s thesis. He did a good job and I applaud his work, but it is still a model and it is not a measurement, he calls it “semi-empirical” and claims it is one step above a “proxy model.” But, semi-empirical is still a model.

            Yeo admits that his model does not predict changes in TSI accurately, he claims it is only 95% accurate (“95% of the variability”), which is not good enough. Magnetic field models work OK in the Sun’s active regions, but not so well in the quiet regions. They are missing some critical component, that is why they always predict a flat solar minimum trend, which is very unlikely to be true, at least in my opinion. Yeo’s model, semi-empirical or not, is clearly not modeling the whole TSI picture. You can blame the measurements all you want, but models, never, ever trump measurements.

            The fact that he matches the corrupt PMOD record “best” does not help his case.

        • The three image panels are cross-correlation plots over several solar rotations of solar proxies with TSI data/models, each with distinct characteristic curves. The data legends in each panel are listed in order of the plots to the left, top-to-bottom with peak TSI lag from sunspot number in days listed first.

          Each characteristic curve depicts the change in correlation over time of each TSI dataset to v2 SN daily data from the appearance, growth, and disappearance of a sunspot(s). For example, the sunspot group that formed in the third week of March 2019 persisted over several rotations, driving TSI spikes with each successive solar rotation through the spring and summer, demonstrating this characteristic response very well:

          https://i.postimg.cc/c49xNpGG/TSIS-SORCE-DSD-2018-2020.jpg

          https://i.postimg.cc/760PK31b/CCFs-of-SN-and-SF-vs-TSI-and-Models.jpg

          The first panel has several CCF plots for v2 SN versus TSI datasets and models as listed. The top four tier of PMOD, CDR (climate data record), RMIB, and ACRIM produce a higher correlation to v2 SN than SORCE TSI and all the other TSI models except SATIRE-S, which is consistently too high in later rotations. The data coverage period differs for each plot here.

          The top four TSI plots in the first panel probably correlate higher than SORCE does at the rotational peaks because SN averaged higher during those years than it did during the SORCE era, warranting further examination later to see if it’s true or by how much.

          If those top four are offset by the difference between them and SORCE, they would correlate fairly well, except SORCE has the TSI peak at the second rotation, 54 days, by a very small amount, compared to the single rotation lag for the top four.

          The second panel of CCF plots are of v2 SN daily data cross-correlated with the NRL2 TSI daily model output from 1882, broken down into four periods; and for the third panel, adjusted F10.7cm was cross-correlated with NRL2 TSI over four periods.

          The magnitude and phase of the correlations change for the worse the further one goes back in time in both panels, revealing a problem with NRL2 TSI.

          Solar Forcing for CMIP6 (v3.2) is partly based on NRL2.

  3. “We can easily see that the UNFCCC and the IPCC have a potential conflict. ”

    No doubt and I’ve been saying this for decades. This conflict of interest is why the IPCC will never correct the errors that have led to their insane position.

    AR6 is even worse where support for policy goals appeared over and over again in the chapters that were supposed to be about science. The policy goals of the IPCC are now the same as the UNFCCC.

    • CO2

      I doubt there is any “new” source of convincing data from any anywhere. The analysis above concerns what was earlier on said to be data-poor, now been filled in nicely. They have no choice but to sink more money into the models: of the oceans, Greenland ice cover, then point to the unusual Arctic ozone depletion as “proof” of something or other. When you are paid to find potatoes, everything dug up looks like a potato.

      The article is superb. It is a good brief history and corrected one misunderstanding I had about a date.

      A quibble about dates mentioned is MBH98 which was included in the referenced documents before it was published which is a violation of IPCC terms. It was accepted as a published article in 1997 and after being rejected in 1998, appeared later than expected. In other words, it was accepted by the IPCC because a journal said they would publish it, which journal, after IPCC acceptance, rejected it. That course of events stinks too.

    • Gee Mods, you effectively neutralize my comments by getting them okayed a way late even if it is pretty bland, straight and on topic. I’ve been commenting since 2007 and never been edited, banned or even warned. I dont use profanity or threats. I’m not calling people things. I’m not selling anything. I am a geologist (who even studied paleoclimate over 60yrs ago) and am an engineer who makes good contributions to the discussion and to society. I’ve donated on many occasions starting with the surfacestations project, donations for a trip or two for Anthony and to individuals like Mark Steyn (buying his books- he won’ttake a donation per se), Peter Ridd, and others. On political topics I don’t think I rant any worse than many others. I’m always a booster for WUWT. Is there a pathway out of purgatory, because clearly my name automatically rings the bell.

      • Hi Gary, It isn’t personal.

        We’ve had months-long system problems that just got fixed last night (personally I think it was sabotage, because somebody edited my main PHP file, something only the wordpress people have access to) and it caused problems being able to approve comments.

        One tech there noticed it last night, and changed it back, and boom, a bunch of problems went away.

  4. “IPCC is unable to find any observational evidence that humans contribute to climate change, much less measure the human impact on climate.”

    Actually, there is overwhelming observational evidence that humans have zero contribution to climate change.
    None of the various regional climates around the world has changed. “Global climate” is a non-sequitur.
    There is no global climate change. Various global temperature proxies show zero warming.
    Climate models are completely irrelevant and completely useless.
    The increased CO2 in the last 100 years has been primarily due to natural variations in source and sink fluxes. Atmospheric anthro-CO2 concentration matches the global anthro-CO2 flux into the atmosphere.
    There is no evidence that human CO2 accumulates in the atmosphere preferentially over natural CO2.
    Anthro-CO2 mixes into the atmosphere and therefore follows the near infinite global sink.
    Since the anthro-CO2 source flux is about 4 percent of the natural flux, the atmosphere CO2 is also 4 percent of the total atmosphere CO2. 4 percent of 400 ppm is 16 ppm. The remainder is natural CO2.

      • Not points at all, just ignorant, erroneous, bias-confirming opinion, cut and pasted from the professional doubt-mongers. You say you were a scientist for 40 years? How can you write “good” in relation to this obvious tripe: “Since the anthro-CO2 source flux is about 4 percent of the natural flux, the atmosphere CO2 is also 4 percent of the total atmosphere CO2.” Doubt-mongers like you. Shame.

        • You are partly right if you are able to prove that no anthro CO2 has been “eaten” by natural siink and all is still circulating in the athmosphere. Are you ?
          😀

        • The anthro-CO2 is going up slowly. Thew total atmospheric CO2 is going up slowly in seasonal steps. As atmospheric CO2 rises the annual anthro-CO2 becomes a smaller percentage of total atmospheric CO2.
          “the atmosphere CO2 is also 4 percent of the total atmosphere CO2”, doesn’t even make any sense. Not that you ever do.

          • They’re not my words, they’re bwegher’s nonsense. Here are some more which Andy appears to concur with – “There is no evidence that human CO2 accumulates in the atmosphere preferentially over natural CO2.” It’s too fascile to be a strawman, lol.

            Are you actually disputing that the rise from 280ppm to 416 is entirely the responsibility of human activities? Because that is what bwegher is suggesting and you’re going along with that?

        • Loydo, I think you mean 0.04%. You are off by 2 orders of magnitude. Bwegher is correct, you are way off base.

          • I suggested that she learn how to work a calculator on another thread. I think I may have been ahead of her curve as I’m not sure now if she knows what a calculator is.

            I also wonder why I see an image of Dame Edna Everage whenever I read a Loydo post. She’s the most successful “climate crisis (TM)” doubt-monger, to use her words, ever to grace this Board.

          • Beat me to it! LOL

            “It ain’t ignorance causes so much trouble; it’s folks knowing so much that ain’t so.”
            -Josh Billings

          • … and where’s the evidence of a Covid-19 induced decline in the output of “anthro CO2” in Mauna Loa data?

    • Humans have had observable, measureable effects on local and regional climates. Globally, not so much.

      Consider Las Vegas. Before 1905, it was the eponymous meadows, fed by springwater. While incorporated in 1911, it wasn’t until legalized gambling and Hoover Dam in the 1930s that its growth and heat island effect took off, supercharged by air conditioning. It’s now the 28th largest US city and anchors a populous SMSA.

      Similar urbanization has gripped the world in the past century. There were no conurbations of tens of millions until recently in history. NYC became the first such megacity in AD 1950.

      In AD 1800, just three percent of humanity was urban. By 2000, it was 47%, and is now over half. If the trend continues, plan for more pandemics.

    • bwegher
      I think you missed out the only palpable change in climate there is, the thing that heat worriers won’t talk about for obvious reasons. The Great Greening of the planet! Expansion of forest cover and habitat for our fellow creatues (and “leafing out” of other plants) by 20 percent in 40yrs and bumper harvests. This puts the benefits of ‘carbon’ several orders of magnitude greater than costs. Indeed, even the proponents of climate disaster have had to push this off into the future several times over the past 40 years of hype.

    • bwegher,
      One correction:
      I think you meant:

      the anthropogenic atmospheric CO2 is also 4 percent of the total atmospheric CO2.

      You left out the “anthropogenic.”

  5. I can’t decide whether to be impressed or depressed by reading about the machinations of so many intelligent, educated adults over the latest (suspected) poofteenth rise in the roller-coaster that is (and always has been) Earth’s prevailing temperatures.

    Here’s my observations –
    the data are unreliable;
    the motives are suspect;
    the models are pure conjectures;
    the objectives are ongoing/increasing $$$$$s for the players.

    And as Steyn’s book chronicles – the perfidy is blatant.

  6. Andy, nice review and technical deep dive.

    There is a simpler way to show the mess that is IPCC attribution, which originated with Lindzen at MIT. The warming from roughly 1920-1945 is both visually and statistically indistinguishable from that of roughly 1975-2000. Yet the IPCC itself (AR4, SPM WG1) said the former period was mostly natural. Simply not enough rise in CO2. Whatever those natural forcings were, they did not disappear in 1975.

  7. That was once part of the IPCC praeamble in the early beginning:

    The Intergovernmental Panel on Climate Change (IPCC) is an intergovernmental body of the United Nations[1][2] that is dedicated to providing the world with objective, scientific information relevant to understanding the scientific basis of the risk of human-induced[3] climate change, its natural, political, and economic impacts and risks, and possible response options.

    https://en.wikipedia.org/wiki/Intergovernmental_Panel_on_Climate_Change

    • Krishna, Yes, there are a lot of different mission statements for the IPCC. Perhaps they don’t know where they are going or what they are doing?? Could that be? 😉

      • They know very well what they do, or why are thea always hide the early preammble ?
        I had several links to it in several IPCC indroductions by themselves, but piece by piece, all error 404, finally this early statement is pinned at Wiki 😀 and so at least for the moment abvailable 😀

  8. As a simple physicist, with a casual interest in atmospheric/climate science, it would be very helpful to me if long posts like this led off with a few paragraphs equivalent to an abstract, methods, results, and significance section summarizing what was to come. Often I find myself slogging through a little at first, then just skimming the figures, without ever coming to the gist. Not a criticism, just a suggestion. It might even be nice to standardize the format across posts, or at least the meaty technical posts like this one.

    • fah, Thanks. I will consider being more formal in the future. This was a post that is longer than I like to post, but it was a big topic and hard to compress.

    • Andy always appreciated your posts. I echo fah’s request and also reminder you that posts in these pages are anonym hell. Many of us are not as conversant in the history of IPCC and others. A legend or brackets would be most helpful in understanding, without stopping to fill in the gaps.

  9. I heard Judith Lean speak at NOAA/GFDL, when she presented a seminar on solar activity and TSI; this was in the early 2000s. After her talk, I approached her and brought up the subject of natural variability, especially cyclicity of naturally occurring features, such as ocean phases. While she agreed with the subject on a general level, she was dismissive that these cycles could cause the warming that has been seen up to that time, since 1980. It was and has been apparent to me that this group-think, combined with political agendas of people of a certain political persuasion, has corrupted our research and academic institutions, and this was exceedingly depressing for me.

  10. They are missing for me as well. Firefox browser. Only Figure 1 displays, the others have figure text but no pictures.

  11. ”If it is invariant, which is difficult for a variable star like the Sun, most of the warming can be attributed to humans.”

    If the astronomy-astrophysics community wants another opportunity to see a real variable flare star in action, our nearest neighbor, Proxima Centauri, is likely to put on quite the flare show Starting June 10, 2020 (error estimate due to range uncertainty is -2 days to + 6 days, that is June8 to June 16). Proxima Centauri though is SH astronomical object (dec = −62° 40′ 46.1631″ ) so telescopes in Chile and Australia are in the best position to see these super-flares. As a red dwarf star, PC is well known for its flaring variability. This June’s event though should be quite notable.

  12. I see only Figure 1, as well. The captions for the other figures (2-8) are present…. but no graphs/figures.
    I’m running Firefox browser and Windows 10. No indications of any blocking of cookies or malicious websites….

    As a test, I started Windows Edge and brought up WUWT, now I can see all 8 graphs/figures with their respective captions. Is Figure 1 embedded differently in the article, when compared to Figures 2-8? Some difference there that Firefox is sensitive to but Edge is not???

    • J Mac,
      No they were all uploaded at the same time. Use Edge, the problem must be with firefox. Chrome works also.

      • Andy,
        I have a enduring revulsion for the intrusive nature of Microsoft Edge. I’m using Firefox right now, and for what ever reason, your figures/graphs are displayed correctly today. The last electron finally fell into the correct valence shell????

  13. Variation in cloud cover is not mentioned, variation particularly over the tropics that are mostly ocean apparently correlates with the global average temperature 1983 – 2000 (HadCRUT 3) — not forgetting the risk of the single cause fallacy.
    “… They are reduced to creating models of climate and measuring the difference between models that include human forcings and those that do not …”.
    They assume what they set out to prove, hence they are trapped in a vortex of circular reasoning.
    https://www.buzzle.com/img/articleImages/371786-54522-8.jpg

  14. If your theory is that the industrial economy since the industrial revolution inserted a human cause into the rate of warming and then when you look at the data you can find that human cause only after 1950 and change your theory accordingly and present that data as empirical evidence to support your theory, it is a form of circular reasoning because the data used to construct a hypothesis may not be used to test that hypothesis.

    https://tambonthongchai.com/2019/12/25/earth-day-wisdom/

  15. Regarding the attribution of global warming in the late 20th century, my 2018 paper produced two separate estimates that the sun was responsible for about 35% of warming between 1980 and 2001. Here is a rough summary.

    My paper “On the influence of solar cycle lengths and carbon dioxide on global temperatures”, published in 2018 by the Journal of Atmospheric and Solar-Terrestrial Physics (JASTP) is a rare example of a peer-reviewed connection between solar variations and climate which is founded on solid statistics. It is available at https://doi.org/10.1016/j.jastp.2018.01.026 (paywalled) or in publicly accessible pre-print form at https://github.com/rjbooth88/hello-climate/files/1835197/s-co2-paper-correct.docx .

    The paper builds on the work in the 1990’s by Friis-Christensen and Lassen, by adding a CO2 element to their model based on solar cycle lengths (SCLs). By using a linear regression model, statistical significance levels can be measured for these two effects. Using HadCRUT4 as the global temperature series, averaged over 11-year periods starting 4 years before solar cycle maximum, the CO2 variable is hugely significant, as it explains the overall upward trend, while the length of the previous cycle has a 1.3% significance level (more on this below), and this variable explains the wiggles in the graph which is Figure 1 of the paper.

    The upshot of the analysis is an estimate of TCR (Transient Climate Response) of 1.93K, and of ECS (Equilibrium Climate Sensitivity) of 2.22K. Under an assumption of continuing increases of CO2 concentration by 2 parts per million each year, the expected HadCRUT4 anomaly in AD2100 is only 1.1K higher than it was during the period 1996-2006.

    Allowing for the SCLs does make material (but not vast) differences:
    • without them the TCR estimate would be 2.37K instead of 1.93K
    • they explain 37% of the observed warming between 1980 and 2001
    • without them the residual errors significantly differ from the model assumptions

    A model based on radiative model gives a very similar estimate for the Sun’s contribution to global warming between 1980 and 2001, namely 33%. That model also implies that global temperatures are much more sensitive to solar (short-wave) radiation than to greenhouse (long-wave) radiation, by a factor of nearly 3.

    Rich.

    • Thanks Rich, very interesting paper. I haven’t read all of it yet, but I will. This stands out:

      37% of the recent warming from 1980 to 2001 was due to solar effects

  16. “The natural component is very complex and works on multiple time frames, at its root it is driven by solar variability and ocean oscillations..”

    Ocean oscillations are an inverse response to indirect solar variability, with weaker solar wind states driving a warmer AMO and increased El Nino conditions, and which drives a decline in low cloud cover.

  17. All the figures came through on Chrome, however I copied and pasted it to Word and saved it to OneDrive. The Word document only had the first figure. All the other figures were a place holder empty block.

  18. “The fact that some people could use [the ACRIM group’s] results as an excuse to do nothing about greenhouse gas emissions is one reason, we felt we needed to look at the data ourselves. Since so much is riding on whether current climate change is natural or human-driven, it’s important that people hear that many in the scientific community don’t believe there is any significant long-term increase in solar output during the last 20 years.” (Lindsey 2003)

    Thanks Andy. My comment directly addresses her quote and your article headline.

    Judith Lean et al came to what I think is the wrong conclusion back in 1995, and climate science followed.

    Lean and her colleagues found that the Sun may have contributed half of the changes since 1610 and less than a third of the changes since 1970, contrary to earlier research suggesting that the Sun may be entirely responsible. This meant that solar variability was not the primary cause of global warming in the past decades. – wikipedia

    I came forth here at WUWT over five years ago in the comments section with a solar-climate theory that explains climate change as a function of solar activity via TSI, then took my poster works to the 2018 LASP Sun-Climate Symposium, the 2018 AGU Fall Meeting, and the 2020 Sun-Climate Symposium, where I learned the hard way after spending thousands of dollars they’re not at all particularly interested in changing their minds in spite of the overwhelming evidence I bring to bear, because they are all in with Judith Lean et al.

    She is very likeable. Look at the impressive wiki list of honors she’s earned. Her colleagues who hired her in at LASP after her NRL retirement also gave her a rousing special heart-felt show of appreciation and tribute.

    How could NASA ever change their mind about AGW if she doesn’t first? She was actually asked by the also warmist 2020 Symposium meeting leader to not bring up politics again after she overtly promoted activism over emissions and climate change in this country.

    NASA says that greenhouse gas emissions coming from human activity has had more than 50 times the influence of the slight increase in solar energy in causing warming temperatures on Earth since 1750.Spaceflightnow

    This Spaceflightnow article discusses the end of the SORCE mission and the first TSIS mission but doesn’t say anything about there being a new competitive sun-climate theory or any doubts in AGW over TSI.

    Dong Wu, quoted in that article, suggested to me in February I should think about introducing my work here at WUWT in a post or two as the climate is controversial [and political] and the public should know there are opposing ideas and evidence for solar variability climate control other than Svensmark’s theory.

    Isn’t it important that people hear that many in the scientific community don’t believe there is any significant human-caused climate change?

    • Thanks Bob. Certainly I will not believe that human-caused climate change is significant until I am shown some evidence besides models. These models change like the weather. Yeo (who Leif refers to in other comments) does try and model the quieter parts of the Sun, that would presumably cause long term changes in the solar minima, but he fails. He cannot model wavelengths shorter than 300 nm (see page 20 of his thesis). He fudges this calculation with a “semi-empirical” correlation with SORCE/SOLSTICE since 2003, missing all the increasing activity from 1980 to 2000. Plus he assumes LTE (local thermodynamic equilibrium) which breaks down in parts of the Sun. As a result he is missing some critical component of the sun’s variability. He can model most of one solar cycle, not perfectly, but he gets close, but nothing beyond that. That is why his models are always flat on the bottom and unrealistic. In the figure below, from Scafetta’s paper, page 12, the Yeo’s model is shown in red. Notice the significant difference from 1985-1996 relative to the unaltered satellite measurements (DuDok de Wit). The model does OK relative to SORCE after 2000 (which it is calibrated to, “semi-empirically”), but is way off before 1996, which is roughly the peak of a possible grand solar maximum. The differences are exaggerated a bit due to scaling errors, see the paper for an explanation.
      Scafetta figure 6

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