by Nicola Scafetta
Two Equilibrium Climate Sensitivity Testing (ECS) publications were recently published in Climate dynamics:
Scafetta, N. (2022a). CMIP6 GCM complex members relative to global surface temperature.
Lewis, N. (2022). Objectively incorporate climate-sensitive evidence.
These two papers are important because they take different but free approaches and achieve the same result – ECS <3°C. Scafetta (2022a) extended and validated Scafetta (2022b) previously published on GRL.
Lewis’ research was discussed in a previous postLet us briefly present here the main findings of Scafetta (2022).
The articulated model comparison project (phase 6) (CMIP6) global circulation model (GCM) projects equilibrium climate sensitivity (ECS) values between 1.8 and 5 ,7°C. To reduce this range, 38 GCMs were divided into low ECS subgroups (1.5 .)
Satellite-based recordings were added because surface records are susceptible to many biases, including urban heat, among others (Connolly et al., 2021; Scafetta, 2021a). Validation tests were performed using 688 GCM member simulations, 143 mean GCM population simulations, and a Monte Carlo model of internal GCM variation that complied with the three model accuracy requirements. replacement image.
The period from 1980 to 2021 was chosen because that is the time when global temperature records are thought to be least affected by uncertainty. Furthermore, the same time period covered by satellite measurements gives an independent estimate.
The important finding of the paper is that most of the simulations of the medium and high ECS GCMs run overheated. From 1980–1990 to 2011–2021, only simulations of the low ECS GCM group seem to have accurately predicted the warming shown by surface-based records. For example, while all temperature data show warming below 0.6°C, all GCM mean values from the medium and high ECS group predict warming above 0.6° C up to 1.3°C. They are clearly visible in Figures 1 and 2.
Figure 1: Global surface temperature records of GCM (yellow area, ±1σ) versus HadCRUT5 (filled data), GISTEMP v4, NOAAGlobTemp v5 and UAH-MSU-lt v6 temperature recordings (color) black, 12-month moving average).
Figure 2: Average temperature change (2011–2021 minus 1980–1990) predicted by 38 simulations of the CMIP6 GCM average. The vertical lines in blue represent the temperature change measured with the HadCRUT5 (populated data), ERA5-T2m, GISTEMP v4, NOAAGlobTemp v5 and UAH-MSU-lt v6 temperature records.
If internal model variability is also considered, the conclusion remains unchanged because, as the study clearly shows, 95% and 97% of the ECS composite membership simulations, respectively. Medium and high run hotter than all temperature records. These findings are summarized in Figure 3.
Figure 3: Boxed frames of CMIP6 group members for each CMIP6 GCM; # represents 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 (data filled in), ERA5-T2m, GISTEMP v4, NOAAGlobTemp v5, and UAH -MSU-lt v6 records the corresponding temperature.
Figure 1-3 clearly shows that the GCM warming forecast increases as the ECS increases, and only the low ECS group of GCMs can be considered consistent with the data. The study also demonstrated that the results hold regardless of the degree of internal variability of the models being modeled. Furthermore, it is not statistically significant that a small number of simulations of medium and high ECS GCMs appear to coincide with the evidence. Therefore, the actual ECS must be lower than 3°C, as Lewis (2022) has also found.
However, Figure 1-3 also shows that if actual warming from 1980-1990 to 2011-2021 is better represented by the UAH-MSU-lt v6 temperature record, the low GCM ECS will also overrun. hot. In fact, while the various available surface-based temperature records show a warming of between 0.5 and 0.6°C, the UAH-MSU-lt v6 temperature record shows a warming by about 0.4°C while low ECS GCMs show warming of 0.6 ± 0.1°C. It is worth mentioning that according to GCMs, the troposphere must experience a larger warming trend than the surface surface (Mitchell et al., 2020) so that UAH-MSU-lt v6 might even overestimate surface warming. Therefore, GCMs with low ECM would need to be scaled by about 33%, assuming that the warming of UAH-MSU-lt v6 is accurate and representative of warming at the surface. This implies that the actual ECS can also be between 1 and 2°C.
Future warming will be moderate and not particularly worrisome if the actual ECS is between 1.5 and 3.0°C. IPCC projections of future climate catastrophe if the amount of gases CO2 emissions that are not severely reduced to essentially zero would be unwarranted if the actual ECS were significantly lower, which is 1-2°C. It is therefore important to assess whether warming trends, as many studies have revealed, can affect the temperature data on the surface or not.
To test the last point, the work adds an extension where observed warming and GCM patterns on land and in the ocean are compared. As a result, land warms 2.0–2.3 times faster than the ocean according to surface temperature records, 1.5 times faster according to satellite-based temperature records, and somewhere in between. according to GCMs: 1.75 ± 0.20. In addition, surface temperature records on land show warming about 0.4°C higher than those recorded on satellites, while surface temperature records over the ocean shows warming only slightly greater (up to 0.1°C) than satellite observations. These results suggest that the warming reported by surface temperature records, especially over land, is too large and incompatible with both satellite measurements and land/land ratio predictions Ocean of models. These findings imply that the warming indicated by surface temperature records, especially over land, is excessive and inconsistent with both satellite observations and theoretical model predictions. about the land/ocean ratio.
Based on the aforementioned findings, it is determined that surface temperature records can be reduced by at least 10% when it comes to actual warming. By reducing the ECS by 10% of the low-ECS GCMs, the ECS range changes from 1.8-3.0°C to 1.6-2.7°C, which is consistent with Lewis’ conclusion (1.7 -2.7°C).
However, if warming is indeed close to the levels indicated by the UAH-MSU-lt v6 temperature record, or if the climate system is controlled by multi-level and millennial natural oscillations that the GCMs If it is not reproducible, the ECS can be much lower (e.g. 1-2°C). For example, Scafetta (2013, 2021b) inferred ECS between 1.0 and 2.3°C by assuming natural (solar-induced) climate fluctuations over a period of almost 20 , 60, 115 and 1000 years, observed in many climate data during the Holocene but not reproduced by the GCM. Similar results were obtained using solar records showing large secular variability, while GCM used solar devices derived from solar proxy reconstructions for found the least secular variation (Connolly et al., 2021).
Because they imply that anthropogenic global warming in the coming decades will certainly be moderate, the results of Scafetta (2022a) and Lewis (2022) cast serious doubts importance of climate alarm.
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Connolly R, Soon W, Connolly M et al. (In 2021). How much has the Sun influenced temperature trends in the Northern Hemisphere? A debate is going on. Res Astron Astrophysics 21:131. https://doi.org/10.1088/1674-4527/21/6/131
Lewis, N. (2022). Objectively incorporate climate-sensitive evidence. Climate dynamics https://doi.org/10.1007/s00382-022-06468-x
Mitchell DM, Lo YTE, Seviour WJM, Haimberger L, Polvani LM (2020). Vertical plot of recent tropical temperature trends: continuum model deviation in the context of internal variability. The letter of the environment 15:1040b4. https://doi.org/10.1088/1748-9326/ab9af7
Scafetta N (2013). Discussion of climate oscillations: The CMIP5 general circulation model versus a semi-empirical harmonic model based on astronomical cycles. Earth Sci Rev 126: 321–357. https://doi.org/10.1016/j.earscirev.2013.08.008
Scafetta N (2021a). Detect non-climate biases in land surface temperature records by comparing climate data and simulating their models. Climb Dyn 56:2959–2982. https://doi.org/10.1007/s00382-021-05626-x
Scafetta N (2021b). Recreate annual to millennial scale models of global surface temperatures. Atmosphere 12:147. https://doi.org/10.3390/atmos12020147
Scafetta, N. (2022a). CMIP6 GCM overall members compared to global surface temperatures. Climate dynamics. https://doi.org/10.1007/s00382-022-06493-w
Scafetta N (2022b). Advanced testing of low, medium and high ECS CMIP6 GCM simulations compared to ERA5-T2m. Geophys Res Lett 49: e2022GL097716. https://doi.org/10.1029/2022GL097716
