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Attribution of anthropogenic global warming – Increase by that?


Featured cartoon is by Josh who graciously gave me permission to use it, his website is this. This cartoon is on Josh’s 2022 calendar, I think he still has a few for sale.

By Andy May

My newest book,[1] just released, talks about the climate change debate between Professor David Karoly of the University of Melbourne and Professor Emeritus William Happer of Princeton. One of the most interesting debated topics is about the causes of global warming. The host is James Barham of TheBestSchools.org. Barham asks David Karoly in interview: “If CO in the atmosphere increases2 not responsible [for global warming]what is?” TheBestSchools notes that skeptics point to several factors that need to be considered before we can draw this conclusion:

  1. After all, we’re still recovering from the last ice age (in the truest sense of the term), which lasted about 100,000 years and ended only about 12,000 years ago; hence why moderate warming is not simply what we should expect (null hypothesis)…
  2. Across the geologic record, it seems that the warming trend frequently precedes the increase in CO2 level, not vice versa.
  3. Several studies show a strong correlation between the Sun’s activity cycles and the Earth’s surface temperature.

(Karoly, 2021a, p. 21)

Karoly is convinced That CO2 and “other greenhouse gases from human activity” are the main drivers of the observed warming (Karoly, 2021a, p. 23). He pointed to the AR5 IPCC report. Specifically, he directs us to Figure 10.5,[2] that’s our Figure 1.

IPCC calculates human contribution to climate change with models, nine of the fifteen models are listed on the left of Figure 2. Since anthropogenic effects on climate have never been observed or measured, the “climate strongholds” are plotted in Figure 1. and Figure 2 depends only on the assumptions built into the model. Figure 1 is a summary of the data and analysis illustrated in Figure 2.

Figure 1. The IPCC has modeled the range for causal warming trends from 1951 to 2010. The observed warming of about 0.66°C is shown in black. IPCC AR5 (IPCC Core Writing Group, 2014, page 6).

Figure 2 is part of IPCC AR5 Figure 10.4 (IPCC, 2013, page 882). It displays the modeled greenhouse gas (GHG) caused by warming, in green. The left panel, panel (a), is scaled in degrees Celsius and shows the range of patterned change in temperature, from 1951 to 2010. The bars shown are based on regression analysis. scale of multiple computer runs of each model. PC runs systematically changing conditions, for example some runs with little change in CO2 and some have large variations in CO2. The central line in table (a) is unchanged for the analyzed climate pressures.

The required color schemes in Figures 1 and 2 are the same, so green represents changes caused by GHGs, yellow is caused by other humans, and blue is variation nature. The green bars vary from negative values ​​(GISS-E2-H) to more than 1.5°C (GISS-E2-R and H, CSIRO-Mk3-6-0). Human-made or other OA estimates are also subject to change. The most likely outcomes from several statistical analyzes of model outcomes are shown as squares, diamonds, and black triangles in table (b).

Table (b) shows the same data, but as a scale factor. The scaling factor is the amount that each component must be multiplied by to get HadCRUT4 global temperature record. While the rate of individual-factor warming is model-based, the total warming is therefore constrained by the HadCRUT4 profile.

This means that the coefficients used to calculate AGW (anthropogenic or anthropogenic global warming) and natural warming are calculated from model results, not observations. The observations are only a limitation of the total.[3] Nathan Gillett and colleagues observed that some of the 15 models they studied produced very unusual negative scaling coefficients, as shown in Figure 2(b). Their statistical evaluation of the model results is shown as triangles in Fig. 2(b). Negative scaling factors are the result of negative warming coefficients. This means the component is cooling as its pressure increases. They single out the GISS-E2-H model and note that it is not “well constrained”.

Figure 2. Part of AR5 Figure 10.4. In table (a), the green bars are the estimated GHG warming temperatures for the model listed on the left. The yellow bars are “other man-made” in Figure 1. The little blue bars are natural pressures. The panel (b) is the scaling factor that must be applied to the model results to fit the HadCRUT global temperature dataset. The vertical dashed line at (a) is 0.66°C, which is the observed warming, and at (b) it is a scale factor of 1. Source: AR5 (IPCC, 2013, p. 882) .

Gillett et al. commented that: “The assumption is often made that a model TCR [transient climate response] proportional to the trend of warming caused by greenhouse gases over the historical period. ” GISS-E2-H appears to violate this assumption in a particular way, but to a lesser extent most models violate this assumption, which concerns Gillett and colleagues. The predictions for warming were the same, but the TCR values ​​were very different, indicating that something was wrong in the models, they found that because the desired result was a multi-model estimate of susceptibility with climate, so the pattern violates hypothesis should be investigated. They therefore concede that the link between GHGs and warming is clear assumptions, but the model results do not match the assumptions. They don’t say it out loud, but it’s also possible that the models are incorrect.

In Figure 2b, the central dashed line is a scaling factor of one, which means that the sum of the model’s components is equal to the warming (HadCRUT4) observed over the time period. Most models obviously overestimate warming because most of the component ratio factors are less than one. Model CSIRO-Mk3-6-0 is a notable exception.

Interested readers are directed to Gillett, et al. for details of the statistical analysis of the fifteen climate models included in their study. Technically, the statistical technique used is known as the empirical orthogonal function (EOF) or Principal Components Analysis (PCA). I don’t have a serious problem with their statistical methods, my problem is that it’s a study of the model’s results and the models are untested there. Furthermore, why do the scaling factors in Figure 2b, cluster below significantly?

The top set of results, labeled “multi”, are the averages of multiple models. The multi-model average overestimates warming over HadCRUT4 for all bastions in Figure 2b. However, the IPCC believes that it is valid to generate it from these diverse values ​​and uses the average to “calculate” the amount of anthropogenic global warming. The IPCC’s summary assessment of values ​​from all 15 models is what is shown in the neater Figure 1, the much less neat Figure 2 which is an intimidating look under the hood.

Some models do not detect any GHG forcing,[4] but most did. An IPCC statistical analysis of the models found that: “Overall, there is some evidence that some CMIP5 models have a higher transient response to GHGs and a greater response to the devices. caused by humans is different… from the real world (average confidence). ”[5] This conclusion agrees with William Happer’s assessment of the models.

Figure 2 shows the same thing. Most scale factors, for the nine models shown, are below unity and some are negative. These models are clearly overestimating GHG-induced warming.

Figure 2 shows a particularly large variation in the calculated greenhouse effect. The range of calculated (or modeled) greenhouse gas warming from 1951 to 2010 is greater than the total observed warming of 0.66°C, dashed in table (a). This gives us no confidence in the values ​​plotted in Figure 1 or in the models.

Averages, especially global averages, hide a lot of important details. 2013 by Nathan Gillett and colleagues paper is a major source in AR5Chapter 10. Gillett is a co-author of Chapter 10, and his 2013 paper is cited 25 times in the chapter. He and his coauthors have the following to say about the assumptions they make to attribute recent warming to anthropogenic “carbon” emissions:

“For TCRE [the transient climate response to cumulative CO2 emissions] depends on both the carbon cycle and the physical climate system, limiting it to observations requires both observations of the carbon cycle and temperature. … The most direct approach is to first use surface temperature observations to estimate CO2-can allocate warming to current and then divide this value by an estimate of cumulative CO2 emissions. Estimating TCRE in this way is based on the assumption that TCRE is constant as a function of the cumulative emissions between the current cumulative emissions and the cumulative emissions at CO.2 double. …Note that we estimate CO2-can be warmed using consistent with 150-year temperature observations … Because of the historical increase of radiant radiation associated with aerosols and non-CO2 the greenhouse gases have almost eliminated each other and major trends in volcanic and solar destruction did not occur during this period, CO2 Concentration change caused by reaction temperature with zero CO2 the fortress is probably small. ”

Let me translate. First, we assume that TCRE is a constant function of cumulative CO2 emissions, we then compare the 150-year record of CO2 surface temperature emissions reached a record over the same period. We assume that aerosols and other greenhouse gases cancel each other out and that all solar and volcanic devices are negligible. Therefore, they assume that CO2 emissions were the only significant effect on climate and then found that CO2 is the only significant effect on climate, big surprise!

This is just one example of the “proof” circle that CO2 Emissions that lead to climate change, there are many others. There is no evidence, other than models, that human CO2 emissions drive climate change, and ample evidence suggests that the Sun, along with natural climate cycles, drives most, if not all, of recent climate changes, as documented description in Connolly, et al., 2021.[6]

Much of this post is from my latest book, The Great Climate Debate, Karoly v Happer, available for purchase on Amazon.com and BarnesandNoble.com. If you purchased and read it, please leave a favorable review at one or both of the sites, otherwise mark the favorable reviews as “helpful”, this also counts.

Folder can be downloaded this.

  1. (May 2022)

  2. on page 884 in Chapter 10 of AR5

  3. (Gillett et al., 2013) and (IPCC, 2013, p. 882)

  4. (IPCC, 2013, page 882)

  5. (IPCC, 2013, page 884)

  6. (Connolly et al., 2021): “How much has the sun influenced temperature trends in the Northern Hemisphere? An ongoing debate”. Available data suggest that the Sun could explain anywhere from 0 to 100% of recent warming.



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