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Lewis's study was discussed in a previous post, so the main findings of Scafetta (2022) will be briefly presented here.
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Satellite-based records were added because surface-based records are susceptible to many biases, including urban warming (Connolly et al., 2021; Scafetta, 2021a). Validation tests were performed using 688 GCM member simulations, 143 averaged GCM ensemble simulations, and Monte Carlo modeling of within-GCM variability that met three surrogate model accuracy requirements.
The years 1980–2021 were chosen because they are believed to be the period in which the global temperature record appears to have the least uncertainty. In addition, satellite measurements that provide independent estimates also cover the same period.
The main finding of the paper is that the majority of simulations run by GCMs with medium and high ECS become too warm. From 1980-1990 to 2011-2021, only the GCM ensemble simulations with low ECS appear to accurately predict the warming shown by the surface record. For example, while all temperature data indicated a warming below 0.6 °C, all GCM averages for the mid-elevation ECS ensemble predicted warming above 0.6 °C to 1.3 °C. These are clearly visible in Figures 1 and 2.
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Figure 1: GCM global surface temperature ensemble (yellow area, ±1σ) with HadCRUT5 (populated data), GISTEMP v4, NOAAGlobalTemp v5, and UAH-MSU-lt v6 (black, 12-month moving average).
Figure 2: Average temperature change (2011-2021 minus 1980-1990) reported by the average simulation of 38 CMIP6 GCMs. Blue vertical lines represent temperature changes measured by HadCRUT5 (populated data), ERA5-T2m, GISTEMP v4, NOAAGlobalTemp v5 and UAH-MSU-lt v6 temperature records.
The conclusions remain the same if intra-model variability is also taken into account, since, as the study clearly shows, 95% and 97% of the simulation runs for medium and high ECS ensemble members, respectively, were conducted at higher temperatures than all temperature records. Figure 3 summarizes these findings.
Figure 3: Box plot of CMIP6 ensemble members for each CMIP6 GCM; # indicates the number of simulations available for each GCM. The horizontal blue lines represent global surface warming from 1980-1990 to 2011-2021 as reported by HadCRUT5 (populated data), ERA5-T2m, GISTEMP v4, NOAAGlobalTemp v5 and UAH-MSU-lt v6 temperature records.
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Figures 1–3 clearly show that the GCM warming effect increases with ECS, and only the low ECS GCM ensemble can be considered consistent with the data. The study also showed that the results held regardless of how the variation within the model was statistically modelled. Furthermore, simulations from a small number of moderate and high ECS GCMs appear to fit the evidence, which is statistically insignificant. Therefore, as Lewis (2022) also stated, the true ECS should be below 3 °C.
However, Figure 1-3 also shows that if the actual warming from 1980-1990 to 2011-2021 is better represented by the UAH-MSU-lt v6 temperature record, even the low ECS GCMs are warming too much. While the various available surface temperature records show a warming of about 0.5–0.6 °C, the UAH-MSU-lt v6 temperature record shows a warming of about 0.4 °C, while the low ECS GCM shows a warming of 0.6 ± 0.1. °C. It is worth mentioning that according to the GCM, the troposphere should have a larger warming trend than the surface (Mitchell et al., 2020), so UAH-MSU-lt v6 may even overestimate surface warming. Therefore, assuming UAH-MSU-lt v6 warming is accurate and representative of surface warming, the low ECM GCM must be reduced by about 33%. This should mean that the actual ECS is also likely to be 1-2°C.
If the actual ECS is between 1.5 and 3.0 °C, future warming is modest and of little concern. If the actual ECS is significantly lower, i.e. 1-2°C, the IPCC's predictions of a future climate catastrophe without a significant reduction in CO2 emissions will be essentially groundless. Therefore, it is crucial to assess whether warming biases affect surface temperature data, as several studies have already revealed.
To investigate this last point, the work adds an extension that compares observed and GCM-simulated warming over land and ocean. Thus, the land is warming 2.0-2.3 times faster than the ocean according to surface-based temperature records, 1.5 times faster according to satellite-based temperature records, and somewhere in between according to the GCM: 1.75±0.20. In addition, land surface temperature records show about 0.4 °C warmer than satellite readings, while sea surface temperature records show slightly warmer (up to 0.1 °C) warmer than satellite observations. These results suggest that the warming reported by surface temperature records, especially over land, is too large to match satellite measurements and modeled land/ocean ratio predictions. These findings suggest that the warming shown by surface temperatures, especially over land, is exaggerated and inconsistent with satellite observations and theoretical model predictions of land-ocean relations.
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Based on the above observations, it can be concluded that the surface temperature record is likely to deviate by at least 10% from the actual warming. By reducing the ECS of the low-ECS GCM by 10%, the ECS range changed from 1.8–3.0 °C to 1.6–2.7 °C, which is in good agreement with the conclusion of Lewis ( 1.7–2.7 °C).
However, the ECS may be much lower if the actual warming is closer to that shown by the UAH-MSU-lt v6 temperature record, or if the climate system is dominated by natural oscillations over decades and millennia that cannot be reproduced by GCMs (for example, 1- 2°C). For example, Scafetta (2013, 2021b) assumes an ECS between 1.0 and 2.3 °C assuming (astronomically induced solar) quasi-20, 60, 115, and 1000-year natural climate oscillations observed in many climates throughout the Holocene. Both can be observed in the data, but not reproduced by the GCM. The same results are obtained using solar records that show large secular variations, while GCMs use solar forcing derived from solar proxy reconstructions that show minimal secular variations (Connolly et al., 2021).
The results of Scafetta (2022a) and Lewis (2022) seriously question climate alarmism, as they suggest that anthropogenic global warming is bound to moderate in the coming decades.
Connolly R, Soon W, Connolly M, et al. (2021). How much does the sun affect temperature trends in the Northern Hemisphere? continuous conversation. Res Astron Astrophysics 21:131. https://doi.org/10.1088/1674-4527/21/6/131
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Mitchell DM, Lo YTE, Seviour WJM, Haimberger L, Polvani LM (2020). Vertical components of recent tropical temperature trends: Persistent model bias in the context of internal variability. Environmental Research Letters 15:1040b4. https://doi.org/10.1088/1748-9326/ab9af7
Scaffetta N (2013). Discussion of climate oscillations: CMIP5 atmospheric general circulation model and a semi-empirical harmonic model based on the astronomical circulation. Geosciences Rev. 126:321–357. https://doi.org/10.1016/j.earscirev.2013.08.008
Scaffetta N (2021a). Detecting climate change in surface temperature records by comparing climate data with their model simulations. Clem Dynamics 56:2959–2982. https://doi.org/10.1007/s00382-021-05626-x
Scaffetta N (2021b). Reconstructing annual and millennial-scale patterns of global surface temperature. Mood 12:147. https://doi.org/10.3390/atmos12020147
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Scaffetta N (2022b). Low, medium and high ECS CMIP6 GCM simulation with advanced testing of ERA5-T2m. Geophysical Research Letters 49:e2022GL097716. https://doi.org/10.1029/2022GL097716 Flash memory manufacturer Nextorage has announced (machine translated) the world's fastest CFexpress Type B memory card.
According to internal tests, when B1 Pro series cards support VPG400 (guaranteed data transfer rate of at least 400 MB/s), read and write speeds can be 1950 MB/s and 1900 MB/s. For now, the cards appear to be limited to the Japanese market, but other Nextorage products are already available at international retailers a few months after their debut.
Currently, the fastest memory cards on the market are Lexar's Professional CFexpress Type B Card DIAMOND series, with a read and write speed of 1990MB/s and a write speed of 1700MB/s. While an extra 50MB/s read or even 200MB/s write performance probably won't make a difference in actual usage, there's always bragging rights to take the “world's fastest” crown from someone else's storage company.
Nextorage says the cards use Dynamic Auto Power Save, a power-saving technology developed by Nextorage that the company claims can “reduce power consumption”.
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