By Andy May
Nicola Scafetta has written a new work in Atmosphere (Scafetta, 2021) about a new millennial reconstruction of surface temperature. This is his latest “What If the Models Are Accurate?” Analysis. Scafetta’s idea is, let’s say a model is correct, what are the implications?
Previously, he examined the PMOD solar model, assuming it was correct and the sun is immutable in the long run. Does this make sense? Another example: if we smooth the urban heat island effect (UHI), do we eliminate it or do we just smear it so the data looks better but is just as inaccurate? In the latter post about UHI I wrote:
“Compared to 1940 through 1960, the original HadCRUT curve shows a warming of 0.59 ° C and 0.48 ° C using Scafetta’s corrections. The UAH record shows 0.44 ° C. The CMIP5 climate models show a warming of 0.78 ° C.
After Scafetta’s correction, it is possible that non-climatic prejudices have caused a fifth of the reported global warming by HadCRUT since 1940-1960. It is also possible that the CMIP5 climate models overestimate the warming by a third. These are serious problems. “
Computer models have one disadvantage. They can be used by those on the other side of the debate. If they lead to absurd or inconsistent conclusions, they will make you look stupid. What if Scafetta assumed that HadCRUT’s current temperature record is correct? Then he assumes that the mean value of the CMIP5 ensemble (Coupled Model Intercomparison Project Phase 5) is correct. What does this mean for past temperatures? Do we find inconsistencies?
Since 1860, Scafetta has performed frequency analysis of temperature records and reconstructions of the Holocene climate and identified at least eight significant frequencies. One of these is a well-known and powerful millennial signal that is often associated with the vortex solar cycle. This is the vibration that likely caused the Medieval Warm Age (MWP) and Little Ice Age (LIA). We can assume that these vibrations are natural and are likely due to fluctuations in solar power. Javier has discussed the evidence for the millennial cycle, or vibration, and says:
“Within the paleoclimatological scientific community, millennial cycles are widespread during the Holocene because their effects are observed in most climatic proxies and there is sufficient agreement on certain periodicities that emerge from frequency analysis and are in phase from multiple proxies in different locations.”
It is known, but little known, that natural climate variability consists of natural vibrations. Examples are the Atlantic Multidecadal Oscillation (AMO), the El Nino Southern Oscillation, the Pacific Decadal Oscillation (PDO), and others. These and many others have been observed but never declared to everyone’s satisfaction. As a result, the extent of their impact on our climate is unknown, and CMIP5’s global circulation models (GCMs) simulate them poorly. It goes without saying that if we cannot measure the natural variability of the climate, we cannot calculate the human impact on it. We have a temperature record of unknown accuracy, models of unknown accuracy, and natural climatic vibrations that we don’t understand. What should we do?
Scafetta assumes the HadCRUT4 temperature record is correct. Then he models it as a function of the more closely observed climatic fluctuations, records of volcanic eruptions and anthropogenic influences. His analysis of the model results suggests that natural climatic vibrations accounted for about half of the warming from 1970 to 2000, when the AMO was in a warm phase. When the various shorter oscillations (70 years or less) are combined, they result in a climatic oscillation of ~ 60 years, which is shown in historical, archaeological and paleoclimatic data for many millennia. Figure 1 shows the HadCRUT4 dataset (blue) and the Scafetta model in red. The model shown uses a calculated anthropogenic input based on the CMIP5 models. While they use an assumed Climate Sensitivity to CO2 (ECS) of ~ 3 ° C, Scafetta uses 1.5 ° C / 2xCO2 to account for its estimate of natural forces.
Figure 1. HadCRUT4 in blue and Scafetta’s model in red. This is from Figure 4 in his work. The HadCRUT4 data will be stopped as a test in 2013. Note that Scafetta’s model accurately reproduces the 2016 and 2020 temperature peaks. For more details on the model, see his article. Scafetta also appears to have predicted the sudden drop in temperature in late 2020 and early 2021.
Next, he extends the CMIP5 models with data from a reconstruction of air conditioning by Thomas Crowley (Crowley, 2000). The reconstruction depends on Crowley’s data as well as on the CMIP5 models of anthropogenic, volcanic and solar “forces”. The extension to 1000AD is shown in Figure 2, with the blue line representing Scafetta’s CMIP5 extension and the pink line representing the mean CMIP5 for several models. The CMIP5 solar radiation model is shown in red, and the same model that has been expanded in the past is shown in black. Basically, the models assume that the sun hardly varies over the long term.
Figure 2. The CMIP5 temperature anomaly in purple and the corresponding solar signature in red. Scafetta’s extension of the CMIP5 model is shown in blue and the matching solar signature is shown in black.
In Figure 3 we compare the results with the temperature reconstruction by Moberg et al. (Moberg, Sonechkin, Holmgren, Datsenko & Karlen, 2005) from the year 1000 AD.
Figure 3. The Moberg reconstruction is shown in red, the expanded CMIP5 temperature record is shown in light blue, the mean CMIP5 for multiple models is shown in dark blue, and the HadCRUT4 record is shown in green.
It is clear that the CMIP5 model does not successfully capture the Medieval Warm Period (MWP). There was no significant difference in volcanic activity, land use, or CO2 or other greenhouse gas concentrations, and this is what drives the CMIP5 models. As you can see in Figure 4, the CMIP5 models from the IPCC AR5 synthesis report (page 6) assume that the natural variation and the internal climate variability are zero. So you have no way of calculating the MWP warming shown in Figure 3.
Figure 4. The factors influencing the results of the CMIP5 climate model. It is believed that natural forces such as sun fluctuations and internal fluctuations, the oceans, decrease to zero on climatic timescales.
The assumptions shown in Figure 4 work perfectly according to the Little Ice Age (LIA). This is likely because natural forcing tends to increase, similar to CO2 concentration. If they assume that the sun is immutable, they can attribute all of the warming to CO2. This assumption that there is no natural variability over long periods of time leads the IPCC to the false assumption that the ECS is 3 ° C / 2xCO2. This assumption collapses in the MWP.
It cannot be proven that solar variability caused the MWP, but there is little else that could have caused it. By solar variability we mean the amount of solar radiation that reaches the earth, especially the earth’s surface. The radiation reaching the surface of the earth can vary as the sun changes like stars like the sun do. Or it could be due to other natural astrophysical effects, such as: B. Fluctuations in solar wind, cosmic rays or the orbits of the moon or other planets.
Summary and conclusion
The millennial oscillation was very sustained throughout the Holocene or the past 12,000 years. It gave us the Roman Warm Age, the Dark Age, the Medieval Warm Age, the Little Ice Age, and the Modern Warm Age. The modern warm period is at least partially influenced by a human contribution. However, Scafetta believes that at least 50% of the warming since 1950 has been natural. Figure 5 shows Scafetta’s last semi-empirical model in blue, Moberg’s reconstruction in pink and HadCRUT4 in red.
Figure 5. Scafetta’s model in blue, Moberg’s reconstruction in pink, HadCRUT4 in red and the CMIP5 extension (greenhouse gases, aerosols and volcanic eruptions) in black. The black curve is shifted down for clarity.
Scafetta’s semi-empirical model generally agrees with Moberg’s reconstruction, but shows a colder little ice age.
In general, Scafetta’s model shows us that the CMIP5 models lack important climatic “forces” or mechanisms. The smooth nature of the CMIP5 ensemble mean suggests that the CMIP5 models do not model the short-term climatic vibrations, and the mismatch with the Medieval Warm Period indicates that they do not model the longer-term vibrations either. Basically, CMIP5 ignores nature.
Nothing has really changed since Guy Callendar or the second IPCC report called “SAR”. The original draft of SAR Chapter 8, which was changed by politicians from the original, contained the following statement:
“We don’t have a yardstick by which we can measure the artificial effect. If long-term natural variability cannot be established, we are back with Callendar’s criticism in 1938 and we are no better than Wigley in 1990. “(Lewin, 2017, p. 277)
Guy Callendar was the first to use empirical data to calculate the temperature effect of adding CO2 to the atmosphere. One of the main criticisms of his work was how did he know? If you don’t know natural climate variability, how can you calculate the effects of CO2? This was also the criticism that Tom Wigley faced while working on similar calculations for the chapter on the detection of the greenhouse effect for the first IPCC report “FAR” in 1990. The criticism is still valid today.
Crowley, T. (2000). Causes of Climate Change in the Last 1000 Years. Science, 289 (5477). Retrieved from https://science.sciencemag.org/content/289/5477/270.abstract
Lewin, B. (2017). In search of the disaster signal. Global Warming Policy Foundation. Retrieved from https://www.amazon.com/Searching-Catastrophe-Signal-Origins-Intergovernmental/dp/0993118992
A. Moberg, D. Sonechkin, K. Holmgren, N. Datsenko & W. Karlen (2005). Highly variable northern hemisphere temperatures reconstructed from low and high resolution proxy data. Nature, 433, 613- 617. Retrieved from https://www.nature.com/articles/nature03265
Scafetta, N. (2021). Reconstruction of the interannual to millennial patterns of global surface temperature. Atmosphere, 12 (2). Retrieved from https://www.mdpi.com/2073-4433/12/2/147
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